BIOLOGICAL FIELD STATION Cooperstown, New York

48th ANNUAL REPORT 2015

Otsego bass/lake whitefish (Coregonus clupeaformis)

STATE UNIVERSITY OF NEW YORK COLLEGE AT ONEONTA

OCCASIONAL PAPERS PUBLISHED BY THE BIOLOGICAL FIELD STATION

No. 1. The diet and feeding habits of the terrestrial stage of the common newt, Notophthalmus viridescens (Raf.). M.C. MacNamara, April 1976 No. 2. The relationship of age, growth and food habits to the relative success of the whitefish (Coregonus clupeaformis) and the cisco (C. artedi) in Otsego Lake, New York. A.J. Newell, April 1976. No. 3. A basic limnology of Otsego Lake (Summary of research 1968-75). W. N. Harman and L. P. Sohacki, June 1976. No. 4. An ecology of the Unionidae of Otsego Lake with special references to the immature stages. G. P. Weir, November 1977. No. 5. A history and description of the Biological Field Station (1966-1977). W. N. Harman, November 1977. No. 6. The distribution and ecology of the aquatic molluscan fauna of the Black River drainage basin in northern New York. D. E Buckley, April 1977. No. 7. The fishes of Otsego Lake. R. C. MacWatters, May 1980. No. 8. The ecology of the aquatic macrophytes of Rat Cove, Otsego Lake, N.Y. F. A Vertucci, W. N. Harman and J. H. Peverly, December 1981. No. 9. Pictorial keys to the aquatic mollusks of the upper Susquehanna. W. N. Harman, April 1982. No. 10. The dragonflies and damselflies (Odonata: Anisoptera and Zygoptera) of Otsego County, New York with illustrated keys to the genera and species. L.S. House III, September 1982. No. 11. Some aspects of predator recognition and anti-predator behavior in the Black-capped chickadee (Parus atricapillus). A. Kevin Gleason, November 1982. No. 12. Mating, aggression, and cement gland development in the crayfish, Cambarus bartoni. Richard E. Thomas, Jr., February 1983. No. 13. The systematics and ecology of Najadicola ingens (Koenike 1896) (Acarina: Hydrachnida) in Otsego Lake, New York. Thomas Simmons, April 1983. No. 14. Hibernating bat populations in eastern New York State. Donald B. Clark, June 1983. No. 15. The fishes of Otsego Lake (2nd edition). R. C MacWatters, July 1983. No. 16. The effect of the internal seiche on zooplankton distribution in Lake Otsego. J. K. Hill, October 1983. No. 17. The potential use of wood as a supplemental energy source for Otsego County, New York: A preliminary examination. Edward M. Mathieu, February 1984. No. 18. Ecological determinants of distribution for several small mammals: A central New York perspective. Daniel Osenni, November 1984. No. 19. A self-guided tour of Goodyear Swamp Sanctuary. W. N. Harman and B. Higgins, February 1986. No. 20. The Chironomidae of Otsego Lake with keys to the immature stages of the subfamilies Tanypodinae and Diamesinae (Diptera). J. P. Fagnani and W. N. Harman, August 1987. No. 21. The aquatic invertebrates of Goodyear Swamp Sanctuary, Otsego Lake, Otsego County, New York. Robert J. Montione, April 1989. No. 22. The lake book: a guide to reducing water pollution at home. Otsego Lake Watershed Planning Report #1. W. N. Harman, March 1990. No. 23. A model land use plan for the Otsego Lake Watershed. Phase II: The chemical limnology and water quality of Otsego Lake, New York. Otsego Lake Watershed Planning Report Nos. 2a, 2b. T. J. Iannuzzi, January 1991. No. 24. The biology, invasion and control of the Zebra Mussel (Dreissena polymorpha) in North America. Otsego Lake Watershed Planning Report No. 3. Leann Maxwell, February 1992. No. 25. Biological Field Station safety and health manual. W. N. Harman, May 1997. No. 26. Quantitative analysis of periphyton biomass and identification of periphyton in the tributaries of Otsego Lake, NY in relation to selected environmental parameters. S. H. Komorosky, July 1994. No. 27. A limnological and biological survey of Weaver Lake, Herkimer County, New York. C.A. McArthur, August 1995. No. 28. Nested subsets of songbirds in Upstate New York woodlots. D. Dempsey, March 1996. No. 29. Hydrological and nutrient budgets for Otsego lake, N. Y. and relationships between land form/use and export rates of its sub -basins. M. F. Albright, L. P. Sohacki, W. N. Harman, June 1996. No. 30. The State of Otsego Lake 1936-1996. W. N. Harman, L. P. Sohacki, M. F. Albright, January 1997. No. 31. A self-guided tour of Goodyear Swamp Sanctuary. W. N. Harman and B. Higgins (Revised by J. Lopez),1998. No. 32. Alewives in Otsego Lake N. Y.: A comparison of their direct and indirect mechanisms of impact on transparency and Chlorophyll a. D. M. Warner, December 1999. No.33. Moe Pond limnology and fish population biology: An ecosystem approach. C. Mead McCoy, C. P. Madenjian, V. J. Adams, W. N. Harman, D. M. Warner, M. F. Albright and L. P. Sohacki, January 2000. No. 34. Trout movements on Delaware River System tail-waters in New York State. Scott D. Stanton, September 2000. No. 35. Geochemistry of surface and subsurface water flow in the Otsego lake basin, Otsego County New York. Andrew R. Fetterman, June 2001. No. 36 A fisheries survey of Peck Lake, Fulton County, New York. Laurie A. Trotta. June 2002. No. 37 Plans for the programmatic use and management of the State University of New York College at Oneonta Biological Field Station upland natural resources, Willard N. Harman. May 2003.

Continued inside back cover Annual Reports and Technical Reports published by the Biological Field Station are available at: http://www.oneonta.edu/academics/biofld/publications.asp

48th ANNUAL REPORT 2015

BIOLOGICAL FIELD STATION COOPERSTOWN, NEW YORK bfs.oneonta.edu

STATE UNIVERSITY COLLEGE AT ONEONTA

The information contained herein may not be reproduced without permission of the author(s) or the SUNY Oneonta Biological Field Station

BFS 2015 ANNUAL REPORT CONTENTS

INTRODUCTION: W. N. Harman…………………………………………………………………...….1

ONGOING STUDIES:

OTSEGO LAKE WATERSHED MONITORING: 2015 Otsego Lake water levels. W.N. Harman and M.F. Albright……………………….9 Otsego Lake limnological monitoring, 2015. H.A. Waterfield and M.F. Albright..….…12 A survey of Otsego Lake’s zooplankton community, summer 2015. M.F. Albright and M.D. Robinson……….……………………………..…….…..24 Chlorophyll a monitoring on Otsego Lake, Cooperstown, NY, summer 2015. C. Garfield……………………………………………………………………..36 Water quality monitoring and analysis of fecal coliform of five major tributaries in the Otsego Lake watershed, summer 2015. B. Wells. ..…………44

SUSQUEHANNA RIVER MONITORING: Upper Susquehanna River water quality monitoring, summer 2015. B. Shaw………………………………………………………………………..64

REPORTS:

Preventing zebra mussel (Dreissena polymorpha) veliger attachment using potassium permanganate. E. Clifton and M.F. Albright………………………...….81 Introduction to drones as tools for research and monitoring. P.T. Booth……..…….…………86 Drone ecology on a budget. P.T. Booth……………………………...... ……………………99 Report on migration of Butternut Creek in Wheeler’s field. L. Hasbargen, P.T. Booth and D. Busby…………………………………………………………..…119 A characterization of the riparian corridor of the Oaks Creek Blueway Trail with emphasis on Otsego Land Trust properties. N. Pedisich and D. Vogler………..…...129 Annual trap net monitoring of fish assemblages in the weedy littoral zone at Rat Cove and the rocky littoral zone at Brookwood Point, Otsego Lake, 2015. J.B. Casscles……………………………………………………………………….…136 Characterization of spawning rainbow smelt (Osmerus mordax) in the Mohican Canyon Creek, Otsego Lake, NY. M. Best and J.R. Foster.…...... 144 Effects of zebra mussels (Dreissena polymorpha) on lake trout (Salvelinus namaycush) fry recruitment in Otsego Lake. J.B. Casscles, J.R. Foster, D.M. Lucykanish and N.M. Sawick..………………………………….…..154 Dominant algae of Otsego Lake, Cooperstown, NY. C. Garfield……………………………...162 A quantitative FlowCAM analysis of diatoms in Otsego Lake, New York, with an emphasis on method implications. B. Wells.…………..…………...….……….…….173 Benthic macroinvertebrate survey of the upper Susquehanna River using two sampling methods. B. Shaw…………………………………….…………….………..193 An update on the fish parasites of Otsego Lake and nearby water bodies. F. Reyda.………..205 Continued monitoring of the Moe Pond ecosystem in conjunction with biomanipulation. D. Busby and J.B. Casscles……..…………………………….….210 Benthic macroinvertebrate survey of Otsego Land Trust Properties on Oaks Creek, Otsego County, NY. M.D. Robinson and J.S. Heilveil..…………………………….222 Evaluation of phosphorous and nitrogen uptake by Phalaris arundinacea plants in a wastewater treatment wetland, Cooperstown, NY. S. Bouillon…………………...... 230 The fish assemblages of the selected Otsego Lake tributaries. J.B. Casscles……………...... 243 Total mercury concentration in fish tissue relative to length and weight. D.M. Snyder, C.R. Parker and K. Yokota.………..…………….….………….……..251 Summer 2015 BioBlitz series. E. Clifton…….…………………………….…….….……….255 Survey of zooplankton in Brant Lake, Horicon, NY. S. Newtown and A. Reyes….….….…274 Aquatic macrophyte management plan facilitation, Lake Moraine, Madison County, NY 2015. B.P. German and M.F. Albright……………………………..…..………..280 Preliminary prey density analysis of wood turtles (Glyptemys insculpta) in Central New York. E. Clifton, A. Robillard and D. Vogler…..…………….…….292 Deer population impacts on biodiversity in Glimmerglass State Park. B. Panensky…...... …295 Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 2015. M.F. Albright………………………………………………………..302 Dynamics of Galerucella spp. and purple loosestrife (Lythrum salicaria) in Goodyear Swamp Sanctuary, summer 2015 update. H. Waterfield and M.F. Albright………...317 Hydroacoustic survey and bathymetric map creation for Brant Lake, New York. H. Waterfield……………………………………………………………..……….….324 Watershed characterization for Goodyear Lake, New York: Watershed subbasins, land use and cover, surficial geology, and soils. H. Waterfield…………….…….….329

INTRODUCTION

Willard N. Harman

Interns:

Elizabeth Clifton, a SUNY Oneonta Ecology and Field Biology major, received the SUNY Oneonta Biology Department Internship. Working under the direction of Matt Albright, she evaluated potassium permanganate as an agent to disrupt zebra mussel colonization on artificial substrates. She also did some preliminary investigation on prey densities of wood turtles with graduate student Alexander Robillard, and Donna Vogler, and she oversaw three “bioblitzes” arranged jointly between the Biological Field Station and the Otsego Land Trust. Matt Robinson and Nicole Pedisich, both of SUNY Oneonta, were sponsored by the Otsego Land Trust. Matt worked with Jeff Heilveil on conducting benthic macroinvertebrate surveys on Oaks Creek. He also worked with Matt Albright on describing the Otsego lake zooplankton community. Nicole worked with Donna Vogler on characterizing the riparian corridor of the Otsego Land Trust’s “Blueway Trail” along Oaks Creek.

Britney Wells, a Biology major with a minor in Water Resources at SUNY Oneonta, was supported by the Otsego County Conservation Association and held the R.J. Thayer Otsego Lake Research Assistantship. She evaluated water quality across the Otsego Lake watershed to evaluate changes attributable to agricultural Best Management Practices She also worked under the direction of Les Hasbargen and Holly Waterfield on a methodological report on using the BFS’ NSF-funded FlowCam® particle analyzer to evaluate diatoms from Otsego lake sediments. And, she worked with Kiyoko Yokota on evaluating the influence of cosmetic microplastics on algal growth.

Peter Booth, a GIS/Geography major at SUNY Oneonta, developed usage protocols regarding applications of drones for field research. He was sponsored by the Otsego County Conservation Association. The purchase of the drone was made possible through a donation from the Otsego Lake Association.

David Busby, an Environmental Science major at SUNY Oneonta, worked with Ben Casscles, the Robert C. MacWatters Intern in the Aquatic Sciences, on evaluating the fishery of a pond following the unauthorized stocking of largemouth bass and developing protocols for their future control. David also worked with Peter Booth, under the direction of Les Hasbargen, on three dimensional optical modeling of natural landforms related to stream cut banks. Ben also monitored the littoral fish community of Otsego Lake. With SUNY Cobleskill Professor John R. Foster and students David M. Lucykanish and Nicholas M. Sawick, Ben evaluated the influence of zebra mussel colonization of lake trout spawning success, and he led efforts to describe the fish assemblages in Otsego Lake’s watershed.

1 Sara-June Bouillon, a SUNY Oneonta graduate having majored in Environmental Science, investigated phosphorus and nitrogen uptake by vegetation in the Village of Cooperstown’s wastewater treatment plant. She worked under the direction of Donna Vogler and was funded by the Village of Cooperstown.

Claire Garfield of Oneonta Senior High School and Bethany Shaw of Bainbridge-Guilford Central School both received F.H.V. Mecklenburg Conservation Fellowships. With OCCA support and under the direction of Kiyoko Yokota, Claire monitored chlorophyll a concentrations and surveyed the algae taxa in Otsego Lake. Bethany, supported by the Village of Cooperstown, conducted water quality monitoring along the upper Susquehanna River and conducted benthic macroinvertebrate surveys along the same river stretch.

Faculty and staff activities:

Bill Harman, Kiyoko Yokota, Dan Stitch, Matt Albright and Holly Waterfield (all NALMS Certified Lake Managers), along with MS students Luke Gervase, Christian Jenne, Edward Kwietniewski, Jenna Leskovec, Kathleen Marean, Alejandro Reyes, Maxine Verteramo, Eric Davis, Ben German, Patrick Goodwin, Leah Gorman and 2015 intern Britney Wells all attended the 35th International NALMS Symposium in Saratoga Springs, NY. Matt and Holly were actively involved on the host committee. Kiyoko presented a talk entitled “HAB + Microplastics = ?”. Luke presented the talk “Millsite Lake: Coping with an Invasion”. Ben spoke on “20 Years in Lake Moraine: Long-term Macrophyte Management in a Nutrient-Rich System”. Kathleen presented “Sixberry Lake: Protecting an Oligotrophic Lake from Anthropogenic Eutrophication”. Alejandro spoke on “Invaders at the Doorstep, Preventing the Spread and Establishment of Invaders into Brant Lake”. Jenna presented “Evaluation of Drawdown for Management of Native Aquatic Macrophytes in an Adirondack Lake”. Eric summarized his work on “Iodized Table Salt as a Potential Chemical for Zebra Mussel Decontamination”. Pat gave an overview on “Aeration’s Effect on Algae: A Review of Success and Failures”. Christian presented “Truesdale Lake and its Fight with Eutrophication”. Ed spoke on “Rushford Lake: An Interesting Case of an Extreme Drawdown”. Kathleen, AJ, Christian, Ed and Max also presented posters of their work, with Max receiving the award for best poster at the conference! Max also provided a “Paint Sip Fun” workshop.

Les Hasbargen continued his investigations of lake sediment cores taken from Otsego Lake, working on methods to isolate diatoms, and characterize particle characteristics using automated methods. The continued collaboration with Dr. Christoph Geiss, at Trinity College in Hartford, CT, yielded a paper titled Magnetic and Sedimentological Analyses of Sediment Cores from Otsego Lake Reveal Climate and Possible Delta Dynamics Throughout the Holocene, which was presented at the annual meeting of American Geophysical Union, December 2015 in San Francisco. A second paper titled Needles in the haystack: particle characterization and the hunt for diatoms in Holocene lake sediment from upstate New York was presented by Hasbargen at the annual meeting of the Geological Society of America in Baltimore, MD in November 2015. Britney Wells, a BFS intern, assisted with this effort. Hasbargen also worked with Peter Booth

2 and David Busby, student interns at BFS, to develop techniques for high resolution mapping with aerial imagery from a small unmanned aerial vehicle (UAV). Initial projects included monitoring stream banks to measure erosion rates and wetland mapping. The combination of relatively inexpensive high resolution imagery and structure-from-motion software has resulted in exquisitely detailed topographic maps, which open up a broad spectrum of surface process studies. A paper titled Views of the river: flood records, Lidar, UAV, and canoe based monitoring of meander migration in upstate New York was presented by Hasbargen at the annual meeting of the Geological Society of America in Baltimore, MD in November 2015. A follow up paper on the controls of stream cutbank erosion, titled Flood records, bank erosion mechanisms and meander migration in Butternut Creek, New York, was presented at the Northeastern Section of the Geological Society of America conference in Albany, NY in March, 2016.

Kiyoko Yokota was recently elected to the NALMS board of directors. Over 2015, she utilized BFS resources throughout the academic year to teach BIOL 685 (Studies in Limnology, Fall 2015) and BIOL 691 (Management of Aquatic Biota, Spring 2016). Results of her microplastics project, carried out with Holly Waterfield, 2014 and 2015 BFS summer interns and Lake Management MS student Edward Kwietniewski, has been presented at various scientific conferences including the 34th Phycological Society of America Annual Meeting, the North American Lake Management Society Annual Symposium, and the Northeast Aquatic Plant Management Society Annual Meeting. Colleen Parker, MS Biology student, is conducting a study on fish mercury levels in lakes in the Catskill Region in collaboration with Syracuse University under Kiyoko’s guidance. This is a part of a statewide monitoring project commissioned by the New York State Energy Research and Development Authority. Yokota and her colleagues from SUNY Oneonta, Fredonia and New Paltz have also been working on the SUNY Lakes Ecosystem Observatory Network and held a workshop at BFS Boathouse for Otsego Lake researchers and stakeholders. One of the 2015 summer interns, Claire Garfield, continued her summer project of characterization of Otsego Lake phytoplankton into fall 2015 and presented the results at the Northeast Aquatic Plant Management Society Annual Meeting.

Research in the fish parasitology lab of Florian Reyda involved a diversity of people and activities. Early in the year we had a guest for one month from Brazil. Juliana Primon is an undergraduate student in the laboratory of Reyda's longtime collaborator Fernando Marques at the University of Sao Paulo, in Brazil. Juliana traveled to the Reyda lab to collaborate with him on the description of a new species of tapeworm from a stingray species from the Caribbean Sea. During her visit Juliana completed the necessary components of a species description in the laboratory, while integrating with the other students (and also experiencing one of the coldest Februaries on record!). During the spring 2015 semester Reyda had seven SUNY Oneonta undergraduate students conducting research in the laboratory. Craig Wert and Ashley Mills studied parasitic trematodes from Otsego Lake fishes. A highlight of that work was the discovery of an unusually small–even for a trematode–species from the intestine of chain pickerel that were collected by NYS DEC employee Timothy Pokorny. The trematode (mentioned in this report) is likely a species new to science, but that work is still being undertaken by Craig Wert. Tara Aprill and Kathryn Forti completed the description of a new species

3 of tapeworm from a species of stingray from northern coastal Australia, and Elsie Dedrick and Illari Delgado described a species from a different stingray from coastal Borneo. Nathan Heller made great progress on his own study with Reyda on the pathology caused to white suckers by a species of acanthocephalan (mentioned in the report). The results of that study, which was authored by Nathan, Reyda, and Nathan's father Arthur (an M.D. specializing in garstroenterology) is currently under review in a parasitological journal. A large paper in which the tapeworms studied by Tara, Kathryn, Elsie, Illari and several other authors, including 5 Reyda lab alumni, is also currently under review in a parasitological journal. Both of those two projects were finalized by Reyda in fall 2015 when he was on sabbatical.

Reyda also made the most of BFS resources during his summer offering of Parasitology. That course was based out of campus, but it included 3 weekend field trips to the Thayer Farm where students were able to collect species of fish, amphibians, and invertebrates, and study their parasites in the teaching lab at the Hop House.

John Foster and Mark Cornwell of SUNY Cobleskill continue to use BFS resources for student involvement and research. Students were involved in using the NFS-funded FlowCam particle analyzer to characterize stomach contents of paddlefish. Efforts were initiated to evaluate spawning efforts by Otsego Lake whitefish, and a pilot project was initiated to attempt to rear their fry for augmenting the wild fishery. John Foster and 2014 summer intern Matthew Best evaluated the 2015 smelt spawning run in Mohican Creek.

Bill Harman and Holly Waterfield completed the final report for a NYSDEP sponsored series of surveys of aquatic invasive species in five reservoirs in the Catskills and Lower Hudson regions. Jeff Heilveil was responsible for a contribution to the same report addressing work to develop a low-cost, user friendly species specific genetic markers for invasive species identification.

Bill was co-author of two peer reviewed papers: Eric A. Davis, Wai Hing Wong and Willard N. Harman. 2015. Comparison of three sodium chloride chemical treatments for adult zebra mussel decontamination. Journal of Shellfish Research, 34 (3): 1029–1036, 2015. Jennifer M. Vanasshe, Wai Hing Wong, Willard Harman and Matthew Albright. 2015. Early invasion records of zebra mussels Dreissena polymorpha (Dallas, 1717) in Otsego Lake, New York. Bioinvasions records. 2014. 3 (3): 169-162. One book review: Wong and Gerstenberger (2015) Biology and Management of Invasive quagga and zebra mussels in the Western United states. Management of Biological Invasions (2015) volume 6, Issue 4:429 -21-23. And two articals in lay magazines for recruitment of graduate students: Harman, WN. 2015. A Career on Inland Waters. Master’s Degree Opportunity available. Pond Boss. XXV (1); 36 – 37.

4 Nancy Mueller, Holly Waterfield and Willard Harman. 2016. Lake Management Degree Program Attracts Students. Catskill Life 31(1);26 -28.

Bill made a trip along the East Coast from New York to Florida in the spring of 2015 visiting firms with individuals potentially becoming involved as advisors in our new Professional Science Masters (PSM) degree program in Lake Management and/or employing degree recipients from both Master’s programs. In the spring of 2016 he traveled from New York through Pennsylvania Ohio, Indiana, Illinois, Michigan, Wisconsin, and Iowa for the same purposes while attending the Midwest Aquatic Plant Management Society meetings in Grand Rapids, Michigan. Six Lake Management graduate students also attended that meeting. He accompanied several graduate students to the Northeast Aquatic Plant Management Society meetings in Saratoga, NY and AJ Reyes to the Adirondack Lakes Alliance meetings at Paul Smith’s College. At the 2015 NYS Federation of Lake Associations meetings in Hamilton, NY he provided a lake management workshop; The Lake Doctor Is In, and presented, with BFS faculty, staff and grad students, Limno 101, An Introduction to Lake Management. Five graduate students presented and displayed posters at that venue. He presented at the 6th annual Invasive Species Workshop at Alverna Heights, Fayetteville, NY; Invasive Species Research at the SUNY Oneonta BFS, sponsored by SUNY ESF, NYS Office of Parks Recreation and Historic Preservation, Cornell University Cooperative Extension and the Cortland-Onondaga Federation of Kettle Lake Associations. In September he was Keynote speaker at the Environmental Law Section of the NYS Bar Association at their Annual Meetings in Cooperstown; Threats to, and the status of, Otsego Lake Water Quality.

Graduate Studies:

Danial Stich, recently from the University of Maine, having previous experience modeling fish populations at Virginia Tech. and the Cary Institute of Ecosystem Studies, joined our faculty to fill the position left open by David Wong’s leaving for personal reasons. Dan was a BFS summer undergraduate intern during the summer of 2007 while attending SUNY Cobleskill’s Fisheries and Wildlife Technology program where he received a Bachelor of Technology in Fisheries and Aquaculture.

A new Master’s degree program has recently been approved, a Professional Science Master’s (PSM) in lake management, that is identical with our current MS program but replaces the thesis research with several weeks working at a professional venue in the lake management field. Students entering in the fall of 2016 will be able to choose between the two degree options. Both programs intensively use the resources of the Biological Field Station, most courses being taught on site in Cooperstown.

At this time we have 11 students enrolled in the Master of Science in Lake Management degree program. Two are part-time students; Caitlin Stroosnyder is still working on Goodyear Lake on the Susquehanna River near Oneonta. She holds a position at Delaware Engineering. Ben

5 German continues to work on Moraine Lake in Madison County while employed at SUNY Cobleskill as an Instructional Support Assistant. Students beginning in 2014 include Maxine Verteramo, Christian Jenne, Luke Gervase, Edward Kwietniewski, Jenna Leskovec, Kathleen Marean and Alejandro Reyes. They all appear to be on schedule to receive their degrees in a timely manner. Students completing their first year are Leah Gorman with a degree from SUNY Purchase, who is developing a management plan for DeRuyter Reservoir in Madison County and Pat Goodwin who graduated from the University of South Florida in 2013 and since has worked for Vertex, a lake management consulting firm. The latter is supporting Pat’s efforts financially. Pat is doing research on two lakes; Mohegan Lake in Westchester County and Thunder Lake in Madison County. As indicated above, all of the students in the program have been sponsored to attend and present at multiple professional venues including one not before mentioned, the New York Chapter meetings of the American Fisheries Society.

Daniel Kopec and Owen Zaengle completed their degrees this year. Dan is working at Apex Co., LLC., an environmental consulting firm. Owen is working in his family’s business. All other degree recipients to date are employed by various consulting firms.

Biology Graduate student Eric Davis started his research under David Wong. Eric was the first graduate student supported by external funding (NYS DEC contract) in the history of the Biology Department. He is involved in research with invasive, exotic zebra mussels and is writing the final drafts of his thesis at this time.

6 Otsego Lake boat census data: Year 1975 1976 1977 1978 1979 1980 1981 1991 Date 28-Jul 22-Jul 22-Jul 13-Aug 31-Jul Sailboats 224 186 129 101 92 95 230 243 Rowboats 145 236 160 94 86 42 87 285 Canoes 59 52 28 75

Outboards 636 515 436 456 378 197 445 470 Inboards 73 38 22 36 60

Inboard-Outboards 213

Personal Watercraft 61 Misc. cruisers/houseboats 65 41 40 33 24 23 Total 1070 978 765 783 679 408 896 1332

Year 1992 1993 1994 1995 1996 1997 1998 1999 Date 5-Aug 5-Aug 27-Jul 14-Jul 23-Jul 18-Jul 7-Aug 29-Jul Sailboats 220 181 208 208 207 183 236 238 Rowboats and canoes 243 266 311 313 325 312 372 309 Outboards 407 405 461 430 378 371 377 412 Inboards 22 27 16 13 36 13 20 15 Inboard-Outboards 219 215 227 267 260 275 261 265 Personal Watercraft 32 28 29 47 51 62 84 66 Misc. 40 57 49 Total 1158 1145 1285 1315 1272 1226 1351 1317

Year 2000 2001 2002 2005 2007 2008 2009 2010 Date 10-Aug 9-Aug 22-Jul 23-Aug 27-Aug 26-Aug 31-Aug Sailboats 187 190 171 198 192 153 178 162 Rowboats and canoes 349 389 384 450 383 422 407 458 Outboards 381 375 319 380 344 340 349 363 Inboards 23 9 36 21 24 25 30 14 Inboard-Outboards 287 285 216 297 277 280 251 272 Personal Watercraft 19 23 18 15 22 16 17 9 Misc. 53 66 43 51 43 38 48 44 Total 1299 1337 1187 1412 1285 1274 1280 1322

Year 2011 2012 2013 2014 2015 Date 9-Sep 15-Aug 22-Aug 4-Sep 27-Aug Sailboats 118 140 113 121 141 Rowboats and canoes 450 545 520 551 562 Outboards 227 334 329 361 354 Inboards 15 16 31 16 17 Inboard-Outboards 190 274 247 231 242 Personal Watercraft 14 22 17 11 13 Jetboats* 2 2

Misc. 40 40 41 35 32 Total 1054 1371 1298 1328 1363 * Prior to 2014, jetboats were grouped with Inboard-Outboards

7 Public support makes our work possible. Funding for BFS research and educational programs was procured in 2015 from many citizens and organizations. Special thanks go to the Clark and Scriven Foundations who generously support our annual needs. The OCCA, the Peterson Family Charitable Trust, the Village of Cooperstown, the Otsego Lake Association, The Otsego Land Trust, SUNY Oneonta, and the SUNY Graduate Research Initiative have also supported our endeavors. A diversity of Lake Associations, and the New York State Federation of Lake Associations, and professional lake management consulting firms contribute to the support of students in our Lake Management program.

Willard N. Harman, CLM

8 ONGOING STUDIES:

OTSEGO LAKE WATERSHED MONITORING:

2015 Otsego Lake water levels W.N. Harman and M.F. Albright

Graphs represent Otsego Lake elevation readings at Rat Cove, in centimeters, above or below “0”, which equals the level considered optimal (364.1 m, or 1194.5 ft, above mean sea level).

Ice on (14 Jan)

Ice off (18 Apr)

11 Otsego Lake limnological monitoring, 2015

Holly A. Waterfield1 and Matthew F. Albright2

INTRODUCTION

Otsego Lake is a glacially formed, dimictic lake (max depth 51m) supporting a cold water fishery. The Lake is generally classified as being chemically mesotrophic, although flora and fauna characteristically associated with oligotrophic lakes are present (Iannuzzi, 1991).

This study is the continuation of a year-round monitoring protocol that began in 1991. The data collected in this report run for the calendar year and are comparable with contributions by Homburger and Buttigieg (1992), Groff et al. (1993), Harman (1994; 1995), Austin et al. (1996), Albright (1997; 1998; 1999; 2000; 2001; 2002; 2003; 2004; 2005; 2006; 2007; 2008), Albright and Waterfield (2009), and Waterfield and Albright (2010; 2011; 2012; 2013; 2014; 2015). Concurrent additional work related to Otsego Lake included estimates of fluvial nutrient inputs (Wells 2016), descriptions of the zooplankton community (Albright and Robinson 2016), chlorophyll a (Garfield 2016), and nekton communities (Casscles 2016).

MATERIALS AND METHODS

Physiochemical data and water samples were collected near the deepest part of the lake (TR4-C) (Figure 1), which is considered representative of whole-lake conditions, as past studies have shown the Lake to be spatially homogenous with respect to the factors under study (Iannuzzi 1991). Data and sample collection occurred approximately monthly when the lake was frozen and bi-weekly during open water conditions, 22 January through 16 December 2015. Physical measurements were recorded at 2-m intervals between 0 and 20 m and 40 m to the bottom; 5-meter intervals were used between 20 and 40 m. Measurements of pH, temperature, dissolved oxygen (mg/l and % saturation), specific conductance, and chlorophyll a concentration were recorded with the use of a YSI® 650 MDS with a 6-Series or EXO 1 multiparameter sonde which had been calibrated according to the manufacturer’s instructions prior to use (YSI Inc. 2009). Samples were collected for chemical analyses at 4-m intervals between 0 and 20 m and 40m and 48m; 10-m intervals were used between 20 and 40 m. Methodologies employed for sample preservation and chemical analyses are summarized in Table 1. Nutrient and chlorophyll a concentrations were determined for all sampling dates; alkalinity, calcium, and chloride concentrations were determined for one profile date per month. No data or samples were collected in April due to unsafe ice conditions.

1 CLM. Research Support Specialist: SUNY College at Oneonta Biological Field Station, Cooperstown, NY. 2 CLM. Assistant to the Director: SUNY College at Oneonta Biological Field Station, Cooperstown, NY.

TR4-C

Figure 1. Bathymetric map of Otsego Lake showing sampling site (TR4-C).

13 Table 1. Summary of laboratory methodologies.

Parameter Preservation Method of Analysis Reference Persulfate digestion followed by Total Phosphorus H SO to pH < 2 Liao and Marten 2001 2 4 single reagent ascorbic acid Cadmium reduction method following Pritzlaff 2003; Total Nitrogen H SO to pH < 2 2 4 peroxodisulfate digestion Ebina et al. 1983

Nitrate+nitrite-N H2SO4 to pH < 2 Cadmium reduction method Pritzlaff 2003

Ammonia-N H2SO4 to pH < 2 Phenolate method Liao 2001 Calcium Store at 4oC EDTA trimetric method EPA 1983 Chloride Store at 4oC Mercuric nitrate titration APHA 1989 Alkalinity Store at 4oC Titration to pH= 4.6 APHA 1989 Filter Buffered acetone extraction followed Chlorophyll a immediately; Welschmyer, 1994 o by flourometric detection store at 0 C

RESULTS AND DISCUSSION

Temperature Figures 2a and 2b depict temperatures measured in profile (0 to 48m) at site TR4-C from 22 January through 28 July and 28 July through 16 December 2015, respectively. Observed surface temperature ranged from a low of 0.76oC under the ice to 22.6oC on 28 July, at which point the epilimnion extended through 6m depth (Figure 2a). Temperatures just off-bottom (46- 48m) reached the annual minimum of 1.95oC on 03 February, and maximum of 4.56oC on 03 December. Complete ice-cover formed on 14 January; the lake was completely ice-free on 18 April. Spring mixing occurred prior to the 4 May sampling event and thermal stratification was evident by 19 May. Maximum surface temperature was recorded on 28 July, after which surface temperatures began to decrease and the thermocline occurred at greater depth until fall turnover, which had not yet occurred as of the 16 December sampling event (Figure 2b).

Dissolved Oxygen Isopleths of dissolved oxygen concentration based on the profiles for the calendar year are presented in Figure 3. On 4 May, prior to the onset of thermal stratification, dissolved oxygen ranged from 11.40 mg/l at bottom to 12.02 mg/l at the surface. The minimum observed DO concentration in 2015 was 2.88 mg/l recorded on 04 November at 48m. In most years between 1995 and 2009, the bottom minimum concentration was near or below 1.0 mg/l. The areal hypolimnetic oxygen depletion rate (AHOD), calculated at 0.049 mg/cm2/day (between 21 May and 15 October), remains well below the historical average for the sixth consecutive year (Table 2).

Alkalinity Alkalinity concentrations generally followed a typical pattern of seasonal variation, with concentrations decreasing in the epilimnion and increasing deeper during the growing season. However, there were two dates (14 July and 4 November) on which irregular patterns were

14 observed; on both dates, measured concentrations at 40m were 5 to 10 mg/l (as CaCO3) lower than overlying(30m) waters. Mean annual concentration at TR4-C was 122 mg/l, ranging from 87 mg/l (as CaCO3) at 4m on 14 July to 134 mg/l from 40 to 48m on 19 March 2015.

Temperature (oC) 0 5 10 15 20 25 0 1/22/2015 5 2/18/2015 10 3/19/2015 15

5/4/2015 20 5/19/2015 25 6/3/2015 30

Depth (meters) Depth 6/17/2015 35 7/2/2015 40 7/14/2015

45 7/28/2015

Temperature (oC) 0.00 5.00 10.00 15.00 20.00 25.00 0 7/28/2015 5

10 8/13/2015

15 9/16/2015

20 10/6/2015

25 10/21/2015 30 Depth (meters) Depth 11/4/2015 35 11/30/2015 40 12/16/2015 45

Figure 2. Otsego Lake temperature profiles (oC) observed at TR4-C 22 January through 28 July (2a) and 13 August through 16 December 2015 (2b).

Figure 3. Distribution of dissolved oxygen (isopleths in mg/L) as recorded in 2015 at site TR4-C on Otsego Lake. Points along the x-axis indicate profile observation dates.

Calcium Calcium concentrations followed a typical pattern of seasonal fluctuation similar to that of alkalinity (with similar irregular patterns observed on some late summer/fall dates, when the calcium concentrations at 40m and/or 44m were 5-10 mg/l lower than overlying waters (see Alkalinity, above)). Mean annual concentration at TR4-C was 48.2 mg/l, ranging from 33.7 mg/l at the surface on 04 November to 53.7 mg/l at 20m on 03 June.

Chlorides Mean chloride concentrations in Otsego Lake from 1925 to 2015 are shown in Figure 4. Between 1994 and 2005, mean concentration increased steadily at of rate of 0.5 to 1.0 mg/l per year (Figure 4). Between 2006 and 2014, mean annual concentrations were variable and trended slightly downwards, possibly influenced by flushing of the system that occurred during major flooding events (2006, 2011, 2013). The mean lake-wide concentration in 2014 was 15.0 mg/l. Chlorides in Otsego Lake have generally been attributed to road salting practices, with the greatest influx of the ion during spring snowmelt events (Albright 1996).

Contamination is strongly suspected to have contributed to the irregular high values (i.e. 28mg/l on 19 March, 24mg/l on 17 August); while impossible to verify, chloride concentrations

16 in Otsego Lake have, without exception, displayed vertical and temporal homogeneity during summer stratification within any given year. In 2015, new equipment sanitation protocols which used a salt solution to disinfect sampling equipment were put into place in order to prevent the spread of AIS between lakes. It is possible that incomplete rinsing resulted in salt residue on some equipment that contaminated samples. Specific conductivity readings taken concurrent with sample collections did not reflect the trends observed in chloride concentration determined via titration in the laboratory, as would have been the case if the measured chloride concentrations reflected true conditions.

Table 2. Areal hypolimnetic oxygen deficits (AHOD) for Otsego Lake, computed over summer stratification in 1969, 1972 (Sohacki, unpubl.), 1988 (Iannuzzi, 1991), and 1992-2015.

Time Interval AHOD (mg/cm2/day)

05/16/69 - 09/27/69 0.080 05/30/72 - 10/14/72 0.076 05/12/88 - 10/06/88 0.042 05/18/92 - 09/29/92 0.091 05/10/93 - 09/27/93 0.096 05/17/94 - 09/20/94 0.096 05/19/95 - 10/10/95 0.102

05/14/96 - 09/17/96 0.090

05/08/97 - 09/25/97 0.101 05/15/98 - 09/17/98 0.095 05/20/99 - 09/27/99 0.095 05/11/00 - 09/14/00 0.109 05/17/01 - 09/13/01 0.092 05/15/02 - 09/26/02 0.087 05/16/03 - 09/18/03 0.087 05/20/04 - 09/24/04 0.102 05/27/05 - 10/05/05 0.085 05/04/06 - 09/26/06 0.084

05/18/07 - 9/27/07 0.083

05/08/08 - 10/7/08 0.088

05/27/09 - 10/19/09 0.082 05/26/10 - 10/7/10 0.053 05/19/11 – 10/12/11 0.060 05/24/12 – 10/05/12 0.056 05/21/13 – 10/15/13 0.061 05/21/14 – 10/15/14 0.050 05/19/15 – 10/06/15 0.049

17 20 18 16

14 12 10 8

Chloride (mg/l) (mg/l) Chloride 6 4 2 0 1920 1940 1960 1980 2000 2020 Year

Figure 4. Mean chloride concentrations at TR4-C, 1925-2015. Points later than 1990 represent yearly averages (figure modified from Peters 1987).

Nutrients Total phosphorus averaged 6 µg/l in 2015, ranging from below detection (< 4 µg/l) on multiple dates to 19 µg/l at 48m on 14 July. Concentrations were nearly homogeneous from surface to bottom on many dates during the growing season while higher, more variable, concentrations were observed occasionally (21 May, 18 June, 29 July, and 14 August). No phosphorus release from the sediments was observed prior to fall turnover, as dissolved oxygen was present at concentrations sufficient to maintain iron-phosphorus bonds in sediment materials.

Nitrite+nitrate-N averaged 0.55 mg/l; ammonia-N was not measured, as it is generally below detectable levels (<0.02 mg/l) when dissolved oxygen exists in the bottom of the hypolimnion. Total nitrogen analyses, yielding a mean of 0.69 mg/l, indicate an average organic nitrogen concentration of about 0.14 mg/l over the year.

Chlorophyll a and Secchi Disk Transparency Chlorophyll a concentrations were determined for samples collected on seven dates from June through September 2015. Average 0-20m composite chlorophyll a concentration was 1.8µg/l (range = 0.82 to 2.80 µg/l). Temporal and spatial distribution of chlorophyll a was studied in June and July is discussed by Garfield (2016).

Secchi disk transparency measurements, presented in Figure 5, began the ‘growing season’ at a season-maximum of 11m, reaching the lowest observation of 5.0m on 18 June. Fewer Secchi disk measurements were taken in 2015 than in most years; the temporal variation of transparency was similar to that observed in 2010 and 2013; May-September transparency measurements for 2010 through 2015 are presented in Figure 5. Mean summer Secchi

18 transparencies for all years available (1935-2015) are given in Figure 6. Mean transparency in 2015 was 5.1 m, lower than recent years, and individual observations were more variable (range: 2.7 to 11.0), encompassing a larger range.

2010 2011

0 0

2 2 4 4 6 6 8 8 10 10

Depth (meters) Depth 12 (meters) Depth 12 14 14 16 16 2012 2013

0 0

2 2 4 4 6 6 8 8 10 10

12 (meters) Depth 12 Depth (meters) Depth 14 14 16 16

2014 2015

0 0

2 2 4 4 6 6 8 8 10 10 12 12 Depth (meters) Depth Depth (meters) Depth 14 14 16 16

Figure 5. May through September Secchi transparencies at TR4C, Otsego Lake, 2010 through 2015.

19 Year

0.0

1.0

2.0

3.0

4.0

5.0 Secchi Transparency (m) (m) Transparency Secchi

6.0

7.0

8.0

Figure 6. Mean summer (May through September) Secchi disk transparency collected at TR4-C, 1935-2015.

CONCLUSIONS

Lake conditions continue to vary annually, as they have in recent years (i.e. Waterfield and Albright 2014, 2015). Interactions between management efforts, invasive species, and climate variability continue to develop. Trophic cascade has been described, linking recent changes in water quality to the combined effects of zebra mussel (Dreissena polymorpha) establishment (around 2007) and the walleye (Sander vitreus) stocking program that was intended to control the population of alewife (Alosa pseudoharengus) (an invasive forage fish). Region 4 Fisheries Biologists are currently evaluating the lake trout, walleye, and whitefish populations and have adjusted stocking programs accordingly. The virtual elimination of alewife, while a management success, was faster and more complete than expected and has left the lake trout without an important component of its winter diet. NYSDEC gill net catch in 2014 indicates decreased fitness of adult lake trout and an absence of juveniles, both of which are likely due to decreased forage fish abundance. Gill net catch also included lake whitefish, a native cold-water species, representing multiple age classes; this catch, together with the presence of many lake whitefish fry in larval fish samples, indicates ongoing recruitment by this once-prominent planktivore. In fall of 2015, lake whitefish were captured during the spawn for

20 the collection of eggs and milt. SUNY Cobleskill faculty and students fertilized the eggs and are rearing whitefish in the hatchery in anticipation of stocking Otsego Lake in 2016.

REFERENCES

Albright, M.F. 1996. Hydrologic and nutrient budgets for Otsego Lake, NY and relationships between land form/use and export rates of its sub-basins. Occas. Pap. 29. SUNY Oneonta Biol. Fld.Sta., SUNY Oneonta.

Albright, M.F. 1997. Otsego Lake limnological monitoring, 1996. In 29th Ann. Rept. (1996). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 1998. Otsego Lake limnological monitoring, 1997. In 30th Ann. Rept. (1997). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 1999. Otsego Lake limnological monitoring, 1998. In 31st Ann. Rept. (1998). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2000. Otsego Lake limnological monitoring, 1999. In 32nd Ann. Rept. (1999). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2001. Otsego Lake limnological monitoring, 2000. In 33rd Ann. Rept. (2000). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2002. Otsego Lake limnological monitoring, 2001. In 34th Ann. Rept. (2001). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2003. Otsego Lake limnological monitoring, 2002. In 35th Ann. Rept. (2002). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2004. Otsego Lake limnological monitoring, 2003. In 36th Ann. Rept. (2003). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2005. Otsego Lake limnological monitoring, 2004. In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2006. Otsego Lake limnological monitoring, 2005. In 38th Ann. Rept. (2005). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2007. Otsego Lake limnological monitoring, 2006. In 39th Ann. Rept. (2006). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2008. Otsego Lake limnological monitoring, 2007. In 40th Ann. Rept. (2007).

21 SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. and H.A. Waterfield. 2009. Otsego Lake limnological monitoring, 2008. In 41st Ann. Rept. (2008). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Albright, M.F. and M.J. Best. 2015. A survey of Otsego Lake’s zooplankton community, summer 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

APHA, AWWA, WPCF. 1989. Standard methods for the examination of water and wastewater, 17th ed. American Public Health Association. Washington, DC.

Austin, T., M.F. Albright, and W.N. Harman. 1996. Otsego Lake monitoring, 1995. In 28th Ann. Rept. (1995). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Ebina, J., T. Tsutsi, and T. Shirai. 1983. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res. 17(12):1721-1726.

EPA. 1983. Methods for the analysis of water and wastes. Environmental Monitoring and Support Lab. Office of Research and Development. Cincinnati, OH.

Groff, A., J.J. Homburger and W.N. Harman. 1993. Otsego Lake limnological monitoring, 1992. In 24th Ann. Rept. (1991). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Harman, W.N. 1994. Otsego Lake limnological monitoring, 1993. In 26th Ann. Rept. (1993). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Harman, W.N. 1995. Otsego Lake limnological monitoring, 1994. In 27th Ann. Rept. (1994). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Homburger, J.J. and G. Buttigieg. 1992. Otsego Lake limnological monitoring. In 24th Ann. Rept. (1991). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Iannuzzi, T.J. 1991. A model plan for the Otsego Lake watershed. Phase II: The chemical limnology and water quality of Otsego Lake, Occasional Paper #23. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Leach, J. H. 1993. Impacts of the zebra mussel (Dreissena polymorpha) on water quality and fish spawning reefs in western Lake Erie. In: Zebra Mussels: Biology, Impacts, and Control. Lewis Publishers, Boca Raton, FL p 381-397.

Liao, N. 2001. Determination of ammonia by flow injection analysis. QuikChem ® Method 10- 107-06-1-J. Lachat Instruments, Loveland, CO.

Liao, N. and S. Marten. 2001. Determination of total phosphorus by flow injection analysis colorimetry (acid persulfate digestion method). QuikChem ® Method 10-115-01-1-F. Lachat Instruments, Loveland, CO.

Peters, T. 1987. Update on chemical characteristics of Otsego lake water. In 19th Ann. Rept.

22 (1986). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Pritzlaff, D. 2003. Determination of nitrate/nitrite in surface and wastewaters by flow injection analysis. QuikChem ® Method 10-107-04-1-C. Lachat Instruments, Loveland, CO.

Vanassche, J., W.H. Wong, W.N. Harman, and M.F. Albright. 2014. Zebra mussels and other benthic organisms in Otsego Lake in 2008. In 46th Ann. Rept. (2013) SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2010. Otsego Lake limnological monitoring, 2009. In 42nd Ann. Rept. (2009). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2011. Otsego Lake limnological monitoring, 2010. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2012. Otsego Lake limnological monitoring, 2011. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2013. Otsego Lake limnological monitoring, 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2014. Otsego Lake limnological monitoring, 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2015. Otsego Lake limnological monitoring, 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Wells, S.M. 2015. Personal Communication. New York State Department of Environmental Conservation, Bureau of Fisheries. Region 4 Office, Stamford, NY. (607) 652-2660.

Welschmyer, N.A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol. Oceanogr. 39:1985-1992.

YSI Incorporated. 2009. 6-Series multiparameter water quality sonde user manual. Yellow Springs, OH.

23 A survey of Otsego Lake’s zooplankton community, summer 2015 M.F. Albright and M.D. Robinson1

INTRODUCTION (from Tanner and Albright 2014) This is a continuation of a study that has entailed monitoring the zooplankton community that exists in Otsego Lake, in part to evaluate efforts implemented to control alewife (Alosa pseudoharengus) by the addition of walleye (Sander vitreus) as well as influences by the zebra mussel (Dreissena polymorpha). Historically, Otsego Lake has been characterized as oligo-mesotrophic based on various trophic state indicators (Harman et. al 1997). In the 1970s, data collected on Otsego Lake to evaluate algal standing crops were indicative of oligotrophic conditions (water that has low nutrients along with density of algae, but high dissolved oxygen readings) (Godfrey 1977). However, there was evidence of phosphorus loading rates more indicative of a mesotrophic state (where water contains moderate amounts of dissolved nutrients, promoting moderate algal growth and leading to deep-water oxygen declines) (Godfrey 1977). This was attributed to high rates of algal grazing by the crustacean zooplankton community in the lake that had been larger- bodied and more abundant compared to other lakes in New York studied at that time (Godfrey 1977). In 1986, alewife was documented in Otsego Lake (Foster 1990); by 1990 it was the dominant forage fish (Warner 1999). Being efficient grazers, they virtually eliminated the larger bodied crustacean plankton (Warner 1999). The zooplankton community changed from crustacean dominance to rotifers gaining dominance (Foster and Wigens 1990). Rotifers sequester fewer nutrients and have substantially lower algal grazing rates than crustaceans (Warner 1999). Through the 1990s and early 2000s, higher algal standing crops lead to lower transparencies in the summer and the increased rates of hypolimnetic oxygen depletion (Harman et al. 2002). Though there were mitigative efforts set forth to reduce the nutrient inputs in the lake (Albright 2005), the efforts seemed to be overshadowed by the indirect influence of the still- dominant alewife. Walleye (Sander vitreus) have been stocked into Otsego lake since 2000 (Cornwell 2005) at a targeted rate of 80,000 per year (though most years the numbers have been lower; Sanford 2012). The expectation was that predation on alewife might allow for the re-establishment of crustacean zooplankton through trophic cascading, returning oligotrophioc conditions to Otsego Lake (Cornwell 2005). Zebra mussels were first documented in Otsego lake in 2007 (Waterfield 2009) and by 2010, adults had become widespread throughout substrate all over the lake (Albright and Zaengle 2012). This study helps give insight on the zebra mussel reproductive timing through the detection of veligers, even though the composite samples are not suggestive of the entire lakes condition and it is not certain of the affects by the zebra mussel in the zooplankton community.

1 BFS Intern, Summer 2015. Current Affiliation: State University of New York at Oneonta.

24 METHODS From 4 May to 16 September 2015, samples were taken biweekly at TR4-C (Figure 1) to evaluate the temporal distribution of the zooplankton community at the deepest location of Otsego Lake. This site historically has been monitored regularly for physical, chemical and biological parameters. At each site, a conical 63 µm plankton net with a 0.2m diameter opening was used for collecting zooplankton. The end of the cup was weighted and the net was lowered to, then hauled up from, 12 m (the approximate depth to the thermocline by late summer). A G.O.™ mechanical flow meter was mounted across the net opening, allowing for calculation of the volume of lake water filtered. The concentrated samples were preserved with ethanol. Samples were analyzed one ml at a time on a gridded Sedgwick Rafter cell. Zooplankton were identified, measured and enumerated using a research grade compound microscope with digital imaging capabilities. Typically, at least 100 organisms were viewed per sample. After each slide was assessed as above, cross polarized light was employed and the cell was viewed again to enumerate zebra mussel veligers as described by Johnson (1995). Mean densities and lengths for cladocerans, copepods and rotifers were used to calculate dry weight (Peters and Downing 1984), daily filtering rate (Knoechel and Holtby 1986) and phosphorus regeneration (Esjmon-Karabin 1983) on each date sampled according to the equations provided in Table 1.

Figure 1. Otsego Lake, New York, showing the three sample sites (TR3-C, TR4-C and TR5-C).

Table 1. Equations used to determine zooplankton dry weight (Peters and Downing 1984), filtering rates (Knoechel and Holtby 1986), and phosphorus regeneration rates (Esjmon-Karabin 1983).

Dry weight: D.W. = 9.86*(length in mm)2.1 Filtering Rate: F.R. = 11.695*(length in mm)2.48 Phosphorus regeneration: Cladocerans: P.R. = .519*(dry weight in ug)-.023*e0.039*(temp. in C) Copepods: P.R. = .229*(dry weight in ug)-.645*e0.039*(temp. in C) Rotifers: P.R. = .0514*(dry weight in ug)-1.27*e0.096*(temp. in C)

26 RESULTS AND DISCUSSION Table 2 summarizes the data collected from TR4-C over the summer of 2015, including mean epilimnetic temperature (which influences phosphorus regeneration rates), zooplankton densities, mean lengths and dry weights, dry weights per liter, phosphorus regeneration and filtration rates. Figure 2 provides the calculated dry weights of rotifers, copepods and cladocerans on each date sampled at TR4C over the summer of 2015. Figures 3, 4, 5, 6 and 7 provide comparable data from 2014, 2013, 2012, 2011 and 2010, respectively. It is difficult to discern any seasonal pattern over the recent years, though rotifers continue to comprise only a minor part of the community, in contrast to the period during which alewife were dominant (Warner 1999). Table 3 summarizes the mean crustacean density, mean cladoceran size and mean dry weight, percent of the epilimnion filtered per day and phosphorus regeneration by crustaceans in 2000 and 2002 – 2015 for samples collected at TR4-C. Over the past several years, the zooplankton community in Otsego Lake has changed substantially. In the 2000s the community of cladocerans was predominantly Bosmina longirostris, a small bodied organism, typically around 0.3mm. When Daphnia were present, their measurements were around 0.6 to 0.7 mm (Harman et al. 2002). In the more recent years, the Daphnia have increased relative to Bosmina, and have increased in mean size of over 1.0 mm, leading to an increase in the mean cladoceran length. Throughout the 2012 season, the cladocerans were comprised of 98 percent of Daphnia sp., averaging 21.5/1 and having a mean length of 1.19 mm (Albright 2012). In 2013, the crustacean community was split equally between Bosmina and Daphnia and averaged 5.5 crustaceans/l and 1.0 mm length. Again in 2014, the cladaceran community was approximately split between Bosmina and Daphnia, with both being at somewhat lower densities. That led to lower mean crustacean dry weight, lower rates of epilimnetic filtering rates and lower phosphorus regeneration rates. Despite lower filtering, chlorophyll a concentrations tended to be low (<2ug/l), transparency high (mean= 6.1 m) and the rate of hypolimnetic oxygen depletion, at 0.050 mg/cm2/day, was the lowest ever recorded for Otsego Lake (Waterfield and Albright 2015).

27 Avg Avg Mean Dry Phos. Regen. Rate Phos. Regen. Filtering % Temp. #/L length Dry Wt (ugP*mgdrywt-1 Rate Rates Epilimnion (°C) (mm) Wt (µg) (µg/L) *ind*h-1) (ug/l/day) (ml/ind/day) filtered/day 5/4 6.25Avg Avg Mean Dry Phos. Regen. Rate Phos. Regen. Filtering % Cladocera Temp. 1.20 #/L length1.762 39.86Dry 47.64Wt ugP*mgdrywt0.284 -1 0.324Rate 47.622Rates Epilimnion5.69 Copepoda 20.12 0.616 4.36 87.70 0.113 0.238 3.520 7.08 Rotifers 2.19 0.100 0.09 0.20 1.386 0.007 0.039 0.01 Total 135.53 0.569 12.78 5/19 11.63 Cladocera 4.26 0.943 9.40 40.00 0.488 0.468 10.103 4.30 Copepoda 31.55 0.625 4.69 147.86 0.133 0.472 3.643 11.49 Rotifers 1.25 0.148 0.20 0.25 0.190 0.001 0.102 0.01 Total 188.11 0.942 15.81 6/3 14.67 Cladocera 4.12 0.931 8.53 35.15 0.562 0.474 9.806 4.04 Copepoda 49.47 0.368 2.04 101.12 0.780 1.894 0.979 4.84 Rotifers 0.00 Total 136.27 2.367 8.89 6/17 18.18 Cladocera 2.25 1.307 18.44 41.58 0.539 0.538 22.710 5.12 Copepoda 17.21 0.390 1.85 31.80 0.313 0.239 1.134 1.95 Rotifers 0.00 Total 73.38 0.777 7.07 7/2 18.44 Cladocera 0.83 1.177 17.86 14.82 0.543 0.193 17.509 1.45 Copepoda 11.41 0.396 2.02 23.02 0.296 0.163 1.178 1.34 Rotifers 0.31 0.119 0.11 0.04 0.489 0.000 0.059 0.00 Total 37.87 0.357 2.80 7/14 19.87 Cladocera 1.82 0.352 2.06 3.76 0.893 0.081 0.881 0.16 Copepoda 10.94 0.440 2.59 28.30 0.252 0.171 1.526 1.67 Rotifers 48.02 0.149 0.34 16.24 0.124 0.048 0.103 0.50 Total 48.31 0.300 2.33

Table 2. Summary of site TR4-C of 2014 for mean epilimnetic temperature, zooplankton densities and mean length per taxa, as well as derived values for mean weight per individual and per liter, phosphorus regeneration per individual and per liter, filtering rates per individual and the percent epilimnion filtered per day.

28 (°C) (mm) Wt (µg) (µg/L) *ind*h-1 (ug/l/day) ml/ind/day filtered/day 7/28 21.21 Cladocera 5.91 0.51 3.11 18.37 0.914 0.403 2.151 1.27 Copepoda 21.78 0.314 0.164 1.153 1.13 9.84 0.39 2.21 Rotifers 5.66 0.14 0.27 1.54 0.184 0.007 0.082 0.05 Total 41.68 0.574 2.45 8/10 21.15 Cladocera 0.46 1.164 13.62 6.24 0.649 0.097 17.046 0.78 Copepoda 7.33 0.526 3.10 22.73 0.252 0.137 2.374 1.74 Rotifers 9.85 0.119 0.12 1.13 0.546 0.015 0.060 0.06 Total 30.10 0.249 2.58 8/27 22.11 Cladocera 3.45 0.461 2.77 9.56 0.973 0.223 1.716 0.59 Copepoda 19.95 0.470 2.78 55.44 0.281 0.373 1.798 3.59 Rotifers 14.58 0.114 0.10 1.53 0.641 0.023 0.053 0.08 Total 66.53 0.620 4.26 9/16 21.51 Cladocera 3.44 0.738 6.01 20.66 0.795 0.394 5.514 1.90 Copepoda 43.33 0.561 3.64 157.94 0.230 0.872 2.790 12.09 Rotifers 185.03 0.107 0.09 17.16 0.730 0.301 0.046 0.85 Total 195.76 1.567 14.84

Season mean Cladocera 2.522 0.849 11.059 21.615 0.604 0.291 12.278 2.301 Copepoda 20.105 0.435 2.662 61.608 0.269 0.429 1.827 4.267 Rotifers 24.263 0.110 0.147 4.232 0.477 0.045 0.061 0.173 Total 87.45 0.765 6.74

Table 2 (cont.). Summary of site TR4-C of 2014 for mean epilimnetic temperature, zooplankton densities and mean length per taxa, as well as derived values for mean weight per individual and per liter, phosphorus regeneration per individual and per liter, filtering rates per individual and the percent epilimnion filtered per day.

29 400 Rotifera 350 2015 Copepoda

300 Cladocera 250 200 150 100 Dry weight (ug/l) 50 0 5/4 5/19 6/3 6/17 7/2 7/14 7/28 8/10 8/27 9/16

Figure 2. Dry weight combined by rotifers, copepods and cladocerans in Otsego Lake over the summer of 2015 at TR4-C.

400 Rotifera 350 2014 Copepoda

300 Cladocera 250 200 150 100 Dry weight (ug/l) 50 0 5/7 5/21 6/3 6/18 7/2 7/14 7/29 8/14 9/3 9/17

Figure 3. Dry weight combined by rotifers, copepods and cladocerans in Otsego Lake over the summer of 2014 at TR4-C (Albright and Best 2015).

400 Rotifera 350 2013 Copepoda

300 Cladocera 250 200 150 100 Dry weight (ug/l) 50 0 5/2 5/21 6/5 6/18 7/3 7/17 7/30 8/14 8/27 9/11 9/24

Figure 4. Dry weight combined by rotifers, copepods and cladocerans in Otsego Lake over the summer of 2013 at TR4-C (Tanner and Albright 2014).

30 400 2012 Rotifera 350 Copepoda

300 Cladocera 250 200 150 100 Dry weight (ug/l) 50 0 5/9 5/24 6/7 6/21 7/4 7/19 8/2 8/16 9/5 9/19

Figure 5. Dry weight combined by rotifers, copepods and cladocerans in Otsego Lake over the summer of 2012 (Albright 2013) at TR4-C.

400 Rotifera 350 2011 Copepoda

300 Cladocera 250 200 150 100 Dry weight (ug/l) 50 0 5/19 6/1 6/15 6/28 7/13 7/26 8/8 8/24 9/9 9/27

Figure 6. Dry weight combined by rotifers, copepods and cladocerans in Otsego Lake over the summer of 2011. (Albright and Zaengle 2012) at TR4-C.

400 Rotifera 350 2010 Copepoda

300 Cladocera 250 200 150 100 Dry weight (ug/l) 50 0 5/18 6/4 6/15 7/1 7/15 8/2 8/12 8/26

Figure 7. Dry weight combined by rotifers, copepods and cladocerans in Otsego Lake over the summer of 2010 at TR4-C (Albright and Leonardo 2011).

Table 3. Mean crustacean density, mean cladoceran size and mean dry weight, percent of the epilimnion filtered per day and phosphorus regeneration by crustaceans in 2000 and 2002 – 2014. Samples collected at TR4-C.

Mean: 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 cladoceran size (mm) 0.29 0.30 0.36 0.53 0.55 0.55 0.34 0.54 0.69 0.81 0.76 1.19 1.00 0.98 0.86 crustacean density (#/l) 208 146 132 163 159 159 154 178 97 56.7 59.4 21.5 28.10 19.9 22.6 crustacean dry weight (ug/l) 175 145 177 261 206 206 128 321 142 143 155 122 102 84 83 % of epilimnion filtered/day 11.9 9.9 12.7 25.1 19.2 19.2 12.2 31.9 9.5 10.8 12.1 11.5 7.70 6.8 6.74 phosphorus regeneration (ug/l/day) 4.49 2.60 3.10 4.40 2.70 2.40 3.00 5.80 1.49 1.90 1.80 1.17 1.02 0.69 0.76

Figure 8 illustrates the abundance of zebra mussel veligers in the 0-12 m composite samples collected over the summer of 2014 at TR4-C. The density at TR4-C peaked on 14 July at 46 individuals/l, the highest ever recorded at mid-lake. The peak veliger density of other years since 2000, when monitored, was between 19 and 33/l, with the timing of those peaks varying considerable (from 21 June in 2012 to 24 August in 2010). (Data are not available for 2011).

TR4-C 40

10 Veliger density (#/liter)

0 4/20 5/10 5/30 6/19 7/9 7/29 8/18 9/7 9/27

Figure 8. Abundance of zebra mussel veligers in the 0-12 m composite samples collected over the summer of 2015 at TR4-C.

CONCLUSION Through the 1990s, when alewives were dominant, there were very low numbers of larger bodied crustaceans; plankton filtering rates were low, algal standing crops were high, transparencies were low and hypolimnetic oxygen demand was high (Harman et al. 2002). Following the establishment of walleye, alewife were virtually eliminated from the lake (Waterfield and Cornwell 2013; Stowell 2014) and the above trends were reversed. Secchi transparency was high (mean = 5.6m) and oxygen depletion rates were low (AHOD = 0.05- 0.06

32 mg/cm2/day) (Waterfield and Albright 2013; 2014; 2015) with chlorophyll a being low, generally < 2 µg/l (Bianchine and Tanner 2014; Freehafer 2015; Garfield 2016). While at a lower density than observed in 2012, Daphnia continued to be common and large bodied through the first half of the summer of 2013. Daphnia were present at every sampling date over 2014 at an average length of about 1.0 mm, but had declined in abundance to an average of 2 individuals/l. The character of the lake continued to reflect oligotrophic conditions. The influence the zebra mussels on the zooplankton community is not well understood.

REFERENCES Albright, M.F. 2005. A report on the evaluation of changes in water quality in a stream following the implementation of agricultural best management practices. In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. 2013. A survey of Otsego Lake’s zooplankton community. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. and M.J. Best. 2015. A survey of Otsego Lake’s zooplankton community, summer 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. and O. Zaengle. 2012. A survey of Otsego Lake’s zooplankton community, summer 2011. In 44th Ann. Rept. (2011). SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. and M. Leonardo. 2011. A survey of Otsego Lake’s zooplankton community, summer 2010. In 43rd Ann. Rept. (2010). SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta.

Cornwell, M.D. 2005. Re-introduction of walleye to Otsego Lake: Re-establishing a fishery and subsequent influences of a top predator. Occas. Pap. No. 40. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Esjmont-Karabin, J. 1984. Phosphorus and nitrogen excretion by lake zooplankton (rotifers and crustaceans) in relation to the individual body weights of the animals, ambient temperature, and presence of food. Ekologia Polska 32:3-42.

Foster, J.R. 1990. Introduction of the alewife (Alosa pseudoharengus) in Otsego Lake. In 22nd Ann. Rept. (1989) SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

Foster, J.R. and J. Wigen.1990. Zooplankton community as an ecological indicator in cold water fish community of Otsego Lake. In 22nd Ann. Rept. (1989). SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

Freehafer, M. 2015. Chlorophyll a concnetrations in Otsego Lake, summer 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

33 Garfield, C. 2016. Chlorophyll a concnetrations in Otsego Lake, summer 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Godfrey, P.J. 1977. An alalysis of phytoplankton standing crop and growth: Their historical development and trophic impacts. In 9th Ann. Rept. (1976). SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

Johnson, L.E. 1995. Enhanced early detection and enumeration of zebra mussel (Dreissena sp.) veligers using cross-polarized light microscopy. Hydrobiologia 312:139-147.

Harman, W.N., M.F. Albright and D.M. Warner. 2002. Trophic changes in Otsego Lake, NY following the introduction of the alewife (Alosa pseudohargenous). Lake and Reserv. Manage. 18(3)215-226.

Iannuzzi, T.J. 1991. A model plan for the Otsego Lake watershed. Phase II: The chemical limnology and water quality of Otsego Lake, Occasional Paper #23. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Knoechel, R. and B. Holtbly.1986 Construction of body length model for the prediction of cladoceran community filtering rates. Limnol. Oceanogr. 31(1):1-16.

Peters, R.H. and Downing, J.A. 1984. Empirical analysis of zooplankton filtering and feeding rates. Limnology and Oceanography, 29 (4). pp. 763-784.

Sanford, S. 2012. Pers. Comm. Sanford Bait Farm, Wolcott, NY.

Stowell, S.G. 2014. Trap net monitoring of fish communities within the weedy littoral zone at Rat Cove and rocky littoral zone at Brookwood Point, Otsego Lake. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Tanner, C. and M.F. Albright. 2014. A survey of Otsego Lake’s zooplankton community, summer 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Warner, D.M. 1999. Alewives in Otsego Lake, NY: a comparison of their direct and indirect mechanisms of impact on transparency and chlorophyll a. Occas. Pap. No.32. SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

Waterfield, H.A. 2009. Update on zebra mussel (Dreissena polymorpha) invasion and establishment in Otsego Lake, 2008. In 41st Ann. Rept. (2008). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. and M.F. Albright. 2016. Otsego Lake limnological monitoring, 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. and M.F. Albright. 2015. Otsego Lake limnological monitoring, 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. and M.F. Albright. 2014. Otsego Lake limnological monitoring, 2013. In 46th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. and M.F. Albright. 2013. Otsego Lake limnological monitoring, 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. and M.D. Cornwell. 2013. Hydroacoustic surveys of Otsego Lake’s pelagic fish community, 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

35 Chlorophyll a monitoring on Otsego Lake, Cooperstown, NY, summer 2015

Claire Garfield1

INTRODUCTION

Quantifying chlorophyll-α concentration on a regular basis is quintessential to determining the health of a lake. Chlorophyll a is a pigment that facilitates photosynthesis and is found in species of algae in Otsego Lake (APHA 2012). Therefore, chlorophyll a concentration is indicative of the size of the algal standing crop (Harman et al. 2002); that, in conjunction with nutrient levels and lake morphology, determines the trophic state of the lake. The combination of the relatively low algal biomass and nutrients in Otsego Lake characterize the lake as meso- oligotrophic (Godfrey 1977). Furthermore, chlorophyll a contributes to or correlates with many other parameters that should be monitored as part of a lake management plan making it useful in identifying anomalies that warrant further investigation. One such parameter is dissolved oxygen, which is important when considering the biological capacity of a lake. Algae add oxygen as a byproduct of photosynthesis; however, it also removes oxygen when the algae die and decompose (Wetzel 2001).

Chlorophyll a levels have changed substantially since the late 1990s. This trend can be attributed to the reduction of alewife (Alosa psuedoharengus) and the introduction of zebra mussels (Dreissena polymorpha). Alewife, a planktivorous fish, contributed heavily to a larger algal crop since introduced to Otsego Lake in 1986 (Foster 1990). Alewife predation decreased the abundance of zooplankton thus resulting in increased phytoplankton biomass (Harman et al. 2002). Zebra mussels, exotic filter-feeders that were documented in 2007, filter the water of algae and thus reduce algal biomass (Waterfield 2009).

This study was conducted to ascertain the concentration of chlorophyll a in Otsego Lake thereby gaining a more complete understanding of the biogeochemical processes occurring the lake and helping to better address environmental issues threatening the lake.

METHODS

Samples were collected at site TR4-C, the deepest site on the lake, on a bi-weekly basis. A Kemmerer sampler was used to collect discrete samples at one meter intervals from zero to twenty meters. A composite sample was taken using two garden hoses attached to a weighted

1 F.H.V Mecklenburg Conservation Fellow, summer 2015. Present affiliation: Oneonta High School. Funding provided by the Otsego County Conservation Association.

36 line. It was lowered to 20 m and retrieved from the line attached to the hose bottom. Samples were stored in Nalgene® bottles and placed in a cooler until at the Biological Field Station.

Figure 1. Bathymetric map showing the sampling site for chlorophyll-α during the summer of 2015.

37 Once at the Biological Field Station, samples were filtered through a 47mm Whatman® GF/A Micro Fiber filter with a low-pressure vacuum pump that expedited the filtration process. The filters were then folded, blot-dried, and put in petri dishes. To prevent degradation of the chlorophyll a samples were stored in a freezer until further processing.

Individual filters were then cut into small pieces and combined with buffered acetone

(90% acetone, 10% MgCO3) in a 15mL grinding tube. The samples were ground into a homogeneous pulp with a drill and Teflon pestle drill. Samples were placed in a 15mL centrifuge tube and additional acetone was added so the sample would be approximately 10mL.

Samples were placed in the centrifuge for 10 minutes at 10,000 X G. The liquid portion of the centrifuged sample was placed in the Turner Designs™ TD-700 fluorometer. The final concentration, in parts per billion, was established with the following equation from the methods of Arar and Collins (1997).

(~10 ) ×

(125 ) 푚푚 표표 �푓푓푓푓푓푓푓 푠푠�푠𝑠 푚푚 𝑝푝 푚푚 표표 푠푠�푠𝑠 𝑡 푡�푡 푚푚

RESULTS AND DISCUSSION

Chlorophyll a concentrations over the summer of 2015 have seemed to be somewhat higher than those of recent years (Figure 2). However, two issues may cause this to be misleading. First, the sampling period in 2015 was fairly short and limited to the peak season of algal growth (2 July to 12 August). Surface – 20 m composite samples collected both before (May-June) and after (late August-September) this study were markedly lower than the concentrations reported in Figure 2 (see Waterfield and Albright 2016). Secondly, during this study, the mean concentrations of the discrete profiles of samples consistently exceeded the composite sample concentrations by about 30% (Figure 3). No apparent reason for this can be suggested. The mean concentration of chlorophyll a from composite samples collected from 19 May to 16 September 2015 was 1.8 ug/l (Waterfield and Albright 2016), which is quite similar to concentrations reported since 2010.

The profile of chlorophyll a over the study is presented in Figure 4. Concentrations were constantly higher in the metalimnetic region (8-12 m depths). This region is an area of rapidly changing temperature. The deeper, cooler water is denser, so algae may passively settle out there. Some light sensitive species may actively prefer the reduced light of the thermocline in which to grown.

Figure 5 compares profiles of chlorophyll a measured in 2015 from those of 1997 through 2014. .

38 8

Concentration (ppb) Concentration 2

Year Figure 2. Mean surface-2- m summer chlorophyll a concentrations, summers 1997, 2000-2015.

5 Discrete mean 4.5 Composite 4 3.5 3 2.5 2 1.5

CONCENTRATION (ppb) CONCENTRATION 1 0.5 0 2 Jul 14 Ju 30 Jul 12 Aug

Figure 3. Average chlorophyll-α concentration for sampling dates, summer 2015, comparing mean values of the surface-20 m discrete profile samples and the measured values of the surface- 20 m composite samples.

39 Chlorophyll a Concentration at the TR4-C Sample Site, Summer 2015

Concentration (ppb)

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 0

6 7/2/2015 7/14/2015 8

7/30/2015

10 8/12/2015 Depth (m) Depth 12

Figure 4. Summer 2015 data from site TR4-C shows fluctuations in chlorophyll-α concentrations. Spikes are associated with the thermocline.

40 Average Chlorophyll a Concentrations in Otsego Lake at TR4-C

Concentration (ppb) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 0

4 1997 2000 2001 6 2002 2005 8 2006 2007 10 2010 Depth (m) Depth

2011 12 2012 2013 14 2014 2015 16

Figure 5. Data since 1997 to 2015, with the exception of 2008 and 2009, show fluctuations related to food web alterations. Data from 1997 were collected at two-meter intervals (King 1997) and are represented by markers only (no line).

Chlorophyll a levels have fluctuated since 1997. The high levels in the late 1990s and 2000s can be attributed to the high alewife (Alosa pseudoharengus) population that ate zooplankton, thus significantly reducing algal grazing. The decline since 2005 coincides with the reduction of alewife following the re-establishment of walleye (Sander vitreus) in the lake (Albright and Robinson 2016) The introduction of zebra mussels in 2007 (Waterfield 2009) likely increased filtration of lake water that resulted in lower chlorophyll a.

41 Despite fluctuations, chlorophyll a levels have remained consistent with a meso- oligotrophic lake. Furthermore, since the variation over time is largely attributed to nuisance species, the chemical health of the lake is consistent with years past, suggesting that management practices to reduce nutrient loading have been successful.

CONCLUSION

Algal standing crop, nutrient level, and morphological features of a lake determine the trophic state of the lake (Godfrey 1977). Furthermore, algae heavily influence biologically available dissolved oxygen. Because of the ecological importance of algae, chlorophyll a should be monitored because it is telling of the health of the lake. Based on this year’s data in contrast with data from previous years, Otsego Lake can be classified as meso-oligotrophic.

REFERENCES

Albright, M.F. and M.D. Robinson. 2016. A survey of Otsego Lake’s zooplankton community, summer 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M. and Leonardo M. 2011. A survey of Otsego Lake’s zooplankton community, summer 2010. In 43rd Annual Report (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta

Arar, EJ and GB Collins.1997. Method 445.0, In Vitro Determination of Chlorophyll a and Pheophytin a in Marine and Freshwater Algae by Fluorescence. In Methods for the Determination of Chemical Substances in Marine and Estuarine Environmental Matrices, 2nd Edition. National Exposure Research Laboratory, Office of Research and Development, USEPA., Cincinnati, Ohio. EPA/600/R-97/072.

APHA, AWWA, WPCF. 2012. Standard methods for the examination of water and wastewater, 22nd ed. American Public Health Association. Washington, DC

Chlorophyll-a [Internet]. Detroit Lakes(MN):RMB Environmental Laboratories; [2015, cited 2015 Jul 17] . Available from: http://rmbel.info/chlorophyll-a/

Freehafer, M. 2015. Chlorophyll a concentrations in Otsego Lake, summer 2014. In 47th Annual Report (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Godfrey, P.J. 1977. An analysis of phytoplankton standing crop and growth: Their historical development and trophic impacts. In 9th Ann. Rept. (1976). SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

42 Godfrey, P.J. 1979. Otsego Lake limnology: Phosphorus loading, chemistry, algal standing crop and historical changes. In 10th Ann. Rept. (1978). SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

Harman, W.N., M.F. Albright, and D.M. Warner. 2002. Trophic changes in Otsego Lake, NY following the introduction of the alewife (Alosa Pseudoharengus). Lake and Reservoir Management. 18(3):215-226.

King, D.A. 1998. Analysis of chlorophyll a in Otsego Lake, summer 1997. In 30th Annual Report (1997). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta

Korhonen et al. 2011. Productivity-diversity relationships in lake plankton communities. 10.1371/journal.pone.0022041

Levenstein, A. 2012. Chlorophyll a concentrations in Otsego Lake, summer 2011. In 44th Annual Report (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A., and M.F. Albright. 2016. Otsego Lake limnological monitoring, 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. 2009. Update on zebra mussel (Dreissena Polymorpha) invasion and establishment in Otsego Lake, 2008. In 41st Annual Report. (2008). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Wetzel R.G. 2001. Limnology: lake and river ecosystems. 3rd ed. San Diego, California. Academic Press.

43 Water quality monitoring and analysis of fecal coliform of the five major tributaries in the Otsego Lake Watershed

Britney Wells1

INTRODUCTION

Water quality monitoring of the northern watershed of Otsego Lake continued in the summer of 2015. White Creek, Cripple Creek, Hayden Creek, Shadow Brook, and a stream that runs off Mount Wellington together provide about 70% of inflow to Otsego Lake (Harman et. al 1980). Forty four % of the land within the watershed was used for agricultural purposes (Harman et. al 1997). Otsego Lake was classified as mesotrophic with characteristics of an oligotrophic lake (Iannuzzi 1991). However, throughout the 1990’s, limnological monitoring of Otsego Lake showed a slight trend towards eutrophication (Harman et. al 1997). Eutrophication can ultimately affect the stability of cold water aquatic organisms such as native lake trout (Salvelinus namaycush). Increasing eutrophication rates are attributed to nutrient loading from manmade sources such as wastewater treatment systems (Meehan 2004a), agricultural runoff (Murray and Leonard 2005), and residential land use (Albright 2005).

This report summarizes the overall sources of contaminants by evaluating nutrient (phosphorus and nitrogen) and fecal coliform growth at each site. Analyzing fecal coliform concentrations at the stream sites can help track these nonpoint sources of pollution. Fecal coliform are a group of bacteria that occur in the digestive tracts of mammals, and thus serve as an appropriate indicator of fecal contamination (APHA 2012). The presence of fecal coliform could suggest manure runoff or inadequately treated wastewater.

In 1998, the Otsego Lake Watershed Council (OLWC 1998) created the Plan for the Management of the Otsego Lake Watershed, which focused on the reduction of excess nutrients in the five tributaries that are transported to the Lake (Anonymous 2007). In order to work towards lake ecological stability, USDA’s Natural Resource Conservation Service (NRCS) developed programs known as Best Management Practices (BMPs). Program techniques include conservation tillage, crop nutrient management, manure storage plans, and riparian buffers. This study is an annual assessment of the 23 BMPs that that are currently in place within the Otsego Lake watershed (Hewett 1996). In addition, these projects are expected to reduce levels of fecal coliform in receiving waters by reducing nutrient loading.

METHODS

Water samples were collected on 9 dates between 21 May 2015 and 22 July 2015 at 23 sites along White Creek, Cripple Creek, Hayden Creek, Shadow Brook and Mount Wellington. Each of these sites were originally chosen in 1995 based on factors such as access to the stream,

1 SUNY Oneonta Biological Field Station Intern, summer 2015. Funded by the Otsego Country Conservation Association.

44 the location relative to agricultural areas, and stream characteristics (Hewett 1996). Table 1 contains the site coordinates and description. Figure 1 illustrates each site and its proximity to farms utilizing BMPs.

Tributary Water Quality Monitoring

Physiochemical data were measured in situ at each site using a YSI (6820 V2) multiparameter probe, which was calibrated to the manufacturer’s specifications before data collection (YSI Inc. undated). Parameters measured included pH, temperature, specific conductivity, dissolved oxygen (DO) concentration (mg/L) and percent saturation, and turbidity (NTU).

In order to measure nutrient concentrations, water samples were collected in acid-washed 125-mL bottles for examination. The samples were preserved to <1 pH using sulfuric acid. Nutrients for each site were analyzed for total nitrogen (TN), nitrate+nitrite content (NO3), and total phosphorus (TP) using a Lachat ® QuikChem FIA+ Water Analyzer. Nitrate+nitrite content and total nitrogen were assessed with the cadmium reduction method (Pritzlaff 2003) and total phosphorus with the ascorbic acid method following persulfate digestion (Liao and Martin 2001).

Fecal Coliform

Fecal coliform water samples were collected in acid-washed 500 ml nalgene bottles and kept on ice during transportation. the samples were processed using the membrane filter technique (APHA, 2012). In short, a volume of sample was filtered through a Millipore membrane (45μm) using a low-pressure vacuum assembly. Each sample was analyzed in triplicate at 2 volumes (10 mL and 100 mL) in order to achieve the optimal 20-80 colonies per culture dish. Filters were cultured in petri dishes which had been seated on absorbent pads saturated in 2.2 mL of growth media made with FC Base by Bacto®. All filtering equipment was sterilized with 70% ethanol between sample sites. Dishes were inverted in water-tight containers in a 44.5 (±0.2) °C water bath for 24±2 hours. After some early episodes, during which leaking containers caused the flooding of some culture dishes, the containers were vacuum sealed using a Food Saver® vacuum system. Subsequently, blue colonies were counted and results reported as colonies per 100 mL. Cultures dishes were frozen to disinfect and then discarded.

Table 1. GPS coordinates and physical descriptions of sample locations (modified from Hastings, 2014).

White Creek 1 (WC1): N 42º 49.612’ W 74º 56.967’ South side of Allen Lake on County Route 26 over a steep bank.

White Creek 2 (WC2): N 42º 48.93’ W 74º 55.29’ Plunge-pool side of stream on County Route 27 (Allen Lake Road) where there is a large dip in the road.

45 Table 1 (cont.). GPS coordinates and physical descriptions of sample locations (modified from Hastings, 2014).

White Creek 3 (WC3): N 42º 48.407’ W 74º 54.178’ West side of large stone culvert under Route 80, just past the turn to Country Route 27.

Cripple Creek 1 (CC1): N 42º 50.878 W 74º 55.584’ Weaver Lake accessed from the north side of Route 20.

Cripple Creek 2 (CC2): N 42º 50.603’ W 74º 54.933’ Young Lake accessed from the west side of Hoke Road. The water at this site is shallow; Some distance from shore is required for sampling.

Cripple Creek 3 (CC3): N 42º 49.418’ W 74º 54.007’ North side of culvert on Bartlett Road.

Cripple Creek 4 (CC4): N 42º 48.837’ W 74º 54.032’ Large culvert on west side of Route 80. The stream widens and slows at this point; this is the inlet to Clarke Pond.

Cripple Creek 5 (CC5): N 42º 48.805’ W 74º 53.768’ Dam just south of Clarke Pond accessed from the Otsego Golf Club road. Samples were collected on the downstream side of the bridge.

Hayden Creek 1 (HC1): N 42º 51.658’ W 74º 51.010’ Summit Lake accessed from the east side of Route 80, north of the Route 20 and Route 80 intersections. Small pull off but researcher must wade in the water to place the YSI probe.

Hayden Creek 2 (HC2): N 42º 51.324’ W 74º 51.294’ Downstream side of culvert on Dominion Road.

Hayden Creek 3 (HC3): N 42º 50.890’ W 74º 51.796’ Culvert on the east side of Route 80 north of the intersection of Route 20 and Route 80.

Hayden Creek 4 (HC4): N 42º 50.267’ W 74º 52.175’ North side of large culvert at the intersection of Route 20 and Route 80.

Hayden Creek 5 (HC5): N 42º 49.996’ W 74º 52.501’ Immediately below the Shipman Pond spillway on Route 80.

Hayden Creek 6 (HC6): N 42º 49.685’ W 74º 52.773’ East side of the culvert on Route 80 in the village of Springfield Center.

Hayden Creek 7 (HC7): N 42º 49.279’ W 74º 53.984’ Large culvert on the south side of County Route 53.

46 Table 1 (cont.). GPS coordinates and physical descriptions of sample locations (modified from Hastings, 2014).

Hayden Creek 8 (HC8): N 42º 48.869’ W 74º 53.291’ Otsego Golf Club, above the white bridge adjacent to the clubhouse. The water here is slowing moving and murky.

Shadow Brook 1 (SB1): N 42º 51.831’ W 74º 47.731’ Small culvert on the downstream side off of County Route 30 south of Swamp Road.

Shadow Brook 2(SB2): N 42º 49.891’ W 74º 49.067’ Large culvert on the north side of Route 20, west of County Route 31.

Shadow Brook 3 (SB3): N 42º 48.799’ W 74º 49.839’ Private driveway (Box 2075) off of County Route 31, south of the intersection of Route 20 and Country Route 31 leading to a small wooden bridge on a dairy farm.

Shadow Brook 4 (SB4): N 42º 48.337’ W 74º 50.608’ One lane bridge on Rathbun Road. This site is located on an active dairy farm. The streambed consists of exposed limestone bedrock.

Shadow Brook 5 (SB5): N 42º 47.441’ W 74º 51.506’ North side of large culvert on Mill Road behind Glimmerglass State Park.

Mount Wellington 1 (MW1): N 42º 48.843’ W 74º 52.608’ Stone bridge on Public Landing Road adjacent to an active dairy farm.

Mount Wellington 2 (MW2): N 42º 48.77’ W 74º 53.004’ Small stone bridge is accessible from a private road off Public Landing Road; at the end of the private road near a white house there is a mowed path which leads to the bridge. Water here is stagnant and murky. Sample was taken on the same side as the lake.

Figure 1. All 23 sites along White Creek, Cripple Creek, Hayden Creek, Shadow Brook and Mount Wellington marked with numbers. Asterisks represent the farms that implemented BMP’s. RESULTS AND DISCUSSION

Tributary Water Quality Monitoring

Temperature Temperature is a critical factor within freshwater environments, both in determining water quality, as well as aquatic flora and fauna diversity. Temperature is inversely related to dissolved oxygen, with colder water being able to hold higher amounts of dissolved oxygen (Weiner 2008). Riparian vegetation buffer zones, an application of BMPs, can help stabilize temperature fluctuations by shading the stream channel. In 2015, mean temperatures ranged from 15.26 °C at Cripple Creek 3 to 22.87 °C at Hayden Creek 1 (Figure 2). This temperature range is

48 comparable to the summers of 2013 and 2014 when minimum and maximum temperatures also were seen at these sites (Teter 2013; Hastings 2014).

Mean Temperature 25.0

23.0

21.0

19.0

17.0 Temperature (C) Temperature 15.0

13.0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 2. Mean Temperatures of sampling sites along stream gradients of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

Dissolved Oxygen The health of aquatic ecosystems is partly determined by dissolved oxygen (DO). DO usually increases in streams during the day as aquatic plants photosynthesize and release oxygen, and declines at night while they are consuming oxygen (Weiner 2008). Undisturbed vegetative stream banks would result in a cooler temperature and more oxygen rich water. Mean DO concentrations ranged from 7.54 mg/L at Hayden Creek 1 to 11.39 mg/L at Shadow Brook 3 (Figure 3). These concentrations are significantly higher than DO ranges from the past ten summers. This increase could be potentially due to large amounts of precipitation this summer.

49 Mean Dissolved Oxygen 12.0 11.0

10.0 9.0 8.0 7.0 6.0 5.0 Dissolved Oxygen (mg/l) Oxygen Dissolved 4.0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km) White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 3. Mean dissolved oxygen of sampling sites along the stream gradients of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

pH The geology of a lake basin and watershed plays a large role in pH levels. Limestone, acting as a pH buffer, is abundant in the Otsego watershed (Harman et. al. 1997). Mean pH values were within an anticipated range given past results and the watersheds geology, displaying stable levels near neutral or slightly basic. Mean values in 2015 ranged from 7.7 and 8.2 across the five tributaries (Figure 4).

50 Mean pH 8.5 8.4 8.3 8.2 8.1

pH pH 8.0 7.9 7.8 7.7 7.6 7.5 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km) White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 4. Mean pH of sampling sites along the stream gradients of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

Specific Conductance Similar to pH, the underlying geology is the initial contributor to baseline specific conductance readings. It is also influenced by dissolved inorganic ions such as road salt or nutrient concentrations (Weiner 2008). Specific copnductance ranged from 0.299 mmho/cm at White Creek 2 and 0.618 mmho/cm at Mount Wellington 2 (Figure 5). Conductivity levels remained constant through the sampling period at each site which indicated there was not a sudden discharge of pollutants.

51 Mean Conductivity

0.6

0.5

0.4

0.3

0.2 Conductivity (ms/cm) Conductivity

0.1 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km) White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 5. Mean conductivity of sampling sites along the stream gradients of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

Turbidity Turbidity causes water to appear cloudy and is caused from suspended particles in the water. In 2015, turbidity ranged from near 0 NTU at Cripple Creek 3 to 22.9 NTU at Mount Wellington 1 (Figure 6). These results reflect the difference in site characteristics. Cripple Creek 3 was a straight, rapidly flowing site with stabilizing riparian vegetation situated on lower stream banks. Mount Wellington 2 contained water that was murky and stagnant in which turbidity possibly attributed to algal growth, opposed to suspended sediment. In addition, the Mount Wellington tributary and Shadow Brook 5 had among the highest turbidity values from 2012 to 2015.

52 Mean Turbidity 30

10 Turbidity (NTU) Turbidity 5

0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 6. Mean turbidity of sampling sites along the stream gradients of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

Phosphorus The productivity of Otsego Lake is limited by phosphorus (Harman et al. 1997). Excess phosphorus within lakes can be attributed to factors such as livestock waste, agricultural fertilizers and residential wastewater treatment systems. Forty six% of Shadow Brook’s basin is farmed and it was the largest contributor of phosphorus and sediments to the lake between 1991 and 1993 (Albright 1996). To mitigate this, BMPs riparian conservation buffers reduce the volume of overland flow into the streams that contain excess nutrients. Figure 7 shows the mean total phosphorus content of samples taken at each site in 2015. Figure 8 shows mean total phosphorus at stream outlets from 1996 to 2015. A comparison of the mean phosphorus concentrations at each site, (2000-2015) is displayed in Table 2. Mean summer total phosphorus concentrations ranged from 18 µg/L at Shadow Brook 4 to 70 µg/L at Cripple Creek 1, respectively. Mean total phosphorus has been slightly variable but showed a clear overall decline in most sites. Shadow Brook and Mount Wellington contain the most BMPs in which these tributaries have improved most drastically throughout the years.

53 Mean Total Phosphorus 250.0

200.0

150.0

100.0

Total Phosphorus (ug/L) Total 50.0

0.0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 7. Mean total phosphorus of sampling sites along the stream gradients of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

Mean Total Phosphorus at Stream Outlets 250

200

150

100

50 Total Phosphorus (ug/L) Phosphorus (ug/L) Total

0 White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington Stream Outlets 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Figure 8. Mean total phosphorus concentration (µg/L) at the most downstream sites (outlets) of five tributaries in the northern watershed of Otsego Lake 1996-201.

Comparison of phosphorus concentrations (µg/L), 2000-2015 Site ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 WC1 31 34 72 25 33 51 17 66 46 33 33 25 22 18 26 34 WC2 28 33 23 26 39 61 33 37 34 24 25 25 33 29 27 29 WC3 19 24 12 23 26 36 40 38 19 22 17 21 21 27 30 33 CC1 45 36 112 30 49 49 33 86 89 38 63 49 62 26 30 70 CC2 48 23 46 124 144 172 37 36 25 24 25 28 22 22 22 40 CC3 25 24 10 25 39 37 62 40 22 26 41 30 21 49 27 31 CC4 28 35 19 22 46 55 40 39 34 27 45 30 37 126 28 36 CC5 42 45 51 28 46 70 37 58 59 34 41 40 51 45 31 31 HC1 26 25 60 21 43 33 33 48 43 35 28 53 22 25 21 46 HC2 20 17 14 13 23 34 57 30 27 18 24 20 52 23 22 18 HC3 25 28 47 26 34 39 50 35 54 24 31 24 21 27 26 33 HC4 20 23 17 26 29 41 22 38 27 24 31 24 20 24 31 40 HC5 28 27 27 22 33 43 46 41 37 22 31 27 41 32 26 24 HC6 24 24 21 33 28 40 40 49 32 26 26 27 25 31 25 25 HC7 34 26 19 30 44 54 73 40 42 27 32 28 30 40 31 31 HC8 32 37 54 31 51 120 89 43 71 30 37 32 62 42 39 33 SB1 52 39 57 21 27 103 54 28 19 36 30 33 - - 23 25 19 SB2 56 43 24 31 45 63 50 17 32 34 29 21 27 37 35 31 SB3 28 36 46 24 37 40 30 35 30 25 35 24 32 36 22 18 SB4 48 37 27 27 62 62 22 26 39 38 26 22 42 39 77 18 SB5 39 54 40 34 63 85 38 45 44 37 38 31 45 92 39 32 MW1 38 45 36 50 83 51 23 54 33 29 45 25 26 40 22 35 MW2 142 192 99 136 88 214 69 65 38 57 68 46 71 234 55 62 Table 2. Comparison of total phosphorus concentrations (µg/L) 2000- 2015. * - - stream flow was too low for sample collection; no nutrient data exists for Site SB1 in 2012.

Nitrogen Nitrogen is the secondary nutrient in Otsego Lake, and similar to phosphorus, it is essential for algal production. Anthropogenic influences, namely agricultural practices and wastewater, influence the amount of nitrogen entering Otsego Lake. Total nitrogen (TN) is an all-encompassing summation of nitrate + nitrite, ammonia, and organic forms of nitrogen. Ammonia testing was discontinued in 2010 on the northern watershed of Otsego Lake after concentrations were repeatedly below the minimum detection limit (<0.02 mg/L). All total nitrogen values are displayed in Figure 9. The mean total nitrogen ranged from 0.322 mg/L in White Creek 1 to 1.86 mg/L in Shadow Brook 3.

Figure 10 shows the mean nitrite and nitrate concentrations for all sites sampled in 2015. A comparison of the mean nitrate + nitrite concentration at each stream outlet (1991, 1998-2015) is displayed in Figure 11. Table 3 shows a comparison of mean nitrate concentrations since 1998, including data from 1991. Nitrate + Nitrite concentrations fluctuate annually, which can be explained by its high solubility and correlation with precipitation events.

55 White Creek showed consistent nitrogen concentrations throughout the sampling period. This consistency could be due to the undisturbed land it flowed through which was composed primarily of vegetation that stabilized the soil and decreased erosion. Since loading into the lake is a function of concentration and discharge, loading from White Creek is not very high (Albright 1996).

Conversely, developed streams may be prone to erosion and contamination from nonpoint nutrient sources. This occurs when storm water runoff carried excess nutrients into the tributary, diverging from fixed concentrations over a dry period. As a result, there was a large amount of nitrogen variation in most Shadow Brook sites and Mount Wellington sites. However, loading is lower in Mount Wellington 2 than Shadow Brook 5 since the discharge rate is very low (Albright 1996).

Mean Total Nitrogen 3.5

3.0

2.5

2.0

1.5

1.0 Total Nitrogen (mg/L) Nitrogen Total 0.5

0.0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km) White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 9. Mean total nitrogen of sampling sites along the stream gradient of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis.

56 Mean Nitrate + Nitrite 2.5

2.0

1.5

1.0

0.5

Nitrate + Nitrite Concentrations (mg/L) Concentrations Nitrite + Nitrate 0.0 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 Distance from Otsego Lake (km) White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 10. Mean nitrate + nitrite of sampling sites along the stream gradient of five major tributaries in summer 2015. Distance from the lake increases moving left to right on the x-axis. Mean Nitrite + Nitrate at Stream Outlet 3

2.5

1.5

Nitrate + Nitrite (mg/L) Nitrite + Nitrate 0.5

0 White Creek Cripple Creek Hayden Creek Shadow Brook Mount Stream Outlets Wellington 1991 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Figure 11. Mean nitrate + nitrite at stream outlets of 5 tributaries in the northern watershed of Otsego Lake 1991, 1998-2015.

Comparison of Mean Nitrate Concentrations (mg/L) 1991, 1998-2015 Site ‘91 ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 WC1 0.10 0.05 0.11 0.25 0.31 0.29 0.15 0.27 0.01 0.22 0.21 0.06 0.25 0.09 0.25 0.10 0.13 0.17

WC2 0.31 0.30 0.12 0.16 0.25 0.24 0.15 0.09 0.04 0.11 0.09 0.12 0.10 0.06 0.19 0.08 0.10 0.11

WC3 1.09 0.37 0.41 0.19 0.22 0.33 0.24 0.35 0.31 0.12 0.35 0.24 0.16 0.24 0.13 0.34 0.13 0.17 0.25 CC1 0.07 0.07 0.06 0.08 0.12 0.18 0.13 0.04 0.00 0.02 0.01 0.01 0.03 0.01 0.04 0.23 0.00 0.68

CC2 0.04 0.02 0.24 0.04 0.16 0.34 0.22 0.20 0.01 0.00 0.00 0.01 0.97 0.01 0.02 0.28 0.02 0.82

CC3 1.54 1.19 0.89 1.63 1.20 1.12 1.06 0.60 0.86 0.88 0.97 1.16 1.17 0.97 0.96 1.32 1.24 1.17

CC4 1.42 0.97 0.92 1.77 1.07 1.37 1.05 0.56 0.88 0.97 0.77 1.15 1.19 1.01 0.81 1.28 1.16 1.17

CC5 0.69 0.99 0.37 0.68 1.41 0.77 0.80 0.77 0.27 0.83 0.39 0.38 0.99 0.81 0.69 0.57 1.18 0.92 1.01 HC1 0.82 0.29 0.82 0.68 0.64 0.52 0.26 0.02 0.72 0.07 0.01 0.47 0.33 0.30 0.52 0.90 0.39 0.48

HC2 0.72 0.24 0.71 0.66 0.76 0.52 0.24 0.03 0.84 0.06 0.01 0.59 0.34 0.36 0.34 1.04 0.36 0.46

HC3 1.35 0.64 0.96 1.62 1.44 1.43 1.11 0.60 1.11 0.51 0.44 0.62 0.86 0.70 0.65 1.45 0.96 0.89

HC4 1.34 0.95 1.17 1.73 1.41 1.27 1.11 0.66 1.10 0.55 0.46 0.68 0.88 0.73 0.82 1.39 0.96 0.93

HC5 1.36 0.85 1.19 1.87 1.18 1.34 1.39 0.98 1.64 0.59 0.36 0.94 0.89 0.72 0.64 1.41 0.82 0.96

HC6 1.45 0.90 1.29 1.87 1.51 1.27 1.51 1.38 1.58 0.69 0.45 1.02 0.95 0.77 0.79 1.51 0.91 0.94

HC7 1.45 0.95 1.33 2.00 1.50 1.46 1.31 1.05 2.52 1.22 0.57 1.93 1.00 0.94 0.84 1.57 1.17 1.24

HC8 1.11 1.63 1.21 1.48 1.56 2.09 1.62 1.62 1.31 1.69 0.89 0.70 1.62 1.22 1.17 1.09 1.77 1.32 1.28 SB1 0.21 0.31 0.66 0.53 0.33 0.34 0.32 0.21 0.25 0.09 0.14 0.16 0.21 0.17 - - 0.96 0.26 0.29

SB2 1.86 1.21 1.45 1.40 1.80 1.33 1.39 1.55 0.61 0.98 0.95 1.43 1.34 1.57 1.05 1.79 1.33 1.46

SB3 1.56 0.77 1.57 1.37 1.38 1.36 1.19 0.73 0.94 0.57 0.44 1.34 0.65 0.89 0.64 1.69 1.68 1.61

SB4 1.39 0.87 1.56 1.55 1.43 1.47 1.02 0.73 0.88 0.63 0.57 1.31 0.77 0.94 0.59 1.72 1.55 1.46

SB5 0.90 1.20 0.58 1.27 1.27 1.11 1.05 1.04 0.47 0.87 0.35 0.39 1.22 0.51 0.78 0.52 1.48 1.20 1.23 MW1 0.91 1.11 0.78 1.14 2.31 2.46 1.17 0.67 0.70 0.55 0.23 0.79 0.32 0.50 0.90 1.24 2.13 1.49

MW2 1.47 0.68 1.10 1.06 1.66 2.70 1.58 1.60 1.18 0.83 0.35 0.89 1.07 1.15 0.63 1.15 1.49 1.08

Table 3. Comparison of mean nitrate concentrations (mg/L) 1998-2015. 1991 is also included, but only the values for stream outlets were taken, where available.

Fecal Coliform

According to the New York State Department of Environmental Conservation (2008), fresh water used for contact recreation should not contain more than 200 colonies of fecal coliform bacteria per 100 mL. When fresh water reaches a count over the threshold of 200 colonies per 100 mL, the possibility is higher that pathogenic bacteria are present (NYS DEC 2008). Figure 12 illustrates 2015 fecal coliform averages for all the stream sites. This summer, 19 sites averaged over 200 colonies per 100 mL. Concentrations in 2001 were similar with 16 sites that exceeded the threshold (Albright 2001). Conversely in 2014, only 5 sites contained a concentration over 200 colonies per 100 mL, which is considerably lower than this summer. The significant increase in fecal coliform concentrations from 2014 to 2015 may be associated with higher amounts of rainfall before or during sampling days. Rainfall is very influential to the runoff of fecal coliform into the streams and can cause a spike in concentrations. The majority of the 2015 samples were taken post-rain event while 2014 was a relatively dry summer.

58 Mean fecal coliform counts for all sites in 2015 are illustrated in Figure 13. Moving further away from the outlet showed a correlation with lower fecal coliform concentrations in most streams. This decrease can also be attributed to rainfall, causing an accumulation of fecal coliform transported closer to the stream outlet. This decrease of fecal coliform towards the headwaters is most evident at Shadow Brook and Mount Wellington. These sites run through many agricultural tracts and other residential areas, making the runoff into the streams more likely to contain fecal coliform.

Figure 14 shows average fecal coliform concentrations from each site in 1996 (Miller 1997), 1997 (Pasquale 1998), 1998 (Ingraham 1999), 2001 (Albright 2002) and 2014 (Bakhuizen 2015). A general decline with some variation in average coliform colonies per 100 mL can be suggested over the span of these studies. Shadow Brook and Mount Wellington are tributaries that contain the most BMPs. This decline could be due to the mitigation of bacterial and nutrient runoff into the streams by the Best Management Practices.

Cripple Creek and White Creek contained the lowest average fecal coliform concentrations in 2015 (Figure 13) and are surrounded by relatively undisturbed land. Therefore, fewer sources are present that would cause nonpoint runoff from animal waste, potentially explaining the low number of colonies in these streams. Hayden Creek 4 and Mount Wellington 1 and 2 contained the highest concentrations out of all the sites in 2015 (Figure 13). This is comparable to the study in 2014, during which the largest average concentrations were also counted at HC4, MW1 and MW2.

Fecal Coliform Overall Average 1800 1600

1400 1200 1000 800 600

Colonies per Colonies 100 mL 400 200 0 0 2 4 6 8 10 12 14 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 12. Mean fecal coliform counts for all sites in 2015. Distance from the lake increases moving from left to right on the x-axis.

Average of Fecal Coliform 1800

1600

1400

1200

1000

800

600 Colonies per 100 mL Colonies per 400

200

Site

Figure 13. Fecal coliform averages for White Creek (WC), Cripple Creek (CC), Hayden Creek (HC), Shadow Brook (SB) and Mount Wellington (MW).

Annual Mean Fecal Coliform 100000

10000

1000

100

10 Fecal Fecal Coliform (per 100 mL) 1 White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington Stream Sites 1996 1997 1998 2001 2014 2015

Figure 14. Mean fecal coliform concentrations from each site 1996-1998, 2001, 2014-2015. *Note logarithmic scale on y-axis.

60 CONCLUSIONS

The overall effectiveness of the Best Management Practices seems to have improved throughout sampling years (Figures 8,11,14). Nutrient and fecal coliform analysis potentially showed that the Best Management Practices have been effective in reducing the export of nutrients from land, thereby improving local water quality. Phosphorus levels and fecal coliform concentrations showed an overall decline with slight fluctuations throughout the years. Nitrate + nitrite concentrations fluctuated annually, which can be explained by its ability to dissolve easily in correlation with precipitation events. Overall average cooler temperatures and oxygen rich waters associated with high precipitation during sampling periods this summer. Precipitation increased runoff into the tributaries, which caused high spikes of nitrogen and fecal coliform concentrations at highly disturbed areas with the most BMPs such as Mount Wellington and Shadow Brook. In addition, these concentrations evidently increased closer to the stream outlets. However, relative to water volume at the stream outlets, the amount discharged into Otsego Lake can vary.

A regression plot demonstrated that fecal coliform concentrations in 2015 do not correlate with phosphorus input into the streams. This indicated that the source of pollution was probably not primarily due to manure runoff or failing wastewater systems. A potential trend towards an overall decline in fecal coliform concentrations can be seen from the studies in 1996- 1999, 2001, 2014 and 2015. The numbers of colonies were not very similar to 2014 results, as this year showed a spike in fecal coliform concentrations at most of the sites likely due to high rainfall during sampling. Determining changes in water quality resulting from Best Management Practices requires long term sampling to account for climate and natural variation.

61 REFERENCES

Albright, D. 2002. Analysis of fecal coliform concentrations in Otsego Lake’s northern tributaries, summer 2001. In 34th Ann. Rept. (2001). Bio. Fld. Sta., SUNY College at Oneonta.

Albright, M.F. 2005. Changes in water quality in an urban stream following the use of organically derived deicing products. Lake and Reservoir Management. 21(1):119-124.

Albright, M.F., L.P. Sohacki and W.N. Harman. 1996. Hydrological and nutrient bugets for Otsego Lake, N.Y. and relationships between land form/use and export rates of its subbasins. Occasional Paper #29. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Anonymous. 2007. A Plan for the management of the Otsego Lake Watershed. Otsego County Water Quality Coordinating Committee. Otsego County. New York

APHA, AWWA, WEF. 2012. Procedure 9222 D. Standard methods for the examination of water and wastewater. 22nd Edition.

Bakhuizen, J. 2014. Analysis of fecal coliform bacteria in Otsego Lake’s northern tributaries, summer 2014. In 47th Ann. Rept. (2013). Bio. Fld. Sta., SUNY College at Oneonta.

Harman, W.N., L.P. Sohacki and P.J. Godfrey. 1980. The limnology of Otsego Lake (Glimmerglass), Lakes of New York State, Volume III, Academic Press, 111 Fifth Ave. New York NY, 10003.

Harman, W.N., L.P. Sohacki, M.F. Albright and D.L. Rosen. 1997. The State of Otsego Lake, 1936-1996. Occasional Paper #30. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Hastings, C. 2014. Water quality monitoring if five major tributaries in the Otsego Lake watershed, summer 2014. In 47th Ann. Rept. (2013). Bio. Fld. Sta., SUNY College at Oneonta.

Hewett, B. 1997. Water quality monitoring and the benthic community in Otsego Lake watershed. In 29th Ann. Rept. (1996). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Iannuzzi, T.J. 1991. A model plan for the Otsego Lake watershed. Phase II: The Chemical limnology and water quality of Otsego Lake. Occasional Paper #23. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Ingraham, C. 1999. Analysis of fecal coliform concentrations in Otsego Lake’s northern tributaries, summer 1998. In 31st Ann. Rept. (1998). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

62 Liao, N. and S. Marten. 2001. Determination of total phosphorus by flow injection analysis colorimetry (acid persulfate digestion method). QuikChem®Method 10- 115-01-1-F. Lachat Instruments. Loveland, Colorado.

Meehan, H.A. 2004. Phosphorus migration from a near-lake septic system in the Otsego Lake watershed, summer 2003. In 36th Annual Report (2003). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Miller, C. 1998. Analysis of fecal coliform concentrations of Otsego Lake’s tributaries and the upper Susquehanna River, 1996. In 29th Ann. Rept. (1997) Bio. Fld. Sta., SUNY College at Oneonta.

Murray, K. and P. Leonard. 2005. Water quality monitoring of five major tributaries to Otsego Lake, summer 2004. In 37th Ann. Rept. (2004). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

New York State Department of Environmental Conservation (NYS DEC). 2008. §703.4 Water quality standards for coliforms.

Otsego Lake Watershed Council (OLWC). 1998. A Plan for the Management of the Otsego Lake Watershed. Updated by Otsego County Water Quality Coordinating Committee (WQCC) June 2007.

Pasquale, C. 1998. Analysis of fecal coliform concentration in Otsego Lake’s tributaries, summer 1997. In 30th Ann. Rept. (1997). Bio. Fld. Sta., SUNY College at Oneonta.

Pritzlaff, D. 2003. Determination of nitrate/nitrite in surface and wastewaters by flow injection analysis. QuikChem®Method 10-107-04-1-C. Lachat Instruments. Loveland, Colorado.

Teter, C. 2014. Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Weiner, Eugene R. Applications of Environmental Aquatic Chemistry: A Practical Guide. Boca Raton, FL: CRC, 2008.

63 Susquehanna River Water Quality Monitoring:

Upper Susquehanna River water quality monitoring, summer 2015

Bethany Shaw1

INTRODUCTION

The largest tributary to the Chesapeake Bay, the Susquehanna River is one of the most important rivers in the United States. The Susquehanna supplies 50% of the Chesapeake Bay’s water and it is the largest non-navigable fresh water river that lies entirely within the United States’ borders. From its main branch’s headwaters in Cooperstown, New York and its western branch’s headwaters in western Pennsylvania to where it pours into the Chesapeake Bay at Havre De Grace, Maryland (at a rate of 446 million gallons/day during peak flow), the Susquehanna meanders through 464 miles of the states of New York, Pennsylvania, and Maryland (SRBC 2009).

Following the1972 Clean Water Act, the 2009 Chesapeake Bay TMDL (total maximum daily load) strategy was implemented through the Chesapeake Clean Water Act (2009). This policy was put into place to lower point and non-point source pollution (sediment, nitrogen and phosphorus) that is affecting the Susquehanna and the Chesapeake Bay. Sewage treatment plants and agricultural sites along the river have been the most impacted by this policy. In order to meet the new requirements of the this policy, the Village of Cooperstown, along with Ducks Unlimited and the Army Corps of Engineers, built a wetland that the secondarily treated wastewater (with most total suspended solids and organic carbon having been removed) would move through before entering into the Susquehanna. As the secondarily treated wastewater travels through the wetland, it is the intention that the wetland’s biota will help to take up the effluent’s phosphorus in order to meet these new regulations.

Monitoring the headwaters of the Susquehanna has been in effect since 1991 through SUNY College at Oneonta’s Biological Field Station (Albright et. al. 1992). The annual monitoring of the headwaters of the Susquehanna provides important data and history that can be used to detect meaningful changes, for example if the implantation of the wastewater treatment plant’s wetland has been successful in accordance with its initial purpose.

1 F.H.V. Mecklenburg Conservation Fellow, summer 2015. Present affiliation: Nazareth College. Funding provided by the Village of Cooperstown.

METHODS

Between the start of the Susquehanna at the base of Otsego Lake to its confluence with Oaks Creek, nine sites along the river (Table 1; Figure 1) were monitored for their water quality between 25 June and 20 August 2015. Sites 8, 17 and 18 were monitored on an additional date as part of the benthic macroinvertebrate survey of the Upper Susquehanna (Shaw 2016). The YSI® 6820 V2-2 multi-probe sonde was placed in moving water at each site and was used to measure temperature, pH, specific conductivity, dissolved oxygen, and turbidity.

At each site, water was collected in an acid washed Nalgene® bottle (125 mL) and brought back to the BFS lab. Samples were preserved with 1ml of 5.6N H2SO4. Total phosphorous, total nitrogen, nitrate, and nitrite concentrations were determined using a Lachat® QuickChem FIA Water Analyzer (Pritzlaff 2003; Liao and Marten 2001).

Table 1. Locations and descriptions of the nine Upper Susquehanna River sites.

Site Distance from Description source 3 144m Under the Main Street Bridge; accessed via slope beside the bridge. 6a 1012m Below the dam at Bassett Hospital; accessed from the northern corner of the lower parking lot of Bassett Hospital. 7 1533m Below the dam at Bassett Hospital; accessed from the southern corner of the lower parking lot of Bassett Hospital. 8 1724m Under the Susquehanna Ave. bridge west of the Clark Sports Center; accessed via the slope beside the bridge. 12 4119m Just above the sewage discharge of the Cooperstown Wastewater Treatment Plant, near Cooperstown High School. Accessed by an opening in the fence. 16 5460m Small bridge perpendicular to the road on Clark Property. Accessed by crossing a gated bovine grazing area (cow field). 16a 5939m Distinct bend in river alongside road on Clark Property, in field directly across from large house with hay rolls in front. Accessed by long path found on the right side of the field. Be cautious of barbed wire. 17 8143m Abandoned bridge on Phoenix Mill Road. 18 9867m Railroad trestle about 200m north of the railroad crossing on Rt. 11 going out of Hyde Park, accessed by walking on the railroad tracks.

Figure 1. Map of the Upper Susquehanna site tested, summer 2015.

RESULTS AND DISCUSSION

Temperature Temperature is an important measurement to obtain from water sources since it heavily influences both biological activity and growth. Overly high or low temperatures of water bodies will deter biota such as fish, insects, zooplankton, and phytoplankton that are temperature sensitive. Furthermore, temperature can affect the chemistry of the water, having sometimes negative effects on sensitive biota, decreasing the dissolved oxygen and increasing the conductivity of the water. Temperature values over summer 2015 (Figure 2) fell intermediate of those collected since 2004 (Figure 3).

Figure 2. Average temperature profile for the Upper Susquehanna River, summer 2015.

25 2015 2014 24 2013

23 2012

ºC) 2011 22 2010 21 2009

Temperature ( Temperature 2008 20 2007 19 2006

18 2005 0 2000 4000 6000 8000 10000 2004 Distance from source (m)

Figure 3. Average temperature profile for the Upper Susquehanna, summers 2004 (Hill 2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matus 2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), 2014 (Freehafer 2015), and 2015.

67 Specific Conductivity Specific conductivity is water’s ability to transmit electrical current. This property comes from the dissolved ions in water, such as calcium carbonate and sodium chloride (NaCl). Conductivity can serve as an indicator of a sewage leak since conductivity will increase due to the introduction of phosphate and nitrate ions. Conversely, the conductivity will decrease when organic substances are introduced into the water, since organic molecules do not separate into ions (Kemker 2014). This use of conductivity is particularly important while monitoring the upper Susquehanna since elevated conductivity readings helped reveal illegal, unauthorized discharges of raw sewage being dumped into the river there (Albright, et. al. 1992).

Specific conductance along the upper reaches of the river (Figure 4) was well within the mean values measured since 2004 (Figure 5).

Figure 4. Average specific conductivity profile for the upper Susquehanna River, summer 2015.

68 0.4

2015

2014 0.35 2013 2012 2011

0.3 2010 2009 2008 2007 0.25 Specific Conductivity (mmhb/cm) Conductivity Specific 2006 2005 2004 0.2 0 2000 4000 6000 8000 10000

Figure 5. Average specific conductivity profile for the Upper Susquehanna, summers 2004 (Hill2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matu2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), 2014 (Freehafer 2015), and 2015.

pH pH is the concentration of H+ ions found in solution. The greater concentration of H+ in solution the more acidic a solution is, a lesser concentration of H+ ions indicate an alkaline, or basic, solution, where a pH of 7 is neutral (Wetzel 1975). The pH is determined largely by the geology of a region, as the bedrock and soils supply the minerals that help to buffer against changes in pH. In the northeastern United States, where acid rain is a common occurrence, freshwaters having a pH greater than 7 are generally not at risk of acidification.

pH values over summer 2015 (Figure 6) were within typical ranges measured since 2004 (Figure 7).

Figure 6. Average pH profile for the upper Susquehanna River, summer 2015

8.5 2015 2014 8.25 2013 2012

2011

pH 8 2010 2009 7.75 2008 2007 7.5 2006 0 2000 4000 6000 8000 10000 2005 Distance from source (m)

Figure 7. Average pH profile for the Upper Susquehanna, summers 2004 (Hill 2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matus 2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), and 2014 (Freehafer 2015), and 2015.

70 Dissolved Oxygen Dissolved oxygen is the single most important parameter to measure since it directly affects whether aquatic life will survive in an that particular system. Dissolved oxygen can be affected by water flow, oxygen demand and temperature. Dissolved oxygen concentrations measured over 2015 (Figure 8) were among the highest encountered since 2004.

Figure 8. Average dissolved oxygen profile for the upper Susquehanna River, summer 2015

10 2015

9 2014 2013 8 2012 2011 7 2010 2009 Dissolved Oxygen (mg/l) 6 2008 5 2007 0 2000 4000 6000 8000 10000 2006 Distance from source (m)

Figure 9. Average dissolved oxygen profile for the Upper Susquehanna, summers 2004 (Hill 2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matus 2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), and 2014 (Freehafer 2015), and 2015.

71 Turbidity Turbidity of water is the inverse of clarity. In lotic systems, it is most influenced by the scattering of light by suspended sediments particles in the water (Kumar 2004). It can indicate agricultural runoff or erosion. It can restrict the photic zone by blocking light penetration, and the deposition of sediments can greatly influence the microbenthic communities (Sweeting 1994). Turbidity values tended to increase downstream (Figure 10) and were similar to those over the summers of 2013 and 2014 (Figure 11).

Figure 10. Average turbidity profile for the upper Susquehanna River, summer 2015

10 2015 2014 Turbidity (NTU) Turbidity 5 2013

0 0 2000 4000 6000 8000 10000 Distance from source (m)

Figure 11. Average turbidity profile for the Upper Susquehanna, summers 2013 (Bianchine 2014), and 2014 (Freehafer 2015), and 2015.

72 Total Phosphorous Phosphorous is less abundant than carbon, hydrogen, nitrogen, oxygen, and sulfur in fresh water sources, but as an often limiting nutrient it is considered to be one of the most important (Wetzel 1975). Phosphorous is an important nutrient for algal growth, while too much phosphorous in fresh water ecosystems can have disastrous effects on the biota composition of the stream, primarily due to the promotion of excess algal growth. Excess phosphorous can come from sewage effluent, agricultural and urban runoff.

Total phosphorus over the summer of 2115 is shown in Figure 12. Its concentrations this year were among the lowest since 2004 (Figure 13). Figure 14 shows the summertime average total phosphorus concentrations at each site from 2004 through 2009 vs. 2010 through 2015 in an effort to suggest the influence of changes in treatment at the municipal sewage treatment plant. Beginning in 2009, the Village of Cooperstown’s effluent was directed through an engineered plant intended to remove nutrients prior to its introduction into the Susquehanna River. Phosphorus and nitrogen uptake by the wetland has been demonstrated (Albright 2016). Figure 14 suggests phosphorus removal in the river itself.

Figure 12. Average total phosphorous profile for the upper Susquehanna River, summer 2015.

Figure 13. Average total phosphorous profile for the Upper Susquehanna, summers 2004 (Hill 2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matus 2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), and 2014 (Freehafer 2015), and 2015.

250

200

150

2004 - 2009 100 2010 - 2015 Total Phosphorus (ug/l) Phosphorus Total 50

WWTW outfall 0 0 2000 4000 6000 8000 10000 Distance from source (m)

Figure 14. Mean total phosphorous profile for the years 2004-2009 and 2010-2015. WWTW= wastewater treatment wetland.

74 Total Nitrogen Total nitrogen is the sum of ammonia, nitrite, and nitrate measured in a water body. High nitrogen can lead to severely low dissolved oxygen, can cause brown blood disease in fish, and levels of 50-100mg/l will cause blue blood disease in human infants (Nitrogen and Water, 2015). High levels of nitrogen can indicate agricultural runoff or wastewater effluent. Total nitrogen over the summer of 2115 is shown in Figure 15. Its concentrations this year were intermediate among those since 2004 (Figure 16). Figure 17 shows the summertime average total phosphorus concentrations at each site from 2004 through 2009 vs. 2010 through 2015 in an effort to suggest the influence of changes in treatment at the municipal sewage treatment plant. Beginning in 2009, the Village of Cooperstown’s effluent was directed through an engineered plant intended to remove nutrients prior to its introduction into the Susquehanna River. Phosphorus and nitrogen uptake by the wetland has been demonstrated (Albright 2016). Figure 17 suggests a reduction in total nitrogen downstream of the wastewater treatment wetland outfall, though total nitrogen upstream of this point prior to 2009 was somewhat higher than it has more recently been.

Figure 15. Average total nitrogen profile for the upper Susquehanna River, summer 2015.

Figure 16. Average total nitrogen profile for the Upper Susquehanna, summers 2004 (Hill 2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matus 2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), and 2014 (Freehafer 2015), and 2015.

WWTW outfall

Figure 17. Mean total nitrogen profile for the years 2004-2009 and 2010-2015. WWTW= wastewater treatment wetland.

76 Nitrate + Nitrite Levels of nitrate and nitrite across the Susquehanna River sites are shown in Figure 18. They were intermediate among those over 2004 to 2015 (Figure 19). Mean concentrations prior to vs. after the municipal wastewater was diverted through the wastewater treatment plant show a similar pattern Figure 20) as did total nitrogen (Figure 17).

Figure 18. Average nitrate + nitrite profile for the upper Susquehanna River, summer 2015.

Figure 19. Average nitrate + nitrite profile for the Upper Susquehanna, summers 2004 (Hill 2005), 2005 (Bauer 2006), 2006 (Zurmuhlen 2007), 2007 (Coyle 2008), 2008 (Matus 2009), 2009 (Heiland 2010), 2010 (Bauer 2011), 2011 (Scott 2012), 2012 (Katz 2013), 2013 (Bianchine 2014), and 2014 (Freehafer 2015), and 2015.

77 1

0.8

0.6

2004 - 2009 0.4 2010 - 2015

(mg/l) Nitrite + Nitrate WWTW outfall 0.2

0 0 2000 4000 6000 8000 10000 Distance from source (m)

Figure 20. Mean nitrate + nitrite profile for the years 2004-2009 and 2010-2015. WWTW= wastewater treatment wetland.

CONCLUSIONS

The measurements taken in 2015 of the Upper Susquehanna River express values that are consistent with previous years. In the case of a rainy year such as that of 2015, one would expect that the temperature would be lower and the dissolved oxygen and turbidity to be higher. All of these trends are shown in the data collected from 2015. The temperature lies at the lower end of the averages, the dissolved oxygen is the highest consistent average taken since 2004, and the turbidity is similar to the last two years (however, more data must be collected on turbidity in order to comment on this trend, since this is only the third year that turbidity has ever been collected on the Upper Susquehanna).

Since the implantation of a fully operational wastewater treatment wetland in 2010, the average phosphorus and nitrogen concentrations have changed significantly. Between sites 12 and 16, the treatment wetland’s effluent enters the river. Phosphorous and nitrogen levels historically have increased between these sites. This has been particularly evident prior to 2010, but in more recent years the difference between the total phosphorous, total nitrogen, and nitrate + nitrite levels has become more subtle between these two sites (see Figures 14, 17, and 20). Since 2010, the average total phosphorous output from the effluent pipe has decreased by more than 50 ug/l. The average total nitrogen has decreased by more than 0.2 mg/l and the average nitrate + nitrite levels have decreased by 0.15 mg/l since 2010. More striking than this

78 comparison is the fact that the total nitrogen data since 2010 shows a range of only 0.15 mg/l, compared to the 2004-2009 range of 0.53 mg/l. The nitrate + nitrite levels show the same pattern; the 2010-2015 range is 0.09 mg/l and the 2004-2009 range is 0.31 mg/l. Most interestingly, not only does the total nitrogen and nitrate + nitrite levels not rise between sites 12 and 16, the data shows that they decrease across this section of river where the effluent pipe exists. Since 2010, the wastewater treatment plant’s wetland has seemingly helped to remove a great amount of phosphorous from entering the Susquehanna, as it was intended to do. Bimonthly sampling since 2010 indicates that the wastewater treatment wetland removes, on average, about 30% of the total phosphorus and total nitrogen that is introduced by the treatment facility (Albright 2015).

REFERENCES

Albright, M.F. 2015. Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 2014. In 47th Ann. Rept. (2014) SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F., et. al. 1992. An analysis of water quality in the upper Susquehanna River. In 24th Ann. Rept. (1991) SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Bauer, E. 2006. Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2005. In 38th Ann. Rept. (2005). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Bauer, H. 2011. Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2010. In 43th Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Bianchine, T. 2014. Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Chesapeake Clean Water and Ecosystem Restoration Act. (HR 3852/S 1816) 2009. http://www.cbf.org/Document.Doc?id=398.

Coyle, O.L. 2008. Monitoring water quality and fecal coliform bacteria in the Upper Susquehanna River, summer 2007. In 40th Annual Report (2007), SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Ebina, J.T.Tsutsui, and T. Shirai. 1983. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res. 17(12):1712-1726.

Electrical Conductivity/Salinity Fact Sheet. 2004. http://www.swrcb.ca.gov/water_issues/programs/ swamp/docs/cwt/guidance/3130en.pdf.

79 Freehafer, M. 2015. Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Heiland, L. 2010. Monitoring water quality in the upper Susquehanna River, summer 2009. In 42st Ann. Rept. (2009). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Hill, J.2005. Monitoring the water quality in the upper Susquehanna River, summer 2004. In 37th Ann. Rept. (2004). SUNY Oneonta Bio. Fld Sta., SUNY Oneonta.

Katz, R. 2013. Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Kemker C. 2014 Mar 3. Conductivity, Salinity and Total Dissolved Solids [Internet].Fundamentals of Environmental Measurements. Fondriest Environmental, Inc.; [2014 Mar 3, cited 2015 Dec 24]. Available from: http://www.fondriest.com/environmental-measurements/parameters/water- quality/conductivity-salinity-tds/

Liao, N. and S. Marten. 2001. Determination of total phosphorus by flow injection analysis chloriometry (acid persulfate digestion method). QwikChem Method 10-115-01-1-F. Lachat Instruments. Loveland, Colorado.

Matus, J.E. 2009 Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2008. In 41th Ann. Rept. (2008). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Nitrogen and Water [Internet]. USGS; [2015 Jul 24, cited 2015 Jul 27]. Available from: http://water.usgs.gov/edu/nitrogen.html.

Pritzlaff, D. 2003. Determination of nitrate+nitrite in surface and wastewater by flow injection analysis. QwikChem Method 10-115-01-1-F. Lachat Instruments. Loveland, Colorado.

Scott, B. 2012. Monitoring the water quality and fecal coliform in the upper Susquehanna River, summer 2011. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Shaw, B. 2016. Benthic macroinvertebrate survey of the upper Susquehanna River using two sampling methods. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Susquehanna River Basin Commission. 2009. http://www.srbc.net.about/index.htm.

Zurmuhlen, S. J. 2007. Monitoring water quality in the upper Susquehanna River, summer 2006. In 39th Annual Report (2006), SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

80 REPORTS:

Preventing zebra mussel (Dreissena polymorpha) veliger attachment using potassium permanganate

Elizabeth Clifton1 and Matthew Albright2

INTRODUCTION

Zebra mussels (Dreissena polymorpha) are a fast colonizing invasive species that were first discovered in North America in 1988 (NOAA 2007) and have since spread across much of the United States. They are considered one of the world’s most economically and ecologically damaging invasive species and have been estimated to cost water treatment facilities $1-5 billion dollars every year (Aldridge 2006). Late stage veligers (juveniles) of the mussels will attach to most rough surfaces, including other zebra mussels, using byssal threads to create strong attachments (D’Itri 1997). This has created problems for any water treatment facility that draws water from a source infested with these mussels. Adherence by mussels within the pipe and the treatment facility will clog the pipe and disrupt water filtration. Their management requires the expenditure of considerable effort and resources.

Zebra mussels were first discovered in Otsego Lake (Otsego County, NY) in 2007 (Horvath 2008). They have since colonized hard surfaces lake wide, including the Village of Cooperstown Municipal Water Works. This facility draws water from the lake via a 0.355m diameter cast iron pipe that extends about 1,310m from its source; water is drawn at about 14m depth (Elliot 2015). Recent efforts to control mussel fouling in the pipe have involved pigging (Elliot 2015), or forcing a flexible plastic plug through the length of the pipe. It requires the assistance of SCUBA divers and the threat exists that the pig may eventually become lodged within the intake pipe.

The water treatment facility currently uses potassium permanganate, an oxidizing agent, to disinfect and improve the potable quality of the water (Elliot 2015). To achieve disinfection, every day about 48mg/L of KMnO4 is pumped for about an hour, at a rate of about 70 L/minute, through a small pipe that runs along the bottom of the intake pipe. Diffusers at the mouth of the intake pipe distribute the permanganate as water is drawn from the lake. The configuration of the system’s design potentially poses some challenges related to chemically control zebra mussels, primarily in that contact time is limited. It takes about 2 hours for the potassium permanganate- dosed water to reach the mouth of the pipe and about one hour for it to return through the intake pipe to the treatment plant (so the contact time with the permanganate is limited to 60 minutes). The rate of water withdrawal is about 2,100 L/minute. The calculated dose at the pipe mouth is 1.6 mg/L (70 L/minute / 2,100 L/minute*48 mg/l). Water coming into the treatment plant was

1 SUNY Oneonta Biology Department Intern, summer 2015. Current affiliation: SUNY Oneonta.

2 Assistant to the Director, SUNY Oneonta Biological Field Station.

81 measured contain about 0.62 mg/L permanganate. The difference (~1 mg/L) is presumable lost to reduction as it oxidizes compounds in the raw water. Research conducted by Coyle (2015) indicated that a concentration of 8 ppm would kill about 50% of veligers after a 120 minute exposure. Lower dosing rates and lesser exposure times were not effective at killing veligers.

The goal of this experiment was to determine the concentration of potassium permanganate at one hour contact necessary to prevent zebra mussel veligers from attaching to artificial substrates (which may be substantially lower than that necessary to cause mortality). This oxidizing compound is already used by the Cooperstown facility to treat the lake water, and it has been demonstrated in other studies to control veligers (Claudi and Mackie 1993), making it a candidate for preventing fouling by veligers. These findings could be useful to water treatment operators in that it allows for the determination of dosing rates that are high enough to effectively prevent settling without being excessive. Over dosing not only carries the direct cost of the chemical treatment, but also the cost of removing the permanganate prior to delivery to water users, as permanganate imposes a purple coloration which makes it aesthetically unattractive. Permanganate removal adds to treatment expense as it consumes activated carbon.

METHODS

The original intention of this study was to effectively conduct a semi-controlled experiment using veligers collected directly from the Cooperstown water treatment plant. During the period when veligers were present, the plant operator would adjust the outgoing dose of permanganate in the morning and we planned on running a calculated volume of the return water though a 61 um plankton net. Those veligers would then be transferred to aquaria and cultured to settling stage. This approach would have most closely mimicked the actual treatment scenario. However, we were unable to reliably collect veligers at the treatment plant (possibly due to the intake depth which, at about 14 m, is below the thermocline), so a more controlled approach was undertaken.

Instead zebra mussel veligers were collected from Otsego Lake. A 64 micron zooplankton net was towed between the surface and 5 meters deep for about 10 minutes near the end of July (when veliger numbers were high). After towing, the mussels were brought directly to the lab and their density was determined using an analytical grade digital microscope equipped with cross polarizing filters (Johnson 1995). Once the average number of veligers per milliliter was determined, the sample was diluted with filtered lake water to yield about 100 veligers/mL.

Five 500 mL beakers were half filled with concentrations of 0mg/L, 2mg/L, 4mg/L, 8mg/L, and 16mg/L of potassium permanganate, respectively. Each treatment was conducted in duplicate. Each beaker received a cage, which was constructed of of PVC piping with the top end open and the bottom end closed off by 64 micron mesh. These contained the veligers while being exposed to the permanganate solution. Ten milliliters of the veliger sample was added to each cage using a 1mL Hensen Stempel ten times for each concentration, thus adding approximately 1,000 veligers to each treatment. The veligers were exposed in their respective beaker for one hour, at room temperature, in order to replicate one hour of dosing at the Village of Cooperstown Municipal Water Works. Once the hour was completed, each cage was rinsed off with filtered

82 lake water and transferred to its respective aquarium. Each aquarium contained 20L of filtered lake water, was equipped with an aerator, and had a ~30x30 cm plexiglass plate suspended vertically in its center. These plates had been pre-treated by having been hung in a local pond (free of zebra mussels) for 7 days prior to the experiment in order to create a biofilm to which the veligers could attach (Davis 2015).

The veligers were left in the tanks for 30 days to allow them time to grow, settle and attach to the plexiglass. They were fed 0.25 mL of Isochrysis 1800TM “instant algae” every Monday, Wednesday, and Friday and the temperature of each tank was taken after feeding. Every Monday, water samples were collected for the determination of ammonia in order to ascertain reasons for mortality should the veligers die. Also weekly, half of the tank’s water was replaced with fresh filtered lake water. To remove the water, a zooplankton sieve with a 61 micron mesh was placed over the end of a siphon tube so that water could be drawn out without drawing out any veligers.

After 30 days, the plexiglass plates were removed from each tank and examined under a cross polarized scope. All of the plates were checked under the scope; however the opacity of the plates, as well as the film on them, made it difficult to see anything. For this reason, the biofilm was scraped from the plates and was added it to 125mL of 70% ethanol. In order to determine how many unattached veligers there were, each tank was siphoned through a 64 micron mesh plankton cup. The filtered water was mixed with 70% ethanol to dilute the solution to 125mL and to preserve the samples. Both the scrapings and the filtered samples were examined under a cross-polarized scope. From each sample, 5mL was inspected and all the [whole] veligers were counted. The number counted was divided by 5mL and then multiplied by 125mL to determine how many were either on the board in the end or in the water.

RESULTS AND DISCUSSION

Over the course of the month long study, temperature across the tanks averaged 21.76oC (SD= 0.74oC). A one-way single factor ANOVA was performed on the temperature data which showed no significant difference between the tanks (F=0.278751, df=9, p=0.978956). Ammonia concentrations averaged 0.43 mg/l (SD= 0.05). A one-way single factor ANOVA was done on the ammonia data which revealed that there was no significant difference between the tanks (F=0.337178, df=9, p=0.95511) or over time (F=9.79208, df=3, p=<.001).

Figure 1 summarizes the attached and planktonic zebra mussels (veligers) among the treatment groups. The “unaccounted for” fraction is the discrepancy between the targeted number of veligers initially added (1,000) and the estimated numbers of veligers and attached mussels recovered (the subsamples of 5 x 1 mL subsamples viewed, considering the total concentrate volume of 125 mL). This fraction could also include veligers not surviving long into the experiment. A chi-squared test showed that there was no difference in the number of attached veligers between tanks (χ= 15, df=12, p=0.2414). There was no difference in number of attached mussels or veligers between treatment levels. Potassium permanganate does not appear

83 to be an effective molluscicide for zebra mussels at the concentrations and duration of contact evaluated.

Figure 1. Percent of zebra mussel veligers that were either attached, planktonic, or unaccounted for. A Chi-squared test was done to show no difference in number of attached veligers between tanks.

This project was originally planned to be performed at the Cooperstown Water Treatment facilities; however, we were unable to consistently collect veligers there to represent each of the five trail concentrations. This necessitated the collection of veligers directly from the lake as well as conducting the exposure sequences in the lab rather than that occurring during the water withdraw by the municipal treatment facility. It was also discovered part-way through the experiment that zebra mussel veligers had infested the filtered water system used for culturing at the lab facility (presumably due to a failed filtration system). Therefore, we had to restart the entire project. Also, after this realization, extra care needed to be taken to ensure that veligers were not introduced from this source into the culture tanks, i.e., every time we used the lake water hose we filtered the water through a 61 micron zooplankton net. Lastly, power was lost for several hours halfway through the project. This may have caused a disruption to the aerators in the tanks, but the fact that ammonia concentrations were consistent throughout the experiment implies that oxygen concentrations did not drop meaningfully.

84 REFERENCES

Aldridge, D.C., P. Elliott, and G.D. Moggridge. 2006. Microencapsulated BioBullets for the control of biofouling zebra mussels. Environmental Science & Technology. 40:975-979.

Boelman, S. F., Neilson, F. M., Dardeau Jr., E. A., and Cross, T. 1997. Zebra Mussel (Dreissena polymorpha) control handbook for facility operators, first edition. Miscellaneous Paper EL-91-1, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS.

Claudi, R. and G.L. Mackie. 1993. Practical manual for zebra mussel monitoring and control. Lewis Pubishers.

Coyle, B. P., Lord, P. H., Wong, W. H., and Albright, M. F. 2015. Potassium permanganates effect on zebra mussel adults and veligers. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Davis, E. 2015. Personal communication. SUNY Oneonta MS candidate. Oneonta., NY.

D’Itri, F.M. 1997. Zebra mussels and aquatic nuisance species. Ann Arbor Press, Inc.

Elliot, D. 2015. Personal communication. Cooperstown Water Treatment Plant. Cooperstown, NY.

Horvath, T. 2008. Economically viable strategy for prevention of invasive species introduction: Case study of Otsego Lake, New York. Aquatic Invasions. 3(1): 3-9.

National Oceanic and Atmospheric Administration. 2007. The zebra mussel invasion. United States Department of Commerce. Web. 12Aug2015. http://www.noaa.gov/features/earthobs_0508/zebra.html.

O’Neill Jr., C. R. 1997. Economic impact of zebra mussels- results of the 1995 national zebra mussel information clearinghouse survey. Great Lakes Research Review. 3(1):35-42.

Watters, A., Gerstenberger, S. L., Wong, W. H. 2013. Effectiveness of EarthTec® for killing invasive quagga mussels (Dreissena rostriformis bugensis) and preventing their colonization in the western U.S. The Journal of Bioadhesion and Biofilm Research. 29(1):21-28.

85 Introduction to drones as tools for research and monitoring

Peter T. Booth1

Abstract

The purpose of this manual is to supplement the already available operation manual from the 3DRobotics Iris+. This manual is intended to provide some more detailed insight on some aspects of the safe usage of drones, compliance with local laws, and effective use of ground station software. The primary aim of this document is to provide the reader with the tools necessary to create meaningful data for spatial ecology using a consumer grade unmanned aerial system (drone). This technical manual encompasses in broad strokes the steps taken to achieve aforementioned goals during the summer of 2015. This technical manual is intended to provide BFS staff as well as future researchers with the tools necessary to employ the UAV as a spatial data collection tool, or other component to their experiments.

Safety & Legal Note

The following are guidelines provided by the AMA in conjunction with the FAA. NOTE: Without a 333 exemption, no one is allowed to operate a drone in the United States Airspace in any capacity other than for hobby or recreational reasons. To clarify the FAA’s interpretation of what constitutes “Hobby or Recreational usage”, please refer to the FAA FAQ webpage: https://www.faa.gov/uas/faq/#qn24

In order to conduct flights for compensation of any kind, there are two different avenues. The first avenue requires the operator to apply for something called a 333 exemption. Details of that can be found at this website: https://www.faa.gov/uas/civil_operations/ The second avenue that may apply to the BFS is to obtain a public certificate of operation (Public COA). This is a special grant of permission to conduct flights that are in the public’s best interest, and these are only available to publicly funded organizations. More details on issues of authorization can be found here: https://www.faa.gov/uas/public_operations/

Public Perception

It is common for some people to become very uncomfortable with the presence of drones in the sky. Their concerns are based largely on fears of invasion of privacy or fears related to their belief that the drones in the sky may be looking for violations. As

1 Otsego County Conservation Association intern, 2015. Department of Geography, SUNY Oneonta. Funding provided by the Otsego County Conservation Association.

Funding for the drone and accessories was provided by the Otsego Lake Association.

86 representatives of the Biological Field Station, it is imperative that all interactions with the public be conducted in a friendly, positive and responsible manner. Some people are simply interested in the drone and would like a closer look. The best method for dealing with the curious is to kindly inform them that you will be willing to answer any questions they have after you have landed.

Crew

A drone crew should consist of a minimum of two people: 1. The Pilot In Command (PIC), whose roll is to operate the drone. Note: if the drone is being used for commercial purposes, the PIC must be an FAA licensed pilot. 2. The Spotter: It is the spotter’s job to maintain situational awareness of all activities happening within the active zone of flight, and to alert the PIC of changes to the situation. Examples of changes that the spotter should alert to pilot to are: people approaching the zone of flight, the presence of full-scale aircraft in the sky, and periodic updates from the Ground Control Station.

Know Before You Fly ~ (The following is quoted directly from the FAA Model Aircraft Operations webpage): “The FAA has partnered with several industry associations to promote Know Before You Fly, a campaign to educate the public about using unmanned aircraft safely and responsibly. Individuals flying for hobby or recreation are strongly encouraged to follow safety guidelines, which include:

• Fly below 400 feet and remain clear of surrounding obstacles. • Keep the aircraft within visual line of sight at all times. • Remain well clear of and do not interfere with manned aircraft operations. • Don't fly within 5 miles of an airport unless you contact the airport and control tower before flying. • Don't fly near people or stadiums. • Don't fly an aircraft that weighs more than 55 lbs. • Don't be careless or reckless with your unmanned aircraft – you could be fined for endangering people or other aircraft “.

Simulation Training

Although the 3DR Iris+ is a very capable semi-autonomous platform, I always recommend that prospective pilots spend some of their spare time using a flight simulator. When the autonomous flight modes are working correctly these drones are very easy to fly, but when faced with an in-flight technical malfunction, it is the capabilities of the pilot that make the difference between a safe return to landing, and a lost drone. One very reasonable and accessible flight simulator is called “Real Flight mobile”, available for IOS devices. The app is free but the multi-rotor is a $2.99 in-app purchase. This simulator is not a perfect physics representation of how a multirotor flies, but it is very good at demystifying the control system of the multirotor, as well as helping

87 the operator to develop strategies for certain types of spatial disorientation that happen when the drone is far away and silhouetted against the sky.

Another option for Android: Quadcopter FX Simulator available at: https://play.google.com/store/apps/details?id=com.Creativeworld.QuadcopterFX&hl=en This is a very good simulator that should help a new pilot get started. A solution for the PC exists as well. Two such simulators are: Real Flight 7.5 http://www.realflight.com/ and Pheonix RC5 http://www.phoenix-sim.com/ Both of these are higher end simulators that more closely approximate the experience of flying a drone, though both are more expensive than their mobile counterparts. It is recommended that one visit their pages to determine system requirements etc.

Step 1: In the Lab (GCS). The ground station computer should be co-located with the drone. Before leaving for the field make sure that the ground station computer is fully charged. Ground Control Station (GCS): open GCS computer and log in using the following credentials: User: BFS Drone Password: iris NOTE: 1As of this latest revision of this document this computer is no longer available at the BFS. It is unknown to me whether or not the BFS has replaced the ground station computer with another system. 2 The following directions are accurate as of the most recent mission planner build at the time of publication.

When the computer starts, open up Mission Planner (MP) which should be located on the desktop. After mission planner has finished loading (it takes a little while) click on the “ FLIGHT PLAN” icon located in the upper left hand corner of the screen. Click and drag in the map window to find the location where you intend to conduct your flight. While pressing the “Alt” key on the computer, click and drag a blue rectangular bounding box around the area. With the area selected, right click anywhere inside the map window to invoke a context sensitive menu. Drag down to “maptools” and hover your cursor over that list item until the fly out reveals more options. Select the option “prefetch”. Once this has been done another window will open giving you the status of the map tiles that are being cached for later retrieval in the field. Pay close attention to this dialogue box as it will inform you what levels of zoom are being cached. When you have gotten to zoom level 20, hit escape a few times to stop downloading. There is no point in loading maps that are further zoomed in than 20 as they take a very long time, and they do not provide any useful data in the field. The ground station computer is now ready to work in the field.

Step 2: In the Lab (DRONE) A. Make sure all batteries are charged. It is a convention of mine that all charged batteries are put in the case with their cables sticking up. It is also a convention of mine to place discharged batteries in the case with the cables sticking down into the holes. As an additional memory aid I have labeled each battery with a number. It is a good practice

88 for the operator to take notice of the battery number as it goes into the drone; also it is good practice to run through the batteries sequentially while out in the field. This practice helps prevent flying away with a battery that has almost no charge. There is a battery tester included in the case. The battery Charger must be plugged in as illustrated. A full battery will read around 12.5 (v) (Figure 1). If the battery is reporting anything below 11.5v, take the time to charge it up before the flight. Never start flying with a battery lower than 11.0 (v) and never run a battery down below 10.7 (v) it risks being destroyed.

Figure 1 Voltage Meter. B. Remove the drone from its case, turn it upside down on a counter and remove the GoPro camera using the smallest hex wrench included in the flight case. Make sure the GoPro is charged, and the micro SD card is in place. C. Make sure at this time that all mission critical equipment is in the case, and that it has been checked for operational condition. This inspection should include such points as damage, battery level, memory storage… etc. D. Inspect the propellers for damage. Discard any propellers that have been damaged. Tiny pits on the leading edge of the props are acceptable in some cases, but notches or cracks, or warped propellers will degrade the flight of the drone. E. Remove the Remote Control Box from the case, turn it on, check its battery. If the voltage is lower than 9V do not go flying without first replacing the 8 AA cells in the back.

Step 3: out in the field Choosing a launch site: The launch site should be as clear of overhead obstructions as possible. In general the point of takeoff should be approximately 30 feet diameter minimum. From this location it should be possible to see a large portion of the sky. A good rule of thumb for determining this is for the operator to stand in the center of a proposed launch sight, extend their arm at a 45° angle and make one full rotation while looking at surrounding clutter. Obstructions, which may include buildings, overhead utility wires, or the tree

89 line, all should fall below the operator’s outstretched arm. This is both important for obtaining GPS fixes, and helping in recovery of the drone. The surface from which the drone is launched should also be stable. During arming procedures it is necessary for the drone to recalibrate it’s onboard inertial measurement unit (IMU). If the drone is in motion while this is happening, the drone will not arm and the GCS will return a “BAD GYRO HEALTH” message.

Also it is important to note that the spot on which the drone is sitting when it establishes a GPS fix will double as the RTL (Return to Land) waypoint. This means that this is the spot where the drone will attempt to fly back to if it enters failsafe mode. Since the drone lacks any ability to detect obstacles, it is imperative that the RTL spot be extremely clear of obstructions.

Step 4: out in the field (Establish Base) Open the drone case and start with removing the drone. Place it on the spot where you intend to become the RTL point. Don’t plug it in or put the propellers on yet. Next turn your attention to the Transmitter.

a. Carefully remove controller (transmitter) from box. b. Make sure all switches are in their full back positions. c. Make sure all sticks are centered except for the left stick, which should be in full down position. d. Turn on transmitter. It will emit one beep to let you know that it is working. If the transmitter continues beeping, it is likely that a switch is in a forward position; this will prevent the transmitter from initializing. e. Make note of the transmitter voltage. If it falls below 9 volts, do not attempt to fly without first replacing the batteries. f. Press and hold the button labeled “DN” which is in the 6 o’clock position in the clover leaf button cluster on the lower left hand side of the controller face. This will open the controller’s telemetry screen which includes the following information: Altitude (meters), Speed (MPH), Distance (to ground station), Satellites (how many GPS satellites are locked in), Battery (The voltage of the drone’s battery), and milliamp hour (record of battery consumption in milliamp hours. g. Double check that the two switches in the upper right-hand corner of the controller are set to STD and RTL=OFF. h. Install FPV monitor: i Fold FPV monitor cleat. ii Insert thumb bolt. iii tighten with backside of propeller wrench. Now it is time to plug the drone battery in and let it start acquiring satellite position. Be careful while doing this, as excessive shaking will prevent the drone from arming.

Readying the ground station: a. Turn ground station computer on. b. Attach 915mhz radio transmitter to the Velcro located on the lid of the ground station computer and plug into empty USB port.

90 c. Start “Mission Planner”(windows) “APM Planner”(MAC) which is the ground station application. While waiting for the mission planner application to boot, return your attention to the drone.

Turn the drone over and make sure that it’s power cords are plugged in for the gimbal, and the video transmitter. Turn on GoPro. Turn on the video transmitter, and make sure that it is receiving a strong solid picture.

It is now safe to put the props onto the drone. Refer to the operator’s manual for how to accomplish.

NOTE: Though the use of a ground station is not totally necessary to use the drone, it should be considered mandatory equipment, as it provides a wealth of flight information. This information is often helpful in diagnosing and responding to an inflight problem, which will assist the operator in the task of returning the drone safely to a landing. Mission Planner (windows) receives real time telemetry logs. These logs are like an airliner’s “black box” in that they record the entire flight for playback and analysis. This information is invaluable in locating a lost drone should the unlikely event of a “fly away” occur.

Step 5: Connecting Drone to base station: After the drone has been made ready for flight, plug in the battery and close the battery door. a. In Mission Planner: Notice in the upper right hand corner of the screen there are two pull-down menus and a connect button. The pulldown menus should be set at “COM9” and “57600” respectively. Press the connect button. b. When the drone connects you will see relevant messages in the information display. c. The most common warning is “bad gyro health”, which appears when the drone is experiencing too much motion during IMU initialization. The remedy for this is to power cycle the drone while keeping it stationary. d. When there are no longer any warnings in the status window, you are ready to fly. e. Arm the drone by pulling the throttle stick down and pushing it fully to the right. f. Hold it there until you hear one long tone. g. To take off, raise the throttle stick slowly until the drone is light on its landing gear. h. Be prepared to drop the throttle to land again if the drone is not lifting off in a level attitude. i. Once the drone is in the air, stabilize it in a hover approximately 10 feet off the ground before continuing with the flight. This safe hover will help the operator determine if the controls are functioning correctly.

91 Step 6: Landing: The recovery or landing of the drone is the stage of flight that immediately follows an approach to landing. Since the approach to landing may be flown by either the human pilot or the autopilot, I am not covering that procedure in this manual. To land the drone manually (recommended), carefully position it 10-20 feet above the landing spot as required to assure horizontal clearance of obstacles as it descends. Lower the throttle stick below the middle position, and let the drone come down gently into its own footprint.

Note: the pilot must keep their thumbs on the controller during this stage, as the drone is susceptible to erratic behavior as it loses altitude. This behavior can be caused by several factors related to operating close to the ground. Among these factors are: ground effect turbulence, descending into the vortices of the drone’s props, glitches in GPS caused by descending below the surrounding tree line, or differences in the wind at ground level versus just above the tree line.

Once the drone is safely settled on the ground, it is necessary to disarm it. To do so, drop the throttle stick all the way down and push it to the LEFT. Hold until you hear multiple tones.

Mapping Configuration: For mapping, the only required equipment is the mapping camera, the mapping camera mount and the remote camera trigger (see Figures 2 and 3). The mapping camera is mounted on the front via the mapping camera mount; it faces downward so that its photographic plane is more or less parallel to the ground plane. The mapping camera is triggered remotely by the remote camera trigger, which is connected to the power output from the drone and to receiver output channel 7.

It is optional to use the GoPro and video transmitter as an FPV setup. This will allow you to see what the drone sees and in some cases may help to recover the drone if there is a problem with the autonomous mission and the drone needs to be flown home manually. It should be noted that this optional equipment adds a certain amount of weight to the configuration and will result in reduced flight times.

Figure 2 Mapping camera and mount.

Figure 3. Remote Camera Trigger.

Mapping BFS Camera & Mapping Camera Mount:

Optional: First person view equipment (see Figure 4). Required for real time video downlink, but dramatically increases flight time. GoPro Hero4 Silver GoPro Simple Mount Video Transmitter

Figure 3.” First Person View Equipment”. Equipment to remove: Tarot Gimbal (The drone cannot lift this and the Mapping camera at the same time).

Mapping Mode (assumes no FPV setup): 1. Remove gimbal. 2. Remove video transmitter. 3. Install remote camera trigger. 4. Secure and attach mapping camera. 5. Plug remote trigger into camera usb. Video Mode 1. Remove mapping camera. 2. Remove remote trigger. 3. Attach gimbal, being careful to plug in red power cord and the two wire(yellow and brown) connector before screwing the gimbal plate to the drone body. 4. Install Video Transmitter (Velcro). 5. Plug usb into GoPro port and insert GoPro into gimbal cradle. 6. Secure GoPro with Velcro strap.

Flight Planning It is necessary to obtain as much information as possible about a survey site before conducting any kind of drone related mission. Firstly, the drone operator should consult the ‘Know before you fly” website available at: http://knowbeforeyoufly.org/ for helpful tips regarding how to maintain safe and ethical operations. There is an interactive map available at the following website: https://skyvector.com/ This map will quickly alert the drone pilot to any potential conflicts with local general aviation traffic. In the case of Cooperstown, there is a small airfield located approximately 8 miles south east of the lake which may cause problems for the drone operator depending on where the survey site is located (Figure 5).

Figure 5. Sectional chart showing Cooperstown Westfield Airport.

In addition to obtaining the necessary situational awareness to operate safely within the United States Airspace, the operator should also determine what the hazards are in the area including but not limited to land contour and vertical obstacles. Ownerships of properties the operator intends to overfly, and proximity of appropriate launch site, is outlined in Step 3 of this document.

The simplest type of data to acquire using a drone by far is video, in general It is sufficient to simply follow the directions outlined in this report to install the video camera, the video transmitter, and the video receiver. The trickiest part is to navigate the various control menus for the GoPro and the video monitor. The manuals for each of these do a very good job orienting the user to the operation of that equipment. Those manuals are located in the manila envelope inside the drone case. Although waypoint missions are not required for video, there are some use case scenarios that may benefit from using them. For instance, a splined waypoint mission may be helpful if one is attempting to acquire quality video overflying a riparian corridor. Since the drone can manage the workload of following the river below this frees the operator to concentrate on camera framing.

To set up a splines waypoint mission (assuming that Mission Planner is open and the drone is connected according to the instructions for doing so earlier in this document), in Mission Planner: 1. Click on the “FLIGHT PLAN” tab. 2. Navigate to the area of interest. 3. Click a rough faceted line on the map to represent the corridor you wish to fly a splined mission along. 4. In the mission outline panel at the bottom of the page, select the pulldown icon next to the words that say “waypoint” and change this to “splined waypoint”.

95 5. Notice that the line you drew becomes more smoothly curved. 6. When you are satisfied with the mission parameters, click the “Write WPs” button. Mission Planner will load the mission to your drone.

When you are ready to execute the mission, simply selecting “auto” for the switch position on the control box will send the drone off to execute this mission plan. For more information regarding the various different styles of flight plan for mission planner, the operator is encouraged to familiarize themselves with the Mission Planner Instructions located online at http://planner.ardupilot.com/ Two other flight modes that may be of use are “Orbit” which causes to drone to fly in a circle around a fixed point, all the while pointing toward the center, and Region of Interest which causes the drone to face the same point on a map regardless of where it is flown.

The Autopilot requirements for mapping are much more crucial than those available for videography. This is mainly because mapping requires repeatability, uniform heading, and uniform altitude above target. To complicate matters further, there is a requirement to achieve a minimum picture overlap of 60% in order to reconstruct orthorectified mosaics. During a mapping mission, the drone is instructed by the autopilot to fly a ‘lawnmower’ pattern over a subject parcel. It does so at a fixed height, with a fixed heading.

To create a mapping mission (assuming Mission Planner is running, and the drone is connected to the ground station laptop): 1. Click on the “FLIGHT PLAN” tab. 2. Navigate to the area of interest. 3. Right click on the screen and select the menu item “Draw polygon> Add Polygon Point (then click ‘OK’ to dismiss the warning). 4. Add red polygon vertices around your area of interest, they can also be grabbed and dragged once placed. 5. When polygon has fully enclosed the target survey area, right click and pull out the menu for Auto WP > Survey (Grid). 6. The window that opens is the Survey grid control panel. Select the simple tab in the upper right hand corner of the screen and make sure that the camera pulldown is selecting the Canon a2400 powershot (or whatever camera you are using for the survey). 7. On the Grid Options Tab, make sure side lap and overlap are set to 60%. 8. In “copter options”, check the heading hold box, then check the Unlock from grid box.. 9. Type ‘0’ for North into the heading hold box. 10. Check the window below that survey grid map paying close attention to the Flight Time (est) field make sure that it does not exceed the copter’s battery limitations. ‘It should be less than 15 minutes’ 11. Go back to the ‘simple’ tab and adjust the Altitude (in meters) until the estimated mission time under the mapping pane works for the battery. Remember, it is a hard FAA rule that flights not be conducted in excess of 400 (121 meters) feet AGL. As you raise the altitude you decrease the amount of time the survey will take, while also reducing the ground resolution of the survey data. In situations where the ground

96 resolution or altitude parameters are forcing a smaller survey area, simply plan on doing the surveyed area in multiple parts. Don’t forget to overlap the area of survey so that there are sufficient data to blend them in post production. 12. When everything looks correct, click the “accept” button. 13. Click the ‘Write WPs’ button. 14. Click the ‘Read WP’s’ button (this is an extra step). It assures the operator that the drone has accepted the survey mission and is aware of its current location. This will help prevent fly-aways 15. Now you are ready to take off and execute the survey.

Post Processing Data: After a mission, the operator is left with a number of pictures of their survey area, none of them containing the whole picture. It is necessary to use post processing to turn these data sets into something useful. There are two main choices. If the desired photo absolutely must be ortho-rectified, then the data set needs to be turned into a 3D model from which an orthographic projection of the scene can be produced. In order to produce a truly orthorectified mosaic, the model also needs to have a ground reference plain that is perpendicular to the direction of gravity; in other words, perpendicular to 0 nadir. To achieve this, the photographs in the data set must have a minimum GPS position encoded in the EXIF data.

Mission Planner http://planner.ardupilot.com/ (Application used to create autonomous missions and also georeference photographic datasets): The camera does not code the positional DATA in, so it needs to be appended to the images. Mission Planner has a powerful tool for doing this. It utilizes the time code from the camera and matches it to the GPS logs kept by the autopilot. By doing this MP is able to mark each picture with a relatively accurate positional code related to where it was when the picture was snapped. To outline this process is not reasonably within the scope of this document, and there are a great deal of online resources to walk the drone user through this process. So for the purpose of brevity I will refer the readers of this journal to the following Youtube video: https://www.youtube.com/watch?v=Vth6LXCifOs This is the video I used to learn the process and I can attest that it works as stated.

Photoshop In use case scenarios where orthorectification is un-necessary, Photoshop has the power to create seamless high resolution aerial mosaics. The gain in using Photoshop over a photogrammetric modeling software package like Agisoft Photoscan is that it does not require the additional step of georeferencing the photograph. Instead the image is georeferenced after its creation by inserting it into a GIS application such as Arcmap, QGIS or Global Mapper. In order to do so (assuming the user has access to Photoshop cs4 or later): 1. Open Photoshop. 2. Go to the File>automate>photomerge pullout. 3. Select ‘auto’ for layout.

97 4. Navigate to the folder containing the photos in your data set and shift+select all that are applicable. 5. Make sure that ‘Blend images together’. ‘Vignette removal’, and ‘Geometric Distortion Correction are checked. 6. Click “OK”. 7. Wait… this is going to take a long time.

When Photoshop is done, it will have made a HUGE and very high-resolution file. It may be necessary to re-size the image in order to prevent the GIS that you put it in from crashing. Save the resulting file as a TIFF. Lastly, import the file into your favorite GIS software and georeference according to that software’s operating procedure.

Agisoft Photoscan. (http://www.agisoft.com/) Manual for Agisoft can be downloaded at the following link: http://downloads.agisoft.ru/pdf/photoscan-pro_1_0_0_en.pdf

Photoscan is a photogrammetric modeling software that enables creation of 3d models from aerial photo data sets, provided that there is enough overlap between individual pictures. There are others that are available which can produce the same results. However, Agisoft is the only one that I have used and have had success with. It costs around $500 for an educational license, which is very inexpensive as most others start at around $5000 for annual rental. Describing the steps to process photogrammetric models are similarly outside of the scope of this report and it comes with a very easy to follow manual.

This concludes the Introduction to drones for spatial ecology manual. The knowledge contained herein is enough to turn any drone with ‘waypoint navigation’ capability into a functional spatial analysis data collection tool. Though this report doesn’t probe the absolute depths of what a drone is capable of, it’s intent, which is to demystify and bundle together the essential steps required to create useful data using a small unmanned aerial system is all contained here-in. Special thanks to those members of the BFS that assisted in my research, as well as the Otsego County Conservation Association for providing the funds which enabled my internship. And a special thanks to the Otsego Lake Association who provided the funds which were used to purchase the drone and all ancillary equipment used in this study. This document does not indemnify future operators from litigation resulting from the illegal, unethical, or unsafe usage of drones.

Drone ecology on a budget

Peter T Booth1

INTRODUCTION

There is little doubt that aerial photos are high in value for research, but this value comes at a great cost. Aerial surveys conducted with full-scale aircraft, manned by pilots and technicians, are extremely expensive. They also require a closely located airport, and a pilot that is willing to fly at a moment’s notice. Small Unmanned Aerial Systems (sUAS), by contrast can be on site within moments, and their acquisition costs are less than the cost of just one full-scale aerial survey. With the recent invention and proliferation of consumer grade sUAS’s, that cost less than $2000, these limitations have been reduced. sUAS’s are limited in their own ways. They have relatively low weight carrying capability, which reduces the quality of equipment they can carry. Given these limitations, the actual value of sUAS’s in research requires further study. The goals of the experiments conducted between 26 May and 1 August 2015 at the Biological Field Station (BFS) were to help determine this value.

Using Drones to collect research data is not a new practice. There have been studies conducted in other fields of conservation, such as animal population surveys, and forestry land use monitoring. In 2012 Koh and Wich (2012) performed a series of experiments in Indonesia using a <$2000 fixed wing drone to study human land use change, and to consider the application of low cost uav’s for the purpose of wildlife survey. They reported the following results:

In the images acquired during our transect mission, we could easily distinguish different land uses, including oil palm plantations … maize fields …., human habitation …, forests …logged areas …and forest trails …These geo-tagged photographs and the flight paths of each mission could also be superimposed on Google Earth …, which allows for easy visualization of the location of features of interest from the photographs.

Using commercially available software … we produced geo-referenced mosaics from these aerial photographs … These mosaics are essentially near real-time land use/ cover maps, which could be useful for local conservation workers seeking to monitor land-use change and illegal forest activities. An example is the mosaic produced from our grid mission, which is overlaid on a Landsat- based land use/ cover map … The pixel resolution of our mosaic (5.1 cm) is 600

1 Otsego County Conservation Association intern, 2015. Department of Geography, SUNY Oneonta. Funding provided by the Otsego County Conservation Association.

Funding for the drone and accessories was provided by the Otsego Lake Association.

99 times higher than that of the Landsat-based map (30 m). “ (Koh and Wich 2012).

Understanding what use this technology is to the research ecologist at the BFS requires quantifying the output of these surveys. Since much of the work done at the BFS falls under the categories of continued monitoring, and education, it stands that the output from aerial survey would have to be something capable of mapping interests on the ground, while also applying time signature to this data. For example: If one wanted to monitor the spread of an aquatic invasive species over the course of a growing season, one could use the sUAS to map its distribution on a weekly basis, but only if they had an acute awareness of when the basemap had been made. The sUAS allows us to construct our own base maps with total documentation of the date/time of acquisition. Many researchers believe that this practice is going to become standard operating procedure in the near future. According to Anderson and Gaston (2013),

“Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) – aircraft remotely operated from the ground – and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale- appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range.” -- (Anderson, Gaston 2013)

In March of 2015, I learned that the Biological Field Station had received funding to purchase a drone. I immediately responded by submitting an application for summer internship. I have several years of experience building, designing, and operating drones, so I was a good fit for the position. Understanding what use this technology is to the research ecologist at the Biological Field Station (BFS) requires quantifying the output of these surveys. The logical first step of my experiments with the BFS this summer was to determine exactly what it was the drone could do, and what value its deliverables could possibly add to research. To do so I conducted a series of evaluations for each of the following deliverables: photomosaics, orthomosaics, and video footage.

My first task was to select a drone that would be suitable for the type of research performed at the field station. Given the tight budgetary constraints and the richness of capability that was needed for the drone to be of any research value, I endorsed the purchase of a consumer grade drone from a company named 3D Robotics. For around $2000, I was able to put together a package for them that included: 1 Iris+ drone, a GoPro hero4 capable of 1080p video at 60fps, a

100 3DR/Tarot 2D brushless gimbal stabilization system, a Black pearl combination diversity receiver and video monitor, and several redundant support items including spare batteries and propellers. All of this came with a large waterproof gear case with custom foam insert, which has proven essential for transporting the drone and all of its support gear to various field locations including operations on the lake from motorboats. All of this was acquired for roughly $2000 USD, which was approximately %50 over the budget they originally outlined to me. An ancillary purchase was a 16mp Canon a2400 powershot digital camera, which was converted to a mapping camera using the CHDK script. The CHDK scripts, or Canon Hacker Development Kit is open source software project that in the words of CHDK developers:

“ … enhances the capabilities of your camera in a non-destructive, non- permanent way.” (CHDK 2015)

The reason for this was to enable our mapping camera to be remotely triggered by the autopilot. In order to mount the mapping camera to the drone it was necessary for me to design a camera holder that worked with the existing hardware mounts on the Iris. Since nothing was available from any commercial sources, I opted to design one using a 3d part-modeling program called SolidWorks. (http://www.solidworks.com/). By reverse engineering the existing GoPro system mount on the front of the Iris, a case was created that neatly fits the cannon while also easily attaching to any “two tabbed” GoPro accessory mount. The designed part files were then sent to Allan Anderson, who is the scientific technician for the Science division of SUNY Oneonta. He then printed these parts in his 3D printer. An additional piece of equipment required for aerial mapping was a remote camera trigger that allowed the autopilot to trip the camera shutter at key locations. This item was built by myself using the instructions available in a YouTube video titled: Trigger Canon SX260HS with CHDK Using PIXHAWK or APM posted January 16th 2015 by user: TechnoSys Embedded Systems. (TSES 2015) I donated the materials and equipment required to make the camera trigger.

My decision to purchase the Iris+ from 3DR was informed by past experience with the Iris’s flight controller. The Pixhawk flight controller is a product of 3DR, https://store.3drobotics.com/products/3dr-pixhawk (3DR 2015). Considerable time has been spent perfecting hardware that runs the ardupilot flight controller code. Ardupilot is a flight controller code that has been developed by an open source community and is available to anyone. According to the Ardupilot website:

“[Ardupilot is] A constantly evolving repository of knowledge and innovation.

The DIY Drones community provides the life-source and inspiration for Ardupilot. A comprehensive list of features that are continually born from the needs of the community.” - (Ardupilot 2015)

This is an open source flight controller product that has been a staple for autonomous flight researchers as it is a very capable autopilot that costs a fraction of what some other

101 autopilots with fewer capabilities have. The ardupilot firmware allows the usage of several different ground station solutions on either the windows or android platform. This enables the operator to perform lawnmower-pattern survey missions, which are essential for aerial survey. The Lawnmower pattern survey grid is the item of most interest to us at the BFS.

The logical first step of my experiments with the BFS this summer was to determine exactly what it was the drone could do, and what value its deliverables could possibly add to research. To do so I conducted a series of evaluations for each of the following deliverables: Photomosaics, which are unreferenced images of an area. These are constructed by tiling together a number of photographs obtained from different camera positions. Adjustments to these photographs are required to make them fit together; these adjustments and distortions rule out the photomosaics use as a measuring device. Orthomosaics are similarly constructed of multiple photos taken from different camera positions, but they have the extra step of producing a 3d model using photogrammetry. This extra step enabled the image to be a true 90° projection from the subject to the datum plane. This process results in photographs where distances can be measured. Geo-referencing orthomosaics enables them to be used in conjunction with existing USGS spatial data. To do so one must establish accurate ground control points. Lastly experiments were conducted to ascertain the value of aerial video footage for research.

Evaluation Series #1 Photo-Mosaics Method

This series of experiments was conducted to determine which method of acquiring photographs for photomosaics produced the best results. In experiment #1 I flew several missions collecting only GoPro video footage. Later in post-processing I used QuickTime to export stills from that video at timed intervals. I then used Adobe Photoshop to merge those together. In the Second Experiment, I used the Canon A2400 Mapping Camera running a CHDK intervalometer script which simply snapped one picture every second for the duration of the flight. For the third experiment, I used the mapping camera and the remote camera trigger in conjunction with the Mission Planner flight planning software to fly a precise scanning ground track while assuring uniform overlap in photographs.

Experiment #1: Still images from video: Extracting frames from GoPro footage, obtained while flying video recon at Shadow Brook Inlet on 06-15-2015. These derived photos were then fed through Adobe Photoshop cc’s “photomerge” automation.

Experiment #2: Pilot controlled scan pattern/intermittent camera shutter at 1 sec interval: This Experiment was conducted on 1 July 2015 at Hayden Creek. The drone was set up with the mapping camera only. The drone was then flown in loiter mode in a pilot controlled approximation of a scanning pattern. The purpose of this experiment was to determine the reliability of data collected with little to know mission pre-planning. This condition mimics the use-case-scenario that is often found on the site of a suspected plume event.

Experiment #3: Autonomous photo collection with 65% overlap (every other photograph discarded to simulate %25 overlap). These experiments were conducted at three different locations on 9 July 2015: Hayden Creek, Rat Cove, and Shadow Brook. The survey patterns

102 were created on a laptop in the field. This method was used at these locations for the purpose of establishing baseline control representative of a “no suspended sediment condition”.

The results of each of these three methods were imported into Google Earth, where they were stretched, rotated, or distorted to fit into the Google basemap. Each method was evaluated individually based on how easy it was to make them fit the Google basemap, and how completely they were able to be fit to the Google basemap.

Hypothesis to be tested: The fewer steps required to fit a photomosaic into the Google Earth basemap, the more accurate the method.

Evaluation Series #1 Photo-Mosaics Results

Experiment #1: Still images from video

Resulted in vignette free, tiled photograph that provided a larger overview of the 15 June 2015 Shadow Brook sediment plume. The images obtained were incredibly distorted; because of extreme un-rectified barrel distortion, and lack of geo-referencing, these images serve little measurable purpose (See illustration 1).

103 Illustration 1: Photomosaic created using extracted stills from GoPro video footage. Shadow Brook inlet, Lake Otsego, NY15 June 2015

Experiment #2: Pilot controlled scan pattern/intermittent camera shutter at 1 sec interval: The photomosaic produced during this experiment produced a decent overlay that was then compared to a baseline survey conducted later (Illustration 2). The mosaic provided a clear visual indication that conditions on the day of that survey where different than those conditions on the day of the baseline survey; however, since they are not ortho-rectified or georeferenced, they lacked measurability.

104

Illustration 2: Comparison of Photomosaic areas to Orthomosaic areas: Underlay is a Photomosaic that was created for the purpose of establishing a baseline of a non-sediment condition. Later, the same group of photos was used to create an unreferenced ortho mosaic. The lines in this illustration show the difference between placement of identifiable features in the photomosaic, and the orthomosaic. The heavy solid line represents the ortho rectified image, and the dashed-dot line represents the same identifiable objects as they are shown in the photomosaic. Note that the difference in placement of these features is non-liner.

105 Experiment #3: Autonomous photo collection with 25% overlap. The photomosaics that came out of this survey are by far more accurate, less distorted and more usable than the previous two experiments. These required the least amount of modification to fit into the Google Earth terrain imagery. Since they lack georeferenced ground targets, area measurements taken from photomosaics using this process would be good for estimation purposes only (See Illustration 3).

Illustration 3: Autonomous photos collected with 25% overlap.

Evaluation Series #1 Photo-Mosaics Discussion

Un-referenced photo-mosaics may at first sound like a waste of time for their lack of measurable date, but they do have certain applications that make them attractive. In cases where it is not possible to place the GPC required for ortho-rectified images, the simpler Photo-mosaic is a good alternative. Mosaics can help by showing areas of coverage that are too wide to capture with a single image, and they can, with some work, be fit into Google Earth for the purpose of illustrating change. Making them requires much less expensive software, and in most cases they can be produced within an hour of the flight in which they were captured, versus the four hours or more that it takes to construct an Ortho-rectified image.

Experiment#1: Extracting stills from video. Though this method produces the most distorted mosaics, it is still of some value because it makes it possible to image a large area using only the initial reconnaissance video as a source of images. In some cases this may be a necessary step in order to justify a full blown survey of an area of interest.

Experiment #2: Intermittent photography using mapping camera, while flying a crude lawnmower pattern: This process produced decent enough results for my purposes, and the setup time was very short. This is a completely usable method when the object of the survey is to simply image a large area as quickly as possible. This method is very useful for obtaining a

106 decent aerial survey when ground prep time is too limited to facilitate establishing and executing a survey pattern.

Experiment #3: Mapping camera remote triggered, and autonomous mapping flight plan: this method requires the most setup, but also delivers the best results. This is my preferred method when time allows not only because the photomosaics that are produced by this method are superior in every way, but also because it enables making an un-referenced ortho and 3d models at a later time if it is done with sufficient photographic overlap.

Evaluation Series #1 Photo-Mosaics Conclusion:

The best way to obtain photomosaic is to establish a survey and use the mapping camera trigger to assure consistent overlap. The overlap required for a photomosaic is less than that required for an ortho-mosaic. Overlap should be adjusted to no more than 25% since the requirements of mosaics are less than that of ortho rectification, and few photographs mean longer flight times. However, if the flight is conducted to provide %60 overlap, then that group of photos can be used to also construct a 3D model and Ortho-mosaic as we will discover in the next section.

The true value of the photomosaic over the ortho-mosaic is the ability to launch and complete a mission in shorter time, and also with greater coverage per flight battery. The fewer photographs that are required to capture an area the faster the drone can maneuver over that area. This means that the drone can cover a bigger area at a lower altitude. As altitude decreases resolution increases. In cases where a large area photograph is all that is needed, the %25 overlap method of data acquisition is recommended.

Evaluation Series #2 Ortho-Mosaics Methods

Ortho-Mosaics or orthogonal projection is a two-dimensional graphic representation of an object in which every point on the landscape is projected at right angles to a theoretical datum plane. A true orthograph will not have any error of parallax, nor will it have any linear distortions. Ortho-mosaics require an additional software step in post processing. That software step relies on a process called photogrammetry, to facilitate the building of a 3 dimensional model. This process would take several academic papers to sufficiently explain, so for the purpose of this paper I will only paraphrase. Photogrammetry is a process where by depth can be determined by comparing the apparent locations of items appearing in two or more photographs taken from different camera locations. The following excerpt from the website found at: www.geodetic.com, which outlines the process further.

107 “The fundamental principle used by photogrammetry is triangulation. By taking photographs from at least two different locations, so-called "lines of sight" can be developed from each camera to points on the object. These lines of sight (sometimes called rays owing to their optical nature) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest. Triangulation is also the principle used by theodolites for coordinate measurement. If you are familiar with these instruments, you will find many similarities (and some differences) between photogrammetry and theodolites. Even closer to home, triangulation is also the way your two eyes work together to gauge distance (called depth perception).” (Geodetic 2015)

As mentioned earlier, the drone can be used to collect a photographic scan of an area with controlled overlap. These photos are then fed through a photogrammetry program (AgisSoft Photo Scan) to produce a 3D model from which an orthorectified image can be generated. In order to give meaning to this orthograph, we also need to include geographic data so that the photo can be imported into GIS software packages such as ArcGIS or Global Mapper. Once our models have been imported, very accurate area measurements can be derived from them. In order to make this all work a reliable source of Ground Control is required.

Ground Control Points or (GCP) are items that are located on the ground in an area of interest. These can include targets that were placed for this purpose, or it can be permanent features of the landscape that are easy to locate in the photo data set. Whatever object is chosen for GCP, the same principles apply. GCP must have the following characteristics: It must be easy to see in several of the photographs for any given survey data set. It must have a unique and accurate position associated with it, i.e. latitude, longitude, elevation. It must remain static throughout the process of surveying. And there must be a minimum of three ground control points per survey.

The intent of this study is to inform our expectations for modeling precision given the variability of different methods of locating GCP’s.

Two different types of experiments were used here.

Experiment #:1 Orthography without GCP Since the photographs there were obtained on 9 July 2015 at various locations around Otsego Lake, and were obtained in a manner consistent with mapping survey, with the exception of laying down GCP, I decided it was prudent to post process these in the same manner as any other photogrammetric survey omitting the step of locating GCP. The idea here is to test the theory that Orthography is more or less useless without reliable GCP.

Experiment #2: Orthography with GCP All Orthomosaics were obtained using the same method of sUAS survey. A gridded survey pattern was created and flown, using the Mission Planner software. The drone was commanded to fly a lawnmower pattern over the subject area maintaining the same heading and altitude throughout the mission. The autopilot was told to execute a script that instructed the camera to trigger remotely based on the location of the drone. These shooting locations are determined in the software in order to assure the required photographic overlap for the mission is

108 being met. In the case of orthomosaics, that overlap is %60 minimum. Photogrammetry is then used to construct 3D models of the subject area. Agisoft PhotoScan Professional Edition, Version 1.1.6 Build 2038 (64bit), is the software that was used to perform this task. The workflow of this software is as follows: 1. Import group of images collected during survey 2. Compute pairwise matches between the images (generate sparse point cloud) Intermediate steps: Identify GCP’s in photos Import geodetic locations for GCP’s 3. Generate Dense Point Cloud Settings to High 4. Generate Mesh Settings to High 5. Generate Texture Projection to “heightfield” Vertexes to “high” 6. Export Orthophoto

There are several options for this, but the two that I concerned myself with for these experiments were: Ortho-rectified geo tiff, which will fit into a GIS application in the correct location, assuming the GCP is properly located, & Ortho-rectified.PNG for publication.

The following three methods were used to locate GCP in the WGS84 geodetic datum. The coordinate system used for these studies is UTM Universal Transverse Mercator. All of these test cases were conducted in UTM zone 18N.

Method 1: Total Station / DGPS located Ground Control Points. Post processed to sub- centimeter resolution and adjusted for the ellipsoid height. GCP’s consisted of fluorescent orange compact disks held in place with a dimple headed surveyor’s nail. Later tests were conducted using two Home Depot yardsticks that had been painted white, a hole drilled in the center, and a dimple headed cabin spike driven through two of them to make a white cross on the ground. The GCP were placed in the field where they were located relative to each other using a total station. The position of the total station and local network of points was then tied into the geodetic datum by means of two differential GPS receivers that took averaged readings for the duration of each survey. The data collected from the DGPS receivers was then post processed using a combination of Spectral Precision’s GNSS software and data from the existing network of CORS stations operating within this region.

Method 2: Consumer Grade GPS placed on top of existing ground references. Consumer GPS for this study is a Garmin Etrex20 which has the ability to “average” a waypoint. All of the GCPs in this study where put through such a process until the GPS unit reported 100% certainty. These points were then entered into the software in exactly the same way as the Differential GPS points from step 1.

Method 3: Photo derived (incidental) Coordinates: Identifying items in the subject photographs that are recognizable in corresponding orthographic photos available through the

109 USGS database. Northing, Easting, and Elevation Data for these identifiable ground features are derived after loading these georeferenced images into a GIS application called Global Mapper. The resulting coordinates are then written into a spreadsheet and imported into Photoscan where they are then applied as GCP to the corresponding features in the model/survey photographs. Examples of these features are: manholes, parking lot lines, corners of pavement, etc.

Evaluation Series #2 Ortho-Mosaics Results Experiment #1 Orthography without GCP: The resulting Orthography was neatly rectified, and showed signs of being measurable; however, the scale, placement, and rotation are not verifiable. Once these photos had been placed in Google Earth and manually manipulated to fit, the results were somewhat measurable. Though this process would be useful for providing extremely estimated area measurements of new events, it would not be very optimal for measuring change over time, because it is not possible to certify any of the locations without conducting a proper field survey. The following photographs are a result of applying ortho- mosaic building techniques to the same photographs obtained during the control surveys conducted at three different locations on 9 July 2015: Hayden Creek, Rat Cove, and Shadow Brook (see Illustrations 2&4).

Illustration 4: Ortho rectified images from Hayden Creek, Rat Cove, and Shadow Brook. Created with the same images used for the photomosaics shown in illustration 3.

Experiment #2 Orthography with GCP: Results were as variable as the methods of ground control used. For the Photo CD’s and the yard sticks the results were very good; these surveys produced orthorectified and geo-referenced photographs that had horizontal accuracy at the sub-decimeter level. This is a conservative figure; actual results are closer to 1.5 cm. For the other methods of approximating GCP the results were not as favorable. The GCP placed using consumer grade GPS devices yielded 5-meter discrepancies for horizontal position, and no correlation at all in the vertical. The results of the photo-derived coordinates were even worse, producing orthophotos and models that were less accurate than un-rectified Photomosaics by hand.

110

Illustration 5: Ortho-Rectified, and georeferenced .tiff of the wastewater polishing wetland located south of Cooperstown, NY.

111

Illustration 7: Geomorphological study of cut-bank migration. Conducted 2 July 2015 at the Wheeler Field Bend on Butternut Creek, UTM 4707300.70 m N, 478369.04 m E, 18T.

112 Illustration 8: Vertical Profile of Transect through 3D photogrammetric model of Wheeler Field Bend, Butternut Creek: Showing a comparison between the vegetated surface model created by Hasbargen, Booth, Busby, (2015), and the USGS “bare earth” model created using lidar in 2007. The Model clearly shows terrain match, as well as area of lost cutbank (dashed line).

Evaluation Series #2 Ortho-Mosaics Discussions:

Experiment #1 Orthography without GCP: the main difference between an un-referenced Ortho-mosaic and a photo-mosaic is that the ortho mosaic can be scaled, rotated, and manually inserted with relative ease into a GIS application. Though the manual placement somehow cheapens the value of measurements obtained from this type of deliverable, the errors appear more linear and therefor correctable. In situations where it is not possible or practical to place GCP, the un-referenced ortho-mosaic runs a close second to its fully referenced brother.

Experiment #2 Orthography with GCP:

Though I was able to produce highly precise results using terrestrial ground survey methods that located the GCP at sub-centimeter precision, the equipment and intellectual requirements for placing this GCP makes this process inaccessible to most researchers in this field. Of the alternative methods that I have explored, all produced results that were simply too un-precise and variable to be relied upon for data.

113 Evaluation Series #2 Ortho-Mosaics Conclusion:

Evaluation Series #2 Ortho-Mosaics: The ortho-mosaic is by far the most valuable product in terms of delivering usable data. With this process there is near limitless application for time base studies of anything having to do with area measurements. Just a few examples that leap to mind are: The study of open water areas before and after major storm events, distributions of invasive aquatics before and after mitigation efforts, or the spread of nitrogen fixing plant species in a mitigation area. This is just to name a few, and I am sure time will expose even more uses. One drawback to the Ortho-rectified image is that it requires a lot of “front end” work. By this I refer primarily to the difficult task of setting reliable ground control points. The results of all experiments conducted using methods other than traditional terrestrial survey are unreliable. It is necessary to find another solution for locating GCP. One possible solution is to build permanent survey points that can serve this purpose for subject areas that are to be repetitively surveyed. Another possible solution which I have not had the chance to test, is to obtain a “mapping grade” GPS. These devices are capable of sub-decimeter precision, and they cost a fraction of what a total station, and two differential GPS units would cost.

Evaluation Series #2 HD Video Methods

Video: All video experiments were conducted with the drone outfitted in aerial video gear. That gear list includes: an HD GoPro camera recording at a resolution of 1080p at 60 frames per second, a video transmitter broadcasting a 5.8hz signal at 800mW, and a combination diversity video receiver/monitor. This apparatus allows for the drone operator to see a real-time video feed from the GoPro mounted on the drone. The Video signal resolution is degraded down to 480x640.

Experiment #1 Pilot controlled video: Innumerable flight experiments have been conducted in this fashion. The method is to simply fly around an area using the video screen to “have a look around”. While conducting this type of mission there are two options. The first is to record video only, the second is to record video and still images simultaneously. This is accomplished with settings in the GoPro itself.

Experiment #2 Pilot Controlled/Free Flight: These experiments were conducted in the same way as the previous experiments, with the addition of putting the drone in stabilize mode and allowing it to float downwind in a stabilized fashion. These experiments were conducted throughout the internship mostly in wide-open spaces like those found at Thayer farm.

Experiment #3 Autopilot controlled flight. This experiment was conducted with two different variables. In the first experiment the drone was instructed to fly straight line paths while the camera filmed. In the second experiment the drone was instructed to fly along a “splined curve”. The purpose of these experiments was to determine the best way to obtain smooth video of a subject that twists and turns. Using the example of a riparian corridor, one experiment was conducted flying a mission path in line segments, and another in splines. Although this

114 experiment was designed to evaluate a method for flying riparian corridors, the actual experiment was conducted over Moe pond on 20 July 2015 in conjunction with an electro-fishing survey that was being conducted on that date.

Experiment #4 Pilot controlled from moving platform. The previous experiments in this series were conducted from static ground locations. Differently, in this experiment I operated the drone from a mobile platform (motorboat). The purpose of this experiment was to evaluate the difficulty level and feasibility of conducting such flights. This experiment was conducted at Moe Pond on 22 July 2015 during an electro-fishing survey.

Evaluation Series #2 HD Video Results

Experiment #1: Pilot controlled video: The results of these experiments are varied; factors such as wind speed, and lighting greatly affect the outcome of these videos. This series of experiments did expose a technical issue with the gimbal, which results in the gimbal periodically flopping over and struggling to right itself to the horizontal. All feasible attempts were made to rectify this situation including: making adjustments to the gimbal settings, and adding counterweights to the gimbal itself to accommodate for a shift in weight distribution that occurred between the GoPro 3 series that the gimbal was designed for, and the GoPro 4 series. The results of this tampering appear to be favorable so far.

Experiment #2: Pilot Controlled/Free Flight. This experiment was designed to ascertain if the flying style of the drone was contributing to the gimbal stability issues. Since the gimbal issue seemed more pronounce when the drone was stationary in high winds, or if the drone was flying quickly in one direction, I reasoned that the problem might be exacerbated by airflow around the camera itself. Since “stabilize” mode holds the drone nice and level while leaving it free to drift away with the wind, the drone will then be in a 0 relative wind condition. I reasoned that this would expose the camera gimbal’s tendencies in a 0 wind condition. The results are quite favorable as seen in the video: /BFS DRONE /VIDEO/GoProVideo/2015-06- 11/GOPR0560.MP4 between time marker 6:20 and 6:28 the drone is performing this maneuver and the gimbal is stable.

Experiment #3: Autopilot controlled flight: The test case footage for this can be found at: BFS DRONE /VIDEO/GoProVideo/2015-07-20/GOPR2007.MP4 between the time marker 6:10 and 7:40. This experiment was inconclusive, and further study is needed.

Experiment #4: Pilot controlled from moving platform: video of this experiment can be found at: BFS DRONE /VIDEO/GoProVideo/2015-07-22/GOPR2011.MP4. This experiment was successful. This method worked well for collecting informative and interesting images.

115 Evaluation Series #2 HD Video Discussion:

The research applications for aerial video are still questionable, although I have reason to explore this as an option more in the future. As can be seen in my time study of the Willow Brook sediment plume there are things that can be seen using these techniques that are not macroscopic in the human time frame. It has been my experience that anything that exposes a new perspective must have research value.

Evaluation Series #2 HD Video Conclusion

Experiment #1 Pilot controlled video: This is a staple for recon; it is the perfect method for quickly deploying a reconnaissance flight. This process is essential for gathering spatial information relative to setting up a successful autonomous mission later. The recon flight offers information such as minimum obstacle clearance altitudes, or environmental factors that may affect autonomous flight.

Experiment #2 Pilot Controlled/Free Flight: This is a good method for collecting long stable aerial tracking shots. The research value of these shots is questionable, but they do lend themselves well for inclusion in documentary films and process documentation.

Experiment #3 Autopilot controlled flight: Further study is required to formulate any strong conclusions for this method.

Experiment #4: Controlling drone from moving platform: Even as a seasoned drone pilot, the initial disorientation of flying from a moving platform while looking up into the sky at a drone required some adjustment. I do not recommend using this technique until one has become a very confident pilot. Once an operator becomes confident with this method of flight, the resulting shots can be quite exciting.

Final Recommendation

At the time of this writing I have learned that the FAA has prohibited drones for commercial use of any kind. As to whether or not this applies to the kind of not-for-profit research conducted this summer, I cannot say. The FAA has a protocol for Public organizations to obtain the authorization to fly drones for research purposes. Details on how to obtain this authorization can be found on this website: https://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/systemops/aaim/organizations/uas/coa/ The Certificate of Waiver Authorization is defined by the FAA as follows:

“COA is an authorization issued by the Air Traffic Organization to a public operator for a specific UA activity. After a complete application is submitted, FAA conducts a comprehensive operational and technical review. If necessary, provisions or limitations may be imposed as part of the approval to ensure the

116 UA can operate safely with other airspace users. In most cases, FAA will provide a formal response within 60 days from the time a completed application is submitted.” - (FAA 2015)

I recommend that the BFS begin the process of obtaining this waiver before any future drone related research is conducted.

GLOSSARY

GCP (Ground Control Points): Ground Control Points are defined as points on the surface of the earth of known location used to geo-reference aerial images.

Orthographic projection: Photograph where all of the subject matter has been projected to the reference plane at 90-degree angles. This removes errors cause by parallax, and perspective. Since this error has been removed, orthographic projections can be used to generate linear and area measurements.

Orthomosaics: Orthographic projection generated using data acquired by several cameras in several locations.

Photomosaics: assemblage of photographs that have been stitched together to provide increased ground coverage. These are especially necessary when the altitude restrictions of the drone in concert with the Field of view limitations of the camera, make it impossible for the operator to obtain a singular picture of a wide area event. For example a single digital camera still from our sample camera produces a ground footprint of approximately 1 square acre, yet often times a sediment plume covers 10’s of acres. Photo-mosaics are not referenced to a geodetic grid, and they are not orthorectified, thus they are more useful for demonstrative purposes.

REFERENCES

3DR. 2015. As found on WEB: https://store.3drobotics.com/products/3dr-pixhawk

Ardupilot. 2015. As found on Web: http://ardupilot.com/

Anderson, Karen and Kevin J. Gaston. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11: 138– 146. http://dx.doi.org/10.1890/120150

CHDK. 2015. Retrieved from web August 2015 http://chdk.wikia.com/wiki/CHDK

117

FAA. 2015. https://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/systemops/a aim/organizations/uas/coa/

GeoDetic. 2015. Retrieved from web http://www.geodetic.com/v-stars/what-is- photogrammetry.aspx

Koh, L and S, Wich. 2012. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Mongbay.com Open Access Journal-Tropical Conservation Science Vol. 5(2):121-132,2012

Jones. 1996. parenthetihttp://www.washingtonpost.com/archive/business/1987/09/21/small- airports-nosediving-in-number/6dda3749-c8e8-49be-b963-92993b9f330a/lly ().

TSES. 2015. Video is available at the following: RL:ttps://www.youtube.com/watch?v=x7iN3UeQ2_w

118 Report on migration of Butternut Creek in Wheeler’s field

Les Hasbargen1, Peter Booth2, and David Busby3

INTRODUCTION

Project Overview This report summarizes the activity of a meander loop of Butternut Creek. The channel has been migrating laterally over the last several decades. The report gathers information about channel location over time using aerial imagery and elevation data sets from government agencies, and provides a new highly detailed image and elevation survey using pictures taken with a camera mounted on a remote controlled helicopter provided by SUNY Oneonta’s Biological Field Station in the summer of 2015. In brief, the channel moves laterally about 0.1-1.7 m per year, and has done so at least since the early 1990s. Trees planted by Otsego County Soil & Water District in 2012 are unlikely to grow fast enough to stabilize the bank before being eroded by Butternut Creek.

There are several reasons to perform a new survey of the area. First, while high quality elevation data exist for this area, those data were collected in 2007. The channel has migrated over that time, and another high resolution elevation survey would highlight changes over that time. The new survey would also serve as a new baseline against which future measurements could be compared.

Second, in the last ten years, two very large flood events have hit the region--one in 2006 (several days of heavy rain) and another in September 2011 as remnants of Tropical Storm Lee moved through the area. There has been some concern about the potential for the frequency of large storm events to increase due to global warming. Frequent detailed documentation of the channel shape could supply the information needed to assess the effects of flood size on bank erosion, and thus provide watershed managers with better insight into potential problems with erosion under a changing climate.

Additionally, from a basic science perspective, we would like to know what the main or dominant erosion mechanisms are for this river. The mechanisms which erode banks include bank collapse due to stream undercutting of a bank; soil heave from frost needle growth during freeze- thaw events in winter; fluid shear stress on the soil particles in the bank during flood flows; burrowing, digging, path development etc., by animals; and small mass flows of water and sediment on cutbanks during saturating events (but still above the water level in the creek). Which of these mechanisms is most important in overall erosion is not well known. It could be that concern about increased erosion from larger floods is not warranted, if fluid shear stress, for instance, is not a significant erosive agent. For instance, bank collapse could be most responsible

1 Summer Research Fellow, 2015. Associate Professor, Earth Science Department. SUNY Oneonta. 2 Otsego County Conservation Association Intern, 2015. Department of Geography, SUNY Oneonta. Funding provided by the Otsego County Conservation Association. 3 SUNY Oneonta Biological Field Station Intern, summer 2015. Current affiliation: Department of Environmental Sciences, SUNY College at Oneonta, Oneonta, NY.

119 for bank retreat, and thus undercutting mechanisms become very important. It might be that smaller and more frequent flows that are still capable of eroding the base of the cutbank play a more important role in driving migration via bank collapse than larger less frequent flows do via bank shear. Repeat high resolution measurement of stream banks are required to address these uncertainties.

Geomorphic setting The Wheeler farm extends from upland ridges down to the valley bottoms along Butternut Creek (see Figure 1). Landforms within the confines of the farm include a bedrock gorge with waterfalls; a perennial tributary creek which has built a modern alluvial fan into Butternut Valley; older and higher alluvial fans perched on the valley side, presumably formed by the same tributary during a time when a glacier was still filling part of the main valley bottom; old flow paths of now defunct streams in the main valley of the Butternut; and a modern floodplain etched with flood channel and meander scars--a few of which hold oxbow lakes. The latter in particular are diagnostic of laterally migrating meander loops in Butternut Creek, and they both highlight where the channel used to be, and speak of “behavior” which has persisted over longer times-- from tens to hundreds or thousands of years. Both the old alluvial fans and the floodplain are actively farmed. The floodplain in particular is of high agricultural value, with a soil consisting of silt, sand, and minor amounts of clay.

Figure 1. Shaded relief map of Butternut Valley including Wheeler’s farm, derived from NYSDEC’s lidar elevation data from 2007.

120 Flood Records for Local Rivers Butternut Creek was monitored by the United States Geological Survey (USGS) up through the mid 1990s, but those data are no longer easily available. In place of actual discharge measurements for the Butternut, the USGS stream gage on the Unadilla River at Rockdale, NY downstream from Wheeler’s farm serves to approximate the stream flow in the Butternut. Figure 2 shows the daily discharge record from 2003 up to early July 2015. The two largest floods ever recorded for the Unadilla River occurred in June 2006 (unnamed summer storms) and September 2011 (Tropical Storm Lee). Note that the discharge scale in the figure is shown in a logarithmic scale, which reveals the spectrum of sizes of daily flows.

Figure 2. Daily discharge record for Unadilla River at Rockdale, NY from the USGS.

In the past decade there have been about 7 days when discharge in the Unadilla River matched or exceeded 10,000 cfs. Such flows, when viewed over the entire record for the Unadilla River, are exceeded on 50 days out of 27,967 days of record (76 years). Are these the flows that erode the most, or are smaller more frequent flows more effective in modifying the channel? What flow size is appropriate to monitor? We would like to answer these questions, and they can only be answered with detailed measurements of the river bank over time. This study gathers what we know from prior surveys, and lays the groundwork for future work to fill in gaps of our understanding about erosion and deposition along Butternut Creek.

Past surveys As a neighbor of the Wheelers, one of the authors, Les Hasbargen, has had the opportunity to visit Wheeler’s field along Butternut Creek on numerous occasions throughout each year since 2007. Numerous photographs taken over this time period can be used to qualitatively estimate effects of various floods and note erosion processes visible at the cutbank. Photos were typically taken of the area from the field or from a watercraft on Butternut Creek. The photos in most cases are merely suggestive of change (for example, revealing a recent bank collapse). In a few cases however, the photos were taken with significant overlap in the field of view, and thus are amenable to 3D object reconstruction using photogrammetric software. In addition to those photos, a detailed land survey with differential GPS receivers was conducted in 2013 to map trees planted along the bank, and the location of the top of the cutbank as well. Other excellent sources of information on channel location over time include orthophotographs and topography available from New York State’s Department of Environmental Conservation website. The aerial photos were taken every several years, and extend back to 1994. For topography, NYS DEC teamed with US Federal Emergency Management Agency to collect high resolution topography using light

121 ranging and detection (lidar) equipment in 2007--after the then flood of record of June 2006 (which was exceeded by the flood from Tropical Storm Lee in 2011). We gathered these aerial image data sets and determined changes in bank location from one data set to the next.

SWCD tree plantation In 2012, Otsego County Soil and Water Conservation District (SWCD) implemented a channel stabilization program which planted 7000 trees along unstable channel banks for 33 landowners throughout the county. A total of 242 young trees were planted along active cutbanks on the Wheeler property along Butternut Creek, a few miles south of Morris, NY (Figure 3). Plantings comprised oak, maple, elm and willow. The trees were staked and tubed with plastic netting. The presumed goal of the treeplanting campaign was to reduce erosion of the stream bank by anchoring the soil in place through the growth of tree root networks. In 2013, the tree locations were surveyed by Les Hasbargen with a total station, and georeferenced using a differential GPS system, Magellan’s ProMark 3. A map of these locations appears below. By 2015, some of the trees were in danger of falling into the creek as the cutbank migrated west.

Figure 3. Tree seedling locations along Butternut Creek on Wheeler’s farm. Note that two rows of trees were planted in 2012, and only the row closest to the field was surveyed in 2013. Aerial image taken in April 2014 and provided by New York State Department of Environmental Conservation.

METHODS

2015 Aerial survey with remote controlled aircraft In the last ten years, there has been a very rapid development in the use of small remotely controlled aircraft for aerial imagery and for the recovery of high resolution topography (Pierzchała et al. 2014; Javernick et al. 2014; Passalacqua et al. 2015). In 2015 the BFS acquired funding from the Otsego Lake Association for the purchase of an unmanned aerial vehicle (UAV) with a camera for environmental mapping purposes. This aerial vehicle can be controlled by software to fly a pre-determined path and capture images at a specified interval. Photographs captured with greater than 60% overlap can be mosaiced into a single image. In addition, software was available to recreate the three dimensional land surface from the images. We needed to tie together several systems to take advantage of the aerial imagery, including a total station to

122 survey ground control objects, differential GPS to provide geopositioning information, in addition to control over the flight path and camera orientation of the UAV. These are described briefly below.

The images acquired by UAV can be georeferenced (aligned to north and registered to widely used reference system such as latitude and longitude, or Universal Transverse Mercator) if georeferenced points appear in the images. For Wheeler’s farm, we used several ground control objects (also known as ground control points, or GCPs) constructed from yard sticks painted white and fastened to the ground in a cross pattern with a spike. We surveyed the GCPs with a Sokkia 530R total station and reflecting pole. The survey also recorded the location of two Magellan ProMark 3 GPS receivers. Post-processing the GPS data with GNSS Solutions software provided georeferenced locations with location uncertainties of a few cm. The GCP total station survey coordinates were then transformed into georeferenced coordinates using a two dimensional conformal coordinate transformation (accounts for scale difference, rotation and translation). The GCPs were identified in the photographs during mosaicing and three dimensional object reconstruction in Agisoft’s PhotoScan software. The software georeferenced the photos. In the final mosaic, each pixel was approximately 2.5 cm on the ground. Aerial imagery at this resolution is not readily available anywhere, and represents a significant mapping capability of UAVs. The mosaic could easily be compared to similarly rectified aerial imagery provided by New York State by overlaying the mosaic onto older imagery. This overlay appears below in Figure 4.

123

Figure 4. 2015 mosaic from UAV survey (green vegetation) overlain onto NYSDEC imagery from 2014. The visual goodness of fit without any additional rectification in the GIS software strongly suggests that the UAV survey methods produce reliably geolocated maps.

One of the byproducts of 3D object reconstruction in PhotoScan is an elevation point cloud consisting of geolocated pixels with associated color values. The point cloud was loaded into Global Mapper GIS software, and then gridded at 0.1 m spacing for visualization and topographic comparison with high resolution lidar elevation data from NYSDEC taken in 2007 at 2 m spacing. Vertical height changes of a few cm could be detected in the reconstructed 3D landscape (see Figure 5 for a visual comparison).

Initially, there was a difference in elevation projection of approximately 30 m between the 2007 and 2015 data sets. This is because the 2015 data were collected in an ellipsoid elevation reference frame, while the 2007 profile is in a geoid reference frame. We corrected the UAV to the geoid reference frame. A topographic profile was constructed from the 2007 lidar data and from the 2015 UAV elevation grid, and the two profiles were overlain on top of each other, shown in Figure 5 below. The green dashed line indicates the cross sectional area of the cutbank that was eroded from 2007 to 2015. Note that the 2007 profile is bare earth elevation, while the

124 profile from 2015 represents vegetation elevation. A bare earth elevation has all vegetation from the data set, and thus represents a ‘bare Earth’. This accounts for the substantial differences in elevation of the two data sets between 50m and 100m from the west end of the profile.

Figure 5. Comparison of the elevation of Butternut Creek in Wheeler’s field from 2007 to 2015. Green dashed line shows the eroded part of the cutbank from 2007 to 2015.

A significant goal of our survey was meant to answer the question: can small UAVs provide new and useful data? The answer is a resounding affirmative. Based on the excellent fit in co- location and proper elevation range when compared to other state published surveys, we think UAV mapping holds great promise for future data collection. The high data density from the UAV maps can be used to monitor erosion events down to a few cm in spatial extent. This level of resolution is at a scale where we can determine which process is the most important driver of channel migration. The orthophoto mosaic (all aerial images are merged into a single geroreferenced image) which is part of the product from extracting elevation from the UAV survey captures details in plant communities, among other things, and thus the UAV could serve as a superb vegetation mapping tool.

Comparisons of the channel location over time To expand the temporal coverage of our study of cutbank migration, we utilized New York State Department of Environmental Conservation’s (NYSDEC) online aerial imagery captured at various times in the past. These data can be accessed and downloaded for free (http://www.orthos.dhses.ny.gov/). Errors in location are nominally 4 to 8 feet, and ground distance between pixels is 2 to 4 feet depending on the year the images were captured. While these errors may seem large, the movement of the bank over the last several years is several times these errors, and thus can be measured over time using this data set. We downloaded images from NYSDEC and then loaded them into Global Mapper, a GIS software (see Figure 6 below). We

125 sketched the bank location for each time period. A line was drawn along the uppermost point of the cutbank on the west side of the creek in each of the orthoimages and the photomosaic. The line was drawn wherever the top of the cutbank location could be determined to reasonable accuracy from the images. See Figure 7 for the location of the cutbank over time.

1994 2001

2005 2010 Figure 6. Time series of aerial images for the cutbank on Wheeler’s Farm.

Sketches of the cutbank location reveal varying amounts of erosion occur along the entire length of the cutbank, with maximum amounts in the central portion and diminishing to nil at the ends of the cutbank arc (see Figure 6). To determine an average erosion rate, or more correctly, a bank migration rate, we measured the area of the cutbank that was eroded for the following time periods: 1994 to 2001; 2001 to 2005; 2005 to 2007; 2007 to 2010; 2010 to 2014; and 2014 to 2015. This information is summarized in Table 1. Note, for 2007, we used a high quality elevation data set which was developed after the major flooding of 2006. The data, called lidar elevation data, derives from a laser scanning device carried by an aircraft. Data posting was every 2 m, and vertical resolution is ~10 cm. Our UAV-derived elevation compared quite favorably with the lidar, as is shown in Figure 5 above. The migration rate is determined by dividing the total area lost by the length of the cutbank, divided by the time between aerial images . As an estimate of the length, one can divide the perimeter of the area lost by 2. So, the cutbank migration rate C is given by = . Table 1 summarizes the measurements and calculations.훥훥 푃 퐴 Cutbank migration rates have varied2퐴 from 0.13 to 1.68 m/yr (~5 inches to 5 feet per year). 퐶 푃푃푃 Table 1. Eroded areas and migration rates of Butternut Creek over time.

126

Eroded area Average Migration rate, Years Perimeter, m between sequential migration m/yr photos, m2 distance, m 2001-2005 202.42 172.7 1.71 0.43 2005-2007 206.05 240.9 2.34 1.17 2007-2010 167.51 32.7 0.39 0.13 2010-2014 210.43 106.9 1.02 0.25 2014-2015 191.29 161.1 1.68 1.68 1994-2001 262.15 616 4.70 0.78

Figure 7. Location of the west bank of Butternut Creek in 1994, 2001, 2005, 2007, 2010, 2014, and 2015.

127 SUMMARY

This project investigated the potential of UAVs as a mapping tool. We were able to create a high fidelity map which included a georectified orthoimage and a digital terrain model of the farm field, riparian zone, and stream channel. The orthophoto mosaic was captured at a high resolution, about 2.5 cm/pixel. The vertical resolution of the digital terrain model was remarkable, capturing vegetation roughness changes of a few cm.

It was well known that Butternut Creek was migrating on Wheeler’s property, but quantification of migration was lacking. We utilized a time series of aerial imagery from NYS DEC to determine the cutbank migration rate for 6 time intervals since 1994. The rates vary substantially, by a factor of ~15. Some of this variability is likely due to significant flood events, though there are several contributing factors to erosion of stream cutbanks. These include the material in the cutbank, the location of the channel relative to the bank (for instance, at the outside bend of the channel), flood size and duration, animal activity (trails down to water), large woody debris in the channel, riparian vegetation, and freeze-thaw events (which can be quite vigorous on cutbanks). Which of these factors contribute most is an important question which we have not been able to answer yet. With these new monitoring tools (UAVs and repeat surveys), we should be able to address these issues, and provide more insight into how to control erosion of valuable farmland along the meandering reaches of local streams.

REFERENCES

Aschenbach, J. Using The ProMark 3 for Centimeter Accuracy - Resource ... 2009. Accessed 14 Mar. 2016, http://www.resourcesupplyllc.com/PDFs/News/RSLLC_020609ProMark3.pdf

Javernick, L., J. Brasington, and B. Caruso. 2014. Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology, 213 (2014) 166–182. http://dx.doi.org/10.1016/j.geomorph.2014.01.006

New York State Department of Environmental Conservation Orthoimagery Program, access to aerial imagery and lidar elevation data, http://www.orthos.dhses.ny.gov/ .

Passalacqua, P., C.A. Bode, H. Sangireddy, P. Belmont, C. Crosby, K. Schaffrath, D.M. Staley, S.B. DeLong, D.G. Tarboton, J.D. Simley, N.F. Glenn, T. Wasklewicz, J. R. Arrowsmith, S.A. Kelly, J.M. Wheaton and D.Lague. 2015. Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review, Earth-Science Reviews 148 (2015) 174–193. http://dx.doi.org/10.1016/j.earscirev.2015.05.012

Pierzchała, M., B. Talbot and R. Astrup. 2014. Estimating Soil Displacement from Timber Extraction Trails in Steep Terrain: Application of an Unmanned Aircraft for 3D Modelling, Forests 2014, 5(6), 1212-1223; doi:10.3390/f5061212. http://www.mdpi.com/1999- 4907/5/6/1212/htm

US Geological Survey National Water Information System, Web Interface, Stream gage data for the Unadilla River at Rockdale, NY, http://waterdata.usgs.gov/ny/nwis/uv?site_no=01502500.

128 A characterization of the riparian corridor of the Oaks Creek Blueway Trail with emphasis on Otsego Land Trust properties

Nicole Pedisich1 and Donna Vogler2

INTRODUCTION

The Otsego Land Trust Blueway is a series of Land Trust owned and protected parcels that provide fishing, hiking, paddling, bird watching, and educational opportunities from Canadarago Lake to the Susquehanna River including Brookwood Point on Otsego Lake. (Otsego Land Trust 2014). The trail consists of Fetterly Forest, Deowongo Island, Oaks Creek Preserve, Crave, Parslow Road, Greenough Road, and Compton Bridge. For this project, an assessment of the riparian vegetation communities of Oaks Creek was conducted along a section of the Blueway Trail starting in Schuyler Lake and ending in Cattown. More in-depth characterizations of plant communities were done at Oaks Creek Preserve, the Crave property, and Parslow Road Conservation Area.

Oaks Creek is a stream located in Otsego County, NY. It flows from Canadarago Lake southeast into the Susquehanna River, a distance of approximately 13.8 miles. (Hingula 2004). A majority of the stretch of stream assessed is state-regulated freshwater wetlands (Figure 1, NYSDEC). Oaks Creek Preserve is a 28-acre parcel located along its namesake between Schuyler Lake and Oaksville. Downstream are Crave, a parcel recently acquired by the Otsego Land Trust and Parslow Road Conservation Area, an 86-acre parcel located on the northern edge of Oaksville running a half-mile along Oaks Creek (Figure 2).

1 BFS Intern, summer 2015. Current affiliation: SUNY College at Oneonta. Funding for this project was provided by the Otsego Land Trust.

2 Professor. SUNY Oneonta Biology Dept.

129

Figure1. Map of state-regulated freshwater wetlands (NYSDEC Resource Mapper 2015).

Figure 2. Map of Otsego Land Trust properties on Oaks Creek. From top to bottom: Oaks Creek Preserve, Crave Property, and Parslow Road Conservation Area.

130 METHODS

Otsego Land Trust parcel maps (Figures 3, 4, 5) were obtained and a canoe trip was planned accordingly. Canoe trips were taken on 22 June and 21 July 2015 down Oaks Creek from Route 22 in Schuyler Lake to Cattown Road in Cattown. Dominant, unique and invasive species were noted for the Trail and for each parcel, and various plant specimens were collected and later identified. GPS waypoints were taken along the trail and at points of interest (Table 1). Communities were defined using the taxa lists created from the canoe trips and Edinger et al. (2014).

Figure 3. Oaks Creek Preserve.

Figure 4. Crave Property.

131

Figure 5. Parslow Road Conservation Area.

132 Table 1. GPS way-points taken along Oaks Creek.

RESULTS AND DISCUSSION

Edinger et al. (2014) focuses on seven system types: Marine, Estuarine, Riverine, Lacustrine, Palustrine, Terrestrial and Subterranean. This project focused on two of these systems to characterize the riparian zone of Oaks Creek; the Riverine and Palustrine Systems. Based on the Riverine Systems section, Oaks Creek has characteristics of both a Marsh Headwater Stream and Unconfined River. Looking at elevation on a United States Geological Survey topography map, the gradient and physical characteristics lean more towards that of an Unconfined River. It is dominated by runs with interspersed pool sections, has few riffles, and distinguished meanders. Endemic macroinvertebrates are reflective of Marsh Headwater Stream (Heilveil and Buckhout 2012), and macrophytes are also reflective of a Marsh Headwater Stream (pondweeds, duck weed, water stargrass, bur-reeds and white water-lily and yellow pond lily). The low slope and flow, combined with width in some areas make it more lake-like, and the

133 abundance of pickerel-weed (Pontederia cordata) and pondweeds supports this. The dominant species along the Creek (Table 2) indicate that the major community type is a Floodplain Forest; which is included in the Palustrine Systems section of Edinger et al. (2014). A Floodplain Forest is a hardwood forest that occurs on mineral soils on low terraces of river floodplains and floods annually. Forests of this type are variable and diverse. Some characteristic tree species are silver maple, red maple, ashes, elms and swamp white oak. Characteristic shrubs, vines and ferns include viburnums, multiflora rose, poison ivy, Virginia creeper, jewelweed, and sensitive fern. Overall the Oaks Creek riparian forest has many large, mature trees with a high canopy and little undergrowth. It is distinctive for the area as it is not seen very often.

At Oaks Creek Preserve the canopy was dominated by silver maple and a silver/red maple hybrid, called freeman’s maple (Acer x freemanii). American elm was a unique find here, being singular and mature. There was a monoculture of Phalaris at the water’s edge around many of the bends of the Creek, especially at this parcel. It could be an intermediate between Floodplain Forest and red maple-hardwood swamp, a broadly defined community with several regional and edaphic variants. The composition of the Crave property was similar to Oaks Creek Preserve. There was more pickerel-weed along the Creek at Crave, as most of the water along here seems to be slower and more lake-like. Parslow Road Conservation Area is the most public of the three sites that were surveyed. The most obvious invasive species at Parslow Road was bush honeysuckle (Diervilla sp.) found near the public access fishing points. The major community types here were mixed between Northern White-Cedar swamp, Floodplain Forest and Hemlock-hardwood swamp. The dominant tree species for the Northern White Cedar Swamp is the northern white-cedar (Thuja occidentalis) mixed with red maple and eastern hemlock. Hemlock-hardwood swamps are dominated by eastern hemlock (Tsuga canadensis) and are mixed with red maple and yellow birch (Betula alleghaniensis).

As stated in Edinger et al. (2014), the coarse/fine filter approach that they use is an efficient means of identifying the most sensitive animals, plants and communities of an area. They also state that no two communities are identical, but are similar within a range of variability and that the similarities are not defined quantitatively in their classifications (Edinger et al. 2014). It was intended for the different described communities to be non-overlapping units and for artificial boundaries to be made between ecological gradients. This makes definitively classifying a community a little more difficult. Edinger et al. (2014) say that in the case that a site is equally similar to two different community types, that it should be described as an intermediate between the two most similar community types. This is why Oaks Creek Preserve has been described as an intermediate between a Floodplain Forest and Red maple-hardwood swamp, and why Parslow Road is described as a mix of three community types. More regional information is needed on many of the community types in Ecological Communities of New York State on both flora and fauna. Gathering this information would be a good opportunity for a future study that would facilitate both the research and teaching mission of the Biological Field Station and the Otsego Land Trust.

134 Table 2. List of dominant species found along Oaks Creek.

Common Name Genus Species Red Maple Acer rubrum Silver Maple Acer saccharinum Freeman’s Maple Acer xfreemanii White Ash Fraxinus americana Hemlock Tsuga canadensis Cedar Thuja occidentalis Black Willow Salix nigra White Oak Quercus alba Pickerel-weed Pontederia cordata Water Stargrass Heteranthera dubia Reed Canarygrass Phalaris arundinacea Duckweed Lemna sp. Swamp Dock Rumex verticillatus Moneywort Lysimachia nummularia

REFERENCES

Blueway trail. 2014. Otsego Land Trust. Retrieved July 22, 2015, from http://www.otsegolandtrust.org/

Edinger, G.J., D.J. Evans, S. Gebauer, T.G. Howard, D.M. Hunt, and A.M. Olivero (editors). 2014.Ecological Communities of New York State. Second Edition. A revised and expanded edition of Carol Reschke’s Ecological Communities of New York State. (Draft for review). New York Natural Heritage Program, New York Department of Environmental Conservation, Albany, NY.

Heilveil, J. and B. Buckhout. 2012. Qualitative spot biotic survey of Oaks Creek, White Creek, Cripple Creek, and Moe Pond in Otsego County, New York. In 45th Ann. Rept. (2012). SUNY Oneonta Biol.Fld.Sta., SUNY Oneonta.

Hingula, L. 2004. Benthic macroinvertebrate survey of Oaks Creek, Otsego County, NY, during the initial stages of zebra mussel (Dreissena polymorpha) colonization. In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Plants database. 2015. United States Department of Agriculture. Natural resources conservation service. Retrieved July 25, 2015, from http://plants.usda.gov/java/

135 Annual trap net monitoring of fish assemblages in the weedy littoral zone at Rat Cove and the rocky littoral zone at Brookwood Point, Otsego Lake, 2015

J. Benjamin Casscles1

INTRODUCTION

This study was a continuation of annual monitoring of the littoral fish communities of Otsego Lake. The long term goal of the study is to assess the littoral fish community and determine population dynamics of species utilizing littoral habitats. Rat Cove has been studied since 1979 (MacWatters 1980) and Brookwood Point since 2002 (Wayman 2003). Littoral habitats of sizeable lakes such as Otsego Lake are necessary, providing spawning and nursery habitats for many species of fish. The illegal introduction of Alewife (Alosa pseudoharengus) in 1986 (Foster 1990) altered the trophic balance and physical/chemical characteristics of Otsego Lake, due to the species’ opportunistic behavior and over-effective grazing of the lake’s zooplanktonic community (Harman 2002). Alewives are efficient, opportunistic, epilimnetic planktivores that feed on microcrustaceans, insects, ichthyoplankton, zooplankton, and their own eggs (Cornwell 2005). Long term monitoring of littoral fishes helps to assess the inshore fish communities, as well as alewife abundance and spawning activity. Piscivorous fish provide valuable data regarding forage species abundance. Diet samples were taken from game fish to further investigate the predator/prey relationship with alewife in Otsego Lake. Additionally this study provides useful long term data on non-alewife species. In order to mitigate the detrimental effects alewife have had on the lake’s ecosystem, predatory walleye (Sander vitreus) have been re-established through stocking, which began in 2000. During summer stratification, alewife utilize only the top layer of water (the epilimnion), resulting in spatial separation between them and the cold water predators of Otsego Lake. The separation from their predators allows the alewife to reproduce and feed freely with nothing keeping their population in check. Walleye, however, have been known to forage in the epilimnion, so during summer stratification alewife would be ideal prey. This study continues to document littoral fish communities that could provide insight into changes occurring in Otsego Lake.

1 Robert C. MacWatters Internship in the Aquatic Sciences, summer 2015. Present affiliation: Department of Fisheries, Wildlife and Environmental Science Technology, SUNY Agriculture and Technical College, Cobleskill, NY.

136 METHODS & MATERIALS

Winged Indiana trap nets with a single throat were set out Monday through Friday and checked daily, at both Rat Cove and Brookwood Point (Figure 1) from 20 May to 24 July. At Rat Cove the trap was set perpendicular to the north shore and at Brookwood Point the trap was set due east from the middle of the point. The catch was transferred from the nets into totes, and all metrics were taken on site and fish were promptly returned to the water. Diet samples from any predator fishes (Rock bass, chain pickerel, walleye, smallmouth & largemouth bass) over 200mm were taken using pulsed gastric lavage. Each fish was identified and measured in (mm). Any alewife captured would be kept to be measured and further analyzed at the main lab. Diet samples were preserved in 70% alcohol/water mixture until examined. Contents of the samples were placed into a petri dish and examined under a dissecting microscope. Any prey fish in the diet sample was identified and measured in mm.

Figure 1. Bathymetric contour map of Otsego Lake, NY. Trap nets were set perpendicular to the shore at Rat Cove and due east from the middle of the point at Brookwood Point.

RESULTS

The total catch per week for Rat Cove decreased from the previous year from 18-13 fish per week from 2014-2015. Brookwood Point experienced a more pronounced decrease from 29 to 8 fish per week. From 2005 – 2011 there was an increase in overall mean catch per week at both Rat Cove and Brookwood Point, and a drop off in 2015 (data not available for 2011-2012) (Tables 1 and 2). There was a change in nets used and quality of nets used that could be a factor in these changes (Stowell 2013).

137 Table 1. Mean weekly catch at Rat Cove and catch contributed by each species, 2000-2015 (modified from Best 2014).

Rat Cove Mean Catch Per Week 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2014 2015 Alewife 120.1 67.8 8.0 45.2 2.4 0.4 0.0 3.2 1.1 0.5 0.4 <1 0.0 0.0 0 Golden Shiner 0.6 0.3 0.4 0.7 0.5 0.3 0.0 0.1 0.4 0.2 0.1 <1 1.3 0.5 0.1 Pumpkinseed 9.7 20.8 15.1 32.8 12.9 4.6 2.0 2.2 4.4 5.1 5.1 16 8.6 2.6 2.4 Blue Gill 2.0 2.9 3.7 1.7 1.5 1.4 0.8 3.2 5.9 6.6 4.8 7 1.3 9.8 8.6 Redbreast Sunfish 0.8 0.6 0.3 0.4 0.3 0.1 0.0 0.0 0.1 0.1 0.0 <1 0.0 0.0 0.1 Rock Bass 1.6 1.5 3.8 1.0 1.8 0.5 0.5 0.6 0.9 1.0 1.1 2 0.6 1.9 0.3 Largemouth Bass 0.1 0.6 0.3 0.3 0.1 0.1 0.0 0.6 0.3 0.2 0.3 <1 0.5 0.1 0.1 Chain Pickerel 0.6 0.5 0.1 0.2 0.2 0.1 0.1 0.3 0.8 0.4 0.3 <1 0.6 0.8 0.3 Atlantic Salmon 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0 Yellow Perch 2.5 0.5 1.3 0.3 1.2 0.3 0.6 0.2 0.3 0.0 0.5 4 3.4 1.6 0.9 White Sucker 1.1 0.2 1.1 0.1 1.9 0.2 0.5 0.0 0.0 0.0 0.3 <1 0.6 0.0 0 Common Carp 0.3 0.3 0.2 0.5 0.3 0.7 0.1 0.0 0.0 0.0 0.0 <1 0.5 0.0 0 Brown Bullhead 1.7 0.1 6.4 2.6 1.6 0.1 0.0 0.1 0.0 0.1 0.1 <1 0.5 0.1 0 Spottail Shiner 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0 0.0 0.1 0.2 Smallmouth Bass 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0 Emerald Shiner 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.0 <1 0.3 0.0 0 European Rudd 0.1 0.0 0.3 0.7 0.2 0.0 0.1 0.0 0.4 0.7 1.4 <1 0.0 0.0 0 Margined Madtom 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <1 0.0 0.1 0 35 Total 141.0 96.0 41.0 87.0 25.0 9.0 5.0 11.0 14.0 15.0 14.0 18.0 18.0 13

Table 2. Mean weekly catch at Brookwood Point and catch contributed by each species, 2000-2015 (modified from Best 2014).

Brookwood Point Mean Catch Per Week 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2014 2015 Alewife 224.2 137.3 77.4 94.7 12.6 5.7 1.4 5.5 0.3 0.3 1.4 0 0.0 0.0 0 Golden Shiner 0.3 0.3 1.1 1.8 1.6 0.3 0.1 0.0 0.0 0.0 0.3 <1 0.3 0.3 0.3 Pumpkinseed 3.1 7.4 12.0 13.1 12.2 1.1 0.8 1.0 1.8 1.9 1.3 10 5.1 5.0 1.3 Blue Gill 6.5 0.9 0.9 1.0 0.8 0.5 0.3 0.3 0.9 0.1 0.4 15 0.5 3.9 0.4 Redbreast Sunfish 0.3 0.0 0.9 0.2 0.7 0.1 0.1 0.2 0.0 0.3 0.3 4 0.0 1.5 0.2 Rock Bass 7.7 3.5 4.0 3.8 3.0 1.1 0.3 0.3 0.6 2.3 2.0 14 3.0 12.1 2.4 Largemouth Bass 0.3 0.3 0.7 0.8 0.0 0.1 0.0 0.1 0.3 0.1 0.0 <1 0.0 0.1 0 Chain Pickerel 0.3 0.0 0.3 0.2 0.2 0.2 0.0 0.2 0.1 0.0 0.0 <1 0.1 0.5 0.1 Atlantic Salmon 0.0 0.3 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0 0.0 0.0 0 Yellow Perch 1.8 0.3 0.2 0.0 0.6 0.1 0.2 0.0 0.1 0.3 0.0 1 1.6 2.1 0.1 Walleye 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.1 0.0 <1 0.5 0.8 0.3 White Sucker 4.9 0.0 1.7 0.7 0.6 0.2 0.3 0.0 0.0 0.0 0.1 <1 0.4 0.6 1.2 Common Carp 2.1 0.3 0.6 0.1 0.3 0.0 0.2 0.0 0.0 0.0 0.0 0 0.1 0.0 0 Bluntnose Minnow 0.3 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.1 Brown Bullhead 6.7 0.0 1.0 3.6 4.2 0.0 0.1 0.0 0.0 0.0 0.0 <1 0.6 0.5 0.4 Spottail Shiner 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.3 3 0.0 1.4 0.3 Smallmouth Bass 0.0 0.0 0.0 0.6 0.2 0.0 0.0 0.0 0.1 0.0 0.3 <1 0.0 0.3 0.1 European Rudd 0.0 0.3 0.0 0.1 0.2 0.0 0.1 0.1 0.1 0.0 0.0 0 0.0 0.0 0 Common Shiner 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0 Creek Chubsucker 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <1 0.0 0.1 0 Lake Whitefish 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <1 0.0 0.1 0.1 Rainbow smelt 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.1 0.1 50 Total 259.0 152.0 101.0 121.0 37.0 10.0 4.0 8.0 4.4 5.4 6.3 12.0 29.0 7.4

138

A total of 21 fish were caught per week at Rat Cove and Brookwood Point, which was a decrease from the 2014 study (47 fish per week) but not as high as the 2011 study where 75 fish were caught per week. Brookwood Point caught less fish per week (8) than Rat Cove (13), which was normal compared to recent years with the exception of 2011 (Tables 1 & 2). Rock bass (Ambloplites rupestris) were more abundant at Brookwood Point than at Rat Cove (RC=<1 per week, BW=2 per week), while bluegill (Lepomis macrochirus) were more abundant at Rat Cove than at Brookwood Point (RC=10 per week, BW= <1 per week). The bluntnose minnow (Pimephales notalus) was documented at Brookwood Point, which has not occurred since the first year of trap net surveying in 2000. Lake whitefish (Coregonus clupeaformis) were recorded for the second year at Brookwood Point. The first rainbow smelt (Osmerus mordax) was documented in the summer of 2015 at Brookwood Point. Common carp (Cyprinus carpio), common shiner (Luxilus cornutus), European rudd (Scardinius erythrophthalmus), creek chubsucker (Erimyzon oblongus), Atlantic salmon (Salmo salar), and alewife were species not captured during this year’s survey that have been captured in the past (Figure 2).

100 90 80 70 60 Brookwood Point Rat Cove 50 40 30 20 10 Total fish collected Total 0

Figure 2. Species frequency captured in trap nets at Rat Cove & Brookwood Point, Otsego Lake NY, 2015.

Alewives were not captured in either sampling location in 2015 (Figure 3). The littoral assessment of 2015 was the third consecutive summer no alewives were documented. Their decline has been concurrent with walleye stocking efforts, which commenced in 2000. The decline of alewife is illustrated in Figure 3.

139 250

200

150

100

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2013 2014 2015

Brookwood Rat Cove Figure 3. Historical Alewife Catch per week in Otsego Lake, NY 2000-2015.

Figure 4 summarizes the frequency of occurrence in chain pickerel. Of the adult chain pickerel sampled (n=3), 100% contained bluegill, 67% contained yellow perch, 33 % spottail shiner and 33% rock bass. Every chain pickerel sampled contained various fish bones and tissue.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Various Bones & Tissue Bluegill Yellow Perch Rock Bass Spottail Shiner

Figure 4. Frequency of occurrence of prey items in adult chain pickerel (>200mm) in Rat Cove and Brookwood Point, Otsego Lake 2015.

Rock bass sampled (n=8) contained mostly invertebrates, but 50% contained fish or fish parts (Figure 5). Only two rock bass contained whole fish which were recognizable, bluegill and spottail shiner. Among the invertebrates, adult stoneflies, and amphipods were the most abundant in rock bass stomachs.

140 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Figure 5. Frequency of occurrence of prey items in adult rock bass (>200mm) in Rat Cove and Brookwood Point, Otsego Lake 2015.

Of walleye sampled (n=3), 100% contained bluegill, 67% contained rock bass, 33% contained yellow perch, and 33 % contained spottail shiner (Figure 6). Walleye sampled positively selected for bluegill.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Bluegill Yellow Perch Rock Bass Spottail Shiner

Figure 6. Frequency of occurrence of prey items in adult walleye (>200mm) in Rat Cove and Brookwood Point, Otsego Lake 2015.

DISCUSSION

A total of 202 fish were caught between Rat Cove and Brookwood Point over the 2015 sampling season. 130 fish were captured in a weedy littoral habitat represented by Rat Cove, which was a slight decrease from the 2014 season (158) and a significant decrease from the 2011 season. Bluegill and pumpkinseed were the dominant species at Rat Cove. 71 fish were captured at Brookwood Point, a rocky littoral zone that is dominated by rock bass. The weedy littoral

141 zone has been known to be a nursery for juvenile fish, providing a safe habitat to grow. At Rat Cove the average size of sunfish was 106.2 a subtle increase from 100.1mm in 2014. At Brookwood Point the average size of sunfish was 146 mm, a slight decrease from 147.9mm in 2014. Brookwood Point proved to be more diverse having 14 species, while Rat Cove only had 9 species. Juvenile lake whitefish were documented in both 2014 and 2015 at Brookwood Point. Prior to 2014 whitefish had not been recorded in trap netting surveys. In a lake trout recruitment study conducted at Bissel Point in the spring of 2014 the first lake whitefish fry were documented (Lucykanish 2015). These signs suggest a potential rebound for the species in Otsego Lake.

CONCLUSION

Otsego Lake has seen an increase in clarity, potentially due to two separate factors, first being the introduction and establishment of zebra mussels (Dreissena polymorpha) first documented in 2007 (Harman 2008). Zebra mussels have been documented to cause ecological changes, including increased water clarity, following a successful introduction into a water body (D’Itri 1996). In 2009 (Gillespie 2010) and 2010 (Albright and Leonardo 2011), cladoceran zooplankton mean size and Daphnia sp. abundance had increased, correlating with increased water clarity. (Transparencies through 2011 were even greater (Waterfield and Albright 2012)). This change in the plankton community was likely due to reduced grazing by alewife. Alewife declined steadily following the reintroduction of walleye and they appear to have been virtually eliminated by the mid 2000’s (Figure 3). This is corroborated by hydroacoustics surveys by Waterfield and Cornwell (2013). Lake whitefish (otherwise known as the “Otsego Bass”) are the native coldwater pelagic predator of Otsego Lake, and historically a coveted ice fishery game species. Lake whitefish were once extremely abundant in Otsego Lake. In the mid 1800’s as many as 5,000 whitefish were taken in a single haul seine (DeKay 1842). Although not as abundant in the 1900’s, whitefish, along with cisco, provided a unique, locally important sport and commercial fishery into the 1980’s. Sport and commercial interest of the two species waned as the population and fitness of these fish declined drastically following the introduction and irruption of alewife (Foster, 1996; Keenen and Ketola, 1993). With the absence of alewife, the presence of lake whitefish has the potential opportunity to increase.

REFERENCES

Albright, M.F. and Leonardo. 2011. A survey of Otsego Lake’s zooplankton, summer 2010. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F. and H.A. Waterfield. 2012. Otsego Lake water quality monitoring, 2011. In 44th Ann. Rept. (2001). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Best, M.J. 2014. Trap net monitoring of fish communities within the weedy littoral zone at Rat Cove and rocky littoral zone at Brookwood Point, Otsego Lake. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

142 Cornwell, M.D. 2005. Re-introduction of walleye to Otsego Lake: Re-establishing a fishery and subsequent influences of a top predator. Occas. pap. #40, SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

D’Itri, F. 1996. Zebra Mussels and Aquatic Nuisances Species. Chelsea, NY: Ann Arbor Press. 161-163. Print.

Foster, J.R. 1990. Introduction of alewife (Alosa pseudoharengus) in Otsego Lake. In 22nd Ann. Rept. (1989). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Gillespie, S. 2010. A survey of Otsego Lake’s zooplankton community, summer 2009. In 42nd Ann. Rept. (2009). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Harman, W.N. 2008. Introduction. In 40th Ann. Rept. (2007). SUNY Oneonta Biol. Fld., SUNY Oneonta.

Harman, W.N., L.P. Sohaki, M.F. Albright, and D.L. Rosen, 1997. The state of Otsego Lake 1936-1996.pp.261-261.

Keenen, C.H. and H.G. Ketola. 1993. Composition of whitefish and cisco flesh captured in Otsego Lake in 1969 and 1992 and possible influence of the introduction of alewives on their forage . In 25th Ann. Rept., 1992.pp. 112-119. SUNY Oneonta bio. Fld. Sta., SUNY Oneonta.

Lucykanish, D.M. Unpublished data. SUNY Cobleskill.

MacWaters, R. C. 1980. The fishes of Otsego Lake. Occas. Paper #7. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Waterfield, H.A. and M.D. Cornwell. 2013. Hydroacoustic surveys of Otsego Lake’s pelagic fish community, 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Wayman, K. 2003. Rat Cove and Brookwood Point littoral fish survey, 2002. In 35th Ann. Rept. (2002). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

143 Characterization of spawning rainbow smelt (Osmerus mordax) in the Mohican Canyon Creek, Otsego Lake, NY

Matthew Best1and John R. Foster2

Abstract: Rainbow smelt (Osmerus mordax), a key component of the 1980’s cold-water fish fauna of Otsego Lake, was decimated in the 1990’s by the introduction of alewives (Alosa pseudoharengus). With the recent collapse of the alewife population, the rebound of the rainbow smelt population was expected. The goal of this study was to examine the population dynamics of spawning rainbow smelt in the Mohican Canyon Creek, for evidence of that rebound. Between 15-24 April 2015, (8:35pm and 11:05pm) 152 adult smelt were sampled using a Halltech backpack electrofisher. This study found that the spawning population, average size of spawners, spawning age and sex ratios had not returned to the pre-alewife levels (1983 & 1984) in 2015.

INTRODUCTION

Rainbow smelt (Osmerus mordax) were introduced into Otsego Lake in 1979 and used many of the lakes tributary streams for spawning (Leatherstocking Creek (Foster 2002, Cornwell 2001, Cornwell 2004, Best 2014), Mohican Canyon (Breitan 2001), 3-Mile Point Creek (Cornwell 2004), 6-Mile Point Stream and Shadow Brook (Harman 2002). Smelt abundance was high in the 1980’s and a dip net fishery developed in these tributary streams during the spawning runs (McWatters 1983). Population dynamics of rainbow smelt were studied in Mohican Canyon Creek by MacWatters (1984) and Cornwell (2004) and spawning behavior there was also documented (Cornwell 2001).

Rainbow smelt became a key component of the cold-water fish fauna of Otsego Lake soon after they were introduced. By 1982 smelt were abundant enough to provide a high quality forage base for mid-water, predatory, cold-water game fish, such as Atlantic salmon (Salmo salar; Sanford 1986). However, in 1986, alewife (Alosa pseudoharengus) were introduced into Otsego Lake (Foster 1990) and quickly became the dominant planktivore, reducing the abundance of many fish species, including rainbow smelt (Harman et al. 1997). With the recent collapse of the alewife population (Waterfield and Cornwell 2013, Best 2015), the population dynamics of rainbow smelt is expected to rebound to the pre-alewife levels documented by MacWatters (1984).

The goal of this study was to characterize the population dynamics of rainbow smelt spawning in Mohican Canyon Creek. In order to meet that goal, sex ratio, growth, age & length frequency distribution and catch per unit effort were determined. Data collected here, following the extirpation of alewives from Otsego Lake, will be compared to data collected by MacWatters (1984) before the introduction of alewives and Cornwell (2004) during high alewife abundance.

144 MATERIAL & METHODS

This study was conducted at Mohican Canyon Creek (Latitude 42.764851, Longitude 74.899007; Figure 1) at its confluence with Otsego Lake at 5-Mile Point. This location followed previous studies (MacWatters 1984, Cornwell 2001 & 2004, Best 2015). Mohican Canyon Creek was initially studied by MacWatters due to its high density of spawning smelt and its protection from public access (MacWatters 1984).

This study was conducted 15-24 April 2015, between 8:35pm and 11:05pm (sunset occurred at 7:45 pm). A Halltech backpack electrofisher was used to capture spawning rainbow smelt on 20, 22 and 24 April. Several electrofishing runs were conducted on those dates, at approximately 45 minutes intervals. Each run had a shock time of 350-600 seconds. Electrofishing runs began at the mouth of the stream and continued upstream for approximately 40m. Headlamps remained off until electrofishing began to avoid deterring smelt from the sampling site. Captured smelt were measured (total length), sexed, and a few scales were removed from behind the pectoral fin. Smelt were held in totes until the evening sampling was completed. Then they were released at the upstream culvert 15m above the spawning site.

Figure 1. Study site at Mohican Canyon Creek at its Otsego Lake confluence at 5-Mile Point.

145 While this study and that conducted by Best (2015) used electrofishers to capture smelt on their spawning run, MacWatters (1983 & 1984) used dip nets, and Cornwell (2001 & 2004) used seines.

RESULTS

Spawning Conditions

Spawning rainbow smelt were first detected in Mohican Canyon Creek on 20 April 2015. During the spawning run, water temperature ranged from 8.4°C to 3.7 °C; water turbidity was low except 14.5 NTU, which occurred after a storm; pH ranged between 8.3 and 8.5; and dissolved oxygen ranged from 9.7 to 11.0 mg/L (Table 1).

Table 1. Water parameters in Mohican Canyon Creek during April 2015 sampling.

Date Temp pH Turbidity Conductivity Dissolved O2 (°C) (NTU) (uS/cm) (mg/L) 4/15/15 9.2 8.6 4.6 201 10.8 4/17/15 8.9 8.4 8.1 208 11.0 4/20/15 8.4 8.3 4.9 231 9.7 4/22/15 6.2 8.5 14.5 214 9.9 4/24/15 3.7 8.4 2.5 204 11.0

Length Frequency Distribution

On average spawning males had a total length of 124 mm, and females had a length of 121mm (Figure 2). The size of spawning males and females did not differ in 2015 (T-test P >05).

When measurements of the 152 smelt captured in this study were combined with 13 smelt from Best’s (2015) 2014 study, the post-alewife mean length was 124 mm. The post-alewife smelt size (124mm) did not differ from the size of spawning smelt (125mm) during high alewife abundance (Cornwell 2004). However, smelt were considerably smaller than the average length (149 mm, N=1352) collected in the pre-alewives years of 1983 and 1984.

146 45

40 Male Female 35 30 25 20 15 10

Number of SpawningSmelt 5 0 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185

Total Length (mm)

Figure 2. Length frequency distribution of spawning rainbow smelt captured in Mohican Canyon Creek in April 2015.

Sex Ratio

In Mohican Canyon Creek, female smelt were significantly less abundant than male smelt in 2015 and in 1983 (Chi square test, P < .001, Figure 3). In 2015, only 7% of the smelt captured were females, while in 1983 females made up 32% of the spawning population and in 1984, females were 47% of the spawning population (Chi square test, P < .001).

147 100 Male 90 Female 80 70 60 50 40 30 Per Cent Spawners 20 10 0 1983 1984 2015 Date

Figure 3. Per cent of males and females spawning in Mohican Canyon Creek in 1983, 1984 and 2015.

Age Frequency Distribution

The age-frequency distribution of rainbow smelt spawning in Mohican Canyon Creek was significantly different between all three years studied (Chi square test, P < .001). In 2015 smelt captured in Mohican Canyon Creek were dominated by one year olds, which made up 75% of the spawning population (Figure 4). However, in the pre-alewife years (1983-1984) 2 year old smelt dominated the spawning population (62% & 88%, respectively). The percentage of 3 year old smelt was small in all studies (6% in 1983; 0.3% in 1984; 5% in 2015). In fact there were no smelt sampled that were over 3+ years of age in 1983, 1984 and 2015.

148 100 1983 90 1984 80 2015 70

60 50

Per CentPer 40 30 20 10 0 1 Year Old 2 Year Old 3 Year Old Age of Spawning Population

Figure 4. The age-frequency distribution of rainbow smelt spawning in Mohican Canyon Creek in 1983, 1984 and 2015.

Growth

One year old smelt sampled in 2015 were 124mm, growing 12-14 mm larger than in 1984 and 1983, respectively (Figure 5). However, with an average size of 142mm, 2 year old smelt in 2015 were 10-13mm smaller than in 1983-1984. On average the size of three year old smelt was very similar between the 3 years, with only 6 mm difference between the three sample years.

200 180 160 140 1983 120 1984 100 2015 80

Total Length 60 40 20 0 Age-1 Age-2 Age-3 Age

Figure 5. Size at age for rainbow smelt spawning in Mohican Canyon Creek.

149 Spawning Run Timing

Spawning rainbow smelt were not present in Mohican Canyon Creek on 15-17 April. They were first detected on 20 April 2015.

Numbers of males were present in the stream throughout the evening sampling period (8:35-11:05 pm; Table 2). Female smelt were consistently sampled in the stream between 9:40 and 11:05 pm, similar to MacWatters (1983) observation that the peak time for females to enter the stream was approximately 10:30 pm.

Table 2. The number of male and female rainbow smelt captured along a 40m spawning reach of Mohican Canyon Creek in 2015.

Date Time (PM) Run Males Females 1 9 8:35 4/20/2015 2 19 9:15 9:45 3 27 3 1 3 8:50 9:40 2 13 2 4/22/2015 10:20 3 15 1 11:05 4 8 1 9:40 1 35 2 4/24/2015 10:05 2 7 1 3 6 10:30

In 2015, several walleye and lake trout were observed around the mouth of the Mohican Canyon Creek during the smelt spawning run.

DISCUSSION

The population of rainbow smelt became large enough to support a smelt fishery, and to support the forage base for the introduction of a new mid-water game fish Atlantic salmon shortly after their introduction into Otsego Lake in 1979 (Sanford 1986). When the planktivorous alewife was introduced into Otsego Lake a significant decline in the smelt population occurred. Alewife have significant spatial and diet overlap with smelt, so smelt population dynamics could have been altered through competition and/or predation on their fry (Simonin et al. 2012). In Otsego Lake, alewives had a severe reduction on the size and abundance of large bodied zooplankton (Harman 1997, Warner 1997, Tanner and Albright 2014), which are a significant part of the diet of smelt (Johnson et al. 2004). Smelt population decline may have also been due to alewife predation on their fry (Brandt et al. 1987; Crowder 1980; Wells 1977). By 2011, the alewife population collapsed to zero (Waterfield & Cornwell

150 2014) and has not rebounded to date. Since the smelt forage base of large bodied zooplankton has rebounded (Tanner and Albright 2014) and alewife predation on larval smelt has been eliminated in recent years, population dynamics of smelt should be showing a rebound to the pre- alewife years.

Data collected in this study shows no evidence of a rebound in smelt population dynamics. The size of smelt sampled in 2015 were virtually the same, 124mm, as the smelt sampled when alewives were abundant, 125 mm (Cornwell 2004) and were considerably smaller that the 149mm observed in the pre-alewife samples (MacWatters 1984). Further, the spawning population was significantly younger in 2015 (age-1) compared to the pre-alewife samples dominated by 2 year olds (MacWatters 1984). Growth was initially better in 2015 to age-1, but growth to age-2 was slower than in the pre-alewife samples (MacWatters 1984). The smelt spawning population was overwhelmingly dominated by males in 2015, while there was a more even distribution between males and females to the pre-alewife samples (MacWatters 1984). Possibly more time is needed for the smelt population to adjust to the post-alewife ecology of Otsego Lake. Therefore, the spawning population of smelt should continue to be monitored at Mohican Canyon Creek to document changes in population dynamics.

It was clear in conducting this study that the abundance of rainbow smelt has not returned to the 1983-1984 pre-alewife levels. What is different about the ecology of Otsego Lake between pre-alewife and post-alewife years? The 2015 age frequency distribution (Figure 4) shows good survival of smelt between age-1 and age-2 in the pre-alewife years, but poor survival in post- alewife years. In the pre-alewife years, 1983-1984, there were no mid-water predators in Otsego Lake. In the post-alewife years (2015) there were populations of piscivorous walleye, Atlantic salmon and brown trout, feeding in the preferred smelt habitat around the thermocline. The presence of these mid-water predators may be preventing the rebound of rainbow smelt in Otsego Lake. Perhaps the reason we can’t catch smelt like we did in the good old days is because hungry populations of lake trout, walleye, Atlantic salmon and brown trout have caught them first!

ACKNOWLEDGEMENTS

SUNY Cobleskill’s Fisheries, Wildlife and Environmental Sciences Department provided equipment, volunteers, transportation and guidance in this research. Tom Breiten generously allowed us to use his property in Mohican Canyon Creek. Mark Cornwell, Brent Lehman and Ben German provided assistance in prepping gear and carrying out the field work.

151 LITERATURE CITED

Best, M.J. 2015. Summer 2014 trap net monitoring of the fish communities in the weedy littoral zone at Rat Cove and the rocky littoral zone at Brookwood Point, Otsego Lake. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Brandt, S.B., D.M. Mason, D.B. McNeil, T. Coates and J.E. Ganon. Predation by alewives on larvae of yellow perch in Lake Ontario. Trans. of the American Fisheries Society. 116:641-645.

Breiten, T. 2001. Personal communication State Highway 80. Cooperstown, NY 13326.

Cornwell, M.D. 2000. Monitoring trophic changes following the reintroduction of walleye (Stizostidion vitreum) to Otsego Lake: An executive summary. In 33th Ann. Rept. (2000). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Cornwell, M. D. 2001. Evidence of rainbow smelt (Osmerus mordax) spawning in Mohican Canyon, a tributary of Otsego Lake. In 34th Ann. Rept. (2001). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Cornwell, M. D. 2004. Characterization of rainbow smelt (Osmerus mordax) spawning in Mohican Canyon, 2004. In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Crowder, L.B. 1980. Alewife, rainbow smelt, and native fishes in Lake Michigan: competition or predation? Environmental Biology of Fishes. 5:225-233.

Foster, J. R. 1990. Introduction of the alewife (Alosa Pseudoharengus) into Otsego Lake. In 22th Ann. Rept. (1990). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Foster, J.R. 2002. Personal communication. Professor of Fisheries and Aquaculture. SUNY Cobleskill, Cobleskill, NY 12043.

Harman, W.N., L.P. Sohacki, M.F. Albright and D.L. Rosen. 1997. The state of Otsego Lake 1936-1996. pp.252-266.

Harman, W.N. 2002. Personal communication. Director SUNY Oneonta Biological Field Station, SUNY Oneonta.

Johnson, T.B., W.P. Brown, T.D. Corry, M.H. Hoff, J.V. Scharold, A.S. Trebitz. 2004. Lake herring (Coregonus artedi) and rainbow smelt (Osmerus mordax) diets in Western Lake Superior. Journal of Great Lakes Research. Vol 30, (Supplement 1); 407–413.

Lindhart, F. 2002. Principal Fisheries Technician, New York State Department of Environmental Conservation. Region 4, Stamford, NY 12167 (unpublished data).

152 McWatters R. C. 1983. The Fishes of Otsego Lake (2nd ed.). Occ. Paper #15 SUNY Oneonta Bio. Field Station, SUNY Oneonta.

McWatters R. C. 1984. The Age, growth and food habits of the rainbow trout, Osmerus mordax (Mitchill) in Otsego Lake, New York. In 16th Ann. Rept. (1983). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Sanford, D.L. 1986. Sr. Aquatic Biologist. New York State Department of Environmental Conservation. Region 4, Stamford, NY 12167 (unpublished report).

Simonin, P.W., D.L. Parrish, L.G. Rudstam, P.J. Sullivan & B. Pientka. 2012. Native rainbow smelt and nonnative alewife distribution related to temperature and light gradients in Lake Champlain. J. of Great Lakes Research Vol.38 (Supplement 1); 115-122.

Tanner, C. and M.F. Albright. 2014. A survey of Otsego lake’s zooplankton community, summer 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Warner, D. 1997. Filtering rates of Otsego Lake zooplankton, summer 1997. In 30th Ann. Rept. (1997). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Wells, L. 1977. Changes in yellow perch Perca flavescens populations of Lake Michigan, 1957- 75. Journal of the Fisheries Research Board of Canada 34:1811-1829.

Waterfield, H.A. & M. D.C. Cornwell. 2014. Hydroacoustic survey of Otsego Lake’s pelagic fish community, spring 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

153 Effects of zebra mussels (Dreissena polymorpha) on lake trout (Salvelinus namaycush) fry recruitment in Otsego Lake

J.Benjamin Casscles1, John R. Foster2, David M. Lucykanish3 & Nicholas M. Sawick3

Abstract: Zebra mussels (Dreissena polymorpha) became established in Otsego Lake in 2007 and by 2010 carpeted the lake trout (Salvelinus namaycush) spawning shoal at Bissel Point. Numerous papers suggest that the presence of zebra mussels on lake trout spawning shoals would negatively impact fry recruitment, because of reduced attractiveness of the substrate and the degradation of interstitial water quality within the substrate. In this 3-year study, lake trout fry recruitment was characterized in the presence of zebra mussels and compared to recruitment levels observed (2003-2004) before the zebra mussel invasion. Emergent fry traps were used to capture lake trout fry swimming up from the substrate at Bissel Point during April and May (2013 – 2015). Twelve emergent fry traps (diameter=81 cm; area=0.52 m2) were set on four linear transects in depths of 30, 60 and 90 cm, across the entire inshore shoal area. Fry recruitment was highest (70%) in the shallowest depth, where zebra mussel density was lowest. Overall, both the highest (4.83 m2/day) and lowest (1.59 fry/m2/day) recruitment levels occurred in the presence of zebra mussels. Fry recruitment was 3.44-3.96 fry/m2/day in the absence of zebra mussels. During the peak fry emergence period, 29-April-15 May, when 90% of the fry emerged, significantly more fry were captured in the presence of zebra mussels (2014-2015) than in their absence (2003-2004). Therefore, contrary to expectations from the literature, there was not clear evidence that zebra mussels were hindering lake trout fry recruitment in Otsego Lake.

INTRODUCTION

Zebra mussels (Dreissena polymorpha) became established in Otsego Lake in 2007 (Waterfeild 2009) and by 2010 carpeted the lake trout (Salvelinus namaycush) spawning shoals (Anonymous 2010). Zebra mussels may negatively impact fry recruitment by covering shallow rocky shoals, deep water cobbles and exposed stony lakeshores were lake trout spawn (Marsden et al. 1995). Lake trout broadcast their eggs over these rocky substrates, where they settle into interstitial spaces to incubate overwinter (Muir et al. 2012). For natural fry recruitment to occur, enough eggs must be deposited to survive predation (Gunn 1995). When high densities of zebra mussels thickly encrust the rocky substrates, interstitial spaces may be occluded, making eggs more vulnerable to predation (Marsden & Chotkowski 2001). Excessive mortality during the incubation period is believed to be responsible for recruitment failures in lake trout populations throughout most of the Great Lakes (Jones et al. 1995; Savino et al. 1999).

Recruitment of lake trout into the Otsego Lake fishery is largely dependent on natural reproduction (McBride and Sanford 1997, Tibbits 2007). Although Otsego Lake has been stocked annually by the New York State Department of Environmental Conservation (NYSDEC) with approximately 5,000 fingerlings per year, gill net surveys have shown that natural recruitment accounts for 75% of the adult lake trout population.

154 The goal of this study was to characterize lake trout fry recruitment in Otsego Lake and compare current recruitment levels to fry recruitment before the invasion of zebra mussels. In order to meet that goal, soft mesh-emergent fry traps were deployed in April-May 2013-2015 to capture lake trout fry emerging from the Bissel Point spawning shoal. The data collected here and in earlier studies (Sawick & Foster 2013; Lucykanish & Foster 2014), were compared to Tibbits (2007) study conducted at Bissel Point in the absence of zebra mussels.

MATERIALS & METHODS

This study was carried out at Bissel Point, Otsego Lake (W74° 54.141; N42° 45.550, Township of Otsego, Otsego County, New York), following Tibbits (2007), Sawick & Foster (2013) and Lucykanish & Foster (2014). The 2015 study was conducted over 42 days starting at ice-out (9 April 2015) and extending to 21 May 2015.

The twelve emergent fry traps (81 cm diameter, .52 m2 area) used in this study were the same ones used by Tibbits (2007), Sawick & Foster (2013) and Lucykanish & Foster (2014). They were centered at depths of 30, 60 and 90 cm along four linear transects perpendicular to the shoreline (Figure 1). Traps were checked at 48 hour intervals. Captured fry were counted and released. Water temperature was measured using a HANNA temp/pH electronic meter.

Figure 1. Emergent fry traps were set at depths of 30, 60 and 90 cm along four linear transects off Bissel Point, Otsego Lake, NY.

155 RESULTS

A total of 786 lake trout fry were captured between 2013 and 2015. Overall, 70% of the fry emerged at a depth of 30 cm, 24% at 60 cm, and 6% at 90cm. While there was some variability between years, the pattern of emergence was consistent (Figure 2). No significant difference was observed in the number of fry emerging from different depths in the absence of zebra mussels (2004) or in the presence (2013-2015) of zebra mussels (chi square test, P > .05).

80 30 cm 70 60 cm

60 90 cm 50

% Fry Fry % Emergence 20

0 2004 2013 2014 2015 Fry Emergence Date

Figure 2. The percent of lake trout fry emerging at three depths in the absence of zebra mussels (2004) and in their presence (2013-2015).

160 140 2003 120 2004 2013 100 2014 80 2015 60 40 20 Number of Emergent Fry Number of EmergentFry 0 5/1 5/3 5/5 5/7 5/9 4/15 4/17 4/19 4/21 4/23 4/25 4/27 4/29 5/11 5/13 5/15 5/17 5/19 Date

Figure 3. The number of emergent lake trout fry compared to date of emergence (2003, 2004 and 2013-2015).

156 Throughout the 5 years of this study, peak fry emergence was almost identical (Figure 3). Peak fry emergence occurred on 7 May (2003, 2013, 2015) and 8 May (2004, 2014). Over 90% of the fry emerged over a 17 day period (29 April–15 May). Peak fry emergence seems to be triggered when the water temperature reaches 8oC. In 2015, 93% fry emerged between 8.3oC and 14.9oC, while in 2014, 94% fry emerged between 8oC & 11.1oC (Figure 4 & 5).

70 Temp oC 60 # Fry 50 40 30 vs. Temperature oC

20 Number of Emergent Fry Emergent of Number 10 0 5-1 5-3 5-5 5-7 5-9 4-15 4-17 4-19 4-21 4-23 4-25 4-27 4-29 5-11 5-13 5-15 5-17 5-19 5-21 Date Figure 4. The relationship between date, water temperature and the number of emergent lake trout fry in 2015.

160

140

C Temp oC

o 120 # Fry 100

vs. Temperature 40

Number of Emergent Fry Emergent of Number 20

0 5/1 5/3 5/5 5/7 5/9 4/17 4/19 4/21 4/23 4/25 4/27 4/29 5/11 5/13 5/15 5/17 5/19 5/21 Date

Figure 5. The relationship between date, water temperature and the number of emergent lake trout fry in 2014.

157 Fry recruitment between years was variable (Figure 6). When emergence was measured by the average number of fry captured per m2 of substrate per day, both the highest and lowest fry recruitment levels (4.83 and 1.59, respectively) occurred in the presence of zebra mussels. In 2015 the average number of fry per m2/day was 2.41, lower than 4.83 fry/m2/day captured in 2014, and higher than the 1.59 fry/m2/day captured in 2013.

6.00

4.83 5.00 3.96 4.00 3.44

3.00 2.41

2.00 1.59

1.00 ²/Day Fry/m of Number Average 0.00 2003 2004 2013 2014 2015 Zebra Mussels Absent Zebra Mussels Present

Figure 6. Average number of lake trout fry emerging per m2 of substrate per day in the absence and presence of zebra mussels.

However, lake trout fry emergence in 2014 and 2015 was much higher than shown by the average number fry captured per m2/day. In 2003 the sampling period was 21 days and in 2004 and 2013 it was 28 days. In 2014 and 2015 sampling was extended to 42 days. The extra days added in recent years were well outside the 29 April – 15 May peak emergence period, lowering the average number of fry caught per m2/day. A better way to compare the data is to focus on the period 29 April – 15 May during peak fry emergence. In the presence of zebra mussels (2014- 2015) a total of 700 fry were captured during the 29 April – 15 May peak emergence period, while in the absence of zebra mussels (2003-2004) only 222 fry emerged in that same period (chi square test, P < .0001). This would indicate that lake trout fry emergence was substantially higher in the presence of zebra mussels.

DISCUSION

The impact of zebra mussels on lake trout fry recruitment in Otsego Lake raised concerns shortly after zebra mussels became well established on the rocky spawning shoal at Bissel Point (Sawick & Foster 2013). The presence of zebra mussels on lake trout spawning shoals has been shown to interfere with the deposition of eggs, as well as their survival. The presence of zebra mussels have been reported to reduce egg deposition by discouraging adult lake trout from spawning. Further, zebra mussels have been reported to increase damage to lake trout eggs

158 (Marsden and Chotkowski 2001), as well as increase vulnerability of eggs to predators (Claramunt et al. 2005, Marsden 1997). Zebra mussels are also thought to degrade interstitial water quality within the substrate resulting in a decrease in lake trout egg viability (Marsden et al. 1995. Marsden 1997, Marsden & Chotkowski 2001).

However, other studies have not shown a clear-cut negative impact of zebra mussels on lake trout fry recruitment. For example, Marsden & Chotkowski (2001) showed that lake trout emergence was similar on substrates fouled and not fouled by zebra mussels. Further, Marsden et al. (2005) showed that lake trout fry hatch per egg had some of the highest rates on sites in Lake Champlain that were densely covered with zebra mussels.

There is also the possibility that zebra mussels have a positive impact on lake trout fry recruitment. The presence of zebra mussels increases the surface area and complexity of the substrate, which may provide more refuges from predators (Ozersky et al. 2011). Further, lake trout fry are mobile before swimming up, and they move within and above the substrate (Baird & Krueger 2000). If zebra mussels clog interstitial spaces in the substrate, then fry lateral movements may become restricted, making them more likely to move vertically into emergence traps (Marsden & Chotkowski 2001).

While this study provides an excellent characterization of lake trout fry emergence at Bissel Point, it does not provide an unequivocal answer to the question of whether zebra mussels are impacting lake trout fry recruitment in Otsego Lake. Evidence that there may be a negative impact comes from the negative correlation between fry recruitment and zebra mussel density. In Otsego Lake, trout fry emergence was highest at 30 cm in depth, where zebra mussels are least dense. Fry recruitment decreased at 60 cm and decreased further at 90 cm, as zebra mussel density increased. However, the same pattern of fry recruitment by depth occurred in 2004 in the absence of zebra mussels, indicating that the correlation between fry recruitment with depth had nothing to do with the density of zebra mussels. Evidence that zebra mussels had no impact on fry recruitment comes from the catch rate. Both the highest (4.83 m2/day; Lucykanish & Foster 2014) and lowest (1.59 m2/day; Sawick and Foster 2013) occurred in the presence of zebra mussels. Lake trout recruitment is variable between years and other factors besides the presence or absence of zebra mussels may have a greater impact on the natural year-to-year variation in fry emergence. For example, wave action, ice scour, and predation all impact lake trout fry recruitment and are expected to vary from year to year (Edwards et al. 1990, Krueger et al. 1995, Marsden et al. 1995). Evidence that lake trout fry recruitment was enhanced by the presence of zebra mussels is shown in the comparison of total lake trout fry recruitment during the peak emergent period. In that comparison, over 3 times more fry emerged in the presence of zebra mussels than in their absence. Therefore, quite possibly the presence of zebra mussels may increase lake trout fry recruitment in Otsego Lake.

159 ACKNOWLEDGEMENTS

The SUNY Oneonta Biological Field Station provided the emergent fry traps and facilities. Matthew Albright provided guidance and assistance to this project. Land owners Bevin & Aaron Hall allowed access and use of their property at Bissel Point. SUNY Cobleskill students Eric Malone, Wesley Miller, Michal Soukup, Quinn Buckley, Katherine Williams, Alex Walczyk, Daniel Garret, helped set, check and repair traps. The Schenectady Clear Water Chapter of Trout Unlimited and the Schoharie County Conservation Association supported this project with research grants to the senior author.

LITERATURE CITED

Anonymous. 2010. Otsego Lake zebra mussel update. SUNY Oneonta Reporter. summer/fall 2010, p. 3. SUNY Oneonta Biological Field Station, Oneonta, NY.

Baird, O.E. & C.C. Krueger. 2000. Behavior of lake trout sac fry. vertical movement at different development stages. Journal of Great Lakes Research, 26 (2), 141-151.

Edwards. C.J., R.A. Ryder & T.R. Marshall 1990. Using lake trout as a surrogate of ecosystem health for oligotrophic waters Journal of Great Lakes Research, 16 (4), 591-608.

Gunn, J.M. 1995. Spawning behavior of lake trout: effects on colonization ability. Journal of Great Lakes Research. 21:1. 323–329.

Jones, M.L., Eck, G.W., Evans, D.O., Fabrizio, M.C., Hoff, M.H., Hudson, P.L., Janssen, J., Jude, D., O’Gorman, R., and Savino, J.F. 1995. Limitations to lake trout (Salvelinus namaycush) rehabilitation in the Great Lakes imposed by biotic interactions occurring in early life stages. Journal of Great Lakes Research, 21:1.505–517.

Krueger, C.C., D.L. Perkins, E.L. Mills & J.E. Marsden. 1995. Predation by alewives on lake trout fry in Lake Ontario: roll of an exotic species in preventing restoration of a native species. Journal of Great Lakes Research, 21, 458-469.

Lucykanish D. M. & Foster J. R. 2014, Is lake trout recruitment impacted by zebra mussels in Otsego Lake, NY? In 47th Ann. Rpt. (2014) SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta.

Marsden, J.E. 1997 Common carp diet includes zebra mussels and lake trout eggs. Journal of Freshwater Ecology 12: 491-492.

Marsden, J.E., J.M. Casselman, T.A. Edsall, R.F. Elliott, J. D. Fitzsimons, W.H. Horns & B.L. Swanson 1995. Lake trout spawning habitat in the Great Lakes—a review of current knowledge. Journal of Great Lakes Research, 21, 487-497.

160 Marsden, J.E., & Chotkowski, M. A. 2001, Lake trout spawning on artificial reefs and the effect of zebra mussels: fatal attraction?. Journal of Great Lakes Research, 27:1. 33-43.

Marsden, J.E., B.J. Elliott, R.M. Claramunt. J.L. Jonas & J. D. Fitzsimons. 2005. A comparison of lake trout spawning, fry emergence, and habitat use in lakes Michigan, Huron, and Champlain. Journal of Great Lakes Research, 31 (4). 492-508.

Muir, A., Blackie, C., Marsden, J., & Krueger, C. (2012). Lake charr Salvelinus namaycush spawning behaviour: new field observations and a review of current knowledge. Reviews In Fish Biology & Fisheries, 22(3), 575-593.

Ozersky, T., D.R. Barton and D.O. Evans. 2011. Fourteen years of dreissenid presence in the rocky littoral zone of a large lake: effects on macroinvertebrate abundance and diversity. Journal of the North American Benthological Society 30(4): 913-922.

Savino, J.F., Hudson, P.L., Fabrizio, M.C., and Bowen, C.A. 1999. Predation on lake trout eggs and fry: a modeling approach. Journal of Great Lakes Research, 25:1: 36–44.

Sawick N. M. & Foster J. R. 2013. Natural recruitment of lake trout (Salvelinus namaycush) In Otsego Lake. In 46th Ann. Rpt. (2013) SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta.

Tibbits W. T. 2007. The behavior of lake trout Salvelinus namaycush in Otsego Lake, documentation of strains, movements, and natural reproduction of lake trout under present conditions. Occas. Pap. #42 SUNY Oneonta Biological Field Station, SUNY Oneonta.

Waterfield, H. A. 2009, Update on zebra mussel (Dreissena polymorpha) invasion and establishment in Otsego Lake, 2008. In 41st Ann. Rept. (2008). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

161 Dominant algae of Otsego Lake, Cooperstown, NY

Claire Garfield 1

INTRODUCTION

The diversity and frequency of algal species are important factors in determining limnological conditions because algae serve as a biological indicator of ecological stability and water quality (Bellinger and Sigee 2010). While Otsego Lake is considered a meso-oligotrophic lake, data relating to the composition of the algal standing crop in Otsego Lake would be advantageous in further determining the state of the lake (Godfrey 1977).

Certain species of algae are indicative of water quality. Some groups of diatoms (Bacillariophyceae) and golden-brown algae (Chrysophyceae) grow only in relatively unpolluted water whereas cyanobacteria are more tolerant of pollution (Baker 2012). Therefore the relative abundance of any given group of algae can reveal much about the health of a lake (Bellinger and Sigee 2010).The importance of knowing the taxonomic composition of algae in Otsego Lake is augmented by the fact that many genera of cyanobacteria, often referred to as blue-green algae, produce toxins as secondary metabolites involved in storing nitrogen (Baker 2012). Many of these toxins are neuro- or hepatotoxins. Beta-Methylamino-L-alanine (BMAA), a toxin capable of being produced by multiple species of cyanobacteria has been linked to amyotrophic lateral sclerosis (ALS) or Lou Gehrig’s disease (Cox et al. 2003).

The purpose of this study was to identify and determine the relative dominance of major planktonic algal taxa in Otsego Lake using both conventional microscopy as well as a Fluid Imaging Technnologies FlowCam®, a digital particle analyzer (FlowCam 2011).

METHODS

Samples were taken bi-weekly at the deepest point (50m) in Otsego Lake, TR4-C (Figure 1). Two garden hoses attached to a weighted line were lowered to a depth of 20 meters and retrieved from the bottom, yielding a single composite sample. The contents of the hoses were emptied out into a one gallon Nalgene® bottle made of high density polypropylene.

1 F.H.V. Mecklenburg Conservation Fellow, summer 2015. Present affiliation: Oneonta High School. Funding provided by the Otsego County Conservation Association.

162

Figure 1. Map of Otsego Lake showing the sampling site for algae.

Samples were preserved with Lugol’s iodine solution, which also later helped the algal cells settle out of suspension in Utermöhl chambers. The samples were stored in the cold room until further processing to prevent degradation of the algae.

163 Samples were analyzed in two ways: one with manual counts with an inverted microscope and another with FlowCam® .

Samples viewed with a microscope were inverted several times and placed in a 10-mL KC Denmark A/S Utermöhl chamber and viewed with the Zeiss Axiovert 25 inverted microscope. Cell counts were taken and recorded for the entire bottom area of the chamber.

Samples viewed with FlowCam® were analyzed as follows. Approximately 3mL of undiluted sample was processed through the flow cell. FlowCam® then took images of the first 1000 particles. Those pictures were then identified and sorted into various libraries to be counted.

FlowCam® was much more proficient at identifying cells on new samples; however, on older samples, the microscope proved a better method. A function and asset of using FlowCam® is that it can analyze data quickly and compensate for error involved in taking a subsample. However, using FlowCam® did pose some problems. The most common pictures taken were either of detritus or non-algal particles, and the poor quality of many pictures prevented them from being used.

Algae were counted by cell as opposed to colonies; however, Microcystis and Anabaena were counted by colony and filament, respectively.

RESULTS AND DISCUSSION

Table 1 summarizes the particle counts acquired using the microscope from samples collected from 21 April 2014 to 12 August 2015 (cell counts for most, colony counts for Microcystis and Anabaena). Similarly, Table 2 summarizes particle counts acquired using the FlowCam® on samples collected from 19 May to 12 August 2015. Here, cells are not differentiated from particles (i.e., cells). Figure 2 and 3 summarize the microscope counts and FlowCam® counts, respectively. Similarly, Figures 4 and 5 summarize the total counts using the microscope and FlowCam® during the summer of 2015 (19 May to 12 August).

164

4/21/14 5/7/14 6/18/14 6/21/14 7/2/14 9/7/14 9/13/14 10/27/14 11/10/14 5/19/15 6/3/15 7/2/15 8/12/15 8/27/15 9/16/15 10/6/15 10/21/15 TOTAL

MICROCYSTIS 0 0 0 0 1.3 0 0 0 1.6 0.5 0.1 0 0.2 0.4 0.3 0.6 0.1 5.1 ANABAENA 0 0.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 CYCLOTELLA 0.1 0.3 0.2 0 0.4 0 0 0 0 0.1 0 0.5 1.2 11.5 0 0.2 0.1 14.6 ASTERIONELLA 7.2 5 0.2 2.4 1.4 0 0 2.6 0 0 0 0.8 0 0 0 0 0 19.6 FRAGILARIA 0.3 0 0 0 0 0 0 2.5 5.5 0.4 3.2 0.6 26.7 8 0 2.1 0.1 49.4 PINNATE DIATOMS 0.8 4.3 0 1.5 0.1 0 0.6 0.6 0 0.4 0.2 0.2 0.2 0.3 0 0.1 0.8 10.1 DINOBRYON 0.1 0 5.3 0.2 1.4 0.8 0.7 1 0 0 3.7 1.6 0.6 2 1.8 1.2 7.6 28 PERIDINIUM 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0 0 0.1 0 0.2 CERATIUM 0 0 0 0 0.1 0 0 0 0 0 0 0 0.1 0 0 0 0 0.2 EUGLENA 0 0 0 0 0 0 0.1 0 0 0 0.1 0 0 0 0 0 0 0.2 MOUGEOTIA 15.8 29.3 2.9 13.9 3.2 6.8 0 6.5 2.8 1.1 4 6.6 8.6 5.3 1.3 2.4 15.8 126.3 GLOEOCYSTIS 0 0 0.2 0 8.2 3.6 0 7.1 2.2 32.3 1.5 422.3 445.6 105.7 77.6 4 3.2 1113.5 PEDIASTRUM 0 0 0 0 0 0 0 6.4 0 0 0 0 3 0 0 3 0 12.4

Table 1. The total number of cells (#/ml) (except for Microcystis and Anabaena, which were counted as colonies) counted with the microscope, by genus and date.

165 5/19/15 6/3/15 6/7/15 7/2/15 7/14/15 8/12/15 TOTAL

MICROCYSTIS 2 2 2 0 6 0 12 ANABAENA 0 0 0 1 0 6 7 CYCLOTELLA 0 0 0 0 0 0 0 ASTERIONELLA 0 0 0 0 0 0 0 FRAGILARIA 0 0 0 0 0 0 0 PINNATE DIATOMS 0 0 0 0 0 1 1 DINOBRYON 0 0 14 1 0 0 15 PERIDINIUM 0 0 0 0 0 0 0 CERATIUM 0 0 0 0 0 0 0 EUGLENA 2 0 0 0 0 0 2 MOUGEOTIA 0 0 0 0 0 0 0 GLOEOCYSTIS 20 0 30 97 79 34 260

Table 2. The number of particles counted with FlowCam® for 2015 (#/ml) by genus and date.

Microscope 4/21/14 500 5/7/14 450 6/18/14

400 6/21/14 350 300 7/2/14 250 9/7/14 200 10/27/14 150

AMOUNT OF CELLS 11/10/14 100 50 5/19/15 0 6/3/15 7/2/15 8/12/15

GENERA

Figure 2. The total number of cells (except for Microcystis and Anabaena, which were counted as colonies) counted with the microscope by genus and date.

166 FLOWCAM® 120

100 5/19/15

6/3/15 80 6/7/15 60 7/2/15 40 7/14/15

AMOUNT OF CELLS 8/12/15 20

GENERA

Figure 3. The number of particles counted with FlowCam® for 2015 by genus and date.

Microscope totals 1200

1000 800 600 400

AMOUNT OF CELLS 200 0

GENERA

Figure 4. The total amount of cells counted manually by genus and date between 21 April and 27 August 2015.

167 FLOWCAM® totals 300

250 200 150 100

AMOUNT OF CELLS 50 0

GENERA

Figure 5. Represents total amount of particles counted by genus with FlowCam® between 21 April and 27 August 2015.

The most ubiquitous group of algae in Otsego Lake was Gloeocystis (Figure 6). Gloeocystis is a member of the subkingdom Chlorophyta. The value of Gloeocystis as a biological indicator is limited; however, large quantities, much larger than those of Otsego Lake, can cause an unpleasant smell (Guiry and Guiry 2015).

Figure 6. Gloeocystis. Scale bar = 12.5 µm.

The second most abundant species of algae in Otsego Lake was Mougeotia (Figure 7), a member of charophyceae. Mougeotia is spread throughout the world and is not considered a nuisance (Guiry and Guiry 2015).

168

Figure 7. Mougeotia. Scale bar = 12.5 µm.

Dinobryon, a genus of Chrysophyceae (golden-browns), was also found frequently (Figure 8). Dinobryon is dependent upon oligotrophic conditions making it an excellent biological indicator. Thus the frequency of Dinobryon suggested a more oligotrophic lake (Baker 2012).

Figure 8. Dinobryon. Scale bar = 12.5 µm.

The most diverse group in Otsego Lake and all fresh water is Bacillariophyceae, commonly known as diatoms. Genera collected include Asterionella, Fragilaria, Cyclotella, and other unidentified pinnate diatoms. Specific genera of Bacillariophyceae have particular ecological preferences. The specificity and diversity of diatoms make them good biological indicators (Bellinger and Sigee 2010). The amount and diversity of diatoms suggested that Otsego Lake has fairly clean water.

169 The most common diatom was Fragilaria, a colonial genus that is intolerant of pollution (Bellinger and Sigee 2010). The high abundance of this genus suggested that Otsego Lake has relatively little pollution.

Figure 9. Fragilaria. Scale bar = 12.5 µm. Photo by Kiyoko Yokota.

While cyanobacteria were only found at low frequencies and with low diversity of genera, two genera, Microcystis and Anabaena, were found in Otsego Lake. Anabaena (Figure 10) can dominate in polluted, nutrient rich conditions, but the low density suggests good water quality. Microcystis also thrives in lakes with high phosphorous and nitrogen levels; thus, high densities might indicate cultural eutrophication (Baker 2012). The fact that few colonies of Microcystis were found indicates that nutrient loading is not yet a major problem to the health of the lake, but rather something to monitor for. While both are potentially toxin producing, microcystin and anatoxin from Microcystis and anatoxin from Anabaena, their presence is not a cause for concern because toxin production is strain specific, with not all cyanobacteria being dangerous (Dittmann et al. 2012) and because they were such minor components of the commubnity (Baker 2012). However, in the future, Microcystis counts may be higher because of the infestation of zebra mussels (Dreissena polymorpha); zebra mussels alter the plankton community, often favoring Microcystis (Vanderploeg et al. 2001). The increase in zebra mussels could promote the growth of Microcystis in Otsego Lake so monitoring the algal community composition would provide valuable data for addressing future issues.

170

Figure 10. Anabaena. Scale bar = 12.5 µm.

CONCLUSION

Algae monitoring is important for both environment and human health; certain types of algae can provide insight as to the state of habitats or potential production of deleterious toxins. Furthermore, analyzing algae is even more important in the face of environmental stressors and changes. With potential nutrient loading and the introduction of invasive species, algal composition could change significantly and continuing data collection could prove an important asset in addressing ecological problems. Based on the genera present and the amounts of the dominant genera in Otsego Lake, it can be inferred to be healthy and safe.

REFERENCES

Baker, A.L. 2012. Phycokey -- an image based key to Algae (PS Protista), Cyanobacteria, and other aquatic objects. University of New Hampshire Center for Freshwater Biology. http://cfb.unh.edu/phycokey/phycokey.htm 21 Jul 2015.

Bellinger E.G. and D.C. Sigee. 2010. Freshwater algae: Identification and use as bioindicators. Chichester (West Sussex): Wiley-blackwell.

Dittmann E., D. Fewer and B. Neilan. 2012. Cyanobacterial toxins: biosynthetic routes and evolutionaryroots. Federation of European Microbiological Societies [Internet]. [cited 2015 Aug 19] 10.1111/j.1574-6976.2012.12000.x. Available from: file:///C:/Users/garfc40/Downloads/23.full.pdf

FlowCam® Manual. (3.0) [2011 Sept, cited 2015 Aug 4]. Available from: http://www.ihb.cas.cn/fxcszx/fxcs_xgxz/201203/P020120329576952031804.pdf

171 Godfrey, P.J. 1979. Otsego Lake limnology: Phosphorus loading, chemistry, algal standing crop and historical changes. In 10th Ann. Rept. (1978). SUNY Oneonta Bio Fld. Sta., SUNY Oneonta.

Guiry, M.D. and G.M. Guiry. AlgaeBase. World-wide electronic publication, National University of Ireland, Galway. http://www.algaebase.org; searched on 04 August 2015.

Vanderploeg, H.A., J. R. Liebig, W.W. Carmichael, M.A. Agy, T.H. Johengen, G.L. Fahnenstiel, and T.F. Nalepa. 2001. Zebra mussel (Dreissena polymorpha) selective filtration promoted toxic Microcystis blooms in Saginaw Bay (Lake Huron) and Lake Erie. Can J. Fish. Aquat. Sci. 58: 1208-1221.

172 A quantitative FlowCAM analysis of diatoms in Otsego Lake, New York, with an emphasis on method implications

Britney B. Wells1

ABSTRACT

A study was performed from January to May 2015 that was intended to investigate environmental change as revealed in sediment cores over the last 90 centuries in Otsego Lake, NY. Various conditions of the lake and local environment can be characterized based on the species of diatoms that were present in the cores. To fulfill this purpose, three methods were investigated as to their ability to obtain absolute diatom abundances from different core depths. Diatoms were chosen as a paleoecological proxy for environmental changes because they are highly sensitive to environmental conditions, respond rapidly to changing conditions and preserve well in sediment (Smith and Flocks 2010). This paper documented the results from core analyses and highlighted limitations of the petrographic microscope, FlowCAM and ImageJ software.

Diatoms were separated into two orders of Pennales and Centrales (westerndiatoms.colorado.edu) by creating diluted slide smears to take photomicrographs. Due to the lack of anatomical detail that could be observed using a petrographic microscope, limitations arose that regarded species level identification. FlowCAM, a benchtop particle analyzer, photographed and counted particles from a sample through a fluid stream. Parameters that characterized particles were displayed such as length, circularity and transparency. An image library was created to isolate shapes and sizes that resembled certain diatoms into a folder, which could be useful to obtain absolute abundances. Before samples could be introduced into the FlowCAM, an ImageJ software program was utilized to measure diatom dimensions. ImageJ assisted with the classification of Pennales and Centrales further into more distinct groups based on their minor anatomical and shape differences. Ultimately, due to the steep learning curve of the FlowCAM, methods were ineffective in determining diatoms abundances. However, mitigations could be made to lead to a successful quantitative analysis.

BACKGROUND

Otsego Lake is located in northern Otsego County, New York with Cooperstown at the southern end. Otsego County was completely glaciated during the Pleistocene Epoch, which deposited a variety of dense, unsorted clay, sand, gravel and boulders over the land surface (Isachsen 1991). Due to the glacial erosion, Otsego Lake is found in a glacially carved, over-

1 Independent Study Biol. 399. Current affiliation: SUNY Oneonta.

173 deepened valley, at an elevation of approximately 364 meters. Otsego Lake serves as the headwaters of the Susquehanna River in Otsego County (Harman and Albright 1997).

Sediments contain the remains of diatoms, mollusk shells, algal material, woody debris, mineral particles, and charcoal. These components provide a mosaic of lake processes and changes in the local environment over the last 90 centuries. Siliceous diatom valves rarely decompose in sediment, making them a useful proxy for the ecological analysis of Otsego Lake. The distribution of diatoms can relate to the trophic status of a lake. Their various ecosystem niches are sensitive to changes in pH, salinity, temperature and nutrient concentrations. Generally, lakes with low productivity are considered oligotrophic. Nutrients at higher concentrations lead to mesotrophic and eventually eutrophic lakes (Arnold 2002). Different diatom species function as different environmental indicators. Studies have been done that examined the history of lake conditions as revealed by diatoms in sediment cores (Fritz 1993, Arnold 2002).

Diatoms (Bacillariophyta) are distinguishable in two orders, Pennales and Centrales. Diatoms can be seen in different orientations under a microscope. The ‘valve view’ refers to the view of the ‘face’ of a diatom frustule, which classifies their symmetry. Conversely, the profile view is referred to as the girdle view. Pennales, or pennate diatoms, are bilaterally symmetrical. Their general outline may be boat-shaped or rod-shaped. In the center of many pennate diatoms is an unsilicified groove, known as the rapine. The Centrales, or centric diatoms, are radially symmetrical and lack rapines. The outline of centric diatoms is typically circular, oval or elliptical. These outlining differences are not always true for the two morphological groups. Diatom identification must be based on the anatomical pattern of their internal vales (Round 1990)

Near the Biological Field Station in Cooperstown, NY, six sediment cores were probed in Rat Cove in Otsego Lake by a modified Livingstone-type drive rod corer of which two cores were analyzed for diatom abundances. Each core, or drive, was about 1 meter long. A number of drives were extracted from these locations. For the purpose of this study, samples taken from the cores were mentioned in relation to where the sediment/water interface meets.

At location A (OTS – 2014 – 02 - B), a core was extracted on 1 February 2014 in a deep part of the lake, close to a protruding delta (Figure 1). Total, there were ten drives taken that collected about 6 meters of sediment. Drive 2 began at 100 cm below the sediment/water interface (Figure 2). The core contained very dark sediment with little to no layers. Samples were extracted and analyses were made from 115, 155 and 195 cm below the sediment/water interface.

174 At location C (OTS – 2012 – 05 – 12C), a 1.6-meter long core was probed on 12 May 2012 within three drives. Each subsequent drive overlapped the last drive by 50 cm. This area is closer to the shore than location A (Figure 1). From drive 2, which began at 25 cm below the sediment/water interface, samples at 70 and 97 cm were extracted for analysis (Figure 3). This

Figure 1. Map of core locations in Otsego Lake just north and east of the Biological Field Station, Otsego County, NY, in Rat Cove.

Figure 2. Sediment core that was extracted from a deeper part of Otsego Lake at location A, drive 2. The core began at 100 cm below the sediment/water interface.

175

Figure 3. Sediment core that was extracted from a shallower part of Otsego Lake at location C, drive 2. The core began at 25 cm below the sediment/water interface.

core contained a variety of organic material, few white gastropods in layers of brown-grey mud. Woody debris, leaf remains and peat were dispersed in darker, rich sediment in the middle section of the core.

In addition, at location C, samples at 77-137 cm below the sediment/water interface (sampling every 2 cm) from drive 3, were observed in this study. Drive 3 began at 75 cm below the sediment/water interface. This core contained organic material, shells and gastropods embedded in dark brown/black mud. There was an abrupt mud/clay mixture layered in the middle of the core. Previously, sediment slides were created and analyzed in this study from 77 and 79 cm below the sediment/water interface of drive 3.

METHODS

In this study, the use of an Olympus Petrographic Polarizing Microscope was used to manually identify Centrales or Pennales. Slide smears were created and analyzed of location A and location C at various depths. Samples were introduced into the FlowCAM through a fluid stream for a diatom abundance calculation. Dimensions of the diatoms were measured with ImageJ. Measurements assisted with diatom identification. In addition, measurements helped determine what objective lens and flow cell would be appropriate for the FlowCAM to maximize data collection of a wide range of diatom sizes.

To initially confirm that diatoms were present in the core, photomicrographs were analyzed of previously created slides of location C at 77 and 79 cm below sediment/water interface. Slide smears were created of location C at 97 cm below the sediment/water surface to examine physical characteristics of diatoms on a deeper scale. The core was sampled with a metal spatula in 2 cm increments. We avoided obtaining samples that were potentially disturbed

176 by the coring barrel by digging through the soil surface for collection. Each 1x1x1 cm sample was placed in separate sample bottles, then hydrated with deionized water. A tiny amount of the samples was transferred onto individual glass microscope slides via pipette. The addition of detergent broke up surface tension to allow easier dispersion of particles across the slide. Samples were dried on a hot plate and weighed. Photomicrographs were taken at 40x magnification (Figure 4a and 4b). Diatoms were visually characterized based on their shapes. Any diatoms that appeared elliptical or round with no raphie were assumed to be Centrales (Figure 4a). Oppositely, boat- shaped, rod-shaped or elongated diatoms with possible raphie (central area) were assumed to be Pennales (Figure 4b). However, diatoms cannot be characterized based on their shape outline. Diatom identification was loosely interpreted since anatomical features within their frustule were not always visible (Round 1990).

Inspection of photomicrographs revealed that HCl increased the visibility of diatoms that were on the slide smears. Location C premade slides at 77 and 79 cm below the sediment/water interface (HCl absent), displayed a large amount of organic material, which covered the diatoms partially. In contrast, location C at 97 below sediment water interface (+HCl) resulted in much less organic debris (Figure 4). HCl addition caused samples from location C to effervesce.

The Pennales and Centrales were broken down further into more specific groups based on their minor anatomical and shape differences (Table 2). A species name was not assigned to these diatoms. However, they were classified based on their distinct visual differences between each one. To do this, ImageJ scaled sample photomicrographs and measure diatom dimensions. An aspect ratio, or proportional relationship between an objects width and height, was calculated for each diatom observed of location C at 77 and 79 cm below the sediment/water surface. A value close to 1 indicated that the diatoms distance in length was similar to its distance in width.

177

Figure 4a. A centric diatom found at location C (Drive 2) + HCl at 97 cm below the sediment/water interface.

Figure 4b. A pennate, boat-shaped diatom found at location C, drive 2 (300 µm sieve + HCl) at 97 cm below the sediment/water interface.

178 A dynamic imaging particle analysis tool, known as the bench top FlowCAM, at the Biological Field Station was utilized to retrieve abundances of diatoms (Figures 5 and 6). This machine photographed each individual particle in a flow stream of water and lake sediment. Subsequently, the photographs of diatom shaped particles that the FlowCAM took were compared to previously acquired photomicrographs for species identification. Although a 4x objective allowed for a huge range of sizes to be photographed in a large field of view, any particle under 100 µm was extremely hard to identify.

The following analyses were performed with FlowCAM-stored data of the samples. Particles were sorted by shape and size to isolate certain species of diatoms into an image library as templates. Figure 7 displays Pennales that have been narrowed down based on their similar boat-shaped characteristic within a remote folder. Subsequently, FlowCAM automatically scanned and filtered the database for similar images that matched the criteria provided. The data were then instantly enumerated into concentrations of specific particle characteristics.

Figure 5. FlowCAM, the dynamic imaging particle analysis tool used for quantifying diatoms.

179

Figure 6. Sample addition to the FlowCAM for image processing.

Figure 7. FlowCAM image library of boat-shaped pennate diatoms isolated into a folder. Summary statistics of particles is displayed on the bottom left.

180 The more template images that are placed aside from the particles, the better chance the FlowCAM has in isolating exact shape matches. The diatom library required clear, individual images of the desired shape. This rose to the issue of the FlowCAM’s inability to perform this technique due to plant debris and fibers blocking the field of view of some diatom images (Figure 8).

Figure 8. Image of fibrous material from a run through FlowCAM (4x objective and 300 µm flow cell). Location A at 195 cm below the sediment/water interface (300 µm sieved).

Three samples of location C at 70 and 97 cm below the sediment/water surface were run through the FlowCAM. Due to the fact that creating a library in FlowCAM did not isolate Pennales and Centrales (Figure 6), each image page was roughly counted by the naked eye. The number of diatoms and particles from each total run were added to calculate an absolute abundance of Pennales and Centrales (Table 1).

181 The FlowCAM was equipped with a 4x magnification objective with a correlating 300 µm flow cell and 1mL syringe. These parameters were recommended by the company’s Configuration Optimization Guide in the FlowCAM Manual (2011). The maximum detectable size was 300 µm. The machine was focused by running 50 µm specialized pre-diluted focus microspheres through the field of view. In addition, to balance between grey-scale and color measurement, the intensity mean value was set to be approximately 150. Before and after each use, the system was flushed with ultra pure water to ensure particles did not molt into the flow cell.

Prior to presenting sediment samples into the FlowCAM, samples were sieved through a 300-µm mesh screen to leave a broad range of diatoms (Location A at 115, 155 and 195 cm below sediment/water surface; Location C at 77-137 cm below sediment/water surface sampled every 2cm). The sieve was folded into a funnel and the sediment was completely filtered into a beaker with deionized water. Approximately 1-2 drops of the remaining sediment sample was introduced from a funnel set above the FlowCAM and pumped at a rate of 20 fps (Figure 5). Drawing up prepared sediment of less clumped particles ensured that soil clumps would not get lodged in the flow cell. In addition, detergent was added to ensure smooth flow of sediment by reducing surface tension and adhesion.

The samples were pretreated with 5-15 drops of HCl of location A at 115, 155 and 195 cm below the sediment/water interface and of location C at 70 and 97 cm below the sediment/water interface. The addition of HCl was intended to reduce organic matter and carbonate composition. To observe if diatom visibility increased due to the partial dissolution caused by HCl, slide smears were created of HCl pretreated samples and compared to non- pretreated samples.

RESULTS AND INTERPRETATIONS

The original strategy to isolate certain diatom shapes into a FlowCAM library to retrieve abundances did not work (Figure 9). When the database was scanned for similar particles, the FlowCAM isolated other particles such as minerals, plant debris and other sediment. Therefore, numeric estimations by the naked eye was executed to count the number of diatoms at certain depths at each location. FlowCAM and photomicrograph data of location C at depths of 70, 97 cm (Figure 9), 77 and 79 cm (Table 2; Figure 10) below the sediment/water interface were compared to FlowCAM data of location A at the depths of 115, 155 and 195 cm (Figure 4 + HCl) below the sediment/water surface. Location C showed a Centrale (Figure 4a) and a Pennale (Figure 4b) at 97 cm below the sediment/water surface. Oppositely, location A showed very few shapes that could be classified as diatoms (Figure 9). All of the samples appeared to be fibrous, organic, tissue-like, and contained minerals such as calcite and quartz.

182

Figure 9. Location C at 97 cm below the sediment/water interface. There are 3 unknown, possibly pennate, rectangular-shaped diatoms within the cluster of images.

The core observations at location C, 77 and 79 cm below the sediment/water interface, showed that there were Pennales and Centrales present (Figure 10). There was an array of different diatom species as seen in column 1 of Table 2. However, without having a strong view of raphie, we were limited in terms of accuracy while identifying diatoms by species name.

In Figure 10a, photomicrographs showed approxamately two Centrales and four Pennales. In Figure 10b, the collection of diatoms counted were four Centrales and five Pennales. There were seven Pennales and one Centrale in the field of view in Figure 10c. Figure 10d displayed one Pennale and Figure 10e displayed two Pennales. In Figure 10f, there was a possible broken diatom valve present along with an assortment of algal material covering many Centrales and Pennales.

In Table 1, location C at a depth of 97 cm below the sediment/water surface, revealed absolute abundances of 1.52E-3 for Pennales and 2.2E-4 for Centrales. At a depth of 45 cm, Pennales were 7.58E-4 and Centrales were 2.53E-4. This also indicated that there were a higher abundance of diatoms in the shallower water (location C) in comparison to deeper in the lake (location A). Figure 9 provides an example of a FlowCAM photosequence of these images.

183 Depth (cm) Pennales Centrales Total number of particles 45 3 (7.58E-4) 1 (2.53E-4) 3956 72 26 (1.52E-3) 4 (2.2E-4) 17076

Table 1. Location C, drive 2 + HCl, 300 µm sieve and 4x objective in FlowCAM. The absolute abundance of diatoms is in parenthesis beside the numerial output of diatoms.

In Table 2, diatoms at location C at 77 and 79 cm below sediment/water surface were characterized and separated into groups that highlighted their individual features. There were six different possible species found shown in column 1 of Table 2. The pennate, boat-shaped diatoms in row 1 displayed an aspect ratio between 3.06-4.86 µm. The longer diatoms with a narrower width in row 2 had slightly higher aspect ratios between 5.77 – 8.68 µm. Diatoms with the same, elongated shape except small size (row 4) had an aspect ratio ranging from 3 – 4 µm and two other outliers with 5.58 and 8.28 µm. It was a possibility that these diatoms belong in column 2, but it was uncertain because photomicrographs did not display enough detail. The Pennale was similar to row 2, except with a thicker width, and had an aspect ratio of 3.95 µm. This ratio, closer to a value of 1, showed that an increased width to be closer to the length size caused the aspect ratio to decrease.

184 Depth below Figure in Length, Width, Aspect ratio sediment/wat Diatom type paper µm µm (length/width) er interface, cm - 104.5 21.5 4.86 77 1. Pennate: 10c 32.6 9.3 3.51 Boat- - 72 20 3.60 shaped; 10a 38.8 12.7 3.06 79 raphe 10f 34.9 9.1 3.84 10e 25.9 8 3.2375 54.4 7.6 7.16 77 - 64.1 11.1 5.77 2. Pennate: 10b - 10.9 - Elongated; - 61.6 7.1 8.68 narrow 79 10e 69 11.5 6 - 72.9 9.6 7.59 35.6 4.3 8.28 10f 79 33.8 7 4.83 25.3 5.4 4.69 3. Pennate: 12.9 2.6 4.96 Smaller 10c 10.9 2.9 3.76 77 species of 24.5 5.4 4.54 above 13.6 3.7 3.68 diatom 25.5 5.9 4.32 10b 27.9 5 5.58 79 24.6 6.7 3.67

4. Pennate: Thicker width, 8b 80.1 20.3 3.95 79 elongated with small, rounded valve tips 8.1 6 1.35 19.7 14.6 1.35 - 5. Pennate: 10.2 8.4 1.21 77 elliptical 6.6 5.5 1.20 14.7 10.6 1.39 - 21 13.2 1.59 6. Centric 10b 13.9 13.9 1.00 79

Table 2. Measured diatoms by ImageJ of location C at 77 and 79 cm below the sediment/water interface.

185

Figure 10a. A diverse assortment of diatoms found from premade slides of location C at 77 cm below the sediment/water interface.

Figure 10b. A diverse assortment of diatoms found from premade slides of location C at 79 cm below the sediment/water interface.

186

Figure 10c. A diverse assortment of diatoms found from premade slides of location C at 79 cm below the sediment/water interface.

Figure 10d. A diverse assortment of diatoms found from premade slides of location C at 77 cm below the sediment/water interface.

187

Figure 10e. A diverse assortment of diatoms found from premade slides of location C at 77 cm below the sediment/water interface.

Figure 10f. A diverse assortment of diatoms found from premade slides of location C at 79 cm below the sediment/water interface.

188 DISCUSSION

When diatom identification was attempted, photomicrographs were compared to FlowCAM images. In the photomicrograph image (Figure 10c), which was treated with HCl (Location C at 97 cm below the sediment/water interface), it was difficult to be certain if the regtangular-shaped diatom was a Pennale or Centrale. The same sample run through FlowCAM, for a partacle of similar shape, lacked the detail which would allow species level identification. Therefore, the diatoms under 100 µm were very difficult to classify due to lack of anatomical detail and less than perfect focus.

For future work, two runs through the FlowCAM with two different sized objectives (4x and 10x) and correlating flow cells should be done to increase shape visibility. This would capture the full size ranges of diatoms. A 10x objective with a 100 µm flow cell would ultimately retrieve larger, more visible images of the smaller diatom sizes up to 100 µm (FlowCAM Manual 2011).

By observing the minor anatomical differences in the diatom valves within photomicrographs, Pennales and Centrales were split into different groups from location C (Table 2). Diatoms were measured in ImageJ to further separate by size. A few of the diatoms in row three (larger; elongated) could be grouped with row two (smaller elongated, possible raphie). The raphie could not be distinguished. When the petrographic microscope was focused to a different degree, diatoms raphie were either highlighted or hidden. This sensitivity and variation presents a huge limitation in diatom identification because there are so many species that resemble one another. An SEM microscope would show the internal anatomy of the diatom in detail, ultimately making the species groups in Table 2 more accurate. To compute an absolute diatom abundance using the FlowCAM, a library was created to isolate certain species shapes into a folder (Figure 7). However, a limitation with this method was that the identification of species based on their common shape outline was not possible. When the database was scanned for similar particles to create the library, the FlowCAM isolated other particles such as plant debris, minerals and other sediment. When plant debris covered part of the diatoms, this caused the FlowCAM to inaccurately isolate material such as mineral fragments and plant fibers. In addition, a diverse library of similar shapes and sizes that matched diatom characteristics increased the probability of isolating particles that resembled diatoms. However, when there were many cluttered and broken diatom valves within the sediment samples, it made the construction of the library difficult because the images to choose from were limited.

Since the amount of photos that displayed a diatom shape was limited, the FlowCAM’s versatility was not utilized to its full potential. Therefore, this assay could not be utilized since diatom abundance results would be skewed, as some of the isolated debris was not diatom-

189 shaped. A way to mitigate this would be to pretreat the samples with hydrogen peroxide to dissolve excess sediment (Battarbee 1973). This would make FlowCAM and photomicrograph diatom images easily distinguished. Since the application of HCl caused increased visibility diatoms in this study, it was believed that an H2O2 technique would greatly increase visibility.

It was possible that the FlowCAM was not utilized to its full imaging potential, as there was a steep learning curve associated with the machine. It would take time to learn all of its features. FlowCAM’s particle parameter table should be utilized to assist with the isolation of diatoms when an image library was created (Figure 7). It was shown in Ide (2007) that the accuracy of plankton identification by image analysis in the FlowCAM was limited. However, Buskey (2006) concluded that the FlowCAM was less tedious and time-consuming method than microscopy. When target cells were added to natural plankton samples, the image recognition software correctly identified 80–90% of the target cells, but incorrectly identified 20–50% of non-target cells.

Petrographic microscope analyses were originally used to identify diatoms by creating slide smears in hopes of retrieving an absolute abundance. However, small samples extracted from large areas of the core created an analysis that was not highly representative to the whole core. Settling-tank assays could obtain a quantitative analysis of diatoms and had been studied since Scherer (1994). Warnock (2015) showed an improvement in this preparation method by using large settling chambers, small samples, and an absence of aliquot subsampling to improve statistical results. Uniform particle distributions were obtained on slide smears for a quantitative analysis, which created a more representative analysis of the core as a whole (Battarbee 1973).

Diatoms were separated from Pennales and Centrales groups based on their minor anatomical and shape differences. Based on whether diatoms were oriented in valve view or profile view onto a slide smear, their anatomical detail was difficult to distinguish, which served as a limitation. Their varying orientations created a difficulty with identifying them. In addition, focus variations of the microscope highlighted different anatomical structures within the diatoms.

CONCLUSION

Due to a steep learning curve, FlowCAM’s automated database scan did not isolate diatoms from other particles completely to compute an absolute abundance (Figure 7). As a result, diatoms were counted manually within the FlowCAM database, which concluded that the shallower core possibly contained a higher number of diatoms than the deeper core. Loose interpretations, through the petrographic microscope, of diatoms were made to classify Pennales and Centrales into further morphological groups based on their common shape outline, size and

190 internal structures (Table 2). The alternative technique of running the same sample through FlowCAM with both 4x and 10x objectives could produce successful quantitative results. This would ultimately photograph a larger range of diatom sizes relative to the field of view.

REFERENCES Arnold, M.R. 2003. The Recent History of Otsego Lake, New York, as determined from analysis of diatoms in a sediment core. In 35th Ann. Rept. (2002). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Battarbee, R. 1973. A new method for the estimation of absolute microfossil numbers, with reference especially to diatoms. Limnol. Oceanogr. 18: 647–653.

Buskey, E.J. and C.J. Hyatt. 2006. Use of the FlowCAM for semi- automated recognition and enumeration of red tide cells (Karenia brevis) in natural plankton samples. Harmful Algae, 5, 685–692.

Diatoms of the United States. Diatoms of the United States. N.p., n.d. Web. 06 May 2015. .

Fritz, S.C., J.C. Kingston and D.R. Engstrom. 1993. Quantitative trophic reconstruction from sedimentary diatom assemblages: A cautionary tale. Freshwater Biology 30.1:1-23.

Harman, W.N., L.P. Sohacki, M.F. Albright and D.L. Rosen. 1997. The State of Otsego Lake, 1936-1996. Occasional Paper #30. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Ide, K., K. Takahashi, A. Kuwata, M. Nakamachi, and H. Saito. 2007. A rapid analysis of copepod feeding using FlowCAM." Journal of Plankton Research 30.3:275-81.

Isachsen, Yngvar W. 1991. Geology of New York: A simplified account. Albany, NY. New York State Museum/Geological Survey, State Education Dept., U of the State of New York. Print.

Manager, Ben Spaulding Fit Laboratory. 2012. FlowCAM® Manual Version 3.2. Fluid Imaging Technologies, Inc.

Round, F.E., R.M. Crawford, and D.G. Mann. 1990. The Diatoms: Biology & Morphology of the Genera. Cambridge: Cambridge UP.

191 Scherer R. 1994. A new method for the determination of absolute abundance of diatoms and other silt-sized sedimentary particles. J Paleolimnol 12:171-179.

Smith, K.E.L. and J.G. Flocks. 2010. Environmental investigations using diatom microfossils: U.S. Geological Survey Fact Sheet 2010–3115.

Warnock, J.P. and R.P. Scherer. 2015. A revised method for determining the absolute abundance of diatoms. Journal Of Paleolimnology. 157. Academic OneFile.

192 Benthic macroinvertebrate survey of the upper Susquehanna River using two sampling methods

Bethany Shaw 1

INTRODUCTION

The Susquehanna River, the largest non-navigable river that lies entirely in the United States, has countless biological, chemical, and physical measurements taken every year. These measurements provide the Chesapeake Bay Foundation with data that will help to maintain the strong integrity of the Chesapeake Bay and its watershed. The Susquehanna is the most influential tributary to the Chesapeake Bay, supplying the bay with over 50% of the bay’s volume of water (SRBC 2015); therefore the river’s monitoring and maintenance is of the upmost importance. These various water quality measurements are taken by a variety of groups, including the state funded Department of Environmental Conservation that only samples every five years (NYSDEC 2015). With this rapid assessment sampling from a federal organization, the responsibility of thorough testing and monitoring of bodies of water becomes the responsibility of local groups, like SUNY College at Oneonta’s Biological Field Station (BFS) in Cooperstown, NY.

Each year the BFS monitors local water bodies and their watersheds in order to supply a range of data and history for water systems in order to detect meaningful changes. Since 1991, the BFS scientists, volunteers, and interns have been monitoring the headwaters of the Susquehanna River in Cooperstown, NY. The last and only study of the macrobenthic community in the Upper Susquehanna was completed in 1998 (Ellsworth 1999). The difference between the Ellsworth 1998 collection and the current collection is stark and illustrates how aquatic communities can change over time. The difference between these two data sets allows for a deeper understanding of the ever-changing ecosystem of the Susquehanna River; this deeper understanding is the reason we must carry out a variety of surveys when trying to create a holistic view of an ecosystem. Furthermore, this study illustrates how the composition of biota in the river changes across different sites and based on the varying habitats. This idea is reiterated in Robinson’s (2016), macroinvertebrate survey of Oaks Creek.

Along with the benthic macroinvertabrate collection on the Upper Susquehanna, water quality measurements were taken (temperature, pH, specific conductivity, dissolved oxygen, turbidity, total phosphorus, total nitrogen, nitrite and nitrate; Shaw 2016). Being able to directly compare the two snapshots in time of an ecosystem allows us to validate the results.

Not only is this study focused on creating a more comprehensive understanding of the biota in the Upper Susquehanna, it is also focused on looking at the varying sampling methods and their biases. While it is true that all sampling methods will have a bias, it is important to understand the best sampling method based on the intent of the study. For this study I compared

1 F.H.V. Mecklenburg Conservation Fellow, summer 2015. Present affiliation: Nazareth College. Funding provided by the Village of Cooperstown.

193 protocols for D-net sampling and Surber sampling according to their different limitations.

METHODS

Historically, a series of sites along the upper Susquehanna River have been monitored for physical and chemical parameters (Figure 1). Of those, sites 8, 17, and 18 (described in Table 1) were used as the benthic macroinvertebrate sampling sites due to their strong riffle habitat that composed at least 50% of the river at that point. Sites 3, 6a, 7, 12, 16, and 16a were not chosen due either to their excessive depth or because their pool habitats would have made sampling with the Surber unsuitable because of its structure and limitations. Site 8 is located 1724 meters from Otsego Lake (the source of the Susquehanna), whereas sites 17 and 18 are respectively located 8143m and 9867m from the source. Between sites 12 and 16 (4119m and 5460m from the source) the Village of Cooperstown’s waste water treatment plant’s effluent discharge pipe exists. Although the amount of nutrient loading has decreased significantly since the implantation of the secondarily treated wastewater wetland in 2010 (Shaw 2016, Albright 2015), the quality of the water changes significantly between these two points, thus causing the biota to change from that point on as well.

Figure 1. Map of the Upper Susquehanna site tested, summer 2015.

194 Table 1. Locations and descriptions of the Upper Susquehanna River sites used in the benthic macroinvertebrate survey

Site Distance from Description source 8 1724m Under the Susquehanna Ave. bridge west of the Clark Sports Center; accessed via the slope beside the bridge. 17 8143m Abandoned bridge on Phoenix Mill Road. 18 9867m Railroad trestle about 200m north of the railroad crossing on Rt. 11 going out of Hyde Park, accessed by walking on the railroad tracks.

Site 8 was sampled on 30 June 2015 beginning at 9:30am and ending at 11:45am. Surber sampling at this site was unable to be fully completed due to impeding precipitation (four 0.3m2 samples were taken as opposed to the standardized five 0.3m2 samples). Sites 17 and 18 were both sampled on 6 August 6 2015 beginning at 9:00am at site 18 and ending at 11:15am at site 17.

At each site on the day of sampling, water quality measurements were taken using the YSI® 6820 V2-2 multi-probe (temperature, pH, specific conductivity, dissolved oxygen, and turbidity). Also, 125ml of the water was collected at each site and brought back to the BFS lab. The samples were preserved with 1ml of H2SO4 and then were analyzed for their total phosphorous, total nitrogen, nitrate and nitrite levels using Lachat QwikChem® FIA and Water Analyzer (Pritzlaff 2003, Liao and Marten 2001).

Concerning D-net sampling, I determined how many different habitats were present at each site and figured out the least number of transects possible required to sample each habitat at least once. Starting from the shore, the sampling would occur once every meter. The D-net would be placed downstream, perpendicular to the current, while someone disturbed the substrate upstream. The substrate would be disturbed for a length one meter starting downstream and working upstream.

With respect to Surber sampling, five haphazardly chosen sampling sites where taken at each site (except for site 8, which there was only four sampling sites chosen). The substrate would be thoroughly disturbed (1cm – 2cm into the fine sand particles) and all the larger rocks (≥ 5cm) within the 0.9m2 that the sampler covered were overturned and any aquatic invertebrate larvae attached to the rocks were scraped off and into the net.

Samples were dumped in respective glass jars with 70% ethanol to preserve the samples. The samples were then taken back to the lab to be sorted, identified, and counted.

A Familial Biotic Index (FBI) of the benthic macroinvertebrates in the Upper Susquehanna sites was determined (Hilsenhoff 1988). The FBI index uses the macrobenthic communities to estimate the degree of organic pollution present. It was used to fully juxtapose

195 and validate the data that was received from the YSI and Lachat QwikChem® FIA and Water Analyzer.

A contingency chi-squared value and percent EPT (Ephemeroptera, Pelcoptera, and Trichoptera) were determined in order to juxtapose the sampling techniques accurately.

RESULTS and DISCUSSION

Tables 2 through 10 summarize the benthic macrobenthic assemblages collected at the Susquehanna sampling sites during 2015 as well as those of 1998 (Ellsworth 1999). Tables 2, 5 and 8 summarize the 2015 collections. In the 1998 study, two discrete collections were made at each site. Qualitative searches employed various collection methods at each site, and those lists include Tables 3, 6 and 9. Qualitative collections employed the use of a Hess sieve, and those data are provided in Tables 4, 7 and 10.

The Ellsworth 1998 collection had two unique orders and one unique family at site SR 8 as opposed to the 2015 collection. The more recent collection had three unique orders and six unique families, as compared to the 1998 collection, at SR 8. Overall, the 2015 collection indicated drastically higher abundance then the 1998 collection. Notable changes include the 1308 Hydropsychidae (a family of Trichoptera, or caddisfly) that were collected between the two sampling methods in 2015, but in 1998 no Hydropsychidae were collected (Tables 2 vs.4).

Table 2. Taxonomic list and the abundance of the benthic macroinvertebrates found at site SR 8, summer 2015.

Site and Date Abundance Abundance Order Family Collected in Surber in D-net Ephemeroptera Baetidae 41 210

SR 8 Ephemerellidae 0 3

7/30/2015 Heptageneiidae 38 145 Plecoptera Perlidae 0 5 Coleoptera Elmidae 151 583 Gyrinidae 7 16

Psephenidae 88 100

Trichoptera Glossosomatidae 0 2 Hydropsychidae 450 858 Philopotamidae 1 0 Diptera Athericidae 34 78 Chironomidae 5 7

Simuliidae 0 4 Tipulidae 0 4

196 Table 3. Taxonomic list of the benthic macroinvertebrates found at site SR 8, summer 1998 (Ellsworth 1999).

Site and Date Order Family Genus Collected Ephemeroptera Neoephemeridae Neoephemera Odonata Coenagrionidae Anomalagrion SR 8 Ischnura 6/23/1998 Hemiptera Gerridae Gerris Diptera Chirnomidae

Table 4. The abundance of the benthic macroinvertebrates found at site SR 8, summer 1998 (Ellsworth 1999).

Site and Date Order Family Abundance Collected Ephemeroptera 1 SR 8 Trichoptera 1 6/23/1998 Diptera Excluding Chironomidae 16 Chironomidae 3

The Ellsworth 1998 collection had six unique families at site SR 17 that the 2015 collection did not obtain. The 2015 collection had two unique orders and eleven unique families as compared to the 1998 collection at SR 17. Overall, the number of organisms in the 2015 collection were markedly higher than in the 1998 collection at this site (Tables 5-7).

197

Table 5. Taxonomic list and the abundance of the benthic macroinvertebrates found at site SR 17, summer 2015.

Site and Date Abundance Abundance Order Family Collected in Surber in D-net Baetidae 26 15 Ephemeroptera Caenidae 1 6

SR 17 Heptageneiidae 73 74 8/06/2015 Isonychiidae 1 1

Potomanthidae 2 0

Odonata Aeshnidae 0 3 Plecoptera Perlidae 1 4 Megaloptera Sialidae 1 1

Coleoptera Elmidae 81 138 Gyrinidae 0 1

Psephenidae 75 25 Hydropsychidae 58 17 Trichoptera Hydroptilidae 27 0

Limnephilidae 2 0

Polycentropidae 2 1

Rhyacophilidae 2 0

Uenoidae 3 0 Diptera Athericidae 16 23 Chironomidae 4 21

198 Table 6. Taxonomic list of the benthic macroinvertebrates found at site SR 17, summer 1998 (Ellsworth 1999).

Site and Date Order Family Genus Collected Ephemeroptera Ephemerellidae Ephemerella Heptageniidae Epearus SR 17 Baetidae Centraptilurn 6/23/1998 Leptophlebiidae Paraleptophlebia Plecoptera Chloroperlidae Utaperla Trichoptera Hydropsychidae Macrostemum Philopotamidae Dolophilodes Coleoptera Hydrophilidae Hvdrochus Elimidae Stenelmis Psephenidae Psephenus Diptera Chironomidae Simuliidae Nematocera Blepharicera

Table 7. The abundance of the benthic macroinvertebrates found at site SR 17, summer 1998 (Ellsworth 1999).

Site and Date Order Family Abundance Collected Ephemeroptera 3 Trichoptera 1 SR 17 Coleoptera 3 6/23/1998 Diptera Excluding Chironomidae 16 Diptera Chironomidae 10

The Ellsworth 1998 collection had no unique families at site SR 18 as compared to the 2015 collection. The 2015 collection had four unique orders and six unique families as compared to the 1998 collection at SR 18. Overall, the 2015 collection showed substantially higher abundance than the 1998 collection at this site. An exception to this included 56 Chironomidae flies that were collected in 1998 compared to the 7 Chironomidae flies that were collected in 2015 at the same site (Tables 8-10).

199

Table 8. Taxonomic list and the abundance of the benthic macroinvertebrates found at site SR 18, summer 2015.

Site and Date Abundance Abundance Order Family Collected in Surber in D-net Baetidae 13 28 Ephemeroptera SR 18 Ephemerellidae 3 0 8/06/2015 Ephemeridae 0 1

Heptageneiidae 2 10 Potomanthidae 2 5 Odonata Gomphidae 1 0 Plecoptera Perlidae 0 2 Megaloptera Sialidae 1 0

Coleoptera Elmidae 21 79 Psephenidae 159 17 Trichoptera Hydropsychidae 58 24 Uenoidae 3 0 Athericidae 18 5 Diptera Chironomidae 5 2

Tabanidae 0 1 Tipulidae 1 1 Simuliidae 1 4

Table 9. Taxonomic list of the benthic macroinvertebrates found at site SR 18, summer 1998 (Ellsworth 1999). Site and Date Order Family Genus Collected Trichoptera Hydropsychidae Macrostemum SR 18 Coleptera Elimidae Ancyronyx 6/23/1998 Diptera Chironomidae

Table 10. The abundance of the benthic macroinvertebrates found at site SR 18, summer 1998 (Ellsworth 1999).

Site and Date Order Family Abundance Collected Trichoptera 8 SR 18 Coleoptera 5 6/23/1998 Diptera Excluding Chironomidae 12 Chironomidae 56

200 The evaluation of percent EPTs (the orders of insects Ephemeroptera, Plecoptera, or Trichoptera, which tend to be intolerant of pollution and so whose presence indicate unimpaired conditions) allowed for a rudimentary measurement of water quality. Based on this test, neither one of the sampling methods was consistently better than the other concerning the collection of EPTs (Table 11). Because of its ability to pick up more attached species, the Surber sampler is more likely to pick up EPTs (Storey et al. 1991). If the Surber is more likely to collect EPTs, there is also a good chance that it will over-estimate the stream’s integrity at that particular site, whereas D-net sampling is less likely to collect EPTs, according to Storey et al. (1991), although this was not specifically shown in these data.

Table 11. Percent EPT shown for each of the three sites sampled juxtaposing the two sample methods done in 2015.

Site Surber % EPT D-net % EPT SR 8 65.0 60.7 SR 17 52.8 35.8 SR 18 28.1 39.1

The numbers of unique taxa between the utilization of the D-net and the Surber sampler is given in Table 12. The D-net collected nine families that the Surber sampler did not collect; for three of these families, the D-net only collected one member while the Surber sampler collected none. The Surber sampler collected ten families that the D-net did not collect; for three of families, the Surber sampler only collected one member while the D-net collected none. The D-net collected more organisms that swim in the water column. This result is not surprising since the D-net has a design that is more apt to collect in the swimmer organism’s habitat. The Surber sampler collected more organisms that attached themselves to rocks. This is also not surprising since the protocol for using the Surber sampler involved scraping off all the attached organisms on the rocks within the 0.9m2 sampling area. There is no apparent relationship between the sampling method and the organisms that appear either in the water column and near the sediment (mixed), those are associated with the rocky substrate, or those that are found in the sediment. It is important to point out that the most of the taxa that were found uniquely by one sampler did not transcend to multiple sites. For example, the Surber sampler collected three Ephemerillidae at site SR 18 when the D-net collected none. However at site SR 8, the D-net collected three Ephemerillidae when the Surber sampler collected none. The only taxon that was collected by one sole method (the Surber sampler) at more than one site was Uenoidae (Tables 2, 5, 8).

Table 12. The unique families found in either the D-net or the Surber sampler across all three sites in 2015, collected organisms that live in water column, either in the water column or near the sediment (mixed), associated with the rocky substrate, attached to the rocks, or in the sediment.

Unique Families Water Column Mixed Associated Attached Sediment Found In: D-net 3 1 1 2 2 Surber 0 1 1 6 2

201 The water quality measurements collected at each site in 2015 are given in Table 13 (and the FBI designations are interpreted in Table 14). The physical water quality parameters taken with the YSI, and the nutrient concentrations determined using the Lachat QwikChem® FIA Water Analyzer seem to corroborate the macroinvertabrate survey: that the river is of good health. According to the FBI of all three sites, the Upper Susquehanna has “good” water quality with a biotic index value of 4.7. This test tells us that that the Upper Susquehanna probably has some organic pollution.

Table 13. Water quality measurements that were taken on the date and time of the benthic macroinvertebrate survey. Turbidity and pH were not able to taken for sites SR 17 and SR 18. The FBI value for each site is also presented.

Site Specific Dissolved Total Total Nitrate+ Temp Turbidity FBI Date Conductivity pH Oxygen Phosphorous Nitrogen Nitrite (˚C) (NTU) value Sampled (usm/cm) (mg/l) (ug/l) Mg/l (mg/l) SR 8 23.73 .311 7.63 8.10 3.3 13 0.41 0.34 4.8 6/30/2015 SR 17 20.05 .324 N/A 7.66 N/A 30 0.46 0.36 4.5 8/6/2015 SR 18 19.84 .327 N/A 7.90 N/A 30 0.47 0.37 4.5 8/6/2015

Table 14. Water quality based on Family Biotic Index (NYSDEC 2012, Piacente 2015)

A comparison between the 1998 and the 2015 overall water quality measurements show a great difference between the phosphorus and the nitrite + nitrate levels. In 1998, the highest phosphorus level neared 250 ug/L at site SR 18 and nitrite + nitrate levels were the greatest at site SR 16 nearing 1 mg/L (Dewey 1999). In 2015, however, summer-long phosphorus levels were the greatest at SR 18 at 47 ug/L and nitrite + nitrate levels were the greatest at SR 8 at 0.51 mg/L (Shaw 2016). This difference is nutrient levels may be responsible for the differences between the two macroinvertebrate surveys (Tables 2-10).

The contingency chi2 value was determined in order to see a consistent overestimation or underestimation of the organisms collected between the two sample techniques. The p-value was

202 lower than 0.05, so one can conclude that there is no difference between the sampling methods or if they consistently overestimate or underestimate the population abundances.

CONCLUSION

While it is true that the two sampling techniques are very different in their protocol, structure, and sampling bias, neither one should be discredited as a sampling method that is not accurate. Their different biases provide a different, however comprehensive in their own right, understanding of the benthic macroinvertebrate community. Thus, based on the intent of a particular study, one must consider the benefits and the concerns of each method in order to obtain the most relevant data based on what they are looking for. For example, the Surber sampler provides the data with the bio-density of the macroinvertebrates because of its premeasured 0.9m2 sampling area, as opposed to the D-net that would not accurately measure the bio-density because of the nature of its structure and protocol. Also, if one is more interested in the EPT community, the Surber sampler is the most ideal to use since it is more associated with organisms that live near or attached to the rocks in the substrate (Storey et al. 1991). A benefit of using the D-net sampling method is that it is not limited by the depth of the water or varying habitats, thus its samples would show a more holistic view of the site as compared to the Surber sampler that is limited to 0.3m water depth. Also, the D-net’s protocol in this study makes it not have a bias towards the stream width/size since one tests once every meter across a transect that spans across all habitats and from bank to bank. Conversely, the Surber sampler will end up sampling a higher percentage of the stream when the width is small since the number of times one samples with the Surber is not proportional to the size of the stream.

Another aspect to consider is the varying collection and sorting time requirements. Even though the Surber sampler took significantly more time in the field, its samples were easier to sort and count in the lab. Conversely the D-net sampling method was by far quicker in the field (allowing multiple sites to be done in a day); its samples, however, took more time to sort through and count, especially because the D-net collected more organisms overall. Furthermore, the fact that D-net collects more organisms than the Surber sampler does not mean that it is a better overall sampling technique, since there is no consistent organism that the D-net collects that its counterpart does not.

Thus, one can understand that the two sampling techniques are difficult to directly compare to one another, and future benthic macroinvertebrate sampling must try to understand their various sampling biases and sample sites accordingly to obtain firm results.

The differences between the Ellsworth (1999) 1998 study and the current 2015 surveys could be attributed to climatic events (such as two major floods, in 2006 and 2011) and different procedures concerning the sewage treatment plant in Cooperstown. In 2010, Village of Cooperstown’s sewage treatment facility implemented the use of a wetland designed to remove some of the phosphorus and nitrogen from their secondarily treated wastewater effluent (Albright 2015, Shaw 2016). The significant difference in both phosphorus and nitrogen levels since 1998 (Dewey 1999, Shaw 2016), potentially due to the implementation of the wetland (Albright 2015, Shaw 2016), seems to coordinate with the change in the benthic macroinvertebrate organisms in

203 the upper Susquehanna. Also, there was a significant difference in precipitation amounts in those two years. As Dewey 1999 mentions, the amount of precipitation in Cooperstown, NY was lower than average, which could have attributed to some of the high nutrient values. Conversely, the 2015 summer was very wet with above average snowfall and rainfall amounts. Also in 2006 and 2011, two major floods changed the physical properties of the land and the upper Susquehanna, which in turn, could affect the chemistry and the biota of its ecosystem. No one occurrence is charged with changing the upper Susquehanna so drastically; most likely it is a compilation of all these events that have shown a lasting impact on this ecosystem. Since the change in the water quality has significantly changed since 1998, one would expect to see a change in the water system’s biota as well, which the surveys done in 1998 and 2015 seem to express (Tables 2-10).

REFFERENCES

Albright, M.F. 2015. Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 2014. In 47th Ann. Rept. (2014) SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Dewey, G. 1999. Monitoring the water quality of the upper Susquehanna River summer 1998. In 31st Ann. Rept. (1998). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Ellsworth, A. 1999. Macrobenthic study of the upper Susquehanna River. . In 31st Ann. Rept. (1998). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Hilsenhoff, W. 1988. Rapid Field Assessment of Organic Pollution with a Family-Level Biotic Index. Journal of the North American Benthological Society [cited 2015 Aug 25] Vol. 7, No. 1, pp. 65-68. Available from: http://www.jstor.org/stable/1467832

NYSDEC. 2015. http://www.dec.ny.gov/chemical/30951.html.

Piacente, J. 2015. Using benthic macro invertebrates to assess stream quality of the Unadilla River, Otsego County, NY. . In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Robinson, M. 2016. Macroinvertebrate survey of Otsego Land Trust properties on Oaks Creek, Otsego County, NY. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Shaw, B. 2016 Monitoring water quality in the upper Susquehanna River, summer 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Susquehanna River Basin Commission. 2015. http://www.srbc.net/pubinfo/index.htm.

Storey, A.W., D.H.D. Edward, and P. Gazey . 1991. Surber and kick sampling: a comparison for the assessment of macroinvertebrate community structure in streams of south-western Australia. Hydrobiologia. 1991; 211: 111-121.

204 An update on the fish parasites of Otsego Lake and nearby water bodies

Florian Reyda¹

INTRODUCTION

Since the fall of 2009, 52 undergraduate students and I have been surveying the fish parasites of Otsego Lake and nearby water bodies. The results of those efforts have been reported in previous annual reports of the BFS (Reyda 2009, Hendricks and Reyda 2010, Reyda 2010, Szmygiel and Reyda 2011, Reyda and Willsey 2013, Borden and Reyda 2014, Darpino et al. 2014, Sendkewitz et al. 2014) and in peer-reviewed literature (Bergman et al. 2015). The survey work was done with the primary goal of providing additional knowledge about the natural history of Otsego Lake, given its focus throughout the history of the BFS and its regional importance. Ultimately I would like to provide a comprehensive host-parasite checklist for the fish species that occur in Otsego Lake. Taxonomic issues with the parasites, however, have prevented completion of that checklist. There are one or two new species of parasitic worm in Otsego Lake, including a nematode (see Borden and Reyda 2014) that needs to be described as new. Further, there are several species of cestodes, trematodes, nematodes and acanthocephalans that cannot be identified to species with certainty because of issues with the reference literature, i.e., our current knowledge of those species is limited. In fact, there remains a substantial knowledge gap of the parasites of freshwater fishes in North America (Scholz and Choudhury 2014). Many of the species have not been studied since a set of modern tools has become available for taxonomy, most notably improved light microscopy, scanning electron microscopy, and DNA sequencing. Many species of cestodes, trematodes, nematodes and acanthocephalans need to be re-described (i.e., "fixed") with the incorporation of data from these newer tools.

Students and I are endeavoring to help fill in the knowledge gap of North American freshwater fish parasites by conducting taxonomic and systematic studies of selected helminth species. Currently, one student (Craig Wert) and I are studying a putatively new species of digenetic trematode (see Figure 1 below) that occurs in Otsego Lake, in chain pickerel. Another student (Margaret Doolin) and I are beginning revisionary work on the diverse acanthocephalan genus Neoechinorhynchus.

The aim of the current brief report is to summarize the parasitic worms that were encountered in fish during 2015, and to highlight a couple current projects.

______

¹ Associate Professor of Invertebrate Zoology and Researcher, Biology Department and Biological Field Station, SUNY Oneonta.

205

METHODS

Fish were collected in 2015 via hook and line or electrofishing. Three yellow perch (Perca flavescens) and seven chain pickerel (Esox niger) were collected from Otsego Lake; four white suckers (Catostomus commersoni) and one eastern blacknose dace (Rhinichthys atratulus) were collected from Leatherstocking Creek; five largemouth bass (Micropterus salmoides) and two brown bullhead (Ameiurus nebulosus) were collected from Moe Pond. Fish were killed in Tricaine (MS 222) and subsequently dissected for internal parasites. Internal organs were removed by a ventral longitudinal incision along the body wall. Fish organs were individually examined for parasitic worms, which were subsequently removed, formalin fixed, and transferred to vials containing 70% ethyl alcohol. Permanent whole mount slides were prepared

206 by hydrating worms in a graded ethanol/water series, staining in Delafield's hematoxylin, and subsequently dehydrated in a graded ethanol series. Worms were cleared in methyl salicylate and mounted on glass slides under coverslips using Canada balsam. Subsets of worms were prepared for scanning electron microscopy using conventional techniques, or for DNA sequencing by preservation in 100% ethyl alcohol.

RESULTS

Yellow perch were infected with the stomach trematode, Azygia cf. angusticauda. Multiple parasitic worms were encountered in the yellow perch intestine and/or pyloric caeca, including two acanthocephalans (Leptorhynchoides thecatus and Neoechinorhynchus sp.), two cestodes (Bothriocephalus cuspidatus and Proteocephalus ambloplitis), and one trematode (Cryptogonimus chili). In addition, the fins of yellow perch had multiple leeches of family Piscicolidae. White suckers contained two species of helminths in their intestines, an acanthocephan (Pomphorhynchus bulbocolli) and a cestode (Glaridacris catostomi). Chain pickerel were infected with two stomach trematodes, Azygia cf. longa and Azygia cf. angusticauda, with five species of helminth in the intestine: two acanthocephalans (Leptorhynchoides thecatus and Neoechinorhynchus sp.), a nematode (Spinitectus sp.), and a trematode (Caecincola cf. parvulum). The single eastern blacknose dace examined contained no adult helminth specimens, though multiple trematode metacercaria were observed in the body cavity. Largemouth bass from Moe Pond were infected with the larval trematode colloquially referred to as yellow grub (Clinostomum marginatum) and two species of helminths in the intestine and/or pyloric caeca: an acanthocephalan (Neoechinorhynchus sp.) and a trematode (Crepidostomum cf. cornutum). The brown bullhead from Moe Pond were infected with yellow grub, and an as-of-yet unidentified species of intestinal trematode.

DISCUSSION

Our survey work in 2015 was limited in terms of number of fish species examined, when compared to previous years. We only examined specimens of six species of fish owing to our focus on taxonomic and other projects in the laboratory. We focused on chain pickerel because we think the tiny intestinal trematode that we first encountered in 2014 is likely new to science. It is identified in Figure 1 as Caecincola cf. parvulum because it is similar to–though not conspecific with–Caecincola parvulum, a trematode only known from centrarchid fishes. The Caecincola cf. parvulum specimens from chain pickerel are different from C. parvulum from centrarchids in that they are much smaller. There are ongoing efforts in my lab to identify other morphological differences between the worms we find in chain pickerel and the C. parvulum previously reported from sunfishes, and we are currently attempting to obtain DNA sequence data of this diminutive creature.

207 Another highlight from research in 2015 on local fish parasites was a study led by undergraduate student Nathan Heller. Nathan performed a histological study to document the damage (pathology) caused to white suckers by the acanthocephalan Pomphorhynchus bulbocolli. White suckers can have heave infections of P. bulbocolli (see Figure 2). By performing histological sections of pieces of intestine to which worms were attached, Nathan was able to document physical damage to the intestine of white suckers, as well as a degeneration of tissue known as hyaline degeneration. Nathan's study was submitted for publication in a peer-reviewed journal and is currently under review. His work further adds to our knowledge on the impact that parasites can have on their host populations.

CONCLUSION

This report provides an update on the fish species that were collected in 2015, and the parasites that were encountered in the digestive system. There are several ongoing efforts in my laboratory to more deeply understand selected parasite species of interest. Two examples of those studies are highlighted in this report.

ACKNOWLEDGEMENTS

I am indebted to Timothy Pokorny (NYS DEC) for once again providing fish during the winter months. I thank the following students from my Lab for their assistance with fieldwork and dissections: Tara Aprill, Elsie Dedrick, Illari Delgado, Kathryn Forti, Nathan Heller, Ashley Mills, and Craig Wert. Parasitology class students Jill Darpino and Genna Schlicht and BFS interns Ben Casscles and Britney Wells also provided assistance collecting and dissecting fish. This work was supported in part by a faculty research grant awarded to me from the SUNY Oneonta Research Foundation. Fieldwork was conducted under the guidelines of collecting permit 1647 issued to F. B. R. by NYS DEC. Fish were handled under the guidelines of SUNY Oneonta IACUC Protocol 201303.

208 REFERENCES

Bergman, M., J. Heilveil, and F. B. Reyda. 2015. Host utilization of Leptorhynchoides thecatus (Acanthocephala: Rhadinorhynchidae) from the Upper Susquehanna River Basin, New York, U. S. A. Comparative Parasitology. 82: 109–114.

Borden, A., & F. B. Reyda. 2014. Nematodes of the fishes of Otsego Lake, New York, including a species that is new to science. In 46th Annual Report of the SUNY Oneonta Biological Field Station. Pp. 129–134.

Darpino, E., R. Russell, & F. B. Reyda. 2014. Gastropods and Fish as hosts of digenetic trematodes in Otsego Lake and nearby waters. In 46th Annual Report of the SUNY Oneonta Biological Field Station. Pp. 123–128.

Hendricks, L. & F. B. Reyda. 2010. A survey of the acanthocephalan parasites of fish species of Otsego Lake, NY. In 41st Annual Report of the SUNY Oneonta Biological Field Station. Pp. 272–275.

Reyda, F. B. 2009. 2008 Research Activity Report: Fish parasite survey. In 41nd Annual Report of the SUNY Oneonta Biological Field Station. Pp. 153–157.

Reyda, F. B. 2010. Parasitic worms of fishes of Otsego Lake and nearby water bodies , 2009. In 42nd Annual Report of the SUNY Oneonta Biological Field Station. Pp. 276–281.

Reyda, F. B., & D. Willsey. 2013. Parasitic worms of fishes in tributaries of Otsego Lake. In 45th Annual Report of the SUNY Oneonta Biological Field Station. Pp. 213–218.

Scholz, T., & A. Choudhury. 2014. Parasite of freshwater fishes in North America: Why so neglected? Journal of Parasitology. 100: 26–45.

Sendkewitz, A., I. Delgado, & F. B. Reyda. 2014. Cestodes of the fishes of Otsego Lake and nearby waters. In 46th Annual Report of the SUNY Oneonta Biological Field Station. Pp. 140–143.

Szmygiel, C. & F. B. Reyda. 2011. A survey of the parasites of Smallmouth bass (Micropterus dolomieu). In 43rd Annual Report of the SUNY Oneonta Biological Field Station. Pp. 235–240.

209 Continued monitoring of the Moe Pond ecosystem in conjunction with biomanipulation

David Busby1 and J. Benjamin Casscles2

INTRODUCTION

Moe Pond is a 15.6 ha man-made water body in Cooperstown, Otsego County, NY. The pond is owned by the SUNY Oneonta Biological Field Station (BFS), and is located about three miles west of the main facility (Figure 1). The mean depth of the pond is 1.8 m (McCoy et al. 2001), which results in its classification as a polymictic water body. A polymictic body of water is one that is shallow enough such that any temperature stratification is ephemeral, resulting in regular circulation during non-frozen months of the year. Due to high nutrient concentrations and frequency of algal blooms, the pond is also considered eutrophic (Sohacki 1972).

Figure 1. Map showing the location of Moe Pond in Otsego County, NY (modified from Piacente 2015).

Monitoring of the Moe Pond ecosystem has been taking place intermittently for quite some time. There are data compilations from 1972, 1994, 2000-2008, and 2012-2014. The survey conducted in 1994 found that brown bullhead (Ameiurus nebulosus) and golden shiner (Notemigonus crysoleucas) were the only fish species present in the pond (McCoy et al. 2001). Because golden shiners are significant consumers of large zooplankton, which feed primary on algae, the abundance of shiners led to frequent algal blooms and a generally eutrophic nature of the pond (Wilson et al. 1999). In either 1998 or 1999, both smallmouth bass (Micropterus dolomieu) and largemouth bass (M. salmoides) were illegally introduced into the pond. While the motives behind this are unclear, the bass were likley introduced simply for sport fishing purposes. Interestingly, it had been suggested by McCoy that a population of bass in the pond

1 SUNY Oneonta Biological Field Station Intern, summer 2015. Current affiliation: Department of Environmental Sciences, SUNY College at Oneonta, Oneonta, NY. 1 Robert C. MacWaters Internship in the Aquatic Sciences, summer 2015. Current affiliation: Department of Fisheries, Wildlife and Environmental Sciences, SUNY College at Cobleskill, Cobleskill, NY.

210 could potentially decrease the golden shiner population, which would in turn help to minimize large algal blooms (McCoy et al. 2001).

Regardless of the particular motive, by 2007, largemouth bass had outcompeted all of the smallmouth bass in the pond and golden shiners had been extirpated (Reinicke and Walters 2007). Since, the bass have become overpopulated with regards to the food supply, which is primarily macroinvertebrates, large zooplankton, and algae, with only occasional brown bullhead fry. This has led to stunted growth of the bass population.

Summer 2015 has seen a continuation of the long-term data acquisition from the pond, with regards to limnology, the zooplankton community and the fish community. In addition, biomanipulation measures were taken to significantly lower the bass population, and hopefully keep it maintained in future years. This involved culling bass via seine and electrofishing in preparation for the introduction of tiger muskellunge into the pond. The intention of introducing tiger muskie would be to keep the bass population in check, due to consumption by muskie. Having a long-running data set from Moe Pond allows for the identification of any sudden changes or continuing trends in the dynamics of the pond, which may come about as a result of the decreased bass population and muskie introduction.

METHODS

Limnology

Water quality sampling took place biweekly throughout the summer, on 29 May, 12 June, 26 June, and 10 July. Sampling took place at the deepest point in the pond, at about 2.8 m (Figure 2). On 29 May, a depth finder was used to locate the deepest point, where an anchored buoy was then deployed. This was to ensure that the location of testing remained consistent throughout the summer. Each sampling day, a 500 ml sample of water was taken from the surface, which would later be tested for nitrate+nitrite, total phosphorous, chloride, and calcium in the lab. In addition, a YSI® multiprobe was used to measure temperature, conductivity, pH, and dissolved oxygen (% and mg/L). YSI® measurements were taken at the surface, one meter, two meters, and the bottom. Finally, a Secchi disk was used to gauge the transparency of the water.

211

Sampling site

Figure 2. Map of Moe Pond showing the sampling site at about 2.8 m deep (modified from Sohacki 1972).

Zooplankton Community

The zooplankton community was also sampled biweekly at the deepest point. On 29 May, the zooplankton were sampled by towing a Wildco® zooplankton net behind a rowboat at a depth of one meter below the surface. The net was dragged from the sampling site to the south shore of the pond. This produced a sample of zooplankton containing a large amount of plant particulate matter, so a different sampling method was utilized on the following three sample days. This was simply to lower the net to the deepest point in the pond and pull it back up to the surface. All the samples were stored in 500 ml bottles and diluted 1:1 with 190 proof reagent alcohol for preservation. IMAGE PRO PLUS software used with an Axioskop 40 microscope was used to identify and measure the first 100 zooplankton found, 1 ml at a time.

Fish Community

A 200 ft. haul seine was used to evaluate the fish community on 29 May, 17 June, 26 June, and 10 July. A rowboat with an electric trolling motor was driven out into the lake from the south shore, while the seine was flaked off into the water. Figure 3 demonstrates the typical deployment of the net in a “teardrop” shape with the bag at the far end furthest from shore. The seine was then dragged to shore, concentrating all the fish in the bag at the end.

212

Figure 3. Aerial photograph of the seine in a “teardrop” shape (Booth 2015).

All the largemouth bass were assigned an ID number, and measured on a measuring board. Bass over 150 mm in length had a gastric lavage performed. The stomach contents were emptied into a Whirl-Pak® bag, which was labeled with the bass’ ID number. On 29 May, scale samples were taken above the left pectoral fin using a pocketknife, to be used to estimate fish ages. On the other three sample days, an operculum was cut from the fish using dissecting scissors, which was placed into the appropriate Whirl-Pak® bag, and later used for aging. The stomach contents were doubled in volume with 190 proof reagent alcohol, and stored in the cooler. On 29 May, all fish caught were returned to the pond following processing. From then on, the largemouth bass were instead euthanized and disposed of in the woods, in an attempt to lower the bass population. Any brown bullhead caught were returned to the water.

In the lab, the stomach contents were poured into a white dissecting tray where they were identified, using a dissecting microscope when necessary. The scales were aged under a compound microscope equipped with INFINITY® software. The opercula were placed in boiling water for a minimum of 30 seconds, causing the skin to fall off, so that they could be aged.

In previous years, the area sampled by the seine has been estimated as 300 m². However, using aerial images captured by a drone and overlaying them into Google Earth, the sampling area of the seine could more accurately be measured each week. For the sake of making year-to- year comparisons of the bass population, an estimation was made using the areal extrapolation method. The average number of bass per m² was calculated, and then multiplied by 155,800 m², which is the total area of the pond.

213 Electrofishing of Moe Pond took place over a period of four nights from 20 July to 23 July. On 20 July, all of the largemouth bass caught were measured, marked with a pelvic fin clip, and released back into the water, so that a Peterson mark and recapture population estimate could be performed. On the next three nights, all of the bass were kept and stored in a cooler. The morning following capture, the bass were measured and had a scale sample taken above the left pectoral fin. Careful note was taken of the bass that had a pelvic fin clip.

RESULTS AND DISCUSSION

Limnology

Table 1 shows changes in the physical water quality parameters of Moe Pond throughout the summer. At the surface, the temperature increased from 21.46 ºC to 24.61 ºC, while at the bottom there was an increase from 18.09 ºC to 21.52 ºC. Specific conductivity remained fairly consistent at about 0.054 mS/cm. pH decreased from an average of 7.70 to 7.16. For the first two sampling days, the Secchi disk was observable at the bottom of the pond, at 2.8 m. Significant rain storms prior to the 26 June sampling likely caused the drop in Secchi reading for the latter half of the sampling days. Dissolved oxygen levels showed no significant trends.

Table 1. Physical water quality parameters of Moe Pond from 29 May to 10 July 2015.

Temp. Sp. Cond. Date Depth (m) (°C) (mS/cm) pH DO (%) DO (mg/L) Secchi (m) 5/29/2015 0 21.46 0.051 8.15 89.9 7.93 2.8 1 21.12 0.051 7.88 85.1 7.56 2 18.95 0.052 7.58 64.6 5.94 2.8 18.09 0.059 7.18 2.4 0.22 6/12/2015 0 22.16 0.053 7.9 86.7 7.55 2.8 1 21.69 0.052 7.67 84.7 7.45 2 20.66 0.053 7.53 76.3 6.84 2.8 19.71 0.081 6.9 34.6 3.17 6/26/2015 0 23.45 0.056 7.35 85.9 7.29 1.8 1 22.83 0.056 7.25 82.8 7.13 2 22.4 0.057 7.07 69.8 6.06 2.8 21.31 0.083 7.01 4.2 0.35 7/10/2015 0 24.61 0.055 7.48 97.2 8.1 2.1 1 24.26 0.055 7.26 94.5 7.91 2 23.47 0.055 7.18 94.4 8.04 2.3 21.52 0.068 6.72 8.5 0.66

214 Mean limnological data from 1972, 1994, 2000-2008, and 2012-2015 is shown in Table 2. The general increase in Secchi depth readings has continued in 2015, likely due to fewer algal blooms since the extirpation of golden shiners. Total phosphorus levels have remained consistent with recent years, at an average of 17.75 ug/L for 2015. Nitrate+nitrite remained below detection this year, at a level less than .02 mg/L. Calcium levels remained in line this year, while chloride saw a significant decrease from 2014, when a seemingly anomalous reading of 8.75 mg/l was recorded.

Table 2. Average values of secchi depth, total phosphorus, nitrate+nitrite, chloride, calcium, and pH in Moe Pond from 1972, 1994, 2000-2008, and 2012-2015 (modified from Piacente 2015).

Total Nitrate+nitrite Chloride Calcium Secchi (m) phosphorus pH (mg/L) (mg/L) (mg/L) Year (ug/L) 1972 NA 40-70 NA NA NA 6.8-10.2 1994 0.85 36.7 <.05 NA NA 7.93 2000 1.2 NA NA NA NA 8.63 2001 1.1 NA NA NA NA 8.66 2002 2.2 26.4 0.14 1.06 10.45 9.08 2003 2.33 29.05 0.11 1.47 NA 6.84 2004 1.26 42.29 0.1 NA NA 7.3 2005 1.26 56.64 0.01 NA NA 7.66 2006 2.2 26.91 0.01 NA NA 7.3 2007 2.62 20.5 <.01 NA NA 7.54 2008 1.35 28.95 0.003 0.54 1.02 7.39 2012 2.24 26.33 <.02 0.52 1.53 7.89 2013 2.33 20 <.02 NA NA 7.57 2014 1.72 17.6 <.02 8.75 9.22 7.31 2015 2.38 17.75 <.02 2.62 8.22 7.38

Zooplankton Community

Table 3 shows the mean length and percent composition of zooplankton species collected from Moe Pond between 29 May and 10 July 2015. Copepods were the most frequently observed, with the exception being on 29 May, when rotifers were the most abundant. Daphnia, cyclopoid copepods, and nauplii (larval copepods) remained in high abundance throughout the summer, with nauplii being the overall most abundant. In 2014, the rotifer Keratella was the most abundant, followed by nauplii (Piacente 2015). On 29 May 2015, Keratella were observed most often, but dropped off in quantity thereafter. No Trichocerca have been reported in recent years, while 16 were observed from 29 May this year. In addition, 10 Polyartha were seen on 10 July, being the first time since 2008 that they were reported (Finger 2009).

215 Table 3. Mean length and percent composition of zooplankton species in Moe Pond between 29 May and 10 July 2015.

29 May 2015 12 June 2015

26 June 2015 10 July 2015

Fish Community

The bass were aged using the scales or operculum, and Figure 4 shows the length distribution of the bass by age class. The greatest variation in length was in the one and two year old fish, which is likely due to an overpopulation of bass and a lack of forage for them to

216 consume. There were very few fish recorded over three years of age, which suggests that feeding primarily on insects, zooplankton, and algae is not enough to sustain an older population. As is, those fish that were four or five years old showed significant stunted growth, reaching a maximum length of 252 mm. In 2014, there were very few bass recorded that were two years old, and none that were less than two years old (Piacente 2015). This shows that 2015 has seen a return to largemouth bass recruitment in Moe Pond.

Length vs. Age of Largemouth Bass 6 5

4 3

Age (years) 2 1 0 0 50 100 150 200 250 300 Length (mm)

Figure 4. Length distributions by age class of largemouth bass collected from Moe Pond between 29 May and 10 July 2015.

A total of 270 largemouth bass were collected in the haul seine in 2015. Figure 5 shows the length distributions of the bass by quantity. There are clearly two distinct populations of bass by length. These are centered between 120 and 150 mm, and between 210 and 230 mm. The gap between these two populations is likely due to the minimal amount of recruitment that took place in 2014 (Piacente 2015).

Length vs. # of Largemouth Bass 50

20 # of # Fish

0 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 Length (mm)

Figure 5. Length distributions by number of largemouth bass collected from Moe Pond between 29 May and 10 July 2015.

217 Table 4 shows the results of the stomach contents of the largemouth bass collected. As per previous years, the most abundant organisms were daphnia, amphipods, chironomids, and damselfly larvae. In 2014, there were significantly greater numbers of beetles (183), amphipods (4307), caddisflies (219), and true flies (252) than in 2015. No flatworms or dixidae were observed in 2014. In addition, 186 brown bullhead fry were observed in 2014 (Piacente 2015), while only 5 were observed in 2015. This could indicate a decrease in recruitment among brown bullhead, perhaps due to increased competition for food items with the bass.

Table 4. Stomach contents of 78 largemouth bass collected from Moe Pond between 29 May and 10 July 2015. Note: due to analysis error, daphnia were only counted in 54 out of the total 78 bass stomachs. The numbers for daphnia take this error into account.

Taxa Mean Per Stomach % Occurrence Total Individuals Acariformes (Water Mites) 0.14 3.85 11 Coleoptera (Beetles) 0.01 1.28 1 Crustacea: Amphipoda 5.35 65.38 417 Daphnia 122.33 68.52 6606 Diptera: Adults (True Flies) 0.17 12.82 13 Diptera: Chironomidae 3.35 24.36 261 Diptera: Culicidae 0.82 32.05 64 Diptera: Dixidae 0.03 2.56 2 Diptera: Unknown pupae 0.40 15.38 31 Ephemeroptera (Mayflies) 0.17 5.13 13 Ictalurus nebulosus (Brown Bullhead fry) 0.06 3.85 5 Mollusca Sphaeriidae (Fingernail Clams) 0.08 7.69 6 Odonata Zygoptera (Damselflies) 1.42 37.18 111 Platyhelminthes (Flatworms) 0.03 2.56 2 Plecoptera (Stoneflies) 0.01 1.28 1 Trichoptera (Caddisflies) 0.24 7.69 19

Using the areal extrapolation method, the population of largemouth bass was estimated as 35,015+/-5,329 for 2015. This is significantly greater than last year’s estimation of 6,361 (Piacente 2015). This difference is likely due to more efficient sampling methods in 2015. A trolling motor was used when deploying the seine this year, while oars were used in 2014, which may have alarmed the fish, causing them to swim away from the boat. The Peterson mark and recapture method was conducted with an electrofishing boat in 2015 as well. The number of fish marked (M), was 600, and out of the 446 bass recaptured (C), 33 were marked (R). Using the formula: population (N) = MC/R, the total largemouth bass population was estimated at 8,109. Table 5 shows the population changes of golden shiner, largemouth bass, and smallmouth bass in Moe Pond from 1994, 1999-2008, and 2012-2015.

218 Table 5. Changes in the populations of golden shiner, largemouth bass, and smallmouth bass in Moe Pond from 1994, 1999-2008, and 2012 to 2015 (modified from Piacente 2015). 1 indicates a population estimate that was achieved using the electrofishing mark and recapture method, rather than areal extrapolation.

Golden shiner Largemouth bass Smallmouth bass (Notemigonus (Micropterus (Micropterus Year crysoleucas) salmoides) dolomieu) 1994 (McCoy et al. 2000) 7,154: +12,701;-6,356 0 0 1999 (Wilson et al. 2000) 3,210+/-1,760 1,588+/-650 958+/-454 2000 (Tibbits 2001) 381+/-296 2,536+/-1,177 945+/-296 2001 (Wojnar 2002) 1,708+/-1,693 3,724+/-3,447 504+/-473 2002 (Hamway 2003)1 3 206 20 2003 (Hamway 2004)1 2 318 1 2004 (Lopata 2005) 0 6,924+/-2,912 0 2005 (Dresser 2006) 0 12,019+/-3,577 223+/-257 2006 (Reinicke & Walters 2007) 0 11,555 0 2007 (Underwood 2008) 0 13,373+/-249 0 2008 (Finger 2009) 0 46,740+/-13,220 0 2012 (VanDerKrake 2013) 0 6,480+/-1,533 0 2013 (Stowell 2014) 0 13,560 0 2013 (Stowell 2014)1 0 4,205 0 2014 (Piacente 2015) 0 6,361+/-1,676 0 2015 (current) 0 35,015+/-5,329 0 2015 (current)1 0 8,109 0

A total of 229 largemouth bass were removed from the pond via the haul seine, while 1,018 were removed by electrofishing, for a total of 1,247 culled bass. Hopefully this decrease in the bass population will decrease competition for food items, and allow the bass to grow to full maturity without being stunted.

CONCLUSION

The long running data set from Moe Pond allows for the easy identification of any trends or significant changes in the Pond ecosystem. Water quality measurements from 2015 remained consistent with previous years. Water transparency has continued to increase, likely due to fewer algal blooms since the extirpation of golden shiners. There has been minimal change within the zooplankton community this year, with the exception that both Trichocerca and Polyartha were observed for the first time in several years. A single specimen of Leptodora Kindtii was collected on 12 June.

It was clear this year that the bass continue to struggle in finding an adequate food source. Only five brown bullhead fry were found in bass stomachs this year, significantly lower

219 than previous years. The bass stomachs were dominated by macroinvertebrates, large zooplankton, and algae, which is likely the reason the fish are stunted. Young-of-the-year and one-year-old bass were abundant this year, after an apparent absence in 2014 (Piacente 2015), indicating that bass recruitment is occurring in full force once again. The Peterson mark and recapture method with an electrofishing boat produced a largemouth bass population estimate of 8,109, which is slightly higher than estimates conducted in recent years. Via the haul seine and electrofishing, a total of 1,247 bass were culled from the pond in 2015. This was in an attempt to reduce bass overpopulation and competition for food.

The next step is the introduction of tiger muskellunge into the pond. Tiger muskellunge, hybrids of muskellunge (Esox masquinongy) and northern pike (Esox Lucius) would aid in managing the largemouth bass population by consuming smaller bass, without posing as a long- term threat due to their inability to reproduce. In addition, continued monitoring of the pond is necessary to observe any changes that may come about by the reduced bass population and/or muskie introduction.

REFERENCES

Albright, M.F., W.N. Harman, W.T. Tibbits, M.S. Gray, D.M. Warner, and R.J. Hamway. 2004. Biomanipulation: A classic example in a shallow eutrophic pond. Lake and Reserv. Manage, 20(4):263-269.

Booth, P. 2015. Personal communication. Intern. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Dresser, K. 2006. Continued monitoring of the Moe Pond ecosystem following the introduction of smallmouth and largemouth bass (Micropetrus dolomieu and M. salmoides, respectively). In 38th Annual Report (2005). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Finger, K. M. 2009. Continued monitoring of the Moe Pond ecosystem following the introduction of smallmouth and largemouth bass (Micropetrus dolomieu and M. salmoides, respectively). In 41st Annual Report (2008). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Hamway, R.J. 2003. Continued monitoring of Moe Pond after the unauthorized stocking of smallmouth and largemouth bass. In 36th Ann. Rept. (2002). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta: 66-80.

Hamway, R.J. 2004. Continued observations of Moe Pond after the unauthorized stocking of smallmouth and largemouth bass. In 37th Ann. Rept. (2003). SUNY Oneonta Biol. Fld. Stat., SUNY Oneonta: 110-120.

220 Lopata, K. 2005. Fifth annual report on the status of Moe Pond following the stocking of Micropetrus dolomieu and M. salmoides. In 37th Annual Report (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

McCoy, C.M. III, C.P. Madenjian, J.V. Adams, and W.N. Harman. September 2001. The fish community of a small impoundment in Upstate New York. Journal of Freshwater Ecology, 16(3): 389-394.

Piacente, J. 2015. Continued monitoring of the Moe Pond ecosystem and largemouth bass (Micropterous dolomieuii) populations following its introduction, summer 2014. In 47th Annual Report (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Reinicke E. and G.M. Walters. 2007. Continued monitoring of fish community dynamics and abiotic factors influencing Moe Pond, summer 2006. In 39th Annual Report (2006). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Sohacki, L. P. 1972. Limnological studies on Moe Pond. In 5th Annual Report (1972). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta

Stowell, S.G. 2014. Monitoring the Moe Pond ecosystem and population estimates of largemouth bass (Micropterus salmoides) post unauthorized introduction. In 46th Ann. Rept. (2013). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Tibbits, W.T. 2001. Consequences and management strategies concerning the unauthorized stocking of smallmouth and largemouth bass in Moe Pond. In 33rd Ann. Rept. (2000). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Underwood, Emily S. 2008. Continued monitoring of the ecosystem dynamics of Moe Pond following the introduction of largemouth bass (Micropterus salmoides) and smallmouth bass (M. dolomieu). In 40th Ann. Rept. (2007). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta: 125-140.

VanDerKrake, A.J. 2012. Monitoring of the Moe Pond ecosystem and largemouth bass (Mircropterus salmoides) population before considering biomanipulation options. In 45th Annual Report (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta 126-136.

Wilson, B.J., D.M. Warner and M. Gray. 1999. An evaluation of Moe Pond following the unauthorized introduction of smallmouth and largemouth bass. In 32nd Ann. Rept. (1998). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta

Wojnar, K.A. 2001. The continuing evaluation of Moe Pond after the unauthorized stocking of smallmouth and largemouth bass. In 34th Ann. Rept. (2001). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta: 166-176.

221 Benthic macroinvertebrate survey of Otsego Land Trust Properties on Oaks Creek, Otsego County, NY

Matthew D. Robinson1 and Jeffrey S. Heilveil2

INTRODUCTION Agricultural practices, stream alteration, and wastewater discharge into aquatic habitats can lead to a long list of negative environmental changes (Carlos, et al. 2014). As a tributary of the Susquehanna River, Oaks Creek is one of many streams that can directly impact the integrity of the Susquehanna River. The Chesapeake Bay receives around 50% of its overall water from the Susquehanna. (Chesapeake Bay Program 2012). This adds importance to the integrity of all streams within the watershed. Benthic macroinvertebrates can be used as indicators of stream integrity as a result of their varying tolerances to pollution and environmental change (Lakew 2015). By providing baseline data in the form of a taxa list on Oaks Creek, we can learn about the community composition of macroinvertebrates, and have a reference point to monitor future changes. This study is meant to add new genera to previous taxonomic lists and create a more comprehensive list that can be used in future studies. Previous surveys performed by Hingula (2005), and Buckhout (2013), focused mainly on riffle habitats downstream. These studies also used sampling techniques that would not be conducive to habitats other than riffles. Another study by Heilveil and Buckhout (2013) used exhaustive sampling in multiple downstream habitats. This survey added many new genera to the list, and did sample pool habitats, but did not extend as far upstream where the habitat is mainly a long pool. This study is meant to create a new sampling protocol that will differ from those used in previous studies of Oaks Creek. Past studies used protocols that were biased towards one stream habitat, while others were ignored. The new protocol focused on the previously overlooked habitats in order to collect insect genera that were not found in earlier studies. Comparisons between sites, and transects within each site, were also compared to note similarities and differences between locations and habitats based on presence/absence, and quantitative data.

1BFS Intern, Summer 2015. Current Affiliation: State University of New York at Oneonta. Funding for this project was provided by the Otsego Land Trust.

2Associate Professor , State University of New York at Oneonta Biology Department .

222 MATERIALS AND METHODS Site Selection Oaks Creek is a fourth order stream that flows approximately 22 km from the base of Canadarago Lake to the Susquehanna River to the south-east (Hingula, 2004). The upper portions of the stream are very slow flowing and consist mainly of long pools and runs. The Otsego County Land Trust owns multiple properties that allow for sampling access to the stream. Of these sites, three were chosen to be sampled. Each site was surveyed previous to sampling to distinguish between different stream habitats. At each site, transects were marked that reflected the main stream habitats. Oaks Creek Preserve is the most upstream parcel of property that includes around 800 meters of the stream. It was decided that this reach was composed of mainly one large pool and only one transect was marked for sampling (42.77105 oN, 75.02010 oW). Crave is the middle and shortest property with a stream reach of around 500 meters. This reach was composed of mainly pools but also had multiple runs with a rocky bottom. The upper and lower Crave transects were made up of both habitats (42.74874 oN, 75.01279 oW, and 42.747708 oN, 75.01445 oW respectively). The Parslow Road property covered just over 800 meters and similar to the Crave property, had pools and runs. Two transects, upper and lower, were sampled to get representative samples of each habitat (42.741405 oN, 75.01254 oW and 42.737974 oN, 75.01117 oW respectively). At each transect, quantitative surveys were conducted using a D-frame net. The samples were taken on each bank edge, and at every meter along the transect. The substrate was disturbed continuously from the transect line to one meter upstream from the transect. Any insects found at a sampling point that were on the water surface were also collected. The samples were preserved in 70% ethanol were brought back to the lab to be identified to lowest practical taxonomic level using Merrit and Cummins (1995). Questionable taxonomic names were checked using the Integrated Taxonomic Information System. After all insects were sorted and identified, a chi square test and Jaccard’s similarity index were used to note similarities or differences between sites. The specimen collecting was permitted under NYS DEC License to Collect or Possess #1225 assigned to WN Harman.

RESULTS AND DISCUSSION The three sites sampled produced 28 genera within 16 families and 7 orders. By sampling pool habitats that had not been previously sampled with a new protocol, 9 new genera had been added to the Oaks Creek taxonomic list (Tables 1, 2 and 3). The studies that were conducted in years past used methods that did not allow for the collecting of insects in pool environments, but instead focused on riffle habitats. The 2004 study used a method that involved brushing insects off rocks into a D-framed net (Hingula 2004). This method was also restricted to sites under 1 meter deep and soft bottomed pools were ignored. Another study (Buckhout 2012) used a cylindrical Wildco® Hess Stream Bottom Sampler. The method is best used in riffle habitats as it needs sufficient stream flow to work correctly. The third study (Heilveil and Buckhout 2012), used a D-framed net as well, but did not sample the mainly pool habitats this study sampled.

223 All five sites were compared using a contingency chi square test (Table 2) and a Jaccard’s similarity index (Table 3). With the exception of the Parslow upper site and the Parslow lower site (≈ 78% similar), all other transects including those within the same site had lower similarities (≈ 24% - 46%). All site comparisons, based on the contingency chi square test, were significantly different. The difference between sites is indicative that even when the habitats appear visually similar, the microhabitats and other unseen factors can produce different taxa and abundances within a stream reach. The resulting taxa lists are different in a few ways based on the habitats sampled, as well as the sampling methods used. The first obvious difference is the number of odonates collected previously and during the new study. Hingula (2004) and Buckhout (2012) both collected a single genus of Odonata, while the new study found four Odonate genera. Two of these genera, Ischnura and Enallagma, would not be expected in a riffle habitat based on their preference for habitats with vegetation, which is not usually associated with riffles (Witt et al. 2013). Another large difference between genera found between studies was the abundance of Ephemeropterans. Although the upper reaches of Oaks Creek supported large populations of Hexagenia, a burrowing mayfly, the downstream habitats had many more mayfly genera common to riffles. The greatest diversity of mayflies are generally found in rocky bottomed streams rather than soft bottom habitats (Merritt and Cummins 1995). These differences can be seen as an indication that when creating a comprehensive taxonomic list, sampling all habitat types is required to find taxa that specialize it certain microhabitats. Using different protocols and methods of collecting may also aid in collecting more diverse genera due to some sampling method and protocol restrictions. The updated taxonomic list and data can be used to reference the presence of certain genera to show diversity of the stream, as well as potentially detect changes in the community structure and stream integrity in the future.

224 Table 1. Insect genera recovered from studies to date on Oaks Creek. Hingula Buckhout Heilveil&Buckhout Robinson New In Order Family Genus 2004 2012 2012 2015 2015 Ephemeroptera Baetidae Baetis X X X Ephemeroptera Baetidae Heterocloeon X X Ephemeroptera Caenidae Caenis X X Ephemeroptera Ephemerellidae Drunella X X Ephemeroptera Ephemerellidae Ephemerella X X X Ephemeroptera Ephemerellidae Timpanoga X Ephemeroptera Ephemeridae Ephemera X X Ephemeroptera Ephemeridae Hexagenia X Ephemeroptera Heptageniidae Epeorus X Ephemeroptera Heptageniidae Stenonema X Ephemeroptera Heptageniidae Heptagenia X X Ephemeroptera Heptageniidae MacCaffertium X X Ephemeroptera Heptageniidae Stenacron X X X X Ephemeroptera Isonychiidae Isonychia X X Ephemeroptera Leptophlebiidae Paraleptophlebia X X Ephemeroptera Potamanthidae Anthopotamus X Ephemeroptera Ameletidae Ameletus X Odonata Aeshnidae Boyeria X X Odonata Calopterygidae Calopteryx X X Odonata Coenagrionidae Ischnura X X Odonata Coenagrionidae Enallagma X X Odonata Coenagrionidae Argia X Odonata Gomphidae Gomphus X Megaloptera Corydalidae Nigronia X X Megaloptera Sialidae Sialis X X X

225 Table 1 (cont.). Insect genera recovered from studies to date on Oaks Creek.

Hingula Buckhout Heilveil&Buckhout Robinson New In Order Family Genus 2004 2013 2013 2015 2015 Plecoptera Chloroperlidae Utaperia X Plecoptera Perlidae Acroneuria X X X Plecoptera Perlidae Neoperia3 X Plecoptera Perlidae Agnetina X X Plecoptera Perlidae Neoperla X X Plecoptera Perlidae Paragnetina X X Plecoptera Pteronarcyidae Pteronarcys X Hemiptera Corixidae Dasycorixa X Hemiptera Corixidae Hesperocorixa X Hemiptera Corixidae Trichocorixa X Hemiptera Gerridae Trepobates X X Hemiptera Gerridae Rheumatobates X X Hemiptera Gerridae Aquarius X X Hemiptera Veliidae Rhagovelia X X Hemiptera Veliidae Microvelia X Coleoptera Dytiscidae Uvarus X X Coleoptera Elmidae Ancryonyx X X X Coleoptera Elmidae Stenelmis X X X X Coleoptera Elmidae Dubraphia X X X Coleoptera Elmidae Macronychus X X Coleoptera Elmidae Optioservus X X Coleoptera Elmidae Oulimnus X Coleoptera Elmidae Promoresia X X Coleoptera Hydrophilidae Paracymus X Coleoptera Hydrophilidae Tropisternus X X Coleoptera Psephenidae Ectopria X X Coleoptera Psephenidae Psephenus X X X X

3 Neoperia is not a genus within the Plecoptera order. This may have been a typographical error from the first study.

226

Table 1 (cont.). Insect genera recovered from studies to date on Oaks Creek.

Hingula Buckhout Heilveil&Buckhout Robinson New In Order Family Genus 2004 2013 2013 2015 2015 Trichoptera Goeridae Goera X Trichoptera Glossosomatidae Glossosoma X X X Trichoptera Hydropsychidae Cheumatopsyche X X X X Trichoptera Hydropsychidae Hydropsyche X X X Trichoptera Hydropsychidae Ceratopsyche X X Trichoptera Lepidostomatidae Lepidostoma X Trichoptera Leptoceridae Ceraclea X Trichoptera Limnephilidae Pychnopsyche X X X Trichoptera Limnephilidae Apatania X Trichoptera Odontoceridae Psilotreta X Trichoptera Philopotamidae Chimarra X X Trichoptera Polycentropidae Cyrnellus X Trichoptera Polycentropidae Neureclipsis X Trichoptera Polycentropidae Paranyctiophalax X Trichoptera Polycentropidae Polycentropus X X Trichoptera Psychomyiidae Psychomyia X Trichoptera Rhyacophilidae Rhyacophila X X X Trichoptera Uenoidae Neophylax X X X Diptera Athericidae Atherix X X Diptera Ceratopogonidae Probezzia X X Diptera Chironomidae N/A X X X Diptera Ephydridae Ephydra X Diptera Pelecorhynchidae Glutops X Diptera Simuliidae Prosimulium X X Diptera Tabanidae Chrysops X X Diptera Tipulidae Antocha X Diptera Tipulidae Hexatoma X X Diptera Simuliidae Simulium X

227 Table 2. Site comparisons based on contingency chi square test.

Site Comparison P-Value All sites 1.14E-137 Parslow up vs Parslow low 0.0013 Parslow up vs Parslow low #2 0.0033

Parslow up vs Crave up 2.47E-09 Parslow up vs Crave Low 1.54E-12 Parslow low vs Parslow low 2 0.0002

Parslow low vs Crave up 4.15E-14 Parslow low vs Crave low 1.36E-14 Parslow low #2 vs Crave up 2.76E-08

Parslow low 2 vs Crave low 5.96E-10 Crave up vs Crave low 0.00048 Oaks Preserve vs Parslow up 2.81E-42

Oaks Preserve vs Parslow low 4.00E-44 Oaks Preserve vs Parslow low #2 1.39E-37 Oaks Preserve vs Crave up 1.10E-08

Oaks Preserve vs Crave low 2.70E-52

Table 3. Jaccard’s similarity index for all sites.

Parslow Upper Parslow Lower Parslow Lower #2 Crave Upper Crave Lower Oaks Creek Preserve Parslow Upper 1 0.778 0.4 0.333 0.462 0.4 Parslow Lower 1 0.438 0.375 0.429 0.364 Parslow Lower #2 1 0.286 0.389 0.235 Crave Upper 1 0.333 0.333 Crave Lower 1 0.267

Oaks Creek Preserve 1

228 REFERENCES

Buckhout, B. 2013. Benthic macroinvertebrate survey of Oaks Creek, Otsego County, NY. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Stat., SUNY Oneonta. Carlos Martínez-Sanz, Sara María Puente-García, Eduardo Rodolfo Rebolledo, and Pedro Jiménez- Prado. 2014. Macroinvertebrate richness importance in coastal tropical streams of Esmeraldas (Ecuador) and its use and implications in environmental management procedures, International Journal of Ecology, vol. 2014, Article ID 253134, 11 pages, 2014. doi:10.1155/2014/253134 Chesapeake Bay Program. 2012. Retrieved August 11, 2015, from http://www.chesapeakebay.net/discover/bay101/facts. Heilveil, J.S. and B. Buckhout. 2013. Qualitative spot biotic survey of Oaks Creek, White Creek, Cripple Creek, and Moe Pond in Otsego County, New York. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Stat., SUNY Oneonta. Hingula L. 2005. Benthic macroinvertebrate survey of Oaks Creek, Otsego County, NY, during the initial stages of zebra mussel (Dreissena polymorpha) colonization. In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Stat., SUNY Oneonta. Lakew, A. 2015. Assessing anthropogenic impacts using benthic macroinvertebrate as bio-indicators in central highland streams of Ethiopia. Ethiopian Journal of Environmental Studies & Management, 8(1), 45-56. doi:10.4314/ejesm.v8i1.5 Merritt R.W. and K.W. Cummins. 1995. An introduction to the aquatic insects of North America, 3rd Edition. Kendall Hunt Publishing Company. Dubuque, IA. Witt, J. W., R.E Forkner and R.T. Kraus. 2013. Habitat heterogeneity and intraguild interactions modify distribution and injury rates in two coexisting genera of damselflies. Freshwater Biology, 58(11), 2380-2388. doi:10.1111/fwb.12217.

229 Evaluation of phosphorous and nitrogen uptake by Phalaris arundinacea plants in a wastewater treatment wetland, Cooperstown, NY

S. Bouillon1

BACKGROUND

Two constructed wetlands were monitored during this study, one of which was a constructed wastewater treatment wetland. The purpose for monitoring both wetlands was to evaluate the differences in nutrient uptake by Phalaris arundinacea (reed canarygrass) plants between a wetland receiving treated wastewater, and one that does not. Treated wastewater from the Village of Cooperstown was the primary inflow of the constructed treatment wetland system. The other constructed wetland served as a control for this study. The control wetland lies adjacent to an active cow pasture. Cow manure can pollute surface water with phosphorous and nitrogen, indicating that the control wetland may be impacted by nutrient loading. Most likely, the control wetland does not receive nutrient loads to the degree of the treatment wetland, which is why it served as a control for this study. Both the treatment wetland and control wetland were created in 2003. The former began actively receiving treated wastewater in 2010. Since 2010, the Village of Cooperstown has been an active cosignatory of the SPDES/Chesapeake Bay Nutrient Reduction Strategy (Albright and Waterfield 2011). From 2014 on, the maximum amount of phosphorus released by the treatment plant has been set at 984 kg/year (or approximately 2 mg/l, given typical annual discharge volumes; Albright 2015). From the time when the treatment wetland began receiving effluent, retention rates for both total nitrogen and total phosphorus have consistently been near 30% (Albright 2015).

INTRODUCTION

Phosphorous and nitrogen are known to contribute to freshwater eutrophication (the overgrowth of cyanobacteria and algae that causes oxygen levels in the water to decline, ultimately changing freshwater ecosystems for the worse; EPA 2004). Since 1990, treatment wetlands have been established more frequently as the final filtering process for phosphorous and nitrogen in treated waste water (Albright 2014). According to the Environmental Protection Agency, constructed treatment wetlands are a sometimes cheaper option for wastewater nutrient removal processes, and they act like natural wetlands with their soil and vegetation characteristics (2004). The Cooperstown, NY, wastewater treatment wetland was intended to reduce nutrient loading to the Susquehanna River and, ultimately, to the Chesapeake Bay via plant uptake and soil absorption (Albright and Waterfield 2011). This study seeks to monitor and compare the

1 Biological Filed Station Intern, summer 2015. Funding provided by the Village of Cooperstown.

230 removal of nutrients via plant uptake in P. arundinacea plants between both wetlands. This was done by utilizing sample site locations and strategies from Gazzetti’s (2012) study of phosphorous uptake in P. arundinacea plants, and comparing them to the phosphorous concentration data we collected. P. arundinacea plant leaves were evaluated because this common species is known to be abundant in wetlands locally, and it demonstrates the ability to effectively remove nutrients, like phosphorous and nitrogen, from water (Cronk and Fennessey 2001, Gazzetti 2010). Both the treatment wetland and nearby control wetland were constructed in 2003 by the Army Corps of Engineers and Duck’s Unlimited. The design of the treatment wetland was atypical for treatment wetlands, in that the depths throughout most of the system exceeded those needed for the establishment of emergent plants (Robb 2012). However, the treatment wetland system has effectively removed approximately 30% of both the phosphorus and nitrogen loads discharged into it (Albright 2015).

Concurrent with the collection of plant leaf material, water samples were collected in each sampling area so that comparisons between nutrient content in leaf material and the adjacent water samples could be evaluated. Nutrient content in leaf material was compared both between the wetlands and between plants immediately proximal to the surface water compared to plants growing further upland from the water’s edge. Higher nutrient content from plants adjacent to nutrient rich waters (as in the treatment wetland) would imply that P. arundinacea plants can assimilate more nutrients than are physiologically necessary for growth (i.e., it demonstrates luxuriant uptake). This work follows that of Olsen (2010) and Gazetti (2012) who demonstrated conflicting evidence regarding excessive phosphorus uptake by P. arundincea plants growing in the phosphorus-rich treatment wetland. In addition, this study will compare P. arundinacea phosphorous concentrations to those found in Gazzettis study of phosphorous uptake levels in emergent wetland plants (2012).

METHODS

Water sampling

At each primary plant sampling site located near water, a single water sample was collected in a 125 mL bottle to test for calcium content using the EDTA titrimetric method (EPA 1983). There were a total of 6 water samples collected at each wetland.

On 20 August 2015, an additional five water samples were taken within the treatment wetland. Weather conditions made it difficult to collect water samples from each primary sampling site in the control wetland on 20 August. The control wetland is an ephemeral wetland, meaning that the water levels change based on current weather conditions (EPA 2014). In the treatment wetland, water samples were collected at the inflow and outflow (see Figure 1), and at sites 2,3,4, and 6.

231 Calcium analysis of water samples

While Gazetti (2012) reported that there was no statistical significance between phosphorous uptake levels by Typha spp. and those of Phalaris arundinacea plants between the wetlands, he did report that the ash content of these plants was higher in the treatment wetland than in the control. Since this study was modeled after his study, it was necessary to analyze areas of potential differences regarding the use of the dry-ashing methods outlined by Bickelhaupt and White (1982). It is apparent that there may be various inorganic components within the dry-ashed plant material contributing to the overall dry weight of the ash used to calculate how much phosphorous was within the plant leaves collected. A suspected source of this difference was calcium. Given local geology, concentrations of this cation were expected to be higher in the treatment wetland than in the control (Albright 2015). Analyzing water samples for calcium in both wetlands, using the EDTA titration method (EPA 1983), was conducted to attempt to verify this relationship.

Nutrient concentration analysis of water samples

All water samples were analyzed for total phosphorous (persulfate digestion followed by single reagent ascorbic acid method; Liao and Marten 2001) and total nitrogen content (cadmium reduction following peroxodisulfate digestion; Pritzlaff 2003, Ebina et al. 1983) using a Lachet autoanalyzer.

Plant Sampling

All plant and water samples were collected on 21 July 2015. Primary sampling sites (1-6) were established for both near and far locations within each wetland (Figures 1 and 2). These primary sampling sites were divided into three sub-sampling sites (A-C) located closely around each primary sampling site. Primary sampling sites were established at locations “near” and “far” from the water to identify differences in nutrients in P. arundinacea plant tissue growing in soils having high (“near”, treatment wetland) vs lower nutrient content concentrations (“far”, treatment wetland and “near” and “far”, control wetland). All “near” sampling sites were taken from Gazetti’s (2012) comparative study on the variation of nutrient concentrations between Typha spp. and P. arundinacea plants between both wetlands. This was done in order to create a comparison of nutrient concentration data in P. arundinacea plant tissue between his 2011 data and the current 2015 data. At each near sub-sampling site, 20 P. arundinacea leaves were collected by cutting them from the base of their ligules. “Far” sites were established at varying distances from the water’s edge where distinct vegetation changes towards upland habitat preferences were visible. At each sub-sampling site located “far” from the water’s edge in each wetland, 20 Phalaris arundinacea leaves were collected as well. Plant tissue samples were processed as described below. The rationale for creating “near” and “far” sites was to compare and analyze the movement of nutrients throughout the wetlands by analyzing the movement of

232 nutrients within P. arundinacea plants. To compare the differences between near and far site nutrient level concentrations, t-tests were applied (two sample, assuming equal variances). This was also used to evaluate the differences in mean nutrient concentrations between “near” and “far” sites within each wetland.

Figure 1. Aerial photograph of the control wetland sampled in 2015. The sites denoted as “CW” indicate “control wetland” sites, whereas the sites prefixed by “Far” indicate sites adjacent to, but upland from, the CW sites. Note that the Google Earth image was taken in 2011, and these ephemeral wetlands have since experienced changes in overall hydrology.

233

Figure 2. Aerial photograph of the treatment wetland sampled in 2015. The sites denoted as “TW” indicate “treatment wetland” sites, whereas the sites prefixed by “Far” indicate sites adjacent to, but upland from, the TW sites. Note that the Google Earth image was taken in 2011, and these ephemeral wetlands have since experienced changes in overall hydrology.

234 Plant sample processing

Upon returning to the Biological Field Station after field sampling was complete, plants were washed immediately with deionized water, patted dry, put into paper bags, and placed into an oven where they were dried at 60-65o C for about 24 hours. Next, the dried plant samples were ground with a Krup’s® coffee bean grinder. Samples were heated again to 65o C in 50 mL beakers for about 1 hour to evaporate moisture before taking dry weights for nitrogen and phosphorous analyses (Bickelhaupt and White 1982, Gazzetti 2012). All water samples were placed into a refrigerated storage unit for subsequent nutrient concentration sampling.

Phosphorous analysis of plant material

Dry-ashing, acid extraction, and phosphorous concentration determination methods were from Bickelhaupt and White (1982), and applied to all sub-samples within each primary sampling site for both wetlands. To prepare plant samples for dry-ashing, about 0.5 g of oven dried, ground plant sample were weighed out and placed into crucibles that had been acid washed, dried, and weighed. All plant samples were dry-ashed within these crucibles for about 4 hours at 475o C in a muffle furnace. For the acid extraction process, 10mL 5N HN03 and 5mL distilled water were slowly added to the crucibles, which were gently boiled until dry on a hotplate. Next, the crucibles were placed into the muffle furnace and heated at 475o C for an additional hour. After being heated in the oven, 5 mL of distilled water and 10mL of 6N HCL were added slowly to each crucible, which were boiled gently until dry again on a hotplate. Crucibles were cooled and 5 mL of distilled water and 10 mL of 6N HCL were slowly added again. This time, polypropylene stirring rods were used to scrape all dry-ashed material from the sides and bottom of each crucible to ensure that all plant material was dissolved in the. This solution was passed through No. 42 Whatman © filter paper folded into funnels that drained into a 100 mL volumetric flask. The crucibles were rinsed with 15 mL of deionized water and passed through the filters. Crucibles were rinsed with 15 mL of distilled water and filtered twice more. All solutions were brought up to a final volume of 100 mL with DI water within the volumetric flasks and stored in a plastic bottle for further determination. The vanadomolybdophosphoric acid colorimetric method was used to determine the phosphorous concentrations from each extraction (APHA 2012). To determine the absorbency, a Milton Roy Spectronic spectrophotometer 501 was used at 440 nm wavelength. The concentration of all acid extracted solutions was used to find percent phosphorous content for each sample (Gazzetti 2011). The equation outlined in Bickelhaupt and White (1982) used to determine the percentage of phosphorous from the dry-weight of each sample is displayed below.

%P of sample = mg/l P in extract * vol. extract * 0.0001 Sample dry wt. (g)

235 Nitrogen analysis of plant material

The peroxidisulfate total nitrogen determination method outlined by Ebina et al. (1983) was used to digest samples for total nitrogen analysis of the dried, ground plant material. About 0.0025g of finely ground plant sample was added into test tubes with 5 mL distilled water. Five mL persulfate oxidizing agent was added to each test tube. The tubes were autoclaved and total nitrogen was determined using the cadmium reduction method (Pritzlaff 2003) using a Lachet autoanalyzer. Finally, the following formula was used to determine total nitrogen:

%N of sample = mg/l N in extract * vol. extract * 0.0001 Sample dry wt. (g)

This total nitrogen procedure is intended for the analysis of water rather than for plant extracts, but it was intended to serve as a means to compare nitrogen levels between both wetlands. So even if the actual concentrations reported are not precise, relative differences between samples should reflect real differences. This should help us gain an understanding of how these wetlands vary, if at all, in nutrient uptake.

RESULTS AND DISCUSSION

The overall total phosphorous concentrations (see Figure 3) in P. arundinacea tissue between the treatment wetland and control wetland show that the treatment wetland (mean=0.373) had higher total phosphorous levels than the control wetland (mean=0.293). These results are statistically significant (p value=0.000115), and they support this study’s hypothesis that nutrient values will be higher at near sites within the treatment wetland because of treated effluent inflows. While comparing the total phosphorous values between the treatment wetlands near (mean=0.369) and far (mean=0.323) sites, the near sites had higher total phosphorous concentrations (p value=0.014).

As seen in Figure 4, the total nitrogen values of the near sites (mean=0.7578) were higher than those at the wetlands far sites (mean=0.587), and were statistically significant from the treatment wetland with a p value of 0.01. This supports this study’s hypothesis, which expected the treatment wetland to have higher total nitrogen values near the input sources of treated wastewater. For the comparison of total nitrogen between the control wetlands near (mean=0.293) and far sites (mean=0.333), there was a slight statistical significance (p value=0.0488). For the comparison of the total nitrogen values between the control wetlands near (mean=0.666) and far sites (mean=0.708), there was no statistically significant difference between the values (p- value=0.156). In Figure 4, it is evident that far sites in both wetlands differed significantly (p value= 0.0124). The control wetland (mean=0.708) had higher total nitrogen values at its far sites than the treatment wetlands far sites (mean=0.589). Expecting the mean total nitrogen values to

236 be higher at the treatment wetland points out potential for error in the sample processing and testing phases (see conclusion). Errors may have been made during the sample processing phase of leaf material. The total nitrogen values for all near sites in the treatment wetland were nearly half the amount of all other total nitrogen values for sampling sites between both wetlands. For total nitrogen analyses, plant material dried in the convection oven (heated at 65 degrees Celsius) before dry weighting may not have been ground evenly enough, or possibly over dried/burnt.

Figure 5 addresses the differences in total phosphorous content of P. arundinacea between both wetlands within my study and Gazzetti’s (2012) work. Overall, this study’s total phosphorous concentrations in plant tissue were higher and statistically significant for the treatment wetland (mean= 0.367, p value= 0.0102) and control wetland (mean= 0.0293, p value= 0.003)

0.90

0.80

0.70

0.60

0.50 near 0.40 far 0.30 Mean % phosphorus 0.20

0.10

0.00 control treatment

Figure 3. The average phosphorous concentrations of P. arundinacea tissue at near and far sampling sites between both wetlands. Error bars represent standard error. See Figure 1 and 2 for site locations.

237 0.900

0.800

0.700

0.600

0.500 near 0.400 far

Mean % Mean nitrogen 0.300

0.200

0.100

0.000 control treatment

Figure 4. The average total nitrogen concentration of P. arundinacea tissue in near and far sampling sites between both wetlands. Error bars represent standard error. See Figure 1 and 2 for site locations.

0.45

0.4

0.35

0.3

0.25 control 0.2 treatment 0.15 Mean % phosphorous 0.1

0.05

0 Bouillon Gazzetti

Figure 5. The comparison of average phosphorous concentrations (as percentage) of P. arundinacea in the treatment and control wetland, between my study and Gazzetti’s (2012) study.

238

Table 1 shows the total phosphorus and total nitrogen content in P. arundinacea leaves in the control and treatment wetlands. Table 2 provides the concentrations of nitrite+nitrate, total nitrogen and total phosphorus of the water proximal to the sampling sites (see Figures 1 and 2 for site locations). Some sites in the control wetland could not be sampled due to low water levels.

Table 1. The concentrations of total phosphorus and total nitrogen in the “far” and “near” sites of the control and the treatment wetlands, 2015. p Control Wetland Treatment Wetland Site Near Far Near Far TP(%) TN (% ) TP(%) TN (% ) TP(%) TN (% ) TP(%) TN (% ) 1A 0.36 0.75 0.46 0.75 0.45 0.82 0.29 0.35 1B 0.30 0.70 0.32 0.73 0.45 1.15 0.30 0.36 1C 0.27 0.69 0.30 0.78 0.30 0.63 0.28 0.30 2A 0.33 0.59 0.34 0.51 0.42 1.36 0.38 0.40 2B 0.36 0.60 0.31 0.60 0.32 0.68 0.28 0.33 2C 0.39 0.63 0.32 0.59 0.47 1.19 0.33 0.29 3A * 0.71 0.38 0.77 0.24 0.64 0.30 0.36 3B 0.21 0.63 0.42 1.07 0.35 0.71 0.32 0.39 3C 0.23 0.51 0.27 0.74 0.37 1.14 0.41 0.34 4A 0.27 0.80 0.41 0.78 0.33 0.70 0.33 0.21 4B 0.31 0.81 0.26 0.84 0.31 0.58 0.35 0.19 4C 0.24 0.67 0.36 0.79 0.36 0.78 0.37 0.33 5A 0.22 0.57 0.39 0.79 0.44 0.48 0.26 0.23 5B 0.31 0.75 0.30 0.64 0.35 0.68 0.25 0.18 5C 0.33 0.73 0.42 0.57 0.34 0.65 0.23 0.18 6A 0.29 0.61 * 0.48 0.43 0.42 0.39 0.24 6B 0.23 0.58 0.22 0.61 0.35 0.50 0.40 0.34 6C 0.35 0.62 0.22 * 0.32 0.55 * 0.27 * sample lost Table 2. Nitrite+nitrate, total nitrogen and total phosphorus concentrations of water near sampling sites.

Date Sampling Site Nitrate+Nitrite (mg/L) Total Nitrogen (mg/L) Total Phosphorus (ug/L) 8/17/2015 T1 1 0.05 0.236 1524 8/17/2015 T2 4.13 9 3900 8/17/2015 T3 2.92 7.44 3740 8/17/2015 T4 2.72 7.62 3860 8/17/2015 T5 2 2.6 7.34 3620 8/17/2015 T6 0.08 2.01 132 8/17/2015 C1 1 0.02 1.12 75.7 8/17/2015 C6 0.11 2.63 632 1 2 inflow, outlow

239 Table 3 highlights the percent phosphorous found in dry-ashed material. I compared these total phosphorous values to the amount of calcium found in water samples. I found that the treatment wetland had higher amounts of calcium in water samples (average calcium= 52.4 mg/l +/- 4.1) than the control wetland (average calcium= 35.7 mg/l =/- 11.6). This likely is responsible for the higher ash content (or, lower percent C lost on ignition) measured in the control wetland as compared to the treatment wetland (see Table 3).

Lastly, Table 4 compares total phosphorus content in P. arundinacea leaves in the “near” wetland sites of both wetlands between Gazzetti’s (2012) study and this current work. Concentrations at both wetlands were somewhat higher in 2015 than in 2011 (Gazzetti 2012), and in both wetlands the concentrations were somewhat higher in the treatment wetland than the control wetland.

Table 3. Total phosphorus content of P. arundinacea leaves at each subsample site in the treatment and control wetlands and the percent carbon lost on ignition.

Near Far Treatment Wetland Control Wetland Treatment Wetland Control Wetland % P of % C Loss % P of % C Loss % P of % C Loss % P of % C Loss Site Dry on Dry on Dry on Dry on Weight Ignition Weight Ignition Weight Ignition Weight Ignition 1a 0.45 94.8 0.36 92.7 0.29 89.8 0.46 92.9 1b 0.45 92.4 0.30 92.2 0.30 92.9 0.32 91.2 1c 0.30 92.6 0.27 93.4 0.28 90.7 0.30 92.2 2a 0.42 93.1 0.33 92.9 0.38 89.7 0.34 91.4 2b 0.32 93.1 0.36 93.5 0.28 91.1 0.31 88.4 2c 0.47 93.6 0.39 93.7 0.33 89.2 0.32 90.6 3a 0.24 92.3 * 94.0 0.30 91.6 0.38 92.0 3b 0.35 92.9 0.21 94.4 0.32 90.2 0.42 91.4 3c 0.37 93.9 0.23 94.9 0.41 89.1 0.27 92.8 4a 0.33 90.9 0.27 93.8 0.33 91.8 0.41 92.7 4b 0.31 92.1 0.31 93.6 0.35 90.7 0.26 91.9 4c 0.36 91.8 0.24 93.9 0.37 91.2 0.36 93.2 5a 0.44 93.6 0.22 91.4 0.26 92.0 0.39 92.9 5b 0.35 93.1 0.31 93.5 0.25 91.8 0.30 92.8 5c 0.34 91.9 0.33 94.4 0.23 90.0 0.42 92.6 6a 0.43 93.0 0.29 94.5 0.39 93.3 ** 6b 0.35 93.3 0.23 94.1 0.40 93.4 0.22 93.2 6c 0.32 92.7 0.35 94.6 ** 0.22 91.5 * sample lost

240 Table 4. Comparison of total phosphorus content in P. arundinacea leaves between this study and that conducted in 2011 (Gazzetti 2011)) for the control and treatment wetlands. y y Control Treatment % P of Dry weight % P of Dry weight Location Site Bouillon(2016) Gazzetti (2011) Bouillon(2016) Gazzetti (2011) Near 1a 0.362070776 0.194 0.452153754 0.407 Near 1b 0.299290187 0.208 0.452206689 0.481 Near 1c 0.267470638 0.248 0.296489066 0.59 Near 2a 0.328881684 0.274 0.415867883 0.415 Near 2b 0.357447254 0.275 0.317220582 0.373 Near 2c 0.390617152 0.301 0.469048555 0.296 Near 3a * 0.248 0.244429971 0.171 Near 3b 0.205099002 0.291 0.346678573 0.196 Near 3c 0.226571508 0.233 0.372189603 0.232 Near 4a 0.267892425 0.217 0.333920063 0.227 Near 4b 0.308620695 0.23 0.306477889 0.296 Near 4c 0.241789964 0.219 0.358413328 0.259 Near 5a 0.217352225 0.253 0.441394612 0.239 Near 5b 0.307531896 0.241 0.347403187 0.252 Near 5c 0.333867425 0.322 0.338491299 0.206 Near 6a 0.293749641 0.247 0.429811424 0.123 Near 6b 0.225336985 0.203 0.354563217 0.214 Near 6c 0.347401838 0.186 0.322612898 0.228 Mean 0.292999488 0.243888889 0.366631811 0.289166667 Std error 0.013721313 0.008864591 0.014891372 0.028159472 * sample lost In conclusion, this study found that total phosphorous levels were higher within the treatment wetland overall, even when compared to Gazzetti’s study (2012), which conforms to our hypothesis. However, the total nitrogen values were higher within the treatment wetland near sites, which did not support our hypothesis. However, this study did find statistically significant differences between near and far site nutrient concentrations within both wetlands, which supported our hypothesis as well. All in all, the treatment wetland seems to be taking up nutrients from the treated wastewater and soil, making it a successful treatment for nutrients in wastewater before it is discharged into the Susquehanna River.

241 REFERENCES

Albright, M.F. 2015. Personal communication. SUNY Oneonta Bio. Fld. Sta.

Albright, M.F. 2015. Monitoring the effectives of the Cooperstown wastewater treatment wetland, 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta..

Albright, M.F. 2013. Monitoring the effectiveness of the Cooperstown wastewater treatment wetland. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F., and H.A.Waterfield. 2012. Monitoring the effectiveness of the Cooperstown wastewater treatment wetland. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Bickelhaupt, D.H. and E.H. White. 1982. Laboratory manual for soil and plant tissue analysis. State University of New York, Environmental Science and Forestry.

Chesapeake Bay Program. The Bay Ecosystem. Web. 28 August 2015. Cronk, J.K. and M.S. Fennessey. 2001. Wetland Plants, Biology and Ecology. Lewis Publishers. Boca Raton, London, New York, Washington, D.C.

Eastwood, G.W. Calcium’s role in plant nutrition. Web. 3 February 2016.

Ebina, J., T. Tsutsi, and T. Shirai. 1983. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res.7(12):1721- 1726.

EPA 2014. Vernal Pools. Web. 16 August 2015.

EPA. 1983. Methods for the analysis of water and wastes. Environmental Monitoring and Support Lab. Office of Research and Development, Cincinnati, OH.

Gazzetti, E. 2012. Efficacy of emergent plants as a means of phosphorus removal in a treatment wetland, Cooperstown, New York. In 45th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

USDA. National Resource Defense Council. New York and Chesapeake Bay Watershed. Web. 13 August 2015. Olsen, B. 2011. Phosphorus content in reed canary grass (Phalaris arundinacea) in a treatment wetland, Cooperstown, NY. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

242 The fish assemblages of the selected Otsego Lake tributaries

J. Benjamin Casscles1

INTRODUCTION

The Otsego Lake watershed encompasses and area of 18,811 ha (Harman et al. 1997). The tributary streams in the Otsego Lake watershed have been individually surveyed by numerous authors (Hayes 1990, Bassista & Foster 1995, Reynolds et al. 2010, Miner 1997, Foster 1996, Jamieson et al. 2004). Early Biological Field Station fisheries surveys (New 1971, 1973; Harman et al., 1980, MacWatters 1980, 1983) focused on developing comprehensive listings of the fish fauna of Otsego Lake and its tributaries. Unfortunately, they did not separate stream fauna from lake fauna, nor did they describe the fish fauna of specific streams in the Otsego Lake watershed (Foster 1996).

The relative abundance and species composition of stream fishes provide good indicators of the impacts of land use, nutrient input, sediment load and alteration of riparian vegetation (Karr 1981). The following research uses changes in fish assemblages to evaluate efficacy of Best Management Practices.

The goal of the study was to characterize current fish assemblages among Cripple Creek, Hayden Creek, Shadow Brook, and White Creek, then to compare fish assemblages to water quality parameters and historical data.

MATERIALS & METHODS

The following tributaries were surveyed; Cripple Creek, Hayden Creek, White Creek and Shadow Brook, Otsego & Herkimer Counties, New York (Figure 1). The research was conducted between 7 July 2015 and 17 July 2015 (Table 1.) Sites evaluated were a subset of sites sampled in a long-term effort to gauge changes in water quality that could be attributed to land use changes (i.e., Wells 2016).

Each site was sampled with a Smith-Root LR-24 backpack electrofisher unit for 500 seconds. At each site, 250 seconds of effort was exerted both above and below the GPS coordinates shown in Table 1. Surveys began downstream of GPS coordinates and ended upstream of coordinates to avoid biased introduced by downstream avoidance. CPUE (fish/hr) catch rates were extrapolated to offer more tangible conclusions regarding fish assemblages. All fish were identified and total length was recorded to the nearest millimeter.

243 Table 1. Date, Elevation GPS coordinates and Physical Descriptions of sample locations (modified from Wells 2016).

Cripple Creek 3 (CC3) Date: 7/7/15 Elevation: 1222.3 feet Latitude & Longitude: N 42º 49.418’ W 74º 54.007’ Physical Description: North side of culvert on Bartlett Road.

Cripple Creek 5 (CC5): Date: 7/6/2015 Elevation: 1199.2 feet Latitude & Longitude: N 42º 48.805’ W 74º 53.768’ Physical Description: Dam just south of Clarke Pond accessed from the Otsego Golf Club road.

Hayden Creek 7 (HC7): Date: 7/7/15 Elevation: 1221.3 feet Latitude & Longitude: N 42º 49.279’ W 74º 53.984’ Physical Description: Large culvert on the south side of County Route 53.

Shadow Brook 3 (SB3): Date: 7/17/2015 Elevation: 1259.9 feet Latitude & Longitude: N 42º 48.799’ W 74º 49.839’ Physical Description: Private driveway (Box 2075) off of County Route 31, south of the intersection of Route 20 and Country Route 31 leading to a small wooden bridge on a dairy farm.

Shadow Brook 5 (SB5): Date: 7/7/15 Elevation: 1204.3 feet Latitude & Longitude: N 42º 47.441’ W 74º 51.506’ Physical Description: North side of large culvert on Mill Road behind Glimmerglass State Park.

White Creek 2 (WC2): Date: 7/6/2015 Elevation: 1431.7 feet Latitude & Longitude: N 42º 48.93’ W 74º 55.29’ Physical Description: Plunge-pool side of stream on County Route 27 (Allen Lake Road) where there is a large dip in the road.

White Creek 3 (WC3): Date: 7/6/2015 Elevation: 1246.0 feet Latitude & Longitude: N 42º 48.407’ W 74º 54.178’ Physical Description: West side of large stone culvert under Route 80, just past the turn to Country Route 27.

244

Figure 1. Map showing sampling locations of four tributaries sampled in the Otsego Lake watershed.

RESULTS & DISCUSSION

Catch per unit effort and relative abundance of each species collected at each site are given in Tables 2-9. The greatest overall species diversity observed was at Cripple Creek (CC3), with 9 fish species collected. It was the only location in which brown trout (Salmo trutta) were present. Cripple Creek (CC3) also was the only site where river chub (Nocomis micropogan) and fantail darter (Etheostoma flabellare) were observed. This suggests that fish were seeking thermal refuge, as the lowest temperatures recorded in previous studies support these indices in fish assemblages there. Over the summer of 2013, the lowest value of 13.17 °C was located at site CC3 (Teter 2013). In 2015, mean temperatures ranged from 15.26 °C at Cripple Creek 3 to 22.87 °C at Hayden Creek 1 (Wells 2016).Greater habitat diversity supported not only fish normally present in cold water lotic systems, but fish of the weedy littoral like largemouth bass (Table 2).

245 Table 2. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in Cripple Creek (CC3).

Species CPUE (fish/hr) Relative Abundance (%) Blacknose dace 166 52% Brown trout 14 5% Creek chub 7 2% Cutlips minnow 14 5% Fantail darter 7 2% Largemouth bass 29 9% River chub 7 2% Tessellated darter 43 14% White sucker 29 9%

Table 3. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in Cripple Creek (CC5).

Species CPUE (fish/hr) Relative Abundance (%) Northern hogsucker 7 13% Pumpkinseed 14 25% Rock bass 7 13% Tessellated darter 7 13% Yellow Perch 22 38%

Table 4. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in Hayden Creek (HC7).

Species CPUE (fish/hr) Relative Abundance (%) Blacknose dace 50 19% Creek chub 86 33% Longnose dace 115 44% Yellow perch 7 3%

246 Shadow Brook (SB3) was the only stream margined madtoms (Noturus insignis) were present and one of two streams cutlips minnows (Exoglossum maxillingua) were present (Table 5). Table 5. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in Shadow Brook (SB3).

Species CPUE (fish/hr) Relative Abundance (%) Cutlips minnow 209 54% Common shiner 14 4% Longnose dace 58 15% Margined madtom 29 7% Tessellated darter 58 15% White sucker 22 6%

Table 6. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in Shadow Brook (SB5).

Species CPUE (fish/hr) Relative Abundance (%) Longnose dace 7 5% Pumpkinseed 7 5% Rock bass 36 25% Tessellated darter 7 5% White sucker 7 5% Yellow perch 79 55%

The lowest species diversity was observed at White Creek (WC2), where previous studies indicate warm summer temperatures of >20 oC (Teter 2013). White Creek (WC2) is dominated by shallow monotypic riffles, minimal sinuosity and prolonged sun exposure (Table 7). WC2, located just north of Wilsey Rd., was a pool surrounded by grassland with no canopy (Cole 1996). Low species diversity was concluded to be a result of limited habitat diversity and high summer temperature fluctuation.

Table 7. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in White Creek (WC2).

Species CPUE (fish/hr) Relative Abundance (%) Longnose dace 338 96% Creek chub 14 4%

247 Table 8. Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in White Creek (WC3).

Species CPUE (fish/hr) Relative Abundance (%) Blacknose dace 252 66% Creek chub 115 30% Tesselated Darter 14 4%

Table 9. Combined Catch Per Unit Effort (Fish/Hour) and Relative Abundance (%) of fish in Cripple Creek (CC3), (CC5);Hayden Creek (HC7);Shadow Brook (SB3), (SB5);White Creek (WC2), (WC3).

Species CPUE (fish/hr) Relative Abundance (%) Blacknose dace 468 25% Brown trout 14 1% Common shiner 14 1% Creek chub 223 12% Cutlips minnow 223 12% Fantail darter 7 <1% Largemouth bass 29 2% Longnose dace 518 27% Margined madtom 29 2% Northern hogsucker 7 <1% Pumpkinseed 22 1% River chub 7 <1% Rock bass 43 2% Tessellated darter 130 7% White sucker 58 3% Yellow Perch 108 6%

248 CONCLUSION

A total of 16 species were identified throughout the seven electro fishing surveys performed. The most abundant species in 2015 were longnose dace (Rhinichthys cataractae) n=27 % and blacknose dace (Rhinichthys atratulus) n=25 %. The only fish considered to be widely distributed throughout the watershed were: blacknose dace (9 sites), longnose dace (7 sites), and creek chub (8 sites) (Foster 1996).

As part of the Susquehanna River drainage, blacknose dace, brook trout and sculpins would be expected to dominate (Holcutt and Wiley 1986). However, the former two species were not observed. Cyprinids accounted for over 50 % of fish species identified, which is historically typical amongst the watershed. Since 1988, 32 species of fish, representing 8 families have been captured in the streams feeding Otsego Lake. These are dominated by minnows (Cyprinidae), which make-up between 52% (Basssita and Foster) and 100% (in Willow Brook, Three-mile Point Stream) of the fish fauna (Foster 1996).

Fish generally occupying weedy littoral were seen lower abundances: largemouth bass (Micropterus salmoides) comprised 6% of the catch, pumpkinseed (Lepomis gibbosus) 1%, and yellow perch (Perca flavescens) 6%. Lotic habitat supporting Centrarchids were characterized by low water velocity and established submergent vegetation.

LITERATURE CITED

Bassista,T.P. and J.R. Foster. 1995. Relative abundance and species composition of fish in Shadow Brook, Otsego County, New York. In 27th Ann. Rept. (1994). SUNY Oneonta Biol. Field Station. SUNY Oneonta.

Craig I. Cole 1996 A Characterization of White Creek/Trout Creek of Otsego County. In 28th Ann. Rept. (1995). SUNY Oneonta Biol. Field Station. SUNY Oneonta.

Foster, J. R.1996. The fish fauna of the Otsego Lake watershed. In 28th Ann. Rept. (1995). SUNY Oneonta Biol. Field Station. SUNY Oneonta.

Hastings, C. 2015. Water quality monitoring if five major tributaries in the Otsego Lake watershed, summer 2014. In 47th Ann. Rept. (2014). Bio. Fld. Sta., SUNY College at Oneonta.

Hayes, S. A. 1991. Preliminary survey of the fisheries ecology of Leatherstocking Creek. In 23rd Ann. Rept. (1990). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Hocutt, C.H., and E.O. Wiley (eds.). 1986. The zoogeography of North American freshwater fishes. New York.

249

Karr, J.R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6(6):21-27.

Miner, M.M. 1997. A fisheries survey of Cripple Creek. In 29th Ann. Rept. (1996). SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta.

Reynolds, R.J., J.C. Lydon and J.R. Foster. 2011. Fish faunal changes in Otsego Lake’s Shadow Brook watershed following application of best management practices. In 43rd Ann. Rept. (2010) SUNY Oneonta Bio. Field. Station. SUNY Oneonta.

Teter, C. 2014. Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Wells, B. 2016. Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

250 Total mercury concentration in fish tissue relative to length and weight

David M. Snyder1, Colleen R. Parker1, Yokota Kiyoko1

ABSTRACT

A 2011 Fish Advisory published by the New York State Department of Health listed several lakes and rivers in Central New York as a concern for mercury. Goodyear Lake in Otsego County was among these waters. Children and women of childbearing age were advised to not consume any of the fish listed, and men should limit their intake to 4 meals per month. As a baseline study, we chose to investigate how mercury is accumulating in fish tissue, based on fish species, length and weight in Goodyear Lake. Fish tissue samples were collected from walleye, yellow perch, smallmouth bass, and largemouth bass, and were sent to Syracuse University Lab for mercury analysis. All 29 fish tissue samples were above the New York State Environmental Protection Agency mercury limit of 0.30 ng/Kg. Four of these samples were above the intolerable limit of 1.00 ng/Kg for consumption. We are currently investigating if these values show any trends based on species, length and weight, and are hoping to collect more fish tissue from Goodyear and other lakes in the future.

INTRODUCTION

In recent years, mercury (Hg) levels in freshwater ecosystems have increased to a point that fish advisories, documentation of which water bodies are affected and to what extent, are placed on freshwater bodies in United States. The main contributor of Hg pollution into today’s environment is anthropocentric (Taylor et al. 2014). As Hg is precipitated from the atmosphere, it enters waterways where it often starts its transformation from Hg to methyl mercury (MeHg) (Taylor et al. 2014). Hg is converted to the more toxic and organic form, MeHg, by bacterial- mediated process (Gilmour et al. 1992). MeHg, considered a neurotoxin, enters the aquatic environment and is bio-magnifies through the food web. By the time humans consume fish, the concentrations of MeHg is considered harmful (UNEP 2013).

We looked at the levels of MeHg in water and soil concentrations from Goodyear Lake (Figure 1) and various locations in the upper Susquehanna River. Fortunately, all locations tested for MeHg, in water were under the United States Environmental Protection Agency (USEPA) detection limits for MeHg (0.02 ng/L) when no background elements or interferences were present (USEPA 1998). As well, Hg in soil concentrations were all under EPA detection limits for MeHg, 5 µg/kg (USEPA 2003). A New York State Department of Health (NYSDOH) report, in 2014 on Chemicals in Sportfish and Game Species, placed a fish consumption advisory on Central New York (NYSDOH 2014). Goodyear Lake, Otsego County, New York was listed as having elevated levels of MeHg in its fish. The advisory stated that, because of the high levels of MeHg, males

251 should only consume approximately one fish per week, and women, of child bearing age, should not consume fish at all (NYSDOH 2014). The aim of this study was to find Hg concentrations in fish, and to test the effect of length and the effect of weight on concentrations of MgHg in fish from Goodyear Lake, Otsego County, New York (Figure 1).

METHODS

We collected four fish species (n=28) (Table 1), in April of 2014, from Goodyear Lake. Within Goodyear Lake, we set up three fish traps in different intake channels. We recorded the length and weight of each fish.

We collected fillets which were analyzed by Syracuse University for analysis (USEPA 1998). Syracuse University performed a Direct Mercury Analyzer (EPA method 7473A) and recorded the total mercury value for each sample (USEPA 1998). We analyzed tissue for total Hg (THg) based on the Simonin et al. study (2008) which found that 95% of the Mg in fish is MeHg. Since 95% of the Mg in fish is MeHg, we use THg as a substitute (Simonin et al. 2008). We collected samples of Micropterus salmoides, Micropterus dolomieu, Perca flavescens, and Sander vitreus.

C B A

Figure 1. Map of Goodyear Lake, Otsego County, NY. (A) site 1, (B) site 2, and (C) site 3 (Google Maps).

RESULTS Table 1 provides the mean weight and length for each species collected. Figure 2 provides the THg for each species. Of the samples, four were found to be above 1.0 µl/L. Of the four samples that were above the 1.0 µl/L, 50% were S. vitreus and the other 50% were M. dolomieu.

252 Table 1. Summary statistics for fish collected from Goodyear Lake, 2014. N – number of composite fish sample analyzed in study.

Figure 2 shows us that 100% of the samples are above the United States Environmental Protection Agency’s fish criterion level of 0.3 µl/L (NYSDOH 2014). As well, we found that 14% of our samples were above the United States Food and Drug Administration action level of 1 µl/L (USFDA 1995). Our data support the NYSDEC’s implementation of the advisory on Goodyear Lake. As well, we also advise that males only eat four fish a month, and women, especially of childbearing age, shouldn’t consume fish from Goodyear Lake. We also found a positive correlation between weight and Hg levels and between length and Hg. We attribute these two correlations to the bio-accumulation potential of older fish. An increase in the length and/or weight has shown to correlate with the age of the fish. We could conclude that the older the fish, the higher bio-accumulation potential of Hg.

USFDA Action level (1 µl/L)

USEPA fish criterion level (0.3 µl/L)

Figure 2. Mean concentrations of Hg/g relative to fish species. Error bars are +/- 1 standard.

Unfortunately, we had problems that could have affected our data. The first problem was inadequate sample size. Due to our limited budget we could only assess 28 fish, and that sample size was insufficient. Another problem was that the weight of some fish was higher than

253 expected. We attribute the inconsistency in weights to a mix of spawned and un-spawned females. At the time we took our samples, it was spawning season, and, due to the added weight of the eggs, the mass of female fish was often elevated. We recommend increased funding to allow for a statistically significant sample size. To reduce the effect eggs had on the weight of the fish, we recommend collecting samples after the spawning period. We further recommend an examination of gut contents to determine if fish dietary patterns of have an effect on Hg content.

REFERENCES Gilmour, C.C., E.A.Henry and R.Mitchell. 1992. Sulfate Stimulation of Mercury Methylation In Freshwater Sediments. Environmental Science and Technology 26:2281-2267.

NYSDOH, 2014. Chemicals in Sportfish and Game. US Department of Health, Herkimer, NY. Simonin, HA, Loukmas JJ, Skinner LC, and Roy KM. 2008. Lake Variability: Key Factors Controlling Mercury Concentrations in New York State Fish. Environmental Pollution 154.1:107-15.

Simonin, H.A., J.J. Loukmas, L.C. Skinner, and K.M. Roy. 2008. Lake variability: Key factors controlling mercury concentrations in New York State fish. Environmental Pollution 154: 107– 115. doi:10.1016/j.envpol.2007.12.032.

Taylor, D.L., N.J. Kutil, A.J. Malek and J.S. Collie. 2014. Mercury bioaccumulation in cartilaginous fishes from Southern New England coastal water: Contamination from a tropic ecology and human health perspective. Marine Environmental Research 99:20-33.

UNEP, 2013. Global Mercury Assessment 2013: Sources, Emissions, Releases and Environmental Transport. United Nation Environmental Protection Chemicals Branch, Geneva, Switzerland.

USEPA, 1998. Methyl Mercury in Water by Distillation, Aqueous Ethylation, Purge and Trap, and Cold Vapor Atomic Fluorescence Spectrometry. US Environmental Protection Agency, Washington, DC.

USEPA, 2000. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories, Volume 1 Fish Sampling and Analysis, Third Edition. US Environmental Protection Agency, Washington, DC.

USEPA, 2003. EPA Field Demonstration Quality Assurance Project Plan, Field Analysis of Mercury in Soil and Sediment. US Environmental Protection Agency, Washington, DC.

USFDA, 1995. Mercury in Fish: Cause for Concern? US Food and Drug Administration, Maryland.

254 Summer 2015 BioBlitz Series

Elizabeth Clifton1

INTRODUCTION

In 2014, a series of Bioblitz events were held at three Otsego Land Trust properties: Fetterley Forest Conservation Area, Brookwood Point, and Compton Bridge Conservation Area. This work was continued in 2015 with repeat Bioblitz events at the larger properties (Fetterley and Brookwood) and one at the Parslow Road Conservation Area. Each event was about 3-4 hours long and all were open to the public in an attempt to promote community involvement.

The history of BioBlitz events was detailed in the 2014 report (Davidson 2015), and is provided here: In 1996, Susan Rudy of the U.S. National Park service coined the term “bioblitz,” short for biodiversity blitz, while organizing the first Bioblitz at Kenliworth Aquatic Gardens in Washington, D.C. (Post 2003; Ruch et al. 2010). A bioblitz is a rapid evaluation of the flora and fauna found in a designated area during a given period of time (Ruch et al. 2010). A bioblitz generally lasts 24 hours to document organisms found at different times of day, but is just a “snapshot” of the organisms found at a particular site, and does not include seasonal variations in communities. Bioblitzes are done to characterize taxonomic diversity, promote citizen involvement and interest in local biodiversity, and to protect species and their habitats (USGS 2009).

The first Bioblitz of 2015 was held on 29 June at Brookwood Point Conservation Area, located on Otsego Lake, north of Cooperstown, NY (Figure 1, Figure 2). The property is about 22-acres in size and shares its eastern boundary with the shore of Otsego Lake. The property has been protected by OLT since October 2011. Ecosystems that can be observed at Brookwood include meadows, young and old forest, a lake, wetlands, a stream, and a well-maintained garden. All of these systems made Brookwood a great first location for this bioblitz series.

The second Bioblitz was held 9 July at the Fetterley Forest Conservation Area (Figure 1, Figure 3). Fetterley Forest is located in Richfield, NY, is about 106-acres, and has been protected by OLT since August 2011. It was donated to the Land Trust by the Fetterley family, who had owned it since 1867. From the overlook on the Western side of Panther Mountain, one can view Canadarago Lake and Deowongo Island, both part of OLT’s Blueway Trail, which is maintained by the Land Trust and is open to the public.

The third Bioblitz of the summer was held 16 July at the Parslow Road Conservation Area, located in Schuyler, NY (Figure 1, Figure 4). The property is about 28-acres and contains Oaks Creek (a trout stream) and a wetland that includes beaver ponds and floodplain forests. The

1 SUNY Oneonta Biology Department Intern, summer 2015. Current affiliation: SUNY Oneonta. This work was conducted with support by the Otsego Land Trust.

255 Oaks Creak Conservation Area is upstream, but is accessible only from the stream-side; however, the Land Trust is working on adding this location to the Blueway Trail to make it more accessible to the public.

Figure 1. Locations of the three bioblitzes held in Otsego County. Each blitz occurred on land owned by the Otsego County Land Trust.

256

Figure 2. Map of Brookwood Point Conservation Area property.

Figure 3. Map of Fetterley Forest Conservation Area property lines and trails.

257

Figure 4. Map of Parslow Road Conservation Area property.

METHODS

The first Bioblitz occurred at Brookwood Point from 9am-12pm on 29 June 2015. The next was Fetterley Forest Conservation Area during the same time frame on 9 July 2015. The last was Parslow Road Conservation Area during the same times on 16 July 2015. Researchers and students working at the SUNY College at Oneonta Biological Field Station, who are knowledgeable on identification of various organisms, met with community members at each location in an attempt to assess as many organisms as possible.

The equipment used at each Bioblitz location included: terrestrial and aquatic nets, forceps, light and compound scopes, various field guides, binoculars, flashlights, magnifying glasses, and backpack electrofishing equipment. Fetterley Forest does not have a water source and thus there were no aquatic surveys done here, such as electrofishing and aquatic invertebrate collection. During the allotted time participants identified as many organisms as possible. Each organism was classified down to species, if possible.

258 RESULTS

At Brookwood Point 217 organisms were identified, Fetterley Forest Conservation Area had 205 identified, and Parslow Road Conservation Area had 226 identified. The breakdown of each taxa by class is shown in Figure 5. A summary of the number of taxa found in each class is shown in Table 1. Complete taxonomic lists for Brookwood Point, Fetterly Forest Conservation Area and Parslow Road Conservation Area are presented in Tables 2-4.

Figure 5. Number of taxa identified in each class for the three Bioblitz locations.

259 Table 1. Summary of the number of taxa identified in each class for the three bioblitzes.

Class Brookwood Fetterley Parslow Aves 22 17 19 Amphibia 1 3 1 Mammalia 3 2 2 Actinopterygii 5 0 12 Insecta 37 52 48

Arachnida 7 7 2

Liliopsida 3 3 0 Magnoliopsida 115 100 124 Pinopsida 3 2 1 Polypodiopsida 2 8 3 Pteridopsida 7 0 0 Algae 8 0 10

Other 3 7 2

Total 216 201 224

DISCUSSION

The total numbers of taxa identified from each Land Trust site ranged from 201-224, which are consistent both between properties and with previous Bioblitz counts. This shows that even though the sizes (22-106 acres) and habitat types varied immensely between the three sites they all still have a good amount of diversity. This is surprising because the number of organismal experts varied from site to site, as did the number of participants.

Although these results are not quantifiable, this Bioblitz series was a great way to get the community involved in science and the outdoors. It was also beneficial because it enabled the community members of Otsego County to meet some of the biological researchers that may do fieldwork near their homes. Although our Bioblitzes were only about 3-4 hours long and we undoubtedly missed many organisms due to absence of identification experts in certain taxonomic areas, this series was an overall great experience for everyone from the community, to the biological community, to the Otsego Land Trust.

260 Table 2. All taxa observed at Brookwood on 29 June 2015 (between 9am and 12pm).

ANIMALS

Class Order Family Genus Species Common Name Kingdom Phylum Aves Anseriformes Anatidae Aix sponsa Wood duck Aves Anseriformes Anatidae Anas platyrhynchos Mallard Aves Anseriformes Anatidae Mergus merganser americanus Common merganser Aves Charadriiformes Laridae Larus delawarensis Ring-billed gull Aves Passeriformes Bombycillidae Bombycilla cedrorum Cedar waxwing Aves Passeriformes Corvidae Corvus brachyrchynchos American crow Aves Passeriformes Corvidae Cyanocitta cristata Blue jay Aves Passeriformes Emberizidae Junco hyemalis Dark-eyed junco Aves Passeriformes Emberizidae Melospiza melodia Song sparrow Aves Passeriformes Hirundinidae Tachycineta bicolor Tree swallow Aves Passeriformes Icteridae Agelaius phoeniceus Red-winged blackbird Aves Passeriformes Mimidae Dumetella carolinensis Gray catbird Aves Passeriformes Paridae Poecile atricapillus Black-capped chickadee Aves Passeriformes Parulidae Setophaga pensylvanica Chestnut-sided warbler Aves Passeriformes Parulidae Setophaga petechia American yellow warbler Aves Passeriformes Parulidae Setophaga ruticilla American redstart Aves Passeriformes Troglodytidae Troglodytes aedon House wren Aves Passeriformes Tyrannidae Tyrannus tyrannus Eastern king bird

Chordata Aves Passeriformes Vireonidae Vireo olivaceus Red-eyed vireo Aves Passeriformes Picidae Colaptes auratus Northern flicker Aves Pelecaniformes Ardeidae Ardea herodias Great blue heron Aves Piciformes Picidae Hylatomus pileatus Pileated woodpecker Aves Piciformes Picidae Sphyrapicus varius Yellow-bellied sapsucker

Amphibia Caudata Plethodontidae Plethodon cinereus Red back salamander

Mammalia Rodentia Sciuridae Sciurus carolinensis Eastern gray squirrel Mammalia Rodentia Sciuridae Tamias striatus Eastern chipmunk Mammalia Rodentia Sciuridae Tamiasciurus hudsonicus American red squirrel

Actinopterygii Cypriniformes Catostomidae Campostomus commersonii White sucker Actinopterygii Cypriniformes Cyprnidae Notemigonus crysoleucas Golden shiner Actinopterygii Cypriniformes Cyprnidae Notropis hudsonius Spottail shiner Actinopterygii Cypriniformes Cyprnidae Phinichthys atratulus Blacknose dace Animalia Actinopterygii Cypriniformes Cyprnidae Semotilus atromaculatus Creek chub

Insecta Coleoptera Cantharidae sp. Soldier beetle Insecta Coleoptera Chrysomelidae Galerucinae sp. Leaf beetle Insecta Coleoptera Elateridae sp. Click beetle Insecta Coleoptera Lampyridae sp. Firefly Insecta Diptera Callophoridae 2 distinct species Blowfly Insecta Diptera Chironomidae sp. Midge Insecta Diptera Chloropidae sp. Grass fly Insecta Diptera Culicidae sp. Mosquito Insecta Diptera Dolichopodidae sp. Long-legged fly Insecta Diptera Drosophilidae sp. Vinegar fly Insecta Diptera Muscidae sp. House fly Insecta Diptera Mycetophilidae sp. Fungus gnat Insecta Diptera Sciaridae sp. Dark-winged fungus gnat Insecta Diptera Sciomyzidae sp. Snail-killing fly or Marsh fly Insecta Diptera Syrphidae 2 distinct species Hover fly Insecta Diptera Tachinidae sp. Parasitoid fly

Arthropoda Insecta Diptera Tipulidae Hexatoma sp. Crane fly Insecta Ephemeroptera Baetidae Baetis sp. Small minnow mayfly Insecta Ephemeroptera Ephemeridae Hexagenia sp. Burrowing Mayfly Insecta Ephemeroptera Heptageniidae Epeorus sp. Mayfly Insecta Hemiptera Aleyrodidae sp. White fly Insecta Hemiptera Cercopidae 5 distinct species Frog hoppers Insecta Hemiptera Ciciadellidae sp. Plant hoppers Insecta Hemiptera Miridae sp. Plant bugs Insecta Hemiptera Membracidae sp. Tree hoppers Insecta Hemiptera Nabidae sp. Damsel bug Insecta Hymenoptera Cynipidae sp. Gall-making wasps Insecta Hymenoptera Formicidae 2 distinct species Ant Insecta Hymenoptera Ichneumonidae sp. Parasitoid wasp Insecta Hymenoptera Pompilidae sp. Spider wasps

261 Table 2 (Con't). All taxa observed at Brookwood on 29 June 2015 (between 9am and 12pm).

Insecta Lepidoptera Erebidae Lymantria dispar North American gypsy moth Insecta Mecoptera Panorpidae sp. Scorpionfly Insecta Odonata Coenagrionidae Coenagrion sp. Narrow-winged damselfly Insecta Odonata Cordulidae Epitheca sp. Dragonfly Insecta Orthoptera Acrididae sp. Grasshopper Insecta Orthoptera Gryllidae sp. Cricket Insecta Plecoptera Capniidae Allocapnia sp. Stonefly Insecta Plecoptera Leuctridae sp. Stonefly Animalia Arthropoda Arachnida Acarina Trombiculidae sp. Red velvet mite Arachnida Araneae Linyphiidae sp. Sheet weavers Arachnida Araneae Lycosidae sp. Wolf spider Arachnida Araneae Tetragnathidae Tetragnatha elongata Elongate stilt spider Arachnida Araneae Thomisiae sp. Crab spider Arachnida Araneae Salticidae sp. Jumping spider Arachnida Opiliones sp. Daddy longlegs/harvestman

PLANTS

Class Order Family Genus Species Common Name Kingdom Phylum Liliopsida Hydrocharitales Hydrocharitaceae Elodea canadensis Canadian waterweed Liliopsida Najadales Potamogetonaceae Potamogeton crispus Curly pondweed Liliopsida Typhales Typhaceae Typha latifolia Broadleaf cattail

Magnoliopsida Alismatales Araceae Arisaema triphyllum Jack-in-the-pulpit Magnoliopsida Alismatales Araceae Symplocarpus foetidus Eastern skunk cabbage Magnoliopsida Apiales Apiaceae Aegopodium podagraria Bishop weed or Goutweed Magnoliopsida Apiales Apiaceae Daucus carota Wild carrot or Queen Anne's Lace Magnoliopsida Asparagales Asparagaceae Maianthemum canadense Canadian mayflower Magnoliopsida Asparagales Iridaceae Iris pseudoacorus Yellow flag Magnoliopsida Asparagales Orchidaceae Epipactis helleborine Helleborine Magnoliopsida Asterales Asteraceae Achillea millefolium Yarrow Magnoliopsida Asterales Asteraceae Ambrosia artemisiifolia Ragweed Magnoliopsida Asterales Asteraceae Arctium minus Lesser burdock Magnoliopsida Asterales Asteraceae Aster sp. Aster Magnoliopsida Asterales Asteraceae Bidens sp. Beggerticks Magnoliopsida Asterales Asteraceae Cirsium palustre Swamp thistle or marsh thistle Magnoliopsida Asterales Asteraceae Echinops ritro Globethistle Magnoliopsida Asterales Asteraceae Erigeron annuus Eastern daisy fleabane Magnoliopsida Asterales Asteraceae Erigeron philadelphicus Fleabane Magnoliopsida Asterales Asteraceae Helenium sp. Sunflower Magnoliopsida Asterales Asteraceae Lapsana communis Nipplewort Magnoliopsida Asterales Asteraceae Nabalus sp. White lettuce Magnoliopsida Asterales Asteraceae Solidago sp. Goldenrod Magnoliopsida Asterales Asteraceae Tanacetum parthenium Feverfew

Plantae Magnoliopsida Asterales Asteraceae Taraxacum officinalis Dandelion

Magnoliophyta Magnoliopsida Asterales Brassicaceae Alliaria petiolata Garlic mustard Magnoliopsida Asterales Brassicaceae Thlaspi arvense Pennycress Magnoliopsida Brassicales Brassicaceae Hesperis matronalis Dame's rocket Magnoliopsida Caryophyllales Polygonaceae Rumex obtusifolius Dock leaf Magnoliopsida Celastrales Celastraceae Celastrus orbiculata Oriental bittersweet Magnoliopsida Cornales Cornaceae Cornus alternifolia Green osier or pagoda dogwood Magnoliopsida Cornales Cornaceae Cornus alba Red osier Magnoliopsida Dipsacales Adoxaceae Sambucus nigra Elderberry Magnoliopsida Dipsacales Caprifoliaceae Lonicera sp. Honeysuckle Magnoliopsida Dipsacales Valerianaceae Valeriana officinalis Common valerian Magnoliopsida Ericales Balsaminaceae Impatiens capensis Spotted jewelweed Magnoliopsida Ericales Ericaceae Epigaea sp. May flower Magnoliopsida Fabales Fabaceae Amorpha fruiticosa False indigo Magnoliopsida Fabales Fabaceae Amphicarpaea bracteata Hog peanut Magnoliopsida Fabales Fabaceae Robinia pseudoacacia Black locust Magnoliopsida Fabales Fabaceae Trifolium agrarium Hop clover Magnoliopsida Fabales Fabaceae Trifolium dubium Least Hop clover Magnoliopsida Fabales Fabaceae Trifolium hybridum Alsike clover Magnoliopsida Fabales Fabaceae Trifolium pratense Red clover Magnoliopsida Fabales Fabaceae Trifolium repens White clover Magnoliopsida Fagales Betulaceae Alnus incana Speckled alder Magnoliopsida Fagales Betulaceae Carpinus caroliniana Musclewood or American hornbeam Magnoliopsida Fagales Betulaceae Corylus americana American hazelnut

262 Table 2 (Con't). All taxa observed at Brookwood on 29 June 2015 (between 9am and 12pm).

Magnoliopsida Fagales Betulaceae Ostrya virginiana Hop hornbean Magnoliopsida Fagales Fagaceae Fagus grandifolia Beech tree Magnoliopsida Fagales Fagaceae Quercus rubra Red oak Magnoliopsida Gentianales Ascleiadaceae Asclepias incarnata Swamp milkweed Magnoliopsida Gentianales Ascleiadaceae Asclepias syriaca Common milkweed Magnoliopsida Gentianales Rubiaceae Galium odoratum Wild baby's breath Magnoliopsida Gentianales Rubiaceae Galium sp. Bedstraw Magnoliopsida Geraniales Geraniaceae Geranium robertainum Herb Robert Magnoliopsida Haloragales Haloragaceae Myriophyllum spicatum Eurasian watermilfoil Magnoliopsida Juglandales Juglandaceae Carya ovata Shagbark Hickory Magnoliopsida Lamiales Boraginaceae Myosotis sp. Forget-me-not Magnoliopsida Lamiales Lamiaceae Clinopodium vulgare Wild Basil Magnoliopsida Lamiales Lamiaceae Prunella vulgaris Self-heal Magnoliopsida Lamiales Oleaceae Fraxinus americana White ash Magnoliopsida Lamiales Plantaginaceae Plantago lanceolata Enlish plantain Magnoliopsida Lamiales Plantaginaceae Plantago major Broadleaf plantain Magnoliopsida Lamiales Plantaginaceae Veronica anagallis-aquatica American brookline Magnoliopsida Lamiales Plantaginaceae Veronica officinalis Common Speedwell Magnoliopsida Liliales Colchicaceae Uvularia sessilifolia Bellwort Magnoliopsida Malpighiales Hypericaceae Hypericum perforatum St. John's wort Magnoliopsida Malpighiales Salicaceae Populus deltoides Cottonwood Magnoliopsida Malpighiales Salicaceae Populus tremuloides Quaking aspen Magnoliopsida Malpighiales Salicaceae Salix sp. Willow Magnoliopsida Malpighiales Violaceae Viola sp. Violet flower Magnoliopsida Malvales Malvaceae Tilia americana Basswood Magnoliopsida Myrtales Lythraceae Lythrum salicaria Purple loosestrife Magnoliopsida Oxalidales Oxalidaceae Oxalis stricta Yellow woodsorrel Magnoliopsida Poales Cyperaceae Carex lurida Sedge Magnoliopsida Poales Juncaceae Juncus effusus Rush Magnoliopsida Poales Poaceae Agrostis sp. Bentgrass Magnoliopsida Poales Poaceae Avena sp. Wild oats Magnoliopsida Poales Poaceae Dactylis glomerata Orchard grass Magnoliopsida Poales Poaceae Microstegium vimineum Grass Magnoliopsida Poales Poaceae Phleum pratense Timothy grass Magnoliopsida Primlales Primulaceae Lysimachia terrestis Swamp candles Magnoliopsida Primlales Primulaceae Trientalis borealis Starflower Plantae Magnoliopsida Ranuculales Papaveraceae Chelidonium majus Greater celandine Magnoliophyta Magnoliopsida Ranunculales Berberidaceae Berberis thunbergii Japanese Barberry Magnoliopsida Ranunculales Berberidaceae Berberis vulgaris European Barberry Magnoliopsida Ranunculales Ranunculaceae Actaea pachypoda Dolls eye or white baneberry Magnoliopsida Ranunculales Ranunculaceae Aquilegia canadensis Columbine Magnoliopsida Ranunculales Ranunculaceae Hepatica acutiloba Liverwort or Liverleaf Magnoliopsida Ranunculales Ranunculaceae Ranunculus acris Buttercup Magnoliopsida Ranunculales Ranunculaceae Ranunculus septentrionalis Swamp buttercup Magnoliopsida Ranunculales Ranunculaceae Thalictrum sp. Rue Magnoliopsida Rosales Cannabaceae Humulus lupulus American hops Magnoliopsida Rosales Elaeagnaceae Elaeagnus angustifolia Russian Olive Magnoliopsida Rosales Rhamnaceae Rhamnus cathartica Common buckthorn Magnoliopsida Rosales Rosaceae Agrimonia sp. Agrimony Magnoliopsida Rosales Rosaceae Amelanchier arborea Shadbush or juneberry or serviceberry Magnoliopsida Rosales Rosaceae Crataegus sp. Hawthorn Magnoliopsida Rosales Rosaceae Filipendula rubra Queen-of-the-prairie Magnoliopsida Rosales Rosaceae Fragaria virginiana Stawberry Magnoliopsida Rosales Rosaceae Geum canadense White avens Magnoliopsida Rosales Rosaceae Physocarpus sp. Ninebark Magnoliopsida Rosales Rosaceae Prunus serotina Black cherry Magnoliopsida Rosales Rosaceae Rosa multiflora Multiflora rose Magnoliopsida Rosales Urticaceae Pilea pumila Canadian clearweed Magnoliopsida Rosales Urticaceae Urtica dioica Stinging nettle Magnoliopsida Sapindales Anacardiaceae Rhus typhina Smooth sumac Magnoliopsida Sapindales Anacardiaceae Toxicodendron radicans Poison Ivy Magnoliopsida Sapindales Sapindaceae Acer platanoides Norway maple Magnoliopsida Sapindales Sapindaceae Acer rubrum Red maple Magnoliopsida Sapindales Sapindaceae Acer saccharum Sugar maple Magnoliopsida Sapindales Sapindaceae Acer saccharinum Silver maple Magnoliopsida Saxifragales Crassulaceae Sedum sp. Sedum Magnoliopsida Saxifragales Grossulariaceae Ribes sp. Currant Magnoliopsida Saxifragales Hamamelidaceae Hamamelis virginiana Witch Hazel Magnoliopsida Solanales Convolvulaceae Convolvulaceae sp. Bindweed Magnoliopsida Vitales Vitaceae Parthenocissus quinquefolia Virginia creeper

263 Table 2 (Con't). All taxa observed at Brookwood on 29 June 2015 (between 9am and 12pm).

Magnoliophyta Magnoliopsida Vitales Vitaceae Vitis sp. Grapevine

Pinopsida Pinales Cupressaceae Thuja occidentalis Northern white cedar Pinopsida Pinales Pinaceae Pinus strobus White pine

Pinophyta Pinopsida Pinales Pinaceae Tsuga canadensis Hemlock

Polypodiopsida Blechnales Thelypteridaceae Phegopteris hexagonoptera Beech fern Polypodiopsida Blechnales Thelypteridaceae Thelypteris noveboracensis New York Fern

Pteridopsida Polypodiales Athyriaceae Athyrium filix-femina Lady fern

Plantae Pteridopsida Polypodiales Dryopteridaceae Dryopteris carthusiana Wood fern Pteridopsida Polypodiales Dryopteridaceae Dryopteris intermedia Wood fern Pteridopsida Polypodiales Dryopteridaceae Dryopteris marginalis Wood fern

Tracheophyta Pteridopsida Polypodiales Dryopteridaceae Matteuccia struthiopteris Ostrich fern Pteridopsida Polypodiales Dryopteridaceae Polystichum acrostichoides Christmas fern Pteridopsida Polypodiales Onocleaceae Onoclea sensibilis Sensitive fern

Equisetopsida Equisetales Equisetaceae Equisetum arvense Horsetail

OTHER

Class Order Family Genus Species Common Name Kingdom Phylum Conjugatophyceae Desmidiales Closteriaceae Closterium sp. Algae Conjugatophyceae Zygnematales Zygnemataceae Lepocinclis spirogyna Algae

Charophyta Conjugatophyceae Zygnematales Zygnemataceae Mougeotia sp. Algae

Chlorophyceae Sphaeropleales Radiococcaceae Gloeocystis sp. Algae

Plantae Chlorophyceae Oedogoniales Oedogoniaceae Oedogonium sp. Algae Chlorophyta

Bacillariophyceae Cymbellales Cymbellaceae Cymbella sp. Algae

Diatoms Bacillariophytina Bacillariophyceae Diatoma sp. Algae Diatoms Radical centric diatomsMelosirids Melosira sp. Algae Stramenopiles

Agaricomycetes Agaricales Tricholomataceae Tricholoma equestre Man on horseback Fungi

Basidiomycota Agaricomycetes Agaricales Physalacriaceae Strobilurus conigenoides Magnolia-cone mushroom

264 Table 3. All taxa observed at Fetterley Forest Conservation Area on 9 July 2015 (between 9am and 12pm).

ANIMALS

Kingdom Phylum Class Order Family Genus Species Common Name Aves Apodiformes Trochilidae Archilochus colubris Ruby-throated hummingbird Aves Passeriformes Bombycillidae Bombycilla cedrorum Cedar waxwing Aves Passeriformes Corvidae Corvus brachyrchynchos American crow Aves Passeriformes Corvidae Cyanocitta cristata Blue jay Aves Passeriformes Emberizidae Junco hyemalis Dark-eyed junco Aves Passeriformes Emberizidae Pipilo erythrophthalmus Rufous-sided towhee Aves Passeriformes Parulidae Geothlypis trichas Common yellowthroat Aves Passeriformes Parulidae Seiurus aurocapilla Ovenbird Aves Passeriformes Parulidae Setophaga pensylvanica Chestnut-sided warbler Aves Passeriformes Parulidae Setophaga virens Black-throated green warbler Aves Passeriformes Sittidae Sitta carolinensis White-breasted nuthatch Aves Passeriformes Troglodytidae Troglodytes aedon House wren Aves Passeriformes Turdidae Catharus fuscescens Veery

Chordata Aves Passeriformes Turdidae Hylocichla mustelina Wood thrush Aves Passeriformes Turdidae Turdus migratorius Robin Aves Passeriformes Tyrannidae Contopus virens Eastern pewee Aves Passeriformes Vireonidae Vireo olivaceus Red-eyed vireo Aves Piciformes Picidae Sphyrapicus varius Yellow-bellied sapsucker

Amphibia Anura Ranidae Rana clamitans Green frog Amphibia Caudata Ambystomatidae Ambystoma laterale Blue-spotted salamander Amphibia Caudata Plethodontidae Plethodon cinereus Red-backed salamander

Mammalia Rodentia Sciuridae Sciurus carolinensis Eastern gray squirrel Mammalia Rodentia Sciuridae Tamias striatus Eastern chipmunk

Insecta Coleoptera Cantharidae Soldier beetle Insecta Coleoptera Carabidae Ground beetle Insecta Coleoptera Cerambycidae Longhorn beetle Insecta Coleoptera Elateridae Click beetle Insecta Coleoptera Lampyridae Firefly Insecta Coleoptera Scarabaeidae Scarab beetle Insecta Dermaptera Forficulidae Earwig Insecta Diptera Agromyzidae Leaf-miner fly Animalia Insecta Diptera Calliphoridae Blow fly Insecta Diptera Dolichopodidae Long-legged fly Insecta Diptera Drosophilidae Vinegar fly Insecta Diptera Muscidae House fly Insecta Diptera Piophilidae Cheese fly Insecta Diptera Scathophagidae Dung-fly Insecta Diptera Sciaridae Dark-winged fungus gnats Insecta Diptera Syrphidae Hoverfly Insecta Diptera Tabanidae Horse-fly Insecta Diptera Tachinidae Parasitoid fly Insecta Diptera Therevidae Stiletto fly Insecta Hemiptera Aphididae Aphid Insecta Hemiptera Cercopidae Froghoppers

Arthropoda Insecta Hemiptera Cicadellidae Leafhoppers Insecta Hemiptera Miridae Plant bug Insecta Hemiptera Nabidae Damsel bug Insecta Hemiptera Psyllidae Jumping plant lice Insecta Hymenoptera Andrenidae Digger bee Insecta Hymenoptera Apidae Bee Insecta Hymenoptera Braconidae Parasitoid Insecta Hymenoptera Chrysididae Cuckoo-bee Insecta Hymenoptera Cynipidae Gall wasp Insecta Hymenoptera Formicidae Ant Insecta Hymenoptera Halictidae Sweat bee Insecta Hymenoptera Ichneumonidae Ichneumon wasp Insecta Hymenoptera Megachilidae Leaf cutter bees Insecta Hymenoptera Pompilidae Spider wasp Insecta Hymenoptera Sphecidae Thread-waisted wasp Insecta Hymenoptera Erebidae Tiger moth Insecta Lepidoptera Geometridae Inchworm Insecta Lepidoptera Lycaenidae Blues and Coppers Butterfly Insecta Lepidoptera Noctuidae Owlet moth

265 Table 3 (Cont'd). All taxa observed at Fetterley Forest Conservation Area on 9 July 2015 (between 9am and 12pm).

Insecta Lepidoptera Nymphalidae Brush-footed butterfly Insecta Lepidoptera Papilionidae Swallowtail butterfly Insecta Lepidoptera Pterophoridae Plume moth Insecta Lepidoptera Pyralidae Snout moth Insecta Lepidoptera Sphingidae Hawk moth Insecta Mecoptera Panorpidae Panorpa Scorpionfly Insecta Odonata Coenagrionidae Narrow-winged damselfly Insecta Odonata Libellulidae Dragonfly Insecta Orthoptera Acrididae Grasshopper Insecta Orthoptera Gryllidae Cricket Insecta Plecoptera Leuctridae Rolled-wing stonefly Insecta Psocoptera Psocidae Booklice Insecta Trichoptera Limnephilidae Caddisfly Arthropoda

Entognatha EntomobryomorphaIsotomidae Springtail Entognatha Symphypleona Sminthuridae Springtail

Arachnida Araneae Linyphiidae Sheet weaver Arachnida Araneae Lycosidae Wolf spider Arachnida Araneae Thomisidae Crab spider Arachnida Opiliones Harvestmen

Animalia Arachnida Oribatida Soil mites Arachnida Pseudoscorpionida False Scorpion Arachnida Trombidiformes Trombiulidae Red velvet mite

Oligochaeta Megadrilacea Earthworm Annelida

Gastropoda Snail

Gastropoda Mollusca Slug

Nematode Nematoda

Tardigrade Tardigrada

Rotifer Rotifera

PLANTS

Kingdom Phylum Class Order Family Genus Species Common Name Liliopsida Cyperales Cyperaceae Carex stipata Awlfruit sedge Liliopsida Cyperales Poaceae Elymus canadensis Canada wildrye Liliopsida Juncales Juncaceae Juncus tenuis Path rush

Magnoliopsida Alismatales Araceae Arisaema triphyllum Jack-in-the-pulpit Magnoliopsida Apiales Apiaceae Aralia nudicaulis Wild sasparilla Magnoliopsida Apiales Apiaceae Daucus carota Queen Anne's lace Magnoliopsida Asparagales Apiaceae Pastinaca sativa Wild parsnip Magnoliopsida Asparagales Asparagaceae Maianthemum canadense Canadian mayflower Magnoliopsida Asparagales Asparagaceae Smilacina racemosa False Soloman's seal Magnoliopsida Asparagales Orchidaceae Epipactis helliborine Helleborine Magnoliopsida Asterales Asteraceae Arctium minus Burdock Magnoliopsida Asterales Asteraceae Aster divaricatus White wood aster Plantae Magnoliopsida Asterales Asteraceae Centaurea jacea Brown knapweed Magnoliophyta Magnoliopsida Asterales Asteraceae Centaurea maculosa Spotted knapweed Magnoliopsida Asterales Asteraceae Chrysanthemum leucanthemum Ox-Eye daisy Magnoliopsida Asterales Asteraceae Cirsium palustre Marsh thistle Magnoliopsida Asterales Asteraceae Cirsium vulgare Bull thistle Magnoliopsida Asterales Asteraceae Erigeron annuus Daisy fleabane Magnoliopsida Asterales Asteraceae Erigeron philadelphicus Common fleabane Magnoliopsida Asterales Asteraceae Euthamia graminifolia Flat top goldenrod Magnoliopsida Asterales Asteraceae Lapsana communis Nipplewort Magnoliopsida Asterales Asteraceae Solidago sp. Goldenrod Magnoliopsida Asterales Asteraceae Tussilago farfara Coltsfoot

266 Table 3 (Cont'd). All taxa observed at Fetterley Forest Conservation Area on 9 July 2015 (between 9am and 12pm).

Magnoliopsida Brassicales Brassicaceae Allaria petiolata Garlic mustard Magnoliopsida Brassicales Brassicaceae Hesperis matronalis Dame's rocket Magnoliopsida Caryophyllales Caryophyllaceae Cerastium fontanum Mouse-ear chickweed Magnoliopsida Caryophyllales Caryophyllaceae Lychnis flos-cuculi Ragged robin Magnoliopsida Caryophyllales Polygonaceae Fallopia japonica Japanese bamboo Magnoliopsida Caryophyllales Polygonaceae Rumex crispus Curled dock Magnoliopsida Cornales Cornaceae Cornus alternifolia Alternate-leaved dogwood Magnoliopsida Cornales Cornaceae Cornus canadensis Bunchberry Magnoliopsida Cornales Cornaceae Cornus sericera Red osier dogwood Magnoliopsida Dipsacales Adoxaceae Viburnum lentago Nannyberry Magnoliopsida Dipsacales Caprifoliaceae Lonicera tatarica Tartarian honeysuckle Magnoliopsida Dipsacales Caprifoliaceae Sambucus nigra Black elderberry Magnoliopsida Dipsacales Valerianaceae Valeriana officinalis Valarian, Garden heliotrope Magnoliopsida Ericales Balsaminaceae Impatiens capensis Impatience Magnoliopsida Ericales Ericaceae Pyrola rotundifolia Shinleaf, Pyrola Magnoliopsida Ericales Ericaceae Rhododendron occidentale Western azalea Magnoliopsida Ericales Ericaceae Vaccinium sp. Blueberry Magnoliopsida Ericales Myrsinaceae Trientalis borealis Star flower Magnoliopsida Fabales Fabaceae Amphicarpaea bracteata Hog peanut Magnoliopsida Fabales Fabaceae Desmodium heterocarpon Tick trefoil Magnoliopsida Fabales Fabaceae Lotus corniculatus Bird's foot trefoil Magnoliopsida Fabales Fabaceae Trifolium aureum Hop-Clover Magnoliopsida Fabales Fabaceae Trifolium dubium Small hop clover Magnoliopsida Fabales Fabaceae Trifolium hybridum Alsike clover Magnoliopsida Fabales Fabaceae Trifolium pratense Red clover Magnoliopsida Fabales Fabaceae Trifolium repens White clover Magnoliopsida Fabales Fabaceae Vicia cracca Cow vetch Magnoliopsida Fagales Betulaceae Betula papyrifera White birch Magnoliopsida Fagales Betulaceae Corylus cornuta Beaked hazelnut Magnoliopsida Fagales Betulaceae Ostrya virginiana Eastern hophornbeam Magnoliopsida Fagales Fagaceae Fagus grandifolia American beech Magnoliopsida Fagales Fagaceae Quercus alba White oak Magnoliopsida Fagales Fagaceae Quercus rubra Northern red oak Magnoliopsida Gentianales Asclepiadaceae Asclepias exaltata Green milkweed Magnoliopsida Gentianales Rubiaceae Gallium sp. Bedstraw Magnoliopsida Gentianales Rubiaceae Mitchella repens Partridge berry Plantae Magnoliopsida Geraniales Geraniaceae Geranium maculatum Spotted cranesbill

Magnoliophyta Magnoliopsida Geraniales Geraniaceae Geranium robertainum Herb Robert Magnoliopsida Lamiales Lamiaceae Clinopodium vulgare Wild basil Magnoliopsida Lamiales Lamiaceae Prunella vulgaris Heal-all Magnoliopsida Lamiales Oleaceae Fraxinus americana American white ash Magnoliopsida Lamiales Plantaginaceae Plantago lanceolata English plantain Magnoliopsida Lamiales Plantaginaceae Plantago major Broadleaf plantain Magnoliopsida Lamiales Scrophulariaceae Verbascum thapsus Mullein Magnoliopsida Lamiales Verbenaceae Verbena urticifolia White verbena Magnoliopsida Liliales Lilaceae Clintonia borealis Bluebeads Magnoliopsida Malpighiales Salicaceae Populus grandidentata Big-tooth aspen Magnoliopsida Malpighiales Salicaceae Salix sp. Willow Magnoliopsida Malvales Malvaceae Malva moschata Musk mallow Magnoliopsida Malvales Malvaceae Tilia americana Basswood Magnoliopsida Myrtales Onagraceae Circaea lutetiana Enchanter's nightshade Magnoliopsida Oxalidales Oxalidaceae Oxalis stricta Yellow wood sorrel Magnoliopsida Poales Cyperaceae Carex lurida Sedge Magnoliopsida Poales Cyperaceae Carex vulpinoidea Fox sedge Magnoliopsida Poales Cyperaceae Scirpus sp. Bullrush Magnoliopsida Poales Juncaceae Juncus effusus Rush Magnoliopsida Poales Poaceae Glyceria sp. Grass Magnoliopsida Poales Poaceae Phleum pratense Timothy grass Magnoliopsida Ranunculales Berberidaceae Podophyllum peltatum Mayapple Magnoliopsida Ranunculales Ranunculaceae Ranunculus acris Common buttercup Magnoliopsida Ranunculales Ranunculaceae Ranunculus repens Creeping buttercup Magnoliopsida Ranunculales Ranunculaceae Taraxicum officinale Dandelion Magnoliopsida Ranunculales Ranunculaceae Thalictrum dioicum Meadow rue Magnoliopsida Rosales Rosaceae Agrimonia sp. Agrimony Magnoliopsida Rosales Rosaceae Amelanchier laevis Shadbush Magnoliopsida Rosales Rosaceae Fragaria virginiana Strawberry Magnoliopsida Rosales Rosaceae Geum canadense White avens Magnoliopsida Rosales Rosaceae Potentilla simplex Common cinquefoil Magnoliopsida Rosales Rosaceae Prunus serotina Black cherry Magnoliopsida Rosales Rosaceae Prunus virginiana Choke cherry Magnoliopsida Rosales Rosaceae Rosa multiflora Multiflora rose

267 Table 3 (Cont'd). All taxa observed at Fetterley Forest Conservation Area on 9 July 2015 (between 9am and 12pm).

Magnoliopsida Rosales Rosaceae Rubus allegheniensis Northern blackberry Magnoliopsida Rosales Rosaceae Rubus idaeus Red raspberry Magnoliopsida Rosales Rosaceae Rubus odoratus Purple flowering raspberry Magnoliopsida Rosales Urticaceae Pilea pumila Canadian clearweed Magnoliopsida Sapindales Aceraceae Acer pensylvanicum Moosewood, Striped Magnoliopsida Sapindales Anarcardiaceae Rhus typhina Staghorn sumac Magnoliopsida Sapindales Sapindaceae Acer rubrum Red maple Magnoliopsida Sapindales Sapindaceae Acer saccharum Sugar maple Magnoliopsida Solanales Solanaceae Solanum nigra Black nightshade Magnoliopsida Vitales Vitaceae Vitus sp. Grapevine

Pinopsida Pinales Pinaceae Pinus strobus Eastern white pine Plantae Pinopsida Pinales Pinaceae Tsuga canadensis Eastern hemlock Magnoliophyta

Polypodiopsida Polypodiales Dennstaedtiaceae Pteridium aquilinum Northern bracken fern Polypodiopsida Polypodiales Dryopteridaceae Dryopteris intermedia Intermediate woodfern Polypodiopsida Polypodiales Dryopteridaceae Polystichium acrostichoides Christmas fern Polypodiopsida Polypodiales Onocleaceae Matteuccia struthiopteris Ostrich fern Polypodiopsida Polypodiales Onocleaceae Onoclea sensibilis Sensitive fern Polypodiopsida Polypodiales Pteridaceae Adiantum pedatum Maidenhair fern Polypodiopsida Polypodiales Thelypteridaceae Thelypteris phegopteris Beech fern Polypodiopsida Polypodiales Woodsiaceae Athyrium filix-femina Ladyfern

268 Table 4. All taxa observed at Parslow on 16 July 2015 (between 9am and 12pm).

ANIMALS

Kingdom Phylum Class Order Family Genus Species Common Name

Actinopterygii Cypriniformes Catostomidae Catostromus commersonii White sucker Actinopterygii Cypriniformes Catostomidae Hypentelium nigricans Northern hogsucker Actinopterygii Cypriniformes Cyprinidae Exoglossum maxilingua Culips minnow Actinopterygii Cypriniformes Cyprinidae Notropus rubellus Roseyface shiner Actinopterygii Cypriniformes Cyprinidae Semotilus atromaculatus Creek chub Actinopterygii Cypriniformes Cyprinidae Semotilus corporalis Fall fish Actinopterygii Perciformes Centrarchidae Ambloplites rupestris Rock bass Actinopterygii Perciformes Centrarchidae Lepomis auritis Red-breast sunfish Actinopterygii Perciformes Centrarchidae Lepomis gibbosus Pumpkinseed Actinopterygii Perciformes Centrarchidae Lepomis macrochirus Bluegill Actinopterygii Perciformes Centrarchidae Micropterus dolomieu Smallmouth bass Actinopterygii Perciformes Centrarchidae Micropterus salmoides Laremouth bass Actinopterygii Perciformes Percidae Etheosoma olmstedii Tesselated darter

Aves Accipitriformes Cathartidae Cathartes aura Turkey vulture Aves Columbiformes Columbidae Zenaida macroura Mourning dove Aves Coraciiformes Alcedinidae Megaceryle alcyon Belted kingfisher Aves Passeriformes Bombycillidae Bombycilla cedrorum Cedar waxwing Aves Passeriformes Corvidae Corvus brachyrhynchos American crow Aves Passeriformes Corvidae Cyanocitta cristata Blue jay

Chordata Aves Passeriformes Emberizidae Melospiza melodia Song sparrow Aves Passeriformes Emberizidae Zonotrichia albicollis White-throated sparrow Aves Passeriformes Fringillidae Spinus tristis American goldfinch Aves Passeriformes Icteridae Quiscalus quiscula Common grackle Aves Passeriformes Mimidae Dumetella carolinensis Gray catbird Aves Passeriformes Paridae Poecile atricapillus Black-capped chickadee Aves Passeriformes Parulidae Geothlypis trichas Common yellowthroat Aves Passeriformes Parulidae Setophaga petechia Yellow warbler Aves Passeriformes Sittidae Sitta carolinensis White-breasted nuthatch Aves Passeriformes Troglodytidae Troglodytes aedon House wren Aves Passeriformes Turdidae Catharus fuscescens Veery Aves Passeriformes Turdidae Turdus migratorius American robin Aves Passeriformes Tyrannidae Sayornis phoebe Eastern phoebe Aves Piciformes Picidae Colaptes auratus Northern flicker Animalia

Amphibia Anura Ranidae Rana clamitans Green frog

Mammalia Artiodactyla Cervidae Odocoileus virginianus White-tailed deer Mammalia Rodentia Sciuridae Tamiasciurus hudsonicus Red squirrel

Mollusca Bivalvia Veneroida Dreissenidae Dreissena polymorpha Zebra mussel

Insecta Coleoptera Carabidae sp. Gound beetle Insecta Coleoptera Cerambycidae sp. Longhorn beetle Insecta Coleoptera Chrysomelidae sp. Leaf beetle Insecta Coleoptera Elateridae sp. Click beetle Insecta Coleoptera Gyrinidae sp. Whirlgig beetle Insecta Coleoptera Lampyridae sp. Firefly Insecta Coleoptera Scarabaeidae sp. Scarab beetle Insecta Dermaptera Forficulidae sp. Earwig Insecta Diptera Asilidae sp. Robber fly Insecta Diptera Calliphoridae sp. Blow fly Insecta Diptera Dolochipodidae sp. Long-legged fly Insecta Diptera Drosophilidae sp. Vinegar fly Insecta Diptera Sarcophagidae sp. Flesh fly

Arthopoda Insecta Diptera Sciomyzidae sp. Marsh fly Insecta Diptera Syrphidae sp. Hoverfly Insecta Diptera Tachinidae sp. Parasitoid fly Insecta Diptera Tephritidae sp. Fruit fly Insecta Ephemeroptera Ephemeridae Hexagenia sp. Mayfly Insecta Hemiptera Aphididea sp. Aphid Insecta Hemiptera Cercopidae sp. Froghopper Insecta Hemiptera Cicadellidae sp. Leafhopper Insecta Hemiptera Membracidae sp. Treehopper Insecta Hemiptera Miridae sp. Plant bugs Insecta Hemiptera Pentatomidae sp. Stink bugs 269 Table 4 (Cont'd). All taxa observed at Parslow on 17 July 2015 (between 9am and 12pm).

Insecta Hemiptera Reduviidae sp. Assassin bug Insecta Hemiptera Veliidae sp. Broad shouldered water strider Insecta Hymenoptera Apidae sp. Bee Insecta Hymenoptera Cynipidae sp. Gall wasp Insecta Hymenoptera Formicidae sp. Ant Insecta Hymenoptera Halictidae sp. Sweat bee Insecta Hymenoptera Ichneumonidae sp. Ichneumon wasp Insecta Hymenoptera Sphecidae sp Thread-waisted wasps Insecta Lepidoptera Erebidae sp. Tiger moth Insecta Lepidoptera Hesperiidae sp. Skipper Insecta Lepidoptera sp. Leaf miner moth Insecta Lepidoptera Noctuidae sp. Owlet moth Insecta Lepidoptera Nymphalidae sp. Brushtail butterfly Insecta Lepidoptera Papilionidae sp. Swallowtail butterfly Insecta Lepidoptera Pieridae sp. Whites and sulphurs Animalia Arthopoda Insecta Lepidoptera Pyralidae sp. Snout moth Insecta Lepidoptera Sphingidae sp. Clearwing Moth Insecta Mecoptera Panorpidae sp. Scorpionfly Insecta Odonata Calopterygidae sp. Damselfly Insecta Odonata Coenagrionadae sp. Damselly Insecta Odonata Lestidae sp. Damselfly Insecta Odonata Libellulidae sp. Skimmer Insecta Orthoptera Acrididae sp. Grasshopper Insecta Orthoptera Gryllidae sp. Cricket Insecta Orthoptera Tettigoniidae sp. Long-horned grasshopper

Arachnida Opiliones sp. Harvestmen Arachnida Araneae Thomisidae sp. Crab spider

PLANTS

Kingdom Phylum Class Order Family Genus Species Common Name Magnoliopsida Alismatales Alismataceae Sagittaria latifolia Broadleaf arrowhead Magnoliopsida Alismatales Alismataceae Sagittaria rigida Arrowhead Magnoliopsida Alismatales Araceae Symplocarpus foetidus Skunk weed Magnoliopsida Apiales Apiaceae Cicuta maculata Water hemlock Magnoliopsida Apiales Apiaceae Daucus carota Queen Anne's lace Magnoliopsida Apiales Apiaceae Pastinaca sativa Wild parsnip Magnoliopsida Apiales Araliaceae Aralia nudicaulis Wild sarsaparilla Magnoliopsida Asparagales Alliodeae Allium canadensis Wild garlic Magnoliopsida Asparagales Asparagaceae Smilacina racemosa False Soloman's seal Magnoliopsida Asterales Asteraceae Achillea millefolium Common yarrow Magnoliopsida Asterales Asteraceae Centaurea maculosa Spotted knapweed Magnoliopsida Asterales Asteraceae Chrysanthemum leucanthemum Ox-Eye daisy Magnoliopsida Asterales Asteraceae Cirsium palustre Marsh thistle Magnoliopsida Asterales Asteraceae Pseudognaphalium obtusifolium Sweet everlasting Magnoliopsida Asterales Asteraceae Gnaphalium uliginosum Low cudweed Magnoliopsida Asterales Asteraceae Lapsana communis Nipplewort Magnoliopsida Asterales Asteraceae Rudbeckia laciniata Cut leaved coneflower Magnoliopsida Asterales Asteraceae Solidago graminifolia Flat top goldenrod Magnoliopsida Asterales Asteraceae Solidago sp. Goldenrod Magnoliopsida Asterales Asteraceae Sow-thistle

Plantae Sonchus sp. Magnoliopsida Asterales Asteraceae Tussilago farfara Coltsfoot Magnoliophyta Magnoliopsida Brassicales Brassicaceae Allaria petiolata Garlic mustard Magnoliopsida Brassicales Brassicaceae Hesperis matronalis Dame's rocket Magnoliopsida Brassicales Brassicaceae Lepidium campestre Poor man's peppergrass Magnoliopsida Brassicales Brassicaceae Rorippa nasturtium-aquaticum Watercress Magnoliopsida Brassicales Brassicaceae Rorippa palustris Yellow cress Magnoliopsida Caryophyllales Caryophyllaceae Stellaria media Chickweed Magnoliopsida Cucurbitales Cucurbitaceae Echinocystis lobata Wild cucumber Magnoliopsida Cyperales Cyperaceae Carex lurida Sedge Magnoliopsida Cyperales Cyperaceae Carex pseudocyperus Sedge Magnoliopsida Dipsacales Adoxaceae Sambucus canadensis Elderberry Magnoliopsida Dipsacales Adoxaceae Viburnum lentago Nannyberry Magnoliopsida Dipsacales Caprifoliaceae Lonicera sp. Honeysuckle Magnoliopsida Ericales Balsaminaceae Impatiens capensis Jewelweed Magnoliopsida Ericales Myrsinaceae Lysimachia ciliata Fringed loosestrife Magnoliopsida Ericales Myrsinaceae Lysimachia nummularia Moneywort Magnoliopsida Ericales Myrsinaceae Lysimachia terrestris Swamp candles Magnoliopsida Fabales Fabaceae Gleditsia triacanthos Honeylocust 270 Table 4 (Cont'd). All taxa observed at Parslow on 17 July 2015 (between 9am and 12pm).

Magnoliopsida Fabales Fabaceae Lotus corniculatus Bird's foot trefoil Magnoliopsida Fabales Fabaceae Melilotus alba Sweet white clover Magnoliopsida Fabales Fabaceae Trifolium campestre Low hop-clover Magnoliopsida Fabales Fabaceae Trifolium dubium Small hop clover Magnoliopsida Fabales Fabaceae Trifolium hybridum Alsike clover Magnoliopsida Fabales Fabaceae Trifolium pratense Red clover Magnoliopsida Fabales Fabaceae Trifolium repens White clover Magnoliopsida Fagales Betulaceae Betula alleghaniensis Yellow birch Magnoliopsida Fagales Betulaceae Alnus incana Speckled alder Magnoliopsida Fagales Juglandaceae Juglans cinerea Butternut Magnoliopsida Gentianales Asclepiadaceae Asclepias syriaca Milkweed Magnoliopsida Geraniales Geraniaceae Geranium robertainum Herb robert Magnoliopsida Hamamelidales Platanacea Platanus occidentalis American sycamore Magnoliopsida Iridaceae Irideae Iris pseudacorus Yellow flag Magnoliopsida Lamiales Boraginaceae Myosotis scorpioides Forget me not Magnoliopsida Lamiales Lamiaceae Clinopodium vulgare Wild basil Magnoliopsida Lamiales Lamiaceae Glechoma hederacea Ground ivy Magnoliopsida Lamiales Lamiaceae Linaria vulgaris Butter and eggs Magnoliopsida Lamiales Lamiaceae Lycopus sp. Bugleweed Magnoliopsida Lamiales Lamiaceae Mentha arvensis Wild mint Magnoliopsida Lamiales Phrymaceae Mimulus ringens Monkey flower Magnoliopsida Lamiales Plantaginaceae Plantago lanceolata English plantain Magnoliopsida Lamiales Plantaginaceae Plantago major Common plantain Magnoliopsida Lamiales Plantaginaceae Veronica anagallis-aquatica Water speedwell Magnoliopsida Lamiales Verbenaceae Verbena hastata Blue vervain Magnoliopsida Liliales Colchicaceae Uvularia sessilifolia Bellwort Magnoliopsida Malpighiales Hypericaceae Hypericum punctatum Spotted St Johnswort Magnoliopsida Malpighiales Salicaceae Populus tremuloides Quaking aspen Magnoliopsida Malpighiales Salicaceae Salix fragilis Willow Magnoliopsida Malpighiales Salicaceae Salix sp. Willow Magnoliopsida Malvales Malvaceae Malva moschata Musk mallow Magnoliopsida Malvales Tiliaceae Tilia americana Basswood Magnoliopsida Myrtales Lythraceae Lythrum salicaria Purple loosestrife Magnoliopsida Myrtales Onagraceae Circaea lutetiana Enchanter's nightshade Magnoliopsida Myrtales Onagraceae Circaea alpina Dwarf enchanter's nightshade Magnoliopsida Myrtales Onagraceae Ludwigia palustris Water weed Magnoliopsida Myrtales Onocleaceae Evening primrose

Plantae Oenothera biennis Magnoliopsida Nymphaeales Nymphaeaceae Nymphaea sp. Water lily Magnoliophyta Magnoliopsida Orchidales Orchidaceae Epipactis helleborine Broadleaf helleborine Magnoliopsida Oxalidales Oxalidaceae Oxalis stricta Yellow wood sorrel Magnoliopsida Poales Cyperaceae Eleocharis palustris Common spikerush Magnoliopsida Poales Cyperaceae Scirpus sp. Bulrush Magnoliopsida Poales Juncaceae Juncus sp. Rush Magnoliopsida Poales Typhaceae Sparganium americanum Burreed Magnoliopsida Poales Typhaceae Typha latifolia Cattail Magnoliopsida Polygonales Polygonaceae Polygonum persicaria Lady's thumb Magnoliopsida Polygonales Polygonaceae Polygonum virginianum Jumpseed Magnoliopsida Polygonales Polygonaceae Rumex crispus Curled dock Magnoliopsida Ranunculales Ranunculaceae Ranunculus acris Common buttercup Magnoliopsida Ranunculales Ranunculaceae Ranunculus recurvatus Hooked buttercup Magnoliopsida Ranunculales Ranunculaceae Ranunculus repens Creeping buttercup Magnoliopsida Ranunculales Ranunculaceae Ranunculus sp. Buttercup Magnoliopsida Ranunculales Ranunculaceae Taraxicum officinale Dandelion Magnoliopsida Ranunculales Ranunculaceae Thalictrum sp. Rue Magnoliopsida Rosales Rhamnaceae Rhamnus cathartica Buckthorn Magnoliopsida Rosales Rosaceae Agrimonia sp. Agrimony Magnoliopsida Rosales Rosaceae Fragaria virginiana Strawberry Magnoliopsida Rosales Rosaceae Geum aleppicum Yellow avens Magnoliopsida Rosales Rosaceae Geum canadense White avens Magnoliopsida Rosales Rosaceae Malus sp. Apple Magnoliopsida Rosales Rosaceae Potentilla simplex Common cinquefoil Magnoliopsida Rosales Rosaceae Prunus serotina Black cherry Magnoliopsida Rosales Rosaceae Prunus virginia Choke Cherry Magnoliopsida Rosales Rosaceae Rosa multiflora Multiflora rose Magnoliopsida Rosales Rosaceae Rubus allegheniensis Northern blackberry Magnoliopsida Rosales Rosaceae Rubus idaeus Red raspberry Magnoliopsida Rosales Rosaceae Rubus odoratus Purple flowering raspberry Magnoliopsida Rubiales Rubiaceae Galium asprellum Rough bedstraw Magnoliopsida Rubiales Rubiaceae Galium mollugo False baby's breath Magnoliopsida Sapindales Aceraceae Acer rubrum Red maple Magnoliopsida Sapindales Aceraceae Acer saccharinum Silver maple Magnoliopsida Sapindales Aceraceae Acer saccharum Sugar maple 271 Table 4 (Cont'd). All taxa observed at Parslow on 17 July 2015 (between 9am and 12pm).

Magnoliopsida Sapindales Anarcardiaceae Rhus hirta Staghorn sumac Magnoliopsida Sapindales Anarcardiaceae Toxicodendron radicans Poison ivy Magnoliopsida Saxifragales Grossulariaceae Ribes sp. Currant Magnoliopsida Saxifragales Haloragaceae Myriophyllum spicatum Eurasian milfoil Magnoliopsida Scrophulariales Oleaceae Fraxinus americana American white ash Magnoliopsida Scrophulariales Oleaceae Fraxinus nigra Black ash Magnoliopsida Solanales Solanaceae Solanum nigra Black nightshade Magnoliopsida Urticales Ulmaceae Ulmus americana American elm Magnoliopsida Urticales Ulmaceae Ulmus rubra Slippery elm Magnoliopsida Urticales Urticaceae Laportea canadensis Wood nettle Magnoliopsida Urticales Urticaceae Pilea pumila Clearweed Magnoliopsida Urticales Urticaceae Urtica dioica Stinging nettle

Magnoliophyta Magnoliopsida Urticales Urticaceae Boehmeria cylindrica False nettle Magnoliopsida Vitales Vitaceae Parthenocissus quinquefolia Virginia creeper Magnoliopsida Vitales Vitaceae Vitus sp. Grape

Equisetopsida Equisetales Equisetaceae Equisetum sp. Horsetail

Polypodiopsida Polypodiales Dryopteridaceae Dryopteris intermedia Intermediate woodfern Polypodiopsida Polypodiales Onocleaceae Matteuccia struthiopteris Ostrich fern

Plantae Polypodiopsida Polypodiales Oncleaceae Onclea sensibilis Sensitive fern

Pinophyta Pinosida Pinales Pinaceae Tsuga canadensis Eastern Hemlock

Charophyta Conjugatophyceae Zygnematales Zygnemataceae Mougeotia sp. Algae

Chlorophyceae ChlamydomonadalesChlamydomonadaceaeChlamydomonas sp. Algae Chlorophyta Chlorophyceae ChlamydomonadalesChlamydomonadaceaeChlamydomonas sp. Algae Chlorophyceae Sphaeropleales Microsporaceae Microspora sp. Algae

Fragilariophyceae Licmophorales Ulnanaceae Synedra sp. Algae Bacillariophyta Fragilariophyceae Tabellariales Tabellariales Meridion sp. Algae

CyanobacteriaCyanophyceae Oscillatoriales Oscillatoriaceae Oscillatoria sp. Algae

Chlorophyceae ChlamydomonadalesChlamydomonadaceaeChlamydomonas sp. Algae Chlorophyceae Sphaeropleales Microsporaceae Microspora sp. Algae

Chlorophyta Trebouxiophyceae Chlorellates Oocystaceae Oocystis sp. Algae

272 REFERENCES

Davidson, Emily. 2015. Summer 2014 Summer BioBlitz Series. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta.

Gleason, H.A., and Cronquist, A. 1991. Manual of Vascular Plants of Northeastern United States and Adjacent Canada. New York Botanical Garden Pr. Dept.

Integrated Taxonomic Information System. Retrieved SEPT, 2015. System On-line Database.

Otsego Land Trust. 2014. http://otsegolandtrust.org/

Post, S.L. 2003. Biodiversity blitz: A day in the life of… The Illinois Steward 12(1):1-8.

Ruch, D.G., Karns, D.R., McMuray, P, Moore-Palm, J., Murphy, W., Namestnik, S.A., and Roth, K. 2010. Results of the Loblolly Marsh Wetland Preserve Bioblitz, Jay County, Indiana. Proceedings of the Indiana Academy of Science. 119:(1)1-3.

Tree of life Web Project. 2005. http://tolweb.org/tree/

USGS. 2009. Bioblitz Home. http://www.pwrc.usgs.gov/blitz.html

273

Survey of zooplankton in Brant Lake, Horicon, NY

Sarah Newtown1, Alejandro Reyes2

INTRODUCTION

Located in the middle of the aquatic food chain, zooplankton provide a critical energy pathway for higher trophic levels while also being able to exert grazing pressure on primary producers, limiting algae growth. Changes in population size and composition as a result of these factors make zooplankton a good indicator of the impact of invasive species (Havel and Shurin 2004). Invasion by dreissenid mussels (Dreissena spp.) can result in a decrease in small bodied rotifers due to predation and competition for niche space (Mihuc et al. 2012). It has also been shown that alewife (Alosa pseudoharengus) predate upon large bodied copepods and cladocerans, thereby resulting in an assemblage shift to small bodied crustaceans (Mihuc et al. 2012). The same effect can be seen with an invasion of spiny water flea (Bythotrephes longimanus) (Yan et al. 2011). A significant change in the community composition of native zooplankton, as a result of invasive species introduction, can unbalance the ecosystem by reducing filter feeding daphnia and removing the forage base for native fish (Walsh et al. 2016). The results are amplified both up and down the food chain, altering the ecology of the system (Harman et al. 2002).

Brant Lake is a 584 ha lake located in Warren County, NY within the Adirondack Park. It is a public access lake which receives boaters from Lake George, Lake Champlain, and the Great Lakes, all of which contain populations of invasive species such as zebra mussels, spiny water flea, and alewife. As a consequence of Brant Lake’s proximity to these water bodies, there is an increased risk of introduction. The goals of this study were to characterize zooplankton community assemblage and dynamics within Brant Lake and to detect any new invasive species. Because of the planktonic life stages of various invasive species (zebra/quagga mussels and spiny water flea), they have the potential to show up in routine zooplankton monitoring. The results of this study will provide the stakeholders with baseline information on the current community composition of zooplankton in the lake, better preparing them to respond to and quantify an early invasion.

METHODS

Zooplankton samples were collected from Site 1 in Brant Lake (Figure 1) in conjunction with standard limnological monitoring. This sample site was chosen because it is the deepest part of the lake at approximately 18m (Holdren et al. 2001). One vertical tow was conducted using a 153 micron mesh Wisconsin net starting from 2m off the bottom (16m). The net was retrieved at a rate of approximately 1m/s. Samples were collected from April 26, 2015 to

1 SUNY Oneonta Biology Department. 2 MS graduate candidate. SUNY Oneonta.

274 October 27, 2015. All samples were stored at room temperature in 125 mL plastic bottles and preserved with 70% ethanol containing a rose-bengal stain.

Figure 1. Map showing the location of the sample site, Site 1. Site 2 was not used in this study.

In the lab, 1 mL aliquots were taken using a Henson Stemple pipette and placed on a Sedgewick Rafter gridded cell to be enumerated. In order to maintain consistency when quantifying zooplankton in all the samples, at least 100 zooplankton were counted per sample; as many subsamples were taken as needed to reach a count of at least 100 organisms. All identifications were made using Carling et al. (2004). All identifications were verified by staff at the Lake Champlain Research Institute.

RESULTS AND DISCUSSION

Fifteen taxa representing seven families were observed during the study (Table 1). The zooplankton community is dominated by small rotifers, namely Keratella cochlearis, Kellicottia longiseta, and Polyarthra major. The rarest organisms were Asplanchna spp., Tropocyclops, Daphnia longiremis, and Bosmina coregoni, which were only present once in all eight samples.

275

Table 1. All species observed from Brant Lake, 2015, and their final counts.

Two mixing events occurred during the sample period; the first was around 4/26/2015 and the second was around 10/27/2015 (Figure 2). Between these events, thermal stratification was present, with the thermocline forming between 6-8m.

Figure 3. Temperature data for all date’s samples were collected.

276

Early June had the highest abundance of organisms, with a second, smaller peak of organisms during September. Cladocera abundance peaked in June with Bosmina longispina being the most dominant organism. The larger Daphnia, galeata mendotae, showed a slight increase in abundance in late summer, but overall was in low abundance throughout the survey. Copepod abundance was generally low during the season, with Diacyclops thomasi being the dominant species observed. Interesting to note was the differential peaks of Leptodiaptomus and Skistodiaptomus oregonensis. There may be some temporal separation taking place between these taxa, however our sample sizes are too low to draw any definitive conclusions. Rotifer abundance peaked in June, with Keratella cochlearis being the dominant species. All other species do not show a major trend seasonally.

Overall, the zooplankton community of Brant Lake is dominated by smaller bodied organisms such as rotifers and unknown juvenile stages (copepodites and nauplii). The small bodied nature of the community can be partially attributed to predation pressure. Predation pressure has been implicated in changes in zooplankton and abundance vertically (Iwasa 1982). On 27 October, the sample collected had a high number of Chaoborus present. Chaoborus (Chaoboridae) is a genus of aquatic midges that are voracious predators on zooplankton. It is known that Chaoborus instar IV primarily feed on cladocerans for most of the year (Moore et al. 1994). Predation by Chaoborus could explain why cladocerans are in such low abundance for the entire sampling period. Recently, a fisheries survey of Brant Lake found a large population of rainbow smelt (Osmerus mordax) (Reyes unpublished data). Rainbow smelt show a preference for feeding on large bodied copepods and cladocerans, resulting in a population of small bodied crustaceans (Sheppard et al. 2012). We believe that the combined predation pressure of both Chaoborus and rainbow smelt had has a significant impact on the zooplankton community assemblage.

No larval stages of invasive species were found during the course of the survey. This result can mean one of two things. First, there are no invasive species present. Second, invasive species are at such a low abundance in the lake, that their larvae are not being detected in our sampling. For zebra mussels, they require a calcium level of around 20mg/L to successfully produce larvae (Hincks and Mackie 1997). Mean surface calcium levels in Brant Lake are 8.37mg/L (Reyes, unpublished data), making successful reproduction unlikely, implying they are not in the lake at this moment. Spiny water fleas in Lake Champlain show up during the fall of 2014 (Mihuc unpublished data) and they are known to inhabit the deeper portions of lakes (Yan et al. 2011). Since we have samples in the fall, and from the deep portion of the lake, we are confident that our sampling approach would have captured spiny water flea if present.

277

Figure 3. Monthly abundances of Cladocera, Copepoda and Rotifera, Brant Lake, 2015. Note change in x-axis values for each graph.

CONCLUSIONS

The goal of this study was to provide the stakeholders of Brant Lake with a list of zooplankton taxa and any new invasive species found during sampling. No new invasive species were observed in any of the samples collected. These baseline data can be used to start a monitoring program aimed at detecting new invasive species and changes within the

278 zooplankton community. This will enable the stakeholders to detect a new invasive species introduction before management becomes cost prohibitive.

REFERENCES

Carling, K.J., I.M. Ater, M R. Pellam, A.M. Bouchard, and T.B. Mihuc. 2004. A guide to the zooplankton of Lake Champlain. Scientia Discipulorum 1(1):4.

Havel, J.E., and J.B. Shurin. 2004. Mechanisms, effects, and scales of dispersal in freshwater zooplankton. Limnology and Oceanography 49(4part2):1229–1238.

Hincks, S.S., and G.L. Mackie. 1997. Effects of pH, calcium, alkalinity, hardness, and chlorophyll on the survival, growth, and reproductive success of zebra mussel (Dreissena polymorpha) in Ontario lakes. Canadian Journal of Fisheries and Aquatic Sciences 54(9):2049–2057.

Holdren, C., B. Jones, and J. Taggart. 2001. Managing lakes and reservoirs. North American Lake Management Society; Terrene Insitute.

Iwasa, Y. 1982. Vertical migration of zooplankton: A game between predator and prey. The American Naturalist 120(2):171–180.

Mihuc, T.B., F. Dunlap, C. Binggeli, L. Myers, C. Pershyn, A. Groves, and A. Waring. 2012. Long-term patterns in Lake Champlain’s zooplankton: 1992–2010. Journal of Great Lakes Research 38:49–57.

Moore, M.V., N.D. Yan, and T. Pawson. 1994. Omnivory of the larval phantom midge (Chaoborus spp.) and its potential significance for freshwater planktonic food webs. Canadian Journal of Zoology 72(11):2055–2065.

Sheppard, K.T., A.J. Olynyk, G.K. Davoren, and B.J. Hann. 2012. Summer diet analysis of the invasive rainbow smelt (Osmerus mordax) in Lake Winnipeg, Manitoba. Journal of Great Lakes Research 38:66–71.

Walsh, J.R., S.R. Carpenter, and M.J.V. Zanden. 2016. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proceedings of the National Academy of Sciences:201600366.

279 Aquatic macrophyte management plan facilitation, Lake Moraine, Madison County, NY 2015

B.P. German1 and M.F. Albright

BACKGROUND (From Harman et al. 2010)

Located in Madison County NY, Moraine Lake (42o 50’ 47” N, 75o 31’ 39” W) was formed by a deposited glacial moraine damming a valley. The lake, which has been artificially raised, is divided into two basins separated by a causeway and interconnected by a submerged culvert. The north basin is approximately 79 acres, has a mean depth of 1.1m, and a maximum depth 3.7m. The south basin occupies 182 acres, has a mean depth of 5.4m, and a maximum depth of 13.7m. Most of the recreational activities such as fishing, boating and swimming take place in the south basin (Harman et al. 1997).

Moraine Lake has been regarded as meso-eutrophic due to the high productivity of algae and macrophytic plants, low transparency, and depleting levels of dissolved oxygen in the hypolimnion during summer stratification. Development of lakeside residences and nearby agricultural activities are believed to have contributed to the current productivity status of the upper and lower basins (Anon. 1991). Nutrient loading as a result of faulty septic systems from the residences are believed to be a significant source of the problem in nutrient introduction (Harman et al. 1998). Many of the systems are out of date, undersized, and extremely close to the lake (Brown et al. 1983). Furthermore, soils surrounding the lake have poor percolation rates, steep slopes, shallow depths to bedrock, and fractured bedrock make the lake vulnerable to nutrient loading (Harman et al. 2008).

INTRODUCTION

The aquatic macrophyte communities of Moraine Lake have been monitored by the SUNY Oneonta Biological Field Station (BFS) since 1997. The purpose of monitoring these plant communities has historically been directed towards controlling Eurasian water-milfoil (Myriophyllum spicatum), though in recent years the expansion of the exotic starry stonewort (Nitellopsis obtusa) has been a matter of increased focus. Eurasian water-milfoil is an invasive species that grows rapidly and its extensive canopies cause problems for recreation and other species growth (Borman et al. 1999). Numerous methods of control have been applied to reduce the abundance of Eurasian water-milfoil (Harman et al. 2006). Since 1998, efforts have focused primarily on applications of Sonar®, which has been demonstrated to control Eurasian water- milfoil with some specificity. Other efforts have involved stocking the weevil Euhrychiopsis lecontei, which had been shown to limit the growth of Eurasian milfoil in some instances (Harman et al. 2002). The goal of managing the Eurasian water-milfoil in the past has been to achieve a balance of species (Lembi 2000, Harman et al. 2008). Most recent activities have

1 MS in Lake Management candidate.

280 included a Sonar® application in the north basin in 2010 and in the south basin in 2011. Copper Sulfate was applied in 2013-2014, and Renovate® was used in the north basin in July 2014

MATERIALS AND METHODS

Sampling took place 4 June, 28 July and 16 September 2015. Five collection sites were sampled, two in the north basin and three from the south basin (Figure 1). The sampling method used was the Point Intercept Rake Toss Relative Abundance Method (PIRTRAM) (Lord and Johnson 2006). It was evaluated in 2008 by comparing the PIRTRAM and dry weight methods such that the rake toss method “could prove useful, if not too much value is placed on actual abundance estimates. …an adequate number of replicate samples could provide insight into species dominance and extent related to exotic nuisance species as well as efforts to control them” (Harman et al. 2008).

For this method two heads of garden rakes were welded together and connected to a 10m nylon cord. At each of the 5 sites, the rake was thrown out randomly 3 times. The rake was allowed to settle to the bottom of the lake and slowly pulled into the boat. Once in the boat, species were separated and each was assigned an abundance category. The 5 abundance categories are “no plants” (denoted by “Z”), “fingerful” (“T”= trace), “handful” (“S” = sparse), rakeful (“M” =medium), and “can’t bring into the boat” (“D” = dense). Table 1 provides biomass range estimates associated with each category. Each rake toss triplicate sample’s category was converted to its corresponding mid-point (Harman et al. 2008). The mid-points were averaged for each species at each site. These species averages were then summed together to look at overall biomass at each site.

In each basin at the deepest location, water quality parameters were measured with a YSI® multiprobe. From surface to substrate, temperature, dissolved oxygen, conductivity, and pH were measured. A water sample was taken from each basin and returned to the lab to be analyzed using the Lachat QuickChem FIA+Water Analyzer®. The ascorbic acid method following persulfate digestion (Liao and Marten 2001) was used to determine total phosphorus. For total nitrogen, the cadmium reduction method (Pritzlaff 2003) was used following peroxodisulfate digestion as described by Ebina et al. (1983). The phenolate method (Liao 2001) was used to measure ammonia and the cadmium reduction method (Pritzlaff 2003) for nitrate+nitrate-nitrogen. (Harman et al. 2008)

281

Figure1. Bathymetric map of Moraine Lake, Madison County, NY. Contours in feet. WQ1 and WQ2 represent were water quality data were collected, sites 1-5 represent where plant biomass and rake toss methods were performed (Harman et al. 2008).

Table 1. Categories, field measurements, midpoint of each category (g/m2) and dry weight ranges applied for the rake toss method and used to generate Tables 2-6 (Harman et al. 2008).

Abundance Categories Field Measure Total Dry Weight (g/m^2) mid low high "Z" = no plants Nothing 0 0 0 0 "T" = trace plants Fingerful .0001 - 2.000 1 0.0001 2 "S" = sparse plants Handful 2.001 - 140.000 71 2.001 140 "M" = medium plants Rakeful 140.001 - 230.000 185 140.001 230 "D" = dense plants Can't bring in boat 230.001 - 450.000+ 340 230.001 450

282 RESULTS

Plant Biomass

Tables 2-6 proved biomass estimates, by species, during 2015 for sites 1-5 at Moraine Lake. Eurasian water-milfoil was entirely absent from the south basin sites throughout the summer (Tables 2-4). It was present in moderate abundance in the north basin in July and September, however many of the collected stems were in a state of decomposition (Tables 5 and 6). Starry stonewort (Nitellopsis obtusa) continues to dominate the plant community in the south basin. At sites 1, 2 and 3; the biomass estimates given in Tables 2, 3 and 4 undoubtedly underestimate actual values because masses of this plant would collapse and fall off the rake as it was being pulled into the boat. Beds of this plant were often so thick that the perception was that a false bottom existed over 1 m from the actual bottom. 2013 was the first year that it was noted at site 2, and by summer’s end it dominated there. In 2014, starry stonewort was first documented in the north basin, and by September it was established at both sites there (Tables 5 and 6). In 2015 starry stonewort became abundant in the north basin during mid-summer, but tapered off sometime before the September sample. As starry stonewort becomes established, Eurasian milfoil is reduced.

Figures 2-6 graphically summarize the plant biomass contributed by starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species between 2008 and 2015. While not the original focus of study, starry stonewort is highlighted along with milfoil because of its recent increase in abundance in Lake Moraine. Like Eurasian milfoil, starry stonewort is also an exotic nuisance species and management efforts ought to focus upon controlling both species. Also, the temporal variations implied by these figures may be misleading. The highest midpoint that can be assigned by any given species is 340 g/m2 (see Table 1). In some visits prior to 2012, more than one species was assigned this highest value, though the cumulative biomass value, in actuality, likely did not exceed the monocultural beds encountered in 2012 and 2013.

283 Table 2. Mean biomass (g/m2) category mid-points for each species found at Site 1 during 2015 sampling events.

Site 1 6/4/2015 7/28/2015 9/16/2015 Myriophyllum spicatum 0.0 0.0 0.0 Megalodonta beckii 0.0 0.0 0.0 Zosterella dubia 0.0 0.0 61.7 Najas spp. 0.0 0.0 0.0 Ceratophyllum demersum 0.0 0.0 0.0 Chara vulgaris 0.0 0.0 0.3 Vallisneria americana 0.3 0.0 0.0 Elodea canadensis 0.0 0.0 0.0 Ranunculus aquatilis 0.0 0.0 0.0 Ranunculus trichophyllus 0.0 0.0 0.0 Stuckenia pectinata 0.3 0.0 0.3 Potamogeton crispus 71.0 0.0 0.0 Potamogeton zosteriformis 0.0 0.0 61.7 Potamogeton pusillus 0.0 0.0 0.0 Nitellopsis obtusa 85.3 185.0 227.0 Total 157.0 185.0 351.0

Table 3. Mean biomass (g/m2) category mid-points for each species found at Site 2 during 2015 sampling events.

Site 2 6/4/2015 7/28/2015 9/16/2015 Myriophyllum spicatum 0.0 0.0 0.0 Megalodonta beckii 0.0 0.0 0.0 Zosterella dubia 0.0 0.0 0.0 Najas spp. 0.0 0.0 0.0 Ceratophyllum demersum 0.0 0.0 0.3 Chara vulgaris 0.0 0.0 0.0 Vallisneria americana 0.0 0.0 0.0 Elodea canadensis 0.0 0.0 0.0 Ranunculus aquatilis 0.0 0.0 0.0 Ranunculus trichophyllus 0.0 0.0 0.0 Stuckenia pectinata 147.0 0.3 0.0 Potamogeton crispus 0.0 0.0 0.0 Potamogeton zosteriformis 0.0 0.0 0.0 Potamogeton pusillus 0.7 0.0 0.0 Nitellopsis obtusa 0.0 185.0 340.0 Total 147.7 185.3 340.3

284

Table 4. Mean biomass (g/m2) category mid-points for each species found at Site 3 during 2015 sampling events. Site 3 6/4/2015 7/28/2015 9/16/2015 Myriophyllum spicatum 0.0 0.0 0.0 Megalodonta beckii 0.0 0.0 0.0 Zosterella dubia 0.0 0.0 0.0 Najas spp. 0.0 0.0 0.0 Ceratophyllum demersum 23.7 0.0 0.0 Chara vulgaris 0.0 0.0 0.0 Vallisneria americana 0.0 0.0 0.0 Elodea canadensis 0.0 0.0 0.0 Ranunculus aquatilis 0.0 0.0 0.0 Ranunculus trichophyllus 0.0 0.0 0.0 Stuckenia pectinata 0.0 0.0 0.0 Potamogeton crispus 0.0 0.0 0.0 Potamogeton zosteriformis 0.0 0.0 0.0 Potamogeton pusillus 0.0 0.0 0.0 Nitellopsis obtusa 147.0 273.3 340.0 Total 170.7 273.3 340.0

Table 5. Mean biomass (g/m2) category mid-points for each species found at Site 4 during 2015 sampling events.

Site 4 6/4/2015 7/28/2015 9/16/2015 Myriophyllum spicatum 0.0 0.0 0.0 Megalodonta beckii 0.0 0.0 0.0 Zosterella dubia 0.0 0.0 0.0 Najas spp. 0.0 0.7 0.3 Ceratophyllum demersum 24.3 24.3 288.3 Chara vulgaris 0.0 0.0 0.0 Vallisneria americana 0.0 0.0 0.0 Elodea canadensis 0.0 0.0 0.0 Ranunculus aquatilis 0.0 0.0 0.0 Ranunculus trichophyllus 0.0 0.0 0.0 Stuckenia pectinata 85.7 0.0 0.0 Potamogeton crispus 147.0 0.3 0.0 Potamogeton zosteriformis 0.7 0.3 0.0 Potamogeton pusillus 0.0 0.0 0.0 Nitellopsis obtusa 0.3 85.3 0.0 Total 258.0 111.0 288.7

285 Table 6. Mean biomass (g/m2) category mid-points for each species found at Site 5 during 2015 sampling events.

Site 5 6/4/2015 7/28/2015 9/16/2015 Myriophyllum spicatum 0.0 0.3 23.7 Megalodonta beckii 0.0 0.0 0.0 Zosterella dubia 0.3 0.0 0.0 Najas spp. 0.0 0.0 109.0 Ceratophyllum demersum 85.7 85.3 147.0 Chara vulgaris 0.0 0.0 0.0 Vallisneria americana 0.0 0.3 0.0 Elodea canadensis 0.0 0.0 0.3 Ranunculus aquatilis 0.0 0.0 0.0 Ranunculus trichophyllus 0.0 0.0 0.0 Stuckenia pectinata 24.3 0.3 0.3 Potamogeton crispus 71.0 1.0 0.7 Potamogeton zosteriformis 0.0 0.7 0.0 Potamogeton pusillus 0.3 0.0 0.0 Nitellopsis obtusa 0.0 85.3 0.0 Total 181.7 173.3 281.0

Site 1 Plant Community 800 Nitellopsis obtusa 700 Myriophyllum spicatum

600 Others 500

400

300

Dry Weight (g/m^2) 200

100

Figure 2. Comparison of biomass (g/m2) of starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species present, 2008 (Harman et al. 2009), 2009 (Harman et al. 2010), 2010 (Harman et al 2011), 2011 (Harman et al. 2012), 2012 (Harman and Albright 2013), 2013 (Harman and Albright 2014), 2014 (German and Albright 2015), and 2015, Site 1 (see Figure 1 for sites).

286 Site 2 Plant Community 800 Nitellopsis obtusa 700 Myriophyllum spicatum

600 Others 500

400

300

Dry Weight (g/m^2) 200

100

Figure 3. Comparison of biomass (g/m2) of starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species present, 2008 (Harman et al. 2009), 2009 (Harman et al. 2010), 2010 (Harman et al 2011), 2011 (Harman et al. 2012), 2012 (Harman and Albright 2013), 2013 (Harman and Albright 2014), 2014 (German and Albright 2015), and 2015, Site 2 (see Figure 1 for sites). Site 3 Plant Community 800 Nitellopsis obtusa 700

Myriophyllum spicatum 600 Others 500 400 300

Dry Weight (g/m^2) 200 100 0

Figure 4. Comparison of biomass (g/m2) of starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species present, 2008 (Harman et al. 2009), 2009 (Harman et al. 2010), 2010 (Harman et al 2011), 2011 (Harman et al. 2012), 2012 (Harman and Albright 2013), 2013 (Harman and Albright 2014), 2014 (German and Albright 2015), and 2015, Site 3 (see Figure 1 for sites).

287 Site 4 Plant Community 800 Nitellopsis obtusa 700 Myriophyllum spicatum 600 Others 500

400

300

Dry Weight (g/m^2) 200

100

Figure 5. Comparison of biomass (g/m2) of starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species present, 2008 (Harman et al. 2009), 2009 (Harman et al. 2010), 2010 (Harman et al 2011), 2011 (Harman et al. 2012), 2012 (Harman and Albright 2013), 2013 (Harman and Albright 2014), 2014 (German and Albright 2015), and 2015, Site 4 (see Figure 1 for sites).

Site 5 Plant Community 800 Nitellopsis obtusa 700 Myriophyllum spicatum 600 Others 500

400

300

Dry Weight (g/m^2) 200

100

Figure 6. Comparison of biomass (g/m2) of starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species present, 2008 (Harman et al. 2009), 2009 (Harman et al. 2010), 2010 (Harman et al 2011), 2011 (Harman et al. 2012), 2012 (Harman and Albright 2013), 2013 (Harman and Albright 2014), 2014 (German and Albright 2015), and 2015, Site 5 (see Figure 1 for sites).

288 Water Quality Analysis

Water quality parameters over summer 2015 were comparable to those of recent years. In the south basin, waters below 8m were essentially anoxic by the first sampling date (4 June). pH was typically between 6.8 and 8.5. Transparency was between 3.5 and 5.6 m over the sampling dates. Total phosphorus at the surface ranged from below detection limits to 15 ug/l. Total nitrogen declined from 0.57 mg/l at the surface on 4 June to 0.20 mg/l at the surface on 28 July. By 4 June, nitrate was below detection at the surface.

In the shallower north basin, stratification was evident in the June visit, with bottom waters being intermittently anoxic. pH ranged from 7.0 to 8.5. Transparency ranged from 1.9 to 3 m. Total phosphorus ranged from 9 to 19 ug/l. Total nitrogen was between 0.20 and 0.35 mg/l, and nitrate levels were below detection on most samples (when it was detected concentration was <.05 mg/l).

DISCUSSION

The spread of starry stonewort continued throughout 2015. This was the second year in which this macroalga was documented in the north basin. On 4 June and 28 July, starry stonewort was more abundant than Eurasian milfoil in the north basin. By 28 September the starry stonewort was absent from collection sites allowing Eurasian milfoil and coontail to dominate the north basin.

Starry stonewort was present at all sites in the south basin on all sampling dates (except site 2 on 4 June). On all sampling occasions it dominated the sample sites. Eurasian milfoil was absent from collections in this basin. The reduction in the diversity of the macrophytic community of both basins is marked. Native species, such as Chara vulgaris, Vallisneria americana, Potamogeton zosteriformes and Elodea Canadensis, that were routinely collected as recently as 2006 (Harman et al. 2007) are now rarely encountered. Similar reductions in plant diversity following the establishment of starry stonewort have been described elsewhere (Pullman and Crawford 2010).

Curiously, bladderwort (Utricularia spp.) was documented for the first time in 2015. It was found only in the north basin, though it did occur at both sites and was found on multiple sampling occasions throughout the summer. This genus has several species native to New York and does not typically pose management concerns. It is interesting to note that this plant appeared during a year where Eurasian milfoil was at the lowest recorded abundance of the past few years. Though no assumptions/explanations can be made at this time about the plants sudden appearance, it will be interesting to monitor its status in the coming years.

The continued spread of starry stonewort throughout the lake is worrisome. It has become a serious pest- particularly to the native plant community- in many lakes, and in Moraine Lake it continues to become increasingly common and problematic. It is worth noting that starry stonewort forms dense mats just above the bottom; this suppresses native plant growth, but it

289 seems not to have as big of an impact on recreational activities as Eurasian milfoil canopies. We are not aware of proven methods of selective control of this species.

REFERENCES

Anon. 1988. Madison County septic system survey. Madison County Planning Department, Wampsville, NY 13163.

Borman, S., R. Korth, and J. Tempte. 1999. Through the looking glass. A field guide to aquatic plants. Wisconsin Lakes Partnership.

Crow, G. E. and C. B. Hellquist. 2000a. Aquatic and wetland plants of Northeastern North America. V.1. Pteridophytes, gymnosperms, and angiosperms: dicotyledons. The University of Wisconsin Press.

Crow, G. E. and C. B. Hellquist. 2000b. Aquatic and wetland plants of Northeastern North America. V.2. Angiosperms: monocotyledons. The University of Wisconsin Press.

Fuller, R. 1997. Unpublished data. Colgate University, Hamilton, NY 13346.

German, B.P. and M.F. Albright. 2015. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W.N. and M.F. Albright. 2014. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2013. In 46th Ann. Rept. (2013). SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W.N. and M.F. Albright. 2013. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W.N. and M.F. Albright and O. Zaengle. 2012. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2011. Tech. Rept. #30. SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W.N. and M.F. Albright and T.F. Smith. 2011. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2010. Tech. Rept. #29. SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W.N., M.F. Albright, H.A. Waterfield and M. Rubenstein. 2010. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2009. Tech. Rept. #27. SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

290 Harman, W.N. and M.F. Albright and L. Zach. 2009. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y., 2008. Tech. Rept. #26. SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W. N. and M. F. Albright, and C. M. Snyder. 2008. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison County, NY., 2007 Tech. Rept. #25. SUNY Oneonta Bio. Fld. Sta., Oneonta, NY.

Harman, W. N. and M. F. Albright, and A. Scorzafava. 2006. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison County, NY. Tech. Rept. #23. SUNY Oneonta Bio. Fld. Sta., Oneonta NY.

Harman, W. N. and M. F. Albright, P.H. Lord and M. E. Miller. 2002. Aquatic macrophyte management plan facilitation of Lake Moraine, Madison County. Tech. Rept. #13. SUNY Oneonta Bio. Fld. Sta., Oneonta NY.

Harman, W. N. and M. F. Albright, P.H. Lord and M. Miller. 2000. Aquatic macrophyte management plan facilitation of Lake Moraine, Madison County. Tech. Rept. #9. SUNY Oneonta Bio. Fld. Sta., Oneonta NY.

Harman, W. N. and M. F. Albright, P.H. Lord and D. King. 1998. Aquatic macrophyte management plan facilitation of Lake Moraine, Madison County. Tech. Rept. #5. SUNY Oneonta Bio. Fld. Sta., Oneonta NY.

Harman, W. N. and M. F. Albright. 1997. Aquatic macrophyte survey of Lake Moraine, Madison County, summer 1997, as related to management efforts by Sonar ® application. SUNY Oneonta Bio. Fld. Sta., Oneonta NY.

Lembi, C.A. 2000. Aquatic Plant Management. Purdue University, Cooperative Extension Service. West Lafayette, IN 47907.

Lord, P.H. and R.L. Johnson. 2006. Aquatic plant monitoring guidelines. http://www.dec.ny.gov/docs/water_pdf/aquatic06.pdf

NYS Department of Environmental Conservation, Water Division. A Primer on Aquatic Plant Management in NYS. http://www.dec.ny.gov/docs/water_pdf/ch6p2.pdf. 8.12.2009.

Pulman, G.D. and G. Crawford. 2010. A decade of starry stonewort in Michigan. Lakeline, summer 2010. North American Lake Management Society.

291 Preliminary prey density analysis of wood turtles (Glyptemys insculpta) in Central New York

Elizabeth Clifton1, Alexander Robillard2, and Dr. Donna Vogler3

ABSTRACT Known as an opportunistic species, wood turtles (Glyptemys inscultpa) require a calcium rich diet in order to maintain the integrity of their shells. In the northern parts of their range it is believed that snail species provide a great source of nutrition to their calcareous shells. During a Master's Thesis study in Central New York, a handful of wood turtles were observed with snail and slug remains on their beaks. This study examined the density of snails and other invertebrates relative to identified wood turtle locations. Groups of three cardboard squares (30cm x 30cm each) were soaked in nearby streams and placed 1-2 meters apart in known turtle locations. Control groups were placed in areas thought to be suitable habitat, but where no turtles were previously found. Boards were left for 12-24 hours and then checked for any slugs, snails, insects, and other invertebrates. Any data collected in this survey will increase the knowledge on wood turtles and help protect and conserve them more effectively.

INTRODUCTION

Habitat fragmentation is one of the primary drivers for decline in species of turtles (Klemens 2000). Specifically, wood turtles (Glyptemys inscultpa) are threatened throughout their range and are listed as a species of “special concern” in New York State (Breisch and Behler 2002). Despite this classification, many aspects of their life history are still unknown, such as their diet and specific habitat preferences. Wood turtle are generally identified as opportunists and are known to eat a variety of plants and invertebrates including snails, slugs, worms, insects, raspberries and strawberries (Sherwood and Wu 2012; Compton et. al. 2002). While surveying for these turtles in Central NY, numerous turtles were found with bits of snail shells and slugs on their beaks. This suggests they had recently preyed upon gastropods. Consuming snail shells is believed to be advantageous to a wood turtle because they may be able to absorb the calcium from the snail shells and use it to help build and repair their own shells. This survey focused on gastropod densities in areas where these turtles were observed in the past. Cardboard sheets have been shown to be one of the most effective methods for estimating relative densities of gastropod communities, while also being one of the least damaging and least labor intensive approaches (Hawkins et. al. 1998; Oggier et. al. 1998). This method was used in order to preserve the integrity of the study sites as much as possible. A positive correlation between gastropod and turtle populations could indicate that these turtles prefer habitats with more gastropods because they would have more nutritious food for

1 SUNY Oneonta Biology Department Intern. 2 SUNY Oneonta Masters of Science candidate. 3 SUNY Oneonta Biology Department Professor.

292 themselves and their offspring. If this is the case, future studies surveying for these turtles could more easily identify areas that are more or less likely to have wood turtles based on gastropod densities (which are much easier to determine than turtle densities).

METHODS Four freshwater creeks in Otsego County, NY were chosen for this survey (locations not provided here to help protect them from collection for the illegal pet trade). The streams were selected because they had the highest abundance of turtle captures during earlier summer surveys. These sites were also identified by Dr. John New as having wood turtle populations in the 1970s, suggesting the populations have survived multiple generations. Corrugated ventilators were cut into 30cm x 30cm squares. Each stream had eight sites that consisted of three cardboard plots about 1-2 meters apart. Four sites were placed in the exact location a wood turtle had been previously found and four control plots were placed at each stream in an area that was determined to be suitable habitat for turtles, but where one had not been previously sighted. The cardboard was left in each area for 12-24 hours and was checked early the next day while there was still dew/ moisture (Oggier 1998). Data were recorded for any organisms found on top, stuck to the bottom, or underneath each piece of cardboard. A Mann-Whitney U test was used to determine if there was a difference in gastropod density between turtle and control locations.

RESULTS AND DISCUSSION A Mann Whitney-U test was performed on gastropod density in turtle and control sites and showed no significant difference (p= 0.3964) between the two. The median number of gastropods at turtle sites was 1.5 and at control sites was 1.0 (Figure 1). A possible explanation for these results could be that the sites that were used as “control” locations may have turtles living there and that have not yet been identified. These locations were chosen because they were determined to be suitable for the wood turtles, and so it is possible that turtles are in these areas. If this study were to continue, different streams should be used as the control sites. If it is possible to find streams similar to the ones in this study that do not have large wood turtle populations, they would be much better controls. The controls in this study may have been too close to the turtle sites and thus may be why there was no significant data.

293

Figure 1. Median gastropod densities for turtle and control sites. Turtle sites had a median count of 1.5 gastropods, while control sites had a count of 1.0.

REFERENCES Breisch, A. and J.L.Behler. 2002. Turtles of New York State [Internet]. New York State Department of Environmental Conservation. New York State Conservationist; [cited 2015Aug]. Retrieved from http://www.dec.ny.gov/docs/administration_pdf/turtles2.pdf. Compton, B.W., J.M. Rhymer, and M. McCollough. 2002. Habitat selection by wood turtles (Clemmys insculpta): an application of paired logistic regression. Ecological Society of America. 83(3):833-843. Hawkins, J.W., M.W.Lankester, and R R.A. Nelson 1998. Sampling terrestrial gastropods using cardboard sheets. Malacologia. 39(1-2):1-9. Klemens, M.W. 2000. Turtle conservation. Smithsonian Institute Press. Washington, DC. Oggier, P., S. Zschokke, and B. Baur, 1998. A comparison of three methods for assessing the gastropod community in dry grasslands. Pedobiologia. 42(4):348-357. Sherwood, N. and M. Wu, 2012. Terrestrial habitat preference by Wood Turtles (Glyptemys insculpta) in New Jersey. N. J. Academy of Science. 57(1):5-7.

294 Deer population impacts on biodiversity in Glimmerglass State Park

Brandon Panensky1

ABSTRACT Glimmerglass State Park is like other places in New York, overwhelmed with white- tailed deer. With the loss of keystone predators, deer populations continue to grow. In this study, I document how predator diversity in the park is affected by a large deer population. By counting the deer through spotlighting, I formed an average estimate of deer entering the park each night, and estimated population density. Using a bait and camera trap, I observed the number of predator species within the park area in the winter. I conclude that the deer are having an indirect negative impact on local predators through their consumption of resources. This study provides two alternate post study recommendations for handling the growing deer population within the park.

INTRODUCTION

Much of New York is known for it’s over abundant white-tailed deer (Odocoileus virginiamus) population. Millions of dollars are spent each year, repairing damages on homes and cars due to white-tailed deer (Curtis 2001). Deer grow to be an average of 90-113 kg of dense muscle. Daily dry matter consumption of vegetation by whitetail deer varies from 2 to 4% per day (Craven and Hygnstrom 1994). They mainly eat grasses, green leaves, shrubs, berries, and the small woody branches of shrubs and trees, which usually include seedlings and saplings (Fulbright 2013).

White-tailed deer have serious impacts on their environment, affecting plant life and wildlife. Deer occupy various habitats, and are flexible to change (Rooney 2003). Deer can also reproduce under various conditions, starting at age one year (Curtis 2001).

With consumption rates, the more the deer that are in an area, the more saplings and shrubs are browsed (Rooney 2003). This has adverse affects on ecosystem dynamics. The loss of saplings limits the amount of canopy that will develop in future years, and changes the chemistry of the soil with leaf litter changes (Rooney 2003). The loss of shrubs also affects the microclimates on the ground with the presence, or absence, of light, and even the chemistry with rate of decay of shrubs and fruits (Rooney 2003). This change in plant life allows other plants, such as invasive species, to outcompete native plants, since there is now room to grow and weakened plants for competitors (Fulbright 2013).

1 Undergraduate of Environmental Sciences at State University of New York at Oneonta, 108 Ravine Parkway Oneonta, New York 13820

295

With these effects, the composition of ground level resources removes habitats for smaller animals, as well as food sources. This, then, forces the animals to leave the area in search of resources, and the predators of those smaller animals follow. This lowers biodiversity (Bestcha 2012). Without natural predators, and insufficient hunting to keep the deer population low, the deer consume resources until they starve themselves. Predators, such as wolves, keep local herds moving, and in balanced numbers, so they do not consume all of the plant life in one area (Bestcha 2012). Without these keystone predators, the deer population is free to do what it does best, consume, and reproduce (Cote 2004).

METHODS

I estimated the deer population in Glimmerglass State Park Cooperstown, New York. I conducted spotlight surveys using the methods of the National Park Service in Shenandoah National Park (National Park Service, 2009). The surveys were held one day a week for four weeks during the month of November 2014. The spotlight survey started at dusk, and used six logical and equal transects of 200m x 1000m strategically placed around the park. I walked for ~2 hours with a high-powered light, counting each deer. Once counted, I removed an outlier (a single data set that did not fit the pattern of the majority of data sets due to an weather abnormality). I calculated the average estimate of deer that entered the park on a daily basis for the remainder of data sets.

I also estimated predator diversity in the park. First, I obtained a DEC license to obtain a deer carcass from the Department of Transportation (DOT) to bait a trail camera (Trilliet 2014, Boast 2013). The park manager placed the carcass in a location within part of the park (UTM18 511235 E 4737148) during the month of February. The carcass and camera were used for the entire month of February to capture pictures of predators that fed on it.

I downloaded the photos from the camera, and then individually examined the photos for carnivore species that fed on the carcass. I noted repeat visits by animals of the same carnivore species.

RESULTS

The average number of deer that entered the park on a study day was ~46 deer (± 0.36 deer margin of error) in the 259ha park. Deer data provided a population density estimate for the park of ~0.19 deer/ha. Otsego County is included in the DEC’s Southeastern hunting region, which has a density of 0.09 deer/ha. The state, as a whole, yields 0.08 deer/ha. The highest count in the six transects was 57 deer.

296 Camera data documented two predator species visiting the carcass, a single red fox (Vulpes vulpes) and two red tailed hawks (Buteo jamaicensis).

DISCUSSION

The timeframe for this project was severely constrained due to delays in obtaining a DEC license. Coordination issues limited carcass bait trap time.

Another variable impacting the data was weather. The winter of 2014-2015 was abnormally harsh for the Northeastern United States. Temperatures were below freezing on a consistent basis, with sharp winds, and snow reaching heights of several feet. This cold caused small nearby water bodies to freeze, as well as did Otsego Lake. The water bodies were also covered in snow. This forced many raptors, such as bald eagles (Haliaeetus leucocephalus), that depended on the fish in the nearby waters, to move toward the Susquehanna River in search of food. What remained were a few large birds of prey that scoured the landscape for any small signs of possible edible life. The deer population appears to have destroyed much of the shelter and resources for many of the park’s smaller animals, such as weasels and rabbits, to survive during the winter, and most likely, leave the park in search of those resources (Bestcha 2012).

The park’s ecosystems range from dry grasses, to lakeshores, streams, and forests. Despite a variety of ecosystems, and microhabitats, there is a low diversity of animals to fill them. The only fox that was seen on the trail camera was small, and gaunt, which indicates that there is barely enough to sustain it, let alone a healthy fox population. The carcass that was baiting the camera possibly saved its life over the winter.

Coyotes could pose a threat to a small white-tailed deer herd in a rough winter. Deer trapped in a dense snow covered yard leave themselves exposed, where coyotes, single or multiple, can then consume the deer. White-tailed deer need proper nutrition and room to escape from attack (Messier 2011). Deer cannot receive proper nutrition when trapped in a yard, and, when confronted by coyotes, they lack the room and energy to escape and coyotes can consume multiple deer during the winter (Messier 2011). Fawns are also extremely vulnerable during their first few weeks of life, and coyotes can consume many during fawning season (Nelson 2015).

Coyotes also feed on various small game, such as mice, as well as red foxes (Way 2013). This exposure has caused an outbreak of Lyme disease among the coyote population (Way 2013). With Lyme disease affecting the health of the coyotes, the harsh winter may have caused a decrease in their population.

297 Without an apex predator, the deer are free to grow in population, and consume resources, until their own population cannot be sustained (Bestcha 2012). A single apex predator can severely impact the deer population in such a small area like Glimmerglass State Park. It could force the deer to disperse more throughout the area, allowing plant life to recover over time, allowing animal diversity to return (Bestcha 2012).

I conclude that the state park cannot sustain such a large deer population without any repercussions on the surrounding ecology. With contrast to larger areas, Glimmerglass yields a high number of deer which consume ~3% (~3kg) of their body weight in vegetation per day (Curtis 2001). The deer provide a negative impact on biodiversity, thereby, lowering it (Rooney 2003). For a long time, humans filled this role once we removed the nonhuman apex predators from the area, and the deer population stayed relatively balanced, but hunting by humans has since decreased (Riley 2003). With possible social and political variables to be considered, I offer two recommendations. 1) Close the park to regular visitors to open a short bow-hunting season for a limited number of hunters in the park during the region’s regular hunting season. The hunters would be required to sign a contract and pay a fee to the park. 2) Extend the length and dimensions of the survey being described into a continuing study.

Hunting within the park will provide a more immediate addressal to the problem. Other state parks, like Rockefeller State Park, already use this method to manage their deer problem and report positive results. Hunters will fill the role of predators, decreasing the population, and forcing the animals to disperse. Some have proposed deterrents like fences, or a perimeter of repellant to limit the deer that come into the park, but this only sends the deer problem somewhere else (Curtis 2001).

An extension to the study is a commitment, but may not have as much public opposition compared to hunting. Staff, or student interns, can carry this out. If the study is extended, I recommend that it be carried out through all seasons, for at least one full year. The study should include a seedling and sapling inventory, a small mammal survey to obtain an estimate of biodiversity in the park, and continuation of the spotlighting and baited camera trap. The study could provide more time for deer to cause ecological damage, but will provide much more detailed information for informed decisions regarding an effective deer management program.

REFERENCES

Bengsen, A. 2014. Analysis of camera trap surveys to detect effects of population management. Camera Trapping Wildlife Management and Research (2014): 325-330

Beschta, R.L., and W. J. Ripple. 2012. Berry-producing shrub characteristics following wolf reintroduction in Yellowstone National Park. Journal of Forest Ecology and

298 Management. (2012) 276: 132-138. Department of Forest Ecosystems and society, Oregon State University.

Boast, L., U. Molefe, M. Kokole, and R. Klein. 2013. Results of a motion camera survey in Jwana Game Park, Jwaneng, Botswana. Debswana Mining Company and the Botswana Department of Wildlife and National Parks. (2013) Okavango Guiding School.

Cote, S. D., T.P. Rooney, J-P Tremblay, C. Dussault, and D.M. Waller. 2004. Ecological impacts of deer overabundance. Annual Review of Ecology, Evolution, and Systematics, (2004) 35: 113-147. Annual Reviews.

Craven, S.R. and S.E. Hygnstrom. 1994. Deer. The Handbook: Prevention and Control of Wildlife Damage. Cooperative Extension Division Institute of Agriculture and Natural Resources University of Nebraska – Lincoln.

Curtis, P.D. 2015. White-tailed deer. Gettsburg, Wildlife Damage Management Fact Sheet Series, (2001). Cornell University.

Fulton, D.C., et al. 2004. Beliefs and attitudes toward lethal management of deer in Cyuahoga Valley National Park. The Wildlife Society, (2004) 32 (4) 1166-1176. Wiley.

Keyser, P.D., DC Guynn Jr, and H.S. Hill Jr. 2005. Density-dependent recruitment patterns in white-tailed deer. The Wildlife Society, (2005) 33 (1): 222-232. Wiley.

Lind, E.M., E. P.Myron, J. Giaccai, and J.D.Parker. 2012. White-tailed deer alter specialist and generalist insect herbivory through plant traits. Environmental Entomology, (2012) 41(6):1409-1416. National Center for Biotechnology Information.

Messier, F. and C. Barrette. 1985. The efficiency of yarding behaviour by white-tailed deer as an antipredator strategy. Canadian Journal of Zoology (1985) 63 (4): 785-89. NRC Research Press.

Monterroso, P. S., L. N. Rich, A. Serronha, P. Ferreras, and P. C. Alves. 2014. Efficiency of hair snares and camera traps to survey mesocarnivore populations. European Journal of Wildlife Research (2014) 60: 279-289. Springer- Verlag Berlin Heidelberg.

299 Nesldon, J.L., M.P. Scroggie, and C.A. Belcher. 2014. Developing a camera trap survey protocol to detect a rare marsupial carnivore the spotted-tailed quoll (Dasyurus maculatus). Camera Trapping Wildlife Management and Research c. (2014): 271-78. CSIRO.

Nelson, M.A., M. J. Cherry, M. B. Howze, R.J. Warren, and L. M. Conner. 2015. Coyote and bobcat predation on white-tailed deer fawns in a longleaf pine ecosystem in southwestern Georgia. Journal of the Southeastern Association of Fish and Wildlife Agencies (2015) 2: 208-13. Wisconsin Department of Natural Resources.

Pittet, M., and P. Bennett. 2014. Examining the state of biodiversity using camera traps in the Pacaya Samiria National Reserve, Peru. Camera Trapping Wildlife Management and Research b. (2014): 54-60. CSIRO.

Rich, L. N., M. J. Kelly, R. Sollmann, A. J. Noss, M. S. Di Bitetti, L. Maffei, R. L. Arispe, A. Paviolo, C. D. De Angelo, and Y. E. Di Blanco. 2014. Comparing capture-recapture, mark- resight and spatial mark-resight models for estimating puma densities via camera traps. Journal of Mammalogy (2014) 95 (2): 382-391. American Society of Mammologists.

Riley, S.J. 2003. Deer Populations up, hunter populations down: Implications of interdependence of deer and hunter population dynamics on management. Écoscience (2003)10 (4): 455-61. Riley.fw.msu.edu. Michigan State University,

Rooney, T.P., and D.M. Waller. 2003. Direct and indirect effects of white-tailed deer in forest ecosystems. Forest Ecology and Management, (2003)181:165–176. Department of Botany, University of Wisconsin. Roth II, T.C. and S.L. Lima. 2003. Hunting behavior and diet of cooper’s hawks: An urban view of the small-bird-in-winter paradigm. The Condor, (2003) 105(3):474-483. Cooper Ornithological Society.

Rovero, F., E. Martin, M. Rosa, J.A. Ahumada, and D. Spitale. 2014. Estimating species richness and modelling habitat preferences of tropical forest mammals from camera trap data. PLOS ONE (2014) 9(7).

Silver, S.C., E.T. Linde, L.K. Ostro, L. M. Maffei, A. J. Noss, M. J. Kelly, R.B. Wallace, H. Gomez, and G. Ayala. 2004. The use of camera traps for estimating jaguar (Panthera onca) abundance and density using capture/recapture analysis. Oryx: International Journal of Conservation, (2004) 38 (2): 148-154. Fauna and Flora International.

National Park Service. 2015. Spotlight counts of deer in the big meadows area. Natural Resource Facts Sheet (2009): 1091-098. National Park

300 Service.. Visited 24 June 2015.

Taillon, J., D.G. Suave, and S.D. Cote. 2006. The effects of decreasing winter diet on foraging behavior and life-history traits of white-tailed deer fawns. Journal of Wildlife Management (2006) 7 (5): 1445-1454. The Wildlife Society.

Trolliet, F. 2014. Use of camera traps for wildlife studies: A review. Biotechnol. Agron. Soc. Environ. 8.3 (2014): 446-54. Orbi.ulg.ac.be. Presses Agronomiques De Gembloux.

Way, J.G., and B.N. White. 2013. Coyotes, red foxes, and the prevalence of Lyme disease. (2013) Northeastern Naturalist, 20 (4): 655-665. Eagle Hill Institute.

Wuczynski, A. 2005. Habitat Use and hunting behaviour of common buzzards (Buteo buteo) wintering in south-western Poland. ACTA Ornithologica, (2005) 40(2):147-154. Museum and Institute of Zoology, Polish Academy of Sciences.

301 Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 20151

M.F. Albright

BACKGROUND (from Albright and Waterfield 2011)

In 2002, the US Army Corp of Engineers (ACE) initiated a 1.6 million dollar Upper Susquehanna River Watershed-Cooperstown Area Ecosystem Restoration Feasibility Study And Integrated Environmental Assessment. Authorized by the U.S. Congress, the pilot program was to “use wetland restoration, soil and water conservation practices, and non-structural measures to…improve water quality and wildlife habitat…in the Upper Susquehanna River Basin…” (ACE 2001). Initially identified were eight Field Assessed Benefit and Design Strategy sites (FABADS) in Otsego County. During 2003, the SUNY Oneonta Biological Field Station (BFS) monitored two restored sites which receive agricultural runoff as well as a local “pristine reference site”. Comparisons were made between inflows and outflows, and between the wetlands, of concentrations of different nutrient fractions, suspended sediments and fecal coliform bacteria. This short term study did indicate water quality improvements when nutrient levels at the inflow were elevated (Fickbohm 2005), though it is probable that not enough time had elapsed to allow these systems to naturalize to the point where treatment potential was realized.

A third ACE restoration wetland was sited in the outskirts of the Village of Cooperstown adjacent to the municipal sewage treatment facility (Figure 1). The primary function of this 3 acre wetland was phosphorus and nitrogen removal, potentially by converting this site into a treatment wetland for the Village’s municipal effluent. However, at that time, funds to deliver the effluent to the wetland were lacking. The wetland design, provided by Ducks Unlimited, did not necessarily follow that generally utilized for treatment wetlands.

In 2009, funding was provided by the Village of Cooperstown’s Sewer Reserve Fund to hire the services of Lamont Engineering to evaluate alternatives to address nutrient reduction from the wastewater treatment plant (Jackson 2009). A more restrictive SPDES permit by the NYSDEC regarding nutrient loading to the Susquehanna River is consistent with New York State being cosignatory with the Chesapeake Bay Nutrient Reduction Strategy. The engineering report evaluated approaches to reducing phosphorus and nitrogen introduced into the Susquehanna River, their capital and annual operational costs, and expected nutrient reductions. At that time, it was recommended that utilizing the existing wetland for tertiary treatment would likely meet the nutrient reduction goals while costing substantially less than other approaches (i.e., addition of chemical coagulants, modification to the treatment plant, etc.). However, beginning in June 2014 the village was mandated by the Chesapeake Bay TMDL Implementation to limit phosphorus output from the plant to 2,170 lb (984.3 kg) per year (Cankar and Folts 2015). This report summarizes strategies employed to date to address this.

1 Funding for this project was provided by the Village of Cooperstown.

302

Figure 1. Bathymetric map of the wastewater treatment wetland, Cooperstown, NY (modified from Robb 2012).

Rationale for monitoring (from Albright and Waterfield 2011)

Wetlands have been used as water treatment cells for a number of years, but, until recently, only on a very limited basis. Since the mid 1990s, however, the number of constructed wetlands, having a broad range of system configurations and treatment applications, has increased markedly (Kadlec and Wallace 2009). When associated with municipal sewage outfalls, the parameters that are most often targeted for reduction are phosphorus, various nitrogenous compounds (ammonia, nitrate, total nitrogen), suspended solids and biological oxygen demand. The demonstrated effectiveness of the removal of these constituents has been promising, though quite variable, as design and site characteristics are, in practically every case, unique. Because of this, every time a treatment wetland is utilized, the opportunity exists to collect meaningful data which can aid in the design of future systems. More directly, data collection at some level is necessary to evaluate whether or not the goals of the treatment wetland, and the regulated limits of the parameters, are met.

From June 2010 through 2014, the concentration limit requirements of the effluent for total phosphorus, total nitrogen, ammonia and nitrates were not more stringent under the new SPDES/Chesapeake Bay Nutrient Reduction Strategy than they had been. However, refinements of those regulations more recently require reductions in total phosphorus. Continued monitoring of all nutrient species may provide insight into a reduced phosphorus load as nutrient dynamics may be inter-related.

303 METHODS

Early efforts to quantify flow from the wetland were frustrated by the apparent unreliability of water level logging equipment and by interference of the V notch weir by muskrat and beaver. However, work conducted in 2011 focused on direct reading of the gauge on the weir face. Robb (2012) mounted a programmable Reconyx® trail camera so that it would capture images of the gauge at 15-minute intervals. In the absence of moderate rainfall (> ~ 1 cm/24 hr), the mean daily inflow from the wastewater treatment plant (which is gauged) nearly equaled the outflow from the wetland (determined by the photos of the gauge on the weir). During wetter periods, a small stream entering the west side of the wetland can contribute enough flow so that the outflow exceeds the input from the treatment plant for short periods of time. Since 2012, the flow of effluent into the wetland was assumed to equal the flow out of it. Sampling did not coincide with runoff events in order to minimize their influence.

Sampling began in February 2010, and was done monthly through May 2010 to evaluate nutrient conditions prior to the diversion of effluent to the wetland (which commenced on 17 June 2010). Thereafter, samples were collected two to four times a month from the wastewater treatment plant (effluent), the wetland’s outlet, and the stream feeding the wetland (to evaluate contributions from this source, though flow here was often too low to allow sampling). Nutrient loading to the wetland was cumulative monthly discharge X mean monthly effluent concentration; export from the wetland was monthly discharge X mean concentration at the outflow. The retention of each nutrient fraction was calculated as the difference between the load to the wetland and the load from it, and the percent retention was calculated as the mass retained / the mass loaded to the wetland (X 100).

This report summarizes results through December 2015. Samples were processed according to automated methods using a Lachat QuikChem FIA+ Water Analyzer. Samples were analyzed for total phosphorus using ascorbic acid following persulfate digestion (Liao and Martin 2001), total nitrogen using the cadmium reduction method (Pritzlaff 2003) following peroxodisulfate digestion Ebina et.al (1983), ammonia using the phenolate method (Liao 2001), and for nitrate+nitrite nitrogen using the cadmium reduction method (Pritzlaff 2003). Missing values were approximated by interpolating existing data.

RESULTS

Prior to the wetland receiving effluent, the outflowing concentrations of ammonia were below detection, nitrate averaged 0.28 mg/l, total nitrogen averaged 0.72 mg/l and total phosphorus averaged 0.043 mg/l. For the tributary inflow to the wetland from February 2010 through December 2015, mean nutrient concentrations were 0.01 mg/l (SE= 0.003) for ammonia, 0.25 mg/l (SE= 0.026) for nitrite+nitrate, 0.42 mg/l (SE= 0.027) for total nitrogen and 0.050 mg/l (SE= 0.006) for total phosphorus. The relevance of these low nutrient concentrations, coupled with low flows, indicate that its influence on calculating nutrient retention rates, and investigating nutrient transformations, is minimal.

304 Summaries of the annual retention, as both net volumes (kg) as a percent of the inputs, of ammonia, nitrite+nitrate, total nitrogen and total phosphorus following the diversion of effluent to the wetland are provided in Table 1. Tables 2-5 provide mean monthly concentrations of the wastewater effluent and wetland’s outfall, as are total monthly nutrient volumes (kg), the volume of nutrients retained (kg) and the mean retention rate (%).

Between June 2010 and December 2015, the total amount of nutrients retained by the treatment wetland included 1,400 kg of ammonia-N, 10,455 kg of nitrate-N, 14,700 kg of total nitrogen-N and 1,716 kg of total phosphorus-P. Over the course of 2015, the retention of ammonia was actually negative (more was exported from the wetland than was loaded to it). The concentrations in the effluent were somewhat higher than other years and they were quite variable (mean= 2.75 mg/l, SE= 0.41); concentrations were somewhat higher and less variable at the wetland’s outlet (mean= 2.90 mg/l, SE= 0.11). The ammonia outfall concentrations were nearly three times higher than they had averaged from 2010 to 2014.

Over 2015, retention of nitrate (at 29%), and total phosphorus (at 28%) were similar to those over most years since this work began. Total nitrogen (at 22%) was 10-20% lower than it has been over 2010-2014 due to the negative retention of ammonia over the year. The fact that total phosphorus retention has not declined may imply that the mechanism is, in part, related to biological uptake rather than simply sediment binding (which would reduce as the sediments become phosphorus-saturated) (Kadlec and Wallace 2009).

Given that this wetland was designed more for waterfowl habitat than for water quality improvement, the nutrient removal capacity seems promising. As vegetation densities increase, so should nutrient reduction, both directly though vegetative uptake and enhanced microbial denitrification due to increased microsites (Kadlec and Wallace 2009). Investigations into phosphorus uptake by rooted plants at the Cooperstown wetland provided conflicting results. Olsen (2011) found elevated phosphorus content in the leaf tissue of reed canary grass (Phalaris arundinacea) within the wetland than that of plants in nearby areas not influenced by the treatment wetland. However, similar investigations in 2011 on reed canary grass and cattail (Typha sp.) did not show meaningful differences in phosphorus uptake (Gazzetti 2012; Bouillon 2016).

305 Table 1. Summary of ammonia, nitrate, total nitrogen and total phosphorus retention by the Cooperstown treatment wetland, 2010 through 2012.

Ammonia retention Nitrate retention T. Nit. retention T. Phos. retention Kg % Kg % Kg % Kg % 2010 (17 June-Dec) 235 42 797 28 1149 30 252 36 2011 366 27 2017 28 2685 28 251 15 2012 116 24 2637 46 3302 42 306 22 2013 514 45 1995 23 3769 43 315 22 2014 206 27 1719 35 2439 33 352 27 2015 -39 -3 1290 29 1360 22 241 28 total 1398 27 10455 32 14704 33 1716 25

In accordance to the Chesapeake Bay Nutrient Reduction Strategy, a limit of phosphorus release was imposed upon the Cooperstown Sewage Treatment Plant beginning in June 2014. That limit is 984 kg/year (or 2.7 kg/day). Given that the plant typically discharges about 500,000 cubic meters of sewage/year, total phosphorus concentrations of <2 mg/l would satisfy that requirement. As concentrations since 2010 have averaged 2.5 mg/l, the percent reduction needed, at 20%, is modest. There have been several approaches since this effort commenced (Cankar and Folts 2015). The first, beginning on 18 June 2014, was using K2001®, a polyaluminum chloride coagulant. It was injected after the rack and prior to the primary clarifier at a rate of approximately 1 part to 50,000 sewage. Beginning on 3 September 2014, the product used was switched to PAX-WL8®, also a polyaluminum chloride solution. It was also injected at the primary clarifier at varying rates, typically about 1:35,000. Beginning on 17 September 2014, the same product was used at a similar rate, but the injection point was changed to the final clarifier. On 7 October 2014, alum (aluminum sulfate) was injected at the final clarifier at a variable rate, typically about 1:35,000. Beginning on 24 October 2014, SLACK PLUS®, an aluminum chloride coagulant, was used. It was injected at both the primary clarifier and the final clarifier at a combined rate of between 1:40,000 and 1:20,000.The phosphorus reduction using all approaches was modest, though phosphorus discharge at all checks was below the regulated amount of 2.7 kg/day (6 lb/day). From 4 December though 17 June 2015, the injector system was off line as the plant prepared for the installation of in-pipe injectors (of which there is two, each accommodating approximately 50% of the effluent flow).

Beginning on April 17 2015, the dual injection system introduced SLACK PLUS® at a rate of about 1:16,000 to 1:28,000. On 27April 2015, the coagulant was switched to alum (which is considerably less expensive). Rates used were initially between about 1:15,000 to 1:22,000. On 17 December 2015, rates were increased to about 1:13,000. Over the course of 2015, total phosphorus concentration in the effluent averaged 1.90 mg/l, the first time since at least 2010 the levels averaged less than 2.0 mg/l (Figure 5).

306 Table 2. Mean monthly concentrations of ammonia in the wastewater effluent and wetland’s outfall (mg/l), total monthly ammonia volumes (kg) entering and leaving the wetland, the volume of ammonia retained (kg) and the mean retention rate (%). (Projected).

Month Eff flow NH4 (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jun-10* 1728 4.10 1.41 63.8 21.9 41.8 65.6 Jul-10 1692 0.31 1.03 16.1 54.0 -37.9 -235.5 Aug-10 1526 5.35 2.46 252.9 116.2 136.8 54.1 Sep-10 1186 2.19 1.17 77.9 41.6 36.3 46.6 Oct-10 1476 2.19 1.17 100.2 53.5 46.7 46.6 Nov-10 1447 0.60 0.45 26.0 19.7 6.3 24.3 Dec-10 1330 0.60 0.48 23.9 19.1 4.9 20.3 2010 1509 2.46 1.28 560.9 326.1 234.8 41.9 Jan-11 1222 0.68 0.478 24.9 17.5 7.4 29.6 Feb-11 1319 2.68 2.488 98.8 91.8 6.9 7.0 Mar-11 2707 4.30 1.995 360.4 167.4 193.0 53.6 Apr-11 2824 1.96 1.883 166.2 159.5 6.7 4.0 May-11 2816 0.68 1.134 59.6 99.0 -39.4 -66.0 Jun-11 2495 2.15 1.816 161.1 135.9 25.2 15.6 Jul-11 1862 0.54 0.953 31.1 55.0 -23.9 -77.0 Aug-11 1859 3.99 2.073 230.0 119.4 110.6 48.1 Sep-11 2532 0.87 0.758 66.0 57.5 8.5 12.9 Oct-11 2120 0.71 0.341 45.2 21.7 23.5 52.0 Nov-11 1896 0.75 0.461 42.5 26.2 16.3 38.3 Dec-11 1961 0.77 0.262 46.8 15.9 30.9 66.0 2011 2134 1.67 1.22 1332.6 967.0 365.6 27.4 Jan-12 1972 0.80 0.45 47.3 26.6 20.7 43.8 Feb-12 1722 0.69 0.44 33.3 21.2 12.1 36.2 Mar-12 1832 0.55 0.44 31.2 25.0 6.2 20.0 Apr-12 1427 0.55 0.44 23.5 18.8 4.7 20.0 May-12 1718 0.41 0.14 21.8 7.5 14.4 65.9 Jun-12 1442 0.68 0.61 29.4 26.4 3.0 10.3 Jul-12 1340 2.46 2.28 102.2 94.7 7.5 7.3 Aug-12 1344 1.39 1.50 57.9 62.5 -4.6 -7.9 Sep-12 1124 0.63 0.55 21.2 18.5 2.7 12.7 Oct-12 1196 0.52 0.43 18.7 15.4 3.2 17.3 Nov-12 1128 0.77 0.86 26.1 29.1 -3.0 -11.7 Dec-12 1245 1.68 0.40 64.9 15.4 49.4 76.2 2012 1458 0.93 0.71 477.5 361.2 116.3 24.4 Jan-13 1431 0.28 0.25 12.4 11.3 1.1 9.1 Feb-13 1321 1.21 0.46 44.9 17.1 27.8 61.9 Mar-13 1473 1.55 1.00 70.5 45.7 24.8 35.2 Apr-13 1893 4.08 2.36 231.7 134.0 97.7 42.2 May-13 1522 0.81 0.81 38.2 38.3 -0.1 -0.2 Jun-13 2192 2.26 0.89 148.6 58.5 90.1 60.6 Jul-13 2332 1.58 1.13 113.9 81.5 32.3 28.4 Aug-13 1703 3.96 1.58 209.1 83.2 125.9 60.2 Sep-13 1647 2.77 1.08 137.0 53.6 83.4 60.9 Oct-13 1348 1.32 0.92 55.0 38.3 16.7 30.4 Nov-13 1242 0.92 0.87 34.3 32.4 1.9 5.4 Dec-13 1427 1.21 0.93 53.5 41.1 12.4 23.1 2013 1627 1.83 1.02 1149.0 635.0 514.0 44.7

307 Table 2 (cont.). Mean monthly concentrations of ammonia in the wastewater effluent and wetland’s outfall (mg/l), total monthly ammonia volumes (kg) entering and leaving the wetland, the volume of ammonia retained (kg) and the mean retention rate (%).

Month Eff flow NH4 (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jan-14 1552 1.91 0.37 88.9 17.4 71.6 80.5 Feb-14 1215 1.42 1.34 48.3 45.5 2.8 5.8 Mar-14 1476 3.76 1.69 171.8 77.5 94.3 54.9 Apr-14 1855 2.77 2.75 154.3 153.2 1.1 0.7 May-14 1817 0.87 0.36 49.2 20.4 28.8 58.5 Jun-14 1480 1.15 0.73 50.8 32.5 18.3 36.0 Jul-14 1631 1.58 1.56 79.9 78.9 1.0 1.3 Aug-14 1389 0.21 0.00 9.0 0.0 9.0 100.0 Sep-14 1139 0.23 0.36 7.9 12.2 -4.2 -53.5 Oct-14 1154 1.77 1.16 61.3 40.1 21.2 34.5 Nov-14 1003 0.04 0.63 1.3 19.0 -17.7 -1384.0 Dec-14 1351 0.94 1.43 39.4 59.7 -20.3 -51.6 2014 1422 1.39 1.03 762.2 556.3 205.9 27.0 Jan-15 1269 1.72 1.39 65.3 52.9 12.4 19.0 Feb-15 985 2.71 2.33 74.7 64.1 10.5 14.1 Mar-15 1133 2.71 2.33 95.1 81.6 13.4 14.1 Apr-15 1617 3.70 3.26 179.5 158.2 21.4 11.9 May-15 1223 1.33 3.37 50.4 127.8 -77.4 -153.4 Jun-15 1364 3.72 3.60 152.2 147.2 5.0 3.3 Jul-15 1519 6.11 3.83 287.7 180.1 107.6 37.4 Aug-15 1295 3.22 2.70 129.1 108.4 20.7 16.0 Sep-15 1254 2.41 3.34 90.8 125.6 -34.9 -38.4 Oct-15 1208 1.71 2.82 62.0 102.0 -40.1 -64.6 Nov-15 1189 0.75 3.02 26.8 107.8 -81.0 -302.7 Dec-15 1140 2.90 2.80 102.5 99.0 3.5 3.4 2015 1266 2.75 2.90 1316.0 1354.8 -38.8 -2.9 To date 5598.2 4200.4 1397.8 25.0

308 Table 3. Mean monthly concentrations of nitrite+nitrate in the wastewater effluent and wetland’s outfall (mg/l), total monthly nitrite+nitrate volumes (kg) entering and leaving the wetland, the volume of nitrite+nitrate retained (kg) and the mean retention rate (%).

NO2+NO3 Month Eff flow (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jun-10* 1728 11.85 8.05 184.3 125.2 59.1 32.1 Jul-10 1692 9.37 7.65 491.2 401.0 90.2 18.4 Aug-10 1526 9.13 5.75 432.2 272.1 160.1 37.0 Sep-10 1186 10.85 5.80 385.8 206.2 179.6 46.6 Oct-10 1476 10.70 6.43 489.6 294.2 195.4 39.9 Nov-10 1447 11.38 8.33 493.9 361.4 132.4 26.8 Dec-10 1330 9.15 9.65 365.1 385.0 -20.0 -5.5 2010 1509 10.55 7.00 2842.0 2045.1 796.9 28.0 Jan-11 1222 13.35 13.150 489.5 482.2 7.3 1.5 Feb-11 1319 12.70 11.280 468.9 416.5 52.4 11.2 Mar-11 2707 5.31 4.100 445.5 344.0 101.5 22.8 Apr-11 2824 6.75 3.707 571.6 314.0 257.5 45.1 May-11 2816 8.83 6.665 770.9 581.9 189.0 24.5 Jun-11 2495 10.48 5.575 783.9 417.2 366.7 46.8 Jul-11 1862 9.44 7.850 545.0 453.2 91.8 16.8 Aug-11 1859 12.83 8.095 739.3 466.4 272.9 36.9 Sep-11 2532 9.12 5.075 692.8 385.5 307.3 44.4 Oct-11 2120 7.81 6.350 496.6 403.8 92.8 18.7 Nov-11 1896 8.45 5.585 480.8 317.8 163.0 33.9 Dec-11 1961 10.30 8.420 626.1 511.8 114.3 18.3 2011 2134 9.61 7.15 7111.0 5094.3 2016.7 28.4 Jan-12 1972 8.47 7.94 501.1 469.7 31.4 6.3 Feb-12 1722 9.55 7.81 460.5 376.6 83.9 18.2 Mar-12 1832 9.75 7.82 553.7 444.1 109.6 19.8 Apr-12 1427 12.12 9.02 518.8 386.1 132.7 25.6 May-12 1718 9.52 4.39 507.1 233.9 273.3 53.9 Jun-12 1442 12.92 3.71 559.0 160.5 398.4 71.3 Jul-12 1340 10.10 3.08 419.5 127.9 291.6 69.5 Aug-12 1344 7.80 2.75 324.9 114.5 210.4 64.7 Sep-12 1124 8.56 1.96 288.7 66.1 222.6 77.1 Oct-12 1196 10.20 2.73 366.0 98.0 268.0 73.2 Nov-12 1128 15.70 8.65 531.3 292.7 238.6 44.9 Dec-12 1245 17.00 7.25 656.3 279.9 376.4 57.4 2012 1458 10.97 5.59 5686.8 3050.0 2636.8 46.4 Jan-13 1431 10.50 9.43 465.8 418.1 47.7 10.2 Feb-13 1321 11.18 8.43 413.4 311.6 101.7 24.6 Mar-13 1473 7.03 4.42 320.7 201.5 119.1 37.2 Apr-13 1893 7.47 5.12 424.2 290.7 133.4 31.5 May-13 1522 10.90 4.65 514.2 219.4 294.8 57.3 Jun-13 2192 12.10 12.90 795.6 848.2 -52.6 -6.6 Jul-13 2332 9.44 3.83 682.0 276.9 405.2 59.4 Aug-13 1703 9.20 2.61 485.8 137.6 348.3 71.7 Sep-13 1647 9.46 1.50 467.4 74.0 393.5 84.2 Oct-13 1348 7.19 6.00 300.4 250.7 49.7 16.6 Nov-13 1242 9.19 10.20 342.3 379.9 -37.6 -11.0 Dec-13 1427 11.80 7.46 522.0 329.8 192.2 36.8 2013 1627 9.62 6.38 5733.7 3738.3 1995.4 34.8

309 Table 3 (cont.). Mean monthly concentrations of nitrite+nitrate in the wastewater effluent and wetland’s outfall (mg/l), total monthly nitrite+nitrate volumes (kg) entering and leaving the wetland, the volume of nitrite+nitrate retained (kg) and the mean retention rate (%).

NO2+NO3 Month Eff flow (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jan-14 1552 8.00 7.55 372.4 351.5 20.9 5.6 Feb-14 1215 7.33 5.33 249.2 181.2 68.0 27.3 Mar-14 1476 5.68 3.86 260.0 176.5 83.5 32.1 Apr-14 1855 4.38 2.94 243.7 163.5 80.2 32.9 May-14 1817 9.19 4.61 517.6 259.5 258.1 49.9 Jun-14 1480 15.03 8.33 667.1 369.8 297.2 44.6 Jul-14 1631 9.66 5.67 488.7 286.7 201.9 41.3 Aug-14 1389 15.90 8.11 684.7 349.2 335.5 49.0 Sep-14 1139 12.56 7.44 429.3 254.1 175.2 40.8 Oct-14 1154 12.61 6.10 436.7 211.3 225.5 51.6 Nov-14 1003 8.71 11.53 261.9 346.8 -84.9 -32.4 Dec-14 1351 7.70 6.31 322.5 264.3 58.2 18.1 2014 1422 9.73 6.48 4933.9 3214.4 1719.4 34.8 Jan-15 1269 10.59 8.44 403.1 321.3 81.8 20.3 Feb-15 985 11.66 10.56 321.5 291.2 30.3 9.4 Mar-15 1133 4.88 3.29 171.3 115.5 55.8 32.6 Apr-15 1617 6.97 3.82 338.2 185.1 153.1 45.3 May-15 1223 6.41 7.43 242.9 281.6 -38.7 -15.9 Jun-15 1364 11.59 8.79 473.9 359.6 114.3 24.1 Jul-15 1519 12.03 6.44 566.5 303.0 263.5 46.5 Aug-15 1295 10.49 5.22 421.1 209.4 211.6 50.3 Sep-15 1254 9.29 5.21 349.3 196.0 153.3 43.9 Oct-15 1208 10.22 8.54 370.5 309.6 60.9 16.4 Nov-15 1189 12.04 6.69 429.6 238.7 190.9 44.4 Dec-15 1140 8.26 7.91 291.9 279.6 12.4 4.2 2015 1266 9.53 6.86 4379.9 3090.6 1289.3 29.4

310 Table 4. Mean monthly concentrations of total nitrogen in the wastewater effluent and wetland’s outfall (mg/l), total monthly total nitrogen volumes (kg) entering and leaving the wetland, the volume of total nitrogen retained (kg) and the mean retention rate (%).

Month Eff flow TN (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jun-10* 1728 17.30 9.51 269.0 147.9 121.2 45.0 Jul-10 1692 14.10 12.50 739.6 655.7 83.9 11.3 Aug-10 1526 16.03 9.38 758.3 443.6 314.7 41.5 Sep-10 1186 13.47 7.32 479.0 260.2 218.7 45.7 Oct-10 1476 12.40 7.29 567.4 333.3 234.0 41.3 Nov-10 1447 13.65 10.05 592.6 436.3 156.3 26.4 Dec-10 1330 10.90 10.40 434.9 415.0 20.0 4.6 2010 1509 14.49 9.34 3840.8 2692.0 1148.8 29.9 Jan-11 1222 16.48 16.375 604.1 600.4 3.7 0.6 Feb-11 1319 19.28 14.875 711.7 549.2 162.5 22.8 Mar-11 2707 10.20 6.775 855.8 568.4 287.4 33.6 Apr-11 2824 9.38 5.942 794.9 503.4 291.6 36.7 May-11 2816 11.45 9.075 999.7 792.3 207.4 20.7 Jun-11 2495 17.58 8.338 1315.3 624.0 691.3 52.6 Jul-11 1862 14.46 11.038 835.0 637.2 197.7 23.7 Aug-11 1859 11.11 12.350 639.8 711.6 -71.7 -11.2 Sep-11 2532 11.65 6.275 885.1 476.7 408.4 46.1 Oct-11 2120 9.83 7.720 625.1 491.0 134.2 21.5 Nov-11 1896 9.97 6.365 567.2 362.1 205.1 36.2 Dec-11 1961 11.80 9.045 717.3 549.8 167.5 23.3 2011 2134 12.76 9.51 9551.0 6866.2 2684.8 28.1 Jan-12 1972 10.20 9.01 603.4 533.0 70.4 11.7 Feb-12 1722 11.20 8.84 540.1 426.3 113.8 21.1 Mar-12 1832 13.23 12.28 751.3 697.4 54.0 7.2 Apr-12 1427 19.08 12.30 816.8 526.5 290.2 35.5 May-12 1718 13.98 6.35 744.7 338.3 406.5 54.6 Jun-12 1442 14.40 6.04 623.0 261.3 361.7 58.1 Jul-12 1340 17.00 8.05 706.1 334.4 371.8 52.6 Aug-12 1344 13.55 7.00 564.4 291.6 272.8 48.3 Sep-12 1124 14.88 4.27 501.8 144.0 357.8 71.3 Oct-12 1196 17.20 5.48 617.2 196.6 420.5 68.1 Nov-12 1128 18.28 9.45 618.6 319.8 298.8 48.3 Dec-12 1245 20.30 12.95 783.6 499.9 283.7 36.2 2012 1458 15.28 8.50 7871.0 4569.1 3302.0 42.0 Jan-13 1431 14.78 13.03 655.4 577.8 77.6 11.8 Feb-13 1321 16.70 11.23 617.7 415.2 202.5 32.8 Mar-13 1473 11.73 8.03 535.2 366.3 168.9 31.6 Apr-13 1893 21.46 12.19 1218.5 692.2 526.4 43.2 May-13 1522 15.00 6.69 707.6 315.6 392.0 55.4 Jun-13 2192 14.45 6.47 950.1 425.4 524.7 55.2 Jul-13 2332 12.80 5.75 925.3 415.3 510.0 55.1 Aug-13 1703 13.41 4.99 707.9 263.6 444.3 62.8 Sep-13 1647 14.34 3.88 708.2 191.9 516.3 72.9 Oct-13 1348 11.80 9.33 493.0 389.6 103.4 21.0 Nov-13 1242 12.50 10.80 465.6 402.3 63.3 13.6 Dec-13 1427 15.95 10.55 705.6 466.5 239.1 33.9 2013 1627 14.58 8.58 8690.1 4921.6 3768.6 43.4

311 Table 4 (cont.). Mean monthly concentrations of total nitrogen in the wastewater effluent and wetland’s outfall (mg/l), total monthly total nitrogen volumes (kg) entering and leaving the wetland, the volume of total nitrogen retained (kg) and the mean retention rate (%).

Month Eff flow TN (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jan-14 1552 13.40 11.35 623.8 528.4 95.4 15.3 Feb-14 1215 12.75 9.63 433.7 327.4 106.3 24.5 Mar-14 1476 10.17 7.93 465.5 362.9 102.6 22.0 Apr-14 1855 12.76 8.54 709.8 475.2 234.6 33.1 May-14 1817 15.62 8.70 879.6 490.2 389.4 44.3 Jun-14 1480 19.25 10.38 854.7 460.9 393.8 46.1 Jul-14 1631 14.79 9.18 748.0 464.4 283.5 37.9 Aug-14 1389 18.70 9.16 805.3 394.4 410.8 51.0 Sep-14 1139 13.36 9.05 456.8 309.2 147.6 32.3 Oct-14 1154 17.75 7.96 614.7 275.7 339.1 55.2 Nov-14 1003 16.04 19.80 482.5 595.8 -113.3 -23.5 Dec-14 1351 9.22 8.04 386.2 336.8 49.4 12.8 2014 1422 14.48 9.98 7460.6 5021.3 2439.3 32.7 Jan-15 1269 12.95 9.49 493.0 361.3 131.7 26.7 Feb-15 985 16.86 14.45 464.9 398.5 66.5 14.3 Mar-15 1133 11.50 8.64 403.8 303.3 100.4 24.9 Apr-15 1617 9.15 7.00 444.0 339.4 104.6 23.6 May-15 1223 9.24 13.87 350.3 525.9 -175.6 -50.1 Jun-15 1364 15.90 14.05 650.5 574.8 75.7 11.6 Jul-15 1519 18.40 10.70 866.4 503.8 362.6 41.8 Aug-15 1295 15.15 8.37 608.4 335.9 272.5 44.8 Sep-15 1254 12.48 9.12 469.5 343.2 126.4 26.9 Oct-15 1208 10.92 8.96 395.9 324.8 71.0 17.9 Nov-15 1189 13.83 9.07 493.3 323.5 169.8 34.4 Dec-15 1140 12.45 10.95 440.0 387.0 53.0 12.0 2015 1266 13.24 10.39 6079.9 4721.4 1358.6 22.3 To date 43493.5 28791.4 14702.0 33.8

312 Table 5. Mean monthly concentrations of total phosphorus in the wastewater effluent and wetland’s outfall (mg/l), total monthly total phosphorus volumes (kg) entering and leaving the wetland, the volume of total phosphorus retained (kg) and the mean retention rate (%).

Month Eff flow TP (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jun-10* 1728 4.36 1.49 67.7 23.2 44.5 65.7 Jul-10 1692 1.49 0.81 78.3 42.6 35.7 45.6 Aug-10 1526 3.20 2.15 151.3 101.6 49.7 32.8 Sep-10 1186 3.39 2.32 120.6 82.5 38.1 31.6 Oct-10 1476 2.41 1.56 110.2 71.4 38.8 35.2 Nov-10 1447 2.28 1.52 99.2 66.2 33.0 33.3 Dec-10 1330 1.76 1.45 70.2 57.7 12.6 17.9 2010 1509 2.85 1.64 697.4 445.1 252.3 36.2 Jan-11 1222 2.205 2.030 80.8 74.4 6.4 7.9 Feb-11 1319 2.225 1.865 82.2 68.9 13.3 16.2 Mar-11 2707 1.168 0.701 98.0 58.8 39.1 39.9 Apr-11 2824 1.088 0.700 92.2 59.3 32.9 35.7 May-11 2816 1.645 1.160 143.6 101.3 42.3 29.5 Jun-11 2495 3.092 2.402 231.4 179.7 51.6 22.3 Jul-11 1862 2.807 2.870 162.1 165.7 -3.6 -2.2 Aug-11 1859 3.700 4.125 213.2 237.7 -24.5 -11.5 Sep-11 2532 1.419 1.308 107.8 99.3 8.5 7.8 Oct-11 2120 4.400 3.950 279.8 251.2 28.6 10.2 Nov-11 1896 1.480 0.681 84.2 38.8 45.4 54.0 Dec-11 1961 0.996 0.818 60.6 49.7 10.9 17.9 2011 2134 2.19 1.88 1635.8 1384.8 251.0 15.3 Jan-12 1972 0.941 0.810 55.7 47.9 7.7 13.9 Feb-12 1722 1.230 1.237 59.3 59.6 -0.3 -0.6 Mar-12 1832 2.020 1.462 114.7 83.0 31.7 27.6 Apr-12 1427 3.100 2.193 132.7 93.9 38.8 29.3 May-12 1718 2.535 1.437 135.0 76.5 58.5 43.3 Jun-12 1442 3.340 2.578 144.5 111.5 33.0 22.8 Jul-12 1340 3.052 3.152 126.8 130.9 -4.2 -3.3 Aug-12 1344 3.360 2.645 140.0 110.2 29.8 21.3 Sep-12 1124 4.060 3.345 136.9 112.8 24.1 17.6 Oct-12 1196 2.930 1.985 105.1 71.2 33.9 32.3 Nov-12 1128 3.367 2.502 113.9 84.7 29.3 25.7 Dec-12 1245 3.480 2.860 134.3 110.4 23.9 17.8 2012 1458 2.78 2.18 1399.0 1092.8 306.2 21.9 Jan-13 1431 1.170 1.183 51.9 52.5 -0.6 -1.1 Feb-13 1321 2.453 1.645 90.7 60.9 29.9 32.9 Mar-13 1473 1.610 1.148 73.5 52.4 21.1 28.7 Apr-13 1893 2.835 2.475 161.0 140.5 20.4 12.7 May-13 1522 1.880 1.380 88.7 65.1 23.6 26.6 Jun-13 2192 1.376 1.115 90.5 73.3 17.2 19.0 Jul-13 2332 3.093 2.315 223.5 167.3 56.2 25.1 Aug-13 1703 2.990 1.989 157.9 105.0 52.9 33.5 Sep-13 1647 2.569 1.928 126.9 95.2 31.7 25.0 Oct-13 1348 2.838 2.393 118.5 99.9 18.6 15.7 Nov-13 1242 3.335 2.340 124.2 87.2 37.1 29.8 Dec-13 1427 2.230 2.075 98.7 91.8 6.9 7.0 2013 1627 2.36 1.83 1406.0 1091.1 314.8 22.4

313 Table 5 (cont.). Mean monthly concentrations of total phosphorus in the wastewater effluent and wetland’s outfall (mg/l), total monthly total phosphorus volumes (kg) entering and leaving the wetland, the volume of total phosphorus retained (kg) and the mean retention rate (%).

Month Eff flow TP (kg) CM/day EFF (mg/l) Out (mg/l) EFF (kg) OUT (kg) RET. (kg) % RET. Jan-14 1552 2.187 1.969 101.8 91.7 10.1 9.9 Feb-14 1215 3.017 2.209 102.6 75.1 27.5 26.8 Mar-14 1476 2.638 2.017 120.7 92.3 28.4 23.6 Apr-14 1855 2.097 1.532 116.7 85.2 31.4 26.9 May-14 1817 2.998 2.148 168.8 120.9 47.9 28.4 Jun-14 1480 2.890 2.083 128.3 92.5 35.9 27.9 Jul-14 1631 2.108 1.963 106.6 99.3 7.4 6.9 Aug-14 1389 3.035 1.860 130.7 80.1 50.6 38.7 Sep-14 1139 1.873 1.653 64.0 56.5 7.5 11.7 Oct-14 1154 2.880 0.940 99.7 32.6 67.2 67.4 Nov-14 1003 2.598 1.690 78.2 50.9 27.3 34.9 Dec-14 1351 1.469 1.220 61.5 51.1 10.4 16.9 2014 1422 2.48 1.77 1279.7 928.1 351.6 27.5 Jan-15 1269 1.81 1.57 68.7 59.9 8.8 12.8 Feb-15 985 2.26 2.09 62.3 57.6 4.7 7.5 Mar-15 1133 2.15 1.78 75.4 62.6 12.8 17.0 Apr-15 1617 1.34 0.90 65.0 43.7 21.4 32.8 May-15 1223 3.06 2.69 116.2 102.1 14.1 12.1 Jun-15 1364 1.64 1.23 67.3 50.4 16.8 25.0 Jul-15 1519 1.77 0.96 83.2 45.3 38.0 45.6 Aug-15 1295 2.23 1.21 89.4 48.5 40.9 45.8 Sep-15 1254 1.65 1.05 62.2 39.4 22.8 36.6 Oct-15 1208 1.86 1.19 67.3 43.0 24.3 36.1 Nov-15 1189 1.55 1.05 55.2 37.4 17.8 32.2 Dec-15 1140 1.46 0.94 51.6 33.4 18.2 35.3 2015 1266 1.90 1.39 863.8 623.3 240.5 27.8 To date 7281.7 5565.2 1716.4 23.6

314 CONCLUSION

The bathymetry of the wetland (see Figure 1) implies that much of the wetland is considerably deeper than that recommended for maximum nutrient removal; shallower systems allow for the colonization of suitable plants, preferably emergents (Kadlec and Wallace 2009). Also, work by Robb (2012), considering nutrient concentrations across the standing water and by using fluorescent tracers, indicate that the most suitable portion of the system regarding depth (the southeast arm) is ineffective since it is not in the flow path of the effluent. It may be worth considering maintaining the water level lower than the original design, as it was through the later months of 2013 to alleviate plugging issues, in order to encourage plant growth.

The reduction of total phosphorus, nitrate and total nitrogen have remained between 25- 35%. Ammonia retention was actually negative over 2015, a function of concentrations at the wetland’s outfall slightly exceeding those of the treatment plant’s effluent. It remains to be seen how the reduction in nitrogenous compounds will be affected as phosphorus is more effectively removed from the effluent before its discharge to the wetland.

REFERENCES

ACE. 2001. Upper Susquehanna River Watershed-Cooperstown Area Ecosystem Restoration Feasibilty Study and Integrated Environmental Assessment. Project management Plan. United States Army Corps of Engineers, Planning Division. Baltimore, MD.

Albright, M.F. and H.A.Waterfield. 2011. Monitoring the effectiveness of the Cooperstown wastewater treatment wetland. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Bouillon, S. 2016. Evaluation of phosphorous and nitrogen uptake levels by Phalaris arundinacea plants in a wastewater treatment wetland, Cooperstown, NY. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Cankar, J. and S. Folts. 2015. Pers. Comm. Cooperstown Sewage Treatment Plant. Cooperstown, NY.

Ebina, J., T. Tsutsi, and T. Shirai. 1983. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res.7 (12):1721-1726.

Fickbohm, S.S. 2005. Upper Susquehanna River Watershed- Cooperstown Area Ecosystem Restoration Feasibility Study And Integrated Environmental Assessment: Post- restoration water quality and wildlife analysis of the FABADS sites (2003-2004). In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Gazzetti, E. 2012. Efficacy of emergent plants as a means of phosphorus removal in a treatment wetland, Cooperstown, New York. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

315

Jackson, M.H. 2009. Wastewater treatment facility modifications engineering report. Lamont Engineers, Cobleskill, NY.

Kadlec, R.H and S.D. Wallace. 2009. Treatment wetlands (second ed.). CRC Press, Boca Raton.

Liao, N. 2001. Determination of ammonia by flow injection analysis. QuikChem ® Method 10-107-06-1-J. Lachat Instruments, Loveland, CO.

Olsen, B. 2011. Phosphorus content in reed canary grass (Phalaris arundinacea) in a treatment wetland, Cooperstown, NY. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Pritzlaff, D. 2003. Determination of nitrate/nitrite in surface and wastewaters by flow injection analysis. QuikChem ® Method 10-107-04-1-C. Lachat Instruments, Loveland, CO.

Robb, T. 2012. Insight into a complex system: Cooperstown wastewater treatment wetland, 2011. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

316 Dynamics of Galerucella spp. and purple loosestrife (Lythrum salicaria) in Goodyear Swamp Sanctuary, summer 2015 update

Holly Waterfield1 and Matthew Albright2

INTRODUCTION

The distribution and effectiveness of Galerucella spp. populations as a biocontrol agent of purple loosestrife (Lythrum salicaria) were monitored within Goodyear Swamp Sanctuary as part of an ongoing monitoring regime that began in 1997. Annual spring and fall monitoring of the impact of Galerucella spp. on purple loosestrife is updated in this report. Details of the history of this study can be found in Albright et al. (2004); the concise summary below was presented in Albright 2013.

Lythrum salicaria is an emergent semi-aquatic plant that was introduced into the United States from Eurasia in the early 19th century (Thomson 1987). It is an aggressive and highly adaptive invasive species which inhabits wetlands, flood plains, estuaries and irrigation systems. Once established, purple loosestrife often creates monospecific stands, displacing native species including cattails (Typha spp.), sedges (Carex spp.), bulrushes (Scirpus spp.), willows (Salix spp.) and horsetails (Equisetum spp.). Recent efforts, which include both chemical application and the use of biocontrol methods, have focused on controlling L. salicaria where stands impede well-diversified wetland communities (Thomson 1987).

In June 1997, 50 adults each of Galerucella calmariensis and G. pusilla were introduced into Goodyear Swamp Sanctuary (N42°48.6’ W74°53.9), located at the northeastern end of Otsego Lake (Austin 1998). The beetles were initially released into cages at sites 1 and 2 (Figure 1). In 1998, sites 3-5 were introduced into the study in order to monitor the distribution of Galerucella over time to other stands of purple loosestrife (Austin 1999). Sampling sites were established to monitor the qualitative and quantitative effects of the beetles on purple loosestrife and also to examine the extent of any recovery by the native flora (Austin 1998). It was expected that these beetles would lessen the competitive ability of purple loosestrife by feeding upon their meristematic regions, resulting in defoliation, impaired growth, decreased seed production, and increased mortality (Blossey et al. 1994).

METHODS

Spring and fall monitoring surveys were performed according to protocols established by Blossey et al. (1997). Observations of the insects and plants were made within the five 1m2 quadrats, marked by four visible stakes (Figure 1).

1 CLM. Research Support Specialist, SUNY Oneonta Biological Field Station, Cooperstown, NY. 2 CLM SUNY Oneonta Biological Field Station.

317

Figure 1. Map of Goodyear Swamp Sanctuary showing sampling sites. Sites 1 and 2 are 1997 Galerucella spp. stocking sites; sites 3-5 were established to evaluate the spread of Galerucella spp. within the Sanctuary over time.

Spring monitoring was completed on 04 June 2014, which is about 2 weeks later than most years. This first assessment is typically completed within 2-3 weeks after overwintering adults appear (Blossey 1997). Galerucella spp. abundance was estimated in each life stage (egg, larva, adult) according to the established abundance categories (Table 1). The number of stems of L. salicaria within each quadrat were counted, and the five tallest were measured. The percent cover of L. salicaria and the percent damage attributable to Galerucella spp. were both estimated according to established frequency categories. Fall monitoring, which was completed on 08 September 2014, consisted of the same metrics measured in the spring monitoring along with measurements to gauge the vigor of L. salicaria plants, including the number of inflorescences per plant and per quadrat, as well as the number of flowers per inflorescence.

Table 1. Categories prescribed by Blossey’s (1997) protocol for reporting abundance and frequency categories.

Abundance Categories Frequency Categories Number category range category mid point 0 1 0% A 0% 1-9 2 1-5% B 2.5% 10-49 3 5-25% C 15% 50-99 4 25-50% D 37.5% 100-499 5 50-75% E 62.5% 500-1000 6 75-100% F 87.5% >1000 7 100% G 100%

318 RESULTS & DISCUSSION

Monitoring data are represented by abundance and frequency categories (defined in Table 1), and total stem counts. Changes between abundance/frequency categories from year-to-year or plot-to-plot can represent a substantial change in abundance due to the broad ranges covered by each category (Albright 2004). Variation in the number of stems between years or plots may not correspond with a shift in percent cover category, due to the inherent lack of sensitivity in a categorical classification scheme.

Spring Monitoring (27 May 2015) Eggs of the Galerucella beetle were present in all of the five of the quadrats at moderate densities (Figure 2). Larvae were found in two quadrats; typically, spring surveys seem to occur prior to the emergence of larvae (Figure 3). Adult beetles were found in 2 of 5 quadrats at moderate densities (Figure 4).

Abundance category 2

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 2. Comparison of Galerucella spp. egg abundance from yearly spring samplings. Abundance categories taken from Table 1. 6

2 Abundance category 1

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 3. Comparison of Galerucella spp. larval abundance from yearly spring samplings. Abundance categories taken from Table 1.

319 6

2 Abundance category 1

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 4. Comparison of Galerucella spp. adult abundance from yearly spring samplings. Abundance categories taken from Table 1.

Lythrum salicaria abundance in 2015 was similar to recent years (2011-2014), which is substantially lower than abundance estimates (based on stem counts) prior to 2008 (Figure 5). Estimated percent cover was also similar than in recent years, with stems of loosestrife being observed in all five quadrats but at low numbers (Figure 6). Damage by herbivory to loosestrife stems within all quadrats exceeded 35% (Figure 7).

100 90 80

70 60 50 40 30

Number of Stems 20 10 0

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 5. Comparison of the number of purple loosestrife stems from yearly spring sampling observations.

320 70

- point 60 50 40 30 20

Frequency Category Mid 10 0

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 6. Comparison of percent cover estimates by purple loosestrife from yearly spring samplings. Frequency category mid points derived from Table 1.

- point 60 50 40 30 20

Frequency Category Mid 10 0

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 7. Comparison of percent damage estimates to purple loosestrife leaves from yearly spring samplings. Frequency category mid points derived from Table 1.

Fall Monitoring 15 Sept 2015)

Lythrum salicaria was absent at the fall survey (Figures 8 and 9). As in most years, stems of L. salicaria were in bloom elsewhere in the swamp, though all lacked vigor.

Galerucella are host-specific and as such feed exclusively on purple loosestrife. This characteristic results in a beetle population that is directly dependent upon loosestrife densities within the swamp. Abundance patterns observed within the swamp since 1998 illustrate the population dynamics of host-specific organisms and their dependency upon host populations (Fagan et al. 2002). After several cycles of such population fluctuations, it seems that percent damage estimates exceeding 50% during the spring survey indicate a shortage of food resources for the Galerucella spp. beetles, thus limiting the population. A year with such high herbivory is

321 typically followed by a year with lower beetle abundance (Figures 2, 3, 4) and herbivory (percent damage, Figure 7).

120

100

20 Number Number Stemsof NA 0 1997 2000 2001 2002 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 8. Number of purple loosestrife stems per quadrat during fall monitoring, 1997, 2000- 2015. Flooding in fall 2011 precluded sampling.

100

point 90 - 80 70 60 50 40 30 20 10 Frequency Category Mid Category Frequency NA 0 1997 2000 2001 2002 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 9. Estimated percent cover (category midpoints) of purple loosestrife during fall monitoring, 1997, 2000-2015. Categories as presented in Table 1. Flooding in fall 2011 precluded sampling.

CONCLUSIONS

Spring 2015 monitoring indicated that L. salicaria abundance continues to be less (based on percent cover and number of stems) than in most years since monitoring beganNA in 1997, while enough exists to sustain a Galerucella population, as evidenced by the moderate abundance of both eggs and adults. Fall monitoring reveals that Galerucella spp. are effective at controlling not only the abundance of L. salicaria, but also the overall vigor and fitness based on reduced plant height and low production of flowering bodies. Observations related to the presence of

322 Galerucella spp. at sites outside of Goodyear Swamp Sanctuary (i.e., Lydon 2008) indicate that the dispersal of Galerucella spp. continues from the original site and it shows promising potential as a biological agent against the invasive plant.

REFERENCES

Albright, M.F. 2013. An update on the dynamics of Galerucella spp. and purple loosestrife (Lythrum salicaria) in Goodyear Swamp Sanctuary, summer 2012. In: 45th Ann. Rept. (2012) SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Albright, M.F., W.N. Harman. S.S. Fickbohm, H.A. Meehan, S. Groff and T. Austin. 2004. Recovery of native flora and behavior responses by Gallerucella spp. following biocontrol of purple loosestrife. Am. Midl. Nat. 152:248-254.

Austin, T. 1998. Biological control of purple loosestrife in Goodyear Swamp Sanctuary using Galerucella spp., summer 1997. In 30th Ann. Rept. (1997). SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta.

Austin, T. 1999. Biological control of purple loosestrife in Goodyear Swamp Sanctuary using Galerucella spp., summer 1998. In 31st Ann. Rept. (1998). SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta.

Blossey, B. 1997. Purple loosestrife monitoring protocol, 2nd draft. Unpublished document. Dept. of Natural Resources, Cornell University.

Blossey, B., D. Schroeder, S.D. Hight and R.A. Malecki. 1994. Host specificity and environmental impact of two leaf beetles (Galerucella calmariensis and G. pusilla) for the biological control of purple loosestrife (Lythrum salicaria). Weed Science. 42:134-140

Fagan, W.F., M.A. Lewis, M.G. Neubert, P. van den Driessche. 2002. Invasion theory and biological control. Ecology Letters 5(1) 148.

Lydon, J.C. 2008. Monitoring the dynamics of Galerucella spp. and purple loosestrife (Lythrum salicaria) in the Goodyear Swamp Sanctuary and along the shorelines of Otsego, Weaver and Youngs Lakes, summer 2007). In 40th Ann. Rept. (2007). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Thompson, Daniel Q., R.L. Stuckey, E. B. Thompson. 1987. Spread, Impact, and Control of Purple Loosestrife (Lythrum salicaria) in North American Wetlands. U.S. Fish and Wildlife Service. 55 pages. Jamestown, ND: Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/plants/loosstrf/loosstrf.htm (04JUN99).

323 Hydroacoustic survey and bathymetric map creation for Brant Lake, New York

Holly A. Waterfield CLM1

INTRODUCTION

Brant Lake is located in Warren County, New York, within the bounds of the Adirondack Park. The Brant Lake Association contracted the Biological Field Station (RF Contract # 2015- 38) to conduct a survey of the lake’s bottom depths (bathymetry) and create a bathymetric map. This report details the methods used to collect hydroacoustic data and analyze those data to yield a bathymetric map with 2-meter contours; digital files of these map products as well as supporting files for use in Geographic Information Systems (GIS) software will be provided to the Brant Lake Association.

Prior to this survey, bathymetric data available for Brant Lake were limited to those presented in the map available through the NYS DEC website, Figure 1, which has 20-foot (~6- meter) depth contours (NYS DEC 2015). A more detailed understanding of the lake’s bathymetry was needed ahead of the development of a comprehensive lake management plan. The original map does not include islands, shoals, or other bathymetric and near-shore features; such features influence estimates of lake volume, a parameter that is essential to the full understanding of lake trophic state. Analyses of nutrient dynamics, evaluation of external and internal nutrient loading, oxygen dynamics, fish habitat, littoral zone (plant habitat) and many others are all based on an understanding of the lake’s morphometry, specifically, the bottom characteristics.

Figure 1. Bathymetric map of Brant Lake, New York, as accessed from the NYS Dept. of Environmental Conservation website (2015).

1 Research Support Specialist, SUNY Oneonta Biological Field Station, Cooperstown, NY.

324 METHODS

Overview of Bathymetric Survey and Mapping Process Creation of the bathymetric mapped involved both an on-lake survey of bottom depths followed by data processing and mapping in GIS software. Details are provided below. Bottom depths were surveyed using hydroacoustic equipment (SONAR); depth measurements were obtained once per second, yielding over 56,000 data points. These points, along with those representing the shoreline, islands, and exposed rocks, were used to estimate depths across the entire lake (a continuous surface) and from this surface, contours were created.

The survey was conducted over the course August 13, 19, and 26, 2015. Lake level and transducer depth were recorded at the start of each field day. Lake level was measured at the Rte 23A bridge, as the distance from top of the steel curtain to water surface. Transducer depth was measured from the transducer face up to the water surface. On 13 August and 26 August water temperature was measured every meter from the surface to 18 meters depth. The route surveyed is shown in Figure 2. Transects were irregular in shape and orientation; boat traffic on the lake was heavy at times making it difficult to maintain a regular course and pattern. Additionally, the route was altered as bottom features were discovered, as in the case of several off-shore shoals, and to survey around natural jetties and islands. Hydroacoustic data collection details are provided below in Table 1.

Table 1. Data collection settings used during the Brant Lake hydroacoustic survey on 13, 19, and 26 August 2015.

Transducer Frequency 123 kHz Transducer Beam Width (3 dB) 7.4 degrees Ping Rate 1 ping per second (pps) Pulse Width 0.4 ms Survey Speed 5 mph off-shore, 2-3 mph near-shore

Using acoustics analysis software, Sonar5 Pro, raw data were corrected for sound speed (based on water temperature profile), transducer depth and heave (boat movement). Initial analysis for bottom detection was completed in Sonar5 Pro; the bottom line was inspected and edited before the data were exported for use in ArcMap. Excel files were added to ArcMap as tables, displayed, and then exported as shapefiles. All survey points were merged into a single shapefile and points were adjusted for the lake level on each survey date.

The lake shoreline polygon was extracted from the National Hydrography Dataset waterbodies shapefile, accessed via The National Map (nationalmap.gov). High resolution orthoimagery (1-foot) downloaded from the New York State GIS Clearinghouse was used to edit the lake shoreline polygon and digitize islands, jetties, and exposed boulders. The shoreline polygon was densified (to increase the number of vertices) and then converted to a point file. The attribute table was modified to match that of the bottom depth points and all features were assigned a depth of 0.0 meters.

325 Figure 2. Survey route driven during August 2015 hydroacoustic survey of Brant Lake, NY. Data points were collected once per second while the collection gear were in operation.

326 Figure 3. Bathymetry of Brant Lake, as derived from August 2015 hydroacoustic survey data interpretation. Dashed line indicates estimated 1-meter contour; solid lines indicate 2-meter intervals from 2 to 18 meters.

327 A Triangulated Irregular Network (TIN) was created in order to generate a continuous surface from the point data; datasets used included shoreline, islands, exposed boulders, and all survey points. From this TIN, a gridded raster surface was created with a cell size of 5 meters, and contours were generated (as polygons) using the 3D Analyst Toolset. Contours were visually inspected and edited manually with depth points overlaid. Contours were smoothed minimally, with a 1-meter horizontal tolerance to preserve accuracy. The final map was generated with a dashed line indicating the estimated 1-meter contour and solid black lines representing 2-meter intervals from 2 to 18 meters (Figure 3). The maximum depth measurement was 18.93 meters.

Figure 4 illustrates the large bay on the northwestern shore of the lake; it was difficult to navigate due to irregularity of the bottom, particularly along the north and east of the bay, indicated by diagonal striping. The abundance of boulders along with deployment of our survey equipment beneath the boat made sufficient survey of this area nearly impossible. Depth contours in this area are not precise and do not necessarily reflect shallow waters over boulders; this map is NOT intended for use in navigation in such areas.

Figure 4. Bathymetric map of Brant Lake enlarged to show the northwestern bay. Diagonal striping indicates a portion of the shoreline where contours may not accurately represent water depth. Boulders are prominent in this area; boaters should navigate with caution.

328 Watershed characterization for Goodyear Lake, New York: Watershed subbasins, land use and cover, surficial geology, and soils

H.A. Waterfield CLM1

INTRODUCTION

Goodyear Lake is an impoundment of the Susquehanna River, created by the Colliersville Dam, in Otsego County, New York. This report provides characterization and maps of land area in the watershed draining to Goodyear Lake in support of work to draft a comprehensive management plan for the lake and its watershed. This report includes the following maps: Goodyear Lake Watershed and Subbasins, Land Use and Land Cover, Surficial Geology, Bedrock Geology, and Soil Erodibility.

Watershed characterization is an important component of watershed management planning and prioritization, generally leading resource managers to more effective use of limited financial and volunteer resources. The soils, surface geology, and underlying bedrock dictate when, where, and how water moves through the landscape; when combined with land use and cover, this information can be used to assess potential impacts on water quality locally and in downstream receiving waters. Many states have developed and implemented GIS-based analysis packages to streamline the characterization process to aid in use of watershed assessment data by both technical and non-technical personnel involved in the management process (e.g. West Virginia and Minnesota).Several datasets that are common amongst these approaches have been compiled for this report, including land use and cover, geology, and soils.

Land use and cover directly influence hydrology and water quality. A general characterization of land use and cover throughout the watershed helps in the understanding of potential impacts within a given watershed. Geology determines the stability of landscape features, mineral availability and influences the chemical composition of water, among other factors. Knowledge of the nature and origin of surface deposits makes it possible to understand and predict how water will interact with the landscape.

Sediment accumulation within Goodyear Lake is a major concern of active stakeholders in the Goodyear Lake Association; an understanding of the susceptibility of watershed soils to erosion by water may help to target management activities aimed at reducing soil erosion. The USDA’s soil survey database contains a wealth of use-specific ratings for soils throughout the nation. “Erosion factor Kw” indicates the erodibility of the soil, including rock fragments (Soil Survey Staff 2016). Values range from 0.02 to 0.69, where the higher the value, the more susceptible the soil is to sheet and rill erosion by water. The USDA uses this factor along with several others to estimate soil loss by erosion; watershed assessment programs in Minnesota and West Virginia make use of such data in a similar fashion.

1 Research Support Specialist, SUNY Oneonta Biological Field Station.

329

METHODS

A Geographic Information Systems (GIS) based approach was taken to characterize the watershed using ESRI’s ArcMap 10.2 and associated software components. Watershed delineations were performed using the online USGS StreamStats watershed delineation tool for the entire Goodyear Lake watershed as well as each subbasin corresponding to the “focus watersheds” addressed in the ongoing work by C. Stroosnyder. A shapefile (.shp) and the StreamStats Basin Characteristics were downloaded for each subbasin. Land Use and Cover data were clipped from the 2011 National Land Cover Dataset (NLCD 2014), obtained from The National Map (nationalmap.gov). Surficial and bedrock geology datasets were obtained from the New York State Museum (1999; Gerhard 2000) and clipped to the watershed boundary.

Soils data for Herkimer and Otsego Counties were downloaded from the Web Soil Survey, the USDA NRCS online soil mapping site. Queries and thematic mapping of soil data were performed using the Soil Survey Add-In for ArcMap (USDA 2016). Soil units intersecting a stream segment were selected and the database queried for K factor, whole soil, a measure of the potential for erosion by water. A thematic map was created to illustrate the location of these streamside soils along with the associated K factor (whole soil) values. For graphical purposes, a summary was generated to compare the erodibility of soils between the subbasins in terms of soils with slight and moderate-to-high susceptibility to erosion by water. “Slight susceptibility” includes soils with KWS 0.01-0.24; “moderate to high susceptibility” encompasses those with KWS values ranging from 0.28-0.49.

RESULTS AND DISCUSSION

Watershed and Subbasins

The Goodyear Lake watershed and subbasins are illustrated in Figure 1. The entire watershed encompasses an area of roughly 352 square miles (225,203 acres); the largest subbasin is the Cherry Valley Creek watershed (91.7 acres). Areas are summarized in Table 1.

Table 1. Land area (in acres and square miles) of the Goodyear Lake Watershed and subbasins. Land Area acres sq. miles Watershed Total 225,203 352 Cherry Valley Creek 58,692 91.7 Lower Oaks Creek 23,962 37.4 Red Creek 8,192 12.8 Main Stem 42,880 67.0 Canadarago Lake 41,776 65.3 Otsego Lake 49,702 77.6

330 Figure 1. Goodyear Lake Watershed Subbasins. Full color maps are included in the digital version of this report, available on the BFS webpage: www.oneonta.edu/academics/biofld/publications.asp

331 Land Use and Cover

Land Use and Cover are generally summarized in Table 2, providing a comparison of the entire watershed and the focus watersheds. A detailed overview of land use and cover for the entire watershed and each subbasin is presented in Table 3. Data are based on the 2011 National Land Cover Dataset, published and maintained by the U.S. Geological Survey (NLDC 2014). Across the entire watershed, the dominant land cover is forest (including woody wetlands), occupying nearly 55% of the total land area; when considering the focus watersheds only, roughly 65% of land area is forested (including woody wetlands). Lands in hay and pasture cover roughly 20% of the watershed. Low-intensity land cover types (emergent wetlands, shrub/scrub, and open fields) occupy an additional 5% of land within the focus watersheds.

Active land uses that are typically associated with soil disturbance include agriculture (cultivated crops, intensive livestock operations, grazing within stream corridors), roadside ditching, forest operations (logging, road building, etc.), construction (especially on steep slopes), etc. In terms of watershed health and water quality protection, the large proportion of land in forest cover indicates that an opportunity exists for outreach and prevention of soil erosion through Forestry Best Management Practices (BMPs). Along the same lines, optimization of grazing practices on pastured lands and attention to road maintenance and ditching to reduce the potential for soil and stream bank disturbance.

Table 2. Watershed land use and cover (in percent of land area) for the entire Goodyear Lake Watershed.

Watershed Total Focus Watersheds Forest 45.6 56.2 Agriculture: Crops and Pasture 30.9 24.0 Woody Wetlands 8.7 9.1 Developed, Open Space 4.0 3.8 Shrub/Scrub/Open Meadows 3.7 2.7 Emergent Wetlands 2.3 2.4 Open Water 3.7 1.0 Developed, All Intensities 0.9 0.8

332 Figure 2. Land Use and Cover within the Goodyear Lake Watershed (2011 National Land Cover Dataset). Full color maps are included in the digital version of this report, available on the BFS webpage: www.oneonta.edu/academics/biofld/publications.asp

333 Table 3. Watershed Land Use, area (in acres) per watershed subbasin.

Land Use Description Use Cherry Valley Lower Oaks Canadarago Otsego Watershed Code Creek Creek Red Creek Main Stem Lake Lake Total Open Water 11 236.4 197.7 57.2 801.7 2,299.6 4,766.8 8,359.4 Developed, Open Space 21 1,849.9 922.7 305.6 1,966.4 1,889.7 2,117.4 9,051.7 Developed, Low Intensity 22 219.5 151.7 20.2 420.1 460.6 347.6 1,619.7 Developed, Medium Intensity 23 40.3 23.4 0.9 124.1 106.7 87.0 382.3 Developed, High Intensity 24 5.8 1.3 0.0 27.6 20.0 23.4 78.1 Barren Land 31 0.0 0.0 0.0 0.0 105.2 0.0 105.2 Deciduous Forest 41 26,734.7 7,339.0 2,096.5 13,013.0 7,821.2 10,386.9 67,391.3 Evergreen Forest 42 3,438.4 988.5 530.6 3,326.4 1,216.3 2,429.4 11,929.7 Mixed Forest 43 6,856.2 3,199.4 1,309.5 6,379.2 1,822.1 3,813.2 23,379.4 Shrub/Scrub 52 741.0 546.6 175.7 268.2 1,333.7 1,387.1 4,452.3 Herbaceuous 71 737.2 568.2 118.3 518.8 813.7 1,134.0 3,890.3 Hay/Pasture 81 8,722.3 5,694.0 2,205.9 7,591.9 12,465.0 10,017.1 46,696.2 Cultivated Crops 82 2,930.9 1,170.2 271.1 3,480.3 6,491.0 8,616.5 22,960.0 Woody Wetlands 90 4,928.7 2,506.8 783.1 3,934.2 3,929.3 3,599.7 19,681.7 Emergent Herbaceous Wetlands 95 1,250.3 652.7 317.1 1,027.9 1,001.9 976.1 5,226.0 Total Land Area 58,691.7 23,962.3 8,191.7 42,879.7 41,775.9 49,702.1 225,203.3

334

Soil Erodibility

Streamside soils that are considered to be moderately to highly susceptible to erosion by water occupy a total of 12,213 acres within the focus watersheds. Figure 4 graphically presents land area of streamside soils rated as slightly susceptible and moderate-to-highly susceptible; Figure 5 illustrates the distribution of said soils along with an indication of the erodibility based on K factor values (darker colors represent higher K factor value). While the focus on streamside soils does not reflect all soils subject to erosion, those most likely to contribute to the sediment load during storm events have been included (streamside soils). This will provide an avenue for identifying areas of high priority for community outreach and projects to reduce soil erosion and stabilize vulnerable stream banks.

Of the focus watersheds, the Cherry Valley Creek subbasin contains the greatest land area with moderate to high susceptibility for erosion. The erodible soils within the Otsego Lake basin occupy more land area, but lie at the northern end of the lake; eroded materials would settle out of the water column before they could be exported downstream to Goodyear Lake.

335

16000 Slight Susceptibility 14000 Moderate to High Susceptibility 12000 10000 8000 6000 4000 2000 Area of Streamside (acres) Soils Streamside Area of 0 Red Creek Cherry Valley Lower Oaks Main Stem Canadarago Otsego Lake Creek Creek River Lake Watershed Subbasin

Figure 3. Land area (acres) of soils of slight (light grey) and moderate/high (dark grey) susptibilty to erosion by water. “Slight susceptibility”= soils with KWS 0.01-0.24; “moderate to high susceptibility” = KWS values ranging from 0.28-0.49.

Surficial and Bedrock Geology

According to The Geology of New York, the watershed lies along the Northern boundary of the Allegheny Plateau (Isachsen et al. 1991). The underlying geology of the region is comprised of sedimentary rock formations of Devonion origin that have been altered by glacial action during the Pliestocene glaciation, roughly 14,000 years ago. Bedrock in the northern portions of the Canadarago Lake and Otsego Lake subbasins include limestone formations and exposed shale bedrock. Moving southward through the watershed, shale and sandstone bedrock predominate. Surface deposits are primarily glacial in nature, with much of the valley bottoms associated with former glacial lake bottoms; these deposits are generally flat and the silt and clay materials are particularly prone to erosion.

336 Figure 4. Susceptibility of Streamside Soils to water erosion within the Goodyear Lake watershed, based on K factor (whole soil). Full color maps are included in the digital version of this report, available on the BFS webpage: www.oneonta.edu/academics/biofld/publications.asp

337 Figure 5. Bedrock geology of the Goodyear Lake Watershed. Full color maps are included in the digital version of this report, available on the BFS webpage: www.oneonta.edu/academics/biofld/publications.asp

338 Figure 6. Surficial geology of the Goodyear Lake Watershed. Full color maps are included in the digital version of this report, available on the BFS webpage: www.oneonta.edu/academics/biofld/publications.asp

339 REFERENCES

Gerhard, D. 2000. Surficial Geology. New York State Museum Technology Center. http://www.nysm.nysed.gov/gis.html

Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

Isachsen, Y.W., E. Landing, J.M. Lauber, L.V. Rickard, and W.B. Rogers, eds.1991. Geology of New York A Simplified Account. New York State Museum / Geological Survey. Albany, NY.

NYS Museum. 1999. Bedrock Geology of New York State. https://www.nysm.nysed.gov/gis/ Accessed 2/25/2016.

NLCD. 2014. National Land Cover Dataset (NLCD) - 2011. U.S. Geological Survey https://www.sciencebase.gov/catalog/item/513624bae4b03b8ec4025c4d

Soil Survey Staff. 2016. Web Soil Survey. Natural Resources Conservation Service, United States Department of Agriculture. http://websoilsurvey.nrcs.usda.gov/. Accessed 02/22/2016.

USGS. 2015. StreamStats Program: New York State Interactive Map Application. United State Geological Survey. http://streamstats.usgs.gov/new_york.html

USGS. 2016. The National Map. United States Geological Survey. http://nationalmap.gov

340

OCCASIONAL PAPERS PUBLISHED BY THE BIOLOGICAL FIELD STATION (cont.)

No. 38. Biocontrol of Eurasian water-milfoil in central New York State: Myriophyllum spicatum L., its insect herbivores and associated fish. Paul H. Lord. August 2004. No. 39. The benthic macroinvertebrates of Butternut Creek, Otsego County, New York. Michael F. Stensland. June 2005. No. 40. Re-introduction of walleye to Otsego Lake: re-establishing a fishery and subsequent influences of a top Predator. Mark D. Cornwell. September 2005. No. 41. 1. The role of small lake-outlet streams in the dispersal of zebra mussel (Dreissena polymorpha) veligers in the upper Susquehanna River basin in New York. 2. Eaton Brook Reservoir boaters: Habits, zebra mussel awareness, and adult zebra mussel dispersal via boater. Michael S. Gray. 2005. No. 42. The behavior of lake trout, Salvelinus namaycush (Walbaum, 1972) in Otsego Lake: A documentation of the strains, movements and the natural reproduction of lake trout under present conditions. Wesley T. Tibbitts. 2008. No. 43. The Upper Susquehanna watershed project: A fusion of science and pedagogy. Todd Paternoster. 2008. No. 44. Water chestnut (Trapa natans L.) infestation in the Susquehanna River watershed: Population assessment, control, and effects. Willow Eyres. 2009. No. 45. The use of radium isotopes and water chemistry to determine patterns of groundwater recharge to Otsego Lake, Otsego County, New York. Elias J. Maskal. 2009. No. 46. The state of Panther Lake, 2014 and the management of Panther Lake and its watershed. Derek K. Johnson. 2015. No. 47. The state of Hatch Lake and Bradley Brook Reservoir, 2015 & a plan for the management of Hatch Lake and Bradley Brook Reservoir. Jason E. Luce. 2015. No. 48. Monitoring of seasonal algal succession and characterization of the phytoplankton community: Canadarago Lake, Otsego County, NY & Canadarago Lake watershed protection plan. Carter Lee Bailey. 2015. No. 49. A scenario-based framework for lake management plans: A case study of Grass Lake & A management plan for Grass Lake. Owen Zaengle. 2015. No. 50. Cazenovia Lake: A comprehensive management plan. Daniel Kopec. 2015.

Annual Reports and Technical Reports published by the Biological Field Station are available at: http://www.oneonta.edu/academics/biofld/publications.asp