BIOLOGICAL FIELD STATION Cooperstown, New York

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49th ANNUAL REPORT 2016

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

49th ANNUAL REPORT 2016

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 2016 ANNUAL REPORT CONTENTS

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

ONGOING STUDIES:

OTSEGO LAKE WATERSHED MONITORING: 2016 Otsego Lake water levels. W.N. Harman and M.F. Albright……………………….7 Otsego Lake limnological monitoring, 2016. H.A. Waterfield and M.F. Albright..….…10 A survey of Otsego Lake’s zooplankton community, summer 2016. M.F. Albright, C. Murch and K. Johnson .……………………………..…….…..22 Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2016. J. Perry. ..……………………………………………34

SUSQUEHANNA RIVER MONITORING: Upper Susquehanna River water quality monitoring, summer 2016. L. Courter……………………………………………………………………..54

REPORTS:

Evaluation of chlorophyll a extraction techniques. M. Mehlrose and K. Yokota………...….66 Efficacy of a potassium peroxymonosulfate-based disinfectant (VirkonTM) against zebra mussel (Dreissena polymorpha) adults and veligers. J. Perry, M. Albright and D. Stich……..…….…………………………………….…76 Evaluation of citric acid, a common food additive and calcium descaler, for use in adult zebra mussel decontamination. J. Perry, M. Albright and D. Stich…….….…90 Trap net monitoring of fish assemblages in the weedy littoral zone at Rat Cove and the rocky littoral zone at Brookwood Point, Otsego Lake, NY 2016. Z.R. Diehl…...... 102 Freshwater pearly mussel (Unionidae) survey of Otsego Lake following zebra mussel (Dreissena polymorpha) introduction. Z. Piper…………………………...... 112 Succession of BFS Upper Site forest communities 40 years later. N. Muehlbauer..……..……122 Resurgence of spawning rainbow smelt (Osmerus mordax) in the Mohican Canyon Creek, Otsego Lake, NY. M.K. Mulvihill and J.R. Foster...... 137 Comparison of amphibian abundance in vernal pools at two different sites on Rum Hill. T. Franzem and D. Stich………… ………………………………..….…..145 Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 2016. M.F. Albright………………...... ……………….…………...156 Comparison of parasite communities of yellow perch (Perca flavescens) from Otsego and Canadarago Lakes. M. Doolin, J. Darpino, E. Iwanyckyj, S. Macchiarelli, Z. Piper, S. Vandemark, T. Pokorny, and F. Reyda...………..…….171 Water quality monitoring and analysis of fecal coliform of Canadarago Lake tributaries and outlet. T. Perry and M. Brown………….…………….…….…..176 Macroinvertebrate survey and biological assessment of water quality: tributaries of Canadarago Lake; Otsego County, NY. M. Brown….…………………………..…..188 Fish assemblages of selected Canadarago Lake tributaries and outlet. T. Perry…..….….….198 Annual monitoring of Moe Pond in conjunction with bio-manipulation. Z.R. Diehl and N.J. Muehlbauer……………………….…………………………….207 Reproductive phenology of E. complanata in Otsego County, NY T. Franzem, P. Lord, A. Gascho-Landis, and R. LaRochelle….……………...…...... 218 Comparison of gill raker morphology of alewife from recent and bygone introductions. D. Stich and C. Ducey…………………….…………...... 225 Lotic fish communities of selected Otsego Lake tributaries. Z.R. Diehl.….………….……..230 Summer 2016 BioBlitz series. J. Perry….…….…………………………….…….….……….240 Aquatic macrophyte management plan facilitation, Lake Moraine, Madison County, NY 2015. W.N. Harman and M.F. Albright……………………………..…..………..265 The effectiveness of three fry emergence traps in measuring lake trout (Salvelinus naymayacush) recruitment in Otsego Lake, NY. N.R. Winter, B.A. Winter, and J.R. Foster………………………………………………………………………...277 Dynamics of Galerucella app. and purple loosestrife (Lythrum salicaria) in Goodyear Swamp Sanctuary, summer 2016 update. H. Waterfield ……………………………….283 Progress Report on the Study of Neoechinorhynchus (Acanthocephala) in fishes from Otsego Lake and elsewhere. M. Doolin and F. Reyda…...………………...…….…….290

INTRODUCTION

Willard N. Harman

Interns: Zach Piper, an Ecology and Field Biology major from SUNY Oneonta, held the Biology Department Internship. He worked primarily with Paul Lord evaluating the status of freshwater pearly mussels in the Allegany River. He also surveyed mussel abundance in Otsego Lake. Joseph Perry, a Biology major at SUNY Oneonta, was supported by the Otsego County Conservation Association. He conducted water quality monitoring throughout the Otsego lake watershed to evaluate changes potentially attributable to agricultural Best Management Practices there. He also oversaw three “Bioblitz”events arranged jointly between the Biological Field Station and the Otsego Land Trust. He worked with Matt Albright to evaluate the effectiveness of VirkonTM as a disinfecting agent for zebra mussel veligers and adults, and he continued this work into the fall 2016 semester using citric acid. Marissa Mehlrose, a Marine Biology/Environmental Science major at University of New Haven, held the Rufus J. Thayer Internship, funded by the Otsego County Conservation Association. She worked with Kiyoko Yokota evaluating the effectiveness of various laboratory techniques for the extraction of chlorophyll a. Together they conducted nutrient enrichment studies on algae in Otsego Lake. Nick Muehlbauer, a Field Biology/Ecology major from SUNY Oneonta, held a Biological Field Station Internship. Under the direction of Sean Robinson, he conducted a study on successional changes in forest communities on BFS Upper Site property around Moe Pond. Tara Perry, an Environmental Science Major from Ithaca College and Marina Brown, a Biology major from SUNY Oneonta, were both supported by the Otsego Land Trust. Together they conducted water quality monitoring on tributaries to Canadarago Lake. Working at the same sites, Tara conducted fisheries surveys while Marina evaluated the macrobenthic invertebrate communities. With Dan Stich, they also evaluated the brook trout community of Leatherstocking Creek, a tributary to Otsego Lake. Zachary Diehl, a Fisheries and Aquaculture major at SUNY Cobleskill, held the Robert C. MacWatters Internship in the Aquatic Sciences. Zach continued monitoring of the littoral fish community in Otsego Lake. He worked with Dan Stich on evaluating the fishery of Moe Pond during efforts to control populations of largemouth bass. They also conducted fisheries surveys in Otsego Lake’s tributaries. Chase Ducey of Cooperstown High School was supported by the Otsego County Conservation Association. He held a F.H.V. Mecklenburg Conservation Fellowship. Under the direction of Dan Stich, he evaluated physiological differences among various alewife populations. Luke Courter of Milford Central School also held F.H.V. Mecklenburg Conservation Fellowship. He monitored water quality in the upper Susquehanna River and was supported by the Village of Cooperstown.

- 1 - Connor Murch and Kayla Johnson, both of Laurens Central School, volunteered at the BFS, spending a few hours a day throughout the academic year. They were involved in numerous activities, including water quality testing, zebra mussel research, alewife dissections and zooplankton monitoring. Connor is planning to attend SUNY Oneonta in the fall, while Kayla is headed to SUNY ESF.

Faculty and staff activities: Bill Harman, Kiyoko Yokota, Dan Stich, and Matt Albright (all NALMS Certified Lake Managers), along with MS students Patrick Goodwin, Leah Gorman, Ryan Elliot, Joseph O’Reilly, David Pfuhler, George Smith and Alexa Tumbarello all attended the 36th International NALMS Symposium in Banff, Alberta, Canada. Kiyoko presented “In situ determination of nutrient limitation in Otsego Lake, NY”, co-authored by undergraduate intern Marissa Mehlrose. Pat gave a presentation entitled “An evaluation of past and present aeration designs: An Ohio case study”, and Leah presented “DeRuyter Reservoir, Madison County, NY: A case study on invasive plant management strategies and a look into the future”. The BFS was presented with a 2016 “Friends of the North American Lake Management Society (NALMS)” award for our work with graduate students and leadership roles in their organization.

Kiyoko Yokota currently serves as the Region 2 (NY, NJ, PR) Director on the NALMS board of directors and as a member of the Student Programs Committee. She utilized BFS resources throughout the academic year to teach BIOL 685 (Studies in Limnology, Fall 2016) and BIOL 691 (Management of Aquatic Biota, Spring 2017). Results of her microplastics project, carried out with Holly Waterfield, 2014 - 2016 BFS summer interns including the 2016 intern Marissa Mehlrose and Lake Management MS student Edward Kwietniewski, has been presented at various scientific conferences including the North American Lake Management Society Annual Symposium and Association of the Sciences of Limnology and Oceanography Aquatic Sciences Meeting. A manuscript is currently in review by a peer reviewed journal. 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. Parker has presented at various venues including the NYS Chapter of American Fisheries Society and the SUNY Graduate Research Conference, and her research has been highlighted by publications by the SUNY Research Foundation. Yokota, Mehlrose and her colleagues from SUNY Oneonta, Fredonia and New Paltz have also been working on the SUNY Lakes Ecosystem Observatory Network and conducted a coordinated multi-lake nutrient limitation experiment with collaborators from the Northeast GLEON, which is part of the Global Lake Ecological Observatory Network (GLEON). A temperature and light monitoring buoy was deployed on Otsego Lake in Fall 2016, which will be upgraded in 2017 to an autonomous monitoring buoy, similar to those at Fredonia and New Paltz, with funding from a National Science Foundation grant. Matt Albright, Holly Waterfield, and Yokota have also been involved in a couple of GLEON projects that utilize long-term monitoring data from Otsego Lake.

- 2 -

Research in the fish parasitology lab of Florian Reyda continues to involve a diversity of people and activities. Notably, this year Reyda welcomed his first graduate student to the lab: Maggie Doolin. Maggie Doolin began her studies here in January and immediately focused on a taxonomic and systematic problem: the acanthocephalan genus Neoechinorhynchus in freshwater fish. Maggie’s work has included field work here at Otsego Lake, as well as in Canadarago Lake, Oneida Lake, and in various water bodies in New Hampshire. Her project is using both morphological and molecular techniques to characterize species–an integrative taxonomic approach. Last spring, Maggie and SUNY Oneonta undergraduate students Jill Darpino, Elise Iwanyckyj, Sisina Macchiarelli, Zachary Piper (also an intern, see other studies in this volume), and Sam Vandemark completed a study that is provided in this volume, in which they characterize the parasites of yellow perch from Otsego and Canadarago Lakes using full necropsies. That study was made possible thanks to the collecting efforts of Tim Pokorny who is also an author of that report in this volume. Among that list, Elise Iwanyckyj and Sisina Macchiarelli continued in the Reyda lab in fall 2016 to take on their own projects, focusing on stingray tapeworms, and local trematodes, respectively. SUNY Oneonta undergraduate student Craig Wert continued his work on characterizing a new species of trematode from chain pickerel last spring. Last fall, SUNY Oneonta undergraduate student Cheyenne Pommelle joined the lab, assisting Sisina on her project before undertaking her own study of a locally occurring tapeworm that is actually an invasive species from China. 2016 marked the publication of three peer- reviewed studies for Reyda, who continues to occupy himself between mentoring student projects and other projects involving collaborators from elsewhere. The three studies authored by Reyda last year include a total of 10 undergraduate student coauthors, as well as three Chinese scientists and a Japanese scientist, and two collaborators from West Virginia State University. Reyda also utilized BFS resources during his fall offering of Invertebrate Zoology with a weekend long field trip based out of Thayer Farm.

Dan Stich began working with students at the BFS during the 2015-2016 academic year. He has taught graduate courses in lake management through the BFS, and uses BFS resources to teach ichthyology and advanced ichthyology, the first shared-resources course that co-mingles undergraduate and graduate students at SUNY Oneonta. During 2016, Stich offered ichthyology for undergraduates as a summer field course, and students surveyed waters throughout Otsego County to learn about fish biology, ecology, and management while participating in research projects at the BFS. These courses rely heavily on the BFS resources, and focus on exposing students to applied problems in fishery and aquatic resource management through active service in the community. In addition to teaching activities, Stich has been heavily involved in mentoring student researchers, graduate students, and interns, who have conducted studies ranging from assessment of vernal pool occupancy by frogs and salamanders (with involvement by Tom Franzem, SUNY Oneonta) to regional monitoring of fish communities in Otsego County, lake whitefish monitoring in Otsego Lake, characterization of walleye population dynamics,

- 3 - understanding changes in alewife growth and life-history from introduction to collapse, and the development of comprehensive management plans for lakes throughout the state.

On July 21 2016, SUNY Oneonta Assistant Professor Beth Bastiaans, Field Station Senior Staff Assistant Matt Albright, and Matt's son Stephen, collected and successfully sampled several green frogs (Lithobates clamitans) for the presence of Batrachochytrium dendrobatidis (Bd). Dr. Bastiaans returned on July 28, 2016 to perform further sampling with BFS interns Nick Muehlbauer and Tara Perry. They had an amphibian-filled day, successfully sampling two-lined salamanders (Eurycea bislineata), Allegheny mountain dusky salamanders (Desmognathus ochrophaeus), red-backed salamanders (Plethodon cinereus), one late-season spotted salamander (Ambystoma maculatum), several recently metamorphosed pickerel frogs (Lithobates palustris), and several recently metamorphosed American toads (Anaxyrus americanus). Bd is a fungal disease that has been linked to amphibian population declines all over the world, but many Northeastern amphibians appear tolerant of the fungus and may serve as natural reservoirs. This research will help shed more light on the distribution of this pathogen in areas of the world where it does not appear to be actively contributing to mass extinctions, but may still be spreading.

John Foster and Mark Cornwell of SUNY Cobleskill continued 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 continued to evaluate spawning efforts by Otsego Lake whitefish. Smelt spawning in Mohican Creek was also evaluated by John and Cobleskill student Mary K. Mulvihill.

Bill Harman was co-author of one peer reviewed paper, and another in a lay magazine marketing the lake management degree programs: 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. Nancy Mueller, Holly Waterfield and Willard Harman. 2016. Lake Management Degree Program Attracts Students. Kaatskill Life 31(1);26 -28. On 4.13.16 he presented "Biological Field Station Resources Relevant to Community Chamber of Commerce Functionality" to Leadership Otsego in Cooperstown.

Graduate Studies: At this time we have 15 students enrolled in the Master of Science in Lake Management degree program. Students completing their second 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 financially supporting Pat’s efforts. Pat is doing research on two lakes; Mohegan Lake in Westchester County and Thunder Lake in Madison County. First-year graduate students Ryan Elliot, Eric Hanss, Joseph O’Reilly, David

- 4 - Pfuhler, George Smith, Alexa Tumbarello and Sonja Wixom are beginning their research to develop Comprehensive Management Plans for Lake of the Woods in St. Lawrence County, Song, Crooked and Tully Lakes in Onondaga and Cortland Counties, Cassadaga Lake in Chautauqua County, Crumhorn Lake in Otsego County, Big Bowman Lake in Rensselaer County, Paradox Lake in Essex County, Beaver and Mud Lakes at Koinonia in Sullivan County respectively. As indicated above, all of the students in the program have been sponsored to attend and present at multiple professional venues.

Alejandro Reyes and Ben German completed their degrees in lake management this year. Alejandro is working at the University of North Carolina in an extension position on Gaston Lake while concurrently enrolled in a PhD program. Ben is a staff member in SUNY Cobleskill’s Department of Fisheries, Wildlife and Natural Resources Environmental Science

Biology Graduate student Eric Davis, working with David Wong and Bill Harman completed his biology master’s degree and concurrently published two papers: Eric A. Davis, Wai Hing Wong and Willard N. Harman. 2016. Livewell flushing to remove zebra mussel (Dreissena polymorpha) veligers. Mgt. of Biol. Invasions. 7 (4):399-403. 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 Eric was the first graduate student supported by external funding (NYS DEC contract) in the history of the Biology Department.

Jeane Bennet O’Dea retired from the BFS Secretary position after many years of service. Nancy Devins, with an AOS Degree Administrative Specialist, is now in the position and is becoming rapidly indispensable.

Public support makes our work possible. Funding for BFS research and educational programs was procured in 2016 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

- 5 - 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 2016 Date 9-Sep 15-Aug 22-Aug 4-Sep 27-Aug 1-Sep Sailboats 118 140 113 121 141 130 Rowboats and canoes 450 545 520 551 562 600 Outboards 227 334 329 361 354 373 Inboards 15 16 31 16 17 21 Inboard-Outboards 190 274 247 231 242 216 Personal Watercraft 14 22 17 11 13 16 Jet-boats* 2 2 15

Misc. 40 40 41 35 32 36 Total 1054 1371 1298 1328 1363 1407 * Prior to 2014, jet-boats were grouped with Inboard-Outboards

- 6 - ONGOING STUDIES:

OTSEGO LAKE WATERSHED MONITORING:

2016 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).

January'16 0 3 6 9 12 15 18 21 24 27 30 30

20 Ice on (14 Feb) 10

-10 Lake (cm) level Lake

-20

-30

March '16 0 3 6 9 12 15 18 21 24 27 30 30

20 Ice off (12 Mar) 10

-10 Lake level (cm) level Lake

-20

-30

- 7 -

September '16 0 3 6 9 12 15 18 21 24 27 30 30

-10 Lake level (cm) level Lake

-20

-30

- 8 -

- 9 - Otsego Lake limnological monitoring, 2016

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 past 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; 2016). Concurrent additional work related to Otsego Lake included estimates of fluvial nutrient inputs (Perry 2017), descriptions of the zooplankton community (Albright et al. 2017), freshwater pearly mussels (Piper 2017), and nekton communities (Deihl 2017).

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 bi-weekly during open water conditions, 14 April through 08 November 2016. No data or samples were collected between January and mid-April; unsafe ice conditions during the brief period of ice-cover (Feb- March 2016) prevented access to the lake profiling location. 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.

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.

- 10 - TR4-C

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TR4-C

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

- 11 - 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 by flourometric detection store at 0 oC

RESULTS AND DISCUSSION

Temperature Figures 2a and 2b depict temperatures measured in profile (0 to 48m) at site TR4-C from 14 April through 18 August and 18 August through 08 November 2016, respectively. Complete ice-cover formed on 14 February; the lake was completely ice-free on 13 March. Observed surface temperature ranged from a low of 4.7oC on 14 April to 24.4 oC on 18 August, at which point the epilimnion extended through 8m depth (Figure 2a). Spring mixing occurred prior to the 14 April sampling event and thermal stratification developing by 19 May. Maximum surface temperature was recorded on 18 August after which surface temperatures decreased and the thermocline occurred at greater depth until fall turnover, which had not yet occurred as of the 08 November 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 5 May, prior to the onset of thermal stratification, dissolved oxygen ranged from 11.69 mg/l at bottom to 12.04 mg/l at the surface. The minimum observed DO concentration in 2016 was 3.80 mg/l recorded on 08 November at 42m. 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 0.046 mg/cm2/day (between 19 May and 05 October),was the lowest ever recorded, remaining well below the historical average for the seventh 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 (6 July and 8 November) on which irregular patterns were observed; in July, measured concentrations from 40 -48m were 40-50 mg/l (as CaCO3) lower than

- 12 - overlying waters. In November alkalinity of surface to 8m samples were unusually low. Mean annual concentration at TR4-C was 123.3 mg/l, ranging from 70.5 mg/l (as CaCO3) at 4m on 08 November to 145.6 mg/l at 48m on 22 June 2016.

Temperature (oC)

2a. 0 5 10 15 20 25 0 4/14/2016 2a. 5 5/5/2016 10 5/19/2016 15 6/2/2016 20 6/7/2016 25 6/22/2016

30 7/6/2016 Depth (meters) 35 7/19/2016

40 8/4/2016

45 8/18/2016

Temperature (oC)

0 5 10 15 20 25

2b. 5 8/18/2016

10 8/30/2016

15 9/14/2016

20 10/5/2016

25 11/8/2016

Depth (meters) 35

Figure 2. Otsego Lake temperature profiles (oC) observed at TR4-C 14 April through 18 August (2a) and 18 August through 08 November 2016 (2b).

- 13 -

Figure 3. Distribution of dissolved oxygen (isopleths in mg/L) as recorded in 2016 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 48.8 mg/l, ranging from 40.9 mg/l at the surface on 19 May and 22 June to 56.1 mg/l at 40 and 44mm on 08 November.

Chlorides Mean chloride concentrations in Otsego Lake from 1925 to 2016 are shown in Figure 4. The mean lake-wide concentration in 2016 was 17.5 mg/l, approximately 2 mg/l higher than in 2015. 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). 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 an irregular high value recorded at 16m on 14 September (38 mg/l). Specific conductivity readings taken concurrent

- 14 - with sample collections did not reflect the chloride concentration determined via titration in the laboratory, as would have been the case if the measured chloride concentrations reflected true conditions. A possible source of chloride contamination was a salt water bath that some were using to decontaminate gear for zebra mussel control.

Table 2. Areal hypolimnetic oxygen deficits (AHOD) for Otsego Lake, computed over summer stratification in 1969, 1972 (Sohacki, unpubl.), 1988 (Iannuzzi, 1991), and 1992-2016.

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 0.053 05/21/13 – 10/15/13 0.061 05/21/14 – 10/15/14 0.050 05/19/15 – 10/06/15 0.049 05/19/16 – 10/05/16 0.046

- 15 - 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, 1925-2016. Points later than 1990 represent yearly averages (figure modified from Peters 1987).

Nutrients Total phosphorus averaged 11 µg/l in 2016, ranging from below detection (< 4 µg/l) on multiple dates to an abnormally high 78 µg/l at 12m on 30 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. Equipment disinfection procedures led to contamination of some samples. Data from 6 and 19 July were omitted due to suspected contamination by residue of the disinfectant VirkonTM. Further investigation into the P content of VirkonTM suggests that the dry product contains about 10% phosphorus by mass (Perry et al. 2017). Disinfection procedures for sampling gear at the BFS no longer utilize this product. Unusually high phosphorus concentrations were reported for samples collected later in the year; just below thermocline on 30August (78ug/l) as well as a portion of the profile samples from 08 November. While contamination is a possibility, there is no evidence to prove that these concentrations were not accurate and so were included in the annual average.

Average nitrite+nitrate-N concentration was 0.52 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 0.86 mg/l, indicate an average organic nitrogen concentration of about 0.34 mg/l over the year. This estimate is higher than usual (e.g. 0.14 mg/l in 2015).

Chlorophyll a and Secchi Disk Transparency Chlorophyll a concentrations were determined for samples collected on eight dates from May through September 2016. Average 0-20m composite chlorophyll a concentration was 3.2µg/l (range = 0.1.31 to 4.83 µg/l). Peak growing season chlorophyll a concentrations were

- 16 - observed between 22 June and 19 July (4.19 to 4.83 µg/l). The maximum concentration recorded for the year was 5.58 µg/l on 5 October 2016.

Secchi disk transparency measurements are presented in Figure 5. Mean growing season (May through September) transparency was 7.38m. The season-maximum of 15m was observed 19 May, coinciding with the highest recorded dry weight of crustacean zooplankton ever recorded (Albright et al. 2017) The minimum (3.8m) was observed on 14 September. Mean summer Secchi transparencies for all years available (1935-2016) are given in Figure 6.

0.0

2.0

4.0

6.0 8.0

10.0

Secchi Transparency (m) 12.0

14.0

16.0

Figure 5. Secchi disk transparency collected at TR4-C, May through September, 2016.

- 17 - 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, 1935-2016.

CONCLUSIONS

Lake conditions vary annually, as the interactions between management efforts, invasive species, and climate variability continue to develop. NYS DEC Region 4 Fisheries, the BFS and SUNY Cobleskill are continuing to assess changes in the fishery including lake trout fitness and fry survival, and the spawning and recruitment of lake whitefish, rainbow smelt and walleye. Efforts to raise lake whitefish at the SUNY Cobleskill hatchery were unsuccessful in providing fish for stocking in 2016.

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- 21 - A survey of Otsego Lake’s zooplankton community, summer 2016 M.F. Albright1, C. Murch2 and K. Johnson2

INTRODUCTION (from Tanner and Albright 2014) This is a continuation of a study that has entailed monitoring the zooplankton community 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) were stocked in Otsego lake beginning in 2000 (Cornwell 2005) at a targeted rate of 80,000 per year (though most years the numbers have been lower; Sanford 2012), continuing through 2015. 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 SUNY Oneonta Biological Field Station. 2 BFS volunteers, Laurens Central School.

- 22 - METHODS From 5 May to 14 September 2016, 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. On 2 June and 4 August, the meter malfunctioned, so the summer mean meter reading was used to estimate lake volume filtered. The concentrated samples were preserved with ethanol to about 50%. The preserved volume was recorded 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. Plankton densities were recorded (# recorded*ml concentrated sample/ml sample viewed/l lake water filtered). 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.

- 23 -

Figure 1. Otsego Lake, New York, showing the sample site (TR4-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)

- 24 - RESULTS AND DISCUSSION Table 2 summarizes the data collected from TR4-C over the summer of 2016, 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 2016. Figures 3, 4, 5, 6, 7 and 8 provide comparable data from 2015, 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 – 2016 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 cladoceran 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 recorded by that date for Otsego Lake (Waterfield and Albright 2015). In 2016, the two sampling dates of May were associated with crustacean dry weights (evenly divided between cladocerans and copepods) being the highest ever recorded in Otsego Lake. On 19 May 2016, both crustacean dry weight (at 615 mg/l) and Secchi transparency (at 15 m) were the highest ever recorded. Chlorophyll a remained low (averaging 3.2 ug/l), and the rate of hypolimnetic oxygen depletion, at 0.046 mg/cm2/day, was lower than ever recorded (Waterfield and Albright 2017).

- 25 - 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 5/5 7.96 Cladocera 22.70 0.855 8.06 183.04 0.438 1.924 7.927 17.99 Copepoda 55.03 0.614 4.10 225.45 0.126 0.681 3.495 19.23 Rotifers 4.13 0.073 0.05 0.19 2.268 0.010 0.018 0.01 Total 408.68 2.615 37.23 5/19 10.27 Cladocera 28.91 1.060 12.25 354.07 0.435 3.700 13.513 39.06 Copepoda 44.17 0.738 5.93 261.69 0.108 0.681 5.507 24.32 Rotifers 0.00 0.000 0.000 0.000 0.00 Total 615.76 4.381 63.39 6/3 15.95 Cladocera 5.15 1.250 16.42 84.54 0.508 1.031 20.334 10.47 Copepoda 7.03 0.466 2.49 17.52 0.784 0.329 1.759 1.24 Rotifers 1.00 0.118 0.12 0.12 Total 102.18 1.360 11.70 6/22 18.15 Cladocera 3.41 1.447 23.06 78.73 0.512 0.967 29.242 9.98 Copepoda 6.42 0.606 3.77 24.21 0.197 0.115 3.372 2.16 Rotifers 0.27 0.140 0.16 0.04 1.528 0.002 0.089 Total 102.98 1.083 12.15 7/6 19.57 Cladocera 5.49 0.828 7.53 41.31 0.662 0.656 7.313 4.01 Copepoda 5.33 0.573 3.25 17.33 0.217 0.090 2.937 1.57 Rotifers 1.57 0.208 0.40 0.63 0.100 0.002 0.238 0.04 Total 59.27 0.748 5.62 7/19 21.71 Cladocera 5.78 1.114 15.73 90.90 0.559 1.219 15.268 8.82 Copepoda 11.35 0.472 2.57 29.19 0.253 0.177 1.816 2.06 Rotifers 1.44 0.132 0.14 0.21 0.366 0.002 0.076 0.01 Total 120.29 1.398 10.89 Table 2. Summary of site TR4-C of 2016 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.

- 26 - 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 8/4 22.32 Cladocera 0.92 0.77 5.87 5.40 0.825 0.107 6.210 0.57 Copepoda 9.93 0.56 3.57 35.42 0.241 0.205 2.756 2.74 Rotifers 6.62 0.13 0.14 0.91 0.458 0.010 0.073 0.05 Total 41.73 0.322 3.36 8/18 23.22 Cladocera 0.78 0.758 5.50 4.32 0.867 0.090 5.875 0.46 Copepoda 29.04 0.352 1.54 44.78 0.428 0.460 0.880 2.56 Rotifers 43.96 0.129 0.13 5.92 0.486 0.069 0.073 0.32 Total 55.02 0.619 3.34 8/30 22.74 Cladocera 2.24 1.022 10.32 23.16 0.737 0.409 12.341 2.77 Copepoda 40.41 0.299 0.92 37.30 0.585 0.524 0.588 2.38 Rotifers 226.72 0.130 0.14 31.00 0.468 0.348 0.074 1.67 Total 91.46 1.282 6.82 9/14 22.7 Cladocera 1.10 1.227 15.14 16.61 0.673 0.268 19.412 2.13 Copepoda 37.29 0.383 1.69 63.10 0.395 0.599 1.082 4.03 Rotifers 78.96 0.123 0.12 9.72 0.534 0.125 0.065 0.51 Total 89.42 0.992 6.68

Season mean Cladocera 6.953 0.939 10.900 80.189 0.565 0.943 12.494 8.752 Copepoda 22.363 0.460 2.712 68.726 0.303 0.351 2.199 5.663 Rotifers 36.468 0.118 0.140 4.430 0.621 0.057 0.071 0.290 Total 153.34 1.351 14.70

Table 2 (cont.). Summary of site TR4-C of 2016 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.

- 27 - 615 600 2016 Rotifera 500 Copepoda Cladocera 400

300

200

Dryweight (ug/l) 100

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 2016 at TR4-C.

600 2015 Rotifera 500 Copepoda Cladocera 400

300

200

Dry weightDry(ug/l) 100

0 5/4 5/19 6/3 6/17 7/2 7/14 7/28 8/10 8/27 9/16

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

600 2014 Rotifera 500 Copepoda Cladocera 400

300

200

Dryweight (ug/l) 100

0 5/7 5/21 6/3 6/18 7/2 7/14 7/29 8/14 9/3 9/17

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

- 28 - 600 2013 Rotifera 500 Copepoda Cladocera 400

300

200

Dryweight (ug/l) 100

0 5/2 5/21 6/5 6/18 7/3 7/17 7/30 8/14 8/27 9/11 9/24

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

600 2012 Rotifera 500 Copepoda Cladocera 400

300

200

Dryweight (ug/l) 100

0 5/2 5/21 6/5 6/18 7/3 7/17 7/30 8/14 8/27 9/11

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

600 2011 Rotifera 500 Copepoda Cladocera 400

300

200

Dryweight (ug/l) 100

0 5/19 6/1 6/15 6/28 7/13 7/26 8/8 8/24 9/9 9/27

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

- 29 - 600 2010 Rotifera 500 Copepoda Cladocera 400

300

200 Dryweight (ug/l) 100

0 5/18 6/4 6/15 7/1 7/15 8/2 8/12 8/26

Figure 8. 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 – 2016. Samples collected at TR4-C.

Mean: 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 cladoceran 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 1.07 size (mm) crustacean 208 146 132 163 159 159 154 178 97 56.7 59.4 21.5 28.10 19.9 22.6 32 density (#/l) crustacean dry 175 145 177 261 206 206 128 321 142 143 155 122 102 84 83 162 weight (µg/l) % of epilimnion 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 14.7 filtered/day phosphorus 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 1.35 regen. (µg/l/day)

Figure 9 illustrates the abundance of zebra mussel veligers in the 0-12 m composite samples collected over the summer of 2016 at TR4-C. The density at TR4-C were highest from mid-June through mid-July, peaking at 35 individuals/l. 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).

- 30 -

10 Veliger density (#/liter)

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

Figure 9. Abundance of zebra mussel veligers in the 0-12 m composite samples collected over the summer of 2016 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 mg/cm2/day) (Waterfield and Albright 2013; 2014; 2015; 2016) with chlorophyll a being low, generally < 2 µg/l (Bianchine and Tanner 2014; Freehafer 2015; Garfield 2016). Through May 2016, Daphnia continued to be common and large bodied, leading to algal filtering rates higher than have previously been recorded and concurrent with the maximum recorded Secchi transparency (15 m). Daphnia were present at every sampling date over 2016 and averaged over 1.0 mm in length. 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.

- 31 - 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.

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.

- 32 - 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. 2017. Otsego Lake limnological monitoring, 2016. In 49th Ann. Rept. (2016). 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.

- 33 - Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2016. Joseph Perry1

INTRODUCTION As one of the region’s most valuable natural resources, Otsego Lake is considered to be chemically mesotrophic with biotic characteristics of an oligotropic lake (Iannuzzi, 1991), making it a prime cold-water fishery. From the 1990s through 2010, the lake had been showing a trend towards eutrophication (Harman et al. 1997 and Albright 2004), threatening to damage its standing. The eutrophication process is marked by changes towards hypoxia, or low dissolved oxygen, in the deepest levels of a water body. This can be undesirable as it affects habitat availability, health, abundance, and trophic interactions among fishes and other aquatic organisms that rely on an oxygenated water body floor (Breitburg et al., 2009). Recently, hypolimnetic oxygen deficits in the deepest part of Otsego Lake have dropped from levels close to 0.1 mg/cm2/day in the early 2000s down to a near-historical low of 0.049 mg/cm2/day in 2015 according to the most current data (Waterfield and Albright 2016) Not only does eutrophication have the potential to affect the existing community of aquatic organisms and the food webs that rely on them, but it may also be detrimental to the local economy if allowed to proceed. Eutrophication has been implicated in annual economic losses in the United States totaling to $0.3-2.8 and $0.37-1.16 billion per year in real estate and aquatic recreation respectively (Dodds et al., 2008). A major contributor to the eutrophication of water bodies is nonpoint source chemical pollution (Smith et al. 1999). As opposed to point sources of chemical pollution which include wastewater effluent, nonpoint sources are generally more difficult to address because of their diffuse nature. Agricultural runoff is among the largest contributors to nonpoint source pollution and is considered to be one of the most serious threats to freshwater resources in the United States (USEPA, 1996). In 1998, the Otsego Lake Watershed Council authored a Plan for the Management of the Otsego Lake Watershed to address and limit agriculture-sourced nutrient loading from the five main tributaries of Otsego Lake (OLWC 1998). The major concern with agricultural runoff is that it is often rich in phosphorus and nitrogen which are leeched away from fertilized soil. When these nutrients are flushed from the land to a waterbody, they can contribute to eutrophication. This effect can be minimized by the use of Best Management Practices, or BMPs, which have been implemented on a total of 23 farms in the Otsego Lake watershed to date. These practices are numerous and include conservation tillage and crop nutrient management which help keep the nutrients in the soil and reduce the application of soluble inorganic fertilizers. In order to track the success of BMPs in the Otsego Lake Watershed, annual monitoring of each major tributary at has been performed every summer since 1995.

- 34 - METHODS Water quality data were obtained from five major tributaries in the Otsego Lake watershed over the course of the summer 2016 season. Data were collected weekly from three sites on White Creek, five sites on Cripple Creek, eight sites on Hayden Creek, two sites on Mount Wellington stream, and four sites on Shadow Brook over a period of 10 weeks from 26 May 2016 to 20 July 2016 (Table 1 and Figure 1). Sampling locations were maintained from the previous year with the exception of Shadow Brook site number 1, from which no data were collected due to diminished stream flow. Collection sites have not changed appreciably since regular monitoring began on Cripple Creek, Hayden Creek, and Shadow Brook in 1995 and on White Creek and Mount Wellington stream in 1997 (Wells 2015, Heavy 1995, and Miller 1997).

Figure 1. Sampling sites used on the five major tributaries of the Otsego Lake watershed. The individual watersheds of White Creek, Cripple Creek, Hayden Creek, Shadow Brook and Mount Wellington stream are delineated. Asterisks represent the farms that are implementing best management practices (Wells 2015).

- 35 - Table 1. GPS coordinates and descriptions of sample locations (modified from Wells, 2015).

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

White Creek 2 (WC2): N 42º 48.93’ W 74º 55.29’ Plunge-pool located on the north side of County Route 27 (Allen Lake Road) 1.2 miles west of Route 80.

White Creek 3 (WC3): N 42º 48.407’ W 74º 54.178’ West side of large stone culvert under Route 80, 0.2 miles north of the turn onto Country Route 27.

Cripple Creek 1 (CC1): N 42º 50.878 W 74º 55.584’ Pull-off on Weaver Lake used as a small watercraft launch. Located on the north side of Route 20.

Cripple Creek 2 (CC2): N 42º 50.603’ W 74º 54.933’ Pull-off on 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 next to an active farm.

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.

- 36 - Table 1. (Continued)

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.

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. This site was consistently dry over the summer, so no data was collected.

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 that 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.

- 37 - Physical and chemical characteristics of water quality were evaluated at the 22 sites using a multiparameter probe (6820 V2-2 Multi-Parameter Water Quality Sonde, YSI Inc.) calibrated to the manufacturer’s specifications prior to each data collection. Characteristics measured with the probe included pH, temperature, specific conductivity, dissolved oxygen, and turbidity. Water samples were also collected from each site for laboratory analysis. Samples were collected from sub-surface with acid-washed plastic 125mL Nalgene bottles and stored on ice during transport. Each sample was acidified to <1 pH with sulfuric acid before analysis of total nitrogen, nitrate + nitrate, and total phosphorus using a semi-automated Lachat® QuikChem FIA+ Water Analyzer according to the methods in Table 2.

Table 2. Water chemistry analysis methods.

Detection Parameter Preservation Method Reference Limit Persulfate digestion followed by Liao and Marten Total Phosphorus H SO to pH < 2 4 µg/l 2 4 single reagent ascorbic acid 2001 Cadmium reduction method Pritzlaff 2003; Total Nitrogen H SO to pH < 2 0.04 mg/l 2 4 following peroxodisulfate digestion Ebina et al. 1983

Nitrate+nitrite-N H2SO4 to pH < 2 Cadmium reduction method Pritzlaff 2003 0.02 mg/l

Ammonia-N H2SO4 to pH < 2 Phenolate method Liao 2001 0.02 mg/l if low, use Calcium Store at 4oC EDTA trimetric method EPA 1983 more sample if low, use Chloride Store at 4oC Mercuric nitrate titration APHA 1989 more sample if low, use Alkalinity Store at 4oC Titration to pH= 4.6 APHA 1989 more sample

- 38 -

RESULTS AND DISCUSSION Temperature Temperature readings were similar to previous years’ data with the exception of Mount Wellington stream, which showed an average of 15.05 ºC (±0.75) at site 2 compared to five years’ previous average of 19.17 ºC (±0.65) (Wells 2015; Hastings 2014; Teter 2013; Mehigan 2012; Zaengle 2011). Flow was almost nonexistent at the moderately shaded site for most of the summer and was likely a major contributing factor to the diminished temperature readings.

Mean Temperature 26.0

24.0

C) 22.0 º

20.0

Temperature ( Temperature 18.0

16.0

14.0 0.00 2.00 4.00 6.00 8.00 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 2016. Distance from the lake increases moving left to right on the graph.

- 39 - Specific Conductivity Conductivity is the ability of a material to conduct an electical current. The conductivity of water is directly tied to its ionic concentration, or ammount of dissolved salts. Although conductivity is most strongly tied to the geologic composition of the streambed, deviations may suggest the presence of runoff polluted with road salts, fertilizer, and other pollutants (Field et al. 1996). Conductivity was relatively stable in most of the water bodies, although conductivity at Hayden site 3 spiked from 0.340 to 0.830 ms/cm in one week (the highest value of the season) and spiked from 0.430 ms/cm to 0.754 ms/cm at Mount Wellington site 2 over the course of two weeks. The lowest reading of the season was at Cripple Creek site 2 (Young Lake), with a specific conductivity of 0.152 ms/cm.

Mean Specific Conductivity 0.7

0.6

0.5

0.4

0.3 Specific conductivity (mS/cm) Specific conductivity

0.2 0.00 2.00 4.00 6.00 8.00 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 3. Mean specific conductivity of sampling sites along stream gradients of five major tributaries in summer 2016. Distance from the lake increases moving left to right on the graph.

- 40 - pH Like conductivity, pH is strongly linked to the underlying geology of the stream. In the Otsego Lake watershed, limestone is the predominant form of bedrock and influences the water by buffering it to slightly alkaline levels. As expected, data obtained this year is fairly consistent with levels observed in previous years (Wells 2015; Hastings 2014; Teter 2013; and Mehigan 2012).

Mean pH

8.2

8.0

7.8 pH

7.6

7.4 0.00 2.00 4.00 6.00 8.00 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 4. Mean pH of sampling sites along stream gradients of five major tributaries in summer 2016. Distance from the lake increases moving left to right on the graph.

- 41 - Dissolved oxygen Its levels strongly correlated with overall stream health, dissolved oxygen (DO) is essential to the survival most stream life. Mean DO concentrations ranged from 5.96 gm/L at Mount Wellington Stream 1 to 11.70 mg/L at Shadow Brook 2. Overall, DO concentrations were par for the course historically with the exception of Mount Wellington stream site 2, which was much lower than expected due to low flow and stagnation.

Mean Dissolved Oxygen

12.0

11.0

10.0

9.0

8.0

7.0

6.0 Dissolved oxygen (mg/L) oxygen Dissolved

5.0

4.0 0.00 2.00 4.00 6.00 8.00 Distance from Otsego Lake (km)

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 5. Mean dissolved oxygen (DO) of sampling sites along stream gradients of five major tributaries in summer 2016. Distance from the lake increases moving left to right on the graph.

- 42 - Turbidity Turbidity is a metric of water clarity, with higher values indicating cloudiness and a greater proportion of suspended particles or algae. Turbidity is often a function of streambed characteristics and turnover time. Faster streams with sturdy riparian buffers, rocky streambeds, and straight courses tend to have the lowest levels of turbidity (Wells 2015). Each tributary had a trend of increasing turbidity towards their outlets at Otsego Lake with the exception of White Creek, which remained relatively stable. Over the course of the summer, Hayden Creek showed the greatest variability in turbidity ranging from an average of 23.02 NTU at site 1 (Summit Lake) to 1.58 NTU at Hayden Creek 2. Site 1 of Cripple Creek (Weaver Lake) tied for highest average turbidity with Hayden site 1. Because these sites are located on lakes, algal growth was likely a much larger contributor to turbidity than suspended sediment. Turbidity data collected from Cripple Creek sites 3-5 on 26 May were excluded from Figure 6 because they were out of the analytical range (readings were showing turbidity upwards of 40,000 NTU). This was believed to be due to a beaver dam break between sites 2 and 3 which produced abnormally turbulent waters and high suspended sediment. At the time of collection, high, muddy water flowed well out of the normal channel, despite having dry summer conditions.

Mean Turbidity 45

Turbidity (NTU) Turbidity 15

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

White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 6. Mean turbidity of sampling sites along stream gradients of five major tributaries in summer 2016. Distance from the lake increases moving left to right on the graph.

- 43 - Phosphorus Phosphorus is a primary limiting nutrient in Otsego Lake (Harman et al. 1997). Although water bodies naturally contain a base level, an excess of the nutrient may lead to algal blooms and eutrophication. While it is a natural component of aquatic systems, phosphorus may also enter stream water as runoff from inorganic fertilizer, sewage, or residential wastewater systems. Figures 7a and 7b show mean phosphorus concentrations of water tested in the summer of 2016, with levels in Cripple Creek out of scale in comparison to the other tributaries. This was due what was believed to be the failure of a beaver dam between sites 2 and 3 during the first week of collection which lead to abnormally turbulent and high stream levels, contributing to very high phosphorus leach. The maximum phosphorus value recorded was 9,660 ug/L at Cripple Creek site 4 (inlet to Clark Pond) on 21 May 2016, the date of the dam failure, while the lowest recorded was 9 ug/L at Cripple Creek site 2 (Young Lake) on 24 June 2016. Apart from Cripple Creek, Mount Wellington Stream site 2 had notably high phosphorus concentrations throughout the summer.

Mean Total Phosphorus 200.0 180.0

160.0 140.0 120.0 100.0 80.0 60.0 Total Phosphorus (ug/L) Total 40.0 20.0 0.0 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 Distance from Otsego Lake (km)

White Creek Hayden Creek Shadow Brook Mount Wellington Figure 7a. Mean total phosphorus of sampling sites along the stream gradients of four major tributaries in summer 2016. Distance from the lake increases moving left to right on the x-axis.

- 44 - Mean Total Phosphorus 1200.0

1000.0

800.0

600.0

400.0

200.0

Total Phosphorus (ug/L) Total 0.0 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 Distance from Otsego Lake (km) Cripple Creek

Figure 7b. Mean total phosphorus of sampling sites along Cripple Creek in summer 2016. Because of the scale of phosphorus concentrations in Cripple Creek, this figure was separated from Fig. 7a. Distance from the lake increases moving left to right on the x-axis. Table 2 shows historical phosphorus concentrations at all collection sites from 2000-2016. White Creek, Hayden Creek, and Shadow Brook all had fairly average phosphorus levels compared to previous years’ data. Figure 8 shows historical phosphorus levels at each of the stream outlets. At its outlet, Mount Wellington stream phosphorus levels were higher than they have been eleven years. High phosphorus combined with abnormally low DO levels (Figure 5) suggest that an algal bloom occurred in the stream prior to the start of seasonal water collection and analysis. Because stream flow was virtually nonexistent at the outlet and much cooler than it had been in previous years, this may be due to a new beaver dam being constructed or some other blockage in the stream.

- 45 - Table 2. Comparison of total phosphorus concentrations (ug/mL) 2000-2016.

Comparison of phosphorus concentrations (µg/L), 2000-2016 Site ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 WC1 31 34 72 25 33 51 17 66 46 33 33 25 22 18 26 34 31 WC2 28 33 23 26 39 61 33 37 34 24 25 25 33 29 27 29 39 WC3 19 24 12 23 26 36 40 38 19 22 17 21 21 27 30 33 22 CC1 45 36 112 30 49 49 33 86 89 38 63 49 62 26 30 70 97 CC2 48 23 46 124 144 172 37 36 25 24 25 28 22 22 22 40 43 CC3 25 24 10 25 39 37 62 40 22 26 41 30 21 49 27 31 912 CC4 28 35 19 22 46 55 40 39 34 27 45 30 37 126 28 36 1110 CC5 42 45 51 28 46 70 37 58 59 34 41 40 51 45 31 31 69 HC1 26 25 60 21 43 33 33 48 43 35 28 53 22 25 21 46 49 HC2 20 17 14 13 23 34 57 30 27 18 24 20 52 23 22 18 25 HC3 25 28 47 26 34 39 50 35 54 24 31 24 21 27 26 33 30 HC4 20 23 17 26 29 41 22 38 27 24 31 24 20 24 31 40 30 HC5 28 27 27 22 33 43 46 41 37 22 31 27 41 32 26 24 34 HC6 24 24 21 33 28 40 40 49 32 26 26 27 25 31 25 25 40 HC7 34 26 19 30 44 54 73 40 42 27 32 28 30 40 31 31 35 HC8 32 37 54 31 51 120 89 43 71 30 37 32 62 42 39 33 40 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 35 SB3 28 36 46 24 37 40 30 35 30 25 35 24 32 36 22 18 28 SB4 48 37 27 27 62 62 22 26 39 38 26 22 42 39 77 18 24 SB5 39 54 40 34 63 85 38 45 44 37 38 31 45 92 39 32 47 MW1 38 45 36 50 83 51 23 54 33 29 45 25 26 40 22 35 29 MW2 142 192 99 136 88 214 69 65 38 57 68 46 71 234 55 62 155 * - - stream flow was too low for sample collection; no nutrient data exists for Site SB1 in 2012 and 2016.

- 46 - Mean Total Phosphorus at Stream Outlets 300

250

200

150

100

50 Total Phosphorus (ug/L) Total

0 White Creek Cripple Creek Hayden Creek Shadow Brook Mount Stream Outlet Wellington 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure 8. Mean total phosphorus concentration (µg/L) at the most downstream sites (outlets) of five tributaries in the northern watershed of Otsego Lake 1996-2016.

Nitrogen Like phosphorus, nitrogen is a limiting nutrient in algal growth. Excess nitrogen enters the surface water in a similar way, primarily as agricultural leach stemming from the use of inorganic fertilizers with little to no riparian buffer. Total nitrogen includes ammonia, nitrate + nitrite, and organic nitrogen (Figure 9). Total nitrogen remained fairly consistent in all tributaries with the exception of Mount Wellington stream, for which abnormally high and variable levels were recorded. The seasonal low value of total nitrogen was 0.14 mg/L at White Creek site 1 on 13 June 2016, while the seasonal high was 21.45 mg/L at Mount Wellington stream on 7 June 2016.

- 47 - Mean Total Nitrogen

10.0

8.0

6.0

4.0

2.0 Total Nitrogen (mg/L) Nitrogen Total 0.0 0.00 2.00 4.00 6.00 8.00 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 2016. Distance from the lake increases moving left to right on the x-axis.

In addition to total nitrogen, nitrate + nitrite was measured specifically for this report (Figure 10). Ammonia was also measured until 2010 when it was discontinued due to being consistently below the lower limit of detection (<0.02 mg/L). As with total nitrogen, Mount Wellington had higher than normal levels of nitrate + nitrite. White Creek, Cripple Creek, and Hayden Creek all had very low values with Cripple Creek sites 1 and 2 having nitrate + nitrite concentrations below the lower limits of detection with the exception of one time during the entire season.

- 48 - Mean Nitrate + Nitrite 8.0

7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 0.00 2.00 4.00 6.00 8.00 Nitrate + Nitrite Concentrations Concentrations Nitrite (mg/L) Nitrate + Distance from Otsego Lake (km) White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington

Figure 10. Mean nitrite + nitrate of sampling sites along the stream gradient of five major tributaries in summer 2016. Distance from the lake increases moving from left to right on the x- axis.

Historical nitrate + nitrite levels for each stream outlet are shown in Figure 11, while historical values for each site are shown in Table 3. Overall, levels were more or less average in Shadow Brook and in White and Hayden creeks. Levels were higher in Mount Wellington stream and lower in Cripple Creek.

- 49 - Mean Nitrite + Nitrate at Stream Outlet

3.5

2.5

1.5 Nitrate (mg/L) Nitrate 1

0.5

0 White Creek Cripple Creek Hayden Creek Shadow Brook Mount Wellington Stream Outlets 1991 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure 11. Mean nitrate+nitrite at stream outlets of 5 tributaries in the northern watershed of Otsego Lake 1991, 1998-2016.

- 50 - Table 3. Comparison of mean nitrate concentrations (mg/L) 1998- 2016. Data from 1991 is also included, but only the values for stream outlets were taken, where available.

Comparison of Mean Nitrate Concentrations (mg/L) 1991, 1998-2016 ‘91 ‘98 ‘99 ‘00 ‘01 ‘02 ‘03 ‘04 ‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 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 0.08 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 0.16 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 0.31 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 0.00 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 0.00 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 0.65 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 0.54 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 0.26 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 0.19 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 0.15 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 0.71 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 0.94 0.94 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 0.70 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 0.89 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 0.98 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 1.18 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 1.54 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 1.64 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 1.22 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 0.79 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 6.68 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 3.55 * - - stream flow was too low for sample collection; no nutrient data exists for Site SB1 in 2012 or 2016.

- 51 - CONCLUSION Aside from effects of what appeared to be the beaver dam failure on Cripple Creek and possible blockage in Mount Wellington stream, most water quality indicators were fairly constant when compared to the previous years’ data (Wells 2016) and followed characteristic site-by-site trends within each tributary. For the most part, water temperature and dissolved oxygen were slightly higher across the board this when Mount Wellington stream data is excluded. Both may be explained by the relatively warm, sunny weather of 2016 when compared with the previous year, which is more hospitable to algal growth. This is further supported by higher turbidity readings of sample sites on Weaver, Young, and Summit Lakes – relatively calm areas of the watershed with lower levels of suspended particles. Although there has been some seasonal variation year-to-year, these data are part of an overall trend of improving water quality since the implementation of BMPs in the northern watershed of Otsego Lake. Fluctuation of water quality due to natural events such as beaver dam breakages, year-to-year variation in weather, and stream blockages make it important that a study like this is continued long-term to collect aggregate water quality data.

REFERENCES Albright, M.F. 2004. Otsego Lake limnological monitoring, 2003. In 36th Ann. Rept. (2003). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Breitburg D.L., D.W. Hondorp, L.A. Davias and R.J. Diaz. 2009. Hypoxia, nitrogen, and fisheries: Integrating effects across local and global landscapes. Annual Review of Marine Science. 1:329-349. Crane, S.R., J.A. Moore, M.E. Grismer and J.R. Miner. 1983. Bacterial pollution from agricultural sources: A review. Transactions of the American Society of Agricultural Engineers. Dodds, W.K., W.W. Bouska and J.L. Eitzmann. 2008. Eutrophication of U.S. freshwaters: Analysis of potential economic damages. Environmental Science Technology. 43(1):12-9. Field, C.K., P.A. Siver and A. Lott. 1996. Estimating the effects of changing land use patterns on Connecticut lakes. Journal of Environmental Quality 25(2). 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. Heavy, K. 1995. Water quality monitoring in the Otsego Lake Watershed. In 28th Ann. Rept. (1994). Bio. Fld. Sta., SUNY College at 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.

- 52 - 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. Mehigan, K. 2013. Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2012. In 45th Ann. Rept. (2012). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Miller, C. 1997. Water quality monitoring of Otsego Lake's five major tributaries. In 30th Ann. Rept. (1996). Bio. Fld. Sta., SUNY College at Oneonta. Smith, V.H., G.D. Tilman, and J.C. Nekola. 1999. Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environmental Pollution. 100: 179-196 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. United States Environmental Protection Agency (USEPA). 2009. National water quality inventory: Report to Congress. 2004 Reporting cycle. USEPA Office of Water: Washington, DC, USA. United States Environmental Protection Agency (USEPA). 1996. Nonpoint Source Pollution: The Nation’s Largest Water Quality Problem. USEPA Office of Water: Washington, DC, USA Wells, B. 2016. Water quality monitoring if five major tributaries in the Otsego Lake watershed, summer 2015. In 48th Ann. Rept. (2015). Bio. Fld. Sta., SUNY College at Oneonta. Zaengle, O. 2012. Water quality monitoring of five major tributaries in the Otsego Lake watershed, summer 2011. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

- 53 - SUSQUEHANNA RIVER MONITORING Upper Susquehanna River water quality monitoring, 2016

Luke Courter1

INTRODUCTION (from Shaw 2016) 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 the headwaters of its main branch in Cooperstown, New York and the western branch headwaters in western Pennsylvania, to Havre De Grace, Maryland, where it pours into the Chesapeake Bay (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). 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.

METHODS Nine sites along the upper Susquehanna River were monitored over the summer of 2016 (Table 1, Figure 1). Starting on 22 June and ending 12 August, temperature, specific conductivity, pH and dissolved oxygen were monitored using a YSI® 6820 V2-2 multi-probe. Water samples were taken from each of the nine sites in 125ml Nalgene sample bottles and put on ice until returned to the lab, where they were preserved with sulfuric acid to a pH<1.0. The samples were processed on a Lachat® QuickChem FIA Water Analyzer and tested for the levels of phosphorus, total nitrogen and nitrate.

1 F.H.V. Mecklenburg Conservation Fellow. Current affiliation: Milford Central School.

- 54 - 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.

- 55 -

Figure 1. Map of the Upper Susquehanna sites tested, summer 2016.

- 56 - RESULTS

Temperature Temperature across the sites over 2016 is given in Figure 2. Figure 3 compares temperatures of 2016 with those of 2004-2015.

21 2016

20 Temperature Temperature (ºC) 19

18 0 2000 4000 6000 8000 10000 Distance from source (m) Figure 2 Temperature (Co) of upper Susquehanna River sites, 2016.

2015 25 2014 24 2013

23 2012 2011 22 2010 21 2009 2008 Temperature Temperature (ºC) 20 2007 19 2006 18 2005 0 2000 4000 6000 8000 10000 2004 Distance from source (m)

Figure 3. Temperature of upper Susquehanna River sites 2005-2015.

- 57 - Conductivity Specific conductivity across the sites over 2016 is given in Figure 4. Figure 5 compares values of 2016 with those of 2004-2015. Specific conductivity is water’s ability to transmit electrical current (Wetzel 2001). 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. 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 1992; Shaw 2015)

0.4

0.35

0.3 2016

0.25

Specific Conductivity(mmho/cm) 0.2 0 2000 4000 6000 8000 10000 Distance from source (m) Figure 4. Specific conductance measurements of upper Susquehanna River sites, 2016.

0.4 2016

2015 0.35 2014 2013 2012 0.3 2011 2010

0.25 2009 2008

Specific Conductivity(mmhb/cm) 2007 0.2 2006 0 2000 4000 6000 8000 10000

Figure 5. Specific conductance measurements of upper Susquehanna River sites 2007-2016.

- 58 - pH pH is the measure of acidic levels in the water (Wetzel 2001). The level is determined by H+ ions, with higher concentrations corresponding to lower pH values. The less concentration of H+ ions indicate a basic solution, where a pH of 7 is neutral (Wetzel 2001). The pH is largely determined by the geology of the river where 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. (Shaw 2015). Specific conductivity across the sites over 2016 is given in Figure 6. Figure 7 compares values of 2016 with those of 2004-2015.

8.5

8.25

8 pH 2016

7.75

7.5 0 2000 4000 6000 8000 10000 Distance from source (m)

Figure 7. pH measurements of upper Susquehanna River sites, 2016.

- 59 - 8.5 2015 2014 8.25 2013 2012

2011 8 pH 2010 2009

7.75 2008 2007 2006 7.5 2005 0 2000 4000 6000 8000 10000 2004 Distance from source (m)

Figure 8. pH measurements of upper Susquehanna River sites 2004-2015.

Dissolved Oxygen Dissolved oxygen is the measure of oxygen levels in the water and directly affects aquatic animals, and plants. The amount is determined by temperature, water flow and demand. The levels are higher since the previous study in 2015. Dissolved oxygen across the sites over 2016 is given in Figure 9. Figure 10 compares values of 2016 with those of 2004-2015.

2016 7

Dissolved Oxygen (mg/l) 6

5 0 2000 4000 6000 8000 10000 Distance from source (m)

Figure 9. Dissolved oxygen measurements of upper Susquehanna River sites, 2016.

- 60 - 10 2015 2014

9 2013 2012 8 2011 2010 7 2009 2008 Dissolved Oxygen (mg/l) 6 2007 2006 5 2005 0 2000 4000 6000 8000 10000 2004 Distance from source (m)

Figure 10. Dissolved oxygen measurements of upper Susquehanna River sites 2004-2015.

Nutrients 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 2001). Phosphorous is a necessary nutrient for algal growth, though too much phosphorous in fresh water ecosystems can have disastrous effects on the biota of the stream, as it promotes excess algal growth. Excess phosphorous can come from sewage effluent, agricultural and urban runoff (Shaw et al. 2015). Total phosphorus across the sites over 2016 is given in Figure 11. Figure 12 compares values of 2016 with those of 2004-2015. Total nitrogen across the sites over 2016 is given in Figure 13. Figure 14 compares values of 2016 with those of 2004-2015. Nitrate nitrogen across the sites over 2016 is given in Figure 11. Figure 12 compares values of 2016 with those of 2004-2015.

- 61 - 250

200

150

100 2016

Total Phosphorus (ug/l) 50

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

Figure 11. Total phosphorus measurements of upper Susquehanna River sites, 2016.

250

2015 200 2014

2013 2012 150 2011 2010 100 2009 2008 Total Phosphorus (ug/l) 50 2007 2006

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

Figure 12. Total phosphorus measurements of upper Susquehanna River sites 2004-2015.

- 62 - 2

1.5

1 2016

Total Nitrogen(mg/l) 0.5

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

Figure 13. Total nitrogen measurements of upper Susquehanna River sites, 2016.

2 2015 2014 2013

1.5 2012 2011 2010 1 2009 2008

Total Nitrogen(mg/l) 0.5 2007 2006 2005 0 2016 0 2000 4000 6000 8000 10000 Distance from source (m) Figure 14. Total nitrogen measurements of upper Susquehanna River sites 2004-2015.

- 63 - 1

0.8

0.6

0.4 2016 Nitrate + Nitrtrite(mg/l) 0.2

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

Figure 15. Nitrate nitrogen measurements of upper Susquehanna River sites, 2016.

2016 0.8 2015

2014 2013 0.6 2012 2011 2010 0.4 2009

Nitrate + Nitrite(mg/l) 2008 0.2 2007 2006 2005 0 2004 0 2000 4000 6000 8000 10000 Distance from source (m)

Figure 16. Nitrate nitrogen measurements of upper Susquehanna River sites 2004-2016.

- 64 - REFERENCES

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.

Shaw, B. 2016. Upper Susquehanna River water quality monitoring, summer 2015. In 48th Ann. Rept. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

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

Wetzel, R.G. 2001. Limnology, Lake and River Ecosystems, 3rd edition. Academic Press. San Diego, California.

- 65 - Evaluation of chlorophyll a extraction techniques

Marissa Mehlrose1 and Kiyoko Yokota2

INTRODUCTION

Chlorophyll a is a pigment essential for electron transport during photosynthesis, making it vital to the success of all photosynthetic organisms. Because it is found in all organisms that photosynthesize, and its concentration is relative to the biomass of the organism, it is commonly used as an indicator of the planktonic algal biomass in water samples (Wasmund et al. 2006). The process of extracting the chlorophyll a from the sample appears to be a key step in chlorophyll a analysis; in order to get the accurate and most consistent results, it is important to determine which method of extraction is the best (Simon and Helliwell 1998).

Typically, chlorophyll a determinations involve passing raw water through glass fiber microfilters to remove algae from the samples, then performing methods to extract chlorophyll a from these filters through chemical and physical means (APHA 1989). Despite the extraction being such an important step, there is still conflicting evidence on which methods are most effective (Simon and Helliwell 1998; Nelson 1981). Some suggest that disrupting the cell wall mechanically and creating a homogeneous mixture is the best way to ensure all chlorophyll a is extracted (Simon and Helliwell 1998), while others suggest that leaving the cells intact in solution is just as efficient (Saratory and Grobbelaar 1984).

The ability of certain solvents to facilitate extraction has also been debated, as some report that acetone is not effective on green algae (chlorophytes) and blue-green algae (cyanophytes) (Arvola 1981; Shoaf and Lium 1976), though it has been commonly used (APHA 1989). It has also been suggested that ethanol is a better solvent choice because it is safer to work with, and that it works well with all phyla of algae (Arvola 1981). Filter type could also influence reported chlorophyll a concentrations, as filters with smaller pores would be expected to collect smaller-celled (nano- and pico-) phytoplankton. Additionally, chlorophyll a extraction could be a function of the amount of time that the processed filter is subjected to chemical extraction by the solvent.

Historically, chlorophyll a processing at the BFS lab has involved passing water through a GF/A glass microfiber filter under low pressure vacuum. Filters were cut into pieces, placed in a grinding tube to which 90% buffered acetone had been added, and ground into a slurry with a

1 Department of Marine Biology and Environmental Science, University of New Haven. 2 Assistant Professor of Biology, SUNY Oneonta.

- 66 - grinding pestle. After approximately two hours of incubation followed by centrifugation, chlorophyll a in the supernatant was determined fluorometrically according to Arar and Collins (1997). While providing reasonably reproducible results, the process was time consuming and not proven to be optimal.

This experiment aimed to evaluate the combinations of solvent type, glass filter pore size, and the inclusion of physical disruption (i.e., use of a tissue grinder) on the effectiveness of chlorophyll a extraction from surface waters. After this work commenced, preliminary results indicated that measuring chlorophyll a immediately after processing the filters resulted in underestimation of chlorophyll a concentrations. Therefore, the additional variable included in the matrix was whether chlorophyll a was determined immediately after extraction or whether it was held to “steep” (soak) in the solvent for three hours prior to determination.

METHODS

Samples were analyzed from two water bodies in Otsego County, New York. Otsego Lake (Figure 1) is a deep oligotrophic lake of glacial origin (Waterfield and Albright 2016). Conversely, Moe Pond (Figure 2) is a shallow, eutrophic pond (Busby and Casscles 2016). Sampling water from two such distinct sources was intended to represent samples having distinct and variable algal communities, including differing taxa and cell size distributions.

In order to determine which method of chlorophyll a extraction worked best, different combinations of filter paper, solvent, and sample extraction methods were used to determine which combination produced the highest and most consistent concentrations, thus providing the best results. Initial work compared a 3 hour soaking (or “steeping”) step during the extraction process vs. immediate sample processing following sample filtration and placement of the filter into the solvent. The combinations of each of these four variables yielded sixteen distinct iterations, which are summarized in Table 1. These sixteen methods were evaluated on both Otsego Lake and Moe Pond samples, with each evaluation having five trials.

Table 1. Thirty-two (= 25) combinations of the five major factors in chlorophyll a extraction, each with two levels, that were evaluated. N = 5 for each of 32 combinations, resulting in 25  5 = 160 samples total.

Solvent type Grinding Filter Type Steeping Sample source Buffered acetone Ground GF/A No steeping Otsego Lake (oligotrophic) Ethanol Not ground GF/F 3 hour steeping Moe pond (eutrophic)

- 67 - Collection

Using a 20-liter carboy, 12 liters of water were collected from Rat Cove in Otsego Lake (Figure 1) on 7 July 2016 and from Moe Pond (Figure 2) on 20 July 2016. These samples were transported in low light conditions, and all work was performed in low light to minimize degradation of chlorophyll a (APHA 1989). Sub-samples of the water from the lake and pond were preserved with approximately 1 mL of Lugol’s iodine for later microscopic evaluation of algal communities.

Sample site

Figure 1. The location of sampling in Otsego Lake in Cooperstown, NY (from Waterfield and Albright 2016 ).

- 68 -

Figure 2. The location of Moe Pond in Cooperstown, NY (from Busby and Casscles 2015).

Processing Samples were filtered immediately to prevent degradation of chlorophyll a. For Otsego Lake samples, a volume of 250 mL was filtered, and for Moe Pond samples, 100 mL was filtered. This was based on the chlorophyll a concentration ranges anticipated. Two types of 4.7 cm diameter Whatman® glass microfiber filters were compared: GF/A filter papers (having a pore size of 1.6 μm) and GF/F filter paper (having a pore size of 0.7 μm). All samples were filtered using a low pressure (15 psi) vacuum pump. Once filtration was complete, filters were folded in half with the exposed side inside, gently pressed onto paper towel to remove excess water, and the edges not contacted by the sample were trimmed. Each filter was stored in a petri dish wrapped in aluminum foil and stored at -4 °C for 21- 24 hours.

A total of 160 filters were preserved, 80 for each Otsego Lake and Moe Pond samples (Table 1). Half of the filters used were GF/A filters and half were GF/F filters (see above). In all cases, using fine scissors, the filters were cut into small (~ 3 mm x 3 mm) pieces. Half of each of these filters were extracted using buffered acetone, the other half using ethanol. Half of each of these treatments were ground to a slurry using a Teflon pestle chucked in an electric drill and a grinding tube (with the pestle and tubes being rinsed well between samples to avoid cross- contamination), the other half were not ground. Both the cut up filter pieces and the ground slurries were transferred to 15 ml centrifuge tubes and the volume of solvent was taken up to 10 ml. Half of each of these treatments were allowed to steep for three hours under refrigeration, the other half were processed immediately.

At the appropriate times (either immediately or after three hours), the samples were centrifuged at 10,000 xG for ten minutes and chlorophyll a concentrations were determined on the supernatant using a Turner Designs TD 700® fluorometer using the methods of Arar and Collins (1997).

- 69 - Algal Analysis Ten milliliters of algal samples preserved with Lugol’s iodine were poured into an Utermöhl settling chamber and refrigerated for a minimum of 24 hours to allow the phytoplankton cells to sink to the bottom of the chamber. The algal cells were identified and enumerated using an inverted microscope under a 40 x objective using taxomic keys by Bellinger and Sigee (2010) and Prescott (1964).

RESULTS

Figure 3 compares chlorophyll a concentrations from samples collected from the oligotrophic Otsego Lake, processed using different filter types, extraction solvents, filter grinding vs no grinding, and steeping vs no steeping. When compared to the same filter and extraction method, all filters with an extraction time of 3 hours showed higher chlorophyll a concentrations than those that were centrifuged immediately after filtering (no steep).

7.5

6.5 g/L)

μ 5.5 4.5 GF/A 23 hr. steep 3.5 GF/A 1 no steep 2.5 GF/F 2 3 hr. steep 1.5 GF/F 1 no steep 0.5

-0.5 Acetone Ground Acetone Not Ground Ethanol Ground Ethanol Not Ground Chlorophyll a concentration (

Method

Figure 3. Comparison of Otsego Lake chlorophyll a values when the filters were allowed to steep for three hours compared to values being obtained immediately after filtering. GF/A “no steep” and GF/F “no steep” were processed immediately (without a steeping period), while GF/A “3 hr. steep” and GF/F “3 hr. steep” had an extraction time of 3 hours before processing.

Otsego Lake chlorophyll a concentrations derived from the different filter types were similar when those filters were ground as part of the extraction process. However, the GF/F filters provided higher chlorophyll a concentrations when the filters were not ground. Not grinding the samples resulted in more consistent chlorophyll a concentrations, and when the

- 70 - filters were not ground, acetone was somewhat more effective as an extractant than was ethanol (Figure 4). 7.5

6.5 g/L)

μ 5.5

4.5

3.5 GF/A 2.5 GF/F 1.5

0.5

-0.5

Chlorophyll a concentration ( Acetone Ground Acetone Not Ground Ethanol Ground Ethanol Not Ground

Method Figure 4. Comparison of methods used to obtain chlorophyll a concentrations on two different filter types and extraction methods using water from Otsego Lake, NY.

The eutrophic Moe Pond samples also indicated that GF/F filters were more effective than GF/A filters, and not grinding the filters resulted in the highest chlorophyll a concentration (Figure 5). Acetone resulted in somewhat higher chlorophyll a concentrations than ethanol in the Moe Pond water samples.

35 g/L) μ 30 25 20 GF/A 15 GF/F 10 5

Chlorophyll aChlorophyll concentration ( 0 Acetone Ground Acetone Not Ground Ethanol Ground Ethanol Not Ground Method

Figure 5. Comparison of methods used to obtain chlorophyll a concentrations on two different filter types and extraction methods using water from Moe Pond, NY.

- 71 - The relative abundance of algal genera was quantified in order to assess relative abundance of green algae and cyanobacteria in the oligotrophic Otsego Lake and the eutrophic Moe Pond, to evaluate the effectiveness of acetone and ethanol as solvents for extracting chlorophyll a from two different types of phytoplankton samples. In Otsego Lake, the majority of the identifiable taxa were diatoms (bacillariophytes), and only a small percentage of the algae were cyanobacteria (cyanophytes) or green algae (chlorophytes) (Figure 6).

450 411 400 350

300 240 250 200 150 Number Number Cells of 75 82 100 72 60 32 50 16 30 27 16 5 8 1 0

Genera

Figure 6. The abundance of genera found in a 10 mL sample of Otsego Lake water. Striped bars indicate green algae (chlorophytes), a dotted bar indicates a cyanobacterium (cyanophyte).

There was much less algal diversity in the Moe Pond samples than in the Otsego Lake samples, with Rhodomonas (a cryptophyte) being the most abundant, and Anabaena (a cyanophyte) and Monoraphidium (a chlorophyte) being the second and third most abundant (Figure 7).

- 72 - 700 686

600

500

400

300 216 210 Abundance 200

100 1 2 17 22 9 0

Genera

Figure 7. The abundance of genera present found in 10 mL of preserved Moe Pond water. Striped bars (incl. Mougiotia) indicate green algae (chlorophytes), dotted bars indicate cyanobacteria (cyanophytes).

DISCUSSION

In both the eutrophic Moe Pond and the oligotrophic Otsego Lake water samples, the highest chlorophyll a concentrations (and, presumably, the most effective method of extraction) came from the samples passed through GF/F filters, used buffered acetone as a solvent, were not ground, and were allowed to steep for three hours in the solvent before being analyzed fluorometrically. Replicates of samples that were not ground had smaller within-group variance and therefore better reproducibility. The process of grinding seemed to introduce several additional uncontrolled variables such as pressure and speed of pestle, pulsing vs. continuous grinding, etc. that increased error, which would account for the larger variance, especially when a large number of individuals perform the same protocol. Also when grinding, it is possible that incomplete or inconsistent rinsing of the pestle and grinding tube could lead to cross- contamination, or sample loss by spilling or evaporation (Wasmund et al. 2006) could cause concentration and overestimation of chlorophyll a. Frictional heat generated by the grinding process could cause chlorophyll a degradation as well as evaporation of solvents.

Implementing an extraction steep of 3 hours significantly increased the effectiveness of chlorophyll a extraction when evaluated on Otsego Lake water samples. By allowing the samples to steep for that duration, maximum extraction efficiency of the ethanol was achieved

- 73 - (Arvola 1981). Evidence has suggested that acetone is an insufficient solvent for chlorophyll a extraction from chlorophytes and cyanophytes (Shoaf and Lium 1976; Arvola 1981; Saratory and Grobbelaar 1984; Wasmund et al. 2006). However, when allowed to steep for three hours, data presented here suggest that for the algal taxa present in both Otsego Lake and Moe Pond (14.3% of Otsego Lake and 37.5% of Moe Pond samples and samples were composed of green and blue- green algae, respectively, by cell counts), acetone was a more effective solvent than was ethanol.

CONCLUSIONS

When sampling water bodies as diverse as the large, deep and oligotrophic Otsego Lake to small, shallow and eutrophic Moe Pond, the most effective chlorophyll a extraction was achieved by using GF/F filters, buffered acetone, not grinding the filters and allowing for an extraction time of at least 3 hours. This combination consistently yielded higher chlorophyll a concentrations and also decreased variability between sample replicates compared to other combinations. Not having to expend the time required to grind the filters reduces the laboratory processing time and effort considerably. One factor that remains untested is the duration of filter storage in a freezer at - 20 °C between sample filtration and further processing; it was about 24 hours for filters in this study. The effect of varying this time, or of eliminating it, was not evaluated.

REFERENCES

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

Arar, E.J. and G.B. 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.

Arvola, L. 1981. Spectrophotometric determination of chlorophyll a and phaeopigments in ethanol extractions. Annales Botanici Fennici 18 (3): 221-227.

Bellinger, E.G. and Sigee D.C. 2010. Freshwater algae: Identification and use as bioindicators. West Sussex (UK): John Wiley & Sons, Ltd.

Busby, D. and Casscles B. 2016. Continued monitoring of the Moe Pond ecosystem in

- 74 - conjunction with biomanipulation. In 48th Annual Report (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Nelson, D.J. 1981. Improved chlorophyll extraction method. Science 132 (3423): 351.

Prescott, G.W. 1964. How to know the freshwater algae. Dubuque (IA): WM. C. Brown Company.

Saratory, D. P. and J.U. Grobbelaar. 1984. Extraction of chlorophyll a from freshwater phytoplankton for spectrophotometric analysis. Hydrobiologia 114: 177-187.

Shoaf, W.T. and B.W. Lium. 1976. Improved extraction of chlorophyll a and b from algae using dimethyl sulfoxide. Limnology and Oceanography 21(6): 926-928.

Simon, D. and S. Helliwell. 1998. Extraction and quantification of chlorophyll a from freshwater green algae. Wat. Res. 32 (7): 2220-2223.

Wasmund N., L. Topp and D. Schories. 2006. Optimising the storage and extraction of chlorophyll samples. Oceanologia 48 (1): 125-144.

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.

- 75 - Efficacy of a potassium peroxymonosulfate-based disinfectant (VirkonTM) against zebra mussel (Dreissena polymorpha) adults and veligers

Joseph Perry,1 Matthew Albright,2 and Daniel Stich3

INTRODUCTION Zebra mussels pose a major problem for the waterways into which they are introduced. In the United States alone, the economic impact of the species is estimated to be up to $1 billion dollars per year (Pimentel et al. 2005) and includes costs stemming from zebra mussel biofouling of intake pipes and materials. Economic impact is wide-ranging, affecting many economic sectors, including power, water, agriculture, fishing, and recreation (Connelly et al. 2007; Elliott et al. 2005; MacIssac 1996). In addition to economic impacts, zebra mussels are considered to be ecological engineers, producing changes in ecosystem structure upon introduction. Increased water clarity has been consistently reported due to their feeding on phytoplankton and has been implicated in contributing to cycles of mass macrophyte growth and die-offs. Zebra mussels have also been shown to compete with other planktivores, including smaller zooplankton (MacIssac et al. 1995), indigenous freshwater mussels (Ricardi et al. 1998), and others. These factors may be contributing to the benthification of freshwater food webs by redirecting energy and resources from pelagic to benthic zones. The life history and lifecycle characteristics of the zebra mussel augment the species’ ability to spread to new water bodies. Aquatic recreation, considered a serious vector for zebra mussel proliferation (Johnson et al., 1996), peaks in summer months when zebra mussel reproduction is most active. Zebra mussels have incredible fecundity rates and are known to participate in mass spawning events, producing as many as 500,000 veliger larvae per square meter (Ludyanskiy et al. 1993). Adults may be transported on hulls and engines of watercraft, while immature veliger larvae are microscopic and hardy with the potential to live in live wells, bilge tanks, sinks, showers, and even engine cooling systems during overland transport of small- craft boats (Dalton and Cottrell 2013). Veligers also have the potential to survive in damp equipment and gear, including fishing nets, wet suits, rope, and clothing (Timar and Phaneuf 2009). Many disinfection treatments exist for craft and equipment moving between water bodies, each with its own unique costs and benefits. One broad-spectrum disinfectant, Virkon Aquatic ™ (referred to hereafter as Virkon), has shown to be effective in destroying individuals of many aquatic invasive and nuisance species, including the vase tunicate (Ciona intestinalis) (Paetzold and Davidson 2011), red-rimmed melania (Melanoides tuberculate) (Mitchell et al. 2011), freshwater Asian clam (Corbicula fluminea) (Barbour, et al., 2013), quagga mussel (Dreissena bugensis) (Moffitt et al. 2015), and others. Virkon has a long history and broad application as a disinfectant in the medical and veterinary fields and was originally developed as an effective and relatively safe treatment for microorganisms. It is composed of a strong oxidizing agent (potassium peroxymonosulfate triple salt), organic acids (malic and sulphamic acid), an inorganic buffer (sodium hexameta

- 76 - phosphate), a surfactant (sodium toluenesulphonate), and sodium chloride (Antec 1994; Antec 2015). While effective on their own, the components of Virkon also interacts in a regenerative cyclic reaction involving the oxidation of sodium chloride to form hypochlorous acid (Figure 1).

Figure 1. Schematic describing the cyclical reaction of potassium peroxymonosulfate, sodium chloride, and sulphamic acid to produce hypochlorous acid. Potassium peroxymonosulfate liberates chlorine which joins with sulphamic acid to create an intermediate. The intermediate is hydrolyzed to form hypochlorous acid, which decomposes into oxygen gas, hydroxide, and chloride, which is free to once more be acted upon by the oxidizer (Antec 1994).

Virkon has been shown to be relatively safe for mammals and fish, yielding LD50 values of 2,200 mg/kg in rabbits on dermal exposure and 4,123 mg/kg in rats on oral administration. A similar formulation of the product also yielded a 96h LC50 value of 53mg/L for rainbow trout (Antec 2015). In addition, the environmental impact of Virkon is considered by the distributor to be minimal (DuPont undated). Aside from recreational boating as a vector for zebra mussel dispersal, the likelihood of introductions by lake researchers and managers themselves seems plausible. Particularly, porous or absorbent materials which remain moist for some time after use (such as anchor and mooring lines and fishing nets) can harbor living mussels and veligers for several weeks (Timar and Phaneuf 2009). Establishing an effective protocol to disinfect these types of material is an important component of a broader disinfection procedure. Since Virkon has proven to be an effective and multifaceted disinfectant for a broad spectrum of invasive species, we hypothesize that it will be effective for treating materials that potentially move between zebra mussel infested water bodies. To evaluate this, zebra mussel veligers and adults were collected from Otsego Lake and were exposed to Virkon solutions of varying concentrations and periods of time. Resulting mortality was measured.

- 77 - MATERIALS AND METHODS Phase 1: Efficacy of Virkon against zebra mussel veliger larvae Veliger collection and water quality assessment: Veligers were collected before each of five replicate trials using a series of horizontal plankton tows on the surface of Otsego Lake at one of three sites chosen for their relative shallowness and presence of a rocky lakebed (Figure 2): Site A: South-central Otsego Lake N 42° 42' 35.4'' W 74° 55' 14.1'' Site B: Blackbird Bay N 42° 42' 26.2'' W 74° 55' 32.1'' Site C: North-central Otsego Lake: N 42° 47' 33.7'' W 74° 53' 43.5''

Figure 2. Veligers used in this study were collected from Otsego Lake at three different sites: South-central (A), Blackbird Bay (B), and North-central (C). Horizontal tows were performed from a boat moving at a speed of approximately 3 to 5 knots for about 15 minutes per collection. Speed and path were variable in order to sample multiple levels of the water column. A 63μm mesh plankton net and a 61μm mesh collection cup (Wildco®) were used for collection before being emptied into an opaque 1-L plastic bottle. Veliger density was ascertained immediately after returning to the lab (see below).

- 78 - After veliger collection, water quality of each collection site was assessed using a multiparameter probe (6820 V2-2 Multi-Parameter Water Quality Sonde, YSI Inc.) at a depth of 1 meter. Treatment preparation: Treatment solutions were prepared at least one week in advance of each replicate trial by dissolving the appropriate mass of solid Virkon™ Aquatic (DuPont, Lot# 1410174000) in 63μm- filtered lake water to yield 3.00, 2.00, 1.00, 0.500, and 0.250, and 0% treatments. Virkon solutions were stored in the dark at ambient laboratory temperature in 500 mL covered plastic Nalgene bottles. Before each treatment, 100mL aliquots of each solution were poured into 250mL borosilicate glass beakers. Veligers from each sample were identified morphologically and quantified using a 1mL Sedgwick-Rafter cell at 100x magnification under cross-polarized light (Johnson 1995). To maintain a level of consistency between experimental runs, sample volumes were adjusted to attain veliger concentrations between 150-300 individuals per milliliter. Dilutions were made with filtered lake water while concentration was achieved by gravity filtration through a 63μm mesh cup. Once brought to the appropriate concentration, 4mL of veliger-concentrated lake water sample were dispensed into each of thirty 63μm mesh-bottomed veliger holding devices (VHDs) constructed by Davis et al. (2015) which were partially submerged in a 20 x 30 x 5cm porcelain holding pan containing 1400mL of filtered lake water. Individual holding devices consisted of inverted 15mL plastic Corning® centrifuge tubes with a quarter inch (6.35 mm) hole drilled into each screw cap, with the holes being covered with 63μm Nitex mesh so that water and small particles could flow through while retaining veligers. Conical ends of each tube were cut off, leaving an open top. VHDs were stabilized with a double layer of 3 x 3.5cm mesh steel chicken wire fastened to the top of the holding pan. When moving veligers to and from the VHDs, pipette tips were cut to increase their internal diameter to approximately 5mm to decrease the likelihood of damage to the veliger’s fragile valves. After dispensing, veligers were allowed to settle in the lake water for a minimum of 30 minutes before treatment (Figure 3).

Figure 3. Mesh-bottomed VHDs allowed veligers to be moved in and out of treatment solutions with minimal handling. Before and after treatment, VHDs were placed in a porcelain pan containing 1400mL of filtered lake water.

- 79 - Treatment: VHDs were taken out of the holding pan, rinsed with lake water, and were submerged and briefly swirled in the appropriate treatment solution. VHDs were held in their respective treatments for 5, 15, 30, 45, or 60 minutes. At the end of the treatment, VHDs were rinsed three times and the outside patted dry before being partially submerged in a tank containing 20 L of filtered lake water, drawing water into the VHD multiple times to thoroughly rinse and dilute any residual treatment that may have remained. VHDs were patted dry and rinsed once more before being placed back into the holding pan for 1-2 hours before mortality assessment. Treatment start times were staggered in five minute intervals among, and ten minutes between, each time class to compensate for handling time and mortality assessments. Mortality assessment: After 1-2 hours of holding in ambient lake water, veligers were assessed for mortality using a 1mL Sedgwick-Rafter cell at 100x magnification under cross-polarized light. A clean pipette widened to approximately 5mm internal diameter was used to transfer veligers to the cell from each VHD. During each assessment, a range of field depths was used under the microscope to observe individual veligers in full. Veligers exhibiting obvious mechanical damage (ie. cilia and/or organs showing movement with cleanly fractured valves) or those which could not be enumerated (i.e. erratic valve fragments) were ignored. Mortality was considered in cases where internal organs and cilia were still, where internal organs were spilling out of the valves, or if the veliger was observed with empty valves altogether. Veligers with valves so darkened by oxidation that no internal structures could be observed were also considered dead. Barring one exception, mortality assessments were conducted for each treatment until at least 30 veligers were observed. To maximize significance of the data analysis, some assessments were continued well past 30 individuals provided that the assessment had not run past 2 hours post-holding and that recovery from the holding device had not appreciably diminished.

Phase 2: Efficacy of Virkon against adult zebra mussels. Mussel collection and setup Adult zebra mussels were harvested from rocks collected from the source of the Susquehanna River (N 42° 41’ 58.9” W 74° 55’ 13.2”) in water 1 – 2 meters deep. Mussels were removed from rocks with a paint scraper (Davis et al. 2015) and transported to the lab for placement into mesh bags (Doc Foster CE-22541) in sets of eleven live mussels each. A target range of 15-30 mm valve length was sought during selection and bagging in order to exclude mussels in the juvenile life stage. Bags were suspended by dowels in one of two 50-L aquaria

- 80 - with slow, constant flows of aerated lake water for at least 48 hours to acclimate mussels to ambient laboratory conditions. After the acclamation period, fifteen 20-L aquarium tanks were filled with filtered lake water and dosed with Virkon to yield three replicates of the following concentrations: 1.00, 0.50, 0.25, 0.10 % with three negative control tanks filled only with filtered lake water. Each tank was lightly aerated throughout the experiment with compressed air. Mussels in each bag were assessed for mortality before the experiment: Adult mussels with tightly closed valves were considered to be alive while those with gaping valves and no response to stimulus with a blunt probe were considered dead. Dead mussels were removed and replaced to a total of ten mussels per bag. If no dead mussels were present, one mussel was removed at random. Treatment Ten bags of mussels were suspended by dowels in each 20-L aquarium. One bag was removed from each tank at ten predetermined time points (5 and 30 minutes; 1, 2, 4, 6, 8, 12, 24, and 72 hours) before rinsing in a 500-L aquaculture tank filled with lake water to remove any residual Virkon. Bags were then moved to one of two 50-L aquaria with slow, constant flows of aerated lake water for 48-72 hours post-treatment. This recovery period was intended to allow those mussels that might have appeared dead to show signs of recovery, as mussels treated with disinfectants sometimes falsely appear dead immediately after chemical exposure (Pucherelli et al. 2014). Mortality and measurement Mussels were individually evaluated for mortality after holding in post-treatment aquaria according to the criteria listed above. Mussel lengths were obtained using a digital caliper and recorded along with mortality. Water quality assessment A calibrated multiparameter water quality sonde (YSI Incorporated, Model Number: 6820V2-M) was used to monitor physical and chemical parameters of water quality in each experimental, holding, and recovery tank. Data on conductivity, pH, dissolved oxygen, and temperature were obtained at 8:00 each morning. Statistical analysis Binomial logistic regression models were used to analyze the effects of Virkon concentration and exposure time on the mortality of zebra mussels. Mortality was analyzed using separate models for veligers and adult mussels. A Bayesian hierarchical approach was used to model variation in mortality (p) due to the interactive effect of Virkon concentration and exposure time (TIME) for each trial (i). To incorporate potentially different responses to exposure time between the different doses, we modeled dose as a random effect on the slope

(βtime, j) of the linearized relationship between mortality and time:

β β logit pi, j = 0+ time, j∙ TIMEi � �

- 81 - The approach assumed that the number of dead mussels in each ith trial (Di) was drawn from a binomial distribution defined by the probability of mortality in each jth Virkon concentration (pi,j) and the number of zebra mussels in each trial (Ni): B Di ~ inomial(pi,j, Ni) We used uninformative prior distributions for model parameters to allow data to guide conclusions about the process of interest. We assumed a shared intercept (β0) among all doses because all trials started at time zero, with zero mortalities. We used a diffuse normal prior distribution with a mean of zero and a variance of 10 on the intercept. β 0 ~ Normal(0, 10) We hypothesized that increased exposure time would have a positive effect on the number of dead zebra mussels in each trial across all doses, but that the effect of exposure time

(βtime, j) would vary between doses. In order to incorporate variability in the intensity of this effect (i.e. shape of the dose-response curve), we assumed that the effect of exposure time was drawn from a global population of possible effects represented by a normal distribution with hyperparameters µ and σ2.

2 time, j ~ Normal(μ, σ )

The mean of the global distributionβ for βtime, µ, was assigned a diffuse normal prior with a mean of zero and a variance of ten, and we used a uniform prior distribution on σ2 that ranged from zero to ten. This approach allowed us to share information across all trials to estimate hyperparameters for the global distribution of βtime to improve parameter estimation while allowing the effect of time to vary between Virkon concentrations. We used Markov chain Monte Carlo (MCMC) methods to estimate model parameters in JAGS using the ‘R2jags’ package (Su & Yajima, 2015) in R (R Development Core Team, 2017). We used a burn-in of 3,000 runs, and simulated an additional 30,000 samples from each posterior distribution, saving every 30th sample to reduce autocorrelation between samples and increase the number of independent samples from the posterior distribution (Krushke 2010). We ran a total of three Markov chains for each parameter, resulting in a total of 3,000 samples from which to construct posterior distributions. We assessed convergence among the three chains for each parameter using the Gelman-Rubin convergence diagnostic (Gelman & Rubin, 1992), and visually inspected plots of Markov chains to ensure adequate mixing.

RESULTS AND DISCUSSION Phase 1: Efficacy of Virkon against zebra mussel veligers Although water quality data collected at veliger sampling locations in the lake varied slightly over the course of the experiment, parameters measured were within the range suitable for veliger growth and survival as described by Baker et al. (1993). Mean temperature at collection was found to be 20.93 ºC, mean pH was 8.15, mean conductivity was 0.18 mS/cm, and mean dissolved oxygen was 9.44 mg/mL.

- 82 - Veligers exhibited 100% mortality when exposed to 3% and 2% Virkon solutions for 15 minutes or longer, or 1% Virkon solution for 30 minutes or longer. While solutions containing 0.5% Virkon or less did hinder veliger survivorship significantly, no solution in this range resulted in 100% mortality. After 30 minutes, each treatment yielded 70% mortality or higher (Figure 4). A total of 9,343 veligers were assessed over five replicate trials, six concentrations, and six time points (Figure 4). The average number of veligers assessed in each time point, concentration, and replicate was 260 individuals.

3.00% 2.00% 1.00%

0.50% 0.25% 0.00%

Figure 4. Individual posterior predictive dose-response curves for veligers exposed to Virkon. Outer lines indicate the 95% credible interval while percentages indicate dose.

When veligers were observed after exposure, evidence of oxidation was scalable with regard to treatment time and concentration (Figure 5). Veligers exposed to low concentrations of Virkon for shorter periods of time had little if any darkening of their valves, but a larger number were observed to be stationary or had odd, jumpy patterns of movement when compared to control individuals in the same time cohort.

- 83 - fication fication a 3%a (m/m) similarly sized veliger a in veliger sized similarly stage veligers over time. Veligers were exposed to - -polarized light source. To minimize the effect of heat on the reaction, the light source was switched off between heat reaction, of on the source wasoff -polarized minimizeeffect light To switched the light source. the

Figure 5. The effect of 3% of 5. Thesolution (m/m) Virkon effect on umbonal Figure 1 minute (A), of over515 minutes a 30 minutes treatment course (D). Virkon minutesand A- (B), (C), captures. ofindividual same the captured100x (A). solution shown were also Imagesare B-D and under withoutmagni is Virkon with a cross

- 84 - Phase 2. Efficacy of Virkon against adult zebra mussels Average mussel length was 22.53 mm with a standard deviation of 2.61. Average mussel length for dead individuals was 22.55 and average length for living individuals was 22.51, suggesting no effect of size on susceptibility of adult zebra mussels to Virkon. Adults exhibited 100% mortality following exposure to all concentrations after 48 hours and no treatment was 100% effective in any of the three replicates before that time. During the study, 11 individuals in control treatments died, with one dead individual each at the 5 and 30 minute and 24-hour time points, two at 12 hours, and three each at 38 and 72 hours. Wider 95% credible intervals in the posterior predictions for adult mussels reflect more variation in the adult response to Virkon treatment than in veliger response.

1.00% 0.50% 0.25%

0.100% 0.00%

Figure 6. Individual posterior predictive dose-response curves for adult zebra mussels exposed to Virkon. Outer lines indicate the 95% credible interval while percentages indicate dose. Exposure to Virkon at higher dose-time sets yielded brittle, pitted, and bleached valves when compared to individuals from the control group (Figure 7). This is presumably due to Virkon’s mechanism as a strong oxidizing agent.

- 85 -

Figure 7. The effect of a 1% Virkon solution on adult mussels at 72 hours. Treated mussels are in the foreground and untreated mussels are in the background.

CONCLUSION Due to its complex action mechanism, low toxicity, high biodegradability, and success in producing 100% mortality in two life-stages of zebra mussels, Virkon seems to be a potential candidate for disinfecting materials moving between zebra mussel infested water bodies so long as ample exposure time is provided. Mortality rates of 100% were achieved in 15 minutes with 2% or higher solutions. Since adult mussels are large and can be seen with the naked eye, a combination of manual removal of adults and treatment with a Virkon solution to rid materials of veligers may be the most practical approach, as the 48 hours of treatment for adult eradication may be too long of a time to wait or too damaging to the material being disinfected.

Like any treatment, Virkon has its drawbacks. It should not be used to disinfect any equipment or container that might contact water samples intended to be analyzed for phosphorus, as it appears to contain approximately 10% phosphate by mass (personal observation); a 1.0% solution was measured to contain approximately 1.09 g total phosphorus per liter (approximately five orders of magnitude higher than that of local surface waters). Exposure of sampling gear to a 1.0% solution required soaking in hydrochloric acid to remove phosphorus residues. According to the distributor, Virkon is only stable for 1-2 weeks in solution, is very hydroscopic, and as such must be kept completely dry when stored. It also may be deactivated by sunlight or by high concentrations of organic material (Western Chemical 2016). Its cost is moderate, at approximately $70 US (2016) for 10 lb ($15 US/kg). The SDS lists it as an irritant in its powdered form, and personal experience is that multiple water rinses are needed to remove its residue after use. Lastly, since it contains a strong oxidizer under acidic conditions, it may not be compatible for use with some metals or other materials.

- 86 - Additional work that may be performed includes increasing sample size and replicates for both phases of the project to improve statistical power, performing a thorough validation of stability over time and with exposure to organic material and sunlight, conducting a materials compatibility study, and evaluating the disinfectant for use against other aquatic invasive species. According to Diggins (2001) and Costa et al. (2008), adult zebra mussels may alter their filtration rates by season and may vary by a factor of 22 between the summer and winter. The downscaling of filtration by the adult mussels in the fall and winter seasons (when phase 2 of this study was performed) may make the mussels less susceptible to chemical treatments. Because of this, the study should also be performed in the warmer months to determine whether less Virkon may be used for shorter periods of time.

REFERENCES Baker, P., S. Baker, and R.Mann. 1993. Criteria for predicting zebra mussel invasions in the Mid-Atlantic region. School of Marine Science, College of William and Mary, Gloucester Point VA. Barbour, J.H., S, McMenamin, J.T.A. Dick, M.E. Alexander and J. Caffrey. 2013. Biosecurity measures to reduce secondary spread of the invasive freshwater clam, Corbicula fluminea (Müller, 1774). Management of Biological Invasions. 4(3):219-230. Connelly, N.A., C.R. O’Niel, B.A.,Knuth and T.L. Brown. 2007. Economic impacts of zebra mussels on drinking water treatment and electric power generation facilities. Environmental Management. 40(1):105-112. Costa, R., D.C. Aldridge and G.D. Moggridge. 2008. Seasonal variation of zebra mussel susceptibility to molluscicidal agents. Journal of Applied Ecology 45:1712-1721. Dalton, L.B. and S. Cottrell. 2013. Quagga and zebra mussel risk via veliger transfer by overland hauled boats. Management of Biological Invasions. Short Communitcation – Corrected proof. 4. Diggins, T.P. 2001. A seasonal comparison of suspended sediment filtration by quagga (Dreissena bugensis) and zebra (D. polymorpha) mussels. Journal of Great lakes Research. 27:457-466. Elliott, P., D.C., Aldridge, G.D. Moggridge and M. Chipps. 2005. The increasing effects of zebra mussels on water installations in England. Water and Environment Journal. 19(4):367- 375. Johnson, J.E. 1995. Enhanced early detection and enumeration of zebra mussel (Dreissena sp.) veligers using cross-polarized light microscopy. Hydrobiologia. 312:139-147. Johnson, L.E. and J.T. Carlton. 1996. Post-establishment spread in large-scale invasions: Dispersal mechanisms of the zebra mussel Dreissena polymorpha. Ecology. 77(6):1686- 1690.

- 87 - Ludyanskiy, M.L., D. McDonald and D. MacNiell. 1993. Impact of the zebra mussel, a bivalve invader. Bioscience 43(8):533-544. MacIsaac, H.J., C.J. Lonnee and J.H. Leach. 1995. Suppression of microzooplankton by zebra mussels: importance of mussel size. Freshwater Biology. 34(4):379-387. MacIssac, H. 1996. Potential abiotic and biotic impacts of zebra mussels on inland waters of North America. American Zoologist. 36:278-299. Mitchell, A.J., M.S. Hobbs and T.M. Brandt. 2007. The effect of chemical treatments on red-rim melania Melanoides tuberculata, an exotic aquatic snail that serves as a vector of trematodes to fish and other species in the USA. North American Journal of Fisheries Management. 27(4):1287-1293. Moffit, C.M., A. Barenberg, K.A. Stockton and B.J. Watten. 2015. Efficacy of two approaches for disinfecting surfaces and water infested with quagga mussel veligers. In: Wong, W.H. and S.L. Gerstenberger, editors. Biology and Management of Invasive Quagga and Zebra Mussels in the Western United States. CRC Press. p. 467–477. O’Neil, C.R. Jr. and MacNeill. 1991. The zebra mussel (Dreissena polymorpha): and unwelcome North American invader. Sea Grant. Cornell Cooperative Extension, State University of New York. Coastal Resources Fact Sheet, Nov. 1991. Paetzold, S.C. and J. Davidson. 2011. Aquaculture fouling: Efficacy of potassium monopersulphonate triple salt based disinfectant (Virkon® Aquatic) against Ciona intestinalis. Biofouling. 27(6):655-665. Pimentel, D., R. Zuniga and D. Morrison. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics. 52(3):273-288.\ R Development Core Team. 2008. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0 Ricardi, A., R.J. Neves and J.B. Rasmussen. 1998. Impending extinctions of North American freshwater mussels (Unionoida) following the zebra mussel (Dreissena polymorpha) invasion. Journal of American Ecology. 67(4):613-619. Su, YS. and Yajima M. 2015. R2jags (R Package version 0.5-7). Timar, T. and D.J. Phaneuf. 2009. Modeling the human-induced spread of an aquatic invasive: The case of the zebra mussel. Ecological Economics. 68(12):3060-3071. Upper Mississippi River National Wildlife and Fish Refuge, Comprehensive Conservation Plan, Alternative E, Modified Wildlife and Integrated Public Use: Environmental Impact Statement. US Fish and Wildlife Service, 2006, p258) Virkon Aquatic; MSDS Ref. 130000036337. Antec International Ltd., Sudbury / Suffolk, UK. Apr. 13, 2015. http://www.europharma-uk.com/wp-content/uploads/2015/11/Virkon- Aquatic-MSDS-Version-30.pdf (Accessed 10 Sep, 2016).

- 88 - Virkon Aquatic. Western Chemical, Inc., Ferndale, WA. 2016. http://www.wchemical.com/ featured/virkon-aquatic-10-lb-tub-virkdlb0010.html (Accessed 10 Jan, 2016). Virkon: Background Information. Antec International Ltd., Sudbury / Suffolk, UK. 1994. https://extranet.fisher.co.uk/webfiles/uk/web-docs/SLSGD05.PDF (Accessed 10 Sep, 2016).

- 89 - Evaluation of citric acid, a common food additive and calcium descaler, for use in adult zebra mussel decontamination Joseph Perry,1 Matthew Albright,2 and Daniel Stich3

INTRODUCTION Zebra mussels are costly in both economic and environmental terms. As of 2005, the economic cost of zebra mussel proliferation is estimated to be as high as one billion dollars in the United States annually (Pimentel et al. 2005) and is wide-ranging, affecting many economic sectors including power, water, agriculture, fishing, and recreation (Connelly et al. 2007; Elliott et al. 2005; MacIssac 1996). The billion-dollar figure accounts for tangible costs such as property value depreciation, biofouling of intake pipes in the power, municipal water, and industrial sectors, and resources allocated by nonprofits and local, state, and federal governments to manage their spread. Zebra mussels consume microscopic phytoplankton indiscriminately, competing with other planktivores including smaller zooplankton and indigenous freshwater mussels (MacIssac et al. 1995 and Ricardi et al. 1998) among others. Because zebra mussels are efficient filter feeders, their presence has contributed to increased clarity in many water bodies, causing mass macrophyte growth and die-offs. These factors may be contributing to the benthification of freshwater food webs by redirecting energy and resources from pelagic to benthic zones.

Once zebra mussels have been introduced in a water body, populations tend to rise quickly due to mass spawning events. Such events in mature populations have been reported to yield up to 500,000 veliger larvae per square meter of the water column (Ludyanskiy et al. 1993). To date, little if anything can be done to eradicate populations once established. Like many other aquatic invasive species, zebra mussels are primarily introduced by the overland transport of boats and equipment from infested waters. Mussels can survive overland trips attached to boat hulls, motors, chains, pipes, buoys, and livewells (Davis 2016).

While overland transport of zebra mussels is generally attributed to recreational boating, lake-to-lake movements by those involved in lake research or management activities may themselves serve as vectors of a variety of aquatic invasive species. Particularly, absorbent material which can remain damp for extended periods of time, such as fishing nets, can harbor living zebra mussels and their veligers for weeks. Similar conditions are afforded by anchor and mooring lines, foul weather gears such as waders, sampling gear, etc. As part of a larger set of procedures intended to minimize the spread of exotic species, effective protocols for disinfecting such materials need to be adopted. Here, citric acid was evaluated as such a disinfection agent. To evaluate this, zebra mussel adults were collected from Otsego Lake and were exposed to citric acid solutions of varying concentrations and periods of time. Resulting mortality was measured.

While there are many commercially available treatments proven to disinfect materials moving between water bodies, many are less than ideal in terms of cost, safety, ease of

1 SUNY Oneonta Biology Department Intern, summer 2016. 2 Assistant to the Director, SUNY Oneonta Biological Field Station. 3 Assistant Professor of Biology, SUNY Oneonta. - 90 - application in remote locations, environmental impact, and material compatibility (NOAA Fisheries Service 2017). Citric acid is an inexpensive biodegradable food additive that may show promise in application as a targeted disinfectant for invasive mussel species on its own or in conjunction with other compounds. In addition to its use as a food additive, citric acid is commonly used as a commercial descaler for limescale (calcium carbonate) deposits in pipes, boilers, kettles, and espresso machines. As a triprotic species, citric acid undergoes ionization in aqueous environments in a stepwise fashion shown in equations 1-4 below (Al-Khaldi, et al. 2007):

- + H3AOH ↔ H2AOH (aq) + H (aq) pKa (25°C): 3.13 (1) - 2- + H2AOH (aq) ↔ HAOH (aq) + H (aq) pKa (25°C): 4.76 (2) 2- 3- + HAOH (aq) ↔ AOH (aq) + H (aq) pKa (25°C): 6.40 (3) 3- 4— + AOH (aq) ↔ AO + H (aq) pKa (25°C): 11.6 (4)

Where A = C6H4O6

Because citric acid typically yields an acidic environment in solution without the addition of a strong base such as sodium hydroxide, equation 4 is unlikely to apply to the conditions of this study. Once ionized, citrate may participate in a reaction with calcium carbonate, the primary mineral constituent of zebra mussel valves (Pathy and Mackie 1992). The primary stepwise reaction of citric acid and calcium carbonate under acidic conditions is shown in equations 5-7 below (Al-Khaldi et al. 2007):

+ 2+ 2 H (aq) + CaCO3 ↔ Ca (aq) + H2O + CO2 pH: 1.8 – 4 (5) - 2+ + H2AOH (aq) + Ca (aq) ↔ CaH2AOH (aq) pH: 1.8 – 4 (6) 2+ + Ca (aq) + 2(CaH2AOH )(aq) ↔ Ca3(AOH)2(s) pH: 6 (7)

Where A = C6H4O6

In addition to reacting with protein components in the soft tissues, we hypothesize that citric acid will also directly react with the mineral components of zebra mussel valves, specifically calcium carbonate crystals which predominate the valve structure. Attacking the structure of the valves may be of some importance since zebra mussels are known to close tightly on exposure to noxious chemicals, potentially protecting soft tissues for long periods of time (Rajagopal et al. 2002).

- 91 - MATERIALS AND METHODS Mussel collection and setup Adult zebra mussels were harvested from rocks collected from the source of the Susquehanna River (N 42° 41’ 58.9” W 74° 55’ 13.2”) in water 1 – 2 meters deep. Mussels were removed from rocks with a paint scraper (Davis 2016) and transported to the lab for placement into mesh bags (Doc Foster CE-22541) in sets of ten live mussels each. A target range of 15-30 mm valve length was sought during selection and bagging in order to exclude mussels in the juvenile life stage. Bags were suspended by dowels in one of two 50-L aquaria with slow, constant flows of aerated lake water for at least 20 hours to acclimate mussels to ambient laboratory conditions. After the acclamation period, fifteen lightly aerated 20-L aquarium tanks were filled with filtered lake water and dosed with food grade anhydrous citric acid (Duda Energy) to yield three replicates of the following concentrations in the low-dose trail: 1.00, 0.50, 0.25, and 0.10 %. Nine 20-L tanks filled with filtered lake water were dosed with citric acid to yield concentrations of 4.00, 2.00, and 0.500 % in the high-dose trial. Additionally, three negative control tanks filled only with filtered lake water were used in each trial. Treatment Ten bags of mussels were suspended by dowels in each 20-L aquarium. One bag was removed from each tank at ten predetermined time points (5 and 30 minutes; 1, 2, 4, 6, 8, 12, 24, and 72 hours) before rinsing in a 500-L aquaculture tank filled with lake water to remove any residual treatment. Bags were then moved to one of two 50-L aquaria with slow, constant flows of aerated lake water for 48-72 hours post-treatment. This recovery period was intended to allow those mussels that might have appeared dead to show signs of recovery, as mussels treated with disinfectants sometimes falsely appear dead immediately after chemical exposure (Pucherelli et al. 2014). Mortality and measurement Mussels were individually evaluated for mortality after holding in post-treatment aquaria according to the criteria listed above. Mussel lengths were obtained using a digital caliper and recorded along with mortality. Water quality assessment A calibrated multi-parameter water quality sonde (YSI Incorporated, Model Number 6820V2-M) was used to monitor physical and chemical parameters of water quality in each experimental, holding, and recovery tank. Data on conductivity, pH, dissolved oxygen, and temperature were obtained at 8:00 each morning. Calcium concentrations were determined on of each high-dose and control tanks approximately one week after the study was completed using the EDTA titrimeteric method (APHA 2012). Statistical analysis: Binomial logistic regression models were used to analyze the effects of citric acid concentration and exposure time on the mortality of zebra mussels. A Bayesian hierarchical approach was used to model variation in mortality (p) due to the interactive effect of citric acid

- 92 - concentration and exposure time (TIME) for each trial (i). To incorporate potentially different responses to exposure time between the different doses, we modeled dose as a random effect on the slope (βtime, j) of the linearized relationship between mortality and time:

β β logit (pi, j) = 0+ time, j∙ TIMEi

The approach assumed that the number of dead mussels in each ith trial (Di) was drawn from a binomial distribution defined by the probability of mortality in each jth citric acid concentration (pi,j) and the number of zebra mussels in each trial (Ni):

Di ~ Binomial(pi,j, Ni) We used uninformative prior distributions for model parameters to allow data to guide conclusions about the process of interest. We assumed a shared intercept (β0) among all doses because all trials started at time zero, with zero mortalities. We used a diffuse normal prior distribution with a mean of zero and a variance of 10 on the intercept.

β0 ~ Normal(0, 10) We hypothesized that increased exposure time would have a positive effect on the number of dead zebra mussels in each trial across all doses, but that the effect of exposure time

2 βtime, j ~ Normal(μ, σ )

The mean of the global distribution for βtime, µ, was assigned a diffuse normal prior with a mean of zero and a variance of ten, and we used a uniform prior distribution on σ2 that ranged from zero to ten. This approach allowed us to share information across all trials to estimate hyperparameters for the global distribution of βtime to improve parameter estimation while allowing the effect of time to vary between citric acid concentrations. We used Markov chain Monte Carlo (MCMC) methods to estimate model parameters in JAGS using the ‘R2jags’ package (Su and Yajima 2015) in R (R Development Core Team 2017). We used a burn-in of 3,000 runs, and simulated an additional 30,000 samples from each posterior distribution, saving every 30th sample to reduce autocorrelation between samples and increase the number of independent samples from the posterior distribution (Kruschke 2010). We ran a total of three Markov chains for each parameter, resulting in a total of 2,700 samples from which to construct posterior distributions. We assessed convergence among the three chains for each parameter using the Gelman-Rubin convergence diagnostic (Gelman & Rubin, 1992), and visually inspected plots of Markov chains to ensure adequate mixing (Kruschke 2010).

- 93 - RESULTS AND DISCUSSION Low-dose trial A total of 1,500 mussels were assessed over three replicate trials, ten time points, and five concentrations of citric acid treatments (including negative controls). The overall average mussel length was 21.98 mm with a standard deviation of 3.107 Average lengths of dead and live mussels in this trial were 21.88 mm and 22.00 mm respectively. Although no concentration in the low-dose trial yielded 100% mortality at any time point, mortality was above 90% in the 0.500% and 1.00% treatments after 72 hours. In the negative control group, 3 of 300 mussels were dead with one mortality at two, four, and seventy-two hours (Figure 1).

- 94 - 1.00% 0.500%

0.250% 0.100%

Figure 1. Individual posterior predictive dose-response curves for zebra mussels exposed to low doses of citric acid. Outer lines indicate the 95% credible interval while percentages indicate dose.

- 95 - High-dose trial In the high-dose trial, the overall average mussel length was 21.50 mm with a standard deviation of 9.061. Living mussels averaged 21.89 mm and dead mussels averaged 20.83 mm. Because of major valve degeneration in mussels at higher time and doses, a total of 77 mussels were not able to be measured. Treatment concentrations of 2.00 and 4.00% each yielded greater than 50% mortality after 2 hours, while the treatment of 0.500% yielded greater than 50% mortality after 48 hours. Complete mortality was achieved in 0.500% citric acid after 72 hours, 2.00% citric acid after 24 hours, and 4.00% citric acid after 24 hours. All 300 mussels in the negative control group survived (Figure 2).

4.00% 2.00%

0.500% 0%

Figure 2. Individual posterior predictive dose-response curves for zebra mussels exposed to high doses of citric acid. Outer lines indicate the 95% credible interval while percentages indicate dose.

- 96 - Calcium levels were 70.3 mg/L (SD = 1.00) in the 0.500% citric treatment tank, 114 mg/L (SD = 2.00) in the 2.00% tank, and 172.3 mg/L (SD = 12.02) in the 4.00% tank. There was a perfect linear correlation between the citric acid and calcium concentrations in the tanks (R2 = 1). The negative control tank was found to have a calcium concentration of 23.4 mg/L (SD = 0.200), which is not inconsistent with normal calcium levels in the lake from which water was sourced (Figure 3).

200.0

172.3 R² = 1 150.0

100.0 114.2 Calcium concentration (mg/L) concentration Calcium 70.3

50.0

23.4

0.0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Citric acid concentration (%)

Figure 3. Calcium concentrations of high-dose treatment tanks recorded after the completion of the study.

- 97 - Like EDTA, citrate also binds calcium to form an insoluble product, calcium citrate (Equation 7 above). Although this may theoretically deflate numbers obtained from the titrimetric method used to quantify calcium in each sample, the concentration of EDTA is almost 50 times higher than the concentration of citrate in the highest dose treatment solution (100 mM vs 2.12 mM, respectively), so any interference is considered to be negligible. Mussels exposed to higher dose and time conditions had valves that were soft, pitted, and occasionally harbored a white, crystalline substance. While the compound was not tested, it may be reasonable to assume that it could be calcium citrate, the product of equation 7. Mussels exposed to 2.00% and 4.00% citric acid at higher time points were reduced to piles of slime which made the measurement of mussels in these cohorts impossible (Figure 4).

Figure 4. Mussels exposed to 0% (A), 0.500% (B), 2.00% (C), and 4.00% (D) citric acid solutions over 72 hours’ time.

- 98 - CONCLUSION

Citric acid produced 100% mortality of adult zebra mussels at the higher concentrations tested (2-4%), when the contact time was between 12 and 24 hours (Figure 2). Because of this, further investigation is recommended to assess if mortality may be achieved more quickly when using higher concentrations of citric acid. That said, citric acid may hold some promise as an anti-biofouling agent due to significant calcium leeching and near dissolution of zebra mussel valves when exposed to the concentrations tested. The effect of citric acid on materials to be defouled, the evolution of CO2 gas during the process (equation 5), and the effect of higher concentrations of citric acid on dissolution time should all be considered before application. Additionally, mortality should be evaluated on zebra mussel veligers, as this juvenile life stage is that which is most likely to be accidentally transported in field sampling gear, and is the stage generally most susceptible to chemical decontamination (Perry et al. 2017; Davis 2016; Kennedy et al. 2006). Additional work that may be performed includes increasing sample size and replicates for both phases of the project to improve statistical power and performing a thorough validation of solution stability over time. Solutions of relatively low concentrations of citric acid are very biodegradable – fungi and bacteria readily grew in uncovered beakers of citric at 2.00% and were observed in high-dose tanks two weeks after the conclusion of this study. Food grade citric acid is also relatively safe to handle and it is not expensive, at $75 US (2016) for 50 lb ($3.30/kg). According to Diggins (2001) and Costa et al. (2008), adult zebra mussels may alter their filtration rates by season and may vary by a factor of 22 between the summer and winter. The downscaling of filtration by the adult mussels in the fall and winter seasons (when this study was performed) may make the mussels less susceptible to chemical treatments. Because of this, the study should also be performed in the warmer months to determine whether less citric acid may be used for shorter periods of time.

REFERENCES

Al-Khaldi M.H., H.A. Nasr-El-Din, S. Mehta, and A.D. Al-Aamri. 2007. Reaction of citric acid with calcite. Chemical Engineering Science. 61(21):5880-5896.

APHA, AWWA, WEF. 2012. Standard methods for the examination of water and wastewater. American Public Health Association.

Connelly, N.A., C.R. O’Niel, B.A.,Knuth and T.L. Brown. 2007. Economic impacts of zebra mussels on drinking water treatment and electric power generation facilities. Environmental Management. 40(1):105-112.

- 99 - Costa, R., D.C. Aldridge and G.D. Moggridge. 2008. Seasonal variation of zebra mussel susceptibility to molluscicidal agents. Journal of Applied Ecology 45:1712-1721. Davis, E.A., 2016. Determining effective decontamination methods for watercraft exposed to zebra mussels, Dreissena polymorpha (Pallas 1776), that do not use hot water with high pressure spray. Occas. Pap. No. 52. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Diggins, T.P. 2001. A seasonal comparison of suspended sediment filtration by quagga (Dreissena bugensis) and zebra (D. polymorpha) mussels. Journal of Great lakes Research. 27:457-466. Elliott, P., D.C., Aldridge, G.D. Moggridge and M. Chipps. 2005. The increasing effects of zebra mussels on water installations in England. Water and Environment Journal. 19(4):367- 375. Gelman, A. and D.B. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical Science. 7(4):457-472.

Johnson, L.E. and Carlton, J.T. 1996. Post-establishment spread in large-scale invasions: Dispersal mechanisms of the zebra mussel Dreissena polymorpha. Ecology. 77(6):1686- 1690.

Kennedy, A.J., R.N. Millward, J.A. Steevens, J.W. Lynn and K.D. Perry. 2006. Relative sensitivity of zebra mussel (Dreissna polymorpha) life stages to two copper sources. Journal of Great Lakes Research. 32(3):596-606. Kruschke, J. 2010. Doing Bayesian data analysis. Academic Press, Cambridge, Massachusetts. ISBN:0123814855 9780123814852.

Ludyanskiy, M.L., D. McDonald and D. MacNiell. 1993. Impact of the zebra mussel, a bivalve invader. Bioscience 43(8):533-544.

MacIsaac, H.J., C.J. Lonnee and J.H. Leach. 1995. Suppression of microzooplankton by zebra mussels: importance of mussel size. Freshwater Biology. 34(4):379-387.

MacIssac, H. 1996. Potential abiotic and biotic impacts of zebra mussels on inland waters of North America. American Zoologist. 36:278-299.

NOAA Fisheries Service. 2017. Preventing invasive species: Cleaning watercraft and equipment. Retrieved 12 March 2017.

Pathy, D.A. and Mackie, G.L. 1992. Comparative shell morphology of Dreissena polymorpha, Mytilopsis leucophaeata, and the “quagga” mussel (Bivalva: Dreissenidae) in North America. Canadian Journal of Zoology. 71(5): 1012-1023.

Pimentel, D., R. Zuniga and D. Morrison. 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics. 52(3):273-288.\

- 100 - R Development Core Team. 2008. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.

Rajagopal, S., G. Van der Velde, M. Van Der Gaag, and H.A. Jenner. 2002. How effective is intermittent chlorination to control adult mussel fouling in cooling water systems? Water Research. 37: 329-338.

Ricardi, A., R.J. Neves and J.B. Rasmussen. 1998. Impending extinctions of North American freshwater mussels (Unionoida) following the zebra mussel (Dreissena polymorpha) invasion. Journal of American Ecology. 67(4):613-619.

Su, YS. and Yajima M. 2015. R2jags (R Package version 0.5-7).

Timar, T. and D.J. Phaneuf. 2009. Modeling the human-induced spread of an aquatic invasive: The case of the zebra mussel. Ecological Economics. 68(12):3060-3071.

- 101 - Trap net monitoring of fish assemblages in the weedy littoral zone at Rat Cove and the rocky littoral zone at Brookwood Point, Otsego Lake, NY 2016

Zachary R. Diehl1

INTRODUCTION

Oligotrophic lakes tend to be systems sensitive to disturbance because the communities, which have often evolved in recent geological periods, are sensitive to low, stable nutritive bases (Li and Moyle 1981). Littoral zones of larger lakes such as Otsego Lake provide spawning and nursery habitats for various species of lentic fish. Therefore, the fishes that use these habitats can be indicators of other processes that are happening in the lake. Littoral zones represent the most diversified, productive, and heterogeneous portions of lakes (Wetzel 1990). As an example of this, the illegal introduction of alewife (Alosa pseudoharengus) into Otsego Lake, NY in 1986 (Foster 1990) altered the trophic balance as well as the physical/chemical environment of the lake. Analysis of the alewife stomach contents showed that the alewives consume the largest zooplankters available (Hutchinson 1971). In Otsego Lake, this led to a shift in the zooplankton community toward smaller-bodied individuals. Coupled with nutrient loading, it led to an increased algal standing crop (Harman et al. 1997). In oligotrophic lakes, zooplankton are the critical link that allow nutrients to flow from phytoplankton to the fish community (Wigen 1991).

This study was a continuation of the annual monitoring of the littoral fish community of Otsego Lake. The goal of this survey is to describe the littoral fish community and monitor indices of population dynamics of species that use the littoral zone. Rat Cove (Figure 1) has been studied since 1979, when Pennsylvania style trap nets were initially used to assess the fish community in that part of the lake (MacWatters 1980). A similar monitoring effort began at Brookwood Point (Figure 1) in 1999. These two sites have been used to provide annual snapshots of fish communities in different littoral zones of Otsego Lake, one of which is a shallow cove with abundant plant growth (Rat Cove), and the other of which is representative of the many gravelly points in the lake with relatively steep slopes (Brookwood Point).

The primary objective of long-term monitoring of fishes in the littoral zone has been to characterize changes in the population abundances of fishes utilizing the littoral habitats of Rat Cove and Brookwood Point. During the past two decades, these data have been especially useful for monitoring the abundance of invasive alewife during the spawning season. Current and future

1 Robert C. MacWatters Internship in the Aquatic Sciences, summer 2016. Present affiliation: Department of Fisheries, Wildlife and Environmental Science Technology, SUNY Agriculture and Technical College, Cobleskill, NY.

- 102 - monitoring will help to assess whether the depletion of alewife continues to be successful. In the absence of alewife, it is expected that abundance of other forage fishes will shift to utilize resources for which they historically could not compete. Diet analyses of piscivorous fishes provide essential data regarding forage species abundance and their utilization in higher trophic levels (Casscles 2015). Therefore, as a secondary objective of this survey, diet samples were collected from game fish using a gastric lavage to investigate predator/prey relationships.

METHODS & MATERIALS

Winged Indiana trap nets with a single lead line were set from Monday through Thursday between May 31 and August 5. Trap nets were checked every 24 hours at Rat Cove and Brookwood Point (Figure 1). Trap nets were set no deeper than 3 meters. Fish caught in the trap nets were transferred into totes and were identified and measured (fork length, mm). All fish were returned to the lake promptly. Diet samples were collected from all piscivorous fish (walleye Sander vitreus, chain pickerel Esox niger, largemouth bass Micropterus salmoides, and smallmouth bass Micropterus dolomieu) using a gastric lavage (Foster 1977). Diet samples were preserved in 70% ethanol until examined.

Figure 1. Bathymetric contour map of the southern end of Otsego Lake, NY. Trap nets were set perpendicular to shore, parallel to arrows, in Rat Cove and Brookwood Point.

The Proportional stock density (PSD) index was calculated for each species (PSD= #of quality size/ # stock size). PSD is the ratio of quality sized fish to the available stock sized fish, and is a useful index for tracking changes in predator and prey community dynamics. Stock length was defined is the approximate minimum creelable length for a species, and is the minimum size at which a fish is considered to recruit to fisheries (Kohler and Hubert 1999). The

- 103 - actual stock of each species is the number of fish that are at or above the stock length. Quality length is the minimum size of fish that most anglers like to catch (Anderson 1978).

RESULTS

The total catch per week in Rat Cove and Brookwood Point over summer 2016 is shown in Figure 2. Mean catch at Rat Cove decreased from the previous year (2015) from 13 to 6 fish per week (59 fish total). Brookwood Point weekly catch also decreased, from 8 to 5 fish per week (49 fish total) during the same time period. From 2005 to 2011, there was an increase in overall mean catch per week at both Rat Cove and Brookwood Point (German 2011). Initially the use of new nets had seemed to increase catch rates (German 2011) although the catch rate has continued to decline since 2011 (Figure 3).

Panfish (i.e. sunfishes and yellow perch) were the dominant group of species collected in both the weedy littoral zone in Rat Cove and the rock littoral zone at Brookwood Point, with the exception of a late-spawning catch of white suckers (Figure 2). Multiple prey species that were collected recently for the first time in decades were also collected in 2016, including bluntnose minnow (Pimephales notalus), lake whitefish (Coregonus clupeaformis) and tadpole madtom (Noturs gyrinus).

30 25 20 15 10 5 0

Rat Cove Brookwood Point

Figure 2. Weekly average catches of all fish at Rat Cove and Brookwood Point, Otsego Lake, NY 2000-2016.

- 104 - Total weekly catch rates over time (2000-2016) are given in Figure 3. Similarly, mean weekly alewife catch rates are given in Figure 4. Comparing these figures makes evident the fact that prior to 2005, alewife comprised the majority of the total catch. Alewife were not captured in either sampling location in 2016 (Figure 3, Tables 1 and 2). They were last collected in 2012, when two were taken (Hurlbut 2013) Their decline is concurrent with walleye stocking efforts, which began in 2000 (Cornwell 2000).

300

250

200

150

100

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2014 2015 2016

Rat Cove Brookwood Point

Figure 3. Weekly average catches of all fish at Rat Cove and Brookwood Point, Otsego Lake, NY. 2000-2016.

- 105 - 250

200

150

100

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2013 2014 2015 2016

Rat Cove Brookwood Point

Figure 4. Weekly average catches of alewife in Otsego Lake, NY 2000-2016 at Rat Cove and Brookwood Point.

Despite the absence of alewife in littoral sampling, gastric lavage samples from predators indicated their stomachs were not empty (Figure 5). Walleye and chain pickerel collected had been consuming bluegill and, to a lesser degree, spottail shiner. The proportional stock density (PSD) graph (Figure 6) indicates a healthy predator to prey ratio, indicating a top-heavy community dominated by large predators.

- 106 -

Table 1. Mean weekly catch at Brookwood Point and catch contributed by each species, 2000-2016 (modified from Casscles 2016).

Species 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2014 2015 2016 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 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 <0.1 0.3 0.3 0.3 0.0 Pumpkinseed 3.1 7.4 12.0 13.1 12.2 1.1 0.8 1.0 1.8 1.9 1.3 10.0 5.1 5.0 1.3 1.0 Bluegill 6.5 0.9 0.9 1.0 0.8 0.5 0.3 0.3 0.9 0.1 0.4 15.0 0.5 3.9 0.4 0.5 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 1.5 0.2 0.0 0.1 Rock bass 7.7 3.5 4.0 3.8 3.0 1.1 0.3 0.3 0.6 2.3 2.0 14.0 3.0 12.1 2.4 0.6 Largemouth bass 0.3 0.3 0.7 0.8 0.0 0.1 0.0 0.1 0.3 0.1 0.0 <0.1 0.0 0.1 0.0 0.0 Chain pickerel 0.3 0.0 0.3 0.2 0.2 0.2 0.0 0.2 0.1 0.0 0.0 <0.1 0.1 0.5 0.1 0.0 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 0.0 0.0 Yellow perch 1.8 0.3 0.2 0.0 0.3 0.1 0.2 0.0 0.1 0.3 0.0 1.0 1.6 2.1 0.1 0.1 White sucker 4.9 0.0 1.0 0.7 0.6 0.2 0.3 0.0 0.0 0.0 0.1 <0.1 0.4 0.6 1.2 2.0 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 0.1 0.0 0.0 0.0 Brown bullhead 6.7 0.0 1.0 3.6 4.2 0.0 0.1 0.0 0.0 0.0 0.0 <0.1 0.6 0.5 0.4 0.0 Spottail shiner 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.3 3.0 0.0 1.4 0.3 0.0 Smallmouth bass 0.0 0.0 0.0 0.6 0.2 0.0 0.0 0.0 0.1 0.0 0.3 <0.1 0.0 0.3 0.1 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 0.0 0.0 Walleye 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.1 0.0 <0.1 0.5 0.8 0.3 0.2 Lake whitefish 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <0.1 0.0 0.1 0.1 0.1 Creek chubsucker 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <0.1 0.0 0.1 0.1 0.0 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 0.1 0.1 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 0.1 0.1 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 0.0 0.0 Total 259.0 152.0 101.0 121.0 37.0 10.0 4.0 8.0 4.4 5.4 6.3 50.0 12.0 29.0 7.4 4.8

- 107 - Table 2. Mean weekly catch at Rat Cove and catch contributed by each species, 2000-2016 (modified from Casscles 2016).

Species 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2014 2015 2016 Alewife 120.1 67.8 8 45.2 2.4 0.4 0.0 3.2 1.1 0.5 0.4 <0.1 0.0 0.0 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 <0.1 1.3 0.5 0.1 0.0 Pumpkinseed 9.7 20.8 15.1 32.8 12.9 4.6 2.0 2.2 4.4 5.1 5.1 16.0 8.6 2.6 2.4 1.0 Bluegill 2.0 2.9 3.7 1.7 1.5 1.4 0.8 3.2 5.9 6.6 4.8 7.0 1.3 9.8 8.6 0.9 Redbreast sunfish 0.8 0.6 0.3 0.4 0.3 0.1 0.0 0.0 0.1 0.1 0.0 <0.1 0.0 0.0 0.1 0.0 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 0.6 1.9 0.3 0.1 Largemouth bass 0.1 0.6 0.3 0.3 0.1 0.1 0.0 0.6 0.3 0.2 0.3 <0.1 0.5 0.1 0.1 0.0 Chain pickerel 0.6 0.5 0.1 0.2 0.2 0.1 0.1 0.3 0.8 0.4 0.3 <0.1 0.6 0.8 0.3 0.4 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 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 4.0 3.4 1.6 0.9 2.4 White sucker 1.1 0.2 1.1 0.1 1.9 0.2 0.5 0.0 0.0 0.0 0.3 <0.1 0.6 0.0 0.0 0.4 Common carp 0.3 0.3 0.2 0.5 0.3 0.7 0.1 0.0 0.0 0.0 0.0 <0.1 0.5 0.0 0.0 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 <0.1 0.5 0.1 0.2 0.2 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 0.1 0.2 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 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 <0.1 0.3 0.0 0.0 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 <0.1 0.0 0.0 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 <0.1 0.0 0.1 0.0 0.0 Tadpole madtom 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 0.0 0.0 0.1 Walleye 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 0.0 0.0 0.5 Total 141.0 96.0 41.0 87.0 25.0 9.0 5.0 11.0 14.0 15.0 14.0 35.0 18.0 18.0 13.0 6.2

- 108 - 80% 70% 60% 50% 40% 30% 20% 10% 0% Bluegill Spottail shiner Sculpin spp. Yellow Perch Various tissues and bones

Walleye Chain pickerel

Figure 5. Frequency of occurrence of prey items in adult walleye (Sander vitreum) >200 mm (n=6) and chain pickerel (Esox niger) (n=4) in Rat Cove and Brookwood Point, Otsego Lake 2016.

Figure 6. Proportional size structures of predator (n=15) and prey (n=49) species in Rat Cove and Brookwood Point, Otsego Lake 2016.

- 109 - CONCLUSIONS

Alewife were first documented in Otsego Lake in 1986 (Foster 1990) and quickly became the dominant species. Predators of Otsego Lake made extensive of this overly abundant prey species. With walleye stocking efforts beginning in 2000 (Cornwell 2000), alewife populations steadily declined until the species was undetectable in fisheries sampling efforts. The community of prey fishes in Otsego Lake appears to have begun to shift away from alewife and move towards a community dominated by panfish and cyprinids. This seems to be supported by the relative increase in abundance of sunfishes compared to recent years, and also by the dominance of sunfishes in the diet content of predators.

The predator community in the littoral zone seems to be responding favorably to changes in the forage base available in the littoral community of Otsego Lake based on the PSD of predators and prey. The 2016 sampling data indicate a “top-heavy” fish community dominated by large predators. Predators seem to be using the most abundant prey species available in the habitats sampled. It remains unclear whether predators use prey preferentially, or if they were foraging in proportion to availability. However, it may be increasingly important to improve understanding of predator-prey dynamics to manage these two components of an economically valuable fishery in the absence of the contemporary prey base (alewife) moving in to the future.

The near-shore fish community of Otsego Lake appears to be in a state on transition, but more research is needed to determine whether this represents a long-term trend or just annual variability in the absence of alewife.

LITERATURE CITED

Anderson, R.O., and A.S. Weithman. 1978. The concept of balance for coolwater fish populations. Pages 371-381 in R.L. Kendall, editor. Selected coolwater fishes of North America. American Fisheries Society. Special Publication 11, Bethesda, Maryland. Bonar, S.A. 2002. Relative length frequency: a simple, visual technique to evaluate size structure in fish populations. North American Journal of Fisheries Management 22:1086-1094 Casscles J.B. 2016. 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. In 48th Ann. Rept. (2015) SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Cornwell M.D. 2000. Monitoring trophic changes following the reintroduction of walleye (Stizostidion vitreum) to Otsego Lake: An executive summary. In 33rd Ann. Rept. (2000). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Foster, J.R. 1977. Pulsed Gastric Lavage: An efficient method of removing the stomach contents of live fish. Prog. Fish Cult. 39(4):166-169

- 110 - Foster, J.R. 1990. Introduction of alewife (Alosa pseudoharengus) in Otsego Lake. In 22nd Ann. Rept. (1989). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. German, B. 2012. Summer 2011 trap net monitoring of fish communities utilizing the weedy littoral zone at Rat Cove and rocky littoral zone Brookwood Point, Otsego Lake. In 44th Ann. Rept. (2011). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Harman, W.N., L.P. Sohacki, M.F. Albright, and D.L. Rosen. 1997. The state of Otsego Lake 1936-1996. Occ. Paper #30. SUNY Oneonta Biol. Fld. Sta., Cooperstown, NY. Hurlbut, J. 2013. Unpublished data. Hutchinson, B.P. 1971. The effect of fish predation on the zooplankton of ten Adirondack lakes, with particular reference to the alewife, Alosa pseudoharengus, 100: 325-335 Kohler, C.C. and W.A. Hubert. 1999. Inland fisheries management in inland North America. American Fisheries Society, Bethesda, MD. Li, H.W. and P.B. Moyle. 1981. Ecological analysis of species introductions into aquatic systems. Tran. Amer. Fish Soc. 110: 772-782 MacWaters, R.C. 1980. The fishes of Otsego Lake. Occas. Paper #7. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Wetzel, R.G. 1990. Land–water interfaces: Metabolic and limnological regulators. Verh. Int. Ver. Theor. Angew. Limnol. 24: 6–24. WIENS, J. A. 1976. Weithman, A.S., and R.O. Anderson. 1978. A method of evaluating fishing quality. Fisheries 3(3):6-10. Wigen J.D. 1991. Zooplankton community structure as an indicator of the fish community structure in Otsego Lake. In 23rd Ann. Rept. (1990). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

- 111 - Freshwater pearly mussel (Unionidae) survey of Otsego Lake following zebra mussel (Dreissena polymorpha) introduction Zach Piper1

ABSTRACT Surveys in Otsego Lake have documented pearly mussel (Family Unionidae) population changes over the last few decades. Beginning with the first comprehensive survey in 1969, six native species were known to inhabit Otsego Lake, New York. Recent surveys have documented considerable unionid declines in both species diversity and overall densities. Forty-two historical and additional sites were surveyed for pearly mussels in July and August 2016. This is the first comprehensive survey in Otsego Lake to find no live unionids. Additionally, increases in sedimentation and zebra mussel (Dreissena polymorpha) colonization on spent valves was documented. These and other factors are likely to have caused the native pearly mussels to be extirpated from Otsego Lake.

INTRODUCTION Freshwater pearly mussels of the order Unionida are among the richest and most diverse groups of fauna in Eastern North America (Strayer and Jirka 1997). Several species within this group are native to Otsego Lake, situated to the north of Cooperstown, New York. These animals live by filter feeding microorganisms, nutrients, and other particulates from the water while burrowed into the benthic substrate (Strayer and Jirka 1997). Unionids are a significant part of many freshwater ecosystems for their ability in regulating particle, toxin, and nutrient movement (Bowen et al. 1994; Strayer and Jirka 1997). Despite their apparent importance, pearly mussels have limited abilities to move along the substrate, making it difficult for these organisms to relocate to areas with more suitable environmental conditions (Strayer and Jirka 1997). This characteristic, combined with their sensitivity to invasive species, sediment accumulation, and various pollutants make pearly mussels useful bio-indicators for water quality and bio-monitors for environmental contaminants in Otsego Lake and other water bodies (Fuller 1974; Strayer and Jirka 1997). The longevity of pearly mussels also allows for greater accumulation of toxins to occur, providing information on environmental contamination (Carell et al. 1987; Pugsley et al. 1988). Previously, six species of unionids were known to exist in Otsego Lake: Alasmidonta undulata, Anodontoides ferussacianus, Elliptio complanata, Lampsilis radiata, Pygandon cataracta, and Strophitus undulatus (Harman 1970). Past surveys by Harman in 1969 (1970), Ferrara in 2000 (2001), and Caracciolo in 2013 (2014) have shown troubling declines in both overall abundance of mussels and also of species richness. This decline has been attributed in large part to the introduction of invasive zebra mussels (Dreissena polymorpha), first documented in 2007 (Harman 2008). They have since come to dominate the benthic communities of Otsego Lake (Horvath 2008; Waterfield 2009; Yoo et al. 2014). These organisms are particularly problematic for native bivalves due to their propensity to colonize the

1 SUNY Oneonta Biology Department intern, summer 2016. Current affiliation: SUNY Oneonta.

- 112 - posterior ends of other bivalves in groups of hundreds or even thousands, thus interfering with their capacity to feed and respire (Haag et al. 1993; Ricciardi et al. 1995). Massive native pearly mussel population losses since the introduction of Dreissena polymorpha have already been reported in the Great Lakes and are likely to continue to occur in Otsego Lake (Haag et al. 1993; Ricciardi et al. 1995). The decline in biomass and diversity of native bivalves in Otsego Lake between 1969 and 2013 is also hypothesized to be due to increased siltation as discussed by Harman (1970), Weir (1977), Ferrara (2001), and Caracciolo (2014). This study reports a survey of historical pearly mussel sites in Otsego Lake to determine current abundance and diversity of native bivalves. In addition, counts of zebra mussels colonizing bivalves and siltation levels were considered to conclude as probable causes of native bivalve losses.

METHODS Pearly mussels were surveyed in July and August 2016 via SCUBA diving. Sites surveyed included those from past studies (Harman 1970; Ferrara 2001; Caracciolo 2014) along with additional sites which were selected based on historical locations of live unionids, bottom composition, and proximity to other study locations. In total, 42 sites were utilized in this study to extensively cover most of Otsego Lake’s shoreline (Figure 1, Table 1). Divers followed a timed transect parallel to shore at each site based upon a chosen compass orientation for approximately ten minutes. We chose compass bearings for each site to maintain an approximate bottom depth of 1-3 meters. Transects at greater depths were taken if any obstructions (i.e. weedbeds, docks) interfered with surveying. Extra time was allotted for locations with ideal conditions for live unionids and evidence of recently deceased unionids. Any live unionids were to be either identified underwater or brought to the surface for further examination and then immediately returned to their original habitation with their posterior ends directed upwards. Search times were totaled and quantified as Catch Per Unit Effort (CPUE, the number of valves or pairs of valves collected per hour). Depth, GPS coordinates, and substrate composition were also recorded at each survey site.

- 113 -

Figure 1. Unionid survey sites on Otsego Lake, July and August 2016.

- 114 - Table 1. UTM coordinates of Otsego Lake unionid survey sites, July & August 2016.

- 115 - Empty valves found at each site were collected for further examination. Valves were identified to species level, enumerated, and subsequently analyzed for zebra mussel and siltation coverage. Only zebra mussels attached to dead valves were removed and counted. Siltation levels were determined based on sediment accumulation characteristics described in Table 2. Table 2. Siltation level description used in Table 3 (from Box & Mossa 1999; Caracciolo 2014).

Average Siltation Description

Shell color and texture are easily seen, virtually no Low visible silt, sand or mud. Valve is not filled with silt

Majority of shell is clean of sediment, most of shell markings, color and texture are visible, some silt Medium accumulation inside empty valve. Could have effect on health of unionid Color and texture of shell are completely covered, majority of valve buried when found, empty valve High filled with sediment. Could cause severe health effects/death

RESULTS No living unionids were collected. A total of 220 dead unionids were collected during this study, with representatives from three of the six species that were native to Otsego Lake: 6 Elliptio complanata, 200.5 Lampsilis radiata, and 13.5 Pygandon cataracta. Total counts for each species, depth, bottom composition, number of zebra mussels attached, and siltation levels for each site surveyed are listed in Table 3. This study was the first comprehensive survey to not find live unionids in Otsego Lake. The most abundant dead unionid found was L. radiata, with E. complanata as the least abundant. Of the 42 number of sites, 31 were found to have unionid shells. The overall CPUE for the entirety of this study was 0.00 unionids/hour, a continued decrease from previous studies. In addition to the lack of live unionids, most empty valves were found with dozens of D. polymorpha or more attached via byssal threads. A total of 2,356.5 zebra mussels were removed from shells during this study. The most zebra mussels removed from a single shell was 193. Higher silt deposits on and inside unionid shells than previously recorded were also discovered throughout this study. These findings indicate that it is likely that Otsego Lake’s native pearly mussel populations have been extirpated.

- 116 - Table 3. Unionid survey data collected in July & August 2016. Zebra mussel counts (# ZM) were only considered if attached to shells. See Table 2 for descriptions of Average Siltation (Avg. Siltation) classifications.

- 117 - DISCUSSION Results from this study and previous publications show an alarming negative trend in the abundance and diversity of native unionid populations. This particular study alone suggests that living native unionids are no longer present in Otsego Lake, evident by the fact that no living individuals were discovered at any of the 42 sites surveyed. These data are supported by credible reports from divers diving in Otsego Lake since 2013 when the last live unionid was found (Lord 2016). A number of hypotheses have been developed to explain the exact cause for this decline. Past studies on Otsego Lake have proposed that poor habitat, increased siltation and invasive species are likely causes. Past unionid surveys conducted in Otsego Lake reported decreases in both abundance and diversity in native species in association with loss of suitable habitat (Ferrara 2001). Ferrara (2001) explored the possibility that lack of habitat combined with increases in siltation is associated with these mussel declines. The U-shape basin of Otsego Lake has been hypothesized to provide inadequate substrate due to steep, unstable slopes located along shorelines (Ferrara 2001). In addition, lentic environments are found to have smaller mussel populations than lotic waters likely due to poorer nutrient availability and lower productivity (Weir 1977). Weir (1977) described the bottom composition preferences and abundance of Otsego Lake’s native species. He concluded that L. radiata was the most abundant species, although our second most abundant species, P. cataracta, was listed as not abundant. P. cataracta was also reported to have a preference for gravel and fine sand (Weir 1977), a bottom composition that it was rarely found in throughout this survey. Other publications have stated that while pearly mussels may have varying substrate preferences, these are unreliable due to other possible factors that ultimately determine what substrates these organisms inhabit (Box and Mossa 1999; Downing et al 2000; Johnson et al. 2014). An increase in the amount of silt at various Otsego Lake survey sites has been reported as well (Ferrara 2001; Caracciolo 2014). Forty out of the forty-two sites we surveyed were described as having bottom compositions comprised partly of silt (Table 3). Levels of silt accumulation on and inside dead valves were also high in the majority of sites (Tables 2 & 3), however it should be noted that qualitative observations such as these are not the most reliable (Box and Mossa 1999; Green et al. 1989). Regardless, siltation has been documented as having detrimental effects on pearly mussels in numerous studies (Box and Mossa 1999; Downing et al. 2000; Johnson et al. 2014, Harman 2016). Fine particulates in the water column can be dealt with to an extent before a mussel’s health is threatened severely. Certain species such as E. complanata, L. radiata, and P. cataracta are more tolerable of silt (Downing et al. 2000) which could be why valves of these species were found in recent surveys. Increased sediment loading in rivers and other water bodies impairs normal feeding and respiration processes in mussels, although the exact effects are not fully understood (Box and Mossa 1999; Downing et al. 2000; Johnson et al. 2014; Harman 2016). The sources of increased siltation has been attributed to human activities such as logging and construction, however, the exact source of these microscopic particles are difficult to determine and cannot be confirmed with certainty (Johnson et al. 2014; Strayer and Jirka 1997). Zebra mussel data from this survey are concerning as well and likely associated with the observed Otsego Lake unionid declines (Caracciolo 2014). These invasive bivalves are known to attach to the posterior ends of native pearly mussels in large colonies, frustrating normal life

- 118 - processes (Haag et al. 1993; Ricciardi et al. 1995). Unionids infested with zebra mussels likely cannot draw in adequate nourishment with their siphons nearly or completely covered. Studies have also shown that zebra mussels may out-compete native unionids by filtering large amounts of algae, phytoplankton, and small zooplankton out of the water column (Parker et al. 1998; Strayer 2009). With regards to respiration, dissolved oxygen in the water column has been shown to significantly decrease in the presence of zebra mussels which could harm unionids further (Caraco et al. 2000; Effler et al. 2004; Strayer 2009). Pearly mussel declines have been observed throughout New York State within the last century (Strayer and Jirka 1997). Many unionid species are currently threatened and endangered largely due to human impacts and their subsequent effects such as pollution, sedimentation, loss of habitat, and introduction of invasive species. The loss of pearly mussels in Otsego Lake is troubling and should serve as a reminder that increased efforts need to be administered to protect remaining pearly mussel populations.

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Caraco, N.F., J.J. Cole, and S.E.G. Findlay. 2000. Dissolved oxygen declines in the Hudson River associated with the invasion of the zebra mussel (Dreissena polymorpha). Environmental Science and Technology 34: 1204-1210.

Carell, B., S. Forberg, E. Grundelius, L. Henrikson, A. Johnels, U. Lindh, H. Mutvel, M. Olsson, K. Svärdström, and T. Westermark. 1987. Can mussel shells reveal environmental history? Ambio 16: 2-10.

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Fuller, S.L.H. 1974. Clams and mussels (Mollusca: Bivalvia). pp. 215-273 in C. W. Hart and S.L H. Fuller (eds.). Pollution ecology of freshwater invertebrates. Academic Press, NY.

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Haag, W.R., D.J. Berg, and D.W. Garton. 1993. Reduced survival and fitness in native bivalves in response to fouling by the introduced zebra mussel (Dreissena polymorpha) in western Lake Erie. Canadian Journal of Fisheries and Aquatic Science 50: 13-19.

Harman, W.N. 1970. Biological studies- Otsego Lake. In 3rd Ann. Rept. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

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Johnson, G.C., J.L. Krstolic, and B.J. Ostby. 2014. Influences of water and sediment quality and hydrologic processes on mussels in the Clinch River. Journal of the American Water Resources Association 50(4): 878-897.

Lord, P.H. 2016. Personal communication. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Parker, B.C., M.A. Patterson, and R.J. Neves. 1998. Feeding interactions between native freshwater mussels (Bivalvia: Unionidae) and zebra mussels (Dreissena polymorpha) in the Ohio River. American Malacological Bulletin 14(2): 173-179.

Pugsley, C.W., P.D.N. Herbert, and P.M. McQuarrie. 1988. Distribution of contaminants in clams and sediments from the Huron-Erie corridor. II-Lead and cadmium. Journal of Great Lakes Research 14: 356-368.

Ricciardi, A., F.G. Whoriskey, and J.B. Rasmussen. 1995. Predicting the intensity and impact of Dreissena infestation on native unionid bivalves from Dreissena field density. Canadian Journal of Fisheries and Aquatic Science 52: 1449-1461.

Strayer, D.L. and K.L. Jirka, 1997. The pearly mussels of New York State. New York State Education Department. Fort Orange Press Incorporated. pp. I-I13; plates 1-27.

Strayer, D.L. 2009. Twenty years of zebra mussels: lessons from the mollusk that made headlines. Frontiers in Ecology and the Environment. 7(3): 135-141.

- 120 -

Waterfield, H. 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.

Weir, G.P. 1977. An ecology of the Unionidae in Otsego Lake with special references to. Immature stages. Occasional Paper No. 4. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Yoo, A., P. Lord, and W.H. Wong. 2014. Zebra mussel (Dreissena polymorpha) monitoring using navigation buoys. Management of Biological Invasions. 5(2): 159-163.

- 121 - Succession of BFS Upper Site forest communities 40 years later Nick Muehlbauer1

INTRODUCTION

The purpose of this study was to determine successional changes in the Biological Field Station’s Upper Site forest communities. This study will help to determine long term successional changes in the Upper Susquehanna River watershed. Mapping of forest communities on the Biological Field Station’s 360 acre Upper Site property was first conducted in 1974 (Dayton 1975). This study defined the criteria for sampling and serves as the baseline from which 2016 data are compared. Given that forest communities change over long periods of time, it was fitting that the next study of these forest communities was done 40 years later. Data from the forest communities sampled can inform management decisions by the Field Station and other land owners in the Upper Susquehanna watershed. In order to quantify successional changes within a forest, it takes decades for new recruitments to occur in the understory of a mature deciduous forest (Chapman and McEwan 2016). Given that recruitment events in a forest occur on a “lottery based” model system, each species initially has an equal chance of survival (Hurtt and Pacala 1995). However, depending on the abundance of a species within an area of disturbance, the more abundant the species, the greater the chance of long term survival (Chesson and Warner 1981). This indicates that species with greater density within the community will have a higher recruitment rate. Changes in disturbance regimes, introduction of exotic species, pathogens and changes in human uses are driving factors changing forest composition, structure and function and may have lasting effects on successional developmental changes (Cox and Hart 2015).

METHODS

The approximate boundaries of 20 distinct forest communities, in 41 total areas, were determined in 1974 by Dayton (1975) using a combination of aerial photographs, soil moisture and composition, and ground trothing (Figure 1). Four of the twenty distinct communities were resurveyed in 2016, those plots being, 5, 9, 13, and 26. Using the supplemental map in the 1974 report (see Figure 1), I was able to find plots based property lines or relative to the location of forest trails. To sample these communities, transects running perpendicular to the slope of the plot were established. If slope was not evident in a plot, a compass was used to determine direction

1 Oneonta Biological Field Station intern, summer 2016. Current affiliation: SUNY Oneonta.

- 122 - and transects were kept from intersecting with one another. The start of each transect was marked using a GPS waypoint. Points were downloaded to Google Earth and provide an indication of where the forest communities begin (See Appendix A).

Figure 1. Map of Upper Site, Town of Otsego, NY, showing the community plots established in 1974 (Dayton 1975). The shaded plots were we-chacterized in 2016.

- 123 - The point-centered quarter method (Barbour et al. 1999) was used for each plot. For each plot, data were taken on the canopy (trees > 10cm DBH), sub-canopy (trees < 10cm DBH) and ground cover. As previously stated, transects were conducted perpendicular to slope, when present, and transects were at random intervals within the plot without crossing over one another. In large plots, three transects were run with 30 paces between points of sampling. Basal area of tree species in the canopy and sub-canopy were determined in each plot. Frequency of shrubs, herbs and seedlings were recorded at every other sample point (ground cover). A 10 m2 circular plot was employed to determine density relative to the size of the plot. Density was estimated by the surveyor based upon how much of ground was covered by a species within the circular plot. Additional species sightings were recorded as a record of presence absence within each plot. Forest communities were previously delineated by Dayton (1975) and were defined as follows: Hemlock forests are characterized by low divisity of the understory due to acidic soils from needles and lack of sunlight. Canopy cover is high and dominated by hemlocks (Tsuga Canadensis). Appalachain Oak forests are charctierized by a more open canopy with greater understory growth. Shrubs are present more often, in addition to high light species such as braken fern ((Pteridium spp.). Oaks (Quercus spp.) are the dominate tree species and the soils are drier and more sandy than mesic soils found in hardwood forests. Swamp forest types are characterized by the presence of hydric soils. The canopy cover varies but is dominated by hemlock (Tsuga Canadensis) and yellow birch (Betula alleghaniensis). Moist conditions allow for a high diversity of understory species. Birch Beech Maple forest is the most common forest type found in our area. It has a mosaic of tree species but is dominated by birches (Betula spp.), beech (Fagus spp.) and maples (Acer spp.). Soil moisture is mesic and less acidic than soils of hemlock forests. Understory diversity is low due to high canopy cover.

RESULTS

For the hemlock forest type, the changes in canopy cover are as follows: Total number of species has decreased from 8 species in 1974 to 5 species in 2016. This difference can be attributed to the lack of Betula alleghaniensis, Fraxinus americana and Quercus alba. The overall basal area of the trees increased from 1.07 m2/ha to 4.85 m2/ha (Appendix A). The subcanopy had a decrease in the number of species from 5 species in 1974 to 3 species in 2016, this loss being attributed to the loss of Fraxinus americana, Betula papyrifera and Acer rubrum. Total basal area increased from 0.14 m2/ha in 1974 to 1.06 m2/ha in 2016 (Appendix B). Ground cover dropped from 19 species in 1974 to 4 species in 2016. Acer rubrum, Mitchella repens and Fagus grandifolia were found in 2016 and were also present in the first study in 1974 (Appendix C). For Appalachain oak forest type survey the canopy cover changed as follows: There were 11 species found in 1974 and 8 species found in 2016. This change is marked by the absence of Acer saccharum, Amelanchier arborea, Betula papyrifera, Fraxinus americana, Picea abies, and Prunus serotina in 2016. Carpinus caroliniana and Betula lenta were found in

- 124 - the canopy in 2016 but not in 1974. The total basal area increased from 0.47 m2/ha in 1974 to 6.44 in 2016 (Appendix A). The number of sub-canopy species decresed by half, going from 18 species in 1974 to 9 species in 2016. This is marked by thte loss of Acer pensylvanicum, Acer saccharum, Carpinus caroliniana, Carya obvata, Cornus rugosa, Ostrya virginiana, Picea abies, Quercus rubra, Quercus alba and Rhodendron roseum. One new species was found in 2016, that being Betula lenta. Total basal area decreased from 0.11 m2/ha in 1974 to 0.04 m2/ha in 2016 (Appendix B). Ground cover also changed with an increase in the number of species from 13 in 1974 to 16 in 2016. A few speices were the same between 1974 and 2016, those being Gaultheria procumbens, Maianthemum canadense, Medeola virginiana, Mitchella repens and Pteridium aquilinum (Appendix C). For the swamp forest type, species number and composition of canopy differed as follows: There was a decrease in the number of species from 11 species in 1974 to 8 species in 2016. The current study did not find the species Fagus grandifolia, Fraxinus nigra, Quercus rubra, Tilia Americana or Ulmus rubra. However, the current study found Acer saccharum, Larix decidua and Populus tremuloides. Total basal area increased from 0.63 in 1974 to 1.66 in 2016 (Appendix A). The total number of sub-canopy species decreased from 11 in 1974 to 9 in 2016. This change marked by the absence of Fraxinus nigra, Pinus strobus, Quercus rubra, Tilia americana and Ulmus rubra in 2016. However, Acer saccharum, Carpinus caroliniana, Hamamelis virginiana and Ostrya virginiana were found only in 2016. Total basal area increased from 0.01 m2/ha in 1974 to 0.08 m2/ha in 2016 (Appendix B). Ground cover increased from 36 species in 1974 to 41 species in 2016. A complete species list can be found in Appendix 1. For both years this is highest diversity of species found in the survey (Appendix B). For the birch beech and maple forest type, the total number of canopy species consisted of 10 species in 1974 and 11 species in 2016. Hamamelis virginiana was absent from the the 2016 survey. The total basal area for the canopy increased from 0.40 m2/ha to 1.53 m2/ha (Appendix A). For the sub-canopy, the number of species decreased from 11 in 1974 to 8 in 2016. This can be attributed to lack of Acer pennsylvanicum, Amelanchier arborea, Quercus rubra and Tilia americana. Total basal area decreased slightly from .012 m2/ha in 1974 to .01 m2/ha in 2016 (Appendix B). Total number of species increased from 15 in 1974 to 25 in 2016. The total list of species can be found in Appendix C.

DISCUSSION

Many of the differences seen in the study can be attributed to the reduced number of sampling points in 2016 compared to 1974. For the Hemlock forest type, Appalachain oak forest type and birch beech maple forest type, there were 24 points of data taken. For the swamp forest type there were 16 points of data taken. The decreased number of canopy and subcanopy species can likely be attributed to this less intensive sampling. The increase in basal area for the canopy of all the 2016 plots surveyed can be attributed to normal successional trends in which trees will increase in size given a longer period of time (Chapman and McEwen 2016). The subcanopy

- 125 - varied with two forests types (hemlock and swamp) increasing and two forests types (Appalachain and birch beech maple) decreasing. This change might be attributed to changes in soil composition and light availbalility with the increase in canopy cover. The results for ground cover were a surprise. Hemlock forests typically have lower understory diversity due to the presence of acidic soils and low light availability, hence a decrease in ground cover. The high amount of ground cover recorded in 1974 was most likely due to the lower canopy cover. Ground cover for all other plots has increased despite the increased basal area of the canopy. In part, this could be due to the division made in 1974 by measuring shrubs and saplings separately from herbs. However, this seemed inconsequential because all species included in this category were so small that they were unmeasurable with a dbh tape and, therefore, should be considered ground cover species. Some species were left unidentified either because they were too young for positive identification or they were unknown to the author. To continue this study into the future, resurveying all of the original plots developed by Dayton (1975), sites would allow for an estimation of how forest types in the Upper Susquehanna watershed change over time. This would also help to correlate current data with previous data on aspect and soil compostion to see how these differences affect successional changes. To continue what was done in this study, the point-quartered transect method should continue to be used but needs to include distance from the point of stopping to the tree being measured. This will allow for density (stems/ha) calculations that are not present in this study but present in 1974.

REFERENCES

Barbour M.G., J.H Burk., W.D.Pitts, F.S.Gilliam, and M.W.Schwartz 1999. Terrestrial Plant Ecology. 3: 269-278. Chapman, J.I. and R.W. McEwan. 2016. Thirty years of compositional change in an old growth temperate forest: The Role of topographic gradients in oak-maple dynamics. Plos ONE. 11(7): 1-17. Chesson P.L. and R. R.Warner. 1981. Environmental variability promotes coexistence in lottery competitive systems. Am Nat. 117: 923–943. Cox, L. E. and J.L. Hart. 2015. Two centuries of forest compositional and structural changes in the Alabama Fall Line Hills. The American Midland Naturalist, 2: 218. Dayton B. 1975. Vegetation map of Field Station. In 7th Ann. Rept. (1974). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Hurtt, G.C. and S.W. Pacala.1995. The consequences of recruitment limitation: reconciling chance, history and competitive differences between plants. J Theor Biol. 176: 1–12.

- 126 -

APPENDIX A

Hemlock forest type and Appalachian Oak forest type

- 127 -

Swamp forest type and Birch Beech Maple forest type

APPENDIX B

Hemlock Forest Canopy Species Basal area '16 Species Basal Area '74 Acer rubrum 0.089523761 Acer rubrum 0.148295 Fagus grandifolia 4.04188E-05 Betula alleghaniensis 0.0309875 Quercus rubra 0.006719967 Betula papyrifera 0.04316 Tsuga canadensis 4.757686436 Fagus gradnifolia 0.15936 Total 4.853970583 Fraxinus americana 0.0055325 Quercus alba 0.0077475 Quercus rubra 0.1272675 Tsuga canadensis 0.55223 Total 1.07458

- 128 -

Appalachain Oak Forest Canopy Species Basal area '16 Species Basal area '74 Acer rubrum 0.00267809 Acer rubrum 0.06469 Betula lenta 3.15892E-08 Acer saccharum 0.00209 Carpinus caroliniana 7.52694E-08 Amelanchier 0.00088 arborea Fagus grandifolia 1.30109E-06 Betula papyrifera 0.02386 Pinus strobus 1.27637E-06 Fagus grandifolia 0.0053 Quercus alba 0.000812405 Fraxinus 0.00087 americana Quercus rubra 6.437928517 Picea abies 0.01041 Tsuga canadensis 0.008329615 Pinus strobus 0.00254 Total 6.449751312 Prunus serotina 0.00129 Quercus alba 0.05848 Quercus rubra 0.29834 Tsuga canadensis 0.00358 Total 0.47233

Swamp Canopy Species Basal area '16 Species Basal area '74 Acer sacchaurm 2.84147E-05 Acer rubrum 0.015535 Betula alleghaniensis 0.002926209 Betula 0.10934 alleghaniensis Fraxinus americana 1.434777549 Fagus grandifolia 0.05035 Larix decidua 0.10640761 Fraxinus 0.080265 grandifolia Ostrya virginiana 1.91989E-07 Fraxinus nigra 0.035945 Pinus strobus 0.000340172 Ostrya virginiana 0.0284825 Populus tremuloides 5.78577E-06 Pinus strobus 0.0184375 Tsuga canadensis 0.11747844 Quercus rubra 0.0200525 Total 1.661964372 Tilia americana 0.0567875 Tsuga canadensis 0.2013925 Ulmus rubra 0.01349 Total 0.6300775

- 129 - Birch Beech Maple Forest Canopy Species Basal area '16 Species Basal area '74 Acer rubrum 0.005493133 Acer rubrum 0.0461525 Acer saccharum 0.004094402 Acer saccharum 0.08052 Betula alleghaniensis 3.371E-06 Betula alleghaniensis 0.000255 Fagus grandifolia 0.009606639 Fagus grandifolia 0.08268 Fraxinus americana 0.087518108 Fraxinus americana 0.0278425 Ostrya virginiana 1.66357E-05 Hamamelis virginiana 0.0006275 Quercus rubra 0.008996069 Ostrya virginiana 0.00037 Tilia americana 0.008335194 Quercus rubra 0.09901 Tsuga canadensis 1.414956547 Tilia americana 0.02732 Total 1.539020099 Tsuga canadensis 0.03451 Total 0.3992875

APPENDIX C

Hemlock Forest Subcanopy Species Basal area '16 Species Basal area '74 Fagus grandifolia 2.37212E-05 Acer rubrum 0.013055 Hamamelis virginiana 4.38204E-08 Betula papyrifera 0.001225 Tsuga canadensis 1.055668232 Fagus grandifolia 0.0149925 Total 1.055691997 Fraxinus 0.00815 americana Tsuga canadensis 0.1067525 Total 0.144175

- 130 - App Oak Forest Subcanopy Species Basal area '16 Species Basal area'74 Acer rubrum 0.022040155 Acer pensylvanicum 0.0000125 Amelanchier arborea 0.001061028 Acer rubrum 0.0536975 Betula lenta 3.70039E-06 Acer saccharum 0.0000125 Betula papyrifera 1.76756E-07 Amelanchier sp. 0.005025 Castanea dentata 5.3504E-05 Betula papyrifera 0.000415 Fagus grandifolia 0.002081438 Carpinus caroliniana 0.000455 Fraxinus americana 6.81634E-06 Carya ovata 0.0000875 Hamamelis virginiana 0.010041411 Cornus rugosa 0.00001 Tsuga canadensis 0.000838638 Custeanea dentata 0.0005825 Total 0.036126867 Fagus grandifolia 0.02793 Fraxinus americana 0.000075 Hamamelis 0.011535 virginiana Ostrya virginiana 0.0001025 Picea abies 0.004445 Quercus alba 0.00214 Quercus rubra 0.00611 Rhodendron roseum 0.00004 Tsuga canadensis 0.0009475 Total 0.1136225

Swamp Subcanopy Species Basal area '16 Species Basal area '74 Acer rubrum 0.000146053 Acer rubrum 0.00088 Acer saccharum 4.0511E-05 Betula 0.0004275 alleghaniensis Betula alleghaniensis 0.00465624 Fagus grandifolia 0.002295 Carpinus caroliniana 0.000593832 Fraxinus americana 0.0003375 Fagus grandifolia 9.91773E-06 Fraxinus nigra 0.0010425 Fraxinus americana 0.065232244 Ostrya virginiana 0.001095 Hamamelis virginiana 1.10141E-07 Pinus strobus 0.00049 Ostrya virginiana 5.53939E-05 Quercus rubra 0.0005825 Tsuga canadensis 0.005881063 Tilia americana 0.0021125 Total 0.076615377 Tsuga canadensis 0.0038375 Ulmus rubra 0.000625 Total 0.013725

- 131 -

Birch Beech Maple Forest Subcanopy Species Basal area '16 Species Basal area '74 Acer rubrum 1.70912E-05 Acer 0.0001225 pensylvanicum Acer saccharum 1.72867E-07 Acer rubrum 0.0009075 Carpinus caroliniana 3.03338E-07 Acer saccharum 0.003145 Fagus grandifolia 0.005489253 Amelanchier 0.000075 arborea Fraxinus americana 2.98396E-07 Carpinus 0.0000525 caroliniana Hamamelis 3.00836E-09 Fagus grandifolia 0.00485 virginiana Ostrya virginiana 1.75561E-05 Fraxinus 0.0007975 americana Tsuga canadensis 0.004844743 Hamamelis 0.0003175 virginiana Total 0.01036942 Quercus rubra 0.000205 Tilia americana 0.0002075 Tsuga canadensis 0.002005 Total 0.012685

- 132 - APPENDIX D

Hemlock Forest Species % Frequency '16 Acer rubrum 4.5 Mitchella repens 6 Fagus grandifolia 20 Unkown 1 2

Species % Frequency '74

Acer pensylvanicum 14 Acer rubrum 96 Aralia nudicalis 11

Clintonia borealis 25 Dennstaedtia punctiloba 11 Fagus grandifolia 18 Fraxinus americana 25 Hamamelis virginiana 7 Lonicera canadensis 4 Maianthemum canadense 46

Medeola virginiana 43 Mitchella repens 39 Quercus rubra 71

Rhodendron spp. 7 Taxus canadensis 7 Trientalis borealis 18

Tsuga canadensis 11 Viburnum acerifolium 14 Viburnum alanifolium 7

- 133 - App Oak Forest Species % Frequency '16 Species % Frequency '74 Acer rubrum 9 Aralia nudicaulis 58 Betula spp. 25 Aster divaricatus 50 Fagus grandifolia 22 Carex pensylvanica 58 Gaultheria procumbens 3.5 Clintonia borealis 67 Hamamelis virginiana 10.5 Cornus canadensis 25 Maianthemum canadense 24 Gaultheria 58 procumbens Medeola virginiana 31.5 Maianthemum 83 canadense Mitchella repens 12 Medeola virginiana 33 Picea abies 5 Mitchella repens 67 Polygonatum spp. 17 Monotropa uniflora 50 Prenanthes spp. 1 Polygonatum 42 biflorum Pteridium aquilinum 58 Pteridium aquilinum 67 Quercus alba 5 Trientalis borealis 100 Quercus rubra 6.5 Unknown 1 3 Vaccinium angustifolium 100

Swamp Species % Frequency '16 Species % Frequency '74 Acer pennsylvanicum 16 Actaea rubra 11 Acer rubrum 6 Aralia nudicaulis 16 Acer saccharum 1 Arisaema triphyllum 79 Actaea rubra 16 Aster acuminatus 11 Alliaria petiolata 5 Aster divaricatus 26 Arisaema triphyllum 8 Aster sp. 68 Athyrium angustum 10 Athyrium felix- 37 femina Betula spp. 32 Caltha palustis 16 Caulophyllum 7 Carex spp. 11 thalictroides Clinopodium vulgare 11 Caulophyllum 11 thalictroides Dryopteris marginalis 20 Circaea alpina 11 Equisetum sylvatica 17 Circaea 16 quadrisulcata Fraxinus americana 3 Clintonia borealis 11 Galium trifolium 2 Cyperus spp. 42 Grass 1 5 Epipactus 21

- 134 - helleborine Impatiens capensis 15 Equisetum pratense 11 Lonicera tatarica 60 Equisetum sylvatica 16 Maianthemum canadense 1 Galium triflorum 21 Medeola virginiana 2 Habeneria sp. 21 Onocela sensiblis 100 Lycopodium 11 lucidulum Osmunda claytoniana 66 Maianthenum 79 canadense Ostrya virginiana 3 Medeola virginiana 11 Oxalis stricta 1 Mitchella repens 26 Parthenocissus 2 Onoclea sensibilis 42 quinquefolia Podophyllum peltatum 10 Osmunda clatoniana 11 Quercus rubra 1 Parthenocissus 11 quinquifolia Rannunculus 1 Polystichum 21 acrostichoides Rubus dalibarda 15 Prenanthes altissima 32 Sedge 1 35 Pyrola asarifolia 32 Sedge 2 37 Smilacina racemosa 16 Solidage canadense 2 Smilax herbacea 16 Tiarella cordifolia 1 Tiarella cordifolia 16 Toxicodendron radicans 30 Trientalis borealis 21 Unknown 1 20 Trillium spp. 16 Unknown 2 1 Uvularia sessilifolia 37 Unknown 4 1 Viola spp. 42 Unknown 8 2 Unknown 9 2 Unknown grass 1 5 Veratrum viride 1 Veronica scutellata 5

- 135 - Birch Beech Maple Forest Species % Frequency '16 Species % Frequency '74 Acer rubrum 4 Actea pachypoda 28 Acer saccharum 12 Aster divaricatus 44 Betula spp. 20 Aster spp. 64 Dryopteris spp. 27 Carex spp. 18 Epipactus heleborine 7 Clintonia borealis 15 Eurybia divaricata 2 Epipactus 13 helleborine Fagus grandifolia 42 Hieracium spp. 18 Fraxinus americana 22 Maianthemum 33 canadense Grass 1 27 Mitchella repens 10 Grass 2 2 Smilacina racemosa 28 Hamamelis virginiana 2 Solidago spp. 8 Lonicera tatarica 5 Tillium spp. 36 Medeola virginiana 4 Trientalis borealis 10 Monotropa uniflora 2 Uvularia sessifolia 74 Onoclea sensiblis 5 Viola spp. 54 Osmunda claytoniana 5 Ostrya virginana 5 Parthenocissus quinquefolia 30 Podophyllum peltatum 20 Polygonatum biflorum 1 Polystichum acrostichoides 70 Tiarella cordifolia 3 Unknown 1 2 Veronica arvensis 2 Viola spp. 2

- 136 - Resurgence of spawning rainbow smelt (Osmerus mordax) in the Mohican Canyon Creek, Otsego Lake, NY

Mary K. Mulvihill1and John R. Foster2

Abstract: Rainbow smelt (Osmerus mordax), a key component of the 1980s cold-water fish fauna of Otsego Lake, was decimated in the 1990s by the introduction of alewives (Alosa pseudoharengus). With the recent collapse of the alewife population, the rainbow smelt population was expected to rebound. The goal of this study was to examine the population dynamics of spawning rainbow smelt in the Mohican Canyon Creek, for evidence of that recovery. Between 9-14 April 2016, (9:00pm-12:00am) 1,357 adult smelt were sampled using a Halltech backpack electrofisher. This 2016 study found that the smelt spawning population had increased, however, average size of spawners, spawning age and sex ratios had not return to the pre-alewife levels (1983 and 1984).

INTRODUCTION

Rainbow smelt (Osmerus mordax) were introduced into Otsego Lake in 1978 where they used many of the lake’s tributary streams for spawning (Cornwell 2001, Cornwell 2004, 2014), including Mohican Canyon (Cornwell 2001), 3-Mile Point Creek (Cornwell 2004), 6-Mile Point Stream and Shadow Brook (Harman et al 1997). Smelt abundance was high in the 1980s and a dip net fishery developed in these tributary streams during the spawning runs (MacWatters 1983). Population studies were conducted of rainbow smelt in Mohican Canyon Creek by MacWatters (1984), Cornwell (2004), Best (2015) and Best and Foster (2016). Spawning behavior there was also documented (Cornwell 2001). Rainbow smelt was a major component of the Otsego Lake ecosystem, providing a food source to piscivorous fish species into the mid-1980s. However, following the 1986 alewife introduction (Foster 1990) and their subsequent population explosion (Foster and Gallup 1991) the rainbow smelt population declined (Harmen et al. 1997). The diet of alewife, as well as their habitat within the water column, overlaps the smelt niche (Smith 1970). Thus, smelt populations could have been negatively impacted through species competition or predation on smelt fry by alewife (Simonin et al. 2012). Walleye (Sander vitreum) were introduced into the lake in 2000 to enhance Otsego Lake’s fisheries and to potentially control the overabundant alewife (Golding 2006). This led to the decline and subsequent extirpation of the alewife population (Waterfield and Cornwell, 2014). With its primary competitor gone, rainbow smelt populations were expected to resurge. In order to monitor this comeback, the spawning population in Mohican Canyon Creek was studied. The goal of the study was to characterize the population dynamics of rainbow smelt spawning in Mohican Canyon Creek. To meet this goal, sex ratio, growth, age and length distribution and population size was determined. These data were compared to data collected by MacWatters (1984) before the introduction of alewives and data collected in 2015 (Best and Foster 2016).

1 SUNY Oneonta Biol. Field Station Intern, Fisheries & Aquaculture Student, SUNY Cobleskill, Cobleskill, NY. 2 Professor & Chair, Fisheries, Wildlife & Environmental Science Department, SUNY Cobleskill, Cobleskill, NY.

- 137 - This study was conducted at Mohican Canyon Creek (Latitude 42.764851, Longitude 74.899007) near 5-Mile Point (Figure 1). Smelt in their spawning run have been studied at Mohican Canyon Creek by MacWatters (1984), Cornwell (2001 and 2004) and Best and Foster (2016). MacWatters’ (1984) initial study was conducted at Mohican Canyon Creek, because of the large spawning run and its location on private property away from public access.

MATERIALS & METHODS

This study was conducted 09, 11 and 14 April 2016, between 9:00pm and 12:00 am. A Halltech Backack Electrofisher was used to capture spawning smelt. Several electrofishing runs were conducted at 45 minute intervals with a shock time of 152-690 seconds. Electrofishing started at the stream mouth and continued upstream for 26m, covering an area of 93.3m2. Headlamps were used to conduct the sampling. Captured smelt were measured (total length), sexed, and a few scales were removed from behind the pectoral fin for aging. All sampled fish received an adipose fin clip. Smelt were kept in totes during processing and then released back into Mohican Canyon Creek.

Figure 1. This study was conducted at Mohican Canyon Creek at its confluence with Otsego Lake at 5-Mile Point.

- 138 - RESULTS

This study was based on 1,357 spawning smelt collected from Mohican Canyon Creek on 9 -14 April 2016. Water temperature during spawning runs ranged from 4.3°C to 6.3°C; water turbidity was 5.2 – 9 NTU, pH ranged from 6.4 to 7.7 and dissolved oxygen ranged from 13.2 to 15.5 mg/L (Table 1). Table 1. Water parameters in Mohican Canyon Creek during April 2016 sampling.

Dissolved Date Air Temp Water Temp pH Conductivity Oxygen Turbidity (°C) (°C) (uS/cm) (mg/L) (NTU) 4/9/2016 -2.8 6.3 7.6 152 15.5 5.2 4/11/2016 10 4.3 6.4 155 13.5 9 4/14/2016 4.4 5.2 7.7 168 13.2 6.8

Sex Ratio

In Mohican Canyon Creek, female smelt were significantly less abundant than male smelt in 2016, 2015 and 1983 (Chi square test, P<.001, Figure 2). Of 424 smelt sexed in 2016, only 16% of smelt captured were females. Similarly, in 2015 only 7% were females, while in 1983 females made up 32% of the spawning population and 47% in 1984.

100 90 Male

80 Female 70 60 50 40 30 20 Per cent Spawnerscent Per 10 0 1983 1984 2015 2016 Year

Figure 2. Per cent of males and females spawning in Mohican Canyon Creek in 1983, 1984, 2015 and 2016.

- 139 - Males were present in the stream throughout the evening sampling period (2100-2245 hrs.; Table 2). Female smelt were sampled in each run, however, their numbers increased throughout each night. This was different than the pre-alewife study, showing the peak time females entered the stream was around 2230 hrs. (MacWatters 1983).

Table 2. The number of male and female rainbow smelt captured along a 26m spawning reach of Mohican Canyon Creek, 2016.

Date Run Time Males Females 1 2100 12 2 2016-04-09 2 2130 45 10 3 2245 121 25 1 2045 79 16 2016-04-11 2 2145 235 73 2016-04-14 1 2100 631 108

Size of Spawning Smelt

Spawning smelt were primarily between 121mm and 140mm (Figure 3). Spawning females (mean length 133mm) were significantly larger than spawning males (mean length 129 mm; T-test, P < .03).

250

200 Females Males 150

100

Number of Spawning SmeltNumber of Spawning 0 100-120 121-140 141-160 161-180 181-200

Total Length (mm)

Figure 3. Length frequency distribution of spawning rainbow smelt captured in Mohican Canyon Creek in April 2016.

- 140 - Spawning Age

In 2016, spawning smelt were dominated by one year old fish, which made up 55% of the spawning population (Figure 4). In pre-alewife years (1983 and 1984), 2-year-old smelt dominated the spawning population (62% and 88%). The percentage of 3-year-old smelt in 2016 was 13%, which was greater than the percentages captured in all other studies (6% in 1983; 0.3% in 1984; 5% in 2015). There were no smelt in any of the studies that were over 3+ years of age. The age frequency distribution of rainbow smelt spawning in Mohican Canyon Creek was significantly different between all 4 years studied (Chi square test, P<.001).

100 90 1983 80 1984 70 2015

60 2016 50 40 Per Cent Per 30 20 10 0 1 2 3 Age of Spawning Population

Figure 4. Age frequency distribution of rainbow smelt spawning in Mohican Canyon Creek in 1983, 1984 2015 and 2016.

Growth

On average, the one year old smelt sampled in 2016 were the same size as the smelt captured in 1983 and 1984 (Figure 5). However, the size of the 2-year old smelt captured in 2016 was 10-13mm smaller than in 1983 and 1984. The size of the three year old smelt were 20mm below the average length of the smelt captured in all other studies.

- 141 - 1983 200 1984

180 2015 160 2016 140 120 100 80

Total Lenght (mm) Lenght Total 60 40 20 0 Age1 Age 2 Age 3

Year

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

Spawning Population Size

Estimations of the population size of spawning smelt presents problems. Marked males were much more likely to be recaptured than marked females (Chi square Test, P < .001). The percent of marked males recaptured was 11.3%, while the percent of marked females recaptured was only 4.3%. Therefore, the estimation of the population size of spawning smelt was likely to be biased because males remain on the spawning ground longer than females. Further, since smelt freely move into and out of the spawning area, the population being sampled is not “closed”.

While the population estimate may not be completely accurate, it may be useful for future comparisons. Two methods were used to estimate spawning smelt population in Mohican Canyon Creek in 2016. The Schnabel method (Ricker 1975) estimates the population at 5,709, while Chapman (Ricker 1975) estimates the population to be 9,315.

The number of spawning smelt captured in 2016 was considerably higher than the number of smelt captured in 2015. In 2015, 152 smelt were captured in 9 runs giving an average number captured per run at 17. In 2016, 1357 smelt were captured in 6 runs giving an average number captured per run at 226. Thus, the catch per unit effort, as well as the density of spawning smelt was 13.3 times higher in 2016 compared to 2015.

- 142 - DISCUSSION

Data collected in this study show some evidence of a rebound of the rainbow smelt population in Mohican Canyon Creek in 2016. Smelt density and catch per unit effort increased 13.3 times over that of 2015 (Best and Foster 2016).

However, other measures of smelt population dynamics were more equivocal. The mean size of the smelt sampled in 2016 (130mm) was 5mm larger than in 2015. More importantly it was 5 mm larger than the mean length (125mm) when alewives were abundant (Cornwell 2004). So, while the 2016 smelt spawning size is increasing in the absence of alewives, it is still considerably smaller than the 149mm mean size of pre-alewife smelt (MacWatters 1984).

In pre-alewife years, 2-year olds dominated the spawning population (MacWatters 1984). The spawning age of smelt captured in 2015 and 2016 was dominated by 1-year olds. However, in 2016 the proportion of smelt age-2 and age-3 increased, possibly indicating a change in age structure toward the pre-alewife years.

Growth to age-1in 2016, was faster than in 2015 and the same as in the pre-alewife years (1983 and 1984). However, growth to age-2 and age-3 was far below the pre-alewife years (MacWatters 1984).

The smelt spawning population was mainly males in 2015 and 2016, while pre-alewife sex ratio was more evenly distributed (MacWatters 1984). However, in 2016 the proportion of females increased over that of 2015, which again may indicate movement toward the pre-alewife sex distribution.

The 2015 and 2016 post-alewife studies were based on backpack electrofishing surveys and produced comparable data. However, it is difficult to compare these studies to previous studies which used dip-nets (MacWatters 1983, 1984) and seines (Cornwell 2001, 2004). A further complication is that rainbow smelt populations can have nearly a 10-fold variance over a decade (Rupp 1968). Therefore, to gain a better understanding of the changes in the smelt population in post-alewife years, spawning population should continue to be monitored at Mohican Canyon Creek.

REFERENCES

Best, M.J. 2015. Status of rainbow smelt (Osmerus mordax) in the Mohican Canyon tributary, May 2014. In 47th Ann. Rept. (2014). SUNY Oneonta Biol. Fld Sta., SUNY Oneonta.

Best, M.J. and J.R. Foster. 2016. Characterization of spawning rainbow smelt (Osmerus mordax) in the Mohican Canyon Creek, Otsego Lake, NY. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld Sta., SUNY Oneonta.

- 143 - Cornwell, M. D. 2001. Evidence of rainbowsmelt (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., and S. Gallup. 1991. Irruption of the alewife population of Otsego Lake. In 23rd Ann. Rept., 1990. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta. pp. 56-59. Harman, W.N., L.P. Sohacki, M.F. Albright and D.L. Rosen. 1997. The state of Otsego Lake 1936-1996. Occ. Pap. #30. pp.252-266. 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 smelt, Osmerus mordax (Mitchill) in Otsego Lake, New York. In 16th Ann. Rept. (1983). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Rupp, R.S. 1968. Life history and ecology of the smelt (Osmerus mordax) in inland waters of Maine. Maine Dept, of Fish & Game. 36 pp. Ricker, W.E. 1975. Computation and interpretation of biological statistics of fish populations. Fisheries Research Board of Canada, Bulletin 191. Simonin, P.W., D.L. Parrish, L.G. Rudstam, P.J. Sullivan and 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. Smith, S.H. 1970. Species interactions of the Alewife in the Great Lakes. Trans. Am. Fish. Soc. 99(4): 754-765.

Waterfield, H.A. and 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.

- 144 - Comparison of amphibian abundance in vernal pools at two different sites on Rum Hill

Thomas Franzem1 and Dan Stich2

INTRODUCTION

Vernal pools are seasonal wetlands and are important components of upland ecosystems (Calhoun and DeMaynadier 2007). In the Northeast United States, the pools fill with water during spring and gradually dry during the summer. This prevents fish from establishing and allows amphibians to take advantage of a predator-free environment (Zedler 2003). These habitats are important breeding sites for amphibian species, many of which are declining globally in abundance more quickly than in the past (McCullum 2007), largely as a result of human impacts (Stuart et al. 2004; McCullum 2007; Windmiller et al. 2008). The fungal disease chytridiomycosis contributes to declines in amphibian populations; its continued spread threatens amphibians at higher elevations in particular (Stuart et al. 2004).

One promising tool for conservation of pool-breeding amphibian species and their habitats is the construction of vernal pools (Kusler and Kentula 1989). Research on how newly created pools function for amphibian conservation is limited, which makes it difficult to predict the value of vernal pool construction at specific sites during conservation planning. Some anthropogenic pools have even been observed to function as “ecological traps” that can hurt amphibian populations (DiMauro and Hunter 2002). There is a general lack of knowledge about environmental factors that affect amphibian success in anthropogenic vernal pools, including how the pools interact with the surrounding environment, and how amphibian populations respond to them. Many factors, including pool size, pool location, water depth, and hydroperiod may impact amphibian occupancy and population dynamics in seasonal pools.

As part of an amphibian conservation project, the Upper Susquehanna Coalition constructed 21 vernal pools on the Rum Hill property of the State University of New York College at Oneonta Biological Field Station from 2007 through 2010. The first pools (1 – 8) were constructed in an upland meadow during summer 2007, and an additional 12 pools (9–21) were created in a nearby northern hardwood forest (Figure 1). Prior to spring 2016, those pools had not been formally studied to assess conservation value to pool-breeding amphibians. The goals of this study were to 1) estimate abundances of amphibians at two sets of anthropogenic vernal pools in distinct habitats, 2) and determine whether or not abundance differed between these two sites in an effort to guide further investigation of potential drivers of abundance.

1 Undergraduate Research Aide, SUNY Oneonta. 2 Assistant Professor of Biology, SUNY Oneonta.

- 145 -

METHODS Study site A total of 15 artificially constructed vernal pools were monitored during the spring 2016 breeding season in two distinct sites (upper and lower; Figure 1) on the Rum Hill property of the SUNY Oneonta Biological Field Station in Cooperstown, NY. At the lower site, all pools were surveyed (pools 1–8; Figure 1). We surveyed seven pools (9, 10, 11, 18, 19, 20, and 21; Figure 1) at the upper site to approximately balance the number of pools monitored at each site. We selected these seven pools to be representative of the geographic distribution of the pools over the upper site.

Figure 1. Map of study site on SUNY Oneonta’s BFS Rum Hill Property. Pools 1 – 8 are located in an upland meadow; pools 9 – 21 are in a northeastern hardwood forest.

Amphibian surveys We constructed pitfall traps around the perimeter of each study pool on 19 March 2016

- 146 - using 0.5 m construction-grade silt fence. We erected fencing with wooden stakes placed every 3 meters around the perimeter of each pool, approximately 0.5 m from the shoreline. The bottom edge of fencing was weighed down with rocks to create a more effective barrier. At each pool we cut two gaps in the fencing, with random orientations, and dug a small hole at each opening. We placed a 1-gallon plastic bucket in each hole and filled any gaps between the bucket and hole with dirt. Pool 18 was the largest pool at the upper site and was irregularly shaped; therefore, we used four buckets around this pool to account for that.

Amphibian surveys began on 16 March 2016 to monitor activity prior to the construction of pitfall traps. We checked traps daily for amphibians following fence construction on 19 March 2016. All amphibians collected in the traps were identified, recorded, and released. Daily surveys were conducted until 12 April 2016 because there was a dramatic decrease in the number of amphibians observed per survey that indicated their breeding season was over prior to that date.

Habitat characteristics At the beginning of the survey period, we measured initial depth of all pools, and we noted if the pool had a stream running through it (Table 1). We measured the size of each of the studied pools on 14 June 2016. During the same survey, we also noted the water level in the pools to characterize summer water level (empty or inundated).

Table 1. Habitat characteristics of each vernal pool monitored on the Rum Hill property of the SUNY Oneonta Biological Field Station 16 March 2016 through 12 April 2016, including pool ID, site for each pool, presence of stream connecting a pool to others (Y/N), initial water depth, area, and summer water level (observed 14 June 2016). Pool Site Stream (Y/N) Depth (cm) Area (m2) Summer water level 1 Lower Y 56 124 Inundated 2 Lower Y 70 77 Empty 3 Lower Y 74 112 Inundated 4 Lower N 33 48 Empty 5 Lower N 0 38 Empty 6 Lower Y 34 69 Inundated 7 Lower Y 27 32 Inundated 8 Lower N 0 21 Empty 9 Upper Y 34 41 Inundated 10 Upper Y 53 37 Inundated 11 Upper N 35 10 Inundated 18 Upper N 49 35 Inundated 19 Upper N 41 19 Inundated 20 Upper N 34 13 Inundated 21 Upper N 29 22 Inundated

Abundance estimates We used counts on each day to estimate abundance of each species in each pool over the entire study period. Not all amphibians entering or exiting pools were caught in pitfall traps based on visual observations (probability of capture < 1.00). Therefore, we used a binomial

- 147 - mixture model (Royle 2004) that allowed for temporal separation of estimates (Dodd and Dorazio 2004) to estimate abundance during each week of sampling based on daily trap data. This method also allowed us to account for imperfect detection of individuals in pitfall traps at each site within weeks. We implemented the model using a Bayesian approach (see Kéry 2010). We assumed that amphibian count, at each ith pool during week j (Nij) was represented by a Poisson distribution described by parameter λij:

Nij ~ Poisson(λij)

We used an uninformative prior distributions on the log scale. For each pool during each week, the parameter λij was assigned a prior on the log scale with a mean of zero and a variance of of ten:

log(λij) ~ Normal(0, 10)

In order to estimate detection probability for each species, we modeled counts in each pool during each week (Cij) as the outcome of a binomial process (number of success observed), with the distribution of Cij described by parameters pij (probability of successful detection) and Nij from the Poisson distribution of counts above in order to link the likelihoods for detection probability and estimated population sizes:

Cij ~ Binomial(pij, Nij)

We used Markov chain Monte Carlo (MCMC) methods to estimate model parameters in JAGS using the ‘R2jags’ package (Su and Yajima 2015) in R (R Core Team 2016). We used a burn-in of 3,000 runs, and simulated an additional 30,000 samples from each posterior distribution, saving every 30th sample to reduce autocorrelation between samples and increase the number of independent samples from the posterior distribution (Kruschke 2010). We ran a total of three Markov chains for each parameter, resulting in a total of 2,700 samples from which to construct posterior distributions. We assessed convergence among the three chains for each parameter using the Gelman-Rubin convergence diagnostic (Gelman and Rubin, 1992), and visually inspected plots of Markov chains to ensure adequate mixing (Kruschke 2010).

Seasonal population abundance at each pool for each species was estimated using the week during which species abundance was greatest. We estimated the mean difference in abundance between pools at the upper site and the lower site by subtracting the seasonal population abundance at pools in the lower site from the mean estimated population abundance at pools in the upper site for each iteration of MCMC sampling. We used a similar approach to estimate the mean difference in abundance of amphibians in pools with and without streams, and in pools that were inundated in mid-summer and those that were empty. We present the mean and 95% CRI for these differences, and assume that where the 95% CRI did not contain zero those differences were statistically significant when α = 0.05.

- 148 - RESULTS

Habitat measurements Water depth was variable between pools and throughout the season at both sites. At the lower site, pools 5 and 8 never filled with water; there were zero wood-frogs in either of these pools (Table 2). Pool 4 at the lower site contained water during the breeding period, when multiple species of amphibians were collected in the pool (Table 2). However, this pool was empty during mid-summer (Table 1). The pools at the upper site were filled with water throughout the study period, and higher numbers of all amphibian species were collected in pit fall traps than at the lower site (Table 3).

Table 2. Pit-fall trap counts of amphibians in pools at the lower site during spring 2016. WF: wood frog (Rana sylvatica). JS: Jefferson salamander (Ambystoma jeffersonianum). SS: spotted salamander (Ambystoma maculatum). N: red spotted newt (Notophthalmus viridescens). Date Pool 1 Pool 2 Pool 3 Pool 4 Pool 5 Pool 6 Pool 7 Pool 8 19-Mar-16 0 0 0 0 0 0 0 0 20-Mar-16 0 0 0 0 0 0 0 0 21-Mar-16 0 0 0 0 0 0 0 0 22-Mar-16 0 0 0 0 0 0 0 0 23-Mar-16 0 0 0 0 0 0 0 0 24-Mar-16 1 WF 1 JS 0 0 0 0 0 0 25-Mar-16 0 0 1 JS, 3 SS 4 JS 0 0 0 0 26-Mar-16 1 WF 0 0 2 JS 0 2 JS, 3 SS 0 0 27-Mar-16 0 0 0 0 0 0 0 0 28-Mar-16 0 0 0 0 1 N 1 SS 0 0 29-Mar-16 0 1 WF 1 SS 0 0 0 1 WF, 1 SS 0 30-Mar-16 0 0 0 2 SS 0 0 0 0 31-Mar-16 0 0 0 0 0 0 0 0 1-Apr-16 1 SS 2 SS 2 WF, 6 SS 1 WF, 1 JF, 3 SS 1 N 5 SS 0 0 2-Apr-16 0 1 JS 1 WF 2 SS 0 3 SS 0 0 3-Apr-16 0 0 0 0 0 0 0 0 4-Apr-16 0 0 0 0 0 0 0 0 5-Apr-16 0 0 0 0 0 0 0 0 6-Apr-16 0 0 0 0 0 0 0 0 7-Apr-16 0 0 0 0 0 0 0 0 8-Apr-16 0 0 1 SS 1 JS 0 4 SS 1 JF 0 9-Apr-16 0 0 0 0 0 0 0 0 10-Apr-16 0 0 0 0 0 0 0 0 11-Apr-16 0 0 0 0 0 0 0 0 12-Apr-16 0 0 0 0 0 0 0 0

The average area of vernal pools was 46.5 m2. The largest pool was pool 1, which was approximately 124 m2 (Table 1). During the 14 June survey, we observed that pools 2, 4, 5, and 8 were drained, but the rest of the pools at the lower site held water. Pools 1, 2, 3, 6, and 7 were all connected by a stream that ran through the lower site. At the upper site, all pools retained water through early summer, and pools 9 and 10 were ephemerally connected by a ditch that was likely dug by beavers (Table 1).

- 149 - Table 3. Pit-fall trap counts of amphibians in pools at the upper site during spring 2016. Symbols are defined as in Table 2.

Date Pool 9 Pool 10 Pool 11 Pool 18 Pool 19 Pool 20 Pool 21 19-Mar-16 0 0 0 0 0 0 0 20-Mar-16 0 0 0 0 0 0 0 21-Mar-16 0 0 0 0 0 0 0 22-Mar-16 0 0 0 0 0 0 0 23-Mar-16 0 0 0 0 0 0 0 24-Mar-16 1 WF, 1 JS 3 WF, 2 JS 0 1 JS 1 WF, 1 JS 1 WF, 2 JS 0 25-Mar-16 4 WF, 4 JS, 2 SS 5 WF, 5 JS, 6 SS 2 WF, 7 JF, 1 SS 2 WF, 6 JF, 5 SS 23 WF, I JS, 14 SS 1 JS 3 WF, 3 JS, 7 SS, 3 N 26-Mar-16 1 SS 1 SS 2 WF 1 WF 2 WF, 1 SS 1 WF 1 SS, 1 N 27-Mar-16 0 0 0 1 JS 0 0 0 28-Mar-16 1 SS, 1 N 1 JS, 1 SS 1 WF, 1 SS 1 WF, 1 JS, 2 SS, 1 N 1 WF, 1 JS, 1 N 1 JS, 1 N 1 WF, 3 N 29-Mar-16 3 WF, 1 JS 0 0 18 WF, 2 JS, 2 SS 5 WF, 2 JS, 2 SS, 1 N 1 WF 2 WF, 2 SS 30-Mar-16 0 0 0 0 0 0 0 31-Mar-16 0 0 0 0 0 0 0 1-Apr-16 11 SS 5 SS 3 SS 1 WF, 3 JS, 17 SS, 2 N 3 WF, 8 SS, 1 N 3 SS 1 WF, 13 SS 2-Apr-16 1 JS, 2 SS 1 WF, 2 SS, 2 N 0 4 WF, 1 JS, 2 SS, 8 N 1 WF, 1 N 1 SS, 1 N 2 N 3-Apr-16 0 0 0 0 0 0 0 4-Apr-16 0 0 0 0 0 0 0 5-Apr-16 0 0 0 0 0 0 0 6-Apr-16 0 0 0 0 0 0 0 7-Apr-16 0 0 0 0 0 0 0 8-Apr-16 1 SS 2 JS 1 WF 2 WF, 1 JS, 2 SS 1 JS, 1 SS 1 SS 2 SS 9-Apr-16 0 1 JS 0 1 WF, 1 SS 0 0 2 SS 10-Apr-16 0 0 0 0 0 0 0 11-Apr-16 0 0 0 0 0 0 0 12-Apr-16 0 0 0 0 0 0 0

Wood frog abundance Estimated seasonal abundance of wood frogs in pools at the lower site was 5 wood frogs (95% credible interval [CRI] = 1 – 21 wood frogs), although abundance varied between pools (Figure 2a). We estimated abundance of zero wood frogs at pools 5 and 8, which did not contain water. The greatest abundance of wood frogs at the lower site was documented in pool 3 (mean = 11; 95% CRI = 2 – 52). The 95% CRI for the difference in abundance between pools connected by streams and those not connected by streams showed considerable overlap with zero (95% CRI = -4 – 29), indicating little evidence that abundance differed between pools based on stream connectivity at the lower site. Similarly, we found insufficient evidence to support differences in abundance between pools that retained water through early summer and those that drained seasonally (95% CRI for difference = -5 – 35).

Estimated abundance of wood frogs in a given pool at the upper site (mean = 83; 95% CRI = 29 – 237 wood frogs) was greater than at lower pools (Figure 2a). The greatest abundance occurred in pool 19 where we estimated a population size of about 254 wood frogs (95% CRI = 63 – 780). Interestingly, the lowest abundance of wood frogs at the upper site occurred just meters away in pool 20, where estimated abundance was just 5 wood frogs (95% CRI = 1 – 30).

The mean difference in seasonal abundance at the upper and lower sites was about 77 wood frogs per pool (Figure 3a). The 95% CRI on this estimate (21 – 234 wood frogs) did not include 0, indicating that the difference in abundance was statistically significant. This means that, on any given day during the breeding season, we might expect to find about 20 to 230 more wood frogs in any pool at the upper site than in any pool at the lower site on average.

- 150 -

Figure 2. Estimated seasonal abundance of (a) wood frogs (b) Jefferson salamanders, and (c) spotted salamanders by pool in lower (left) and upper sites (right) during spring 2016. Note the use of different scales for y-axis to allow visualization of results across species.

- 151 -

Figure 3. Differences in estimated seasonal abundance of (a) wood frogs (b) Jefferson salamanders, and (c) spotted salamanders in a given pool at upper and lower sites during spring 2016.

- 152 - Jefferson salamander abundance Estimated seasonal abundance of Jefferson salamanders in pools at the lower site was 7 individuals (95% credible interval [CRI] = 2 – 25), although abundance varied between pools (Figure 2b). As with wood frogs, we estimated abundance of zero Jefferson salamanders at pools 5 and 8, which did not contain water. The greatest abundance of Jefferson salamanders at the lower site was documented in pool 4 (mean = 23; 95% CRI = 4 – 120), which was drained by midsummer 2016. We found virtually no evidence for differences in abundance between pools connected by streams or not (95% CRI = -36 – 18) or differences in abundance between pools that retained water through early summer and those that drained seasonally (95% CRI for difference = -29 – 21).

Estimated abundance of Jefferson salamanders in a given pool was higher at the upper site (mean = 29; 95% CRI = 10 – 101) than at the lower site (Figure 2b). Pool 11 was estimated to have the greatest abundance at the upper site, with an estimated population size of about 58 Jefferson salamanders (95% CRI = 63 – 780). The lowest abundance occurred in pools 19 and 20, both of which were estimated to contain 9 Jefferson salamanders (95% CRI = 2 – 42 for both). We failed to detect significant differences in seasonal abundance of Jefferson salamanders at the upper and lower sites due to the variability among pools (Figure 3b). However, the spread of the 95% CRI on this estimate (- 6–96) indicates that some evidence exists to support this difference.

Spotted salamander abundance Spotted salamander abundance was highly variable between pools at both the upper and lower sites. Seasonal abundance of spotted salamanders was higher than for wood frogs and Jefferson salamanders (Figure 2c), with an estimated mean of 15 individuals per pool (95% credible interval [CRI] = 4 – 55). We estimated abundance of zero spotted salamanders at pools 5 and 8, which did not contain water. The greatest abundance of spotted salamanders at the lower site was estimated in pool 3 (mean = 47; 95% CRI = 7 – 256). As with Jefferson salamanders, no evidence for differences in abundance based on stream connectivity (95% CRI = -14 – 73) seasonal hydro period (95% CRI for difference = -15 – 90).

Estimated abundance of spotted salamanders in a given pool at the upper site (mean = 92; 95% CRI = 36 – 239 wood frogs) was greater than at lower pools (Figure 2c). The greatest abundance occurred in pool 18, where we estimated a population size of about 197 spotted salamanders (95% CRI = 18 – 543). As in the lower site, this species was most consistently abundant in pools at the upper site out of any of the three species modeled. The mean difference in seasonal abundance at the upper and lower sites was about 76 salamanders per pool (Figure 3c). The 95% CRI on this estimate (9 – 224 individuals) did not include 0, indicating that the difference in abundance was statistically significant.

- 153 - DISCUSSION

This work represents the first attempt to quantify population dynamics of amphibians in recently constructed vernal pools on the Rum Hill property of the SUNY Oneonta Biological Field Station. We have developed field protocols and statistical models that can be used as the basis for continued monitoring of the future, and we have noted several important trends in species distribution and abundance between two distinct sites for vernal pool construction.

In general, the vernal pools at the upper site tended to hold more amphibians during the breeding season than pools at the lower site. These differences likely are attributable to differences in habitat quality at these two sites. For example, Calhoun et al. (2003) indicated that a forested environment around a vernal pool improves water quality and serves as a terrestrial nursery for amphibian metamorphs. Although pools 4, 7, and 8 were nearest the forest edge, the pools at the lower site are generally concentrated in the center of the meadow, whereas all pools at the upper site were located within the forest. The different habitats could explain the abundance differences between the sites.

The hydroperiod at each site also may explain the difference in abundance. Four of the eight pools at the lower site were observed to be completely dry on at least one occasion while none of the upper pools were observed dry during our surveys (Table 3). Most importantly, pools 2, 4, and 5 all had egg masses before drying. Pool 4 contained the largest estimated abundance in the entire lower site. As a result, it is highly likely that these pools currently function as ecological traps that attract breeding amphibians to deposit egg masses that very likely result in total loss of reproductive effort.

REFERENCES

Calhoun, A.J., T.E. Walls, S.S. Stockwell, and M. McCollough. 2003. Evaluating vernal pools as a basis for conservation strategies: a Maine case study. Wetlands 23:70–81.

Calhoun, A.J.K., and P.G. DeMaynadier. 2007. Science and conservation of vernal pools in Northeastern North America: Ecology and Conservation of Seasonal Wetlands in Northeastern North America. CRC Press.

DiMauro, D., and M.I. Hunter Jr. 2002. Reproduction of amphibians in natural and anthropogenic temporary pools in managed forests. Forest Science 48:397–406.

Dodd C.K. Jr., and R.M. Dorazio. 2004. Using counts to simultaneously estimate abundance and detection probabilities in a salamander community. Herpetologica 60: 468–478.

Gelman, A. and D.B. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical Science. 7(4):457-472.

Kruschke, J. 2010. Doing Bayesian data analysis. Academic Press, Cambridge, Massachusetts.

Kusler, J. and M. Kentula. 1989. Wetland creation and restoration: the status of the science.

- 154 - United States Environmental Protection Agency, Research and Development, Environmental Research Laboratory.

McCullum, M. 2007. Amphibian decline or extinction? Current declines dwarf background extinction rate. Journal of Herpetology 41:483–491.

R Core Team. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Royle, J.A. 2004 N-mixture models for estimating population size from spatially replicated counts. Biometrics 60: 108–115.

Stuart, S.N., J.S. Chanson, N.A. cox, b.e. young, a.s.l. rodrigues, d.l. fischman, and r. w. waller. 2004. status and trends of amphibian declines and extinctions worldwide. Science 306:1783–1786.

Su, Y.S., and M. Yajima. 2015. R2jags: Using R to Run JAGS. R package version 0.5-7. Available: https://CRAN.R-project.org/package=R2jags.

Windmiller, B., R.N. Homan, J.V. Regosin, L.A. Willitts, D.L. Wells, and J.M. Reed. 2008. Breeding amphibian population declines following loss of upland forest habitat around vernal pools in Massachusetts, USA. Urban herpetology. Society for the Study of Amphibians and Reptiles, Salt Lake City: 41–51.

Zedler, P.H. 2003. Vernal pools and the concept of “isolated wetlands.” Wetlands 23:597–607.

- 155 - Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 20161

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.

- 156 -

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.

- 157 - METHODS

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 calculated as 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 2016. 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 AND DISCUSSION

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 2016, 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.

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 2016, the total amount of nutrients retained by the treatment wetland included about 1,300 kg of ammonia-N, 11,050 kg of nitrate-N, 15,000 kg of total nitrogen-N and 1,850 kg of total phosphorus-P. Over the course of 2015, the retention of ammonia was slightly 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

- 158 - three times higher than they had averaged from 2010 to 2014. Ammonia retention was substantially more negative over 2016, at about -30%. This corresponded with concentrations in the effluent being considerably lower from April through December (generally, less than 0.5 mg/l) then they had been before that.

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% in 2015 and just 5% in 2016) was lower than it had been over 2010-2014 due to the negative retention of ammonia over those years. Over 2016, total phosphorus retention has remained fairly steady at about 25% .

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).

- 159 - Table 1. Summary of ammonia, nitrate, total nitrogen and total phosphorus retention by the Cooperstown treatment wetland, 2010 through 2016.

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 2016 -90 -30 595 17 282 5 131 25 total kg (mean %) 1308 19 11050 29 14985 29 1848 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 between 2010-2014 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; summarized in Albright 2016)). Over 2015, several coagulating agents were used for phosphorus removal, and at different application rates and varying injection sites. These included K2001® (a polyaluminum chloride coagulant), PAX-WL8® (also a polyaluminum chloride coagulant) and SLACK PLUS® (an aluminum chloride coagulant), and lastly alum (aluminum sulfate). 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). The mean total phosphorus concentration over the year was 1.90 mg/l (Table 5).

Over the course of 2016, alum was used (which is considerable less expensive than most other coagulants). It was applied at a rate of 30 gal (115 l)/day, for a rate of about 1:10,000. Over 2016, total phosphorus averaged 1.18 mg/l (Table 5).

- 160 - 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

- 161 - 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 Jan-16 1124 2.11 2.03 71.1 68.5 2.5 3.6 Feb-16 1302 2.11 2.03 76.9 74.0 2.9 3.8 Mar-16 1196 1.31 1.26 48.7 46.8 1.9 3.9 Apr-16 1329 0.24 0.42 9.5 16.8 -7.3 -76.6 May-16 1139 0.27 0.60 9.6 21.0 -11.4 -119.1 Jun-16 1211 0.22 0.80 7.8 29.1 -21.3 -271.4 Jul-16 1423 0.64 1.67 28.2 73.7 -45.4 -160.9 Aug-16 1590 0.52 0.63 25.4 30.8 -5.4 -21.2 Sep-16 1143 0.31 0.33 10.7 11.4 -0.8 -7.2 Oct-16 950 0.09 0.04 2.6 1.2 1.4 54.7 Nov-16 924 0.21 0.20 5.8 5.6 0.3 4.8 Dec-16 1101 0.20 0.41 6.9 14.0 -7.1 -102.2 2016 1203 0.69 0.87 303.4 393.1 -89.7 -29.6 To Date 5901.6 4593.5 1308.1 19.0

- 162 - 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

- 163 - 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 Jan-16 1124 9.45 10.19 318.7 343.5 -24.8 -7.8 Feb-16 1302 5.96 3.32 217.4 121.0 96.5 44.4 Mar-16 1196 2.94 1.54 109.1 57.1 52.0 47.7 Apr-16 1329 10.85 8.54 432.4 340.4 92.1 21.3 May-16 1139 11.80 9.35 416.6 330.0 86.5 20.8 Jun-16 1211 9.07 6.84 329.5 248.6 80.9 24.5 Jul-16 1423 5.95 4.86 262.5 214.3 48.2 18.4 Aug-16 1590 4.18 4.92 205.8 242.3 -36.6 -17.8 Sep-16 1143 10.26 7.77 351.7 266.4 85.3 24.2 Oct-16 950 9.17 9.14 261.4 260.4 1.0 0.4 Nov-16 924 11.20 9.94 310.3 275.4 34.9 11.3 Dec-16 1101 11.17 8.86 381.2 302.3 78.9 20.7 2016 1203 8.50 7.10 3596.5 3001.7 594.8 16.5 To date 34283.9 23234.5 11049.4 31.1

- 164 - 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

- 165 - 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 Jan-16 1124 13.78 14.03 464.6 473.0 -8.4 -1.8 Feb-16 1302 9.87 9.00 359.8 327.9 31.9 8.9 Mar-16 1196 11.62 8.48 430.7 314.5 116.2 27.0 Apr-16 1329 13.50 11.75 538.1 468.3 69.7 13.0 May-16 1139 16.85 14.14 595.1 499.2 95.9 16.1 Jun-16 1211 12.42 11.50 451.2 417.9 33.3 7.4 Jul-16 1423 10.65 14.80 469.9 652.9 -183.1 -39.0 Aug-16 1590 8.30 9.48 409.0 466.9 -57.9 -14.2 Sep-16 1143 12.46 11.44 427.3 392.3 35.0 8.2 Oct-16 950 12.25 10.20 349.1 290.7 58.4 16.7 Nov-16 924 14.50 13.35 401.7 369.9 31.9 7.9 Dec-16 1101 14.05 12.33 479.7 420.8 58.9 12.3 2016 1203 12.52 11.71 5376.2 5094.5 281.8 5.2 To Date 48869.7 33885.9 14983.8 29.1

- 166 - 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

- 167 - 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 Jan-16 1124 0.95 1.01 31.9 34.0 -2.0 -6.3 Feb-16 1302 0.63 0.54 22.8 19.7 3.2 13.9 Mar-16 1196 1.13 0.77 41.9 28.7 13.2 31.4 Apr-16 1329 0.69 0.45 27.4 18.1 9.3 34.0 May-16 1139 1.48 0.91 52.4 32.1 20.3 38.7 Jun-16 1211 2.49 2.15 90.6 78.2 12.4 13.6 Jul-16 1423 1.25 0.63 55.1 27.9 27.2 49.4 Aug-16 1590 1.40 1.58 69.1 77.8 -8.6 -12.5 Sep-16 1143 0.95 0.48 32.4 16.6 15.8 48.8 Oct-16 950 0.99 0.60 28.2 17.2 11.0 39.1 Nov-16 924 1.47 0.57 40.6 15.8 24.8 61.1 Dec-16 1101 0.68 0.55 23.3 18.6 4.7 20.1 2016 1203 1.18 0.85 515.8 384.6 131.1 25.4 To date 7797.5 5949.9 1847.6 25.2

- 168 - 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 emergent vegetation (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.

Over 2015 and 2016, efforts to reduce phosphorus from the effluent prior to its being introduced to the wetland have met some success, with concentrations over 2015 averaging 1.90 mg/l and over 2016 averaging 1.18 mg/l. Further phosphorus removal by the treatment wetland has remained at about 25%, similar to removal rates prior to that pre-treatment. Interestingly, concurrent with this phosphorus removal strategy, ammonia retention (and, to a lesser extent, total nitrogen) has fallen. Over 2016, ammonia retention was actually -30% (Table 2). This is a function of concentrations at the wetland’s outfall exceeding those of the treatment plant’s effluent. The mean concentration of the effluent, however, was just 0.70 mg/l, lower than any year that was monitored.

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. 2016. Monitoring the effectiveness of the Cooperstown wastewater treatment wetland, 2015. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

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

- 169 - 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.

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.

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Comparison of parasite communities of yellow perch (Perca flavescens) from Otsego and Canadarago Lakes

Maggie Doolin1, Jill Darpino2, Elise Iwanyckyj2, Sisina Macchiarelli2, Zach Piper2, Sam Vandemark2, Timothy Pokorny3, and Florian Reyda4

INTRODUCTION

Yellow perch (Perca flavescens) are freshwater fish, widespread and ubiquitous in the lakes of central New York. As both predator and prey, these fish play important roles in lake ecosystems. Since they have an exceedingly generalist diet – plant material, fish, insect larvae and other arthropods, and more – they access prey at multiple trophic levels and interact with many aspects of their shallow-water lake environment (Wilkins et al. 2002). Then, as prey for fish, birds, and mammals that hunt in a lake environment, they serve as junctions between the lowest and highest trophic levels of the lake ecosystem. Because of the important trophic role that they have within their ecosystems, yellow perch serve as common hosts for parasites with complex life cycles that require movement through the food web. Parasites whose final destination is a fish host and parasites that need a bridge from a low trophic level to the top level- predators of the ecosystem both use yellow perch for transport from one life stage to the next.

Although some parasites can be harmful to their host organisms, they are an important component of a healthy ecosystem (Marcogliese 2005). Successful parasite life cycles can clue management professionals into environmental quality, and plant and animal community composition because they rely on several levels of an ecosystem to be functioning correctly to have lasting populations. This is true even for many of the parasites that yellow perch host. For example, the presence of the nematode Eustrongylides tubifex (“red worm”) that is found in the body cavity of yellow perch indicates the presence of piscivorous birds like kingfishers, the final host in the life cycle of that nematode, and the presence of the thorny-headed worm Leptorhynchoides thecatus in the intestine of the fish tells of the presence of a specific group of amphipods, the first hosts in the life cycle of that thorny-headed worm. In central New York, few of the parasites that affect yellow perch seriously decrease their fitness, and none are infective to humans. The benefits of having the parasites in our lake ecosystems outweigh the drawbacks.

The goals of this study were to catalog the full complement of yellow perch parasites found in Otsego and Canadarago Lakes during the winter months, and then to compare the parasite communities found in the fish of each lake. This was the first study in the Otsego area in which full necropsies were performed, as all past work had focused on parasites of the digestive system, and did not examine the other parts of the fish. A total of 31 fish – 13 from Otsego Lake and 18 from Canadarago Lake – that had been caught via ice fishing in January and February 2016 were examined by the authors from January to March 2016. Each organ was thoroughly examined for parasitic infections, and representatives of eight major groups were recovered: Nematoda, Acanthocephala, Cestoda, Trematoda, Monogenea, Annelida, Ciliata and other protists, and Myxozoa. Several differences were discovered between the parasite communities in

1 SUNY Oneonta graduate student, Biology Department, SUNY Oneonta 2 SUNY Oneonta undergraduate student, Biology Department, SUNY Oneonta 3 Aquatic Biologist 1, Region 4, New York State Department of Environmental Conservation 4 Associate Professor and Researcher, Biology Department and Biological Field Station, SUNY Oneonta

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the yellow perch of the two lakes. Given that this was the first effort to complete full necropsies of yellow perch from these two lakes, follow-up work is anticipated in the 2017-2018 academic year. The lakes are isolated from one another, but given their relative geographic proximity, fish stocking, and frequent movement of fishermen (and, therefore, the possibility of fish and water transportation) between the two water bodies, we expected to find great overlap in parasite communities of the two lakes.

METHODS

Yellow perch were collected from Canadarago Lake and Otsego Lake via ice fishing in January and February of 2016. Fish were kept, live, in aquaria at the Biological Field Station until the time of dissection. On the day of dissection, fish were anesthetized following the guidelines of SUNY Oneonta IACUC Protocol 201303 in a solution of Tricaine-S and tap water (ratio of 0.3g/L) for 10 minutes, measured to the nearest millimeter, photographed for the laboratory’s host records, and then examined. Full necropsies were completed, including scale scrapes that were examined under a compound light microscope, and examination of external surfaces and all organs under a stereoscope. Workers used saline solution (7.5g NaCl/1L H2O) to bathe organs, limiting desiccation. Any parasites found upon examination were moved to a clean dish filled with saline solution.

For preservation, parasites were separated by taxon and by host organ. Parasites were preserved in 17 x 60 mm screw thread glass vials in preservative fluids that varied by taxon and purpose of preservation. Specimens that were to be used for morphological examination went in 70% ethanol (leeches, large nematodes, and arthropods), hot formalin (cestodes, trematodes, monogenes, and small nematodes), or tap water for 24 hours, then formalin (acanthocephalans). Specimens of difficult to preserve taxa such as Ciliata were not saved but were photographed using a Leica DSC295 digital camera on a Leica DM2500 compound microscope. Any specimens that were to be used for DNA sequence analysis were preserved in 100% molecular- grade ethanol and then kept in a cold room at 4°C. Permanent whole mount slides of parasites were prepared by hydrating specimens with a graded ethanol/water treatment, staining in Delafield’s hematoxylin, and then dehydrating with a graded ethanol/water treatment. Specimens were then cleared with methyl salicylate and mounted in Canada balsam on 25x75mm glass slides with glass coverslips (22x22mm or 22x50mm).

RESULTS

The authors dissected thirty-one yellow perch – 13 from Otsego Lake and 18 from Canadarago Lake – for this study, attempting to gain perspective on the parasite communities from this species of fish in the two lakes. In total, there were 21 species of parasites representing six major parasitic groups recorded from both lakes (Table 1). The intestine was the organ both most often parasitized and also the organ with the highest species diversity. Although the majority of taxa were found in both lakes, there were some interesting differences. Ichthyophthirius multifiliis (a ciliate), Bunodera sacculata (a trematode), and an unidentified

- 172 -

amoeba were only found in fish from Otsego Lake, while Traenophorus crassus (a tapeworm), Dichelyne cotylophora (a nematode), Clinostomum marginatum and Crepidostomum cornutum (both trematodes), and an unidentified myxozoan were only found in Canadarago Lake. This study is the first record of Otsego Lake fishes carrying Ichthyophthirius multifiliis. Canadarago Lake fishes had highest prevalences of Clinostomum marginatum (10/18 fish), Eustrongylides tubifex (9/18), and unknown species of trematode metacercaria (9/18 fish). In contrast, Otsego Lake fishes had highest prevalences of Proteocephalus sp. (11/13 fish) and the ectocommensal protist Trichodina sp. (10/13 fish).

Table 1. Records of parasite prevalence from 18 Canadarago Lake yellow perch and 13 Otsego Lake yellow perch. Parasites encountered are separated according to taxonomy and identified to as specific of a taxon as possible. Percentages listed represent prevalence of the parasite in the fishes examined from each lake (# of fish infected / # of fish examined).

Canadarago Otsego n=18 n=13 Acanthocephala (thorny-headed worms) Neoechinorhynchus tenellus 28% 23% Leptorhynchoides thecatus 11% 15% Cestoda (tapeworms) Bothriocephalus sp. (adult) 6% 38% Proteocephalus sp. (adult) 17% 85% Traenophorus crassus 6% 0% Unknown (larval) 22% 23% Annelida (leeches) Piscicolaria sp. 11% 62% Monogenea Unknown 22% 38% Myxoza Unknown 6% 0% Nematoda (round worms) Dichelyne cotylophora 6% 0% Eustrongylides tubifex 50% 38% Unknown 11% 8% Ciliata/Protista Amoeba 0% 8% Ichthyophthirius multifiliis 0% 54% Trichodina sp. 33% 77% Unknown 22% 23% Trematoda (flukes) Azygia sp. 33% 46% Bunodera sacculata 0% 15% Clinostomum marginatum 56% 0% Crepidostomum cornutum 6% 0% Other metacercaria 50% 23%

- 173 -

DISCUSSION

Prior to this study, several parasites had been recorded from yellow perch from both Otsego Lake and Canadarago Lake through examination efforts at the Biological Field Station. Since 2008, Dr. Florian Reyda has led a laboratory of undergraduate research students with a focus on recording parasites from local water bodies (e.g., Reyda 2010). Through these years of dedicated research, there had been a heavy emphasis on parasites of the digestive tract of these fish and relatively little investigation of other organs and external surfaces, resulting in incongruent records of parasite prevalence based on their site of infection. This study serves to establish a baseline of relative frequency of a larger range of parasite fauna than has been emphasized in past work in Dr. Reyda’s laboratory, in Otsego and Canadarago Lakes. The study focused on yellow perch because of this species’ ecological habits and its position as both a predatory and prey animal in the shallow water lake ecosystem.

In this study, fish from Canadarago Lake were, on average, smaller than those from Otsego Lake, and had fewer parasites. This finding could be a result of a couple of different factors. First, it could represent the inherent differences between lakes, including nutrient levels, fish growth parameters and general abundance of parasites in the ecosystems. Alternatively, it could be preliminary evidence that parasite communities in central New York yellow perch follow similar trends to those found by Johnson et al. (2004) in which parasite abundance and the rate at which perch acquire parasites increase with fish age and size. It will be interesting to follow up on these possibilities when there are more data available from next year’s study. Hopefully future researchers will be able to shed light on the differences between fish size and parasite community assemblage, if such differences truly exist.

In general, there were several interesting observations about the parasitic communities of these fish. First, Otsego Lake fishes, collected in February, had many fresh infections with juvenile tapeworms (sometimes up to 50 in one fish’s intestine) of the genus Proteocephalus. Observations of this phenomenon had not been previously recorded, and authors hypothesize that this could be related to the Proteocephalus life cycle or to yellow perch feeding ecology, involving a fish’s ingestion of the parasite larval stage in copepods at this time of year, and subsequent parasitic survival in the host gut. Canadarago Lake fish that were collected about one month earlier, in January 2016, did play host to these worms, but there was not the abundance of juveniles in these fish that there was in Otsego Lake fish. Another interesting observation was that researchers encountered the ciliate parasite Ichthyopthirius multifiliis (a.k.a. “Ick”) in Otsego Lake fish. Although the relatively low density of fish in the wild restricts the spread of this organism, it can be an aquarium pest and did spread to all remaining fish in the holding aquarium in the laboratory within a few weeks of the fish being caught. If allowed to fester in a restricted amount of water, infections of Ick can proliferate and result in fish death. It is important to be aware of the presence of highly infective parasites like Ick so that they are not spread to water bodies or to people’s home aquaria.

This work has established baseline knowledge of parasite assemblages in both lakes, and the data gained will serve as important comparison points for future work by the Reyda Lab and others in the Northeastern USA. The authors did find differences in the parasite communities of the two lakes. Parasites unique to Otsego Lake include Ick and Bunodera sacculata, and those unique to Canadarago Lake include Dichelyne cotylophora, Clinostomum marginatum, and

- 174 -

Traenophorus crassus. Next steps include gathering more data to increase confidence that all parasites, even rare ones, that are infecting yellow perch in both lakes are recorded, and then trying to establish a better understanding of what governs the differences in parasite communities between lakes.

REFERENCES

Ecology and Natural Resources Collection. UW-Madison Libraries Digital Collection. 2015. Web.

Great Lakes Fishery Commission. Heterosporis sp. Yellow perch parasite. 2015. Web.

Johnson, M.W., and Dick, T.A. 2001. Parasite effects on the survival, growth, and reproductive potential of yellow perch (Perca flavescens Mitchill) in Canadian Shield lakes. Can. J. Zool. 79: 1980-1992.

Johnson, M.W., Nelson, P.A., and Dick, T.A. 2004. Structuring mechanisms of yellow perch (Perca flavescens) parasite communities: host age, diet, and local factors. Can. J. Zool., 82: 1291-1301.

Marcogliese, D. 2005. Parasites of the superorganism: Are they indicators of ecosystem health? International Journal for Parasitology 35(7): 705-716.

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.

Spear, M. 2013. Infection of yellow perch (Perca flavescens) by black-spot parasites in a public and private lake. Student Project: University of Notre Dame.

Wilkins, J.L., T.J. DeBates, and D.W. Willis. 2002. Food habits of yellow perch, Perca flavescens, in West Long Lake, Nebraska. Transactions of the Nebraska Academy of Sciences 28: 49-56.

- 175 - Water quality monitoring and analysis of fecal coliform of Canadarago Lake tributaries and outlet

Tara Perry1 and Marina Brown2

INTRODUCTION Canadarago Lake, in Richfield Springs, NY, has four main tributaries and empties into Oaks Creek. Physical and chemical water sampling on these waterways has historically been conducted to evaluate the state of Canadarago Lake, its tributaries and outlet (Hart et al. 1980; Albright and Waterfield 2012). The purpose of this current effort of tributary monitoring is to recognize any changes that may reflect changes in land use throughout the watershed. One aspect of lake monitoring is fecal coliform analysis in its tributaries and outlet. Fecal coliform are gram negative, non-sporulating, facultative anaerobic, rod-shaped bacteria that are good indicator organisms used to evaluate water quality (APHA 2012). These bacteria are naturally found in the intestines of mammals and birds. Most fecal coliform bacteria are not harmful to humans, though their presence indicates fecal contamination, which may contain disease-causing pathogens. Federal guidelines recommend no fecal coliform be present in drinking water (AWWA 1990), and no more than 200 colony producing units per 100ml of water (CPU/100ml) should be present in recreational waters (APHA 2012). High fecal coliform levels can also be associated with elevated levels of phosphorous and nitrogen due to a common source, such as agricultural manure runoff or poorly maintained wastewater treatment systems. This study is intended to serve as an extension of work conducted by Albright and Waterfield (2012) as part of The State of Canadarago Lake report. Concurrent with this work, other surveys were conducted on these same tributaries to better characterize the quality and communities of each. These included a fish survey (Perry 2017) and a benthic macroinvertebrate survey (Brown 2017).

METHODS Water samples were collected weekly from 31 May 2016 until 3August 2016 from five sites along the Canadarago Lake tributaries and one site on the lake outlet. These sites are illustrated and labeled in Figure 1 and a summary of the sample sites is given in Table 1. Two sites were included on Ocquionis Creek in order to evaluate the influence of the Richfield Springs Wastewater Treatment Plant, which discharges just upstream from Ocquionis Creek South (Ocq.2), on water quality.

1 SUNY Oneonta Biological Field Station Intern, summer 2016. Funding was provided by the Otsego land Trust. Current affiliation: Department of Environmental Studies and Sciences, Ithaca College, Ithaca, NY.

2 SUNY Oneonta Biological Field Station Intern, summer 2016. Funding was provided by the Otsego land Trust. Current Affiliation: Department of Biology, SUNY Oneonta.

- 176 - Fecal Coliform Fecal coliform samples were collected in 1L Pyrex® glass bottles and stored on ice until being processed. Samples were processed the same day as collection using the membrane filter technique (APHA 2012). Three volumes between 0.1mL and 200mL of each sample were filtered in attempt to produce 20-80 bacteria colonies per petri dish at some volume. Triplicates of each volume were low-pressure vacuum filtered through a 4µm Millipore membrane which was then placed in a sterile petri dish on an absorbent pad that had been saturated with 2.2mL of FC Base by Bacto® growth media. All petri dishes were vacuum sealed (with a FoodSaver sealer to maintain water tightness) in sterile Tupperware and incubated for 24±2 hours in a water bath set at 44.5 (±0.2) °C. After the incubation period, fecal coliform colonies were counted and reported as colony producing units (CPUs) per 100mL.

Figure 1: A map of Canadarago Lake and its tributaries. The sites that were sampled are Herkimer Creek, Hyder Creek, Trout Brook, Ocquionis site 1 and site 2, and the outlet Oaks Creek. (from Albright and Waterfield 2012).

- 177 - Table 1: Outlet, tributaries, and the locations of sampling.

Tributary & Outlet Sampling Sites Oaks Creek (outlet) East of the Village of Schuyler Lake on County Route 22; sampled north of bridge.

Herkimer Creek North of the Village of Schuyler Lake on State Route 28; sampled east of bridge.

Hyder Creek South of Dennison Road (NYSP boat launch access road) on State Route 28; sampled west of bridge.

Trout Brook (Mink Creek) South of County Route 25A on State Route 28; sampled west of bridge.

Ocquionis Creek North The beginning of Elm Street Extension, just south of Bronner Street; sampled south of bridge.

Ocquionis Creek South End of Bloomfield Drive, through the rear gate of the waste treatment plant; sampled downstream of effluent discharge.

Tributary Water Quality Monitoring On each collection day, temperature, specific conductivity, pH and dissolved oxygen (concentration and percent saturation) were measured at each site using a YSI® 6820 V2-2 multi-probe, which had been calibrated prior to use. Water samples from each site were collected in 125mL Nalgene® bottles, kept on ice during transportation, and preserved for nutrient analyses with sulfuric acid to pH<1.0. A Lachat® QuickChem FIA + Water Analyzer was used to determine nitrate+nitrite-N content, total nitrogen, and total phosphorus. The cadmium reduction method (Pritzlaff 2003) was used to determine nitrate + nitrite-N content and total nitrogen (following digestion as per Ebina 1983), and the single reagent ascorbic acid following persulfate digestion method (Liao and Marten 2001) was used to determine total phosphorus.

RESULTS AND DISCUSSION Fecal Coliform The average colony producing units (CPU) per 100mL of fecal coliform bacteria found in the tributaries and outlet is shown in Figure 2. Data from 2010 (Albright and Waterfield 2012) are shown in Figure 3 for comparison. Concentrations of fecal coliform bacteria in Hyder Creek and Trout Brook were significantly higher than they had been in 2010, while Ocquionis Creek sites 1 and 2 have increased only slightly. The cause of the radical increase of fecal coliform in Trout Brook is unknown, though potential sources of such bacteria include agricultural runoff or improperly treated residential wastewater.

- 178 - 9000 8000 7000

6000 5000 4000

(CPU/100mL) 3000 2000 Fecal Coliform Concentration Coliform Fecal 1000 0 Herkimer Hyder Creek Trout Brook Ocquionis Ocquionis Oaks Creek Creek Creek 1 Creek 2

Figure 2: Average fecal coliform concentrations (CPU/100mL) of the six Canadarago tributary and outlet sites sampled for 31 May through 3 August 2016.

9000 8000

7000 6000 5000 4000 (CPU/100mL) 3000

Fecal Coliform Concentration Coliform Fecal 2000 1000 0 Herkimer Hyder Creek Trout Brook Ocquionis Ocquionis Oaks Creek Creek Creek 1 Creek 2

Figure 3: Average fecal coliform concentrations (CPU/100mL) of the 2010 study of Canadarago Lake tributaries and outlet (Albright and Waterfield 2012).

- 179 - Physical Water Quality Temperature Figure 4 represents average temperatures for the six sites tested from 31 May to 3 August 2016. The highest temperature recorded was 22.28°C on 20 July at Oaks creek. The lowest was 11.23°C on 14 June at Hyder Creek. Results from this study are an average of about 5 degrees cooler than the 2010 study (Mazziota 2011).

Herkimer 15 Hyder

10 Oaks Ocquionis site 1

Temperature (ºC) Temperature 5 Ocquionis site 2 Trout 0

Figure 4: Average temperatures for Canadarago Lake tributary and outlet sites from 31 May through 3 August 2016.

Dissolved oxygen Fish rely on dissolved oxygen levels to survive. Values less than 5mg/L can be stressful to aquatic life. Figure 5 summarizes the dissolved oxygen concentrations in the tributaries over the study period. Ocquionis site 2 dropped 28 June and was consistently around 5mg/L. Herkimer Creek dropped quickly during the week of 12 July while the other sites increased but rebounded the week of 26 July. Figure 6 shows the mean dissolved oxygen, as percent saturation, in each tributary over the study period (+/- SE).

- 180 - 14

10 Herkimer 8 Hyder 6 Oaks

4 Ocquionis site 1 Ocquionis site 2 2 Dissolved (mg/l) Oxygen Trout 0

Figure 5: Dissolved oxygen concentrations in Canadarago Lake tributary streams and outlet from 31 May to 3 August 2016.

100 90 80

70 60 50 40

Dissolved Oxygen % 30 20 10 0 Herkimer Hyder Ocquionis 1 Ocquionis 2 Trout Oaks

Figure 6: Percent dissolved oxygen in Canadarago Lake tributary streams and outlet from 31 May to 3 August 2016.

Specific Conductivity and pH Specific conductivity is a measure of dissolved ions present in the water. It doesn’t define specific ions but it is used to document spikes of introduced ions in the stream such as salt-based

- 181 - compounds. An example of the origin of these salt compounds could be agricultural runoff or salts from a nearby road. Figure 7 shows the average specific conductivity of the six sites and Figure 8 shows pH averages for 31 May through 3 August 2016. There are significant differences in conductivity between Trout Brook and Herkimer Creek, reflecting the difference in geology from limestone at Trout Brook to sandstone/shale downstream at Herkimer Creek (Albright et al. 2010). The pH is also affected by the differences in geology.

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Specific Conductivity (µs/cm) Conductivity Specific 0 Herkimer Hyder Ocquionis 1 Ocquionis 2 Trout Oaks

Figure 7: Mean specific conductivity values for Canadarago tributary and outlet sites from 31 May to 3 August 2016.

8.1

7.9

pH 7.8

7.7

7.6 Herkimer Hyder Ocquionis 1 Ocquionis 2 Trout Oaks

Figure 8: Average pH values for Canadarago Lake tributary and outlet sites from 31 May to 3 August 2016.

- 182 - Chemical Water Quality Nitrogen can be introduced by agricultural fertilizer runoff. A flux in nitrogen can increase plant and algal productivity eventually leading to eutrophication of the stream (Wetzel 2001). Phosphorous can enter streams through bedrock erosion, agricultural overspill, and decaying organic materials (Wetzel 2001). The high levels of phosphorous in Trout Brook could indicate manure runoff or inadequately treated wastewater a common source of both.

90 80

70 60 50 40 30

Phosphorous (µg/L) Phosphorous 20 10 0 Herkimer Hyder Creek Oaks Creek Ocquionis 1 Ocquionis 2 Trout Brook Creek

Figure 9: Average concentration of phosphorous found in the Canadarago tributary and outlet sites sampled for 31 May through 3 August 2016.

- 183 - 2.50

2.00

1.50

1.00

Total nitrogen (mg/L) Total 0.50

0.00 Herkimer Hyder Creek Oaks Creek Ocquionis 1 Ocquionis 2 Trout Brook Creek

Figure 10: Average concentration of nitrogen found in Canadarago lake tributary and outlet sites for 31 May through 3 August 2016.

Nitrates and nitrites are bioavailable forms of nitrogen. Organisms convert nitrogen into a more useable form through natural processes. Figure 11 shows the mean concentration of nitrate+nitrite found in the tributaries and outlet.

0.90 0.80

0.70 0.60 0.50 0.40 0.30 0.20 Nitrate+nitrite (mg/L) 0.10 0.00 Herkimer Hyder Creek Oaks Creek Ocquionis 1 Ocquionis 2 Trout Brook Creek

Figure 11: Average concentration of nitrate+nitrite found in Canadarago Lake tributary and outlet sites for 31 May through 3 August 2016.

- 184 - CONCLUSIONS There was significant variation in water quality, nutrients, and fecal coliform present between various Canadarago Lake tributaries and the lake outlet. Fecal coliform fluctuated between weekly samples. However, the source of the fecal coliform in waterways is non-point, meaning the source of the contamination cannot be easily traced. There could be multiple reasons for fluctuations in the presence of fecal coliform, including agricultural runoff and onsite wastewater treatment (septic) system contamination. None of the tributary or outlet sites tested are fit for human consumption as they all consistently tested fecal coliform positive. All of the inflow tributaries (Herkimer, Hyder, Trout, and Ocquionis) are above federal recommended fecal coliform levels for recreational water use (Fig. 2). Oaks Creek, the outflow, had significantly lower levels of fecal coliform than the inflowing tributaries. This suggests that there might be more sources of fecal coliform contamination affecting the inflow tributaries than there are affecting the lake or the outflow tributary. Because fecal coliform lives primarily in the intestines of warm-blooded animals, this disparity could also suggest that inflowing water contaminated with fecal coliform spends enough time in the lake for the fecal coliform to die off before reaching the outlet. There were higher concentrations of total phosphorous, total nitrogen, and nitrate+nitrate in Trout Brook and Ocquionis Creek. The nutrient loading in Trout Brook could be connected to the elevated levels of fecal coliform bacteria found. Though we don’t know the source of the pollution, reasons such as agricultural runoff can cause both elevated levels of fecal coliform and nutrient loading simultaneously. Trout Brook scored 7.047 on the Family-level Biotic Index for the benthic macroinvertebrate community, indicating significant organic pollution and poor water quality (Brown 2017). Trout Brook was also found to contain species of fish that are tolerant to a variety of habitats, conditions, and water qualities (Perry 2017). It seems that the state of Trout Brook has gotten worse since 2010, with levels of fecal coliform and nutrients having increased in recent years. The benthic macroinvertebrates and fish communities found in Trout Brook suggest that the stream has been contaminated for some time now. Hyder Creek was found to have the second highest levels of fecal coliform of the streams tested. However, there was found to be lower levels of total phosphorous, total nitrogen, and nitrate+nitrite in Hyder Creek, which suggests that the elevated levels of fecal coliform isn’t coming from a source that would also cause nutrient loading. Additionally, Hyder Creek scored 4.429 on the Family-level Biotic Index for the benthic macroinvertebrate community, indicating a very low possibility of organic pollution (Brown 2017). This might suggest that the long-term quality of Hyder Creek has been stable and the higher concentration of fecal coliform bacteria is a fairly recent contamination source. Further research should take place on Hyder Creek and Trout Brook to investigate the cause of the significant increase in fecal coliform concentrations since 2010. The microbenthic communities found (Brown 2017) show that Hyder Creek has relatively low pollution, suggesting that there could be an outside source directly causing high bacteria levels. An investigation into runoff or contamination of Trout Brook might help explain the spike in fecal coliform bacteria since 2010.

- 185 - REFERENCES

Albright, M. F. and H.A. Waterfield. 2011. The state of Canadarago Lake, 2011. BFS Technical Report #30. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Albright, M. F., H.A. Waterfield, and N. Mazziotta. 2011. Continued monitoring of Canadarago Lake and its tributaries. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

American Water Works Association. 1990. Water Quality and Treatment, McGraw-Hill, Inc.

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

Brown, M. 2017. Macroinvertebrate survey and biological assessment of water quality: tributaries of Canadarago Lake; Otsego County, NY. In 49th Ann. Rept. (2016). SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta.

Hart, T.E., G.W. Fuhs, D.M. Green, L.J. Hetling, S.B. Smith and S.P. Allen. 1980. Limnology of Canadarago Lake. Pages 129-264. In J.A. Bloomfield (ed.). Lakes of New York State. Academic Press, New York.

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.

Mazziotta, N. 2011. A survey of fecal coliform bacteria in Canadarago Lake and its tributaries. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Oram, M. 2014. Bacteria in the environment and drinking water. Dallas (PA): Water Research Center.

Perry, T.H. 2017. Fish assemblages of selected Canadarago Lake tributaries and outlet. In 49th Ann. Rept. (2016). SUNY Oneonta Bio. 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, Colorado.

- 186 - United States. National Technical Advisory Committee on Water Quality Criteria. 1968. Water Quality Criteria. Washington, D.C.: U.S. Environmental Protection Agency.

Wetzel, R.G. 2001. Limnology, Lake and River Ecosystems, 3rd edition. Academic Press. San Diego, California.

- 187 - Macroinvertebrate survey and biological assessment of water quality: tributaries of Canadarago Lake; Otsego County, NY

Marina Brown1

INTRODUCTION

New York State has used benthic invertebrates to assess stream water quality since 1972 (NYSDEC 2012). Benthic invertebrate communities can be used to conduct a biological assessment of water quality and estimate overall stream health. Invertebrate community assemblages shift with changes in water quality since invertebrate organisms are able to tolerate varying levels of water pollution (Hilsenoff 1988). A degraded site and a clean site will thus be occupied by different communities of benthic organisms. If pollution events occur regularly, invertebrates that are better able to tolerate pollution will dominate over those taxa which are more sensitive. Evaluating water quality will provide insight into changes in pollution on a short term scale, though that might not represent conditions on a longer scale. A benthic invertebrate study may provide more insight into long term water quality conditions and ecosystem health. This study is a reevaluation of a 2009 assessment of Canadarago Lake’s main tributaries (Bailey 2010).

METHODS AND MATERIALS

Benthic invertebrates were collected from the four main tributaries of Canadarago Lake on 2 June 2016. Tributaries that were included are shown in Figure 1. Oaks Creek, the lake’s outlet, was not sampled because of high water levels. Samples were collected using a Wildco Hess sampler fitted with a mesh sock and a 600 um mesh sample cup. The Hess sampler was inserted into the substrate with a current flowing through it. As water flowed through the sampler, the user stirred up the sediment and allowed invertebrates to flow into the mesh sock. Five samples were taken at each site and combined into a single sample. Collected invertebrates were preserved with 70% ethanol and identified to the family level. Data were used in three water quality indices to assess relative water quality and levels of pollution.

Water quality assessments were based on three water quality indices: Ephemeroptera- Plecoptera-Tichoptera (EPT) richness, Percent Model Affinity (PMA), and Family-level Biotic Index (FBI) (Hilsenoff 1988). EPT richness is based on the abundance of three orders of insects that tolerate only low levels of pollution. The Percent Model Affinity test is a comparison to the abundance of specific organisms in a sample compared to New York State’s “ideal” benthic community. The Family-level Biotic Index assigns each family a numerical rating associated with its ability to tolerate pollution. Higher numbers indicate greater tolerance to pollution.

1 SUNY Oneonta Biological Field Station Intern, summer 2016. Funding was provided by the Otsego land Trust. Current Affiliation: Department of Biology, SUNY Oneonta.

- 188 -

Figure 1. Map of Canadarago Lake and its tributaries. Herkimer Creek, Hyder Creek, Trout Brook and Ocquionis Creek were sampled. Oaks Creek was not sampled in this study.

- 189 - RESULTS AND DISCUSSION

A summary of collected organisms in each of Canadarago Lake’s tributaries is presented in Tables 1-4. Results of the indices are given in Figures 1-4. Family-level Biotic Index values from 2010 (Bailey 2010) are compared to 2016 in Table 5. Table 6 provides a comparison of average PMA values of 2010 (Bailey 2010) compared to the data collected in 2016.

Herkimer Creek had the highest diversity of invertebrate organisms (Table 2) and the highest count of EPT (Figure 2). The results of the biotic indices imply that condition of Herkimer and Hyder Creeks have improved since 2010, while the Trout Brook and Ocquionis Creek benthic communities indicate degraded conditions, with Trout Brook having an FBI of 7.0 and Ocquionis Creek with a score of 6.0 (Figure 5). Table 6 compares the PMA values of the 2010 study with recent data.

Trout Brook consistently scored low on the water quality indices. It also has the highest levels of fecal coliform in comparison to the other tributaries (Perry and Brown 2017). Phosphorous levels were the highest over the summer of 2016 and nitrogen levels were the second highest. High levels of nutrients can cause plant and algal production in receiving waters (i.e., Canadarago Lake) to increase, increasing eutrophication. Phosphorous levels could be elevated by inadequately treated residential wastewater, residential development or agricultural runoff.

Bacteria counts were high in Hyder Creek over the summer (Perry and Brown 2017) despite the results from this benthic invertebrate study, which indicate improved conditions since 2010 (Figures 5 and 6). Fecal coliform bacteria, and the associated organic pollution, could have been increased and the invertebrates haven’t responded to the changes. Ocquionis Creek has relatively low levels of fecal coliform but consistently rates low on the water quality indices.

- 190 - Table 1. Summary of collected benthic Table 2. Summary of collected benthic invertebrate organisms found in Hyder invertebrate organisms found in Herkimer Creek on June 2, 2016. (*organisms did not Creek on June 2, 2016. (*organisms did not have specific information to qualify for have specific information to qualify for water quality tests.) water quality tests.)

Order Family Count Order Family Count Plecoptera Capniidae 1 Plecoptera Capniidae 4 Plecoptera Leuctridae 7 Plecoptera Perlodidae 17 Plecoptera Perlodidae 11 Plecoptera Perlidae 9 Tricoptera Hydropsychidae 1 Ephemeroptera Heptageniidae 53 Ephemeroptera Leptophlebidae 5 Ephemeroptera Caenidae 12 Ephemeroptera Heptageniidae 10 Ephemeroptera Baetidae 3 Ephemeroptera Caenidae 5 Tricoptera Limnephilidae 2 Ephemeroptera Baetidae 29 Tricoptera Hydropsychidae 15 Coleoptera Psephenidae 8 Coleoptera Psephenidae 5 Coleoptera Elmidae 54 Coleoptera Dytiscidae 1 Arthropoda Hylellidae 7 Coleoptera Elmidae 147 Isopoda Asellidae 2 Diptera Chironmidae 144 Diptera Chironmidae 21 Diptera Athericidae 1 Diptera Tipulidae 2 Diptera Tipulidae 6 TOTAL 163 Diptera Simulidae 2 Nematoda* Unknown 1 Arthropoda Hylellidae 5 Arthropoda Gammaridae 1 Isopoda Asellidae 1 TOTAL 428

Unionoida* Saphaeriidae 1 Diptera* Unknown Pupa 69 Gastropoda* Physidae 2 Tricoptera* Adult 2

- 191 - Table 3. Summary of collected benthic Table 4. Summary of collected benthic invertebrate organisms found in Trout Brook invertebrate organisms found in Ocquionis on June 2, 2016. (*organisms did not have Creek on June 2, 2016. (*organisms did not specific information to qualify for water have specific information to qualify for quality tests.) water quality tests.)

Order Family Count Order Family Count Plecoptera Perlidae 1 Plecoptera Perlidae 1 Ephemeroptera Heptageniidae 6 Ephemeroptera Leptophlebidae 3 Ephemeroptera Caenidae 19 Ephemeroptera Heptageniidae 3

Ephemeroptera Baetidae 3 Ephemeroptera Isonychiidae 2 Tricoptera Limnephilidae 8 Ephemeroptera Baetidae 6 Coleoptera Psephenidae 2 Tricoptera Hydropsychidae 5 Coleoptera Elmidae 91 Tricoptera Limnephilidae 4 Arthropoda Hylellidae 102 Tricoptera Ryacophilidae 3 Hemiptera Corixidae 6 Coleoptera Psephenidae 1 Diptera Chironmidae 14 Coleoptera Dytiscidae 1

Diptera Athericidae 1 Coleoptera Elmidae 18 TOTAL 253 Diptera Chironmidae 164 Tricoptera* Adult 2 Diptera Tipulidae 6 Diptera* Adult Culicidae 1 Arthropoda Hylellidae 37 TOTAL 254 Diptera* Unknown pupa 3 Nematoda* Unknown 1

- 192 - EPT Taxa EPT Taxa Total Taxa 20 19

18 16 14 14 14 12 11 10 8 8 8 8 6 5 4 Number of Organisms 2 0 Hyder Ocquionus Trout Herkimer

Figure 2. Ephemeroptera-Plecoptera-Trichoptera taxa counts for all sites sampled. EPT values greater than 10 imploy a non-impacted site. Values between 6 and 10 show a slightly impacted site. Values between 2-6 are moderately impacted and any number below 2 is a severely impacted community (NYSDEC 2012).

- 193 - Family Biotic Index 10 9 8 7.047 7 6.063 6 5.308 5 4.429 4 3 2 1 0 Hyder Ocquionus Trout Herkimer

FBI Score WQ Category Level of Organic Pollution 0.00-3.50 Excellent No apparent organic pollution 3.51-4.50 Very Good Possible slight organic pollution 4.51-5.50 Good Some organic pollution 5.51-6.50 Fair Fairly significant organic pollution 6.51-7.50 Fairly Poor Significant organic pollution 7.51-8.50 Poor Very significant organic pollution 8.51-10.0 Very Poor Severe organic pollution Figure 3. Family-level Biotic Index (FBI) results for the sampled tributaries. A number is assigned to a specific family to show its tolerance to polluted water. Values range from 1-10 with 1 being no apparent organic pollution and 10 being severe organic pollution.

- 194 - Percent Model Affinity 100

80 65.85 58.59 60 48.5 40.15 40 Percent

0 Hyder Ocquionus Trout Herkimer

PMA ORDER NYSDEC Model Community Ephemeroptera 40% Plecoptera 5% Trichoptera 10% Chironomidae 20% Coleoptera 10% Oligochaeta 5% Other 10%

Figure 4. Percent model affinity is the total percent of a family in a sample compared to the states stream model for perfect water quality. The results for Canadarago’s tributaries are shown. A result closer to 100% indicates low levels of pollution.

- 195 - Table 5. Family-level Biotic Index values from 2010 are compared to 2016. Values range from 1-10. A number closer to 10 means higher organic pollution.

Tributary 2010 FBI Data 2016 FBI Data Hyder 7.5 4.4 Ocquionis 4.44 6 Trout 6 7 Herkimer 7.7 5.3

Table 6. Average PMA of Carter Baileys study in 2010 is compared to the data collected in 2016 is shown. A number closer to 100% indicated less pollution.

Tributary 2010 PMA 2016 PMA Data Data Hyder 35 66 Ocquionis 46 40 Trout 63 49 Herkimer 42 59

CONCLUSION Trout brook ranked as the most impaired tributary with the lowest ratings in most of the water quality tests and the highest level of fecal coliform (Perry and Brown 2017). The tributary has declined in status in the last 6 years. There is likely an outside process affecting this tributary such as polluted residential wastewater or a change in wastewater treatment. Hyder Creek assessments were consistent and showed moderate pollution across the community indices even though fecal coliform levels were high (Perry and Brown 2017). More research is needed to assess why bacteria levels have increased since 2010. Herkimer creek had the poorest quality in 2010 with an FBI score of 7.70 (Bailey 2010). Today, Herkimer creek has an FBI score of 5.30. Herkimer Creek has improved in quality but the source is unknown. Continued monitoring should be conducted to record any changes in the sites sampled.

- 196 - REFERENCES

Bailey, C. 2010. Macroinvertebrate survey and biological assessment of water quality: tributaries of Canadarago Lake; Otsego County, NY. In 43rd Ann. Rept. (2010). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Heilveil, J. 2014. Personal communication. Associate professor, SUNY Oneonta Dept. of Biology, Oneonta, NY

Hilsenhoff, W.L. 1988. Rapid field assessment of organic pollution with a family-level biotic index. Journal of the North American Benthological Society, Vol. 7 No. 1 pp. 65- 68.

Merritt, R, and K. Cummins. 1996. An introduction to aquatic insects of North America. 3rd ed. Kendall Hunt Publishing. Dubuque, Iowa.

New York State – Department of Environmental Conservation. 2012. Standard operating procedure: Biological monitoring of surface waters in New York State. Albany NY.

Peckarsky, B.L., P.R. Fraissinet, M.A. Penton and D.J. Conklim. 1995. Freshwater macroinvertebrates of northeastern North America. Comstock Publishing. Cornell University Press. Ithaca, NY.

Perry, T. and M. Brown. 2017. Water Quality Monitoring and Analysis of Fecal Coliform of Canadarago Tributaries. In 49th Ann. Rept (2016). SUNY Oneonta Biological Field Station, SUNY Oneonta.

- 197 - Fish assemblages of selected Canadarago Lake tributaries and outlet Tara Perry1 INTRODUCTION As part of an effort to evaluate the water quality and characterize the fauna of tributaries to Canadarago Lake and its outlet, a fisheries survey was conducted. This effort was concurrent with physical, chemical and bacteriological monitoring (Perry and Brown 2017) and a microbenthic survey (Brown 2017). This survey is an effort to use fish species found to determine diversity, abundance, and general water quality of each stream surveyed. Some species of fish are more sensitive to changes in water quality while others are more tolerant of fluctuations and water pollution. Most trout and dace species, along with slimy sculpin (Cottus cognatus) and shield darters (Percina peltata), are sensitive to water quality changes and require cold water streams with rocky substrate (Smith 1985). Some species, such as white suckers (Catostomus commersonii), cutlips minnow (Exoglossum maxillingua), creek chub (Semotilus atromaculatus), pumpkinseed sunfish (Lepomis gibbosus), smallmouth bass (Micropterus dolomieu) and rock bass (Ambloplites rupestris) are more tolerant of changes in water quality and can thrive in more variable or polluted waters (Smith 1985). The composition of fish communities, when evaluated with benthic communities, physical water qualities, and nutrient data, can give insight into the impact of surrounding land use. Potential impacts include alterations to the geography of the streams, agricultural runoff, nutrient loading, sediment loading, and other sources of pollution.

METHODS Five surveys were conducted from 8 July to 29 July 2016 on selected Canadarago Lake tributaries and the lake’s outlet. Hyder Creek, Trout Brook, Ocquionis Creek, and Oaks Creek (outlet) were all surveyed around the same location they were sampled for fecal coliform, physical parameters, nutrients, and benthic macroinvertebrates (Perry and Brown 2017; Brown 2017)). Table 1 lists the date, duration of time sampled, and the location of each site. Figure 1 is a map of the tributaries and outlet around the lake and indicates where each stream was surveyed. Herkimer Creek was not surveyed due to unsuitable stream habitat resulting from ongoing bridge construction. Surveys were conducted in the morning to reduce stress on the cold-water stream species from high temperatures. Each survey was conducted using a Halltech® backpack electro-fisher unit. Seconds of electro-fisher use in each stream was recorded to provide a measure of sampling effort. Starting downstream of the site and working upstream, fish were non-lethally shocked and captured with nets. Fish species were then identified and the number of individuals of each species was recorded. Fish were returned to the stream.

1 1SUNY Oneonta Biological Field Station Intern, summer 2016. Current affiliation: Department of Environmental Studies and Sciences, Ithaca College, Ithaca, NY. Funding was provided by the Otsego land Trust.

- 198 - Table 1. Survey dates, length of time electro-fisher sampling, and descriptions of each survey site. Tributary & Outlet Sampling Sites Hyder Creek Date: 27 July 2016 Seconds: 1856 Location: South of Dennison Road (NYSP boat launch access road) on State Route 28; accessed west of bridge on Route 28.

Trout Brook (Mink Creek) Date: 11 July 2016 Seconds: 3254 Location: South of County Route 25A on State Route 28; accessed west of bridge on Route 28.

Ocquionis Creek Date: 29 July 2016 Seconds: 376 Location: The beginning of Elm Street Extension, just south of Bronner Street; accessed west of bridge.

Oaks Creek site 1 Date: 8 July 2016 Seconds: 2303 Location: East of the Village of Schuyler Lake on County Route 22, below the dam; accessed south of bridge on Route 22.

Oaks Creek site 2 Date: 29 July 2016 Seconds: 2057 Location: About 10 km downstream from Oak Creek’s source. Just east of County Route 26 on County Route 59; accessed north of the bridge on County Route 59.

- 199 - Oak Creek #1 Oak Creek #2 ~10 km

Figure 1. A map of Canadarago Lake, its tributaries and outlet. The sites that were sampled were Hyder Creek, Trout Brook, Ocquionis Creek, and Oaks Creek. Hyder, Trout, and Ocquionis are inflowing tributaries to Canadarago Lake, and Oaks Creek is the outflow of the lake.

RESULTS AND DISCUSSION Hyder Creek is an inflowing tributary to Canadarago Lake. The site sampled contained fairly shallow (up to a few feet deep), moderate paced water with some spots of shade. The bottom substrate is a mix of rocks, pebbles, mud, and sand. The temperature averaged 18.34ºC for 31 May through 3 August 2016 (Perry and Brown 2017). The species and counts found in Hyder Creek are listed in Table 2. The species richness was 14. The dominant species found were smallmouth bass, largemouth bass, and creek chub.

- 200 - Table 2. Fish species found in Hyder Creek, 27 July 2016.

Species Scientific name Count Blacknose dace Rhinichthys atratulus 4 Bluntnose minnow Pimephales notatus 8 Creek chub Semotilus atromaculatus 45 Cutlips minnow Exoglossum maxillingua 19 Largemouth bass Micropterus salmoides 56 Longnose dace Rhinichthys cataractae 6 Margined madtom Noturus insignis 2 Pumpkinseed sunfish Lepomis gibbosus 28 Rock bass Ambloplites rupestris 23 Satinfin shiner Cyprinella analostana 1 Smallmouth bass Micropterus dolomieu 82 Spottail shiner Notropis hudsonius 6 Tessellated darter Etheostoma olmstedi 12 White sucker Catostomus commersonii 13

Trout Brook is another inflowing tributary to Canadarago Lake. The stream site surveyed was shallow with primarily rocky/sandy bottom substrate and a moderate amount of shading. The flow is moderate to fast and the average temperature of Trout Brook was 17.99ºC for 31 May through 3 August 2016 (Perry and Brown 2017). The species and counts found are listed in Table 3. The species richness was 11. The dominant species found were cutlips minnow and tessellated darter. Table 3. Fish species found in Trout Brook, 11 July 2016.

Species Scientific name Count Blacknose dace Rhinichthys atratulus 16 Common shiner Luxilus cornutus 2 Creek chub Semotilus atromaculatus 13 Cutlips minnow Exoglossum maxillingua 37 Largemouth bass Micropterus salmoides 8 Longnose dace Rhinichthys cataractae 15 Margined madtom Noturus insignis 17 Pumpkinseed sunfish Lepomis gibbosus 5 Rock bass Ambloplites rupestris 1 Smallmouth bass Micropterus dolomieu 2 Tessellated darter Etheostoma olmstedi 30

Ocquionis Creek is an inflowing tributary to Canadarago Lake. The site surveyed was deeper than other inflowing tributaries surveyed, often times too deep to survey with an electrofisher, with a lot of suspended sediment. This affected our results as we were only able to

- 201 - electrofish a small section of Ocquionis Creek, totaling only 376 seconds of shocking compared to between 1856 and 3254 for all other streams surveyed, and the dark muddy water hindered our ability to see to the bottom. The substrate was primarily mud, debris, and man-made objects such as cinder blocks and pieces of wood and metal. The water at the Ocquionis Creek site was slow moving but well shaded, averaging about 17.31ºC for 31 May through 3 August 2016 (Perry and Brown). The species and counts found in Ocquionis Creek are listed in Table 4. The species richness was 7. The dominant species found was spottail shiner. Table 4. Fish species found in Ocquionis Creek.

Species Scientific name Count Blacknose dace Rhinichthys atratulus 2 Creek chub Semotilus atromaculatus 2 Cutlips minnow Exoglossum maxillingua 6 Pumpkinseed sunfish Lepomis gibbosus 1 Rock bass Ambloplites rupestris 3 Spottail shiner Notropis hudsonius 12 Tessellated darter Etheostoma olmstedi 6

Oaks Creek is the outlet of Canadarago Lake. This stream is wider and discharges much more water than any of the inflowing tributaries surveyed. Site 1 is closer to the lake, deeper, and slower moving with more suspended sediment. The bottom substrate of the site was primarily mud, muck, and debris with large areas of aquatic vegetation. Oaks Creek site 1 was not well shaded and was consistently a higher temperature than any of the inflowing tributaries surveyed, averaging 20ºC for 31 May through 3 August 2016 (Perry and Brown 2017). The species and counts found in Oaks Creek site 1 are listed in Table 5. The species richness of site 1 was 9. The dominant species found were pumpkinseed sunfish and bluntnose minnow. Table 5. Fish species found in Oaks Creek site 1.

Species Scientific name Count Bluntnose minnow Pimephales notatus 16 Chain pickerel Esox niger 1 Largemouth bass Micropterus salmoides 2 Pumpkinseed sunfish Lepomis gibbosus 24 Rock bass Ambloplites rupestris 2 Spottail shiner Notropis hudsonius 1 Tessellated darter Etheostoma olmstedi 9 White sucker Catostomus commersonii 1 Yellow perch Perca flavescens 1

- 202 - Oaks Creek site 2 is father away from the lake than site 1. It was much more shallow with some deeper pools. The current was faster than at site 1 with more areas of shade and it had a primarily rocky bottom substrate. The species and counts found in Oaks creek site 2 are listed in Table 6. The species richness of site 2 is 15. The dominant species found was fallfish. Table 6. Fish species found in Oaks Creek site 2.

Species Scientific name Count Bluntnose minnow Pimephales notatus 4 Cutlips minnow Exoglossum maxillingua 7 Emerald shiner Notropis atherinoides 9 Fallfish Semotilus corporalis 53 Longnose dace Rhinichthys cataractae 9 Margined madtom Noturus insignis 2 Northern hogsucker Hypentelium nigricans 13 Pumpkinseed sunfish Lepomis gibbosus 2 River chub Nocomis micropogon 1 Satinfin shiner Cyprinella analostana 23 Shield darter Percina peltata 9 Slimy sculpin Cottus cognatus 26 Smallmouth bass Micropterus dolomieu 15 Spottail shiner Notropis hudsonius 6 Tessellated darter Etheostoma olmstedi 2

DISCUSSION Species richness, Shannon-Wiener’s diversity index, and Simpson’s diversity index were measured for each site surveyed and are listed in Table 7. Species richness refers to how many different species make up a given community. Shannon-Wiener’s diversity index gives a measure of the richness and evenness of species within the community. The Shannon index will increase as either richness and/or evenness increases. Simpson’s diversity index is a measurement of the probability that two individuals of the same species be drawn at random from a given community. As Simpson’s index increases, the diversity of a given community decreases. By comparing the two indexes with the Species Richness, one can gauge the diversity and evenness of the fish community in each stream surveyed.

- 203 - Table 7. Species Richness, Shannon-Wiener index, and Simpson’s diversity index for Canadarago Lake tributaries and outlet sites surveyed. The higher the Shannon-Wiener index, the greater the species richness and/or evenness. The higher the Simpson’s diversity index, the lower the diversity of the community.

Site Species Shannon-Wiener index Simpson’s diversity index Richness Hyder Creek 14 2.154199455 0.148015531 Trout Brook 11 2.161992906 0.130358101 Ocquionis Creek 7 1.672347809 0.203629032 Oaks Creek site 1 9 1.468524523 0.281818182 Oaks Creek site 2 15 2.244803689 0.140699816

Hyder Creek, Trout Brook, and Oaks Creek site 2 all had similar habitats with shallow, cold, fast-flowing clear water with a primarily rocky bottom substrate composition. These three streams had the highest richness, evenness, and diversity of fish communities. Ocquionis Creek and Oaks Creek site 1 contained slow-flowing, warmer water with suspended sediment and primarily mud/debris bottom substrate composition. These two sites had the lowest richness, evenness, and diversity of fish communities. Species of dace prefer moderate to swift moving water in small, clear streams with riffles and primarily rock/gravel substrate (Smith 1985). Largemouth bass prefer slow paced and warm water streams or standing water with vegetation. Fallfish prefer small, clear streams. Bluntnose minnow, creekchub, cutlips minnow, pumpkinseed sunfish, rock bass, smallmouth bass, spottail shiner, tessellated darter, and white suckers are tolerant of a variety of habitats. Slimy sculpin, shield darters, species of trout, and species of dace are all more sensitive to changes in water quality and require cold, fast paced water with a rocky bottom substrate (Smith 1985). Hyder Creek’s fish community had high species richness with the dominant species being smallmouth bass, largemouth bass, and creek chub. Hyder Creek exhibited increased fecal coliform bacteria concentrations since 2010 and has a consistently higher fecal coliform bacteria concentration than most other streams surveyed (Perry and Brown 2017). However, there was found to be lower levels of total phosphorous, total nitrogen, and nitrate+nitrite in Hyder Creek than most other tributaries surveyed (Perry and Brown 2017), which suggest that the elevated levels of fecal coliform isn’t coming from a source that would simultaneously cause nutrient loading. Additionally, Hyder Creek scored 4.429 on the Family-level Biotic Index for the benthic macroinvertebrate community, indicating a very low possibility of organic pollution (Brown 2017). This might suggest that the long-term quality of Hyder Creek has been stable and the higher concentration of fecal coliform bacteria is a fairly recent contamination source that has not yet affected the benthic macroinvertebrate community. The fish community found in Hyder Creek seems to be fairly tolerant of these water quality changes thus far. Trout Brook was found to have the highest concentrations of fecal coliform bacteria of the streams surveyed, with a significant increase in concentrations since 2010 (Perry and Brown 2017). Trout Brook was also found to have the highest concentrations of total phosphorus, total nitrogen, and nitrate+nitrite (Perry and Brown 2017). This suggests nutrient loading from an outside source, possibly the same source as the fecal coliform bacteria contamination. Trout

- 204 - Brook scored 7.047 on the Family-level Biotic Index for the benthic macroinvertebrate community, indicating significant organic pollution and poor water quality (Brown 2017). Trout Brook’s dominating species were cutlips minnow and tesselated darter, both of which are tolerant species to a variety of habitats, conditions, and water quality conditions (Smith 1985). The majority of fish species found in Oaks Creek site 1 are tolerant to a variety of habitats or prefer slower flowing, muddy water with vegetation (Smith 1985). The majority of fish species found in Oaks Creek site 2 prefer faster flowing, clear water with rocky bottom substrate (Smith 1985). Oaks Creek was found to have significantly lower levels of fecal coliform bacteria and nutrients (total phosphorous, total nitrogen, and nitrate+nitrite) than any other stream tested (Perry and Brown 2017). Being the outlet of Canadarago Lake, Oaks Creek has a much different stream habitat and water quality than the inflowing tributaries tested. Between the two sites, Oaks Creek has the highest diversity and species richness of the streams surveyed.

CONCLUSION The results found in this study, along with concurrent water quality monitoring (Perry and Brown 2017), and microbenthic surveys (Brown 2017) suggest that Trout Brook has the most variable stream conditions and/or is the stream most contaminated with organic pollutants, nutrient loading, and fecal coliform bacteria. The lake outlet (Oaks Creek) is the most stable and least contaminated stream habitat of the sites surveyed. The decline in quality and spike in nutrients and fecal coliform bacteria in Trout Brook since 2010 suggest a newly introduced source of contamination sometime after the 2010 surveys were conducted. Investigation of land- use and septic systems surrounding Trout Brook might reveal the source of those organic pollutants. Land-use will continue to affect the quality of the streams and the fish communities that can thrive in Canadarago Lake tributaries and outlet. The health of a fish community depends on the quality of the stream habitat in which they reside. Continuing to monitor Canadarago Lake tributaries and outlet more regularly will help gauge the fish communities and overall stream health. This more regular testing of the streams can help inform management decisions, as any sudden observed changes can indicate the introduction of a new source of contamination or a change in land-use near the stream, and appropriate action can be taken.

REFERENCES Albright, M.F., H.A. Waterfield. 2012. The State of Canadarago Lake. Technical Report #30. (2011). Bio. Fld. Sta., SUNY College at Oneonta.

Brown, M. 2017. Macroinvertebrate survey and biological assessment of water quality: tributaries of Canadarago Lake; Otsego County, NY. In 49th Ann. Rept. (2016). Bio. Fld. Sta., SUNY College at Oneonta.

- 205 -

Harr, T.E., G.W. Fuhs, D.M. Green, L.J. Hetlings, S.B. Smith, and S.P. Allen. 1980. Limnology of Canadarago Lake. In J.A. Bloomfield (ed.). Lakes of New York State. Academic Press, New York.

Perry, T.H., M. Brown. 2017. Water quality monitoring and analysis of fecal coliform of Canadarago tributaries and outlet. In 49th Ann. Rept. (2016). Bio. Fld. Sta., SUNY College at Oneonta.

Smith, Lavett, C. 1985. The inland fishes of New York State. Albany, NY: New York State Department of Environmental Conservation. 522p.

- 206 - Annual monitoring of Moe Pond in conjunction with bio-manipulation

Zachary R. Diehl1 and Nicholas J. Muehlbauer2

Introduction

Moe Pond (Figure 1) is a 38 acre man-made polymictic pond located near Cooperstown, New York, and is owned by the State University of New York College at Oneonta Biological Field Station (BFS). Due to high nutrient concentrations and frequency of algal blooms, Moe Pond has historically been considered to be eutrophic (Sohacki 1972). Moe Pond has been surveyed intermittently since 1972 (Sohacki 1972). A survey done in 1994 found that brown bullhead (Amerius nebulosus) and golden shiners (Notemignous crysoleucas) were the only fish species present (McCoy et al. 2001). The abundance of golden shiners caused low abundances of large zooplankton, which resulted in frequent algal blooms and decreased transparency (Wilson et al. 1999). In 1998 or 1999 largemouth bass (Micropterus salmoides) and smallmouth bass (M. dolomieu) were illegally introduced into Moe Pond where they completely altered the trophic balance in the pond. By 2007 the largemouth bass had out competed smallmouth bass and had decimated golden shiner populations leaving little to no forage for this newly established predator (Reinicke and Walters 2007). The disappearance of the golden shiner population lead to an increase in the zooplankton abundance and mean size and increased algal grazing (Albright et al. 2004). Monitoring since has showed conditions fluctuating between dominance by rooted macrophytes and by algae, likely driven by trophic changes (Stowell 2013). Trophic changes led to an altered ecosystem in Moe Pond and caused largemouth bass populations to become stunted in recent years.

During the summer of 2016, the long-term dataset from Moe Pond, with regards to the fish community, the zooplankton community and limnology has continued. As in 2015, largemouth bass were culled from Moe Pond via a haul seine or angling to reduce numbers and determine impacts on fish growth. Likewise, tiger muskellunge (Esox masquinongy x Esox lucius) were stocked as an apex predator to reduce the abundance of bass in Moe Pond. Tiger muskellunge were provided by New York State Department of Conservation (DEC). This project can be used as a model for future studies dealing with similar problems.

2 Oneonta Biological Field Station intern, summer 2016. Current affiliation: SUNY Oneonta.

- 207 - MATERIALS & METHODS

Figure 1. United States Geological Survey (USGS) map of Moe Pond in relation to the SUNY Oneonta biological field station (BFS)

Limnology:

Water quality sampling took place on 3, 16, 21 and 29 June, and on 5 and 14 July 2017. Sampling took place at the deepest point in the pond, at about 2.8 m. On 3 June a depth sounder was used to find the deepest point of Moe Pond and an anchor buoy was deployed to ensure sampling took place in the same location. A YSI multi-probe was used to measure temperature, conductivity, pH and dissolved oxygen (mg/L and % saturation) (Table 1). YSI measurements were taken at the surface, 1m, 2m and the bottom. Finally a Secchi disk was used to gauge the transparency of the water.

Zooplankton Community:

The zooplankton community was sampled each time water quality was sampled. Zooplankton were sampled using a Wildco zooplankton net dragged behind the boat at a depth of about one meter below surface. All samples were stored in 500 ml bottles and diluted with ethanol. IMAGE PRO PLUS software was used with an Axioskop 40 microscope to identify and measure the first 100 zooplankton found, 1 ml at a time.

Fish Community:

A 200ft. haul seine and angling were used to collect largemouth bass from Moe Pond. All largemouth bass collected were euthanized with a lethal dose of MS-222. Fish collected were brought back to the lab and assigned an ID number. Once a fish was assigned an ID number, length (mm) and weight (grams) were recorded for each fish.

- 208 - Scale samples and otoliths were also taken to estimate age of each fish. All scales and otoliths were placed into scale envelopes until they were ready to be aged. Finally, stomachs were taken from each fish and stored in ethanol and placed into Whirl-Pak until they were analyzed. Largemouth bass stomachs were emptied and then everything was identified where possible. Once identified, total count and frequency of occurrence were calculated.

We estimated abundance of the largemouth bass population in Moe Pond by extrapolating haul seine densities to the whole pond. The area of the haul seine is 300m2. The average number of bass collected per haul seine was multiplied by 155,800m2, the total area of Moe Pond.

Tiger muskellunge were stocked into Moe Pond in an attempt to control largemouth bass abundance through trophic manipulation. All tiger muskellunge stocked were implanted with passive integrated transponder (PIT) tags, for later estimation of growth and survival. Ninety eight tiger muskellunge were stocked, with all fish stocked being 225-300mm (Figure 2).

Tiger muskellunge-Moe Pond 2016

40 35 30

25 20

Frequency 15 10 5

0 0 25 50 75 100 125 150 175 200 225 250 275 300 325 Length (mm)

Figure 2. Length frequency histogram of tiger muskellunge stocked in Moe Pond during summer 2016.

- 209 - RESULTS AND DISCUSSIONS

Limnology:

There were no major changes in water quality parameters in Moe Pond throughout the summer of 2016 (Table 1). Conductivity is a measure of the ability of water to carry an electric current, and is the most important water quality parameter for electrofishing. Conductivity levels in Moe Pond were relatively low; this is believed to be because Moe Pond is at the top of the watershed and has no inflowing water sources and is fed by rain water. The local bedrock is low in limestone (McCoy et al.2000) (which generally influences conductivity). The temperature in Moe Pond stayed consistent throughout the summer. Largemouth bass are considered warm- water fish and average temperatures in Moe Pond fall within that range for optimal growth (25- 31 °C). For most of the summer dissolved oxygen levels were within optimal ranges (>5ppm), although near the bottom it was relatively anoxic for much of the summer. Secchi disk readings (meters) stayed relatively the same throughout the summer. Secchi disk readings help gauge how far sunlight can penetrate the water column, with high Secchi disk readings potentially favoring the growth of rooted plants. Moe Pond. pH readings stayed relatively basic for the duration of the summer.

Table 1. Water chemistry parameters for Moe Pond, NY. Summer 2016

Date Depth (m) Temperature Conductivity (mS/cm) pH DO % DO ppm Secchi Disk (m) 6/3/2016 0 23.34 0.052 9.47 111.80 9.52 2.3 1 22.56 0.061 8.61 128.40 11.34 2 19.62 0.093 7.02 27.60 2.53 2.8 19.31 0.095 7.01 13.10 1.16 6/16/2016 0 24.17 0.006 8.51 105.90 8.84 2.5 1 24.14 0.004 8.73 101.60 8.64 2 23.51 0.003 8.58 88.10 7.52 2.8 22.96 0.001 8.32 45.22 2.34 6/21/2016 0 24.73 0.057 9.09 119.50 9.95 2.3 1 25.92 0.057 9.21 123.00 10.39 2 23.62 0.062 8.91 97.40 8.76 2.8 20.46 0.063 8.89 67.89 3.45 6/29/ 2016 0 25.67 0.053 9.03 120.60 9.63 2.2 1 24.38 0.057 9.34 125.40 10.11 2 20.38 0.062 8.87 94.30 8.93 2.8 19.82 0.067 8.91 54.86 2.89 7/5/2016 0 24.66 0.052 9.02 121.70 9.54 2.5 1 23.86 0.052 9.45 124.90 10.23 2 19.82 0.061 8.72 96.80 8.84 2.8 19.14 0.068 8.67 45.88 3.46 7/14/2016 0 25.63 0.059 9.07 120.80 9.67 2.5 1 24.58 0.061 9.48 123.90 10.34 2 19.42 0.063 8.37 97.80 8.94 2.8 18.68 0.069 8.26 56.14 4.16

- 210 - Zooplankton community:

Table 2 shows the mean lengths and percent composition for all the zooplankton collected throughout the summer 2016 surveys. Daphnia were the most frequent zooplankton identified, second being calanoid and cyclopoid copepods. Moe Pond appears to be dominated by larger crustaceans. Nauplius larva, calanoid and cyclopoid copepods and daphnia make up 83% of the total percent composition. Polyartha rotifers were observed for the last two consecutive years, before that they had not been seen since 2008 (Finger 2009). Eight trichocera were collected this summer, which marks the second year in a row that they have been collected. Prior to this no trichocerca have been reported in recent years (Busby and Casscles 2015).

Table 2. Average length (mm) and percent composition of zooplankton species in Moe Pond, NY during summer 2016.

Average plankton samples-Moe 2016 Species: Mean length (mm) Frequency of occurrence Cladocerans: Daphnia 1.362 36% Bosmina 0.160 6% Leptodora 2.671 1% Copepods: Cyclopoid 0.426 20% Calanoid 0.646 10% Nauplius 0.138 17% Rotifers: Asplancha 0.132 4% Keratella spp. 0.108 6% Polyartha 0.128 1% Trichocerca 0.298 1%

- 211 - Fish community:

A total of 200 largemouth bass were collected from Moe Pond via haul seine or angling. Throughout the study, angling proved to be a more successful means of collecting specimens. All fish collected were less than 380mm (15 inches). The length frequency histogram (Figure 3) shows a bottleneck shape with most fish being of similar relative size. There was one fish that was collected (360 mm) that was able to get past the 125-275mm size range.

Largemouth bass collected from Moe Pond were aged two ways; using both scales and otoliths (Figure 4 and 5). Neither method resulted in a large number of fish that were over age 3. This is suspected to be due to the lack of a forage base for the population of largemouth bass. There were many young-of-year largemouth bass collected this year via haul seine. In 2014 no young of the year largemouth bass were collected throughout sampling (Picante 2015).

Largemouth bass-Moe Pond 2016

Frequency 20 10

0 0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 Length (mm)

Figure 3. Length frequency histogram of largemouth bass in Moe Pond during summer 2016.

- 212 - Length (mm) vs. Sclae Age of Largemouth bass 400

350 300

250 200 150

(mm) Length 100 50 0 0 1 2 3 4 5 6 Age

Figure 4. Length (mm) vs. scale age of largemouth bass in Moe Pond during summer 2016.

Figure 5 suggests that there are distinct classes of bass in Moe Pond, based on age and length. Out of the 200 fish collected, only one fish was over 4 years in age (360 mm). This stunting of fish in Moe Pond appears to be due to the lack of a food source.

Length (mm) vs. Otolith Age of Largemouth bass

400 350 300

250 200 150 (mm) Length 100 50 0 0 1 2 3 4 5 6 Age

Figure 5. Length (mm) vs. otolith age of largemouth bass in Moe Pond during summer 2016.

- 213 - The most abundant food source identified in largemouth bass stomachs by far was Daphnia (Table 3). There were 1344 individual Daphnia found throughout all fish collected, comprising 47% of total composition by number. Other dominant food sources includes organisms from the Zygoptera suborder (dragonflies), Anisoptera suborder (damselflies), and Amphipods order (scuds), which also made up a majority of the total composition. There is no optimum food source for the largemouth bass in Moe Pond, after decimating the golden shiner populations they were left with a diet of zooplankton and macroinvertebrates.

Table 3. Stomach contents of 200 largemouth bass from Moe Pond 2016.

Content # of individuals Total count Frequency of occurrence (%) Amerius nebulosus 1 2 <1% Amphipod 48 568 20% Anisoptera 51 158 6% Chironimid 8 91 3% Coleoptera 10 22 1% Cranefly larva 2 3 <1% Daphnia 25 1344 47% Emphemeroptera 1 1 <1% Empty 34 0 <1% Hirudinea 12 78 3% Lepidoptera 1 12 <1% Platyhelminthes 1 2 <1% Plecoptera 1 2 <1% Tricoptera 21 68 2% True Flies 14 107 4% Zygoptera 84 391 14%

- 214 - A population estimate of Moe Pond was done using an aerial extrapolation method (Table 4). For summer 2016 it was estimated that there were 10,500 largemouth bass in Moe Pond. This is significantly greater than the last two’s years Moe Pond has been sampled (2014, 2015). Decline in populations of golden shiner and smallmouth bass are also diagrammed in this table. A total of 306 largemouth bass were euthanized via haul seine or angling. It is hoped with removing largemouth bass and the introduction of tiger muskies that within the coming years a decrease in largemouth bass populations will be observed.

Table 4. Changes in the populations of golden shiner, largemouth bass, and smallmouth bass from 1994-2016 in Moe Pond (modified from Busby & Casscles 2016).

Year Golden shiner Largemouth bass Smallmouth bass 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)* 3 206 20 2003 (Hamway 2004)* 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)* 0 4,205 0 2014 (Picante 2015) 0 6,361+/-1,676 0 2015 (Casscles & Busby 2016) 0 35,015+/-5,329 0 2015 (Casscles & Busby 2016)* 0 8.109 0 2016 (Diehl & Muehlbauer 2017) 0 10,500 0 *Indicates where abundance estimates were conducted using electrofishing mark/recapture surveys rather than areal extrapolation.

CONCLUSION

The water quality of Moe pond has remained fairly consistent throughout the time it has been monitored. Alternatively, the dynamics of fish, both types and amounts, has fluctuated throughout the time of monitoring. These changes in the fish community have contributed to the changes in the zooplankton community. After the reduction of the golden shiners with the introduction of large and smallmouth bass, zooplankton populations were able to recover. Aiding the recovery of the zooplankton community was the low recruitment rate of largemouth bass in recent years, most likely due to intra-specific competition among largemouth bass.

- 215 - Introduction of tiger muskellunge to the system will hopefully continue to reduce the number of largemouth bass and bring greater balance to the system. These fish were chosen for two factors; 1) for their tolerance to higher temperatures and 2) their hybrid vigor. Hybrid vigor can be explained as getting the best genes from each of your parents and essentially growing faster, although the will not grow as large as their predecessors.

Moe pond has been a one-species system for several years and the addition of a predator to balance the system will allow for a more healthy water body. Due to the infertility of tiger muskellunge, a continued stocking program is needed to keep the system balanced. With predation of the largemouth bass and continued population culling through haul seining and angling, a balance will be achieved. This will hopefully allow for the reintroduction of forage species, increasing diversity and stopping cannibalism between largemouth bass. With balanced numbers of fish within the pond, it will allow for an increase in zooplankton and potentially reduce or eliminate the annual algal blooms.

Looking ahead to future studies, monitoring to determine growth and mortality rates of the tiger muskies and bass populations to see how the introduction progresses will shed light on the changes. This will also allow for future decisions as to the feasibility of introduction of forage species. Continuing the monitoring procedures that have been carried out each year will provide the information to make informed management decisions.

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 Reserve Management. 20(04):263-269.

Busby, D. and J.B. Casscles. 2016. Continued monitoring of the Moe Pond ecosystem in conjunction with biomanipulation (2015). In 48th Annual Report (2016). 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 (Micropterus dolomieu and M. salmoides, respectively). In 41st Annual Report (2008). 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.

- 216 - Picante, J. 2015. Continued monitoring of the Moe Pond ecosystem and largemouth bass (Micropterus salmoides) 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., 2013. 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.

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.

- 217 - Reproductive phenology of E. complanata in Otsego County, NY Thomas Franzem1, Paul Lord2, Andrew Gascho-Landis3, and Robin LaRochelle4

ABSTRACT There is uncertainty about the timing of Elliptio complanata glochidia release in central New York. We conducted surveys from late April to early July to determine sex ratios and timing of glochidia brooding and release in a population of Elliptio complanata on the fringes of the Susquehanna watershed. Through mark and recapture and visceral mass and gill samples we found that this population has a slightly higher percentage of males than females. The percentage of males and females was much higher than the percentage of apparent hermaphrodites. By monitoring glochidia presence in the gills of mussels, we were able to determine a time frame for peak glochidia production and release. We concluded that the peak time for production in this population begins in late May, reaches its peak in early June, and tapers off in late June – early July. The findings of this study have answered some of our questions about E. complanata’s reproductive phenology, however, it raised questions too. During future projects, we plan on incorporating detection of hermaphrodites and multiple broods into our study design to hopefully broaden our understanding of E. complanata’s lifecycle. Key words: Elliptio complanata, glochidia, visceral mass.

INTRODUCTION The Eastern elliptio (Elliptio complanata; hereinafter referred to as Elliptio) is the most abundant and widespread unionoid in New York. Elliptio plays a crucial role in the environment by filtering water and consuming algae and zooplankton, making nutrients available to other animals that would otherwise be unavailable (Nedeau 2008). Despite its’ wide distribution and importance to freshwater ecosystems, there are gaps in our understanding of the life cycle of Elliptio. For example, the timing of the release of glochidia (the larval stage of freshwater mussels) is poorly understood. Glochidia are obligate parasites on fish; this is responsible for the dispersal of Elliptio. Researchers have documented different times for unionid glochidia release. For example, Matteson, as cited in Downing (1989), determined glochidia release to be mid to late July while Nedeau (2008) gives the entire summer season as the time of glochidia release. Lellis et al (2013) concluded that American eels (Anguilla rostrata) are the most effective and only host species for Elliptio glochidia in the Susquehanna watershed. Recent research prior to 2015 found no evidence of reproduction of Elliptio anywhere in the Susquehanna River, the Chemung River, the Canisteo River, the Tiougnioga River, the Chenango River, the Unadilla River, Butternut Creek, Catatonk Creek, or Wharton Creek (Lord & Pokorny, 2012; Lord et al., 2010; Lord & Harman, 2010; Lord & Harman, 2009, Harman et al., 2008). It is likely that the lack of reproduction and recruitment of Elliptios throughout the Susquehanna watershed is due to dams and impoundments impeding the movements of the

1 Undergraduate Research Aide, SUNY Oneonta. 2 Instructor, SUNY Oneonta. 3 Assistant Professor, SUNY Cobleskill. 4 Undergraduate, SUNY Cobleskill. - 218 - catadromous A. rostrata. Prior to 2015, there were no recent records of recruitment of Elliptios in the NY Susquehanna drainage. Recently observed recruitment is on the fringes of the NY Susquehanna drainage where watershed waters comingle with other watershed waters. (Landry, pers comm.) This focused our attention on the reproductive phenology of Elliptio. Attempts in the lab at SUNY-Oneonta to identify a glochidia release date for Elliptio have failed (Lentz, pers comm). The purpose of this study is to (1) determine sex ratios for Elliptio, (2) ascertain when females are brooding glochidia, and (3) establish when females are releasing glochidia. A better understanding of their life cycle and biology will inform future conservation efforts and facilitate research on this species and other freshwater mussel species. This study’s results supports efforts to reintroduce A. rostrata to the Susquehanna. The stream reach we studied began at a DEC access point (UTM 18T491010 4702099) off NYS Route 23 in West Oneonta, NY. Starting approximately 30 m north of the Route 23 Bridge, divers worked upstream for approximately 146 m. The river was, on average, 15.4 meters wide. Depth ranged from less than a half a meter at the start of the reach to 1.5 + meters in some parts; the depth increased and decreased no more than .5m in response to precipitation. Concentrated in the center, the substrate was a mix of cobble, gravel, and sand. Both banks were silt. The water flowed through most of the stream at low velocity, however the downstream end of the reach was the beginning of a riffle.

METHODS Surveys Full surveys were conducted on 23 April 2016, 18 May 2016, 1 Jun 2016, and 8 July 2016. In addition, four smaller surveys were conducted on 9 June 2016, 16 June 2016, 20 June 2016 and 27 June 2016 to attain water readings, sample for glochidia, and collect and examine 8+ marked mussels. Starting at the DEC access point, divers searched for mussels as they moved upstream. When an Elliptio was found, the position was marked with a GPS by a dive tender and a rebar stake was hammered into the riverbed to relocate mussels ready for replanting. We collected 20+ mussels per full survey. The mussels were brought to shore, where each captured mussel was tagged with two unique plastic Hallprint tags (Type FPN) (one on each valve). Cyanoacrylate glue (Seachem FlourishTM Glue; Hartmann et al., 2016) was used to attach the tags. Elliptios were pried open 7mm with a clam knife (Dexter Russel 3” Clam Knife) and fingers. A 7mm diameter wooden dowel was inserted between the valves. To extract sperm and eggs, we used the sampling techniques outlined by Saha and Layzer (2008). A syringe (Exel Int.® 3ml Disposable Syringe) with 0.5mL of sterile water was inserted into the visceral mass. The point of injection was below the foot towards the posterior of the mussel. We injected the water and removed 1ml of visceral fluid. The syringes were labeled with the mussels’ tag number and stored in a cooler. The mussels were inspected for swollen or enlarged gills. If they were even slightly swollen, gill fluid was extracted using the same technique to extract the visceral fluid. After sampling, the mussels were provided to a dive tender. Using a combination of GPS points and rebar stakes to determine the location from which each tagged mussel was

- 219 - originally extracted from the sediments, the Elliptio was handed to a diver. The diver dug a small hole using a hand rake and inserted the mussel’s anterior-end into the riverbed and filled the hole, careful to leave the posterior-end of the mussel uncovered. On subsequent visits to the mussel bed, the tag numbers of recaptured mussels were recorded. We examined the recaptured animals for swollen gills; a gill sample was taken if the gills appeared swollen. We did not take visceral mass samples on the recaptured mussels unless there was evidence of hermaphroditism in an Elliptio. Laboratory Work The visceral mass and gill samples were brought to the laboratory and examined using a microscope. We first examined the samples using 10x magnification and inspected for eggs and glochidia. If neither of these were found, a cover slip was placed on the slide, the magnification was switched to 100x, and we examined the sample for sperm. Glochidia Sampling After glochidia was detected in some samples, we deployed a Wildco® stream drift net (100 µm mesh) immediately downstream of the mussel bed (Culp et al. 2008) every time we sampled. Nets were placed in the water for 30 minutes between 10 am and 3 pm. During each sampling occasion, stream flow was measured at the net and the water height on the net was recorded for calculating the volume of water passing through the net during each sampling time. Samples were stained with phloxine B (Culp et al. 2011), numbered, and then brought back to the laboratory where they were examined for glochidia. Water Quality Measurements Starting on 18 May 2016 we recorded water quality readings for temperature, dissolved oxygen (DO), pH, salinity, conductivity, and total dissolved solids (TDS) from a YSI multiprobe.

RESULTS Sex Ratios We determined the sex of 56 captured mussels: 23 were females, 27 were male, and 6 were apparent hermaphrodites (Figure 1). Two of the apparent hermaphrodites showed evidence of protandry and four were apparently protogynous. Glochidia Brooding On 23 April, we captured, tagged, and replanted twenty-one mussels; none were gravid. The following survey we captured twenty mussels, four were recaptures and four were gravid females. On 1 June 2016, we found twelve gravid females out of twenty-one mussels sampled (11 were recaptures). The following survey on 9 June 2016 was not a full survey; only nine mussels (all recaptures) were sampled. Of these nine, four were female. Three of the females were gravid, with either mature glochidia or developing glochidia. On 20 June, thirteen mussels

- 220 - were captured; six were recaptures. The five untagged animals were not tagged during this survey, but visceral mass and gill samples were taken nonetheless. Of the thirteen captured, five were gravid. On 8 July 2016 we conducted our last full survey. Twenty mussels were captured; fifteen were recaptures. Of these twenty mussels only one was gravid (Table 1). The water temperature during the peak time for glochidia release ranged from 18.64°C on 1 June and 20.66°C on 20 June, however, the temperature dropped to 14.77 °C on 9 June.

Figure 1. Number of Eastern Elliptio (Elliptio complanata) males, females, and apparent hermaphrodites in Otego Creek survey area in 2016.

Table 1: Summary of surveys conducted of Otego Creek in West Oneonta (Otsego County) NY. Count of males and females only of newly captured Eastern elliptios (Elliptio complanata) and does not include recaptures; the number of gravid individuals includes both new and recaptured animals.

Male Apparent Female Number of Survey Total (Not Hermaphrodites Recaptures (Not counting Gravid Dates Captured counting (Not counting recaptures) Individuals recaptures) recaptures) 23-Apr-16 21 0 7 10 4 0 18-May-16 20 4 9 5 2 4 1-Jun-16 21 11 4 6 0 12 9-Jun-16 9 9 - - 0 8 20-Jun-16 13 8 4 1 0 5 8-Jul-16 19 15 3 1 0 1

- 221 - Water Quality Measurements Water temperature showed an increasing trend; it was coldest on 18 May with a temperature of 8.67°C and warmest on 8-Jul at 21.45°C. DO steadily decreased across the time frame. Except for a spike in late June, salinity remained relatively consistent over our surveys. Conductivity generally increased from May to July. pH stayed within a range of 6.75 to 7.81 throughout the survey. TDS increased sharply between 20 June and 27 June, but other than that it remained relatively consistent (Table 2). Table 2: Water quality data collected associated with Eastern Elliptio (Elliptio Complanata) collected at Otego Creek in West Oneonta (Otsego County), NY.

Survey Dates Temperature Dissolved Oxygen Conductivity Salinity pH TDS 18-May-16 8.67°C 13.20mg/L 107µs/cm 0.07 7.74 NA 1-Jun 18.64°C 9.10mg/L 159µs/cm 0.08 7.65 0.117 9-Jun-16 14.77°C 8.99mg/L 131µs/cm 0.08 6.75 0.106 16-Jun-16 19.39°C 8.38mg/L 163µs/cm 0.09 7.81 0.118 20-Jun-16 20.66°C 8.24mg/L 181µs/cm 1.09 7.51 0.128 27-Jun-16 19.22°C 7.49mg/L 187µs/cm 0.1 7.4 1.37 8-Jul-16 21.45°C 9.01mg/L 192µs/cm 0.1 7.53 1.34

DISCUSSION Sex Ratios The sex ratio we found is consistent with what we expected (Figure 1); Downing et al (1989) found that across size classes the percentage of males was higher than females and hermaphrodites. Of the six apparent hermaphrodites we found, four of them appeared protogynous, however there is no evidence of protogyny in this species. Downing (1989) concludes that Elliptio is a protandrous sequential hermaphrodite. In his study he defined hermaphrodites in this species as animals that had between 10% and 90% female gonad tissue. It is likely that during visceral mass samples we extracted female gonad tissue and on a subsequent survey recaptured the animal and extracted male gonad tissue when we sampled the visceral mass. It is possible that other captured Elliptios were hermaphrodites that we didn’t detect; nonetheless the percentage of apparent hermaphrodites we found is similar to what Downing found. Apparent hermaphrodites were all found to be brooding glochidia at some point. Based on this, it is likely that these apparent hermaphrodites were functionally female; most hermaphroditic individuals are functionally gonochoristic (Haag 2012). Hermaphroditism in this population would be an interesting topic for future research. Glochidia Brooding We conclude, based on our data, that the peak time for brooding and release of glochidia in this population of mussels begins in mid to late May and continues for several weeks before tapering off in late June – early July (Figure 2). We assume that some Elliptios are brooding and

- 222 - releasing glochidia before and after this time frame, but the bulk of glochidia production and release for this population is within this period.

Figure 2. Percent of female Eastern elliptio (Elliptio Complanata) brooding glochidia in Otego Creek survey area in 2016.

Water temperature can be a cue for spawning and can alter the length of the brooding period and time of glochidia release in many species of freshwater mussels (Watters and O’Dee 2000). It appears that different populations of Elliptio may release glochidia at different times. Furthermore, timing of glochidia brooding and release can change year-to-year within a population. Bearing this in mind, the population we studied has an average temperature preference of 18.37°C for glochidia production and release. Elliptio is able to produce at least two broods across a season (Price and Eads 2011). Detecting multiple glochidia broods were beyond the goals of this study. It is possible that some individuals in this population of Elliptio can produce multiple broods, however we did not detect any; we did not collect a gravid individual, recapture it to find it’s not gravid, then capture it a third time and find it’s gravid again. It could be they produce secondary broods later in the season. Detection of multiple broods should be incorporated into future studies on this population.

- 223 - LITERATURE CITED Culp, J. J., W. R. Haag, D. A. Arrington, & T. B. Kennedy. 2011. Seasonal and species- specific patterns in abundance of freshwater mussel glochidia in stream drift. J. North Am. Benthol. Soc. 30(2):436-445.

Downing, J. A., J. P. Amyot, M. Pérusse, and Y. Rochon. 1989. Visceral sex, hermaphroditism, and protandry in a population of the freshwater bivalve Elliptio complanata. J. North American Benthological Society. 8(1):92-99.

Haag, W. R. 2012. North American Freshwater Mussels: Natural History, Ecology, and Conservation. New York (NY): Cambridge University Press.

Hartmann, J. T., S. Beggel, K. Auerswald & J. Geist. 2016. Determination of the most suitable adhesive for tagging freshwater mussels and its use in an experimental study of filtration behaviour and biological rhythm. J of Molluscan Studies.

Landry, Jenny, A.Personal communication with P. Lord. 2015.

Lellis, W. A., B. St. John White, J. C. Cole, C. S. Johnson, E. van Snik, and H. S. Galbraith. 2013. Newly documented host fishes for the Eastern Elliptio Mussel Elliptio complanata. J. of Fish and Wildlife Management. 4(1):75-85.

Lentz, Vicky. Discussion with P. Lord. 2016.

Nedeau, E. J. 2008. Freshwater Mussels and the Connecticut River Watershed. Connecticut River Watershed Council, Greenfield, Massachusetts. xviii+132 pp.

Price, J. E., and C. B. Eads. 2011. Brooding patterns in three freshwater mussels of the genus Elliptio in the Broad River, South Carolina. American Malacological Bulletin 29:121–126.

Saha, S. & J. B. Layzer. 2008. Evaluation of a nonlethal technique for determining sex of freshwater mussels. J. North Am. Benthol. Soc. 27:84–89.

Watters, G. T. and S. H. O’Dee. 2000. Glochidia release as a function of water temperature: beyond bradyticty and tachyticty. In: R. A. Tankersley, D. I. Warmolts, G. T. Watters, B. J. Armitage, P. D. Johnson, and R. S. Butler, eds., Proceedings of the Conservation, Captive Care, and Propagation of Freshwater Mussels Symposium, 1998. Ohio Biological Survey, Columbus Ohio. Pp. 135-140

- 224 - Comparison of gill raker morphology of alewife from recent and bygone introductions

Daniel Stich1 and Chase Ducey2

INTRODUCTION

The alewife (Alosa pseudoharengus) is an anadromous species of herring native to North America, but along the east coast of the United States, landlocked alewife populations have become well established in inland lakes mostly due to fish stocking. Anadromous adults typically reach 10 to 12 inches in length, whereas adult size in landlocked populations is highly variable but tends to be much smaller (Palkovacs et al. 2007). A high temperature threshold allows them to comfortably live nearly anywhere and helps them survive as they move along streams and rivers to other bodies of water. A reproductive reaction to predation allows them to establish quickly in a new environment, which can sometimes interfere with the pre-existing ecosystem (Gibson and Myers 2003).

Alewives are planktivores, eating large zooplankton and fry (juvenile fish that no longer feed off their own yolk sacs) by filter feeding, which utilizes gill rakers (bony or cartilaginous processes that project off the branchial arch, which supports the gills). As alewife populations grow, competition for food increases, and the abundance of large zooplankton decreases, resulting in the need for alewife to adapt to smaller types of zooplankton. The need to retain smaller food sizes during filter feeding may exert selective pressure on the morphology of gill rakers used in feeding (Palkovacset al. 2007). Landlocked alewife tend to have more gill rakers that are more closely spaced than fish of similar size in anadromous populations (Palkovacs et al. 2007), and the number and spacing of gill rakers in landlocked population is correlated with prey sizes (Palkovacs and Post 2009). The objective of this study was to determine if and how alewife gill rakers change in either number and/or spacing as part of this adaptive process in two landlocked populations of alewife in New York lakes following different periods of introduction (historical and recent).

METHODS

Alewives were collected from Canadarago Lake (Otsego County, NY) and Truesdale Lake (Westchester County, NY) during June 2016. Alewives from Canadarago Lake were obtained from the NYS DEC Region 4 fisheries gill net survey; fish from Truesdale Lake were netted from the surface following a fish kill (Jenne 2016). Alewife have been present in Canadarago Lake since their illegal introduction in 1999 (Brooking et al. 2012). Alewife were first documented in Truesdale Lake in 2016 (Jenne 2016), though the date of introduction is not known.

All fish were frozen until the day of processing, at which time they were thawed in water until flexible. The length (millimeters) and mass (grams) of each fish was recorded and a scale

1 Assistant Professor of Biology, SUNY Oneonta. 2 F.H.V. Mecklenburg Conservation Fellow, summer 2016. Current affiliation: Cooperstown Central High School.

- 225 - sample taken from the left side prior to dissection. Scale samples were stored in labeled coin envelopes. The left gill arch was removed from each fish using a scalpel or scissors and severed at each end of the arch so that no rakers were left behind. Gill arches were then stored in a vial containing 70% ethanol. The head of each fish was removed, and otoliths were harvested using tweezers and stored in empty vials.

Prior to measurement, gill rakers were transferred to vials containing 1 ml 70% ethanol and 1ml glycerin for approximately 1 hour. Next, an equal number of vials were filled with 2 ml glycerin for storage until further processing. A photograph was taken of each gill raker using a dissecting microscope with an Infinity 2 camera attachment. This allowed us to count and measure them easily. The number of gill rakers on each arch was counted and standardized by gill arch length, and the spaces between the first gill rakers on the upper and lower gill arch were measured for each fish. The width of the first gill raker on upper and lower arches also were measured, and the length of the gill arch was measured and standardized by fork length for comparison. A standardized measure of gill raker spacing was calculated for each fish (Palkovacs, Post 2008): GRS = (L – N · W) / N

where GRS was gill raker spacing, L was the sum of the lengths of the upper and lower gill arches, W was the average of the widths of the first gill rakers on the upper and lower gill arches, and N was the total number of gill rakers on both arches. Length-standardized gill raker counts were calculated by dividing the total number of gill rakers by the total length of gill rakers.

- 226 - RESULTS

We collected 86 alewives in total from Canadarago Lake (n = 53), and Truesdale Lake (n = 33). Fish from Truesdale Lake were significantly larger than the fish collected from Canadarago Lake (t-test, t=1.71, df = 33, p < 0.05; Figure 1). Gill arches in fish from Canadarago Lake were 13% longer (95% CI = 7-19 % larger) than gill arches of fish from Truesdale Lake when standardized for fork length (t-test, t = 4.38, df = 33, p < 0.05; Figure 1).

Figure 1. Comparison of fork length (left) and standardized gill arch length (right) of alewife collected from Canadarago (n =53) and Truesdale (n = 33) lakes during June 2016. Dark lines indicate medians, box ends represent the inner quartile range, and whiskers show 99% confidence interval.

Gill raker spacing in alewife from Canadarago (µ = 16.5, S.D = 3.9) and Truesdale (µ = 15.1, S.D = 0.07) lakes was not significantly different at the 95% confidence level (t-test, t=1.71, df = 33, p = 0.09). However, standardized gill raker count was significantly greater for fish collected from Canadarago Lake than for fish collected from Truesdale Lake (Wilcox test, W = 1298, p < 0.05; Figure 2). Similarly, gill raker width was significantly narrower for fish from Canadarago Lake than fish collected from Truesdale Lake (Wilcox test, W = 264, p < 0.05; Figure 2).

- 227 -

Figure 2. Gill raker counts (left) and gill raker width (right) for alewife collected from Canadarago (n = 53) and Truesdale (n = 33) lakes during June 2016. Dark lines indicate medians, box ends represent the inner quartile range, and whiskers show 99% confidence interval.

DISCUSSION This study demonstrated differences in the morphology of the feeding structures of an invasive species following two different timelines for introduction. This work confirms the work of others who have investigated contemporary evolution of gill raker morphology in planktivorous fishes in other locales (Palkovacset al. 2014). While the alewives in Truesdale Lake had only recently been documented (summer 2016), the alewife population in Canadarago Lake has been established for some time. During the time-course of invasion and population growth, the latter population appears to have diverged further morphologically from what might be expected of anadromous populations than has the more recently introduced population in Truesdale Lake. We observed a marked difference in the size of individuals within each population, with alewife in Truesdale Lake being generally larger than alewife from Canadarago Lake. Similarly, the gill raker count in Canadarago Lake was significantly greater than in fish collected from Truesdale Lake, despite the fact that fish from both lakes had a similar gill raker spacing. This indicates that fish in Canadarago Lake, with a smaller mean size, have adapted morphologically during a contemporary time frame to fit a larger number of gill rakers in to a smaller space through by decreasing the width of individual gill rakers and increasing the proportional size of the gill arch. This finding is strongly suggestive that the fish have adapted to differences in the plankton communities between the lakes, and indicates alternative evolutionary strategies for optimizing feeding morphology compared to previous findings (Palkovacs, Post 2008). Future work describing these communities has the potential to confirm or refute this conjecture. Furthermore, few, if any, studies have examined the consequences of the adaptations for the

- 228 - actual fitness of this species as related to growth, fecundity, or survival. The ability to link documented differences in gill raker morphology to these metrics has the potential to be useful in the management of invasive alewife populations.

REFERENCES

Brooking, T., J.R. Jackson, L.G. Rudstam and N.D. McBride. 2012. Fisheries surveys of Canadarago Lake, NY. In M.F. Albright and H.A. Waterfield. The state of Canadarago Lake, 2011. BFS Tech. Rept. #30. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Gibson, A.J.F., and R.A. Myers. 2003. A meta-analysis of the habitat carrying capacity and maximum reproductive rate of anadromous alewife in eastern North America. Amer. Fish. Soc. Symp. 35:211-221.

Jenne, C. 2016. Personal communication. SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta.

Palkovacs, E.P., K.B. Dion, D.M. Post and A. Caccone. 2007. Independent evolutionary origins of landlocked alewife populations and rapid parallel evolution of phenotypic traits. Molecular Ecology. 17(2):582-597.

Palkovacs, E.P. and D.M. Post. 2008. Eco-evolutionary interactions between predators and prey: can predator-induced changes to prey communities feedback to shape predator foraging traits? Evolutionary Ecology Research 10: 699–720.

Palkovacs, E.P. and D.M. Post. 2009. Experimental evidence that phenotypic divergence in predators drives community divergence in prey. Ecology 90(2):300-305.

- 229 - Lotic fish communities of selected Otsego Lake tributaries Zachary R. Diehl1 INTRODUCTION The tributaries to Otsego Lake, NY have been individually surveyed by numerous investigators (Hayes 1990, Bassista and Foster 1995, Foster 1996, Miner 1997, Jamieson et al. 2005, Reynolds et al. 2010). Early Biological Field Station fisheries surveys (New 1971, Harman et al. 1996, MacWatters 1980, 1983) focused on developing listings of the fish fauna of Otsego Lake and its tributaries. However, those surveys did not differentiate stream fauna from lake fauna, nor did they describe the fish fauna of specific streams (Foster 1996). Relative abundance and composition of stream fish communities can be useful indicators of the impacts of land use, sediment load, nutrient input and alteration of riparian vegetation (Karr 1981). A number of changes have occurred in the management of the Otsego Lake, NY watershed during the time since historical fisheries surveys. Studies of the fish communities of tributaries to the lake have been conducted sporadically during the past several decades to understand effects of changing water quality of the Otsego Lake watershed (Hayes 1990). Fish assemblages in Otsego Lake tributaries vary seasonally, thus a general overview is given here. The goal of this study was to characterize current fish assemblages in 10 tributaries of Otsego Lake, and compare those communities to historical data. In order to achieve this goal, back-pack electrofishing was used to survey each community. Data were standardized to facilitate comparisons between streams and allow for comparisons with historical surveys.

MATERIALS & METHODS Fisheries surveys were conducted between 27 June 2016 and 25 July 2016; all sites were within Otsego and Herkimer County, New York. These lotic habitats comprise first- and second- order streams. First order streams include Brookwood Point Creek, Cripple Creek, Glimmerglen Brook, Mohican Canyon Creek, and Three-Mile Point Creek. Second order streams include Hayden Creek, Leatherstocking Creek, Shadow Brook and White Creek. Table 1 describes the sites and provides GPS coordinates. Figure 1 show stream locations. While there are numerous ways to divide stream habitat, this watershed fits best into three ecological zones: headwater tributaries, pool-riffles segments and lowland segments (Hocutt and Wiley 1986). Each stream was sampled with a Halltech HT-2000 backpack electro-fisher unit. Fish communities in the tributaries were sampled during morning hours to minimize heat stress incurred by fish. The majority of stream reaches were sampled for about 1,000 seconds, although larger streams such as Cripple Creek, Shadow Brook, Hayden Creek and Leatherstocking Creek

- 230 - were surveyed more intensively to obtain representative samples of the fish assemblages, therefore, a larger area was sampled in those streams. Streams that were shocked for longer durations are indicated as such. GPS points were recorded at each site to give precise locations of the surveys, and to allow for comparison with data collected in the future. All fish collected were identified and counted, and the fork length of game species were recorded to the nearest millimeter. Catch per unit effort (CPUE-fish/hr.) was calculated for each species based on the amount of time spent sampling in each stream as a standardized index of abundance to facilitate comparison of fish assemblages throughout the watershed. Relative abundance (%) was also calculated to qualitatively asses the dominance of fish species within and among streams. Table 1. GPS coordinates, elevation, date and physical descriptions of sample locations. Stream Date Elevation Start point End point Physical description Brookwood Point 6/28/2016 1,180 N 42 72.636’, N 42 72.761’, Mouth of Creek W -74 W-74 92.089’ Brookwood point 91.655’ Creek, accessed from Brookwood Point.

Cripple Creek 7/21/2016 1,200 N 42 81.334’, N 42 81.333’, Middle of run Downstream W -74 W -74 before mouth of 89.546’ 89.614’ creek to dam below Clarke Pond, accessed from Otsego Golf Club road.

Cripple Creek 7/19/2016 1,263 N 42 82.343’, N 42 82.78’, North side of Upstream W -74 W -74 culvert on Bartlett 89.995’ 89.935’ Road. Glimmerglen Brook 6/30/2016 1,293 N 42 72.013’, N 42.72.293’, Accessed from W -74 W -74 Rat Cove 92.506’ 93.008’ continuing up above to County Route 80 Hayden Creek 7/6/2016 1,163 N 42 81.89’, N 42 82.139’, Large culvert on W -74 W -74 the south side of 88.425’ 88.327’ County Route 53. Leatherstocking 7/1/2016 1,357 N 42 73.223’, N 42 73.306’, Mouth of Creek W -74 W -74 Leatherstocking 91.503’ 92.263’ creek to up above County Route 80 to insurmountable waterfall.

- 231 - Table 1 (cont.). GPS coordinates, elevation, date and physical descriptions of sample locations.

Stream Date Elevation Start point End point Physical description

Mohican Canyon 7/25/2016 1,243 N 42 76.479’, N 42 76.613’, Mouth of Creek W -74 W -74 90.48’ Mohican canyon 89.952’ creek, continuing above County Route 80. Shadow Brook 7/21/2016 1,232 N 42 79.071’, N 42 79.183’, North side of W -74 85.85’ W-74 85.832’ large culvert on Mill Road behind Glimmerglass State Park.

Three Mile Point 7/20/2016 1,202 N 42 74.159’, N 42 74.181’, Mouth of stream Stream W -74 W -74 continuing above 90.823’ 90.897’ County Route 80, accessed through Three-Mile Point Park.

Willow Brook 6/27/2016 1,000 N 42 70.348’, N 42.70287, Mouth of Willow W -74 W -74.92362 Brook up to 92.314’ unsurpassable walkway. Accessed off of Pioneer Street in the Town of Cooperstown. White Creek 7/11/2016 1,250 N 42 80.707’, N 42 80.736’, Mouth of White W -74 W -74 Creek up to white 89.608’ 89.963’ home below wooden staircase.

- 232 -

Figure 1. Map showing sampling locations of 10 tributaries sampled in the watershed of Otsego Lake, NY during summer 2016.

- 233 - RESULTS A total of 25 species was identified during the eleven electrofishing surveys (Tables 2 and 3), nine more than the previous study (Casscles 2016). The most abundant species were blacknose dace (Rhinichthys atratalus) which represented 27% of the total catch and creek chub (Semotilus atromaculatus) which represented 20% of the total catch. The only fish determined to be widely distributed throughout the watershed are blacknose dace (7 sites), creek chub (9 sites) and longnose dace (Rhinichthys cataractae) (7 sites). The greatest diversity of fishes was observed in Hayden Creek, where 13 fish species were collected. Many of the species collected, such as largemouth and smallmouth bass, yellow perch, white sucker and walleye, were young-of-year fish. This indicates that Hayden Creek acts as a nursery for these fish. The capture of young-of-year walleye specifies that natural reproduction is occurring. The lowest species diversity was found in Three-Mile Point Stream. This stream is typically short and dominated by shallow, monotypic riffles. There are also two impassible dams downstream of Highway 80. Low species diversity was concluded to be the result of limited habitat diversity and impassible dams. Species diversity in Glimmerglen Brook was also very low. Pumpkinseeds were the dominant fish species in Glimmerglen, they were found above two impassible dams. Similarly, to Three-Mile Point Stream, the depressed species diversity may be in part due to impassable dams and high summer temperatures.

- 234 - Table 2. CPUE fish/hr. in tributaries to Otsego Lake, NY sampled during summer 2016. Column heading are abbreviated stream names and are defined as follows. BRW (Brookwood Point Stream), CR1 (upstream) & CR2 (downstream) (Cripple Creek), GLM (Glimmerglen Brook), HAY (Hayden Creek), LEATH (Leatherstocking Creek), MOH (Mohican Canyon Creek), SHD (Shadow Brook), 3-MI (Three-Mile Point Stream), WLL (Willow Brook), WH (White Creek).

CPUE fish/hr. Species BRW CR1 CR2 GLM HAY LEATH MOH SHD 3-MI WLL WH Blacknose dace 193 99 8 56 277 18 267 Creek chub 214 7 3 18 459 13 7 25 170 Pumpkinseed 32 6 27 Spottail shiner 4 3 63 3 Bluntnose minnow 4 White sucker 7 83 3 6 7 58 3 Longnose dace 14 81 14 23 31 2 35 Tesselated darter 7 20 32 13 2 7 22 Bluegill 13 35 1 Cutlips minnow 7 4 39 Central mudminnow 2 Smallmouth bass 11 3 13 3 Rockbass 25 7 22 9 Common carp 14 3 Yellow perch 200 68 20 230 Largemouth bass 13 21 3 4 5 Brown bullhead 77 3 1 2 18 Brown trout 137 4 6 Walleye 3 Brook trout 60 Slimy sculpin 1 Northern hog sucker 4 4 Redbreast sunfish 2 2 Central stoneroller 4 7 4 Chain pickerel 3

- 235 - Table 3. Relative Abundance (%) in tributaries to Otsego Lake, NY sampled during summer 2016. Column heading are abbreviated stream names and are defined as follows. BRW (Brookwood Point Stream), CR1 & CR2 (Cripple Creek), GLM (Glimmerglen Brook), HAY (Hayden Creek), LEATH (Leatherstocking Creek), MOH (Mohican Canyon Creek), SHD (Shadow Brook), 3-MI (Three-Mile Point Stream), WLL (Willow Brook), WH (White Creek).

Relative Abundance (%) Species BRW CR1 CR2 GLM HAY LEATH MOH SHD 3-MI WLL WH Blacknose dace 44% 22% 5% 32% 37% 12% 34% Creek chub 54% 50% 2% 10% 61% 9% 50% 16% 22% Pumpkinseed 50% 4% 4% Spottail shiner 1% 2% 42% <1% Bluntnose minnow 1% White sucker 2% 18% 2% 3% 1% 40% <1% Longnose dace 3% 18% 4% 14% 18% 1% 5% Tesselated darter 2% 6% 9% 7% 1% 5% 3% Bluegill 3% 10% <1% Cutlips minnow 1% 1% 22% Central mudminnow 1% Smallmouth bass 3% 2% 9% <1% Rockbass 7% 1% 28% 12% Common carp 4% 1% Yellow perch 53% 39% 25% 30% Largemouth bass 3% 6% 2% 3% 1% Brown bullhead 2% 2% 1% 3% 12% Brown trout 31% 1% 3% Walleye 2% Brook trout 35% Slimy sculpin 1% Northern hog sucker <1% 2% Redbreast sunfish <1% 3% Central stoneroller 3% 50% 4% Chain pickerel <1%

- 236 - Table 4. Fish collected in electrofishing surveys in tributaries to Otsego Lake, NY during years 1995 (Foster 1996), 2015 (Casscles 2016) and 2016.

Family Common Name WLL BRW LEATH 3-MI MO H WH CR HAY SHD GLM Salmonidae Rainbow trout ◊ Atlantic salmon ◊ ◊ ◊ ◊ ◊ Brown trout ◊ ◊ ◊ ◊ ○ ● ◊ ● ◊ ◊ Foster (1996) Brook trout ◊ ● ◊ ◊ ○ Casscles (2015) ● Current study Clupidae Alewife ◊ ◊

Catostomidae White sucker ● ◊ ● ● ◊ ● ◊ ○ ● ◊ ● ◊ ○ ● Northern hog sucker ● ● ○

Cyprinidae Redside dace ◊ ◊ ◊ Satinfin shiner ◊ Common carp ● ◊ ◊ Cutlips minnow ◊ ○ ● ◊ ● ◊ ○ Common shiner ◊ ◊ ◊ ◊ ○ Golden shiner ◊ ◊ Comely shiner ◊ Emerald shiner ◊ ◊ Bridle shiner ◊ Blackchin shiner ◊ Spottail shiner ● ● ● ◊ ● ◊ ● ◊ Northern redbelly dace ◊ ◊ ◊ ◊ Bluntnose minnow ● ◊ ◊ Fathead minnow ◊ ◊ ◊ ◊ ◊ ◊ Blacknose dace ● ◊ ● ◊ ● ◊ ◊ ● ◊ ○ ● ◊ ○ ● ◊ ○ ● ◊ ● Longnose dace ◊ ◊ ● ◊ ● ◊ ● ◊ ○ ● ◊ ○ ● ◊ ○ ● ◊ ○ ● Creek chub ● ● ◊ ● ◊ ● ◊ ● ◊ ○ ● ◊ ○ ◊ ○ ● ◊ ● Fallfish ◊ ◊ Pearl dace ◊ River Chub ○ Central Stoneroller ● ● ●

Umbridae Central mudminnow ◊ ●

Esocidae Chain pickerel ● ◊ ◊

Ictaluridae Brown bullhead ● ◊ ● ◊ ◊ ◊ ◊ ● ◊ ● Channel catfish ◊ Margined madtom ◊ ◊ ◊ ○

Centrachidae Rock bass ● ◊ ◊ ◊ ○ ◊ ◊ ○ ● ● Redbreast sunfish ◊ ● ◊ ◊ ● Pumpkinseed # ◊ ● ◊ ○ ◊ ◊ ○ ● Bluegill ◊ ● ◊ ◊ ● ◊ ◊ Smallmouth bass ● ◊ ● ◊ ● Largemouth bass ◊ ● ◊ ○ ● ◊ ● ◊ ● Black Crappie ◊

Percidae Tesselated darter ● ◊ ● ◊ ◊ ○ ● ◊ ○ ● ◊ ● ◊ ○ ● Yellow perch ◊ ● ◊ ○ ◊ ○ ● ◊ ○ ● Fantail darter ○ Walleye ●

Cottidae Slimy sculpin ● ◊

- 237 - CONCLUSIONS This study provides a valuable contemporary baseline of information about stream fish communities in the Otsego Lake watershed. The work was built upon other recent surveys (Casscles 2016), and allows comparison between past and present community composition in the headwaters of the Susquehanna River. Whereas some tributaries seem relatively unchanged since historical surveys, others have shown notable shifts in the resident fish communities. The Otsego Lake watershed is dominated by cyprinids, which make up 60% of total fish captured throughout the surveys. This family of fishes accounted for 8 species of the total 25 that were collected in the present study. All streams contained at least 1 species of cyprinids, although the composition of each stream appeared to vary with respect to dominant cyprinid species. The three most abundant fish species were blacknose dace, longnose dace and creek chub; historically, this is typical of the watershed (Foster 1996). According to historical accounts, it was expected that sculpin, blacknose dace and brook trout would dominate the watershed (Hocutt and Wiley 1986). All three species were collected this year. Leatherstocking Creek was the only stream in which brook trout and slimy sculpin were collected. The collection of young-of-year brook trout, in addition to larger fish, indicates that the population is reproducing naturally in this system. The habitat in Leatherstocking Creek includes plentiful riparian zone and dense tree cover, keeping the water temperatures lower during the summer months. This high quality, cold-water habitat appears to have allowed this population to persist historically and currently despite the presence of an impassable culvert pipe under Highway 80. Cripple Creek appears to have retained characteristics of a relatively unique, cold-water fishery in this watershed since the time of historical surveys. A large number of brown trout (Salmo trutta) of various sizes were collected in the headwaters of Cripple Creek, several of which (4%) were of trophy size (> 460mm). The wide size distribution, which included young- of-year brown trout and several other age classes, indicates that the population has been reproducing naturally with some success during the past decade. The upstream site on Cripple Creek was the only site in which a central mudminnow (Umbra limi) was collected. Interestingly, this is only the third time a central mudminnow had been collected in the Susquehanna watershed. Notably, the last mudminnow collected in Cripple Creek was two decades ago (Miner 1996). In some cases like Hayden Creek, Shadow Brook and White Creek, warm-water lake species made up significant portion of the fish community. In the watershed, species such as tesselated darter (Etheostoma olmstedii), pumpkinseed (Lepomis gibbosus) and brown bullhead (Amerius nebulosus) invade intermediate sections of streams and in some streams make up a substantial portion of the population (Bassista and Foster1995). Lotic habitat that supported lentic species was characterized by submerged vegetation and low water velocity. Generally, lake species such as yellow perch (Perca flavescens), largemouth bass (Micropterus salmoides) and smallmouth bass (M. dolomieu) were not common in most of the tributaries. Historically, stream length between the lake and impassible barriers was hypothesized to be the major factor

- 238 - determining species diversity (Foster 1996), and this may still influence these communities to a large degree.

REFERENCES 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 Bio. Fld. Sta., SUNY Oneonta. Casscles J.B. 2016. The fish assemblages of the selected Otsego Lake Tributaries. In the 48th Ann. Rept. (2016) SUNY Oneonta Biol. Fld. Sta. 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. Harman, W.N., L.P. Sohacki, M.F. Albright and D.L. Rosen. 1996. The state of Otsego lake, 1936-1996. Occas. Pap. No. 30. SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta. Hayes, S.A. 1990. Prelimanary fish survey of the Otsego Lake watershed. In 22nd Ann. Rept., 1989. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta. pp. 88-89 Hocutt, C.H., and E.O. Wiley (eds.). 1986. The zoogeography of North American freshwater fishes. New York. Jamieson, S.R., T.J. Sommerville and J.R. Foster. 2004. The fish fauna of Weaver and Young lake tributaries, with the first record of the brook stickleback (Culaea inconstans) in the Otsego Lake watershed. In 37th Ann. Rept. (2004). SUNY Oneonta Biol. Fld. Sta., SUNY Oneonta. Karr, J.R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6(6):21-27. MacWatters, R.C. 1980. The fishes of Otsego Lake (1st ed.). Occas. Pap. No. 7. SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta. MacWatters, R.C. 1983. The fishes of Otsego Lake (2nd ed.). Occas. Pap. No. 15. SUNY Oneonta. Biol. Fld. Sta., SUNY Oneonta. Miner, M.M. 1996. A fisheries survey of the species composition and distribution of Cripple Creek. In 29th Ann. Rept. SUNY Oneonta Bio. Fld. Sta., SUNY Oneonta. New, J.G. 1971. Vertebrate studies. In 3rd Ann. Rept. (1970-71). 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 Biol. Fld. Sta., SUNY Oneonta.

- 239 - Summer 2016 BioBlitz series Joseph Perry1

INTRODUCTION In the summer of 2016, a series of three BioBlitzes were held at the following Otsego Land Trust (OLT) properties: Fetterley Forest Conservation Area, Brookwood Point, and the Parslow Road Conservation Area (Figure 1). In addition to being open to the public to promote community involvement, the three events were attended by faculty, interns, and staff from the SUNY Oneonta main campus, the Biological Field Station, and the OLT.

Figure 1: Location of the three OLT properties within Otsego County, NY BioBlitzed over the summer of 2016. Included are Parslow Road (Blitzed on 23 June), Fetterley Forest (18 July), and Brookwood Point (8 August).

- 240 - The primary goal of a BioBlitz is to assess taxonomical diversity in a given space over a short period of time. Because of their short-term nature, these events do not document seasonal variations in taxa and are intended to only serve as a brief snapshot of a local community. While many events tend to last up to 24 hours and are convened only once, the BFS/OLT series is composed of three relatively short events conducted annually during the summer season when communities are most diverse (Clifton 2015). With so many taxa documented annually, the compilation of these data over time could lead to the creation of a valuable historical record of community composition and structure. Applications of this record may include accurate dating of invasive species introduction or local extinctions, tracking long-term shifts in communities due to land use or climate change, and can be used as a tool for educating the public about the vast diversity of life in relatively small spaces.

METHODS Each Bioblitz was attended by Biological Field Station staff and interns, SUNY Oneonta professors, and Otsego Land Trust staff and interns. Biologists of diverse areas of training and varying levels of expertise participated in each event, yielding a fairly broad range of taxa covered. Once identified, each specimen was either written down or checked off of a list compiled in the previous year before being tabulated for this year’s publication. Depending on the geography of the site to be blitzed, equipment including terrestrial nets, aquatic nets, collection trays, hand lenses, forceps, buckets, numerous taxonomic keys, microscopes, binoculars, electrofishing equipment, tables, chairs, pens, paper, taxa lists, and clipboards was utilized. The first BioBlitz in the series was held on 23 June at OLT’s Parslow Road Conservation Area (Figure 2) located at 127 Parslow Road in Hartwick, NY, running from 9 AM to 12 noon. Formerly the site of a tree nursery, the Parslow Road property spans 86 acres and includes a large portion of state and federally designated wetlands, beaver ponds, forested areas, and meadows. In addition, running along the East border of the property is Oaks Creek, a designated trout stream (OLT 2016). The second BioBlitz was held on 18 July at OLT’s Fetterley Forest property located in the town of Richfield, NY running from 9 AM to 12 noon. The largest property blitzed this season, Fetterley spans 106 acres and has been in possession of the OLT since August 2011. The property is dominated by a large, managed hardwood forest community and also includes some meadowlands (OLT 2016). The third BioBlitz was held on 8 August at OLT’s Brookwood Point property in the town of Otsego, NY and ran from 9 AM to 12 noon. While this parcel was the smallest to be blitzed this year, it contained several diverse communities including a hardwood forest, a meadow, a stream, ornamental garden, and a lakeshore habitat (OLT 2016).

- 241 -

Figure 2. Satellite image of the Parslow Road Conservation area, the first property to be blitzed in the 2016 season. Parslow is home to diverse ecological communities.

Figure 3. Map of the Fetterley Forest Conservation Area. The property is dominated by hardwood forest communities and contains no permanent water source.

- 242 -

Figure 4. Satellite image of the Brookwood Point property. While it was smaller than the other properties blitzed this year, it contained numerous and diverse habitats to study.

RESULTS Numbers of taxa identified include 74 taxa on 23 June at the Parslow Road Conservation Area, 186 taxa on 18 July at the Fetterley Forest Conservation Area, and 209 taxa on 8 August at Brookwood Point. Plants and birds generally dominated the taxa lists with the exception of the Parslow Road Conservation Area, for which plant data is notably lacking (Figure 5 and Tables 1, Appendices 1, 2, 3).

250 Other Fungi 200 Algae Tracheophyta 150 Pinopsida Magnoliopsida 100 Liliopsida Arthropoda Actinopterygii 50 Mammalia Amphibia 0 Aves Parslow Fetterley Brookwood

Figure 5. Distribution of taxa recorded at each of the three OLT properties. The “Other” category was a mixed bag including worms, mollusks, and slime molds, and others.

- 243 - Table 1. Summary of taxa identified in each of the three bioblitzes. Dashes (-) indicate no data. 2015 Data sourced from Clifton, 2015. Parslow Fetterley Brookwood Classification 2015 2016 2015 2016 2015 2016 Aves 19 35 17 23 22 31 Amphibia 1 1 3 2 1 2 Mammalia 2 2 2 3 3 2 Actinopterygii 12 14 - - 5 9 Arthropoda 50 3 59 6 44 11 Liliopsida 0 - 3 1 3 0 Magnoliopsida 124 - 100 116 115 127 Pinopsida 1 - 2 2 3 4 Tracheophyta 3 - 8 8 9 10 Algae 10 9 - 7 8 8 Fungi 0 3 2 9 2 5 Other 2 7 5 9 1 1 Total 224 74 201 186 216 210

DISCUSSION A number of invasive species were identified over the course of the three bioblitzes, each having been documented in previous years (Clifton 2015 and Davidson 2014). Included were a zebra mussel (Dreissena polymorpha) identified in Oak’s creek at the Parslow Road Conservation Area, garlic mustard (Alliaria petiolata) identified at both Brookwood Point and Fetterley Forest, and Bishop’s goutweed (Aegopodium podagraria) identified at Brookwood Point (Appendices 1, 2, and 3). Despite the fact that many taxonomic groups had gains in the numbers of species identified in 2016, overall numbers were lower than in 2015 or 2014. The biggest shortfall in 2016 was in arthropod identification, possibly because this year’s team had only one trained entomologist compared to multiple individuals present in previous years. As a result, 133 fewer arthropods were identified among the three properties in 2016 versus 2015. Another factor that contributed to lower counts was the loss of plant data from the Parslow bioblitz by a member of the team. In the future, it is suggested that taxa lists are given directly to the bioblitz organizer at the conclusion of each event. In general, slight gains were seen in numbers of bird, fish, plant, and fungal species identified when compared to the previous years’ data. This trend could be expected to continue as experts returning to the sites year-to-year become more familiar with some of the taxa and the layout of the properties. Although the bioblitzes were fairly short and many taxa surely remain unlisted, the data collected shows that these small spaces are homes to diverse forms of life.

- 244 - REFERENCES

Clifton, Elizabeth. 2016. Summer 2015 BioBlitz Series. In 48th Ann. Rept. (2015). SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta Davidson, Emily. 2015. Summer 2014 BioBlitz Seres. In 47th Ann. Rept. (2014). SUNY Oneonta Bol. Fld. Sta. SUNY Oneonta. Integrated Taxonomic Information System. Retrieved SEPT, 2016. System On-line Database. Otsego Land Trust. 2016. http://otsegolandtrust.org/

- 245 - Appendix 1. All taxa recorded at Parslow Road conservation property. Kingdom Phylum Class Order Family Genus Species Common Name 2015 2016 r

A Arachnida Araneae Thomisidae sp. Crab spider x Arachnida Opiliones sp. Harvestmen x

Insecta Coleoptera Carabidae sp. Gound beetle x Insecta Coleoptera Cerambycidae sp. Longhorn beetle x Insecta Coleoptera Chrysomelidae sp. Leaf beetle x Insecta Coleoptera Elateridae sp. Click beetle x Insecta Coleoptera Gyrinidae sp. Whirlgig beetle x Insecta Coleoptera Lampyridae sp. Firefly x Insecta Coleoptera Scarabaeidae sp. Scarab beetle x Insecta Dermaptera Forficulidae sp. Earwig x Insecta Diptera Asilidae sp. Robber fly x Insecta Diptera Calliphoridae sp. Blow fly x Insecta Diptera Dolochipodidae sp. Long-legged fly x Insecta Diptera Drosophilidae sp. Vinegar fly x Insecta Diptera Sarcophagidae sp. Flesh fly x Insecta Diptera Sciomyzidae sp. Marsh fly x Insecta Diptera Syrphidae sp. Hoverfly x Insecta Diptera Tachinidae sp. Parasitoid fly x Insecta Diptera Tephritidae sp. Fruit fly x Insecta Ephemeroptera Ephemeridae Hexagenia sp. Mayfly x Insecta Hemiptera Aphididea sp. Aphid x Insecta Hemiptera Cercopidae sp. Froghopper x Insecta Hemiptera Cicadellidae sp. Leafhopper x Insecta Hemiptera Membracidae sp. Treehopper x Insecta Hemiptera Miridae sp. Plant bugs x Insecta Hemiptera Pentatomidae sp. Stink bugs x

Animalia Insecta Hemiptera Reduviidae sp. Assassin bug x Insecta Hemiptera Veliidae sp. Broad shouldered water strider x Insecta Hymenoptera Apidae sp. Bee x Insecta Hymenoptera Cynipidae sp. Gall wasp x Insecta Hymenoptera Formicidae sp. Ant x Insecta Hymenoptera Halictidae sp. Sweat bee x Insecta Hymenoptera Ichneumonidae sp. Ichneumon wasp x Insecta Hymenoptera Sphecidae sp Thread-waisted wasps x Insecta Lepidoptera Erebidae sp. Tiger moth x Insecta Lepidoptera Hesperiidae sp. Skipper x Insecta Lepidoptera Noctuidae sp. Owlet moth x Insecta Lepidoptera Nymphalidae sp. Brushtail butterfly x Insecta Lepidoptera Papilionidae sp. Swallowtail butterfly x Insecta Lepidoptera Pieridae sp. Whites and sulphurs x Insecta Lepidoptera Pyralidae sp. Snout moth x Insecta Lepidoptera Sphingidae sp. Clearwing Moth x Insecta Lepidoptera sp. Leaf miner moth x Insecta Mecoptera Panorpidae sp. Scorpionfly x Insecta Odonata Calopterygidae sp. Damselfly x Insecta Odonata Coenagrionadae sp. Damselly x Insecta Odonata Lestidae sp. Damselfly x Insecta Odonata Libellulidae sp. Skimmer x Insecta Orthoptera Acrididae sp. Grasshopper x Insecta Orthoptera Gryllidae sp. Cricket x Insecta Orthoptera Tettigoniidae sp. Long-horned grasshopper x

Branchiopoda Cladocera Daphniidae Daphnia sp. Daphnia x Malacostraca Amphipoda x Maxillopoda Plankton x Actinopterygii Cypriniformes Catostomidae Catostromus commersonii White sucker x Actinopterygii Cypriniformes Catostomidae Hypentelium nigricans Northern hogsucker x x Actionpterygii Cypriniformes Catostomidae Moxostoma macrolepidotum Shorthead Redhorse x Actinopterygii Cypriniformes Cyprinidae Exoglossum maxilingua Culips minnow x x Luxilus cornutus Chordata Animalia Actionpterygii Cypriniformes Cyprinidae Common shiner x Actionpterygii Cypriniformes Cyprinidae Nocomis micropogon River Chub x Actionpterygii Cypriniformes Cyprinidae Notropis atherinoides Emerald shiner x

- 246 - Animali Animali Animali Animali Animalia Animalia Kingdom

a a a Animalia a Nematoda Mollu Mollu Chord Phylum ChordataChordata Chordata s s a iav eeod Dreissenidae Veneroida Gastropoda Bivalva Ranidae Anura Aves Percidae Amphibia Percidae Perciformes Perciformes Actionpterygii Actionpterygii Centrarchidae Perciformes Actinopterygii Cyprinidae Cypriniformes Actionpterygii ciotrgiPriomsPercidae Centrarchidae Perciformes Centrarchidae Perciformes Actinopterygii Centrarchidae Perciformes Actinopterygii Centrarchidae Perciformes Actinopterygii Perciformes Actinopterygii Centrarchidae Actinopterygii Perciformes Cyprinidae Cypriniformes Cyprinidae Actinopterygii Cypriniformes Cyprinidae Actinopterygii Cypriniformes Actinopterygii Actinopterygii amlaRdni Sciuridae Sciuridae Rodentia Leporidae Rodentia Lagomorpha Mammalia Mammalia Mammalia Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Aves Mammalia Class ciirfre Accipitridae Accipitriformes ciirfre Cathartidae Accipitriformes oubfre Columbidae Columbiformes oaifre Alcedinidae Coraciiformes asrfre Bombycillidae Passeriformes asrfre Corvidae Passeriformes Cardinalidae Passeriformes asrfre Corvidae Passeriformes asrfre Corvidae Passeriformes asrfre Emberizidae Passeriformes asrfre Emberizidae Passeriformes Emberizidae Passeriformes asrfre Fringillidae Passeriformes asrfre Hirundinidae Passeriformes asrfre Icteridae Passeriformes asrfre Icteridae Passeriformes asrfre Icteridae Passeriformes asrfre Mimidae Passeriformes asrfre Paridae Passeriformes Mimidae Passeriformes asrfre Parulidae Passeriformes asrfre Parulidae Passeriformes asrfre Parulidae Passeriformes asrfre Parulidae Passeriformes asrfre Sittidae Passeriformes Picidae Passeriformes Picidae Passeriformes asrfre Turdidae Passeriformes Troglodytidae Passeriformes asrfre Turdidae Passeriformes asrfre Tyrannidae Passeriformes asrfre Tyrannidae Passeriformes asrfre Vireonidae Passeriformes iiomsPicidae Piciformes Ardeidae Pelecaniformes ridcyaCervidae Artiodactyla Order Family risn polymorpha Dreissena clamitans peltata flavescens Lithobates olmstedii Percina Perca salmoides macrochirus Etheosoma dolomieu gibbosus Micropterus auritis Micropterus Lepomis rupestris Lepomis corporalis Lepomis atromaculatus Ambloplites rubellus hudsonius Semotilus Semotilus Notropus Notropis aisstriatus hudsonicus sp. Tamiasciurus virginianus Tamias Sylvilagus Odocoileus Accipiter striatus ahre aura Cathartes ead macroura Zenaida eaeyealcyon Megaceryle obclacedrorum Bombycilla ovsbrachyrchynchos cyanea Corvus Passerina ovscorax Corvus ynctacristata Cyanocitta uc hyemalis Junco oorci albicollis melodia Zonotrichia Melospiza pnstristis Spinus ahcnt bicolor Tachycineta glisphoeniceus Agelaius usau quiscula Quiscalus Icterus galbula ueel carolinensis Dumetella ocl atricapillus Poecile Toxostoma rufum etlpstrichas Geothlypis eohg pensylvanica Setophaga eohg petechia Setophaga Vermivora cyanoptera oatsauratus auratus Sitta Colaptes Colaptes rgoye aedon Catharus fuscescens Troglodytes udsmigratorius Turdus Empidonax minimus aonsphoebe Sayornis Vireo re herodias Colaptes Ardea - 247 eu Species Genus 2 uniquespecies carolinensis olivaceus auratus eaoex Nematode er uslxx x Snail x Zebra mussel x x Green frog Shield Darter Yellow perch Tesselated darter Laremouth bass Smallmouth bass Bluegill x Pumpkinseed Red-breast sunfish Rock bass Fall fish Creek chub Roseyface shiner Spottail shiner Red squirrel Eastern chipmunk Cottontail rabbit White-tailed deer hr hne akx Sharp shinnedhawk Turkey vulture Mourning dove Belted kingfisher Cedar waxwing American crow Indigo bunting Northern raven Blue jay Dark-eyed junco ht-hotdsarwxx x White-throated sparrow Song sparrow mrcnglfnhxx x American goldfinch Tree swallow Red-winged blackbird Common grackle Northern oriole Gray catbird lc-apdcikdexx x Black-capped chickadee Brown thrasher omnylotra x x Common yellowthroat hsntsddwrlrx Chestnut-sided warbler mrcnylo abe x x American yellowwarbler Blue-winged warbler ht-ratdntac x White-breasted nuthatch Common flicker Northern flicker Veery House wren American robin Least flycatcher Eastern phoebe Red-eyed vireo Northern flicker Great blueheron Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2015 x x x x x x x x x x x x x x x x 2016 Plantae Fungi Animalia Kingdom

Magnoliophyta Basidiomycota Platyhelminthes Phylum anlosd rclsMyrsinaceae Ericales Magnoliopsida Cyperaceae Cyperales Magnoliopsida Asparagaceae Alliodeae Asparagales Asparagales Apiaceae Magnoliopsida Magnoliopsida Apiales Magnoliopsida Equisetaceae Equisetales Equisetopsida Amanitaceae Amanitaceae Agaricales Agaricaceae Agaricomycetes Agaricales Agaricomycetes Agaricales Agaricomycetes anlosd aae Fabaceae Fabaceae Fabaceae Fabales Fabaceae Fabales Fabaceae Magnoliopsida Fabales Fabaceae Magnoliopsida Fabales Fabaceae Magnoliopsida Fabales Fabaceae Magnoliopsida Fabales Magnoliopsida Fabales Myrsinaceae Magnoliopsida Fabales Myrsinaceae Magnoliopsida Balsaminaceae Magnoliopsida Ericales Ericales Caprifoliaceae Magnoliopsida Ericales Adoxaceae Magnoliopsida Dipsacales Adoxaceae Magnoliopsida Cyperaceae Dipsacales Magnoliopsida Dipsacales Magnoliopsida Cyperales Magnoliopsida Cucurbitaceae Magnoliopsida Cucurbitales Brassicaceae Caryophyllaceae Caryophyllales Magnoliopsida Brassicales Magnoliopsida Magnoliopsida Asteraceae Asteraceae Asteraceae Asterales Asteraceae Asterales Magnoliopsida Asteraceae Asterales Magnoliopsida Asteraceae Asterales Magnoliopsida Asteraceae Asterales Magnoliopsida Asterales Magnoliopsida Asterales Araliaceae Magnoliopsida Apiaceae Magnoliopsida Apiales Apiaceae Apiales Magnoliopsida Magnoliopsida Araceae Apiales Alismataceae Alismatales Magnoliopsida Alismataceae Alismatales Magnoliopsida Alismatales Magnoliopsida Magnoliopsida anlosd seae Asteraceae Asterales Magnoliopsida anlosd rsiae Brassicaceae Brassicaceae Brassicales Brassicaceae Brassicales Magnoliopsida Brassicaceae Asteraceae Brassicales Magnoliopsida Asteraceae Brassicales Magnoliopsida Asteraceae Asterales Magnoliopsida Asteraceae Asterales Magnoliopsida Asterales Magnoliopsida Asterales Magnoliopsida Magnoliopsida Class Order Family rflu repens pratense hybridum Trifolium dubium Trifolium campestre corniculatus Trifolium alba Trifolium Trifolium Melilotus Lotus Gleditsia triacanthos Lysimachia terrestris capensis sp. Lysimachia nummularia Lysimachia lentago Impatiens canadensis Lonicera ciliata Viburnum Sambucus media Carex lurida lobata palustris Carex Echinocystis Stellaria Rorippa pseudocyperus communis palustre P uliginosum Lapsana Gnaphalium maculosa millefolium Cirsium Chrysanthemum leucanthemum canadensis racemosa Centaurea nudicaulis Achillea Smilacina sativa carota Allium maculata Aralia Pastinaca Daucus latifolia foetidus Cicuta rigida Symplocarpus Sagittaria sp. Sagittaria sp. Equisetum gemmata sp. Amanita Amanita Lycoperdon oip nasturtium-aquaticu campestre matronalis petiolata Rorippa Lepidium farfara sp. Hesperis sp. Allaria graminifolia Tussilago Sonchus laciniata Solidago Solidago Rudbeckia seudognaphaliu - 248 Genus m obtusifolium Species m Flatworm ht lvrx x x x x White clover x x Red clover x Alsike clover Small hopclover Low hop-clover Sweet whiteclover Bird's foottrefoil Honeylocust Swamp candles Moneywort Fringed loosestrife Jewelweed Honeysuckle Nannyberry Elderberry Sedge Sedge Wild cucumber Chickweed Yellow cress Watercress Nipplewort Low cudweed Marsh thistle x Ox-Eye daisy Spotted knapweed Common yarrow False Soloman'sseal Wild garlic Wild sarsaparilla Wild parsnip x Queen Anne'slace Water hemlock Skunk weed Broadleaf arrowhead Arrowhead Horsetail Button Gemmed amanita Puffball we vratn x Sweet everlasting ormnsppegasx Poor man'speppergrass Dame's rocket Garlic mustard Coltsfoot x Sow-thistle Goldenrod Flat topgoldenrod Cut leavedconeflower Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2015 x x x x 2016 Plantae Kingdom

Magnoliophyta Phylum anlosd ailsVerbenaceae Plantaginaceae Plantaginaceae Lamiales Lamiales Magnoliopsida Lamiales Magnoliopsida Magnoliopsida Lamiaceae Lamiales Magnoliopsida anlosd olsTyphaceae Polygonaceae Typhaceae Polygonales Juncaceae Polygonales Magnoliopsida Cyperaceae Poales Magnoliopsida Cyperaceae Poales Magnoliopsida Poales Magnoliopsida Poales Oxalidaceae Magnoliopsida Poales Orchidaceae Magnoliopsida Oxalidales Magnoliopsida Onocleaceae Nymphaeaceae Orchidales Magnoliopsida Onagraceae Nymphaeales Magnoliopsida Onagraceae Myrtales Magnoliopsida Onagraceae Myrtales Magnoliopsida Lythraceae Myrtales Magnoliopsida Tiliaceae Myrtales Magnoliopsida Malvaceae Myrtales Magnoliopsida Malvales Magnoliopsida Salicaceae Malvales Magnoliopsida Salicaceae Malpighiales Magnoliopsida Salicaceae Colchicaceae Malpighiales Magnoliopsida Hypericaceae Malpighiales Magnoliopsida Malpighiales Magnoliopsida Liliales Magnoliopsida Magnoliopsida Plantaginaceae Phrymaceae Lamiaceae Lamiales Lamiaceae Lamiales Magnoliopsida Lamiales Magnoliopsida Lamiaceae Lamiales Magnoliopsida Lamiaceae Magnoliopsida Boraginaceae Lamiales Irideae Lamiales Magnoliopsida Lamiales Magnoliopsida Iridaceae Magnoliopsida Platanacea Hamamelidales Magnoliopsida Geraniale Magnoliopsida Magnoliopsida anlosd etaae Asclepiadaceae Gentianales Magnoliopsida anlosd aucllsRanunculaceae Ranunculales Ranunculaceae Magnoliopsida Ranunculales Magnoliopsida anlosd aidlsAceraceae Rubiaceae Rubiaceae Sapindales Rosaceae Rubiales Rosaceae Magnoliopsida Rubiales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rhamnaceae Magnoliopsida Rosales Magnoliopsida Rosales Magnoliopsida Ranunculaceae Rosales Magnoliopsida Ranunculaceae Ranunculales Magnoliopsida Ranunculales Magnoliopsida Ranunculaceae Magnoliopsida Ranunculales Ranunculaceae Magnoliopsida Polygonaceae Ranunculales Polygonales Magnoliopsida Magnoliopsida anlosd aae Betulaceae Fagales Magnoliopsida anlosd aae Betulaceae Fagale Fagales Magnoliopsida Magnoliopsida ls re Family Order Class s s Geraniacea Juglandaceae Polygonaceae e yh latifolia persicaria sp. Polygonum americanum sp. Typha Sparganium stricta palustris Juncus Scirpus helleborine Eleocharis sp. Oxalis biennis Epipactis palustris Nymphaea Oenothera americana salicaria Ludwigia Circaea alpina moschata sp. Circaea fragilis Lythrum Tilia tremuloides Malva lutetiana Salix punctatum sessilifolia Salix hastata Populus Hypericum anagallis-aquatica Uvularia major Verbena lanceolata Veronica arvensis Plantago sp. Plantago vulgaris Mimulus ringens Mentha hederacea Lycopus scorpioides Linaria vulgare Glechoma occidentalis Clinopodium Myosotis Iris Platanus Polygonum Asclepias eaimrobertainum Geranium J aimasprellum mollugo odoratus Acer idaeus Galium allegheniensis Galium Rubus virginia Rubus serotina Rubus Rosa Prunus canadense Prunus aleppicum Potentilla simplex Malus sp. virginiana Geum sp. Geum cathartica Fragaria sp. Agrimonia officinale Rhamnus sp. Thalictrum repens Taraxicum recurvatus Ranunculus crispus acris Ranunculus Ranunculus Ranunculus Rumex euaalleghaniensis incana Betula Alnus uglans cinerea - 249 Genus pseudacorus multiflora s rubrum virginianum yriaca Species ati x x x x Lady's thumb x x Cattail Burreed x Rush x x Bulrush x Common spikerush Yellow woodsorrel x Broadleaf helleborine Water lily x x Evening primrose x x x Water weed x Dwarf enchanter'snightshade Enchanter's nightshade x x Purple loosestrife Basswood x Musk mallow x Willow Willow Quaking aspen Spotted StJohnswort Bellwort Blue vervain Water speedwell Common plantain English plantain Monkey flower Wild mint Bugleweed Butter andeggs Ground ivy x Wild basil Forget menot Yellow flag American sycamore Butternut Herb rober Red maple Rough bedstraw x x False baby'sbreath Purple floweringraspberry Red raspberry Northern blackberry x Multiflora rose Choke Cherry Black cherry Common cinquefoil Apple White avens Yellow avens Strawberry Agrimony Buckthorn Rue Dandelion Buttercup Creeping buttercup Hooked buttercup Common buttercup Curled dock Jumpseed pcldadrx Yellow birch Speckled alder Milkwee Common Name d t x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 2015 2016 Protista Protista Plantae Chromista Bacteria Kingdom Plantae Kingdom

Mycetozoa Ciliophora Charophyta Ochrophyta Bacillariophyta Cyanobacteria Phylum Pinophyta Magnoliophyta Phylum ojgtpyeeZgeaae Zygnemataceae Zygnematales Conjugatophyceae Tabellariales Ulnanaceae Tabellariales Licmophorales Fragilariophyceae Fragilariophyceae Oscillatoriaceae Oscillatoriales Bacillariophyceae Bacillariophyceae Bacillariophyceae Bacillariophyceae Bacillariophyceae Cyanophyceae Bacillariophyceae Bacillariophyceae anlosd iae Vitaceae Vitaceae Vitales Vitales Magnoliopsida Magnoliopsida ioiaPnlsPinaceae Pinales Pinosida Onocleaceae Oncleaceae Polypodiopsida Polypodiales Polypodiopsida Polypodiales Urticaceae Pol Urticaceae Urticaceae Urticales Urticaceae Urticales Magnoliopsida Ulmaceae Urticales Magnoliopsida Urticales Magnoliopsida Urticales Magnoliopsida Magnoliopsida yoyee hsrlsPhysaraceae Physarales Myxomycetes Oligohymenopho Oleaceae Scrophulariales Grossulariaceae Magnoliopsida Saxifragales Magnoliopsida Aceraceae Sapindales Magnoliopsida anlosd riae Ulmaceae Solanaceae Urticales Solanales Magnoliopsida Oleaceae Scrophulariales Magnoliopsida Haloragaceae Magnoliopsida Saxifragales Anarcardiaceae Anarcardiaceae Magnoliopsida Sapindales Aceraceae Sapindales Magnoliopsida Sapindales Magnoliopsida Magnoliopsida yp odio ls re Family Order Class Class p sida Pol

r yblae yblaeeBeisnasp. Brebissonia sp. sp. Cymbellaceae Naviculales sp. Cymbellales Melosira Coscinodiscophycea Cocconeis Coscinodiscales Coscinodiscus Coscinodiscophycea Cocconeidaceae Coscinodiscales Achnanthales hmeihnl Chamaesiphonacea Chamaesiphonale Naviculales hlsisrlsStephanodiscaceae Thalassiosirales oiiaTrichodinidae Mobilida yp Order odiale

s siltasp. Oscillatra Amphipleuraceae tuoedca tuoessp. Stauroneis Stauroneidaceae Dr y o p Family teridacea Algae &Protists e

e ogoi sp. Mougeotia sp. sp. Meridion Synedra rsui sp. Frustulia hmeihnsp. Chamaesiphon yltlasp. Cyclotella sg canadensis Tsuga sensibilis struthiopteris Matteuccia Onclea dioica Vitus quinquefolia Parthenocissus canadensis Urtica rubra cylindrica Pilea Laportea Boehmeria Ulmus uioseptica Fuligo sp. Trichodina D lu americana nigra nigra americana Ulmus Solanum Fraxinus spicatum Fraxinus Myriophyllum saccharinum radicans Ribes Toxicodendron Rhus Acer Acer r y o - 250 p eu Species Genus Genus ei intermedia teris Algae 4 uniquespecies hirta sp. pumila sp. saccharum

Species Algae Algae Algae Algae la x x x x Algae Algae Algae Algae Blue‐green Blue‐green Algae Algae atr elc x Eastern Hemlock Ostrich fern Sensitive fern Grape Virginia creeper Stinging nettle Clearweed Wood nettle False nettle Slippery elm o oi lm odx Dog vomitslimemold mrcnwieahx American el Black nightshade Black ash x American whiteash Eurasian milfoil Currant Poison ivy Staghorn sumac Sugar maple Silver maple Intermediate woodfer Common Name Common Name algae algae m n x x x x x x x x x x x x x x x x x x x x x 2015 x 2015 x x x x x x 2016 2016 Appendix 2. All taxa observed at Fetterley Forest Conservation Area

Year Kingdom Phylum Class Order Family Genus Species Common Name 2014 2015 2016 Clitellata Hirudinea sp. Leech x Oligochaeta Megadrilacea sp. Earthworm xx Oligochaeta sp. Worm x Annelida

Arachnida Acari (subclass) Mite x

Arachnida Araneae Linyphiidae Sheet weaver xx Arachnida Araneae Lycosidae Wolf spider x x Arachnida Araneae Salticidae Jumping spider x Arachnida Araneae Thomisidae Crab spider x Arachnida Opiliones Harvestmen x x Arachnida Oribatida Soil mites x Animalia Arthropoda Ostracoda Seed shrimp x Arachnida Pseudoscorpionida False Scorpion x Arachnida Trombidiformes Trombiulidae Red velvet mite xx

Chilopoda Centipede x

Entognatha EntomobryomorphaIsotomidae Springtail x Entognatha Symphypleona Sminthuridae Springtail x

Insecta Coleoptera Cantharidae Soldier beetle xx Insecta Coleoptera Carabidae Ground beetle x x Insecta Coleoptera Cerambycidae Longhorn beetle x x Insecta Coleoptera Coccienllidae Ladybug x Insecta Coleoptera Curculionoidae weevils x Insecta Coleoptera Elateridae Click beetle x x Insecta Coleoptera Haliplidae Peltodytes Beetle x Insecta Coleoptera Lampyridae Firefly x x Insecta Coleoptera Lycidae Net-winged beetle x Insecta Coleoptera Scarabaeidae Popillia japonica Japanese beetle x Insecta Coleoptera Scarabaeidae Scarab beetle x Insecta Coleoptera Scarabaeidae Scarab beetle x Animalia Insecta Coleoptera Staphylinoidea Beetle x Insecta Coleoptera Tenebrionoidea (superfamily) Beetle x Insecta Coleoptera Vesperidae Beetle x Arthropoda Insecta Dermaptera Forficulidae Earwig x Insecta Diptera Agromyzidae Leaf-miner fly x Insecta Diptera Asilidae Assassin fly x Insecta Diptera Calliphoridae Blow fly x xx Insecta Diptera Culicidae Aedes sp. Mosquito x Insecta Diptera Dolichopodidae Long-legged fly x Insecta Diptera Drosophilidae Vinegar fly x Insecta Diptera Muscidae House fly x Insecta Diptera Piophilidae Cheese fly x Insecta Diptera Scathophagidae Dung-fly x Insecta Diptera Sciaridae Dark-winged fungus gnats x Insecta Diptera Syrphidae Hoverfly x x Insecta Diptera Tachinidae Parasitoid fly x x Insecta Diptera Therevidae Stiletto fly x Insecta Diptera Tipulidae Cranefly x Insecta Hemiptera Aphididae Aphid x Insecta Hemiptera Cercopidae Froghoppers x Insecta Hemiptera Cicadellidae Leafhoppers x Insecta Hemiptera Coreidae Squash bug x Insecta Hemiptera Corixidae Water boatman x Insecta Hemiptera Gerridae Water glider x Insecta Hemiptera Membracidae Treehopper x Insecta Hemiptera Miridae Plant bug x Insecta Hemiptera Nabidae Damsel bug x Insecta Hemiptera Notonectidae Notonecta Backswimmer x Insecta Hemiptera Pentatomidae Stink bugs x Insecta Hemiptera Psyllidae Jumping plant lice x Insecta Hemiptera Reduviidae Assassin bug x Insecta Hempitera Cicadellidae Hoppers or leaf hopper x

- 251 - Animalia Kingdom

Chordata Arthropoda Phylum net yeotr Formicidae Hymenoptera Insecta net eiotr Geometridae Lepidoptera Insecta Erebidae Hymenoptera Insecta net yeotr Sphecidae Hymenoptera Insecta Cynipidae Hymenoptera Insecta net yeotr Pompilidae Hymenoptera Insecta Chrysididae Hymenoptera Insecta vsPseiomsParulidae Passeriformes Fringillidae Aves Passeriformes Emberizidae Emberizidae Passeriformes Corvidae Passeriformes Passeriformes Aves Aves Aves Aves Salamandridae Caudata Amphibia Tetrigidae Gryllidae Acrididae Orthoptera Lestidae Orthoptera Orthoptera Odonata Insecta Insecta Insecta Insecta Nymphalidae Lepidoptera Hesperiidae Lepidoptera Insecta Megachilidae Hymenoptera Insecta Halictidae Hymenoptera Insecta Braconidae Insecta Hymenoptera Insecta vsPseiomsParulidae Passeriformes Parulidae Passeriformes Mimidae Aves Passeriformes Emberizidae Aves Passeriformes Aves Aves Salamandridae Caudata Hylidae Amphibia Anura Amphibia net eotr Panorpidae Mecoptera Insecta mhbaAnura Amphibia Panorpidae Mecoptera Insecta net rcotr Limnephilidae Trichoptera Insecta Sphingidae Lepidoptera Insecta net scpeaPsocidae Psocoptera Insecta Pyralidae Pieridae Lepidoptera Papilionidae Lepidoptera Lepidoptera Insecta Noctuidae Insecta Lepidoptera Insecta Insecta Ichneumonidae Hymenoptera Insecta Apidae Hymenoptera Insecta Aves Aves Plethodontidae Aves Plethodontidae Aves Ambystomatidae Aves Caudata Caudata Caudata Anura Amphibia Amphibia Amphibia Amphibia Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Aves Aves Aves Aves Aves Class asrfre Corvidae Cardinalidae Passeriformes Bombycillidae Passeriformes Columbidae Passeriformes Trochilidae Columbiformes Apodiformes lcpeaLeuctridae Tettigoniidae Plecoptera Orthoptera Libellulidae Coenagrionidae Odonata Odonata Pterophoridae Papilionidae Lepidoptera Nymphalidae Lepidoptera Lepidoptera Lycaenidae Lepidoptera Halictidae Hymenoptera Andrenidae Hymenoptera asrfre ceia glisphoeniceus bicolor Agelaius Paridae Tachycineta Passeriformes Icteridae Hirundinidae Passeriformes Passeriformes asrfre auia eohg arlsesBlack-throatedbluewarbler petechia caerulescens Setophaga Setophaga Parulidae Passeriformes Parulidae Passeriformes Order Bufonidae Ranidae Family euu aurocapilla Seiurus tristis hyemalis melodia Carduelis cristata Melospiza Junco Cyanocitta viridenscens Notophthalmus Lestes plexippus Danaini eohg pensylvanica trichas Setophaga carolinensis Geothlypis erythrophthalmus Dumetella Pipilo crucifer Pseudacris Panorpa Anaxyrus aii glaucus Papilio obclacedrorum Corvus Prianga colubris Bombycilla Zenaida Archilochus cinereus bislineata clamitans laterale Plethodon Eurycea Ambystoma Rana Poecile - 252 eu Species Genus brachyrchynchos americanus olivacea macroura atricapillus American crow Ant Inchworm Tiger moth Thread-waisted wasp Gall wasp Spider wasp Cuckoo-bee Ovenbird American goldfinch Song sparrow Dark-eyed junco Blue jay Red eftorEasternnewt Cricket Cricket Grasshopper Damselfly Monarch butterfly Skipper butterfly Leaf cutterbees Bee Parasitoid Newt Chestnut-sided warbler Common yellowthroat Gray Catbird Rufous-sided towhee Spring peeper Scorpion fly Scorpionfly American toad Caddisfly Hawk moth Booklice Snout moth Butterfly Eastern Tigerswallowtail Owlet moth Ichneumon wasp Bee Rolled-wing stonefly Grasshopper Dragonfly Narrow-winged damselfly Plume moth Swallowtail butterfly Brush-footed butterfly Blues andCoppersButterfly Sweat bee Digger bee Scarlet tanager Cedar waxwing Mourning dove Ruby-throated hummingbird Red-backed salamander Northern two-linedsalamander Blue-spotted salamander Green frog lc apdcikdex Black cappedchickadee Red-winged blackbird Tree swallow American yellowwarbler Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

2014 Year x x x x x x x x xx xx xx xx xx xx x x x x x x x x xx x x x x x x xx x x x x x x xx x x x 2015 x x x x x x x x x x 2016 Plantae Fungi Animalia Kingdom

Basidiomycota Nematoda Mollusca Chordata Phylum vsPseiomsTurdidae Passeriformes Aves anlosd seae Asteraceae Asteraceae Asterales Asterales Magnoliopsida Orchidaceae Magnoliopsida Asparagaceae Asparagales Asparagales Amarylidaceae Magnoliopsida Apiaceae Magnoliopsida Apiaceae Asparagales Apiales Magnoliopsida Apiales Magnoliopsida Magnoliopsida Sciuridae Gastropoda Rodentia Picidae Mammalia Piciformes Tyrannidae Turdidae Passeriformes Passeriformes Aves Turdidae Passeriformes Aves Aves Aves anlosd seae Asteraceae Asterales Magnoliopsida Juncaceae Araceae Poaceae Cyperaceae Juncales Alismatales Cyperales Cyperales Magnoliopsida Liliopsida Liliopsida Liliopsida Gomphaceae Gomphales Agaricomycetes Sciuridae Reptilia Rodentia Vireonidae Mammalia Passeriformes Aves Troglodytidae Sittidae Passeriformes Passeriformes Parulidae Passeriformes Aves Aves Aves anlosd seae Asteraceae Asterales Magnoliopsida Asparagaceae Apiaceae Asparagales Asparagales Magnoliopsida Apiales Magnoliopsida Magnoliopsida Ganodermataceae Polyporales Agaricomycetes Sciuridae Gastropoda Sciuridae Rodentia Rodentia Mammalia Mammalia Aves Aves anlosd seae Asteraceae Asteraceae Asteraceae Asterales Asteraceae Asterales Asterales Magnoliopsida Magnoliopsida Asterales Magnoliopsida Magnoliopsida grcmctsPlprlsPlprca Polypore Polyporaceae Polyporales Agaricomycetes anlosd saaae rhdca ppci hlioieHelleborine helliborine Epipactis Orchidaceae Asparagales Magnoliopsida sp. emetica Russula Russula Inkycap Russulaceae sp. Russulaceae Boletus atramentaria Russulales Russulales illudens Agaricomycetes sp. dryophilus Coprinopsis Panellus Boletaceae Agaricomycetes Psathyrellaceae Omphalotus Mycenaceae Gymnopus Boletales Clavaria Marasmiaceae Agaricales Agaricomycetes Marasmiaceae Agaricales Agaricomycetes Clavariaceae Agaricales Agaricomycetes Agaricales Agaricomycetes Agaricales Agaricomycetes Agaricomycetes Fisher Martespennanti Mustelidae Carnivora Mammalia Aves Aves Aves ls re Family Order Class iiomsPicidae Piciformes Turdidae Passeriformes qaaaColubridae Squamata asrfre yandeTrnu tyrannus Tyrannus Tyrannidae Passeriformes ruticilla Setophaga Picidae Passeriformes Parulidae Passeriformes Apiaceae se prananthoides acuminatus Aster Aster helliborine racemosa tricoccum Epipactis Smilacina sp. nudicaulis Allium Cicuta Aralia hudsonicus Tamiasciurus villosus virens migratorius Picoides guttatus Contopus fuscescens Turdus Catharus Catharus Thamnophis mrsaartemesiafolia Ambrosia triphyllum Arisaema Juncus Elymus Carex floccosus Gomphus monax Marmota olivaceus Vireo carolinensis aedon virens Troglodytes Sitta Setophaga iessp. vimineus divaricatus Carduus crispus Bidens Aster minus Aster Arctium sativa canadense Maianthemum Pastinaca applanatum Ganoderma striatus carolinensis Tamias Sciurus varius Sphyrapicus mustelina Hylocichla Daucus oatsauratusCommon Colaptes - 253 eu Species Genus tenuis canadensis stipata carota sp. Crooked stemaster Whorled woodaster Helleborine False Soloman'sseal Ramps Poison parsnip Wild sasparilla Snail American redsquirrel Hairy woodpecker Eastern pewee Robin Hermit thrush Veery Nematode Garter snake Ragweed Jack-in-the-pulpit Path rush Canada wildrye Awlfruit sedge Scaly chanterelle Groundhog orWoodchuck Red-eyed vireo House wren White-breasted nuthatch Black-throated greenwarbler Slug Welted thistle Bur marigold Small whiteaster White woodaster Burdock Canadian mayflower Wild parsnip Artist's bracket Eastern chipmunk Eastern graysquirrel Yellow-bellied sapsucker Wood thrush Queen Anne'slace Eastern kingbird Mushroom American redstart Vomiting russula Mushroom Mushroom Jack-o-lantern Coral fungus Bolete Common Name flicker x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

2014 Year x xx xx xx xx xx xx xx xx xx xx xx xx x xx xx x xx xx x xx x x x xx x 2015 x x x x x x x x x x x x x x x 2016 Kingdom Plantae

Magnoliophyta Phylum anlosd aae Fabaceae Fabales Magnoliopsida Ericaceae Ericales Magnoliopsida Asteraceae Asteraceae Asterales Asterales Magnoliopsida Magnoliopsida Asteraceae Asterales Magnoliopsida anlosd aae Fagaceae Fagaceae Fagaceae Fagales Fagales Fagales Magnoliopsida Magnoliopsida Magnoliopsida Fabaceae Fabaceae Fabaceae Fabales Fabaceae Fabales Fabaceae Magnoliopsida Fabales Magnoliopsida Fabaceae Fabales Fabaceae Fabales Magnoliopsida Magnoliopsida Fabales Magnoliopsida Fabales Fabaceae Magnoliopsida Magnoliopsida Myrsinaceae Fabales Ericaceae Ericales Magnoliopsida Ericaceae Ericales Magnoliopsida Balsaminaceae Magnoliopsida Ericales Ericales Magnoliopsida Caprifoliaceae Magnoliopsida Dipsacales Magnoliopsida Cornaceae Cornaceae Cornales Polygonaceae Cornales Caryophyllales Magnoliopsida Caryophyllaceae Magnoliopsida Caryophyllaceae Caryophyllales Magnoliopsida Caryophyllales Brassicaceae Magnoliopsida Brassicaceae Asteraceae Magnoliopsida Brassicales Brassicales Asteraceae Asterales Magnoliopsida Magnoliopsida Magnoliopsida Asterales Asteraceae Asteraceae Magnoliopsida Asteraceae Asterales Asteraceae Asterales Asterales Magnoliopsida Asteraceae Magnoliopsida Asterales Magnoliopsida Asterales Magnoliopsida Magnoliopsida anlosd aae Betulaceae Betulaceae Fagales Fagales Magnoliopsida Magnoliopsida Fabaceae Fabaceae Fabales Fabales Magnoliopsida Magnoliopsida Adoxaceae Cornaceae Dipsacales Cornales Magnoliopsida Magnoliopsida anlosd Fagales Fabales Magnoliopsida Magnoliopsida Fabales Magnoliopsida Myrsinaceae Ericales Ericaceae Ericaceae Magnoliopsida Ericales Ericales Valerianaceae Magnoliopsida Caprifoliaceae Magnoliopsida Caprifoliaceae Dipsacales Dipsacales Adoxaceae Dipsacales Magnoliopsida Adoxaceae Magnoliopsida Dipsacales Magnoliopsida Dipsacales Magnoliopsida Magnoliopsida Polygonaceae Caryophyllales Brassicaceae Magnoliopsida Brassicales Asteraceae Asteraceae Magnoliopsida Asteraceae Asterales Asterales Magnoliopsida Asterales Magnoliopsida Magnoliopsida Asteraceae Asterales Asteraceae Magnoliopsida Asterales Magnoliopsida anlosd Fagales Fagales Magnoliopsida Magnoliopsida anlosd aae euaeeBetula Betulaceae Fagales Magnoliopsida glutinosum Desmodium Fabaceae Fabales Magnoliopsida trifolia Cornus Staphylea Cornaceae CrossosomatalesStaphyleaceae Magnoliopsida Cornales fontanum Magnoliopsida Cerastium Caryophyllaceae Caryophyllales Magnoliopsida ls re Family Order Class Betulaceae Fabaceae Fabaceae Betulaceae Betulaceae uru alba rubra grandifolia Quercus Quercus Fagus tetrasperma cracca repens pratense Vicia pratense Vicia hybridum Trifolium Trifolium campestre Trifolium aureum Trifolium Trifolium Trifolium bracteata sp. Amphicarpaea occidentale Lysimachia Vaccinium procumbens Rhododendron capensis ciliata Gaultheria Impatiens sp. Lonicera canadensis alternifolia crispus Cornus media flos-cuculi Cornus Rumex Stellaria vulgaris petiolata Lychnis farfara Barbarea Allaria hirta Tussilago auranticum graminifolia Rudbeckia philadelphicus annuus Hieracium vulgare Euthamia Erigeron altissimum Erigeron Cirsium jacea Cirsium Chrysanthemum leucanthemum Centaurea oyu sp. cornuta Corylus Corylus corniculatus heterocarpon Lotus Desmodium acerifolium sericera Viburnum Cornus retlsborealis rotundifolia Trientalis uniflora Pyrola officinalis Monotropa nigra tatarica Valeriana lentago Sambucus lantanoides Lonicera Viburnum Viburnum japonica Fallopia matronalis Hesperis sp. rugosa communis Solidago Solidago Lapsana palustre Cirsium maculosa Centaurea Betula Vicia Trifolium Ostrya Ostrya - 254 eu Species Genus papyrifera varia dubium virginiana cordifolia florids lehnessYellowbirch alleghaniensis White oak Northern redoak American beech Four seededvetch Cow vetch White clover Red clover Red clover Alsike clover Low hop-clover Hop-Clover Hog peanut Fringed loosestrife Blueberry Western azalea Wintergreen Impatience Honeysuckle Bunchberry Alternate-leaved dogwood Curled dock Chickweed Ragged robin Black mustard Garlic mustard Coltsfoot Black-eyed susan Red paintbrush Flat topgoldenrod Common fleabane Daisy fleabane Bull Thistle Tall thistle Ox-Eye daisy Brown knapweed Hazelnut Beaked hazelnut Bird's foottrefoil Tick trefoil Maple leavedviburnum Red osierdogwood Star flower Shinleaf, Pyrola Dutchman's pipe Valarian, Gardenheliotrope Black elderberry Tartarian honeysuckle Nannyberry Hobblebush Japanese bamboo Dame's rocket Goldenrod Rough-stemmed goldenrod Nipplewort Marsh thistle Spotted knapweed White birch Crown vetch Small hopclover Eastern hophornbeam Musclewood Flowering dogwood Pointedleaf ticktrefoil Bladdernut Mouse-ear chickweed Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

2014 Year xx x xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx 2015 x x x x 2016 Kingdom Plantae

Phylum Magnoliophyta anlosd aucllsRanunculaceae Ranunculales Magnoliopsida Juncaceae Poales Magnoliopsida Geraniaceae Geraniales Magnoliopsida anlosd aucllsRanunculaceae Ranunculaceae Ranunculales Ranunculales Magnoliopsida Ranunculaceae Magnoliopsida Ranunculales Magnoliopsida Ranunculaceae Poaceae Ranunculales Berberidaceae Ranunculales Magnoliopsida Poales Cyperaceae Magnoliopsida Magnoliopsida Cyperaceae Poales Magnoliopsida Poales Onagraceae Onagraceae Magnoliopsida Malvaceae Myrtales Myrtales Malvales Magnoliopsida Magnoliopsida Magnoliopsida Salicaceae Malpighiales Magnoliopsida Colchicaceae Verbenaceae Liliales Lamiales Magnoliopsida Plantaginaceae Magnoliopsida Lamiales Lamiaceae Magnoliopsida Lamiaceae Lamiales Iridoideae Lamiales Magnoliopsida Geraniaceae Iridaceae Magnoliopsida Rubiaceae Geraniales Magnoliopsida Gentianales Magnoliopsida Magnoliopsida anlosd olsCyperaceae Poales Magnoliopsida Violaceae Salicaceae Malpighiales Malpighiales Magnoliopsida Magnoliopsida Hypericaceae Melantahiaceae Malpighiales Liliales Magnoliopsida Magnoliopsida Scrophulariaceae Lamiales Plantaginaceae Magnoliopsida Oleaceae Lamiales Lamiales Magnoliopsida Magnoliopsida Rubiaceae Asclepiadaceae Juglandaceae Gentianales Gentianales Magnoliopsida Fagales Magnoliopsida Magnoliopsida anlosd aucllsRanunculaceae Ranunculaceae Ranunculales Ranunculales Ranunculaceae Magnoliopsida Ranunculaceae Ranunculales Magnoliopsida Ranunculaceae Ranunculales Ranunculaceae Ranunculales Magnoliopsida Ranunculales Magnoliopsida Berberidaceae Magnoliopsida Ranunculales Magnoliopsida Magnoliopsida Poales Magnoliopsida Oxalidaceae Onagraceae Onagraceae Oxalidales Myrtales Myrtales Magnoliopsida Malvaceae Magnoliopsida Magnoliopsida Malvales Salicaceae Magnoliopsida Salicaceae Malpighiales Malpighiales Magnoliopsida Magnoliopsida Verbenaceae Liliales Plantaginaceae Lamiales Magnoliopsida Plantaginaceae Magnoliopsida Lamiales Lamiales Magnoliopsida Magnoliopsida Lamiaceae Lamiaceae Lamiales Lamiales Magnoliopsida Magnoliopsida Rubiaceae Gentianales Magnoliopsida Fagales Magnoliopsida anlosd oae Rosaceae Rosales Magnoliopsida anlosd Poales Magnoliopsida anlosd ailsLmaieLmu lu Whitedead-nettle album Lamium x Lamiaciae Commonmilkweed Lamiales Magnoliopsida syriaca Asclepias Apocynaceae Gentianales Magnoliopsida anlosd apgilsSlcca ouu rniett Whitepoplar Commongreenbrier grandidentata rotundifolia Populus Salicaceae Smilax Malpighiales Smilacaceae Magnoliopsida Liliales Magnoliopsida anlosd olsCprca Carex Cyperaceae Poales Magnoliopsida virginiana Medeola Liliaceae Liliales Magnoliopsida lineareCowwheat Melampyrum Orobanchaceae Lamiales Magnoliopsida anlosd oae oaeeAeacirarborea Amelanchier Rosaceae Rosales Magnoliopsida Class Order Juncaceae Lilaceae Juglandaceae Poaceae Family hlcrmsp. dioicum officinale Thalictrum Thalictrum Taraxicum hispidus Ranunculus pachypoda sp. Actaea sp. Caulophyllum thalictrodes sp. Phleum Juncus Scirpus Carex americana Circaea alpina lurida Circaea Tilia tremuloides lutetiana Populus sessilifolia hastata Uvularia Verbena canadense Plantago lateriflora tetrahit Scutellaria angustifolum robertainum Galeopsis maculatum repens Sisyrinchium Geranium Geranium Mitchella Carex vulpinoidea sp. sp. Viola Salix punctatum erectum Hypericum Trillium thapsus Verbascum lanceolata americana Plantago Fraxinus arvensis exaltata sp. Asperula Asclepias Carya lnoi borealis Clintonia gioi sp. Agrimonia repens recurvatus Ranunculus acris sp. Ranunculus canadensis rubra Ranunculus Clematis Aqualigia Actaea Podophyllum peltatum stricta glandulosum coloratum Oxalis Epilobium moschata Epilobium Malva deltoides grandidentata Populus Populus urticifolia officinalis Verbena major Veronica Plantago vulgaris vulgare Prunella Clinopodium sp Gallium Juncus Carya Glyberia - 255 Genus effusus ovata sp. lurida Species Rue Meadow rue Dandelion Hispid buttercup Doll's eye Blue cohosh Timothy grass Rush Bullrush Sedge Dwarf enchanter'snightshade Enchanter's nightshade Basswood Quaking aspen Bellwort Blue vervain Plaintain Mad-dog skullcap Hemp nettle Blue-Eyed grass Herb Robert Spotted Cranesbill Partridge berry Fox sedge Violet Willow Spotted StJohnswort Red trillium Mullein English plantain American whiteash Woodruff Green milkweed Hickory Bluebeads Agrimony Creeping buttercup Hooked buttercup Common buttercup Woodbine Columbine Baneberry Mayapple Yellow woodsorrel Northern willowherb Purple leavedwillowherb Musk mallow Cottonwood Big-tooth aspen White verbena Speedwell Broadleaf plantain Heal-all Wild basil Bedstraw Rush Shagbark hickory Grass Sedge Serviceberry Indian cucumberroot Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

2014 Year xx xx xx xx xx xx xx xx x xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx x xx xx xx xx xx 2015 x x x x x x x x x x 2016 Chromista Bacteria Kingdom Kingdom Plantae

Ochrophyta Cyanobacteria Phylum Phylum Tracheophyta Pinophyta Magnoliophyta iosd Pinales Woodsiaceae Pinopsida Thelypteridaceae Pteridaceae Polypodiopsida Polypodiales Onocleaceae Polypodiopsida Polypodiales Onocleaceae Polypodiopsida Polypodiales Dryopteridaceae Polypodiopsida Polypodiales Dryopteridaceae Polypodiopsida Polypodiales Polypodiopsida Polypodiales Polypodiopsida Polypodiales Rosaceae Rosales Magnoliopsida iosd iae Pinaceae Osmundaceae Pinales Polypodiopsida Osmundales Pinopsida Solanaceae Dennstaedtiaceae Polypodiopsida Polypodiales Solanales Magnoliopsida Sapindaceae Sapindaceae Anarcardiaceae Sapindales Aceraceae Urticaceae Sapindales Rosaceae Sapindales Magnoliopsida Rosaceae Sapindales Magnoliopsida Rosaceae Rosales Magnoliopsida Rosaceae Rosales Magnoliopsida Rosaceae Rosales Magnoliopsida Rosales Magnoliopsida Rosaceae Rosales Magnoliopsida Rosales Magnoliopsida Rosaceae Magnoliopsida Rosales Magnoliopsida Rosaceae Rosales Magnoliopsida Rosales Magnoliopsida Magnoliopsida anlosd airglsSaxifragaceae Hamamelidaceae Saxifragales Saxifragales Magnoliopsida Magnoliopsida oyoipiaPlpdae Woodsiaceae Thelpteridaceae Pteridaceae Polypodiopsida Polypodiales Polypodiaceae Polypodiopsida Polypodiales Onocleaceae Polypodiopsida Polypodiales Dryopteridaceae Polypodiopsida Polypodiales Dryopteridaceae Polypodiopsida Polypodiales Dennstaedtiaceae Polypodiopsida Polypodiales Dennstaedtiaceae Polypodiopsida Polypodiales Polypodiopsida Polypodiales Polypodiopsida Polypodiales Pinales Pinales Pinopsida Pinopsida Solanaceae Vitales Solanales Magnoliopsida Magnoliopsida Rosaceae Rosaceae Rosales Rosaceae Rosales Magnoliopsida Rosales Magnoliopsida Magnoliopsida ailrohca rglrae rglraeeFaiai sp. sp. Diatoma sp. Fragilaria Caloneis Diatoma Fragilariaceae Pinnulariaceae Anabaena Fragilariaceae Bacillariophyceae Naviculales Fragilariales Bacillariophyceae Fragilariales Nostocaceae Bacillariophyceae Nostocales sp. Hormogoneae Cyanophyceae Chroococcales Chroococcaceae Microcystis Acer Sapindaceae Sapindales Magnoliopsida ls re Family Order Class Class Order Pinaceae Pinaceae Pinaceae Vitaceae Family Algae &Protists Larix sg canadensis cinnamomea Osmundastrum Tsuga phegopteris pedatum Athyrium struthiopteris Thelypteris Adiantum acrostichoides intermedia Onoclea aquilinum Matteuccia Polystichium Dryopteris Pteridium saccharum rubrum hirta pensylvanicum pumila Acer odoratus Acer idaeus Rhus allegheniensis Acer multiflora Pilea virginia Rubus serotina Rubus Rubus simplex Rosa virginianum Prunus Prunus Potentilla sp. Geum Crataegus irlacordifolia virginiana Tiarella Hamamelis Vitus tyimfilix-femina noveboracensis pedatum Athyrium sensibilis virginianum Thelypteris Adiantum acrostichoides intermedia Polypodium aquilinum Onoclea Polystichium punctilobula Dryopteris Pteridium Dennstaedtia nigra Solanum canadense virginiana Geum laevis Fragaria Amelanchier iu strobus rubens Pinus Picea - 256 Genus eu Species Genus decidua filix-femina sensibilis sp. sp. spicatum Species Tamarack Cinnamon fern Eastern hemlock Ladyfern Beech fern Maidenhair fern Sensitive fern Ostrich fern Christmas fern Intermediate woodfern Northern brackenfern Nightshade Sugar maple Red maple Staghorn sumac Moosewood, Striped Canadian clearweed Purple floweringraspberry Red raspberry Northern blackberry Multiflora rose Choke cherry Black cherry Common Cinquefoil Rough cinquefoil Hawthorn Foam flower Witch hazel Grape Ladyfern New Yorkfern Maidenhair fern Rock polypody Sensitive fern Christmas fern Intermediate woodfern Northern brackenfern Hay-scented fern Black nightshade White avens Strawberry Shadbush Eastern whitepine Red spruce Blue-green algae Eastern mountainmaple Blue-green algae Green algae Green algae Plankton Common Name Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Year 2014 2014 Year xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx x xx xx xx x xx 2015 2015 x x x x x x x 2016 2016 Protozoa Plantae Kingdom

Charophyta Phylum ojgtpyeeZgeaae Zgeaaee ogoi sp. sp. Lobosa Myxomycetes Mougeotia Chlorella Zygnemataceae Zygnematales Oocystaceae Conjugatophyceae Trebouxiophyceae Chlorellales Class meiaAobdeAob sp. Amoeba Amoebidae Amoebida Order Family - 257 eu Species Genus Slime mold Plankton Green algae Conjugating greenalgae Common Name

2014 Year 2015 x x x x 2016 Appendix 3. All taxa observed at Brookwood Point Conservation Area

Year Kingdom Phylum Class Order Family Genus Species Common Name 2014 2015 2016 Actinopterygii Cypriniformes Catostomidae Campostomus commersonii White sucker x x x Actinopterygii Cypriniformes Catostomidae Erimyzon oblongus Creek chubsucker x Actinopterygii Cypriniformes Cyprnidae Cyprinus carpio Common carp x Actinopterygii Cypriniformes Cyprinidae Notemigonus crysoleucas Golden shiner x x Actinopterygii Cypriniformes Cyprnidae Notropis atherinoides Emerald shiner x Actinopterygii Cypriniformes Cyprinidae Notropis hudsonius Spottail shiner x x Actinopterygii Cypriniformes Cyprinidae Rhinichthys atratulus Blacknose dace x x Actinopterygii Cypriniformes Cyprinidae Rhinichthys cataractae Longnose dace x Actinopterygii Cypriniformes Cyprinidae Semotilus atromaculatus Creek chub x x x Actinopterygii Cypriniformes Cyprnidae Notemigonus crysoleucas Golden shiner x Actinopterygii Cypriniformes Cyprnidae Notropis hudsonius Spottail shiner x Actinopterygii Cypriniformes Cyprnidae Phinichthys atratulus Blacknose dace x Actinopterygii Cypriniformes Cyprnidae Semotilus atromaculatus Creek chub x Actinopterygii Esociformes Esocidae Esox niger Chain pickerel x Actinopterygii Perciformes Centrarchidae Amblopites rupestris Rock bass x Actinopterygii Perciformes Centrarchidae Lepomis auritus Redbreast sunfish x x Actinopterygii Perciformes Centrarchidae Lepomis gibbosus Pumpkinseed x Actinopterygii Perciformes Centrarchidae Lepomis macrochirus Bluegill x Actinopterygii Perciformes Centrarchidae Micropterus dolomieu Smallmouth bass x x Actinopterygii Perciformes Centrarchidae Micropterus salmoides Largemouth bass x Actinopterygii Perciformes Percidae Perca flavescens Yellow perch x x Actinopterygii Perciformes Percidae Etheostoma olmstedi Tessellated darter x Actinopterygii Perciformes Percidae Sander vitreus Walleye x Actinopterygii Salmoniformes Salmoniformes Salmonidae coregonus Lake whitefish x Actinopterygii Scorpaeniformes Cottidae Cottus cognatus Slimy sculpin x Actinopterygii Siluriformes Ictaluridae Ameriurus nebulosus Brown bullhead x

Amphibia Anura Ranidae Lithobates clamitans Green frog x x Amphibia Anura Ranidae Rana palustris Pickerel frog x Amphibia Caudata Plethodontidae Desmognathus fuscus Northern dusky salamander x Amphibia Caudata Plethodontidae Plethodon cinereus Red back salamander x x Amphibia Urodela Plethodontidae Eurycea bislineata Two-lined salamander x x

Aves Accipitriformes Accipitridae Haliaeetus leucocephalus Bald eagle x Chordata Animalia Aves Anseriformes Anatidae Aix sponsa Wood duck x x Aves Anseriformes Anatidae Anas platyrhynchos Mallard x Aves Anseriformes Anatidae Mergus merganser americanus Common merganser x x Aves Anseriformes Anatidae Branta canadensis Canada goose x Aves Apodiformes Trochillidae Archilochus colubris Ruby throated hummingbird x Aves Charadriiformes Laridae Larus delawarensis Ring-billed gull x x x Aves Columbiformes Columbidae Zenaida marcoura Mourning dove x Aves Galliformes Phasianidae Meleagris gallopavo Wild turkey x Aves Passeriformes Bombycillidae Bombycilla cedrorum Cedar waxwing x x x Aves Passeriformes Corvidae Corvus brachyrchynchos American crow x x x Aves Passeriformes Corvidae Cyanocitta cristata Blue jay x x x Aves Passeriformes Emberizidae Junco hyemalis Dark-eyed junco x x Aves Passeriformes Emberizidae Melospiza melodia Song sparrow x x x Aves Passeriformes Emberizidae Spizella passerina Chipping sparrow x Aves Passeriformes Fringillidae Spinus tristis American goldfinch x Aves Passeriformes Hirundinidae Tachycineta bicolor Tree swallow x x x Aves Passeriformes Icteridae Agelaius phoeniceus Red-winged blackbird x x Aves Passeriformes Mimidae Dumetella carolinensis Gray catbird x x x Aves Passeriformes Paridae Poecile atricapillus Black-capped chickadee x x Aves Passeriformes Parulidae Geothlypis trichas Common yellowthroat x x Aves Passeriformes Parulidae Setophaga pensylvanica Chestnut-sided warbler x x x Aves Passeriformes Parulidae Setophaga petechia American yellow warbler x x x Aves Passeriformes Parulidae Setophaga ruticilla American redstart x x Aves Passeriformes Picidae Colaptes auratus Northern flicker x x Aves Passeriformes Sittidae Sitta carolinensis White-breasted nuthatch x Aves Passeriformes Troglodytidae Troglodytes aedon House wren x x Aves Passeriformes Turdidae Turdus migratorius Robin x Aves Passeriformes Tyrannidae Empidonax minimus Least flycatcher x Aves Passeriformes Tyrannidae Tyrannus tyrannus Eastern king bird x x x Aves Passeriformes Tyrannidae Sayornis phoebe Eastern phoebe x Aves Passeriformes Vireonidae Vireo olivaceus Red-eyed vireo x x x Aves Passeriformes Icteridae Icterus galbula Northern oriole

- 258 - Animalia Kingdom

Arthropoda Chordata Phylum Insecta net itr Culicidae Diptera Insecta net itr Chloropidae Diptera Insecta net itr Chironomidae Diptera Insecta net itr Callophoridae Diptera Insecta net Dermaptera Insecta net oepeaTenebrionidae Scarabaeidae Coleoptera Coleoptera Insecta Insecta Insecta net oepeaPsephenidae Coleoptera Insecta Insecta Insecta net oepeaLampyridae Coleoptera Insecta Insecta net oepeaElateridae Coleoptera Insecta Insecta net oepeaCurculionidae Coleoptera Insecta Insecta Insecta net oepeaCerambycidae Chrysomelidae Coleoptera Coleoptera Insecta Insecta Insecta Insecta net oepeaCarabidae Coleoptera Insecta Insecta net oepeaCantharidae Coleoptera Insecta Insecta rcnd Opiliones Arachnida Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta rcnd rna Thomisiae Tetragnathidae Araneae Araneae Arachnida Arachnida Insecta Insecta rcnd rna Salticidae Araneae Arachnida Insecta rcnd rna Lycosidae Araneae Arachnida Insecta rcnd rna Linyphiidae Araneae Arachnida Insecta rcnd crn Trombiculidae Colubridae Acarina Sciuridae Squamata Sciuridae Arachnida Sciuridae Rodentia Sciuridae Reptilia Rodentia Picidae Rodentia Cervidae Picidae Mammalia Rodentia Picidae Mammalia Artiodactyla Piciformes Mammalia Piciformes Mammalia Piciformes Mammalia Aves Aves Aves Aves Insecta Class eiotr Erebidae Lepidoptera yeotr Vespidae Pompilidae Hymenoptera Hymenoptera yeotr Ichneumonidae Hymenoptera yeotr Formicidae Hymenoptera yeotr Cynipidae Hymenoptera yeotr Apidae Hymenoptera yeotr Apidae Hymenoptera eitr Nabidae Hemiptera eitr Miridae Hemiptera eitr Membracidae Hemiptera eitr Ciciadellidae Cercopidae Hemiptera Hemiptera eitr Aleyrodidae Isonychiidae Hemiptera Heptageniidae Ephemeroptera Heptageniidae Ephemeroptera Ephemerellidae Ephemeroptera Tipulidae Ephemeridae Ephemeroptera Baetidae Ephemeroptera Ephemeroptera Diptera itr Tachinidae Diptera itr Simuliidae Diptera itr Syrphidae Diptera itr Sciomyzidae Diptera itr Sciaridae Diptera itr Mycetophilidae Diptera Diptera eeaiomsArdeidae Pelecaniformes itr Drosophilidae Diptera itr Dolichopodidae Diptera Order Muscidae Family oilajaponica Popillia yati dispar Lymantria obssp. Bombus aeuia sp. Galerucinae snci sp. sp. sp. sp. Isonychia Stenacron sp. Epeorus sp. Drunella Hexagenia Baetis Hexatoma iuimsp. Simulium ergah elongata Tetragnatha hmohssirtalis monax Thamnophis striatus hudsonicus carolinensis Marmota Tamiasciurus sp. Tamias Sciurus varius Odocoileus carolinus herodias pileatus Sphyrapicus Melanerpes Hylatomus Ardea - 259 Genus sp. sp. sp. 2 distinctspecies sp. sp. sp. sp. sp. sp. sp. sp. 2 distinctspecies sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. 5 distinctspecies sp. sp. sp. sp. 2 distinctspecies sp. sp. sp. sp. sp. sp. sp. sp. sp. sp. Species Mosquito rs l x Grass fly ig x x Midge lwl x x Blowfly awgx Earwig etex Beetle aaeebel x Japanese beetle ae-en etex Water-penny beetle ot mrcngpymt x x North Americangypsymoth Yellowjacket Spider wasps iel x x Firefly Parasitoid wasp lc etex Click beetle Ant etex Beetle Gall-making wasps etex Beetle e x Bee x Bumble bee Damsel bug efbel x Leaf beetle etex Beetle Plant bugs ode etex Soldier beetle Tree hoppers ad ogeshreta x Daddy longlegs/harvestman Plant hoppers Frog hoppers White fly Parasitoid fly Crab spider Small minnowmayfly Mayfly Mayfly Mayfly Mayfly Burrowing Mayfly Crane fly Hover fly Blackfly ni-iln l rMrhfyx Snail-killing flyorMarsh lnaesitsie x Elongate stiltspider Jumping spider Dark-winged fungusgnat ofsie x Wolf spider Fungus gnat he evr x Sheet weavers House fly Red velvetmite Vinegar fly Long-legged fly rudo x x x x x x x x Eastern gartersnake x x Groundhog American redsquirrel x x Eastern chipmunk x x x x Eastern graysquirrel White-tailed deer Yellow-bellied sapsucker Red-bellied woodpecker Pileated woodpecker Great blueheron Common Name x x x x x x x x x x x x x x x x x x x x x x x

2014 Year x x x x x x x x x x x x x x x x x x x 2015 x 2016 Plantae Fungi Animalia Kingdom

Magnoliophyta AscomycotaBasidiomycota Platyhelminthes Nematoda Mollusca Arthropoda Phylum etd rtoehldaProteocephalidae Proteocephalidea Monogenea Camallanidea Cestoda Camallanida Dreissenidae Unionoida Secernentea Veneroida Unionoida Gastropoda Bivalvia Bivalvia Cambaridae Subclass:Collembola Cambaridae Decapoda Entognatha Decapoda Malacostraca Malacostraca anlosd lsaae Araceae Alismatales Typhaceae Magnoliopsida Potamogetonaceae Equisetaceae Liliopsida Typhales Liliopsida Najadales Hydrocharitaceae Equisetales Liliopsida Hydrocharitales Equisetopsida Tremellaceae Syzygosporaceae Tremellales Tremellomycetes Tremellales Tremellomycetes Strophariaceae Polyporaceae Boletaceae Strophariaceae Tricholomataceae Polyporales Agaricomycetes Physalacriaceae Boletales Agaricomycetes Agaricales Agaricomycetes Agaricales Agaricomycetes Agaricomycetes aaotaaApioaHyalellidae Amphipoda Malacostraca Insecta Insecta Insecta Insecta Insecta Insecta rmtd zgiaAzygiidae Clinosomatidae Azygiida Strigeidida Trematoda Trematoda Trematoda Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta ls re Family Order Class rcotr Uenoidae Rhyacophilidae Philopotamidae Trichoptera Hydropsychidae Trichoptera Glossosomatidae Trichoptera Trichoptera Trichoptera eaotr Corydalidae Megaloptera lcpeaPerlodidae Perlidae Psocoptera Plecoptera Plecoptera dnt Gomphidae Cordulidae Coenagrionidae Aeshnidae Odonata Odonata Odonata Odonata eotr Panorpidae Pieridae Mecoptera Pieridae Lepidoptera Lepidoptera eiotr Papilionidae Papilionidae Lepidoptera Lepidoptera lcpeaLeuctridae Capniidae Plecoptera Plecoptera rhpeaGryllidae Acrididae Orthoptera Orthoptera Physidae rtoehlssp. Proteocephalus sp. Camallanus polymorpha radiata Physa Dreissena Lampsilis rusticus bartonii Orconectes Cambarus rseatriphyllum latifolia canadensis Arisaema crispus Typha Potamogeton arvense Elodea Equisetum mesenterica mycetophila Tremella Syzygospora sp. versicolor sp. Stropharia equestre Trametes conigenoides Boletus Tricholoma Strobilurus epya sp. sp. sp. Neophylax Rhyacophila sp. sp. Wormaldia Ceratopsyche Glossosoma irnaserricornis Nigronia zgasp marginatum Azygia Clinostomum sprasp. carolinensis Isoperla Acroneuria opu sp. sp. sp. sp. Gomphus Epitheca Coenagrion Boyeria Pieris Papilio Papilio glaucus loanasp. Allocapnia - 260 eu Species Genus sp. sp. sp. 1 sp. sp. sp. sp. sp. sp. sp. sp. Springtail Flatwor Nematode Freshwater snail Zebra mussel Eastern lampmussel Rusty Crayfish Common crayfish Trematode aki-h-uptxxx x x x x x Jack-in-the-pulpit x Broadleaf cattail x Curly pondweed Canadian waterweed x x x Horsetail Witches' butter x Collybia jelly Roundhead x Turkey tail Bolete Man onhorseback Magnolia-cone mushroom Caddisfly Caddisfly Caddisfly Caddisfly Caddisfly okie akie rbrfisx booklice, barklice,orbarkflies Dobsonfly Scorpionfly Butterfly elwgu x Trematode Yellow grub Stonefly Stonefly Stonefly Cricket Grasshopper arwwne asll x x Clubtail Dragonfly Narrow-winged damselfly Dragonfly White butterfly atr ie wloti x Eastern tigerswallowtail Tiger swallowtail Stonefly m Common Name x x x x x x x x x xxx x x x x x x x x x x x x x x x x x x x x

2014 Year x x x x 2015 x x x 2016 Plantae Kingdom

Magnoliophyta Phylum anlosd seae Brassicaceae Brassicaceae Asteraceae Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Asteraceae Magnoliopsida Asterales Magnoliopsida Asterales Xanthorrhoeaceae Magnoliopsida Asterales Orchidaceae Magnoliopsida Asparagales Iridaceae Magnoliopsida Asparagales Asparagaceae Magnoliopsida Asparagales Asparagaceae Magnoliopsida Asparagales Asparagaceae Apiaceae Magnoliopsida Asparagales Asparagaceae Apiaceae Magnoliopsida Asparagales Apiaceae Magnoliopsida Asparagales Magnoliopsida Apiales Magnoliopsida Araceae Apiales Magnoliopsida Apiales Magnoliopsida Alismatales Magnoliopsida Magnoliopsida anlosd rsiae Brassicaceae Caryophyllaceae Brassicaceae Caryophyllales Brassicales Magnoliopsida Brassicales Magnoliopsida Magnoliopsida anlosd Brassicales Magnoliopsida anlosd aypyllsCaryophyllaceae Caryophyllaceae Caryophyllales Caryophyllales Magnoliopsida Magnoliopsida anlosd eatae Celastraceae CelastralesCelastraceae Celastrales Magnoliopsida Magnoliopsida anlosd aypyllsPolygonaceae Polygonaceae Caryophyllales Polygonaceae Caryophyllales Magnoliopsida Caryophyllales Magnoliopsida Magnoliopsida anlosd isclsAdoxaceae Cornaceae Cornaceae Dipsacales Cornales Magnoliopsida Cornales Magnoliopsida Magnoliopsida anlosd aae Fabaceae Fabaceae Fabaceae Fabaceae Fabales Myrsinaceae Fabales Magnoliopsida Ericaceae Fabales Magnoliopsida Balsaminaceae Fabales Magnoliopsida Ericales Valerianaceae Magnoliopsida Ericales Magnoliopsida Ericales Magnoliopsida Dipsacales Magnoliopsida Magnoliopsida anlosd isclsCaprifoliaceae Dipsacales Magnoliopsida anlosd aae Fabaceae Fabaceae Fabaceae Fabales Fabales Magnoliopsida Fabales Magnoliopsida Magnoliopsida anlosd aae Fabaceae Fabales Magnoliopsida anlosd aae Fabaceae Fabales Magnoliopsida anlosd aae Betulaceae Betulaceae Fabaceae Fagales Fagales Magnoliopsida Fabales Magnoliopsida Magnoliopsida Class Asterales Asteraceae Order Brassicaceae Family hap arvense petiolata nigra Thlaspi officinalis Alliaria parthenium sp. Centaurea perfoliatum Taraxacum sp. Tanacetum communis Solidago inula Silphium sp. Nabalus Lapsana aurantiacum Inuleae maculatum graminifolia Helenium philadelphicus Hieracium annuus Eutrochium ritro Euthamia palustre sp. Erigeron sp. Erigeron Echinops minus Cirsium artemisiifolia Bidens millefolium Aster pseudoacorus Arctium helleborine fulva Ambrosia Achillea Hemerocallis sp. Epipactis racemosum Iris canadense Maianthemum majalis sativa Maianthemum carota Hostra Convallaria podagraria Pastinaca Daucus foetidus Aegopodium Symplocarpus tlai griminea nasturtium-aquaticum matronalis Stellaria Rorippa Hesperis Cardamine Dahlia tlai pubera media Stellaria Stellaria unmsalatus orbiculata Euonymus Celastrus escrahydropiper Rumex Rumex Persicaria abcsnigra Sambucus Cornus Cornus oii pseudoacacia dioicus fruiticosa Robinia bracteata Gymnocladus nummularia sp. Amphicarpaea capensis Amorpha officinalis Lysimachia Epigaea Impatiens Valeriana oieasp. Lonicera rflu dubium agrarium varia Trifolium Trifolium Securigera rflu hybridum Trifolium rflu pratense Trifolium rflu repens Carpinus caroliniana Alnus Trifolium - 261 Genus pratensis sp. alternifolia alba obtusifolius crispus incana Species alcmsadxx x x x x x x x x x x x x x x x Pennycress x x Garlic mustard x x Black knapweed Dandelion Feverfew x x Goldenrod x Cup plant White lettuce x x x x Nipplewort x Horse-heal x x x Sunflower x x x Orange hawkweed x x x x Spotted joe-pyeweed x x Grass-leaved goldenrod x x Fleabane x x x x Eastern daisyfleabane x x x Globethistle x x Swamp thistleormarsh Beggerticks Aster Lesser burdock x x Ragweed x x Yarrow Day lily x Helleborine Yellow flag False Solomon'sseal Canadian mayflower x x Hostra x x x Lily ofthevalley x x x Wild parsnip Wild carrotorQueenAnne'sLace Bishop weedorGoutweed Eastern skunkcabbage Cuckoo flower Common stitchwort Wastercress Dame's rocket Dahlia Great chickweed Common chickweed Winged burningbush Oriental bittersweet Dock leaf Curly dock Smartweed re se rpgd owo x x x Elderberry Green osierorpagodadogwood Red osier Black locust Kentucky coffeetree Hog peanut False indigo Creeping Jenny May flower Spotted jewelweed Common valerian Honeysuckle Least Hopclover Hop clover Crown vetch Alsike clover Red clover Speckled alder Musclewood orAmericanhornbea White clover Common Name m xxx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

2014 Year x x x x x x x x x x x x x x x x x x x x 2015 x x x 2016 Plantae Kingdom

Magnoliophyta Phylum anlosd apgilsSalicaceae Salicaceae Melanthiaceae Malpighiales Hypericaceae Liliaceae Magnoliopsida Malpighiales Liliaceae Magnoliopsida Malpighiales Colchicaceae Magnoliopsida Liliales Magnoliopsida Liliales Scrophulariaceae Magnoliopsida Liliales Plantaginaceae Magnoliopsida Liliales Plantaginaceae Magnoliopsida Lamiales Plantaginaceae Magnoliopsida Lamiales Plantaginaceae Magnoliopsida Lamiales Plantaginaceae Magnoliopsida Lamiales Plantaginaceae Magnoliopsida Lamiales Plantaginaceae Magnoliopsida Lamiales Oleaceae Magnoliopsida Lamiales Oleaceae Magnoliopsida Lamiales Lamiaciae Magnoliopsida Lamiales Lamiaceae Magnoliopsida Lamiales Lamiaceae Magnoliopsida Lamiales Lamiaceae Magnoliopsida Lamiales Boraginaceae Magnoliopsida Lamiales Magnoliopsida Juglandaceae Lamiales Magnoliopsida Juglandaceae Lamiales Magnoliopsida Haloragaceae Juglandales Geraniaceae Magnoliopsida Juglandales Magnoliopsida Rubiaceae Haloragales Magnoliopsida Rubiaceae Geraniales Magnoliopsida Ascleiadaceae Gentianales Fagaceae Magnoliopsida Ascleiadaceae Gentianales Fagaceae Magnoliopsida Gentianales Betulaceae Magnoliopsida Gentianales Betulaceae Magnoliopsida Fagales Magnoliopsida Fagales Magnoliopsida Fagales Magnoliopsida Fagales Magnoliopsida Magnoliopsida Magnoliopsida anlosd avlsMalvaceae Malvales Magnoliopsida anlosd ytlsLythraceae Myrtales Magnoliopsida anlosd ytlsOnocleaceae Myrtales Magnoliopsida anlosd olsJuncaceae Cyperaceae Cyperaceae Cyperaceae Poales Poales Oxalidaceae Magnoliopsida Poales Magnoliopsida Poales Magnoliopsida Oxalidales Magnoliopsida Magnoliopsida anlosd olsPoaceae Poaceae Poaceae Poales Poales Magnoliopsida Poales Magnoliopsida Magnoliopsida anlosd olsPoaceae Poaceae Poales Poales Magnoliopsida Magnoliopsida anlosd aucllsBerberidaceae Papaveraceae Ranunculales Ranuculales Magnoliopsida Magnoliopsida anlosd olsTyphaceae PrimlalesPrimulaceae PrimlalesPrimulaceae Magnoliopsida Poales Magnoliopsida Magnoliopsida anlosd olsPoaceae Poales Magnoliopsida anlosd aucllsBerberidaceae Ranunculales Magnoliopsida anlosd aucllsRanunculaceae Ranunculaceae Ranunculales Ranunculales Magnoliopsida Magnoliopsida anlosd aucllsRanunculaceae Ranunculaceae Ranunculales Ranunculaceae Ranunculales Magnoliopsida Ranunculales Magnoliopsida Magnoliopsida anlosd aucllsRanunculaceae Ranunculales Magnoliopsida anlosd oae Elaeagnaceae Rosales Magnoliopsida anlosd oae Cannabaceae Ranunculaceae Rosales Ranunculaceae Ranunculales Magnoliopsida Ranunculales Magnoliopsida Magnoliopsida Class apgilsViolaceae Malpighiales apgilsSalicaceae Malpighiales Order Family ouu tremuloides deltoides perforatum viride parryi Populus lancifolium Populus Hypericum sessilifolia Veratrum Lilium thapsus salicifolia Lilium officinalis Uvularia anagallis-aquatica Verbascum arvensis Veronica major Veronica lanceolata Veronica purpurea Veronica vulgaris Plantago americana Plantago medioides Digitalis vulgaris Syringa hederacea Fraxinus Monarda sp. ovata vulgare Prunella sp. Glechoma Clinopodium Myosotis robertainum Carya sp. spicatum Carya odoratum Myriophyllum syriaca Geranium incarnata Galium rubra grandifolia Galium virginiana Asclepias Asclepias americana Quercus Fagus Ostrya Corylus Viola Salix Tilia yhu salicaria Lythrum eohr biennis Oenothera Juncus Scirpus Carex Carex Oxalis gotssp. odoratum Avena Anthoxanthum Agrostis atlsglomerata vimineum Microstegium Dactylis ebrsthunbergii majus Berberis Chelidonium retlsborealis terrestis americanum Trientalis Lysimachia Sparganium Phleum ebrsvulgaris Berberis qiei canadensis Aquilegia Actaea auclsacris repens acutiloba Ranunculus Ranunculus Hepatica auclsseptentrionalis Ranunculus legu angustifolia Elaeagnus uuu lupulus sp. dioicu Humulus Thalictrum Thalictrum - 262 Genus sp. sp. americana effusus sp. vulpinoidea lurida stricta sp. pratense pachypoda Species otnodxx x x x x x x x x Quaking aspen Cottonwood x St. John'swort x x False hellebore x x x x Lemon lily x x Tiger lily x x x Bellwort x x x x Common mullein x Willow-leaf herb x x Common Speedwell x x American brookline x Speedwell x Broadleaf plantain x Enlish plantain x Common foxglove x x x Lilac x White ash x x Beebalm x x x x Self-heal x x Gill-over-the-ground Wild Basil x x x Forget-me-not x x x x x x Shagbark Hickory x x x Hickory x x Eurasian watermilfoil x Herb Robert Bedstraw Wild baby'sbreath x x Common milkweed Swamp milkweed Red oak Beech tree Hop hornbean American hazelnut Violet flower Willow Basswood Purple loosestrife Evening primrose Rush Bulrush Fox sedge Sedge Yellow woodsorrel Wild oats Sweet vernalgrass Bentgrass Grass Orchard grass Japanese Barberry Greater celandine Starflower Swamp candles Burr-reed Timothy grass European Barberry ol y rwiebnbryxx x Dolls eyeorwhitebaneberry Columbine Buttercup Creeping buttercup Liverwort orLiverleaf Swamp buttercup Russian Olive American hops Rue Meadow rue Common Name x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

2014 Year x x x x x x x x x x x x x x x x x x x x x x x x x x 2015 x 2016 Viridiplantae Plantae Kingdom Plantae Kingdom

Unranked Unranked Phylum Pteridophyta Pinophyta Magnoliophyta Phylum trdpiaPlpdae Onocleaceae Dryopteridaceae Polypodiales Dryopteridaceae Polypodiales Dryopteridaceae Polypodiales Dryopteridaceae Pteridopsida Polypodiales Athyriaceae Pteridopsida Polypodiales Pteridopsida Polypodiales Pteridopsida Thelypteridaceae Pteridopsida Pteridopsida Blechnales Polypodiopsida oyoipiaBehae Thelypteridaceae Blechnales Polypodiopsida Pinopsida Pinales hoohca eooilsOedogoniaceae Chlorophyceae Sphaeropleales Oedogoniales Scenedesmaceae Chlorophyceae Zygnemataceae Closteriaceae Zygnematales Conjugatophyceae Desmidiales Conjugatophyceae Pinaceae Cupressaceae Cupressaceae Pinales Pinales Pinales Vitaceae Pinopsida Vitaceae Pinopsida Pinopsida Solanaceae Pinopsida Vitales Convolvulaceae Vitales Magnoliopsida Solanales Hamamelidaceae Magnoliopsida Solanales Grossulariaceae Magnoliopsida Saxifragales Crassulaceae Sapindaceae Magnoliopsida Saxifragales Sapindaceae Magnoliopsida Saxifragales Sapindaceae Magnoliopsida Sapindales Sapindaceae Magnoliopsida Sapindales Sapindaceae Magnoliopsida Sapindales Anacardiaceae Magnoliopsida Sapindales Anacardiaceae Urticaceae Magnoliopsida Sapindales Urticaceae Magnoliopsida Sapindales Rosaceae Magnoliopsida Sapindales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rosaceae Magnoliopsida Rosales Rhamnaceae Magnoliopsida Rosales Magnoliopsida Rosales Magnoliopsida Rosales Magnoliopsida Rosales Magnoliopsida Magnoliopsida hoohca parpelsRadiococcaceae Sphaeropleales Chlorophyceae Zygnemataceae Zygnematales Conjugatophyceae Class Class iae Pinaceae Pinales Order re Family Order Pinaceae Family

nce sensibilis acrostichoides struthiopteris Onoclea intermedia Polystichum carthusiana filix-femina Matteuccia Dryopteris Dryopteris noveboracensis Athyrium Thelypteris hgpei hexagonoptera Phegopteris Picea Tsuga cndsu sp. sp. Scenedesmus Oedogonium spirogyna sp. Lepocinclis Closterium strobus occidentalis sp. Pinus thyoides Thuja Chamaecyparis nigra quinquefolia Vitis sp. Parthenocissus virginiana sp. sp. Solanum Convolvulaceae saccharum hippocastanum Hamamelis saccharinum Ribes rubrum Sedum platanoides Aesculus Acer typhina Acer dioica Acer pumila radicans Acer sp. Toxicodendron occidentalis Rhus odoratus Urtica multiflora Pilea Rubus serotina Rubus Rubus canadense sp. Rosa sp. Prunus virginiana Physocarpus Peraphyllum ulmaria Geum rubra sp. Fragaria Filipendula arborea Filipendula sp. cathartica Crataegus Amelanchier Agrimonia Rhamnus lecsi sp. Gloeocystis sp. Mougeotia Plankton - 263 Genus Genus canadensis abies Species Species Sensitive fern Christmas fern Ostrich fern Wood fern Wood fern Lady fern New YorkFern Norway spruce Beech fern Hemlock la x Algae Algae Algae Algae x x x x x x x x x x x x x x x x x White pine x Northern whitecedar Atlantic whitecedar x x Grapevine x x x Virginia creeper x x x x Black nightshade x x x Bindweed x x Witch Hazel x Currant x x Sedum x x Horse chestnut Sugar maple x x Silver maple x Red maple x Norway maple x x x x x Poison Ivy x x x Smooth sumac Stinging nettle x x Canadian clearweed x x x x Blackberry x Black raspberry x Purple floweringraspberry x x Multiflora rose x x x Black cherry x x Ninebark x Crab apple White avens x x x Stawberry x Meadowsweet x Queen-of-the-prairie Hawthorn Juneberry orserviceberry Agrimony Common buckthorn Algae Algae Common Name Common Name x x x Year 2014 2014 Year xx xx xx xx xx xx xx xx xx x x x x x 2015 2015 x x x 2016 2016 Unranked Kingdom

Cryptophyta Unranked Phylum Cryptophyceae Cryptomonadales Cryptomonadaceae Bacillariophyceae Bacillariophyceae ailrohca Melosirids Naviculaceae Bacillariophyceae Naviculales Bacillariophyceae Bacillariophyceae Bacillariophyceae ailrohca yblae Cymbellaceae Cymbellales Bacillariophyceae ailrohca ailrohtn Bacillariophyceae Bacillariophyceae Bacillariophytina Bacillariophyceae Bacillariophycea ls Order Class e hlsisrlsStephanodiscaceae Thalassiosirales Catenulaceae Thalassiosirales rglrae Fragilariaceae Fragilariaceae Fragilariales Fragilariales Coscinodiscales Coscinodiscaceae cnnhlsCocconeidaceae Achnanthales Family hdmnssp. Rhodomonas sp. sp. Cyclotella Amphora eoiasp. sp. sp. Melosira sp. Navicula Fragilaria Asterionella yblasp. sp. Cymbella Coscinodiscus itm sp. Diatoma Cocconeis - 264 Genus s p. Species la x x x Algae Algae Algae la x x x Algae Algae Algae Algae la x x Algae Algae la x Algae Algae Common Name x

2014 Year x x x 2015 2016 Aquatic macrophyte management plan facilitation, Lake Moraine, Madison County, NY 2016

W.N. Harman1 and M.F. Albright1

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 SUNY Oneonta Biological Field Station.

- 265 - 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 28 June, 26 July and 18 August 2016 (though plant harvesting activities on the last date limited our access to the south basin) 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)

- 266 -

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

- 267 - RESULTS

Plant Biomass

Tables 2-6 proved biomass estimates, by species, during 2016 for sites 1-5 at Moraine Lake. Eurasian water-milfoil was essentially absent from the south basin sites throughout the summer (Tables 2-4). However, it was abundant in the north basin throughout June and July (Tables 5 and 6). Of particular note is the dominance by starry stonewort (Nitellopsis obtusa) 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. However, stonewort has not been so aggressive in the north basin. 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). It was actually less common over 2016 than it had been the previous few years. Milfoil was prevalent throughout that basin over the summer, as was coontail.

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 2016. While not the original focus of study, starry stonewort is highlighted along with milfoil because it is also an exotic nuisance species and management efforts ought to focus upon control both species. Starry stonewort has come to dominate throughout the south basin throughout much of the summer, while milfoil, coontail and, to a lesser extent, a few native species have comprised the community of the north basin.

- 268 - Table 2. Mean biomass (g/m2) category mid-points for each species found at Site 1 during 2016 sampling events.

Site 1 6/28/2016 7/26/2016 8/18/2016 Myriophyllum spicatum Megalodonta beckii Zosterella dubia Najas spp. Ceratophyllum demersum Chara vulgaris 340.0 113.3 Vallisneria americana Elodea canadensis Ranunculus aquatilis Ranunculus trichophyllus Stuckenia pectinata Potamogeton crispus Potamogeton zosteriformis 0.3 Potamogeton pusillus 0.7 Nitellopsis obtusa 23.7 340.0 227.0 Total 364.3 340.0 340.7

Table 3. Mean biomass (g/m2) category mid-points for each species found at Site 2 during 2016 sampling events.

Site 2 6/28/2016 7/26/2016 8/18/2016 Myriophyllum spicatum Megalodonta beckii Zosterella dubia Najas spp. Ceratophyllum demersum 24.0 Chara vulgaris Vallisneria americana Elodea canadensis Ranunculus aquatilis Ranunculus trichophyllus Stuckenia pectinata 226.7 Potamogeton crispus 47.7 Potamogeton zosteriformis Potamogeton pusillus Nitellopsis obtusa 137.0 340.0 340.0 Total 435.3 340.0 340.0

- 269 - Table 4. Mean biomass (g/m2) category mid-points for each species found at Site 3 during 2016 sampling events.

Site 3 6/28/2016 7/26/2016 8/18/2016 Myriophyllum spicatum Megalodonta beckii Zosterella dubia Najas spp. Ceratophyllum demersum Chara vulgaris 113.3 Vallisneria americana Elodea canadensis Ranunculus aquatilis Ranunculus trichophyllus Stuckenia pectinata Potamogeton crispus 0.3 Potamogeton zosteriformis Potamogeton pusillus Nitellopsis obtusa 340.0 288.3 340.0 Total 340.3 401.7 340.0

Table 5. Mean biomass (g/m2) category mid-points for each species found at Site 4 during 2016 sampling events.

Site 4 6/28/2016 7/26/2016 Myriophyllum spicatum 23.7 227.0 Megalodonta beckii 0.3 Zosterella dubia Najas spp. 23.7 23.7 Ceratophyllum demersum 123.7 123.3 Chara vulgaris Vallisneria americana 61.7 Elodea canadensis 24.0 0.3 Ranunculus aquatilis 0.3 Ranunculus trichophyllus Stuckenia pectinata 123.3 Potamogeton crispus 0.7 Potamogeton zosteriformis Potamogeton pusillus 0.3 Nitellopsis obtusa 85.7 Total 319.3 522.3

- 270 - Table 6. Mean biomass (g/m2) category mid-points for each species found at Site 5 during 2016 sampling events.

Site 5 6/28/2016 7/26/2016 Myriophyllum spicatum 114.0 113.3 Megalodonta beckii Zosterella dubia Najas spp. 0.3 Ceratophyllum demersum 198.7 226.7 Chara vulgaris Vallisneria americana Elodea canadensis 0.3 Ranunculus aquatilis Ranunculus trichophyllus Stuckenia pectinata 0.7 Potamogeton crispus Potamogeton zosteriformis 0.3 0.3 Potamogeton pusillus Nitellopsis obtusa Total 314.0 340.7

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 through 2016, Site 1 (see Figure 1 for sites).

- 271 -

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 through 2016, 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

Figure 4. Comparison of biomass (g/m2) of starry stonewort (Nitellopsis obtusa), Eurasian milfoil (Myriophyllum spicatum) and other plant species present, 2008 through 2016, Site 3 (see Figure 1 for sites).

- 272 - 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 through 2016, 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 through 2016, Site 5 (see Figure 1 for sites).

- 273 -

Water Quality Analysis

Dissolved oxygen concentrations in the south basin extended later into the summer then they typically have, with bottom waters having oxygen of 32% saturation on 28 June. By 26 July, water below about 8 m was essentially anoxic. pH was typically between 7.2 and 8.5. Transparency was between 4 and 6 m over the sampling dates. In the shallower north basin, intermittent mixing was evident. anoxic. pH ranged from 7.4 to 8.9. Transparency was less than 2.5 m.

DISCUSSION

The spread of starry stonewort continued in the south basin throughout 2016. First documented in the north basing in 2014, it has not expanded as aggressively there. In June and July, Eurasian milfoil was dominant in the north basin, with coontail also being common.

Starry stonewort was present at all sites in the south basin on all sampling dates. In July and August it dominated all sites. Eurasian milfoil was practically absent from 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).

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 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.

- 274 - REFERENCES

Anon. 1988. Madison County septic system survey. Madison County Planning Department, Wampsville, NY 13163.0

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.

Harman, W.N. and M.F. Albright. 2014. Aquatic macrophyte management plan facilitation, Lake Moraine, Madison, N.Y. 2013. 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. 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.

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.

- 275 - 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.

- 276 - The effectiveness of three fry emergence traps in measuring lake trout (Salvelinus naymaycush) recruitment in Otsego Lake, NY

Nicholas R. Winter1, Brandon A. Winter1 & John R. Foster1

Abstract: Fry emergence traps have been widely used to monitor salmonid reproduction, egg survival and recruitment. While a variety of different trap designs have been utilized, a side-by-side comparison of their effectiveness has not been conducted. The goal of this study was to compare the effectiveness of circle mesh, circle rigid and square rigid fry emergent traps on measuring Lake Trout recruitment in Otsego Lake, NY. Twenty-four traps were set (12 circle mesh, 6 circle rigid, and 6 square rigid), on Bissel Point, a previously studied lake trout spawning shoal. Traps were set, side by side parallel to shore at 30, 60, and 90cm of water. During the 38 day study (4/10–5/18/2016), 273 fry were captured. Adjusting catch/effort, the circle rigid traps caught 62% of the fry, followed by the circle mesh traps (24%) and the square rigid traps (14%). The circle rigid catch per trap day was 2.6 times higher than the circle mesh traps used at this site since 2003 and out-fished the square rigid traps by 4.4 (P < .001 Test). The circle rigid traps were also easiest to use, and were most effective at the shallowest depths. Future lake trout fry emergence studies should utilize circle rigid traps.

INTRODUCTION

Lake trout are critically important to the cold-water ecology of Otsego Lake where they dominate the cold-water fish fauna and support a substantial recreational fishery (MacWatters 1983, Harman et al. 1996). In the late fall, sexually mature lake trout broadcast spawn on wave- swept shoals covered predominately with clean cobble (MacWatters 1983,, Schreiner et al. 1995, Marsden and Chotkowski 2001). Lake trout eggs and sac fry develop in the rock interstices over the winter, and fry emerge from the substrate in the early spring.

Immediately after the yolk sac is fully absorbed, lake trout fry must swim up to the surface in order to gulp air to fill their swim-bladders (Tait 1960, Balon 1980). Because of this behavior, fry emergence traps have often been used to monitor lake trout reproduction, egg survival and recruitment (Marsden et al. 2005). While a wide variety of different trap designs have been utilized (Collins 1975, Stauffer 1981, Fraley et al. 1986) a side-by-side comparison of their effectiveness has not been made.

The goal of this study was to compare the effectiveness of three fry emergence traps on measuring lake trout recruitment in Otsego Lake, NY. The objective was to determine which

1 Fisheries & Aquaculture Program, Fisheries, Wildlife & Environmental Science Dept., State University of New York at Cobleskill.

- 277 - design (circle mesh, circle rigid, or square rigid traps) was the most effective in capturing emergent Lake Trout fry at different depths.

MATERIAL & METHODS

This study was conducted at Bissel Point, Otsego Lake, a previously studied lake trout fry emergence shoal (Tibbits 2007, Sawick and Foster 2014, Lucykanish and Foster 2015, Casscles et al. 2016). Traps were set for 38 days, from 10 April to 18 May 2016 and checked every other day. All captured lake trout fry were counted and released.

The three emergence traps tested were circle mesh, circle rigid, and square rigid (Figure 1). A total of 24 traps were set (12 circle mesh, 6 circle rigid and 6 square rigid). Groups of three same-type traps were set on clean cobble in 8 transects perpendicular to shore at depths of 30, 60 and 90 cm, across the entire shoal (Figure 2).

Figure 1. Circle mesh, circle rigid and square rigid fry emergent traps.

Figure 2. Trap placement at of Bissel Point spawning shoal at Otsego Lake, NY.

- 278 - RESULTS A total of 273 lake trout fry were captured during this 38 day study. Adjusting catch per unit effort, the circle rigid traps outperformed the other traps, catching 3.61 fry/day (Figure 3). The circle rigid catch per trap-day was 2.6 times higher than the circle mesh traps used at this site since 2003. out-fishing the square rigid traps by 4.4 times (P < .001 Test, Figure 2). Overall, the circle rigid traps caught 62% of the fry, followed by the circle mesh traps (24%) and the square rigid traps (14%).

Trap Design vs. Fry Captured/Trap/Day 4.00 3.50 3.00 2.50 2.00 1.50

Fry/trap/day 1.00 0.50 0.00 Circle Rigid Circle Mesh Square Rigid Trap type

Figure 3. Catch/trap/day for the three trap designs tested.

The circle rigid emergent fry traps outperformed the other traps at all depths (Figure 4). The circle rigid trap performed particularly well at the shallowest (30 cm) depth.

- 279 - Emergent Fry Captured at 30, 60, & 90 cm 3.50 Circle Rigid 3.00 Circle Mesh

2.50 Square Rigid

2.00

1.50

Fry/trap/day 1.00

0.50

0.00 30 cm 60 cm 90 cm Depth

Figure 4. Lake Trout fry captured per trap/day in different traps at different depths (30, 60, 90 cm).

DISCUSSION

Lake trout fry swim up from the substrate to the surface to gulp air to fill their swim- bladders (Tait 1960, Balon 1980). The underlying assumption with fry emergence traps is that they capture all the fry swimming up from the area of substrate directly underneath the trap. Therefore, recruitment from a particular spawning shoal can be calculated by multiplying the shoal area by the number of fry captured in the area covered by the trap. However, the circle rigid traps which covered the same areas as the circle mesh traps captured 2.6 times as many fry. This would indicate that lake trout recruitment from Bissel Point shoal was 2.6 times higher than previously thought using the circle mesh traps (Tibbets 2007, Sawick and Foster 2014, Lucykanish and Foster 2015, Casscles et al. 2016).

There is some evidence the lake trout sac fry and fry move laterally along the substrate before swimming up at relatively shallow depths to gulp air (Baird and Krueger 2000). A consistent inshore pattern of movement may explain their vulnerability to the circle rigid traps that were particularly effective in shallow water (Krueger et al. 1995).

Since 2003, all studies of lake trout emergence in Otsego Lake have utilized the circle mesh traps (Tibbits 2007, Sawick and Foster 2014, Lucykanish and Foster 2015, Casscles et al. 2016). This study clearly shows that the circle rigid trap design was the most effective at capturing lake trout emergent fry. The circle rigid traps were also the easiest to use, held their structure best in onshore waves and current, and were the most effective at capturing lake trout emergent fry at the shallowest depths. Future studies of lake trout reproduction, egg survival and recruitment that are dependent on lake trout fry emergence data should utilize the circle rigid trap design.

- 280 - REFERENCES

Baird, O.E. and C.C. Krueger. 2000. Behavior of lake trout sac fry: Vertical movement at different developmental stages. J. Great Lakes Res. 26(2):141–151.

Balon, E.K. 1980. Early ontogeny of the lake charr, Salvelinus (Cristivomer) namaycush. In Charrs: Salmonid fishes of the genus Salvelinus, ed. E. K.Balon, pp. 485–562. The Hague, The Netherlands: Dr. W. Junk Pub.

Casscles, J.B. and J.R. Foster 2016. Effects of zebra mussels (Dreissena polymorpha) on lake trout (Salvelinus namaycush) fry recruitment in Otsego Lake. In 48th Ann. Rpt. (2015) SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta.

Collins, J.J. 1975. An emergent fry trap for lake spawning salmonines and coregonines. Prog. Fish-Cult.37:140–142.

Fraley, J. J., M.A. Gaub and J.R. Cavigli. 1986. Emergence trap and holding bottle for the capture of salmonid fry in streams. North American Journal of Fisheries Management 6(1) 119-121.

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

Krueger, C.C., D.L. Perkins, E.L. Mills and J.E. Marsden. 1995. Predation by alewives on lake trout fry in Lake Ontario: role of an exotic species in preventing restoration of a native species. J. Great Lakes Res. 21(Supplement 1):458–469.

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

Lucykanish, D.M. and J. R. Foster. 2015. 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.and M.A. Chotkowski. 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. Ellrott, R.M., Claramunt, J.L. Jonas and J.D. Fitzsimons. 2005. A comparison of lake trout spawning, fry emergence, and habitat use in Lakes Michigan, Huron, and Champlain. J. Great Lakes Res. 31:492–508.

Sawick, N.M. and Foster J. R. 2014. Natural recruitment of lake trout (Salvelinus namaycush) in Otsego Lake. In 46th Ann. Rpt. (2013). SUNY Oneonta Biol. Fld. Sta. SUNY Oneonta.

Stauffer, T.M. 1981. Collecting gear for lake trout eggs and fry. The Progressive Fish-Culturist 43(4) 186-193.

- 281 - Tait, J.S. 1960. The first filling of the swim bladder in salmonids. Can J. Zool. 38:179–187.

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.

- 282 - Dynamics of Galerucella spp. and purple loosestrife (Lythrum salicaria) in Goodyear Swamp Sanctuary, summer 2016 update

Holly Waterfield1

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, each marked by four visible stakes (Figure 1). The locations of Quadrats 3 and 5 were estimated due to missing corner posts.

1 CLM. Research Support Specialist, SUNY Oneonta Biological Field Station, Cooperstown, NY.

- 283 -

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 25 May 2016. 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 06 September 2016, 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%

- 284 - 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 (25 May 2016) In contrast to most years since 1998, no life stages of the Galerucella beetle were observed in any quadrat in 2016 (Figures 2, 3, 4). Spring weather conditions combined with low loosestrife abundance may have contributed to the low abundance of beetles.

2 Abundance category Abundance 1

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

Abundance category Abundance 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.

- 285 - 6

1 Abundance category Abundance

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 2016 was lower than in recent years, and substantially lower than abundance estimates (based on stem counts) prior to 2008 (Figure 5). Estimated percent cover remains low, with stems of loosestrife observed in two of five quadrats (Figure 6). No damage from herbivory was perceptible on the few stems present (Figure 7).

100 90 80 70 60 50 40

Number Number Stems of 30 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.

- 286 - 70 point

- 60 50 40 30 20 10

Frequency Category Category Mid Frequency 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.

70 point - 60 50 40 30 20 10

Frequency Category Category Mid Frequency 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 (6 September 2016)

The number of L. salicaria stems and estimated percent cover remained low again in 2016 (Figures 8 and 9, respectively). A total of four loosestrife stems were observed within the quadrats, none of which had inflorescences (flower cluster) present at the time of the survey. Areas of dense loosestrife stems could be seen elsewhere in the swamp and, as in most years, some stems of L. salicaria were in bloom.

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 typically followed by a year with lower beetle abundance (Figures 2, 3, 4) and herbivory (percent damage, Figure 7).

- 287 - 120

100

Number Stems of Number NA 0

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 8. Number of purple loosestrife stems per quadrat during fall monitoring, 1997, 2000- 2016. Flooding in fall 2011 precluded sampling.

100 90 point - 80 70 60 50 40 30 20 10 NA Frequency Frequency Category Mid 0

quadrat 1 quadrat 2 quadrat 3 quadrat 4 quadrat 5

Figure 9. Estimated percent cover (category midpoints) of purple loosestrife during fall monitoring, 1997, 2000-2016. Categories as presented in Table 1. Flooding in fall 2011 precluded sampling.

CONCLUSIONS

Spring 2016 monitoring indicated that L. salicaria abundance continues to be less than in most years since monitoring began in 1997, based on percent cover and number of stems. 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. In the years since their introduction no unintended herbivory has been documented. A balance between L. salicaria control and Galerucella population maintenance exists; the beetles have been resilient in times of low food supply and are able to respond during periods of increased loosestrife abundance. Continued control of loosestrife by Galerucella indicates that the beetles are an effective biological agent against the invasive plant.

- 288 - 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).

- 289 - Progress report on the study of Neoechinorhynchus (Acanthocephala) in fishes from Otsego Lake and elsewhere

Maggie Doolin1 and Florian Reyda2

INTRODUCTION

The phylum Acanthocephala (thorny-headed worms) is a group of obligate intestinal parasites consisting of ~1,300 species that infect a wide variety of host animals (Gazi et al. 2016). Since these worms can severely inhibit proper host intestinal function and hinder host fitness, understanding their life cycles, distributions, and methods of management is of economic interest (Crompton a Nickol 1985). The acanthocephalan genus Neoechinorhynchus contains 115 species that infect fishes and turtles worldwide. Neoechinorhynchus species are characterized in their possession of an attachment structure called a proboscis, with three rows of hooks, the anterior of which is the largest (Figure 1). In North America, 33 species within this genus have been recorded from freshwater fish. These worms are very common in several types of fish, including important game fish like bass and perch, but there is a severe lack of knowledge about most species’ geographic ranges; little information is available with which to identify these worms down to species level.

Since there is no genetic sequence data available for United States members of this genus that parasitize fish, and the current identification keys are nearly impossible to use without expert knowledge of the genus, the group is relatively inaccessible for non-experts. We aim to change this. Our work will allow us to understand diversity and species relationships of Neoechinorhynchus species of Otsego Lake and elsewhere. Plus, on a broader scientific scale, we will add comparative morphological measurement data and the first bank of DNA sequence data to the knowledge base of the genus in the USA. With the combination of morphological observation and molecular investigation, we will better be able to define species boundaries (i.e., which features, either qualitative or quantitative, distinguish one species from another) than if using morphological data alone. The relationships of individuals in the evolutionary tree will serve as clues of where to look for morphological variation. Then, if morphological identification fails, especially for those looking to get fast identification results, future researchers could use sequence-based identifications.

Since the inception of Dr. Reyda’s laboratory at the Biological Field Station in 2008, researchers have recovered Neoechinorhynchus specimens from several types of fish in Otsego Lake and other nearby water bodies, but species identifications have proven challenging. We are addressing this challenge with a direct confrontation of the inadequate information available about Neoechinorhynchus, in a project that aims to understand species diversity and relationships in Neoechinorhynchus in several states of the United States. This report shares preliminary findings of this work as it relates to Otsego County fishes from the families Catostomidae, Centrarchidae, Esocidae, Ictaluridae, and Percidae. Based on the literature that is currently available about this genus, we expected to find variable host specificity (i.e., some species would

1 Graduate Student, Biology Department & Biological Field Station, SUNY Oneonta. 2 Associate Professor and Researcher, Biology Department & Biological Field Station, SUNY Oneonta.

- 290 - be recovered from several fish species, while others may be restricted to only one host species), with the highest degree of specificity exhibited by worms that infect catostomid fishes (Amin and Heckmann 2009, Van Cleave 1949, Lynch 1936).

To this point, the proposed species identifications are tentative diagnoses based on a combination of morphological identification via the available keys and preliminary molecular results (Amin and Heckmann 2009, Amin 2013). The results of molecular analyses are not reported here because we are still in the preliminary stages of interpretation, but they will be an important component of final species identifications. The results of this study will form part of author M. Doolin’s Master’s degree.

METHODS

Fish were collected via angling, electrofishing, trap netting, and haul seining between 2008 and 2016 for investigation and kept, live, in aquaria at the Biological Field Station until the time of dissection. On the day of dissection, fish were anesthetized in a solution of Tricaine Methanosulfonate and tap water (ratio of 0.3g/L) for 10 minutes, measured to the nearest millimeter, photographed for the laboratory’s host records, and then examined. This animal handling protocol has been approved by the SUNY Oneonta IACUC [Institutional Animal Care and Use Committee] as protocol 201303. After discovery, worms were preserved in either 95% ethyl alcohol and then kept at 4°C (for DNA sequencing), or kept in tap water and then transferred to buffered formalin (for morphological study).

Permanent whole mount slides of parasites were prepared after transfer to 70% ethanol solution by staining in Semichon’s acetocarmine and then dehydrating with a graded ethanol/water treatment. Specimens were then cleared with methyl salicylate and mounted in Canada balsam on glass slides (25x75mm) with glass coverslips (22x22mm). Slides are kept in the Reyda laboratory’s permanent slide collection. Mounted specimens were measured and compared qualitatively using a Leica DM2500 compound light microscope (Leica Microsystems, Wetzlar, Germany) and associated LAS computer software.

Specimens kept for molecular work were vouchered and then held in 1 mL snap-top Eppendorf tubes until the time of preparation for sequencing. Forty-seven of 94 samples were sent to us by collaborator Kyle Luth (Wake Forest University) to be included in our molecular analyses. All molecular work was conducted by author M. Doolin at the Smithsonian National Museum of Natural History (NMNH) in their laboratories. Doolin extracted genomic DNA from all specimens, and then carried out PCR for the nuclear gene 28S in four installments, using primers outlined by García-Varela and Nadler (2006). After receiving DNA sequence data, M. Doolin used the software program Geneious, version R10, to assemble raw data into whole gene sequences and then to assemble an evolutionary tree.

- 291 - RESULTS

Neoechinorhynchus specimens were recovered from fishes of five families (Catostomidae, Centrarchidae, Esocidae, Ictaluridae, and Percidae) from Otsego Lake, the Susquehanna River (Cooperstown, NY), Moe Pond, Canadarago Lake, and a tributary of Otsego Lake (e.g. Cripple Creek, above Clark Pond). Based on morphological and molecular data, we have isolated four distinct species of Neoechinorhynchus (Figure 1). Two of these species have been identified as species already described in the literature: N. cylindratus and N. tenellus. Neoechinorhynchus cylindratus (Figure 1A) specimens were recovered from intestinal tracts of centrarchid fishes including largemouth bass (Micropterus salmoides), smallmouth bass (Micropterus dolomieu), and rock bass (Ambloplites rupestris), and from intestinal tracts and livers of brown bullhead, a member of the catfish family. Neoechinorhynchus tenellus (Figure. 1B) was recovered from intestinal tracts of fishes of the perch family – yellow perch (Perca flavescens) and walleye (Sander vitreus)– as well as from chain pickerel (Esox niger), which is a member of the pike family. Two Neoechinorhynchus species were recovered from white suckers, and are currently referred to as Neoechinorhynchus sp. 1 (Figure 1C) and Neoechinorhynchus sp. 2 (Figure 1D). Neither were fully identified to species because they do not match any known species of Neoechinorhynchus. As such, they may be new to science. Members of Neoechinorhynchus sp. 1 were also recovered from white suckers in New Hampshire, differing from historical accounts of the N. cristatus being the only member of the genus recovered from white suckers in that region (Bullock 1963, Muzzall 1980).

- 292 -

Figure 1. Proboscides of Neoechinorhynchus species currently under study. A. N. cylindratus ex largemouth bass from Moe Pond. B. N. tenellus ex yellow perch from Otsego Lake. C. Neoechinorhynchus sp. 1 ex white sucker from North River near Pawtuckaway Lake, New Hampshire. D. Neoechinorhynchus sp. 2 ex white sucker from Cripple Creek (Otsego Lake tributary). All scale bars = 100 m.

- 293 - DISCUSSION

Parasites are incredibly important to the healthy function of an ecosystem (Marcogliese 2005). If we lack knowledge about their diversity and function in their hosts, then we are shortchanging ourselves of important information that could contribute to the understanding of the success and preservation of their greater ecosystems, including grasping food web complexities and understanding tolerance of both parasites and their hosts of environmental stressors. Because Neoechinorhynchus is such a common and widespread parasite in the United States, its success could shed light on these ecosystem traits, and has already done so in our studies of Otsego County water bodies.

Neoechinorhynchus identification keys are challenging to use, and it is possible that the diversity of the genus has not been fully described in the literature. Therefore, we were able to identify only two of four species recovered to species level. Neoechinorhynchus cylindratus, which was originally described from largemouth bass and which we found in largemouth bass, is reported to occur in centrarchid fishes across the United States (Hoffman 1999). So far, our evidence corroborates these reports, and we have identified the worms from centrarchids and from brown bullhead of Otsego and Canadarago Lakes and Moe Pond as this species. Neoechinorhynchus tenellus, which was recovered from chain pickerel and percid fishes in our local waters, has been reported from a wide geographic range and from these hosts (Hoffman 1999). It was therefore not surprising to encounter either of these species.

The more curious results of this investigation center around the species that infect white suckers in the region, since neither can be identified as a species already defined in the literature. It is possible that both species from white suckers are new to science, but more investigation is required. We plan to collect Neoechinorhynchus from several more locations as part of this work, to include in morphological and molecular investigations, so more may be revealed about the identity of these species as our work continues. Some species have exhibited disjointed distributions in the United States (Bullock 1955, Lynch 1936), so collecting specimens from a wide geographic range could add information that helps to identify local Otsego County species and reveal their relationships to other sucker parasites in the genus.

In addition to simply recognizing species, we are interested in better categorizing the range of host specificity in this genus. There is considerable variation in host specificity of the Neoechinorhynchus species recovered from our local collection efforts. From the more general N. cylindratus and N. tenellus, which parasitize hosts in different families, to the much more host specific two unidentified species that parasitize white suckers in these water bodies, it is clear that there is not a consistent level of host specificity across the whole genus. Out of the 33 described Neoechinorhynchus species that parasitize freshwater fishes of North America, thirteen are described from sucker hosts – a disproportionately large number from one single fish family (Amin 2009). This may be a sign that Neoechinorhynchus and Catostomidae have had a special relationship over evolutionary time, resulting in more worm diversity and higher host specificity in the sucker lineages, compared to Neoechinorhynchus species that infect fish of other families. The molecular analyses will enable us to test whether the morphological characters that have traditionally been used to distinguish species of the genus represent true underlying homology and are of phylogenetic value. By extension, the DNA sequencing involved in this work will provide valuable insight into these species relationships and the amount of divergence between

- 294 - species, allowing comparison between that of sucker parasites and those that parasitize other fish.

In the future, author M. Doolin will sequence the mitochondrial gene Cytochrome Oxidase I (COI) and the nuclear gene ITS for the worm specimens for which we have collected DNA sequence data for 28S. Plus, after further collections over a large geographic range this spring and summer, 94 more individuals from fishes from New York, Nebraska, Oklahoma, Louisiana, and Mississippi will be used in further sequencing efforts for all three genes during summer 2017. With sequence data and morphological investigation from all hosts and locations represented in the evolutionary tree that results from this molecular work, we hope to be able to make definitive diagnoses for every species and to better understand the genus as a whole. Without question, we will improve understanding of relationships within Neoechinorhynchus by providing the first hypothesis (through evolutionary trees) of the evolutionary relationships between species in this genus.

ACKNOWLEDGEMENTS

We extend thanks to the New York Department of Environment and Conservation, especially Tim Pokorny and Scott Wells, for assisting us with fish collections. Plus, we thank Tom Brooking for supplying us with fish from Oneida Lake via the Cornell University Biological Field Station at Shackleton Point. Members of the Reyda laboratory between the years 2008 and 2016 contributed specimens and slides to this study. This work was supported in part by student and faculty research grant awards from the SUNY Oneonta Research Foundation, and from an NSF Field Stations and Marine Laboratories Grant (DBI No. 1034744).

REFERENCES

Amin, O.M. 2013. Classification of the Acanthocephala. Folia Parasitologica 60(4):273-305.

Amin, O.M. and R.A. Heckmann. 2009. Description of Neoechinorhynchus (Neoechinorhynchus) buckneri n. sp. (Acanthocephala: Neoechinorhynchidae) from the Blacktail Redhorse Moxostoma poecilurum (Catostomidae) in the Tchoutacabouffa River, Misissippi, U.S.A., with a key to species of Neoechinorhynchus with different dorsoventral body wall thickness. Comparative Parasitology 76(2):154-161.

Bullock, W.L. 1955. The occurrence of Neoechinorhynchus cristatus Lynch, 1936, (Acanthocephala) in eastern North America. Journal of Parasitology 41(3):323-324.

Bullock, W.L. 1963. Neoechinorhynchus prolixoides n. sp. (Acanthocephala) from North American fishes. Proceedings of the Helminthological Society of Washington 30(1): 92- 96.

Crompton, D.W.T., & B.B. Nickol. 1985. Biology of the Acanthocephala. Cambridge University Press. 519 p.

- 295 - García-Varela, M. and S.A. Nadler. 2006. Phylogenetic relationships of Palaeacanthocephala (Acanthocephala) inferred from SSU and LSU rDNA gene sequences. Journal of Parasitology 91(6): 1401-1409.

Gazi, M., J. Kim, M. García-Varela, C. Park, D.T.J. Littlewood, and J. Park. 2016. Mitogenomic phylogeny of Acanthocephala reveals novel class relationships. Zoologica Scripta 45(4): 437-454.

Hoffman, G.L. 1999. Parasites of North American freshwater fishes. Cornell University Press, 2nd Ed. 576 p.

Lynch, J.E. 1936. New species of Neoechinorhynchus from the Western Sucker, Catostomus macrocheilus Girard. Transactions of the American Microscopical Society 55(1): 21-43.

Marcogliese, D. 2005. Parasites of the superorganism: Are they indicators of ecosystem health? International Journal for Parasitology 35(7): 705-716.

Muzzall, P.M. 1980. Ecology and seasonal abundance of three acanthocephalan species infecting white suckers in SE New Hampshire. Journal of Parasitology 66(1): 127-133.

Van Cleave, H.J. 1949. The acanthocephalan genus Neoechinorhynchus in the catostomid fishes of North America, with descriptions of two new species. Journal of Parasitology 35(5): 500-512.

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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. No. 51. Comprehensive lake management plan, Lake Moraine, Madison County, NY. Benjamin P. German. 2016. No. 52. Determining effective decontamination methods for watercraft exposed to zebra mussels, Dreissena polymorpha (Pallas 1776), that do not use hot water with high pressure spray. Eric A. Davis. 2016. No. 53. The state of Brant Lake, & Brant Lake management plan. Alejandro Reyes. 2016.

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