Identifying Abiotic and Biotic Factors Associated with Leedy's Roseroot

Identifying Abiotic and Biotic Factors Associated with Leedy’s Roseroot (Rhodiola integrifolia

subsp. leedyi (Rosend. & J.W. Moore) Kartesz) at Glenora Cliffs, Glenora, New York

Kali Z. Mattingly

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A thesis submitted in partial fulfillment of the requirements for the Master of Science Degree State University of New York College of Environmental Science and Forestry Syracuse, New York May 2016

Approved: Department of Environmental and Forest Biology

______Donald J. Leopold, Department Chair Douglas Morrison, Chair & Major Professor Examining Committee

______S. Scott Shannon, Dean The Graduate School ProQuest Number: 10130756

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ProQuest 10130756

Published by ProQuest LLC (2016). Copyright of the Dissertation is held by the Author.

ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 ACKNOWLEDGEMENTS

Several people have been fundamental in the completion of this thesis. I thank my major professor, Don Leopold, for the opportunity to join his lab and for his guidance over the past two years. I am grateful to John Wiley for initiating this project and seeing it through its conclusion. I thank Russ Briggs for feedback on soils and statistics. Thanks to Joel Olfelt for sharing his wealth of knowledge on Leedy’s roseroot and for letting me accompany his field crew in Minnesota. Thanks also to Phil Delphey and Cheryl Mayer for helping me access the western populations of Leedy’s roseroot. I am thankful for the landowners who allowed me to work on their property, especially Jeff and Laurie Morris, Sayre Fulkerson and family, and Boyd Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 McDowell. For their facilitation of side projects, I thank Danny Fernando and Terry Ettinger.

Census data for Chapter 2 was provided by Steve Young and the New York Natural Heritage Program. Data for Chapter 3 was collected by Frances Delaney in 2012 and by Jacob Peregrim in 2013, and this thesis would be incomplete without their contributions. I am thankful for those who assisted me with fieldwork: Chloe Blaisdell, Josh Crane, Baylee Earl, Gavan Greco, Brianna Rosamilia, Justin Vargas, and especially Mason Clark. For teaching me how to rappel I am grateful to Syracuse University Outing Club and Nick Whites.

I thank the Leopold lab for their daily support: Grete Bader, Kay Hajek, Kristen Haynes, James Johnson, Catherine Landis, Toby Liss, Alex Petzke, Jess Seville, Robert Smith, and Justine Weber. I am also grateful to the members of Ecolunch for sharing their exciting research every week and for their feedback on my own project, especially Monica Berdugo, Martin Dovciak, Greg McGee, Luka Negoita, and Jay Wason.

Thank you to my mother and father, Anita and Bryan Mattingly, and my grandmothers, Lynne Mattingly and Ninette Amis, all retired schoolteachers who collectively taught for 130 years and worked with at least 100,000 young people. I am thankful I got to be their student, too.

Funding for this research was provided by the U.S. Fish and Wildlife Service - Great Lakes Restoration Initiative, SUNY-ESF, the Edna Bailey Sussman Foundation, the Alpha Lambda Delta Christine Conway Fellowship, the Alpha Omicron Pi Nu Iota Scholarship, and the New York Flora Association Grant.

ii TABLE OF CONTENTS

Page

LIST OF TABLES ...... iv LIST OF FIGURES ...... v LIST OF APPENDICES ...... vii ABSTRACT ...... viii Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 CHAPTER 1: INTRODUCTION TO THESIS ...... 1 Rarity ...... 1 Study Species ...... 2 Purpose of Thesis ...... 9 Study Area ...... 10 Literature Cited ...... 11

CHAPTER 2: POPULATION TRENDS AND ABIOTIC FACTORS ASSOCIATED WITH LEEDY’S ROSEROOT IN NEW YORK STATE ...... 16 Abstract ...... 16 Introduction ...... 16 Methods...... 19 Results ...... 27 Discussion ...... 34 Conclusion ...... 45 Literature Cited ...... 45

CHAPTER 3: BIOTIC FACTORS AND IMPACT OF THE INVASIVE SPECIES JAPANESE KNOTWEED ON LEEDY’S ROSEROOT IN NEW YORK STATE ...... 53 Abstract ...... 53 Introduction ...... 53 Methods...... 57 Results ...... 66 Discussion ...... 74 Conclusion ...... 83 Literature Cited ...... 84

CHAPTER 4: CONCLUSION TO THESIS ...... 91 Literature Cited ...... 94

APPENDICES ...... 95

VITA ...... 105

iii LIST OF TABLES Page

Table 1.1 - Summary of demographic and ecological information for Leedy’s roseroot populations. Functionally extinct population at Watkins Glen, NY, omitted. Total and effective population sizes for: Whitewater WMA, Simpson Cliffs, and Deer Creek based on census and harmonic mean demographic data from Ejupovic (2015); Glenora Cliffs and Bear Creek from Olfelt et al. (1998) census. Population sizes for Glenora Falls from USFWS (1998), Harney Peak from Cheryl Mayer (pers. comm.). Site characteristics from USFWS (1998) and pers. obs...... 2 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Table 2.1 - Summary of response variables, explanatory variables, and random effects included in models for census data ...... 23

Table 2.2 - Summary of response variables (Y), explanatory variables (X), and random effects (ai) included in models for: A) Leedy’s roseroot abiotic factor characterization, B) changes in temperature over time, C) cliff face abiotic factor characterization ...... 24

Table 2.3 - Total Leedy’s roseroot count, total fertile individual count, and flowering rate across years sampled ...... 28

Table 2.4 - Model coefficients and statistics for the fixed effects of the LMM describing proximity to Leedy’s roseroot as a function of multiple explanatory variables ...... 31

Table 3.1 - Summary of variables included in Japanese knotweed interaction analysis ...... 63

iv LIST OF FIGURES Page

Figure 1.1 - Top: geographic distribution of Leedy’s roseroot populations. Bottom: three disjuncts with point diameters scaled by number of individuals in each state (Table 1.1). Maps made in ggmap package (Kahle and Wickham 2013) for R (R Development Core Team 2016). Tiles by Stamen Design (2016b) ...... 3

Figure 1.2 - Adapted from USFWS (1998), a stylized depiction of Leedy’s roseroot growing at a seeping crevice on a typical north-facing maderate cliff in Minnesota ...... 5 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

Figure 1.3 - Left: geographic location of the Glenora Cliffs population in box and NOAA weather stations referenced in this thesis, at Penn Yan (2016a) and Mecklenburg, NY (2016b) marked with stars. Right: private property parcels having Leedy’s roseroot at Glenora, NY. Maps made in ggmap package (Kahle and Wickham 2013) for R (R Development Core Team 2016). Tiles by Stamen Design (2016a) ...... 5

Figure 2.1 - Cliff sampling transect, adapted from Harkey (2013) citing Smith (1998). Plot names are the factor location on cliff ...... 22

Figure 2.2 - Total Leedy’s roseroot counts by fertility for each year censused. Fertile individuals were male or female plants with flowers. Sterile individuals lacked flowers ...... 28

Figure 2.3 - Mean number of Leedy’s roseroot individuals across years, grouped by size and fertility. Error bars show standard deviation of the mean. Size classes were as follows: S, small <2 dm2; M, medium, 2 to 5 dm2; L, large, >5 dm2. Fertile individuals were male or female plants with flowers, and sterile individuals lacked flowers ...... 29

Figure 2.4 - Boxplots of significantly different abiotic factors for Leedy’s plots compared to non- Leedy’s plots: A) seepage, B) daily mean temperature (ºC), C) daily max temperature (ºC), D) PAR (µmol/m2/s), E) weathering, F) Distance from anchor (m). Boxes contain values within the first and third quartiles, and the bold line shows the mean ...... 31

Figure 2.5 - Length of the longest Leedy’s roseroot stem (cm) as a function of daily mean temperature (ºC). Line fitted represents the fixed component of the LMM, shown with 95% confidence interval (dark grey) and 95% prediction interval (light grey) ...... 32

Figure 2.6 - Boxplots comparing mean weekly temperatures (ºC) for Leedy’s plots and non- Leedy’s plots. Weeks span May 25-October 2. Asterisks above boxplot pairs represent significantly higher mean temperatures for Leedy’s plots, based on the Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for LMM. Boxes contain values within the first and third quartiles, and the bold line shows the mean ...... 32

Figure 2.7 - Daily mean temperature (ºC) as a function of distance from anchor point (m). Line fitted represents the fixed component of the LMM, shown with 95% confidence interval (dark grey) and 95% prediction interval (light grey) ...... 33

Figure 3.1 - Layout of a Japanese knotweed sampling block, consisting of three treatment plots ...... 59

Figure 3.2 - Boxplots comparing Japanese knotweed SBA across treatments and time. Pre- treatment years were 2012 and 2013. Post-treatment years were 2014 and 2015. Asterisks represent significantly lower SBA within a year, based on the Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for LMM. Boxes contain values within the first and third quartiles, and the bold line shows the mean ...... 68 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Figure 3.3 - Boxplots comparing log-transformed total flowering Leedy’s roseroot count across treatments and time. Different letters represent significant differences, based on Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for LMM. Boxes contain values within first and third quartiles, and bold line shows the mean ...... 68

Figure 3.4 - Boxplots comparing log-transformed Leedy’s roseroot mean stem length (cm) across treatments and time. Different letters represent significant differences, based on Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for LMM. Boxes contain values within first and third quartiles, and bold line shows mean ...... 69

Figure 3.5 - Boxplots comparing cliff temperatures for: A) pre-treatment (2012) cliff max iButton temperatures (ºC), B) post-treatment (2015) cliff rock temperatures (ºC). Uninvaded plot temperatures were not sampled post-treatment. Asterisks represent significantly higher temperature across treatments, based on Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for LMM (A and B analyzed with separate LMMs). Boxes contain values within first and third quartiles, and bold line shows mean ...... 69

Figure 3.6 - Boxplots comparing talus PAR (µmol/m2/s) across treatments and time. Asterisk represents significantly higher PAR based on the Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for the LMM. Boxes contain values within the first and third quartiles, and the bold line shows the mean ...... 70

Figure 3.7 - Boxplots comparing species richness at discrete locations on the cliff. Different letters represent significant differences between locations, based on the Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for the LMM. Boxes contain values within the first and third quartiles, and bold line shows mean ...... 70

Figure 3.8 - Species for NMS ordination of 69 plots, final stress=0.152. Polygons for Leedy’s plots (small inner polygon) and non-Leedy’s plots (large polygon) overlaid. Key to species codes in Appendix F ...... 72

Figure 3.9 - Species for NMS ordination of 69 plots, final stress=0.152. Polygons for three locations on cliff overlaid. Key to species codes in Appendix F ...... 73

Figure 3.10 - NMS ordination of 69 plots, final stress=0.152. Daily min temperature (°C) smoother overlaid ...... 74

vi LIST OF APPENDICES Page

Appendix A - Cliff stability estimation protocol, adapted from Benumof and Griggs (1999). The variable total stability was calculated for a plot by adding the numbers for each category ...... 95

Appendix B - Chapter 2 results of Tukey’s multiple comparisons of least-squared means of LMMs describing: 1) Leedy’s roseroot total as a function of size (degrees of freedom asymptotic), 2) Leedy’s roseroot total as a function of the interaction between size and fertility (degrees of freedom asymptotic), 3) mean weekly temperature as a function of the interaction Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 between week and Leedy’s roseroot presence-absence, 4) maximum weekly temperature as a function of the interaction between week and Leedy’s roseroot presence-absence ...... 96

Appendix D - Chapter 3 summaries of model coefficients for fixed effects of the LMM describing: 1) Japanese knotweed SBA as a function of treatment and time, 2) Japanese knotweed stem diameter as a function of treatment and time, 3) Japanese knotweed stem density as a function of treatment and time, 4) Japanese knotweed percent cover as a function of treatment and time, 5) Japanese knotweed stem diameter as a function of time, 6) Japanese knotweed stem density as a function of time, 7) log-transformed total Leedy’s roseroot as a function of the interaction between treatment and time, 8) log-transformed total flowering Leedy’s roseroot as a function of the interaction between treatment and time, 9) log-transformed mean Leedy’s roseroot stem length as a function of the interaction between treatment and time, 10) pre-treatment cliff max temperature as a function of treatment, 11) post-treatment cliff rock temperature as a function of treatment, 12) post-treatment talus rock temperature as a function of treatment, 13) talus PAR as a function of treatment and time, 14) species richness as a function of location on cliff ...... 98

Appendix F - Species codes associated with NMS ordination matrix ...... 104

vii ABSTRACT

K.Z. Mattingly. Identifying Abiotic and Biotic Factors Associated with Leedy’s Roseroot (Rhodiola integrifolia subsp. leedyi (Rosend. & J.W. Moore) Kartesz) at Glenora Cliffs, Glenora, New York, 106 pages, 6 tables, 20 figures, 2016.

Leedy’s roseroot (Rhodiola integrifolia subsp. leedyi) is a cliff-dwelling glacial relict federally- listed as threatened in the U.S. This research investigated abiotic and biotic factors correlated with Leedy’s roseroot at Glenora Cliffs, Glenora, NY. Results of censuses (2003-2015) showed decreased Leedy’s roseroot flowering in some sections of Glenora Cliffs. Abiotic factors Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 correlated with Leedy’s roseroot occurrence included increased seepage, light, and summer temperatures, decreased weathering, and lower heights on the cliff. Analysis of the Glenora Cliffs community suggested Leedy’s roseroot co-occurs with other cliff-dwelling species. Experimental removal of Japanese knotweed (Fallopia japonica var. japonica) in a three- treatment block design revealed Japanese knotweed was associated with decreased flowering in Leedy’s roseroot. Herbicide removal of Japanese knotweed did not restore Leedy’s roseroot flowering, but was associated with increased temperature and light and shorter Leedy’s roseroot stems. Continued monitoring of Glenora Cliffs is recommended.

Key words: cliffs, invasive species, niche, Rhodiola integrifolia, Sedum

K. Z. Mattingly Candidate for the degree of Master of Science, May 2016 Donald J. Leopold, Ph.D Department of Environmental and Forest Biology State University of New York College of Environmental Science and Forestry Syracuse, NY Donald J. Leopold, Ph.D ______

viii CHAPTER 1: INTRODUCTION TO THESIS Rarity The study of rare plant species has long captivated ecologists, both applied and theoretical, at a variety of scales (Kunin and Gaston 1997). Applied scientists are concerned with biodiversity protection, and biodiversity’s positive effects on ecosystem services, productivity, and stability (Loreau et al. 2001). Conservation of rare plant species is increasingly relevant given current anthropogenically-accelerated species extinctions (MEA 2005), that rare species Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 are more at risk for extinction than common species (MacArthur 1972), and the potential effects of climate change (Lavergne et al. 2006). Rarity has been defined in many ways but is often intuitively understood as referring to taxa with small abundances and/or ranges (Gaston 1994). For plant species, abundance or distribution may be limited by several factors including historical influences like land use and glacial episodes (Smidova et al. 2010; Lavergne et al. 2006), a plant’s own ability to reproduce and disperse (Kunin and Gaston 1997), and external abiotic and biotic factors occurring along temporal and spatial gradients (Wieger Wamelink et al. 2014). Many researchers have tried to generalize factors most often contributing to rarity (Kunin and Gaston 1997), but this effort may be overly simplistic; different factors matter for different taxa and at different scales, and conservation requires consideration of species biology (Holsinger and Gottlieb 1991). Extending Hutchinson’s (1957) niche of n-dimensional hyperspace to rare plant species specifically, Brown (1984) describes rare species as inhabiting multidimensional niche space of narrow breadth, or restricted ranges of tolerance along many abiotic and biotic gradients. Such taxa appear rare because they have become highly adapted specialists in order to exploit narrow multidimensional niches (Brown 1984) and/or because the environmental ranges they inhabit are narrowly distributed in the landscape (Holsinger and Gottlieb 1991). A majority of rare species can be described as restricted (Rabinowitz 1981; Rabinowitz et al. 1986), and in recent decades restricted species with restricted habitats have been prioritized for conservation (Rabinowitz et al. 1986; Marcinko 2007). Identifying abiotic and biotic factors most strongly correlated with a rare plant within its realized multidimensional niche provides information about rare plant biology that can inform conservation. Studies focused on life history and ecology of a single rare plant species may be most conducive to conservation but are often dismissed in favor of study species catering to particular research questions (Holsinger and Gottlieb 1991). This thesis aimed

1 to describe the ecology of a rare subspecies by quantifying abiotic and biotic factors associated with its occurrence at the microscale. Identifying factors correlated with occurrence will help prioritize conservation efforts by identifying potential threats that may be associated with changes in important features of this rare plant’s niche. While conservation is my primary concern, my thesis adds to the broader ecological understanding of rare species and their restriction to narrow multidimensional niches.

Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Study Species Geographic range The rare cliff-dwelling plant Leedy’s roseroot, Rhodiola integrifolia Raf. subsp. leedyi (Rosendahl & Moore) Kartesz (Crassulaceae), is restricted to seven disjunct populations spread among New York, Minnesota, and South Dakota (Fig. 1.1). There is high geographic isolation among the three states, though all populations are at similar latitudes. All are non-alpine, with the exception of the most western population at Harney Peak, South Dakota (Table 1.1). Across Leedy’s roseroot range, cliff habitats are composed of widely differing rock types, but all cliffs are approximately ≤30 m in height (Table 1.1), all experience seepage (USFWS 1998, 2015), and all except those in New York have northern aspects (Table 1.1). North-facing habitats were shown by Cooper (1997) to be associated with water-stress intolerant arctic-alpine plant species.

Table 1.1 - Summary of demographic and ecological information for Leedy’s roseroot populations. Functionally extinct population at Watkins Glen, NY, omitted. Total and effective population sizes for: Whitewater WMA, Simpson Cliffs, and Deer Creek based on census and harmonic mean demographic data from Ejupovic (2015); Glenora Cliffs and Bear Creek from Olfelt et al. (1998) census. Population sizes for Glenora Falls from USFWS (1998), Harney Peak from Cheryl Mayer (pers. comm.). Site characteristics from USFWS (1998) and pers. obs. Effective Dominant Elevation Cliff Population name State Pop. size Aspect pop. size rock (m) height (m) Glenora Cliffs NY >6,000 >4750 shale 136-141 ~20 east Glenora Falls NY 40 - shale 135-141 ~30 south Whitewater WMA MN 1019 181.7 limestone 274-378 ~30 north Simpson Cliffs MN 639 338.7 limestone 274-378 ~30 north Deer Creek MN 322 66.1 limestone 274-378 ~30 north Bear Creek MN 173 97 limestone 274-378 ~30 north Harney Peak SD 100 - granite ~2,200 ~30 north

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The four Minnesota populations are located in the Driftless Area of southeastern Minnesota, in Fillmore and Olmsted Counties (USFWS 1998). These populations of Leedy’s roseroot are the most well-studied (Olfelt et al. 1998; Olfelt et al. 2001; Ejupovic 2015), helping to inform Leedy’s roseroot conservation. It is thought that each Minnesota population is genetically distinct; Ejupovic (2015) found no modern gene flow among three of the four Minnesota populations (the fourth was inaccessible, but the author predicted it was too far from the other populations for pollinators to travel among them). The Minnesota populations occur on north-facing streamside cliffs along the Whitewater River (Whitewater Wildlife Management

3 Area) and Root River tributaries (Simpson Cliffs, Deer Creek, and Bear Creek). These cliffs are composed of thin, horizontally layered limestone and bentonite (USFWS 1998); along the Root River tributaries Shakopee Formation dolostones and Galena group limestones are exposed, while the Whitewater River is bordered by bluffs of the more massively bedded Oneota dolomites (Mossler 1999, Jirsa et al. 2011). These maderate cliffs are formed in karst topography by streams and characterized by surface vents releasing cool air from belowground ice caves

(Fig. 1.2; USFWS 1998). In this region the cooling action of maderate cliff vents and seepage Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 from horizontal bentonite crevices support unique vegetation (Erickson 2014), and may create microsites suitable for the glacial relict Leedy’s roseroot (Sather 1993; USFWS 1998). The slopes above the cliffs in Minnesota support hardwood forests dominated by oak (Quercus spp.), maple (Acer spp.), and basswood (Tilia americana) (USFWS 1998), and Clausen (1975) reported bulblet fern (Cystopteris bulbifera), poison-ivy (Toxicodendron radicans), and wild columbine (Aquilegia canadensis) co-occurring with Leedy’s roseroot. Other special concern species recognized by the state of Minnesota (MNDNR 2013) also occur here, including rock cress grass/rock Whitlow grass (Draba arabisans), smooth rock cress (Arabis laevigata var. laevigata), and Wolf’s bluegrass (Poa wolfii) (USFWS 1998). The two New York populations are located in the Finger Lakes region, near Glenora, Yates County, New York (Fig. 1.3). The New York populations have the majority of all living Leedy’s roseroot individuals (Table 1.1). The Glenora Falls population has about 40 individuals located in the southernmost parcel in Fig. 1.3, alongside Big Stream, upstream of its namesake waterfall. The Glenora Cliffs population, the largest existing population of Leedy’s roseroot in the world, has several thousand individuals inhabiting approximately 3.2 km of east-facing cliff along Seneca Lake (USFWS 1998). New York also has one functionally extinct population of Leedy’s roseroot located at Watkins Glen State Park in Watkins Glen, Schuyler County, New York. Clausen (1975) observed seven individuals at this south-facing, apparently dry location in 1961, but by 1972 only one individual persisted. The presence of only one individual implies functional extinction because Leedy’s roseroot is dioecious, and population persistence would require presence of individuals of both sexes. One male individual was still observable as of 2015; whether it is one of the same individuals observed in 1961 or was introduced later as Clausen (1975) suspected, Leedy’s roseroot is quite long-lived, as has been observed for some other plant species growing on cliffs (Larson et al. 2000).

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Figure 1.2 - Adapted from USFWS (1998), a stylized depiction of Leedy’s roseroot growing at a seeping crevice on a typical north-facing maderate cliff in Minnesota.

5 The Glenora Falls and Glenora Cliffs populations are separated from each other by no more than approximately 0.5 km (Fig. 1.3; Ejupovic 2015), and the two are commonly treated as a single population (Olfelt and Furnier 1995; Olfelt et al. 2001). Based on their proximity, gene flow between Glenora Cliffs and Glenora Falls is likely (Ejupovic 2015), and the populations share several site characteristics (Table 1.1). Both cliff faces are predominantly composed of thin, horizontally layered calcareous shale, interrupted by layers of siltstone (USFWS 1998), part of the Utica Shale formation (Rickard et al. 1970). The plant community of Glenora Cliffs Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 follows the redcedar-oak Finger Lakes community type described by Mohler et al. (2006), and also supports rock Whitlow grass (Draba arabisans) (USFWS 1998), another New York State- listed threatened species (Young 2010). The single South Dakota population is the least studied of the Leedy’s roseroot populations. It was identified in 2014 (Olfelt and Freyman 2014) at Harney Peak in the Black Hills. In 2015 I counted 120 individuals at this population, an estimate similar to the USDA Forest Service count of 100 individuals (Cheryl Mayer, pers. comm.). At this site, the cliff is composed of Harney Peak Granite, a calcium-deficient peraluminous granite (Redden et al. 1982), unlike the calcareous cliffs inhabited by Leedy’s roseroot in New York and Minnesota. The granite cliff is also less weathered than rocks composing the cliffs in New York and Minnesota (pers. obs.), and so contains comparatively fewer crevices and sparser, coarser soil, making it less water-permeable (Rejmanek 1971). This habitat experiences seepage, and Olfelt and Freyman (2014) suggested an upslope reservoir as a potential water source, although the local hydrology has not been studied (Cheryl Mayer, pers. comm.). Harney Peak is the only high-elevation population of Leedy’s roseroot, located at approximately 2,200 m above sea level on the highest peak in South Dakota. Leedy’s roseroot at Harney Peak flowers about a month later (pers. obs.) than usually described for the species (NYNHP 2015), presumably due to climate differences associated with increased elevation. High-elevation parts of Harney Peak are also known to experience orographic effects (Driscoll et al. 2002), which may affect precipitation regimes for this Leedy’s roseroot population. The South Dakota Natural Heritage Program identifies two other sensitive species (Marriott 2001) occurring below the cliff inhabited by Leedy’s roseroot, mountain sorrel (Oxyria digyna) and Selkirk’s violet (Viola selkirkii) (Cheryl Mayer, pers. comm.), within a white spruce (Picea glauca var. densata)-dominated forest community (Hoffman and Alexander 1987).

6 Botanical information Leedy’s roseroot has 4-merous (occasionally 3- or 5-merous) dioecious (rarely perfect) red and yellow cymes (Clausen 1975) which peak in early June in New York (NYNHP 2015). Flowers have floral nectaries and are pollinated by bees and hover flies (Clausen 1975). Fertilized pistillate flowers mature into divergently beaked follicles that dehisce in New York by late July (NYNHP 2015) and have numerous small winged, ribbed seeds (Clausen 1975), indicating dispersal by wind, water, and/or gravity. Fleshy stems can reach up to 45 cm in length Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 (NYNHP 2015), and leaves are oblanceolate or obovate, often glaucous, and sometimes toothed (Gleason and Cronquist 1991). All members of Crassulaceae are characterized by slow-growing (Turner et al. 1966) succulent tissues performing crassulacean acid metabolism (CAM) photosynthesis. Clonal reproduction is common for some members of Crassulaceae but is uncommon in Leedy’s roseroot (Olfelt et al. 1998). Members of the genus Rhodiola are common to subartic-alpine areas and are unified by fleshy rhizomes, scale leaves, and dioecious or hermaphroditic (never monoecious) flowers (Ohba 2003). Marcescent flowering stems (stems of perennial plant species that do not senesce at the end of the growing season) are another trait derived within Rhodiola, perhaps adapted to protect rhizomatous buds from winter exposure (Zhang et al. 2014), and sometimes observed for Leedy’s roseroot (Clausen 1975). Succulent tissues and pachymorph (clumping) rhizomes (Stapleton 1994) are water storage adaptations apparently well suited for the subarctic-alpine and cliff habitats of members of Rhodiola (Ohba 2003).

Taxonomic information Leedy’s roseroot is currently recognized as one of three subspecies of R. integrifolia (Moran 2009), the more widespread subspecies of which are located in Siberia and the Rocky Mountains of the Western U.S. and Canada (Olfelt and Freyman 2014). Most recent work on members of Rhodiola have not focused on ecology, as is the goal of this study. Instead, several researchers have studied the phylogenetic relationships of Rhodiola as part of a larger effort to understand biogeographical relationships between the Asian and North American floras (DeChaine et al. 2013; Wen et al. 2010). Several members of Rhodiola are rare, restricted to subarctic-alpine regions or cliffs (Ohba 2003). Consequently, studies of Rhodiola’s phylogeny and biogeography serve as important tools for studying ultimate causes of rarity (Kunin and

7 Gaston 1997). Additionally, genetic research concerning the evolution of dioecy has focused on Rhodiola; only 6% of all angiosperms are dioecious, but dioecy appears to have evolved multiple times within Rhodiola (Zhang et al. 2014). For R. integrifolia, genetic hypotheses go back to Uhl’s (1952) suggestion that R. integrifolia (n=18) was an allopolyploid species hybrid of R. rosea (n=11) and R. rhodantha (n=7). Modern genetic work has suggested the ranges of these two parents may have historically overlapped, either originally hybridizing in Asia and then dispersing into North America (Guest Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 and Allen 2014) or hybridizing after arrival in North America (Hermsmeier et al. 2012, but see Zhang et al. 2014 for critique of Hermsmeier et al. 2012; Olfelt and Freyman 2014). These findings are especially relevant in the context of rarity, given that hybridization events can in some cases decrease genetic diversity (Levin et al. 1996). There are three currently accepted subspecies of R. integrifolia: integrifolia, neomexicana, and leedyi (Moran 2009). Olfelt and Freyman (2014) suggested R. integrifolia be grouped into two separate species: one including Siberian and Alaskan subspecies integrifolia with Leedy’s roseroot, the other consisting of R. integrifolia located in the central and southern Rocky Mountains and including subspecies integrifolia and neomexicana. This work was consistent with Guest and Allen’s (2014) phylogenetic work and DeChaine et al.’s (2013) niche modeling. Resolution of accurate species and subspecies identities for R. integrifolia will require further research regarding morphological variation, and this work is ongoing (K. Agoglossakis, pers. comm.). Subspecies status for Leedy’s roseroot is supported by its unique ecology, restricted to cliffs throughout its range; disjunct geographical occurrence, existing as seven populations spread among New York, Minnesota, and South Dakota; and genetic evidence (Olfelt et al. 2001; Olfelt and Freyman 2014). Considering Leedy’s roseroot a distinct subspecies also warrants its federal protection, thereby aiding its conservation (Olfelt et al. 2001). Indeed, conservation of this unique plant seems crucial: both demographic work (Olfelt et al. 1998; Ejupovic 2015) and analysis of DNA microsatellite markers (Ejupovic 2015) indicate low effective population sizes and decreased genetic diversity for some populations of Leedy’s roseroot, as well as lack of gene flow among populations.

8 Conservation status Due to its rarity and risk of habitat loss (USFWS 1998), Leedy’s roseroot is federally- listed as threatened under the U.S. Endangered Species Act (ESA) (USFWS 1994). NatureServe (2015) assigns Leedy’s roseroot the conservation status of a globally critically imperiled subspecies of a secure species (G5T1) and state endangered status (S1) in New York and Minnesota.

The U.S. Fish and Wildlife Service Federal Recovery Plan (FRP) for Leedy’s roseroot Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 (USFWS 1998) and its Five-Year Review (USFWS 2015) identified threats to Leedy’s roseroot including natural disturbances like inherent cliff instability, erosion, and weather events; climate change; anthropogenic disturbances such as built structures, exacerbation of erosion, and herbicide contamination of groundwater; and encroachment of exotic species invasions, including Japanese knotweed, Fallopia japonica var. japonica, and black swallow-wort, Cynanchum rossicum, in New York. A recent return on investment analysis of ESA-listed species found that numbers of Leedy’s roseroot in the U.S. had declined slightly more often than increased for the period of 1989-2011, and had received only about 11% of the government funds proposed by its FRP, putting Leedy’s roseroot below a threshold of likely recovery and delisting (Gerber 2016). The present study represents an additional, sizeable investment in the protection and delisting of Leedy’s roseroot.

Purpose of Thesis Of the numerous threats facing Leedy’s roseroot throughout its range, it is unclear how those charged with conserving this federally-protected plant species should proceed in prioritizing conservation efforts. Having observed that several threats listed in the FRP (USFWS 1998) and Five-Year Review (USFWS 2015) involved concern for changes to Leedy’s roseroot’s apparently narrow niche, I aimed in this study to quantify some of the abiotic and biotic factors associated with Leedy’s roseroot by studying the Glenora Cliffs population. In Chapter 2, population trends at Glenora Cliffs over the last 12 years are described. Another objective treated in Chapter 2 is determining degree of correlation of Leedy’s roseroot presence and functional traits with abiotic factors including photosynthetically active radiation (PAR), temperature, seepage, and cliff features. Chapter 3 examines the relationship between Leedy’s roseroot and the plant species community it inhabits, with a specific focus on its interaction with invasive

9 Japanese knotweed. I present the results of a removal experiment examining Japanese knotweed’s impact on Leedy’s roseroot and the mechanism of their interaction. Overall, with this study I aimed to assist Leedy’s roseroot conservation by addressing some the delisting criteria and potential threats described in the FRP (USFWS 1998, 2015). Each chapter serves as a manuscript to be submitted for publication, Chapter 2 to Natural Areas Journal and Chapter 3 to Restoration Ecology. Co-authors for Chapter 2 are Donald Leopold and Steve Young. Steve

Young, New York Natural Heritage Program, provided Glenora Cliffs census data for Chapter 2. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Co-authors for Chapter 3 are Donald Leopold and John Wiley. John Wiley designed the Japanese knotweed removal experiment described in Chapter 3.

Study Area This thesis focused on Glenora Cliffs, near Glenora, Yates County, New York, the population of Leedy’s roseroot with perhaps the greatest impetus for conservation. It is the largest and most genetically diverse population, having the majority of all known Leedy’s roseroot individuals (Table 1.1) and the highest heterozygosity measured by Ejupovic (2015). At Glenora Cliffs, Leedy’s roseroot individuals are distributed patchily along the height of the cliff and occasionally in the talus, but appear to be concentrated at areas of seepage, as has been observed for Leedy’s roseroot throughout its range (USFWS 1998, 2015; Olfelt et al. 2014). Seepage of water between horizontal cliff layers apparently varies both along the cliff and seasonally (pers. obs.). Leedy’s roseroot grows in the sparse soil and loose shale gravel collecting within cliff crevices, where soil pH is 6.8-7.5 (Clausen 1975). Regional climate is characterized by mean annual temperature of 9.1 ºC, mean winter temperature of -2.7 ºC, mean summer temperature of 20.5 ºC, and mean annual precipitation of 81.9 cm with heaviest precipitation occurring during the summer months (NOAA 2016a). Members of the plant community present at Glenora Cliffs roughly follow the description of the redcedar-oak Finger Lakes community type described by Mohler et al. (2006), although eastern redcedar (Juniperus virginiana) is present but uncommon (pers. obs.). Dominant canopy trees on the slopes above the cliffs include eastern hemlock (Tsuga canadensis) and sugar maple (Acer saccharum) (USFWS 1998). On the cliffs and talus, willow (Salix spp.) and staghorn sumac (Rhus typhina) are common (USFWS 1998). Clausen (1975) observed herbaceous members of the Glenora Cliffs plant community including Canada bluegrass (Poa compressa),

10 Virginia creeper (Parthenocissus quinquefolia), herb-robert (Geranium robertianum), and Pennsylvania pellitory (Parietaria pensylvanica), and the cliffs also support rock Whitlow grass (Draba arabisans) (USFWS 1998), another New York State-listed species (Young 2010). A soil invertebrate community of terrestrial isopods (Oniscidea) and earthworms (Oligochaeta) has been observed at this site (pers. obs.), noteworthy due to the complicated effects these organisms can have on soil erodibility and nutrient loss (Le Bayon and Binet 2001).

Land ownership at Glenora Cliffs is divided among about 24 landowners (Fig. 1.3; Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 USFWS 2015). At the time of this study, a majority of Leedy’s roseroot occurred on privately- owned land and were not formally protected, but a small portion of cliff was protected by Finger Lakes Land Trust (USFWS 2015).

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15 CHAPTER 2: POPULATION TRENDS AND ABIOTIC FACTORS ASSOCIATED WITH LEEDY’S ROSEROOT IN NEW YORK STATE Abstract Leedy’s roseroot (Rhodiola integrifolia subsp. leedyi) is a rare cliff-dwelling subspecies federally-listed as threatened in the U.S. Thought to be a glacial relict plant, Leedy’s roseroot is restricted to cliffs throughout its distribution, likely in large part due to the specific environmental factors at these cliffs. In this study, I examined Leedy’s roseroot population trends

at Glenora Cliffs, NY, the largest population of this subspecies in the world, over a 12-year Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 period. I identified abiotic factors associated with Leedy’s roseroot occurrence by comparing the abiotic factors in areas where Leedy’s roseroot was present to adjacent areas where Leedy’s roseroot was absent. Census results indicated sections of the Leedy’s roseroot population at Glenora Cliffs had declined in flowering rate and increased in sterility from 2003 to 2015. Leedy’s roseroot occurrence at Glenora Cliffs was associated with abiotic factors including seepage, light, lower height on cliff, cliff stability features, and higher mean temperatures, with temperature trends varying over the growing season. Increased Leedy’s roseroot stem length was correlated with decreased light and temperature, and cliff features. My estimation of cliff features was effective and unique among cliff ecology studies, but temperature characterization was confined to the microscale and was not as effective as was hoped. Based on my findings, I expect some abiotic factors correlated with Leedy’s roseroot occurrence at Glenora Cliffs, particularly seepage, to be generalizable across Leedy’s roseroot range. My characterization of abiotic factors associated with Leedy’s roseroot supports its glacial relict status, suggests global climate change may impact it differently than most plant species, and informs conservation.

Introduction The rare cliff-dwelling plant Rhodiola integrifolia Raf. subsp. leedyi (Rosendahl & Moore) Kartesz (Crassulaceae) (Leedy’s roseroot) is restricted to seven disjunct populations in the U.S. (Fig. 1.1) and is federally-listed as threatened (USFWS 1994). Its interesting distribution and conservation concerns (USFWS 1998, 2015) have prompted recent genetic and demographic work (Ejupovic 2015; Olfelt and Freyman 2014; Olfelt et al. 2001; Olfelt et al. 1998). The largest and most genetically diverse population of Leedy’s roseroot, having a large proportion of all known Leedy’s roseroot (Table 1.1) and the highest genetic heterozygosity (Ejupovic 2015), is at Glenora Cliffs, along the shore of Seneca Lake in Yates County, NY (USFWS 1998). The present study is the first to focus on and publish censuses of the Glenora Cliffs population of Leedy’s roseroot, as past demography studies have focused on the Minnesota populations (Ejupovic 2015; Olfelt et al. 1998). Monitoring changes in population size and fertility over time at Glenora Cliffs will assist management of this important population. Leedy’s roseroot ecology is also relatively understudied. Many species and their habitats are restricted (Rabinowitz et al. 1986), and a study of Leedy’s roseroot ecology can serve as a

16 case study for understanding other rare plants. Throughout its range Leedy’s roseroot appears to be restricted to cliffs with intermittent seepage (USFWS 1998). Cliffs are also understudied but interesting systems because they present plants with a suite of unique stresses and disturbances (Larson et al. 2000a), have many endemic and restricted species (Nekola 1999; Larson et al. 2000a; Larson et al. 2004; Loehle 2006; Marcinko 2007), act as sources of weeds and pioneer species (Marks 1983; Larson et al. 2004), and can harbor unique species ecotypes such as stands of thousand-year-old Juniperus spp., Thuja occidentalis, and Taxus canadensis (Larson et al. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 2000b). Leedy’s roseroot likely exhibits a disjunct range and restriction to cliffs due to climate change after the Pleistocene, making it a glacial relict: a plant more widespread at the end of the last Ice Age but inhabiting a narrowly restricted modern range. Alternatively, more recent long- distance dispersal events could explain the disjunct distribution of Leedy’s roseroot, although genetic work has not supported this alternative hypothesis (Olfelt et al. 2001). Other subspecies of R. integrifolia inhabit the Rocky Mountains of the western United States, and dispersal from these populations may have colonized the cliffs of South Dakota, Minnesota, and New York. If the long-distance dispersal hypothesis was valid, one would expect Leedy’s roseroot to be most closely related to R. integrifolia in the western United States (Olfelt et al. 2001). However, genetic evidence suggests Leedy’s roseroot is more closely related to R. integrifolia of Alaska and Siberia (Olfelt and Freyman 2014; Olfelt et al. 2001), supporting glacial relict status for Leedy’s roseroot. Likewise, Ejupovic (2015) calculated mean Garza-Williamson indices that suggested a relatively recent genetic bottleneck event affected Leedy’s roseroot, aligning with the idea that Leedy’s roseroot disjuncts represent the remaining fragments of a widespread range at the end of the last Ice Age. Assigning glacial relict status to Leedy’s roseroot has specific conservation implications. Glacial relicts in general, and R. integrifolia (Guest and Allen 2014; DeChaine et al. 2013) and Leedy’s roseroot (Ejupovic 2015) specifically, may be particularly susceptible to human land use and climate change, which may alter species habitats especially in small, isolated populations (Lavergne et al. 2006). Climate change was one of several potential threats listed in the U.S. Fish and Wildlife Service Federal Recovery Plan (FRP) for Leedy’s roseroot (USFWS 1998) and its Five-Year Review (USFWS 2015). Other threats listed in the FRP related to abiotic factors included natural disturbances like inherent cliff instability, erosion, and weather events.

17 Identifying abiotic factors correlated with Leedy’s roseroot occurrence will aid conservation efforts by prioritizing potential threats associated with crucial aspects of the microsite for this rare subspecies. Aspects of Leedy’s roseroot ecology may be predicted based on its glacial relict status. DeChaine et al. (2013) considered climate factors associated with R. integrifolia distribution at a landscape scale and found correlations between modern R. integrifolia ranges and annual temperature ranges, mean daily temperature range, summer minimum temperature, precipitation Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 in the wettest month, and cumulative snowpack variables, but their niche model predictions had no overlap with Leedy’s roseroot range. DeChaine et al. (2013) were concerned with abiotic factors at the macroclimate scale, while Leedy’s roseroot, restricted to isolated populations in the eastern United States, is likely correlated with factors operating at a fine scale within the landscape. The multidimensional niche (sensu Hutchinson 1957) of a rare plant is thought to be restricted to narrow abiotic and biotic gradients (Brown 1987). Sather (1993) hypothesized that Leedy’s roseroot, a glacial relict adapted to the cooler, wetter conditions of the last Ice Age, is confined in the current interglacial period to refugia that are cooler and wetter than the regional climate. Throughout its range, cliff seepage is considered to be a major driver of the moist, temperature-controlled conditions on cliffs where Leedy’s roseroot grows (USFWS 1998). The restriction of Leedy’s roseroot to cliffs may also provide clues to abiotic factors with which it is associated. The heat budget of cliffs is very different from other habitats due to the indirect angle of solar radiation on vertical surfaces (Garnier and Ohmura 1968; Larson et al. 2000) and rock’s ability to operate as a heat source or sink (Larson et al. 2000a). Cliffs can be water, soil, and nutrient limited, and resources are highly heterogeneous across a cliff face (Cooper 1997; Kuntz and Larson 2006). Heterogeneity creates many potential species niches within a small area, as has been observed for other systems (Vivian-Smith 1997). Cliffs may experience high disturbance depending on rock stability (Larson et al. 2000a), and a plant adapted to the harsh conditions present on cliffs may benefit from disturbances that remove competitors and open up sites to be colonized by seedlings (Platt and Weis 1977; Connell 1978), or from the microsites produced by nurse rock features (Steenbergh and Lowe 1969) or areas of seepage (Fernando et al. 2014). For example, other researchers have found the presence of long- lived, stress tolerant relict species to be correlated with harsh conditions like decreased

18 weathering and soil depth (Matthes-Sears and Larson 1995), and increased crevice size (Matthes and Larson 2006). The first objective of this study was to describe abiotic factors correlated with Leedy’s roseroot occurrence. Leedy’s roseroot, a glacial relict, cliff-dwelling succulent with a storage rhizome (Ohba 2003) and marcescent flowering stems (Zhang et al. 2014), is adapted to the stresses of cliff-dwelling and likely benefits from other features of the cliff face. I predicted areas of the cliff where Leedy’s roseroot was present compared to areas lacking Leedy’s roseroot Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 would have higher seepage, higher overall instability, more protective cliff orientations or overhangs, increased crevice size, increased crevices per area, decreased soil depth, and decreased weathering. The indirect angle of solar radiation on vertical cliff faces (Garnier and Ohmura 1968; Buffo et al. 1972) led me to predict lower light levels at sites with Leedy’s roseroot compared to areas lacking Leedy’s roseroot. Following Sather (1993) and DeChaine et al. (2013), I predicted cooler temperatures and higher moisture would be correlated with presence of Leedy’s roseroot. Seepage is often concentrated on the lower half of a cliff face (Larson et al. 2000a), so I expected Leedy’s roseroot presence to be more likely in the lower portion of Glenora Cliffs compared to other sections of the cliff. Plant functional traits also change in response to environmental gradients (Tilman 1990). A second objective of this study was to examine differences Leedy’s roseroot response traits, or functional traits associated with abiotic factors (Lavorel et al. 2007). Under suboptimal, stressful conditions, individuals may allocate resources towards growth and maintenance organs rather than reproduction (Harper and Ogden 1970; Hedi Wenk and Falster 2015). I expected Leedy’s roseroot biomass allocation would follow abiotic gradients, with greater allocation toward growth and maintenance (stem length) rather than reproduction (flowers) under conditions diverging from the optimal abiotic factors identified by the first objective of this study.

Methods Study Site I sampled the Glenora Cliffs population of Leedy’s roseroot located at Glenora, Yates County, New York. Glenora Cliffs has several thousand Leedy’s roseroot individuals inhabiting approximately 3.2 km of east-facing cliff along Seneca Lake, one of the New York Finger Lakes (USFWS 1998). The cliff face is predominantly composed of thin, horizontally layered

19 calcareous shale, interrupted by layers of siltstone (USFWS 1998), part of the Utica Shale formation (Rickard et al. 1970). Regional climate is characterized by mean annual temperature of 9.1 ºC, mean winter temperature of -2.7 ºC, mean summer temperature of 20.5 ºC, and mean annual precipitation of 81.9 cm with heaviest precipitation occurring during the summer months (NOAA 2016a). The study area is within the Laurentian Mixed Forest Province ecoregion of the U.S. (Bailey 1994), and the plant community of Glenora Cliffs roughly follows the redcedar-oak

Finger Lakes community type described by Mohler et al. (2006). Leedy’s roseroot grows in the Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 sparse soil and loose shale gravel collecting within cliff crevices, where soil pH is 6.8-7.5 (Clausen 1975).

Census The New York Natural Heritage Program censused the Glenora Cliffs population of Leedy’s roseroot in 2003, 2005, 2010, and 2015. The entire population was broken into thirds, called sampling sections A (northernmost), B, and C (southernmost), and landmarks delineating each section were established to ensure consistency among census years. Each individual counted was classified by approximate area occupied by an individual: small (S) <2 dm2, medium (M) 2-5 dm2, and large (L) >5 dm2. Fertility (fertile or sterile) of each individual was also assessed by presence or absence of flowers. Flowering rate, the proportion of flowering individuals out of the total population, was calculated for each census year as an estimate of single-season population viability.

Abiotic factor characterization In May 2015, I established 14 transects non-randomly at the New York Glenora Cliffs population (Fig. 2.1), each along a rappel line beginning on the forest slope above the cliff face (hereafter, “plateau”), and continuing vertically down the cliff into the talus in order to capture environmental variability across the cliff face. Transects were selected based on landowner consent and safe access and were approximately stratified across gradients of Leedy’s roseroot density (many, few, and no individuals). Measurements were taken in five 1 m2 plots centered along a transect at ~3 m intervals, and included 1 plateau plot, 1 top cliff plot (mean distance from plateau plot=3.5 m), 1 mid-cliff plot (mean distance from plateau plot=5.5 m), and 1 lower cliff plot (mean distance from plateau

20 plot=8.5 m). If the bottom of the cliff entered the lake, I established a base cliff plot (mean distance from plateau plot=9.2 m), but if the cliff ended in the talus below the cliff, I created a talus plot (mean distance from plateau plot=11.8 m). I measured photosynthetically active radiation (PAR) with a LiCor LI-250A light meter at the top-center of each plot, and rock temperature (to the nearest 0.1 °C) was measured with an Extech dual-laser infrared thermometer. PAR and rock temperature were measured for all transects in late June or early July 2015 on a fair-weather day between 11:00 am and 4:00 pm. In Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 general, measuring PAR for all plots at midday on the same day is best practice (Scanga 2009; Raney 2014) but was impossible due to the size of the area being measured and time required to establish each transect. Long-term temperature readings were collected using Thermochron iButtons waterproofed with silicone and inserted into stable cliff crevices in each plot. Temperature readings were collected to the nearest 0.5 °C every two hours May 25-October 2. I extracted weekly and daily mean, maximum, and minimum temperatures and temperature ranges from iButtons for analysis. I estimated cliff stability for cliff plots (not for talus or plateau) based on the protocol described by Benumof and Griggs (1999). Stability classes for cliff features (Appendix A) were estimated for cliff face plots (base cliff, lower cliff, mid-cliff, and top cliff) and included crevice spacing (crevices per area), crevice dimensions (width, depth), crevice orientation (likelihood of rock-fall), weathering (from smooth edges to cliff rock appearing freshly cut and angular), amount of infill or loose rock debris within crevices, and seepage level/flow rate. My estimation of cliff stability features differed from the Benumof and Griggs (1999) protocol; I omitted their measurement of rock strength with a Schmidt hammer and included a crevice depth measurement. Cliff feature measurements were compiled into a numeric variable called total cliff stability, on the scale of 28.5 to 107 (Appendix A). Each cliff feature was also considered individually as a factor for analysis. Seepage flow rate (mL/s) was intended to be measured quantitatively by placing sponges of consistent surface area against seeping faces/crevices for a standard time, then calculating relative seepage flow rates as the difference in sponge weight before and after sampling. This method was ineffective in the field, and seepage level/flow rates were instead classified as one of five categories from no seepage to point source flow, part of the cliff stability estimation protocol (Appendix A).

21 For plots containing Leedy’s roseroot (hereafter, “Leedy’s plots”), Leedy’s roseroot flower number and longest stem (cm) were measured. For plots not containing Leedy’s roseroot, non-Leedy’s plots, distance was measured to the closest Leedy’s roseroot individual (up to 3 m). Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

Figure 2.1 - Cliff sampling transect, adapted from Harkey (2013) citing Smith (1998). Plot names are the factor location on cliff.

Statistical analysis Census I used R version 3.2.4 (R Development Core Team 2016) for statistical analyses. A significance level of α=0.05 was required to reject null hypotheses. Table 2.1 contains a summary of census data response and explanatory variables. I used linear mixed models fit by the maximum likelihood estimation method (LMM) to test hypotheses about differences in Leedy’s roseroot total based on groupings by explanatory variables, and included a random intercept for sampling section. An LMM was as follows:

22 Y = α + Xβ + ai + ε Formula 2.1 - Linear mixed model (LMM) where Y is the response variable total Leedy’s roseroot count (for whole population, or subset by size class or fertility), X is an explanatory variable, α and β are unknown parameters for intercept and slope respectively, ai is the random intercept included to account for variance of sampling section i, and ε captures the residuals (Zurr et al. 2009). For all LMMs, p-values reported for Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 model estimates and contrasts were calculated based on Satterthwaite’s approximation (SAS Institute 1978), which produces non-integer degrees of freedom (Kline 2016). I used an LMM with the interaction term size*fertility to test the relationship between Leedy’s roseroot size and fertility. To model differences within a sampling section, I used the ordinary least squares method for simple linear regression (LR). Model assumptions for all LR and LMM were checked by examining residual plots, which suggested no serious violations of assumptions. To identify sources of difference between groups for an LMM, I used the Tukey method for computing multiple comparisons of least-squared means; this method adjusts significance to minimize risk for type I error. I used Cohen’s d as a measure of effect size (Cohen 1988).

Table 2.1 - Summary of response variables, explanatory variables, and random effects included in models for census data. Variable type Name Data type Response Total Leedy’s roseroot Count Explanatory Year Ordered factor Explanatory Size Unordered factor Explanatory Fertility Binomial Explanatory Flowering rate Continuous Random effect Section Unordered factor

23 Table 2.2 - Summary of response variables (Y), explanatory variables (X), and random effects (ai) included in models for: A) Leedy’s roseroot abiotic factor characterization, B) changes in temperature over time, C) cliff face abiotic factor characterization. Name Data type A) B) C) Leedy’s roseroot presence-absence Binomial Y X Leedy’s roseroot proximity (cm) Continuous Y Leedy’s roseroot flower number Count Y

Leedy’s roseroot longest stem (cm) Continuous Y Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Daily mean temperature (ºC) Continuous X Y Daily max temperature (ºC) Continuous X Y Daily min temperature (ºC) Continuous X Y Daily temperature range (ºC) Continuous X Y Rock temperature (ºC) Continuous X Y PAR (µmol/m2/s) Continuous X Y Distance from anchor (m) Continuous X X Location on cliff Ordered factor X X Seepage Ordered factor X Y Crevice infill Ordered factor X Y Crevice depth Ordered factor X Y Crevice width Ordered factor X Y Crevice orientation Ordered factor X Y Crevice spacing Ordered factor X Y Weathering Ordered factor X Y Total stability Continuous X Y Weekly mean temperature (ºC) Continuous Y Weekly max temperature (ºC) Continuous Y Weekly min temperature (ºC) Continuous Y Weekly temperature range (ºC) Continuous Y Week Ordered factor X

Sampling transect Unordered factor ai ai ai

Time of recording Unordered factor ai ai

24 Leedy’s roseroot abiotic factor characterization Column A of Table 2.2 summarizes the response and explanatory variables I analyzed to characterize abiotic factors associated with Leedy’s roseroot. I used logistic regression to model the binomial response variable Leedy’s roseroot presence (Leedy’s plots) or absence (non- Leedy’s plots, 3 m+ from nearest Leedy’s roseroot), as a function of a given explanatory variable. Logistic regression followed the general formula:

Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 logit(π) = α + Xβ Formula 2.2 - Logistic regression where α, X, and β are as defined for Formula 2.1, the logit link function bounds fitted values between 0 and 1 as required for presence-absence data, and π is the probability term for a response variable following the binomial distribution. Odds ratios for logistic regression were computed as eβ, where e is the base of the natural logarithm. Outliers were identified by Cook’s distance. I was interested in identifying the combination of abiotic factors most correlated with Leedy’s roseroot. I used LMM with random intercept for sampling transect to model the continuous response variable Leedy’s roseroot proximity as a function of multiple explanatory variables. The multiple variable LMM followed Formula 2.1, but multiple X variables and β parameters were estimated, and ai modeled the random intercept for sampling transect i. Harrell (2001) suggests requiring 10-20 observations per covariate included in a multiple variable model. Maximum sample sizes were n=70 for the abiotic factor sampling design, with observations missing for some explanatory variables, therefore I included no more than four significant covariates in candidate models. To avoid multicollinearity, Pearson correlation coefficients (r) between explanatory variables were calculated, and variables having r>0.85 were not modeled together. To obtain candidate models, I manually performed the backwards elimination method for variable selection: I began with a full model containing the maximum number of explanatory variables fitted by maximum likelihood, then sequentially dropped non- significant explanatory variables and refit the model with each step (Chambers 1992). Candidate models having four or fewer significant explanatory variables were compared by Akaike’s Information Criterion (AIC) to select the best model describing Leedy’s roseroot proximity.

25 Models with AIC differing by <2 were considered equivalent (Millar 2011). Variance inflation factors (VIF) were calculated for the explanatory variables of the best model to detect collinearity among covariates. I considered VIF>3 indicative of collinearity (Zurr et al. 2010). Among Leedy’s plots, I used LMM (Formula 2.1) with random intercept for sampling transect to model Leedy’s roseroot functional traits (flower number, longest stem) as a function of an explanatory variable. Outliers were identified by Cook’s distance. Because of low sample size (n=16), I was unable to model Leedy’s roseroot functional traits using multiple variable Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 LMM (Harrell 2001).

Changes in temperature over time For weekly temperature data, I was interested in characterizing changes in temperature over the time period measured (May 25-October 2) for Leedy’s plots compared to non-Leedy’s plots. Response and explanatory variables included in analyses of changes in temperature over time are summarized in column B of Table 2.2. I used LMM (Formula 2.1) with a random intercept for sampling transect to model a weekly temperature response variable as a function of the interaction week*Leedy’s roseroot presence-absence. I used the Tukey method for computing multiple comparisons of least-squared means to identify significant temperature differences between Leedy’s plots and non-Leedy’s plots within a given week. I reported Cohen’s d as a measure of effect size (Cohen 1988).

Cliff face abiotic factor characterization I was also interested in characterizing variation in abiotic factors across the cliff face. For these analyses, response and explanatory variables are summarized in column C of Table 2.2. I used LMM (Formula 2.1) with a random intercept for sampling transect to model continuous response variables as functions of an explanatory variable (distance from anchor, location on cliff). To determine significant differences between discrete locations on the cliff, I reported the LMM contrasts and Cohen’s d, a measure of effect size (Cohen 1988). One outlier with Cook’s distance>1 was identified in the variable PAR, and I removed this observation for analysis.

26 Results Census The population size over the period censused ranged from 4,053 individuals in 2003 to 4,926 individuals in 2005 (Table 2.3). The most recent total was 4,672 individuals in 2015. More Leedy’s roseroot individuals were fertile than sterile across years (β=-533.58, t=- 5.995, p<0.001, LMM) (Fig. 2.2). Analyzing sterile and fertile totals separately, there was a biologically significant but statistically non-significant decrease in fertile individuals over time Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 (β=-17.104, t=87.770, p=0.055, LMM) (Fig. 2.2). By section, sterile individuals decreased over 2 time in section A (df=3, p=0.017, r adjusted=0.948, LR) but increased over time in section B (df=3, 2 p=0.020, r adjusted=0.943, LR). Without the 2015 data included, there were no significant linear relationships for fertile individuals in the whole population over time (β=-21.846, t=-1.350, 2 p=0.226, LMM), or sterile individuals in section A (df=2, p=0.147, r adjusted=0.896, LR) or 2 section B (df=2, p=0.115, r adjusted=0.935, LR). Flowering rate for the whole population decreased from about 75% in 2003 to about 58% by 2015 (Table 2.3), but the decrease was not statistically significant (β=-0.009, t=-1.603, p=0.135, LMM). Changes in flowering rate were concentrated in Section B, where flowering rate decreased significantly over the period censused 2 (df=3, p=0.006, r adjusted=0.983, LR), from 85% in 2003 to 45% in 2015. The interaction between size and fertility significantly described variation in the census data (β1=-385.08, β2=277.50, t1=-5.179, t2=3.732, p1<0.001, p2<0.001, LMM). Figure 2.3 shows the distribution of fertile and sterile individuals among the three size classes, with the mean calculated across years. Significantly more M individuals were fertile than sterile (p<0.001; d=2.640); 77% of all fertile individuals were M. Significantly more M individuals were fertile compared to S (p<0.001; d=2.384) or L (p<0.001, d=2.786). Among sterile individuals, S was most represented, having 58% of all sterile individuals and significantly more sterile members than M (p=0.002, d=1.332) or L (p<0.001; d=2.691). However, S individuals were no more likely to be sterile vs. fertile (p=0.143; d=0.923). For L individuals, meaningful comparisons were not statistically significant (Appendix B2). For mean size calculated across years, the population was structured significantly by size 2 (β1=630.58, β2=399.67, t1=8.221, t2=5.210, p1<0.001, p2<0.001, LMM) into small (S, <2 dm ), medium (M, 2 to 5 dm2), and large (L, >5 dm2) individuals. In 2015, 61% of all individuals were M, while 31% were S and 8% were L. There were significantly fewer L individuals than M

27 (p<0.001; d=2.684) or S (p<0.001, d=2.368), and more M individuals than S (p=0.016; d=0.841) (Appendix B1). Across years, the size structure of M>S>L was roughly consistent, and number 2 of L individuals decreased marginally over time in section A (df=3, p=0.054, r adjusted=0.842, LR).

Table 2.3 - Total Leedy’s roseroot count, total fertile individual count, and flowering rate across years sampled. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Year Total Leedy’s roseroot Total fertile Flowering rate 2003 4073 3043 0.747 2005 4926 3607 0.732 2010 4220 2758 0.654 2015 4672 2729 0.584 Mean 4472.75 3043.25 0.679

Figure 2.2 - Total Leedy’s roseroot counts by fertility for each year censused. Fertile individuals were male or female plants with flowers. Sterile individuals lacked flowers.

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Abiotic factors Leedy’s roseroot abiotic factor characterization Leedy’s plots had significantly more seepage (df=32, β=-1.733, odds ratio=0.177, p<0.001, logistic regression) (Fig. 2.4A) than non-Leedy’s plots. Nearly all Leedy’s plots (15/16, 93.75%) had at least a wet cliff face, with 68.75% of plots (11/16) experiencing dripping water. Compared to non-Leedy’s plots, Leedy’s plots had higher daily mean temperatures (df=41, β=0.843, odds ratio=2.324, p=0.016, logistic regression) (Fig. 2.4B) and daily max temperatures (df=41, β=0.332, odds ratio=1.394, p=0.024, logistic regression) (Fig. 2.4C) and wider but not statistically significant daily temperature ranges (df=41, β=0.278, odds ratio=1.321, p=0.056, logistic regression). To examine whether these relationships were leveraged by plots in the plateau and talus (where it is known Leedy’s roseroot does not grow often), analyses were performed for only plots on the cliff face; for these analyses, daily mean temperatures (df=30, β=0.808, odds ratio=2.244, p=0.033, logistic regression) were significantly higher in Leedy’s plots compared to non-Leedy’s plots on the cliff. Leedy’s plots had higher PAR than non-Leedy’s plots (df=48, β=0.047, odds ratio=1.048, p=0.007, logistic regression) (Fig. 2.4D). Relationships between Leedy’s roseroot and PAR remained significant when analyses were limited to comparing only cliff face plots (excluding plateau and talus) (df=32, β=0.050, odds ratio=1.051, p=0.015, logistic regression).

29 For cliff stability features, Leedy’s plots and non-Leedy’s plots did not have significantly different weathering, but the odds ratio indicated differences in weathering between Leedy’s plots and non-Leedy’s plots were biologically significant (df=32, β=1.464, odds ratio=4.321, p=0.054, logistic regression) (Fig. 2.4E). Leedy’s plots were located at a greater distance from the anchor compared to non-Leedy’s plots (df=48, β=0.456, odds ratio=1.578, p<0.001, logistic regression) (Fig. 2.4F), with differences persisting for analysis of cliff face plots alone (df=32,

β=0.369, odds ratio=1.446, p=0.020, logistic regression). Among all Leedy’s plots, 43.75% Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 (7/16) were lower cliff, and among all lower cliff plots measured, 50% (7/14) were Leedy’s plots. For LMM of the continuous response variable Leedy’s roseroot proximity, two explanatory variables, daily mean temperature and daily max temperature, were collinear (r=0.86) and were not included together in any candidate model. Model coefficients for the best model (lowest AIC) are summarized in Table 2.4. The best model included four significant explanatory variables: rock temperature, weathering, seepage, and daily mean temperature. Interpreting coefficient signs, Leedy’s roseroot proximity was correlated with lower rock temperatures, less weathering, more seepage, and higher daily mean temperatures. VIF did not indicate collinearity. Relationships between Leedy’s roseroot flower number and abiotic factors were not significant, but several abiotic factors were associated with Leedy’s roseroot longest stem. There was a negative relationship between Leedy’s roseroot longest stem length and daily mean temperature (β=-0.093, t=-3.673, p=0.020, LMM) (Fig. 2.5), and similar trends were observed for daily max temperature (β=-0.236, t=-5.372, p=0.005, LMM), daily temperature range (β=- 0.206, t=-5.545, p=0.005, LMM), rock temperature (β=-0.169, t=-3.326, p=0.008, LMM), PAR (β=-2.324, t=-2.409, p=0.045, LMM), distance from anchor (β=-0.343, t=-3.923, p=0.001, LMM), crevice orientation (β=-0.067, t=-2.637, p=0.019, LMM), and total stability (β=-0.428, t=-3.338, p=0.008, LMM).

30 A) B) C)

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D) E) F)

Figure 2.4 - Boxplots of significantly different abiotic factors for Leedy’s plots compared to non- Leedy’s plots: A) seepage, B) daily mean temperature (ºC), C) daily max temperature (ºC), D) PAR (µmol/m2/s), E) weathering, F) distance from anchor (m). Boxes contain values within the first and third quartiles, and the bold line shows the mean.

Table 2.4 - Model coefficients and statistics for the fixed effects of the LMM describing proximity to Leedy’s roseroot as a function of multiple explanatory variables. Model term β estimate SE df t value p-value VIF (Intercept) 894.894 234.134 26.920 3.822 <0.001 - Rock temperature (ºC) 26.580 9.484 41.990 2.803 0.008 2.222 Weathering -32.047 12.274 41.910 -2.611 0.012 1.064 Seepage 85.623 13.856 37.560 6.180 <0.001 1.038 Daily mean temperature (ºC) -76.299 16.750 41.630 -4.555 <0.001 2.341

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Changes in temperature over time An LMM described changes in weekly mean temperature as a function of the interaction between week and Leedy’s roseroot presence-absence (Appendix C1). Compared to non-Leedy’s plots, Leedy’s plots had significantly higher mean temperatures for several weeks during the period of May 25-October 2 (Appendix B3) (Fig. 2.6), including weeks 10 (p<0.001, d=0.915), 11 (p<0.001, d=0.881), 12 (p<0.001, d=1.046), 13 (p<0.001, d=1.179), 14 (p<0.001, d=1.217), 15 (p<0.001, d=1.044), 16 (p<0.001, d=1.269), 17 (p<0.001, d=1.320), and 18 (p<0.001, d=1.247). For these weeks, mean temperatures were approximately 0.8-1.3 °C higher for Leedy’s plots. Similar trends over time were observed for weekly max temperature (Appendix C2; LMM); Leedy’s plots had significantly higher maximum temperatures than non-Leedy’s plots for weeks 14 (p=0.012, d=0.890), 15 (p=0.017, d=0.957), 17 (p=0.002, d=0.974), and 18 (p=0.013, d=0.985) (Appendix B4).

Cliff face abiotic factor characterization

Seepage differed significantly by location on cliff (Appendix C3; LMM), with less seepage at the top of the cliff compared to the lower portion of the cliff (p<0.001, d=1.194), base cliff (p=0.019, d=0.315), and mid-cliff (p=0.012, d=0.186). Daily mean temperature increased linearly from the anchor point on the plateau (0 m) to the talus (>10 m) (β=0.103, t=4.497,

33 p<0.001, LMM) (Fig. 2.7), and similar trends were found for daily max temperature (β=0.217, t=3.934, p<0.001, LMM), daily min temperature (β=0.069, t=3.042, p=0.004, LMM), daily temperature range (β=0.156, t=2.522, p=0.015, LMM), and PAR (β=4.337, t=5.767, p<0.001, LMM). Crevice orientation became continuously less stable (more likely rock-fall, more overhang) from the bottom to the top of the cliff (=0.117, t=2.334, p=0.024, LMM). Crevice orientation also differed significantly by discrete location on cliff (Appendix C4; LMM), with top cliff plots having significantly less stable orientations than base cliff plots (p=0.006, Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 d=1.222) or lower cliff plots (p=0.032, d=0.800).

Discussion Census It is unknown how well the censuses presented here reflect the actual population size at Glenora Cliffs. For Minnesota populations, Olfelt (2014) estimated census bias by comparing Leedy’s roseroot counts of multiple observers within the same discrete area to obtain a 95% confidence interval and found their counts to be ±14% of the actual population size. Either over- estimation or under-estimation was possible, depending on the observer. I would expect comparable confidence intervals for Glenora Cliffs censuses, which were performed using similar methods to those of Olfelt (2014) (pers. obs.). Even with a 14% correction, the Glenora Cliffs population sizes I present are lower than previously published estimates (Olfelt et al. 1998; USFWS 1998), but this observation should not be considered a population decrease. Rather, the 1998 figure of >6,000 was an estimate (Steve Young, pers. comm.), while 2003-2015 data presented here were the result of full censuses. Accurate reporting of Glenora Cliffs population size is important for Leedy’s roseroot conservation because the FRP requires protection of 4,000 individuals at Glenora Cliffs for delisting (USFWS 1998)--virtually the entire population, assuming accuracy of NYNHP censuses. Protecting such a large proportion of this population, which is owned by about 24 private landowners, will be challenging (Fig. 1.3; USFWS 2015). However, protection is crucial because Glenora Cliffs has approximately two-thirds of all known Leedy’s roseroot and previous work has suggested this population has high genetic diversity (Ejupovic 2015). Over the period censused (2003-2015), some sections of the population showed increases in sterility and decreases in the proportion of individuals flowering. Recent stressors may be of

34 particular concern, given that decreases in fertility were driven by trends captured in 2015 data. NOAA’s (2016b) Climate Extremes Index (CEI) includes several potential stressors of plant species: monthly temperature minimums and maximums, monthly Palmer Drought Severity Index, daily precipitation totals, and wind velocity for tropical storms and hurricanes reaching land (Menne et al. 2009). Some of the highest CEIs ever recorded for the Northeast U.S. (since records began in 1910) occurred during the period censused, including 2011 (#1), 2006 (#2), and

2012 (#5). Moderate droughts struck the Finger Lakes region in May 2010 and June-August Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 2012 (NOAA 2016c). Cumulative spring snowpack, an additional climate variable characterizing R. integrifolia niche (DeChaine et al. 2013), was particularly low in 2013 (NOAA 2016d). A combination of extreme temperature, drought, and decreased snowpack may help explain changes in Leedy’s roseroot fertility in the last five years censused. These observations are anecdotal, and direct effects of climate extremes on Leedy’s roseroot are unclear. Glenora Cliffs should continue to be censused in the future to monitor additional changes in fertility. Changes in fertility could, over time, translate into depletion of genetic diversity, decreased population size, and possible extinction. Section B, the middle third of the population, experienced the most marked increase in sterility and decrease in the proportion of individuals flowering. One potential reason for this change could be that most of section B was invaded by Japanese knotweed (Fallopia japonica var. japonica) growing in the talus below the cliff for the whole period censused. Leedy’s roseroot growing in the bottom portion of the cliff would have experienced Japanese knotweed shade, potentially to the detriment of Leedy’s roseroot fertility. Decreases in light can cause resource allocation towards growth and maintenance rather than reproduction (Harper and Ogden 1970; Hedi Wenk and Falster 2015), and could exacerbate effects on fertility of plants in this section. Stress on section A, the southernmost third of the population, was also apparent over the years censused. Section A experienced decreases in large individuals (>5 dm2) and sterile individuals. Changes in section A could have been caused by changes in light environment or disturbance on the cliff due to a recent black swallow-wort (Cynanchum rossicum) invasion (NYNHP 2015). Section A is also the most developed of the sections (pers. obs.), and human activities and structures could have impacted Leedy’s roseroot in section A.

35 Fertility patterns mapped roughly onto size classes, with sterility most common in smallest individuals and fertility becoming more likely at >2 dm2, and even more likely at >5 dm2. This information could be useful for future censuses of Leedy’s roseroot. Determining fertility for plants growing high on the cliff is often difficult, requiring binoculars to spot minute and perhaps brown (later in the season) flowers. In such cases, results suggest fertility may be inferred if an individual’s estimated size is either very small or very large.

Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Abiotic factors Seepage Of the abiotic factors measured in this study, seepage was most strongly correlated with Leedy’s roseroot occurrence, corroborating the FRP (USFWS 1998) habitat description for Leedy’s roseroot. This finding also provides further support for Leedy’s roseroot as a glacial relict, a species whose restriction is the result of its dependence on abiotic factors similar to the cooler, wetter global conditions at the end of the last Ice Age. During the last Ice Age, Leedy’s roseroot may have inhabited a more widespread distribution outside of cliffs, so long as moisture was sufficient, perhaps growing in gravelly loam on shady slopes like the more common Rocky Mountain subspecies of R. integrifolia (Clausen 1975). Olfelt and Freyman (2014) suggest Leedy’s roseroot may have thrived in the primary successional habitat created at the margins of the receding glaciers. My study did not directly study the mechanism by which additional seepage allows Leedy’s roseroot to colonize or persist in cliff crevices, but I hypothesize ample water assists seedling establishment. Leedy’s roseroot at Glenora Cliffs is probably not dispersal- or germination-limited because flowering females in the field produce many seeds (pers. obs.) which germinate readily in the greenhouse (Olfelt et al. 1998), and mortality at adult life stages is only occasionally observed (Joel Olfelt, pers. comm.). Future work could examine how different abiotic factors interact with Leedy’s roseroot throughout its life history (Stearns 1992). Increased understanding of important threats at different life stages would help estimate this rare plant’s long-term viability. Future work should also identify the source of the seepage at Glenora Cliffs and the other Leedy’s roseroot populations. Cliff seepage is probably fed by a combination of recent upslope precipitation and groundwater, although the proportion of these sources is unknown. Each source has different ecological and conservation implications. Precipitation-fed seepage would be more

36 sensitive to upslope landowner activities (vegetation removal, herbicide use) and climate change impacts on precipitation (USFWS 2015; USFWS 1998). Groundwater-fed seepage may be less sensitive to climate change (Raney 2014) and could accumulate nutrients due to long residence underground (Fernando et al. 2014). Some evidence suggests the seepage at Glenora Cliffs are influenced by groundwater input: groundwater is thought to charge Seneca Lake’s lake floor, circulating the water and impeding freezing (Ahrnsbrak et al. 1996), and Yates County contains an aquifer northwest of Glenora, NY (NYSDOS 2014). Isotopic tracer studies would be required Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 to demonstrate seepage source for Glenora Cliffs and other Leedy’s roseroot populations.

Temperature Surprising temperature trends were another finding of this study. I predicted Leedy’s roseroot would be correlated with temperatures cooler than the surrounding landscape, and my work did suggest that cooler rock temperature, measured as the infrared temperature radiating from cliff rock, was correlated with proximity to Leedy’s roseroot. The continuous variable Leedy’s roseroot proximity a characterized Leedy’s roseroot niche at a scale of up to 3 m2 whereas other variables measured operated on the microscale of a single crevice. Because rock temperature was only significant at this larger scale, this measurement may quantify processes other than those structuring Leedy’s roseroot niche. In contrast, for measurement of temperature at the finer scale of an individual crevice, I was surprised to find mean daily temperatures consistently 0.8-1.3 °C higher from late summer through early fall near Leedy’s roseroot compared to adjacent places lacking Leedy’s roseroot. Temperature probably impacts Leedy’s roseroot in complicated ways. Leedy’s roseroot has several adaptations to drought stress and may, as my results suggest, be restricted to hotter areas of the cliff face, meaning it is competitive on hot, dry portions of the cliff that other plant species cannot colonize. As a glacial relict, Leedy’s roseroot experienced selective pressure during the last Ice Age which led to adaptations including succulent tissue, a storage rhizome (Ohba 2003), and marcescent flowering stems (Zhang et al. 2014). For an individual plant, both heat and cold present individuals with water stress, making these adaptations potentially advantageous to Leedy’s roseroot in novel Holocene habitats, allowing the plant to endure heat so long as seepage is present. However, the magnitude of +0.8-1.3 °C in mean temperature in late summer is not necessarily excessive heat, suggesting that, along the cliff face, temperature

37 conditions measured in areas not occupied by Leedy’s roseroot may not differ to a physiologically relevant extent to exclude the stress-tolerator Leedy’s roseroot. Increased temperature may, however, exclude less stress-tolerant species at Glenora Cliffs that may otherwise compete with Leedy’s roseroot. I did find evidence of temperature gradients along the height of the cliff (supporting Larson et al. 2000a) larger than 0.8-1.3 °C, so perhaps Leedy’s roseroot is absent from adjacent cliff plots not due to exclusionary temperature but because of other abiotic factors present at certain portions of the cliff face (or due to random chance of Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 colonization, a null hypothesis). I showed Leedy’s roseroot was concentrated on the lower cliff, where seepage is also concentrated. The lower cliff also happens to have higher daily mean and maximum temperatures, potentially due to the angle of sun hitting the lower cliff. Indeed, higher PAR was measured on the lower cliff. Leedy’s roseroot is likely able to colonize these areas because of seepage (or because PAR is higher here, or due to the protection from overhangs, or because of other cliff stability features, or as a result of a combination of these things), but this stress-tolerant species fails to be deterred by slightly higher temperatures during certain months in a single year. Indeed, this study only measured temperatures over a relatively short time period. A future study could aim to better characterize effects of temperature regimes and precipitation patterns on the population and individual plants. My results are likely not generalizable due to spatial and temporal limitations of my sampling and because Glenora Cliffs is the only east-facing population of Leedy’s roseroot, experiencing a very different light and heat budget compared to the north-facing populations in Minnesota and South Dakota. At 40°N latitude, east-facing cliffs receive more hours per day of direct solar radiation in summer compared to winter, while north facing cliffs receive no solar radiation in winter (Buffo et al. 1972). Differences in light, heat, and water could have implications for establishment or reproduction of these Leedy’s roseroot populations. In other species, restriction to north-facing aspects has been linked to requirements for decreased direct sunlight, cooler temperatures, and higher moisture (Warren 2010). My sampling design for measuring abiotic factors may have inadequately captured the true temperature regimes present at Glenora Cliffs, although others have found significant temperature variation within 5 m of the cliff face (Bartlett et al. 1990). Temperatures of the whole area including the cliff and lakefront could differ from the wider landscape, but I only sampled up to 20 m away from Leedy’s roseroot and would not have captured potential

38 differences at a larger scale. Due to the temperature and moisture moderating effects of cliffs (Bunce 1968), seepage, and the lake, temperatures around Glenora Cliffs are likely cooler compared to areas on a regional scale, but not on the microscale I measured. For Leedy’s roseroot, cliff rock and water sources (seepage and Seneca Lake) act as larger heat reservoirs than the soil, vegetation, and manmade structures characterizing the rest of the region. During hot conditions (day, summer) the cliff and water are expected to act as heat sinks, while during cold conditions (night, winter) they are heat sources (Larson et al. 2000a). The Glenora Cliffs Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 area has particularly great potential as a heat reservoir. Glenora Cliffs is an outcrop of exposed bedrock, meaning the reservoir extends deep underground, potentially conducting heat inputs and outputs over long distances and times (Larson et al. 2000a). Seneca Lake is also a vast heat reservoir; its surface never completely freezes in the winter because its water circulates due to groundwater springs (Ahrnsbrak et al. 1996) and because it has the largest water volume of any Finger Lake, reaching a maximum depth of 188 m (NYSDOS 2014). Year-round, the heat reservoir effect likely also moderates temperature within Leedy’s roseroot range, but, as well as spatially, my sampling was limited temporally. Due to the reservoir effect as described, I expect Leedy’s roseroot would experience cooler-than-regional temperatures in summer and warmer-than-regional temperatures in winter. Snow melt and spring rains are probably other important drivers of regional temperature regime in early spring. At the microscale, the cliff face temperature regime is also influenced by annual changes in the angle of sunlight hitting the east-facing cliff; more hours per day of direct solar radiation occur in summer compared to winter (Buffo et al. 1972). Comparing my recorded temperatures for June, July, August, and September to NOAA’s (2016a) monthly mean, maximum, and minimum temperatures for the nearest weather station suggests the temperatures recorded in my study were overall cooler than regional temperatures, particularly maximums, during these months, but this comparison is merely anecdotal rather than a true analysis. Further, NOAA records atmospheric temperatures while this study collected soil temperatures from iButtons and rock temperatures from an infrared thermometer, which are most relevant to Leedy’s roseroot niche but may differ from atmospheric temperatures. Future work could examine temperature data at a regional scale to determine whether the lakefront at Glenora Cliffs preserves unique climate conditions. Other studies of factors associated with cliff species might consider a larger scale sampling design to capture temperature variation.

39 Cliff stability In addition to seepage and temperature, I found correlations between Leedy’s roseroot and cliff stability features, although these findings are probably site-specific to Glenora Cliffs and might not inform generalizations about Leedy’s roseroot habitat. Still, my estimation of cliff features was unique among studies of cliff plant ecology and revealed interesting trends supporting the prediction that Leedy’s roseroot is concentrated in areas of moderate instability/disturbance. Leedy’s roseroot was more likely to occur in less weathered areas of the Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 cliff face, where cliff rock appeared freshly cut and angular rather than smooth. Less weathered rock indicated rock-fall may have occurred relatively recently, cleaving out new horizontal crevices for colonization by Leedy’s roseroot. Cliff orientation trends along the whole cliff face could also influence Leedy’s roseroot. Unstable orientations were observed along the top of the cliff creating overhangs, while Leedy’s roseroot was most likely to occur on the lower cliff. Overhangs could protect Leedy’s roseroot from disturbance or help maintain a moist microclimate (Fernando et al. 2014), and may be especially important for facilitating establishment (Steenbergh and Lowe 1969). Growing beneath overhangs, however, could be a risky endeavor because such conformations are inherently unstable and could lead to mortality, shade individuals too thoroughly, or create a rain shadow (Larson et al. 2000a).

Light Leedy’s roseroot was more likely to be found in places with higher PAR, countering the expectation that areas experiencing more direct solar radiation would exclude the glacial relict Leedy’s roseroot. A growth requirement of full sun may be explained by adaptation of Leedy’s roseroot to open areas along glacial margins during the Pleistocene (Olfelt and Freyman 2014). However, there may be a more complicated relationship between Leedy’s roseroot and light. Restriction of Leedy’s roseroot to areas of seepage at the lower portion of the cliff could mean light environment measurements were entangled with these other crucial features. The angle of light would be quite heterogeneous along the cliff face (Garnier and Ohmura 1968; Larson et al. 2000), as are other factors. Additionally, my methods may have been inadequate for characterizing light experienced by Leedy’s roseroot; Matthes-Sears et al. (1997) found traditional methods for measuring PAR underestimated the amount of light available to plants growing on vertical cliffs.

40 Nutrients Soil nutrients and seepage chemistry were not sampled in this study, but are potentially important abiotic factors associated with Leedy’s roseroot. Throughout its range, Leedy’s roseroot grows on cliffs whose bedrock could not diverge more widely: calcareous shale in New York (USFWS 1998; Rickard et al. 1970), calcium- and magnesium-rich limestone and dolostone in Minnesota (Mossler 1999; Jirsa et al. 2011), and peraluminous granite in South

Dakota (Redden et al. 1982). Many edaphic/cliff endemics are calciphiles (e.g. Fernando et al. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 2014), and Leedy’s roseroot may be as well. Regardless of cliff parent material, groundwater- derived seepage may have residence times long enough to dissolve more Ca2+ than is available in soil derived from the same parent material (Fernando et al. 2014). Future work should address whether Leedy’s roseroot experiences seepage rich in Ca2+ or other nutrients. Residence time of water within the cliff face might be measured with isotopic tracers (Fernando et al. 2014; e.g. Wexler et al. 2014). Soil nutrients might be measured using Plant Root SimulatorTM ion- exchange membrane probes (Western Ag Innovations, Inc. 2008). Alternately, leaf tissue could be analyzed for content of Ca2+ or other nutrients.

Abiotic factors affect stem length A final finding of this study regarded Leedy’s roseroot functional trait response to abiotic gradients. Longer Leedy’s roseroot stems were correlated with lower PAR, lower daily mean and maximum temperatures and ranges, lower rock temperatures, lower heights on the cliff, less stable cliff orientations, and less stability overall. Stem length is often assumed to be primarily controlled by light (quantified here as PAR), with high light producing stunted stems and “witch’s broom” foliage and low light causing long, spindly stems that may have increased ability to compete for light (Dudley and Schmitt 1996). Clausen (1975) also observed that R. integrifolia cultivated in increased shade (and, subsequently, increased moisture) grew longer stems. This knowledge may explain the relationship I found between longer stems and decreased PAR, and suggests light and moisture are the primary abiotic factors associated with changes in Leedy’s roseroot stem length. Differences in light may also explain correlations between stem length, temperature, and some cliff stability features. The amount and angle of light hitting the cliff drive temperature regimes (Garnier and Ohmura 1968; Buffo et al. 1972), with cooler temperatures occurring in lower light environments. Likewise, light would be less intense where

41 cliff overhangs are more extreme and less stable, inducing Leedy’s roseroot to grow longer, outward from the cliff face towards the sun.

Climate change implications My work supports the idea that, in the face of climate change after the Pleistocene, seeping cliffs preserved abiotic conditions suitable for Leedy’s roseroot in the eastern U.S., as has been observed for other glacial relicts (Nekola 1999; Larson et al. 2000a; Larson et al. 2004; Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Loehle 2006; Marcinko 2007). At the end of the last Ice Age, global climate changed rapidly in North America, but stable refugia would have been available to Leedy’s roseroot in the Driftless Area of Minnesota, the Black Hills of South Dakota, and the Finger Lakes of New York. In southeastern Minnesota, which was not glaciated during the Wisconsinan glacial advance, paleo- pollen radiometric dating data suggest the regional climate became warmer and drier around 9,000-10,000 years ago (Wright et al. 1998), but the Driftless Area was wet enough to remain dominated by mesic forest rather than the prairie-dominated systems of the rest of Minnesota (Grimm and Jacobson 2004). Regardless of the regional climate, Leedy’s roseroot still could have inhabited southeastern Minnesota’s cliffs at the end of the last Ice Age; the karst of the Driftless Area cliffs was cut primarily around 12,000-30,000 years ago by streams of glacial meltwater (Erickson 2014) and are cooled by maderate ice caves (USFWS 1998) known to support other glacial relict species (Chung et al. 2004; Levsen and Mort 2008). South Dakota’s Black Hills are not glacial in origin, but rather were created by an uplift 60 to 65 million years ago (Driscoll et al. 2002). During the mid-Holocene warming (Fredlund and Tieszen 1997), areas of the Black Hills, like the cliffs of the Finger Lakes and Driftless Area, would have represented refugia. Supporting this idea, Jass et al. (2002) found species of late-Pleistocene mollusk fossils in the southern Black Hills known to be restricted to cooler, more mesic habitats than the prairies covering much of the surrounding region after the last Ice Age. These mollusks were found at lower elevation, and higher elevation areas of the Black Hills likely preserved a climate diverging even more from the surrounding landscape. Today, high elevation locations of the Black Hills have been observed to support interesting plant communities (Hoffman and Alexander 1987) including remnants of the white spruce (Picea glauca) forests that dominated North America during the last glacial maximum (Bonnicksen 2000). New York was glaciated until about 11,000 years ago, and the Finger Lakes were cut by glacial movement and meltwater

42 that also formed the region’s famous gorges around 9,000-10,000 years ago (von Engeln 1961). Afterwards, New York experienced the warmer, drier period of the mid-Holocene (Grimm and Jacobson 2004), likely making the region inhospitable to Leedy’s roseroot and other glacial relicts except in refugia like cliffs and Finger Lakes. Compared to the Black Hills and the Driftless Area, the Finger Lakes are not as renowned for their unique flora, but nearby areas in central New York do harbor glacial relicts like American hart’s-tongue fern (Asplenium scolopendrium var. americanum) (USFWS 2012 citing Evans 1982; USFWS 1993), Acontium Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 noveboracense (USFWS 1983a) and the endemic Chittenango ovate amber snail (Novisuccinea chittenangoensis) (USFWS 1983b). If warming ancient climates led to restriction of Leedy’s roseroot, we should consider how this rare subspecies might respond to additional climate change. My work suggests Leedy's roseroot is more seepage-restricted than it is temperature-restricted, so Leedy’s roseroot and other species restricted to cliff seepage may be less sensitive to rising global temperature than other species, as long as appropriate microsites persist in the face of changing climate. Similarly, other groundwater fed ecosystems are thought to represent refugia from climate change (Raney 2014). Seepage interacts with temperature: at Glenora Cliffs, for example, Leedy’s roseroot experiences the heat reservoir effect conferred not only by seepage, but also by cliff rock and Seneca Lake. More variable precipitation regimes due to climate change may affect Leedy’s roseroot. Future work should determine the source of the seepage at Glenora Cliffs and other populations of Leedy’s roseroot. If seepage is the result of recent upslope precipitation and climate change alters precipitation regimes such that seepage is depleted or seasonality of seepage is changed, Leedy’s roseroot could be negatively impacted. However, if groundwater feeds seepage, Leedy’s roseroot may be less impacted by climate change than species relying exclusively on precipitation. More research regarding climate change’s impacts on groundwater is required (Taylor et al. 2013), but current research suggests depletion is primarily occurring where groundwater is removed for irrigation and precipitation is not sufficient enough to recharge the water table, which experiences relatively small annual additions (e.g. Rodell et al. 2009). If groundwater is identified as the primary source of seepage at Glenora Cliffs, landowner use of groundwater upland of Glenora Cliffs may require monitoring.

43 For some plant species, increased CO2 under climate change may increase growth (Polley et al. 1993), but Leedy's roseroot is a CAM plant that creates biomass more slowly than other species (Turner et al. 1966). If other species in the plant community increase their biomass due to increased CO2, they could outcompete Leedy’s roseroot for resources such as light and soil nutrients. Finally, alpine plants are thought to be particularly susceptible to the negative effects of climate change, as they will be unable to track their moisture or temperature niche upslope, Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 having already occupied the highest elevation in a region (Nogués-Bravo et al. 2007). The high elevation population of Leedy’s roseroot located in the Black Hills may be uniquely susceptible to climate change (USFWS 2015).

Conservation implications In this study I provided additional information regarding potential threats to Leedy’s roseroot described in the FRP (USFWS 1998, 2015). Of all abiotic factors measured, seepage was the factor most associated with Leedy’s roseroot occurrence, supporting the idea that potential disruption of seepage should be considered a major threat (USFWS 1998, 2015). Inherent cliff instability and erosion (USFWS 1998, 2015) may threaten Leedy’s roseroot, but some unstable cliff features I measured were correlated with Leedy’s roseroot, and disturbance may facilitate its establishment. The threat of climate change (USFWS 1998, 2015) may impact Leedy’s roseroot in complicated ways differing from impacts on other plant species. At Glenora Cliffs, fertility of Leedy’s roseroot has decreased over the past 12 years. I suggest the New York Natural Heritage Program continue to monitor this population for changes in fertility as well as inevitable decreases in population size if decreased fertility persists. Population size at Glenora Cliffs could be supplemented by establishing new Leedy’s roseroot individuals, perhaps by out-planting greenhouse grown seedlings. However, an out-planting project is probably unnecessary given that Leedy’s roseroot at Glenora Cliffs does not appear to be dispersal- or germination-limited. An alternative conservation strategy could be out-planting Leedy’s roseroot to new locations using the abiotic factors identified by this study as guidelines to select appropriate areas for planting. Results of this study suggest plantings may be most successful beneath an overhang on the lower cliff, within crevices freshly cleaved rather than weathered, in high light, and,

44 crucially, where seepage wets the cliff face. Temperature may be less important for identifying planting locations, given that Leedy’s roseroot tolerates some of the higher temperatures I recorded along the cliff. North-facing cliff aspect, a feature common to other populations of Leedy’s roseroot (Table 1.1), is likely another a desirable site feature due to its association with other abiotic factors including increased moisture (Warren 2010).

Conclusion Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Through this study I have shown that sections of the population of Leedy’s roseroot at Glenora Cliffs declined in flowering rate and increased in sterility from 2003 to 2015, indicating need for future censuses to monitor changes in population size and fertility. I identified several abiotic factors associated with Leedy’s roseroot, including seepage, light, lower height on cliff, cliff stability features, and, surprisingly, higher mean temperatures, and temperature trends that varied over the growing season. Longer Leedy’s roseroot stem length was correlated with lower light levels and temperatures and with cliff stability features. In short, one can expect to find Leedy’s roseroot in crevices on the lower portion of the cliff, perhaps under an overhang, within crevices that are freshly cleaved rather than weathered, in high light, and where the cliff face is at least wet but ideally dripping, and where late spring-early fall temperatures are higher than elsewhere on the cliff, talus, and plateau. Overall, my findings regarding Leedy’s roseroot support its glacial relict status, suggest global climate change may impact it differently than most plant species, and inform conservation goals. The abiotic factors I described for Leedy’s roseroot may be useful for identifying sites for Leedy’s roseroot establishment and for prioritizing threats listed in the FRP. Some abiotic factors measured may be unique to Glenora Cliffs but others, particularly seepage, are likely generalizable across Leedy’s roseroot range. Future studies of Glenora Cliffs and other Leedy’s roseroot populations should continue to monitor changes in population size and fertility, identify seep hydrology, and measure seepage and soil nutrients.

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52 CHAPTER 3: BIOTIC FACTORS AND IMPACT OF THE INVASIVE SPECIES JAPANESE KNOTWEED ON LEEDY’S ROSEROOT IN NEW YORK STATE Abstract Leedy’s roseroot (Rhodiola integrifolia Raf. subsp. leedyi) is a rare cliff-dwelling subspecies federally-listed as threatened in the U.S. due to its rarity and risk of habitat loss. The invasive species Japanese knotweed (Fallopia japonica var. japonica) threatens Leedy’s roseroot at Glenora Cliffs, NY, the largest known population of this subspecies. This study explored

interactions between Leedy’s roseroot and the other species composing the biotic community at Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Glenora Cliffs, with a focus on the invasive species Japanese knotweed, which was experimentally removed in a three-treatment block design. Observations of Leedy’s roseroot in treatment blocks and in uninvaded areas were made before treatment and after two years of treatment. Compared to uninvaded areas, Japanese knotweed presence was associated with decreased light and temperature and reduced flowering in Leedy’s roseroot for some years. Herbicide removal reduced Japanese knotweed and resulted in increased light and temperature compared to invaded plots. Two years of herbicide removal of Japanese knotweed did not result in restoration of Leedy’s roseroot flowering, but was associated with decreased Leedy’s roseroot stem length. These results suggest large-scale Japanese knotweed removal is not necessary at Glenora Cliffs. I recommend continued monitoring of Leedy’s roseroot fertility and describe potential indicator species for identifying new sites for Leedy’s roseroot establishment. The cliff community was characterized by higher species richness compared to talus and plateau, and species assemblages were unique among each of these locations. Future work should seek to understand Leedy’s roseroot life history and mortality, especially effects of abiotic and biotic factors on the seedling stage. Interspecific interactions of cliff species are understudied and require additional research.

Introduction Leedy’s roseroot, Rhodiola integrifolia Raf. subsp. leedyi (Rosendahl & Moore) Kartesz (Crassulaceae), is a rare cliff-dwelling subspecies of a plant species common in arctic and alpine regions of Western North America and Siberia (Guest and Allen 2014) and is restricted to seven disjunct populations in New York, Minnesota, and South Dakota. Due to its rarity and risk of habitat loss (USFWS 1998, 2015), Leedy’s roseroot is federally-listed as threatened under the U.S. Endangered Species Act (USFWS 1994). A recent return on investment analysis of ESA- listed species found that numbers of Leedy’s roseroot in the U.S. had declined slightly more often than increased for the period of 1989-2011, and had received only about 11% of the government funds proposed by its FRP, putting Leedy’s roseroot below a threshold of likely recovery and delisting (Gerber 2016). NatureServe (2015) assigns Leedy’s roseroot the conservation status of a globally critically imperiled subspecies of a secure species (G5T1) and state endangered status (S1) in New York and Minnesota. New York has approximately two-

53 thirds of all known Leedy’s roseroot individuals. Seepage from horizontal crevices or moisture and temperature regulation from Seneca Lake may create cool, moist microsites suitable for the glacial relict Leedy’s roseroot (Sather 1993; USFWS 1998). Of the two New York populations, the Glenora Cliffs population near Glenora, Yates County, NY, is the largest known population of Leedy’s roseroot, containing >4,000 individuals inhabiting approximately 3.2 km of east- facing cliff along Seneca Lake (USFWS 1998). Glenora Cliffs was the site of this study.

Quantifying abiotic and biotic factors correlated with Leedy’s roseroot is important for Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 conservation because threats to crucial elements of Leedy’s roseroot niche could reduce viability of this rare subspecies, and could eventually result in extinction. Leedy’s roseroot occurrence is structured by its environment in many ways: along abiotic gradients, and in response to autecology (reproductive self-limitation, intraspecific competition) and synecology (plant-plant interspecific competition or facilitation, or plant-animal pollination, dispersal, or herbivory). It is unknown which of these forces limits Leedy’s roseroot most. The U.S. Fish and Wildlife Service Federal Recovery Plan (FRP) (USFWS 1998, 2015) for Leedy’s roseroot identified natural disturbances like inherent cliff instability and anthropogenic disturbances as potential threats to Leedy’s roseroot (USFWS 1998, 2015). The FRP called for more research regarding potentially important threats to Leedy’s roseroot, including autecological biotic aspects including Leedy’s roseroot reproduction and dispersal limitation (treated by Olfelt et al. 1998), abiotic factors restricting this subspecies (treated by Chapter 2 of this thesis), and potential impacts of climate change. The FRP expressed greatest concern for synecology, specifically, for potential impacts of Japanese knotweed (Fallopia japonica var. japonica) shade on Leedy’s roseroot at Glenora Cliffs. Quantifying the impact and mechanism of this interspecific interaction on Leedy’s roseroot was the purpose of this study. I measured the impacts of Japanese knotweed on Leedy’s roseroot by comparing Leedy’s roseroot totals and functional traits before and after removal of Japanese knotweed via a three-treatment block design, and in areas never invaded by Japanese knotweed. Temperature and photosynthetically active radiation (PAR) were measured to quantify the mechanism of the impact of a Japanese knotweed canopy on Leedy’s roseroot and the abiotic conditions experienced by Leedy’s roseroot. Understanding the changes in abiotic factors associated with Japanese knotweed presence is a crucial feature of this study given that many invasive species studies fail to quantify mechanisms of invasion (Roberts et al. 2015). The interspecific interaction between Japanese knotweed and Leedy’s roseroot could be

54 competitive, facilitative, or neutral. I expected Japanese knotweed would impact Leedy’s roseroot negatively by outcompeting Leedy’s roseroot for light. Japanese knotweed and Leedy’s roseroot are not rooted in the same soil, meaning they do not compete for belowground resources. In Chapter 2 of this thesis, I found that Leedy’s roseroot inhabited portions of the cliff face experiencing some of the higher, more extreme light and temperature regimes present in the plateau-cliff-talus system sampled. If Japanese knotweed presence is associated with lower light and temperature conditions, this interaction would represent a negative shift in the available Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 niche for Leedy’s roseroot. Roberts et al. (2015) found potential invasive species impacts had been studied for only about 6.5% of species recognized as threatened by the United States Fish and Wildlife Service; for plants, light competition was the most common mechanism of negative impacts of invasive species on rare species. A recent meta-analysis found invasive species to be a prevalent cause of rare plant species extinction, just behind agriculture, overexploitation, and urban development (Bellard et al. 2016). MacArthur (1972) discussed the ability of a new competitor to drive small, isolated populations to extinction. The role of catastrophes operating on rare populations, in this case perhaps invasions but also rockfall or severe weather events, are often underestimated when considering rare plant population viability (Mangel and Tier 1994). Catastrophic and stochastic events can be the ultimate sources of extinctions for small and isolated populations, which are more at risk for extinction compared to common taxa (Bellard et al. 2016; Lavergne et al. 2006). Few studies have focused on invasive species effects on cliff plants, although it is thought cliffs are less susceptible to invasion (Crawley 1987). Caperta et al. (2014) characterized the rare cliff-dwelling Limonium multiflorum as intolerant of competitors, both native and non-native, but found a particularly negative impact of the invasive vine Carpobrotus edulis. Müller et al. (2006) found improved cover of relict cliff species after removal of trees shading the cliffs. In general, stress tolerant plants like those of cliffs are thought to have poor competitive ability (Grime 1979). Invasive Japanese knotweed may not result in Leedy’s roseroot mortality, but it may decrease reproduction by changing plant biomass allocation (Harper and Ogden 1970; Hedi Wenk and Falster 2015). Alternatively, shading could positively impact Leedy’s roseroot: a glacial relict thought to require cool, moist microsites (Sather 1993), and a member of a genus usually restricted to arctic and alpine regions (Guest and Allen 2014). It has been proposed that facilitative

55 interspecific relationships will be more common than competition in harsh environments (Choler et al. 2001). Cliffs are harsh by several metrics. They can be water, soil, and nutrient limited, may experience high disturbance depending on rock stability (Larson et al. 2000), and resources are highly heterogeneous across a cliff face (Cooper 1997; Kuntz and Larson 2006). Plants may be more concentrated where nutrients and soil accumulate, occasionally where shorebirds defecate (e.g. Baumberger et al. 2012), or in conjunction with nurse plants (Groeneveld et al.

2007; Ren et al. 2010) or nurse rock features (Steenbergh and Lowe 1969). Japanese knotweed Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 could act as a nurse plant or serve other protective functions for Leedy’s roseroot. I also aimed in this study to quantify the impact of Japanese knotweed on life history of Leedy’s roseroot across life stages, seedling to adult. Different mortality regimes may operate at different life stages (Stearns 1992), meaning interspecific interactions with competitors could limit Leedy’s roseroot more strongly at certain life stages. For seedlings, shade from co- occurring species and increased moisture due to less evaporative water loss may facilitate Leedy’s roseroot germination and establishment (e.g. Choler et al. 2001; Groeneveld et al. 2007; Ren et al. 2010). The stress of decreased light could impact adults differently, inducing biomass allocation to growth and maintenance rather than reproduction (Harper and Ogden 1970; Hedi Wenk and Falster 2015). Consistent with the hypothesis of a competitive interaction, I expected Leedy’s roseroot co-occurring with Japanese knotweed to allocate more biomass towards organs like stems and leaves, while Leedy’s roseroot not experiencing invasion would invest more resources into reproductive structures like flowers. Decreased reproduction in populations with sometimes already low effective population sizes (Olfelt et al. 1998; Ejupovic 2015) could exacerbate extinction risk (MacArthur 1972). Another goal of this study was to describe general trends in the cliff plant assemblages with which Leedy’s roseroot co-occurs. Crawley (1987) described cliffs as containing fewer invasive species than other ecosystems and suggested cliff plants experience a relative lack of interspecific interactions. Cooper (1997) proposed harsh conditions and disturbance regimes on cliffs combined with high ecosystem heterogeneity may allow potential competitors to coexist. Recently, Douglas Larson’s cliff ecology research group at the University of Guelph has advanced understanding of cliff species (e.g. Larson et al. 2000; Larson et al. 2004), but interestingly this group was more concerned with cliff flora represented in the ranks of weed, pioneer, and perhaps invasive species (Larson et al. 2004). The Larson group supported Cooper’s

56 (1997) hypothesized relationship between cliff heterogeneity and species diversity (Kuntz and Larson 2006), but did not test cliff resistance to invasion. They also attempted to describe life history strategies of cliff invaders (Larson et al. 2004) and hypothesized that many cliff invaders are opportunists, an observation also made by Zedler and Kercher (2004) for wetland invaders. This work has been discontinued (Douglas Larson, pers. comm.), so there is an apparent need for more research in the area of cliff invasion ecology and community assembly. For Leedy’s roseroot, intermediate disturbance regimes governed by cliff instability may be crucial for Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 removing other plants, including invasive species, which might otherwise compete for space and light (Connell 1978; Grime 1979), and cliff face heterogeneity is thought to facilitate extensive niche partitioning on cliff faces (Matthes-Sears and Larson 1995; Cooper 1997; Kuntz and Larson 2006). At Glenora Cliffs, I expected distinct species assemblages across the cliff, with Leedy’s roseroot co-occurring with a subset of all species present at Glenora Cliffs, specifically associating with other species restricted to the cliff face. This work will inform the FRP for Leedy’s roseroot, which expressed concern for invasion of Japanese knotweed (USFWS 1998, 2015) and black swallow-wort (Cynanchum rossicum) (USFWS 2015) at Glenora Cliffs. Understanding the effects of interspecific interactions on Leedy’s roseroot will also be relevant to conservation of this threatened subspecies. Ultimately, I hope this work will inform the poorly studied area of cliff plant ecology.

Methods Study Site In this study I sampled the Glenora Cliffs population of Leedy’s roseroot located in Glenora, Yates County, New York. Glenora Cliffs has several thousand Leedy’s roseroot individuals inhabiting approximately 3.2 km of east-facing cliff along Seneca Lake, one of the New York Finger Lakes (USFWS 1998). The cliff face is predominantly composed of thin, horizontally layered calcareous shale, interrupted by layers of siltstone (USFWS 1998), part of the Utica Shale formation (Rickard et al. 1970). The study area is within the Laurentian Mixed Forest Province ecoregion of the U.S. (Bailey 1994), and the plant community of Glenora Cliffs roughly follows the redcedar-oak Finger Lakes community type described by Mohler et al. (2006). Leedy’s roseroot grows in the sparse soil and loose shale gravel collecting within cliff

57 crevices, where soil pH is 6.8-7.5 (Clausen 1975). In approximately the northern half of Glenora Cliffs (TNC 1985-1991), Japanese knotweed forms a monoculture in the talus between the cliff base and the lake. The date of first invasion is unknown, but Japanese knotweed has been present in upstate New York since at least the 1920s (Weldy and Werier 2011). Regional climate is characterized by mean annual temperature of 9.1 ºC, mean winter temperature of -2.7 ºC, mean summer temperature of 20.5 ºC, and mean annual precipitation of 81.9 cm with heaviest precipitation occurring during the summer months (NOAA 2016a). Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

Japanese knotweed interaction sampling design Treatments and vegetation sampling Twenty treatment blocks were established within the area of Japanese knotweed invasion at Glenora Cliffs (Fig. 3.1). Blocks were established such that all treatments were within relatively homogenous conditions to reduce error due to environmental or population variability. Blocks were positioned from south (block 1) to north (block 20), covering approximately half of the total area of Japanese knotweed-Leedy’s roseroot interaction and approximately one-quarter of the entire length of the Glenora Cliffs population. Blocks included the basal 2 m of the cliff face (where Leedy’s roseroot interacts with Japanese knotweed) and the talus (where Japanese knotweed occurs), extending a variable distance to the lake shore (where Japanese knotweed distribution ends). Talus area of each plot was measured and used to calculate Japanese knotweed densities (stems/m2). Within each block, three experimental plots were established: Control, Cut, and Herbicide. Placement of plots within each block was ordered, with Control in the southernmost plot of each block, Herbicide in the northernmost unit, and Cut in the middle to minimize Herbicide impacts on Control plots. Each plot was 3 m wide and had a length extending from the cliff base to the end of the talus where the lake shore began. Within each block, plots were separated by a 1 m buffer to minimize additional edge effects. Japanese knotweed removal treatments were applied to treatment blocks in fall 2013 and fall 2014 to assess treatment success for controlling Japanese knotweed and impacts of treatments on Leedy’s roseroot. In fall 2013, Herbicide plots received a cut-stem application of a solution containing 53.8% glyphosate (Rodeo), an herbicide safe to use in aquatic areas and effective for Japanese knotweed control. In fall 2014, Herbicide treatment consisted of foliar

58 spray containing glyphosate (Rodeo, 53.8% glyphosate) diluted to a concentration of 2.69%, imazapyr (Arsenal, 27.8% imazapyr) at a concentration of 0.139%, and Kingpin spray adjuvant at 1% concentration. A cut-stem method was not appropriate for the second treatment because remaining and regrown Japanese knotweed stems in Herbicide plots were stunted from the previous year of herbicide application. Foliar spray was applied carefully to prevent impacts on Leedy’s roseroot. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

Figure 3.1 - Layout of a Japanese knotweed sampling block, consisting of three treatment plots.

Japanese knotweed stems were removed from Cut plots in fall 2013 and 2014. Because the cut-stem herbicide application consisted of a two-step treatment (cutting, then herbicide), Cut plots were included to control for a possible herbicide effect. Cut was not expected to result in decreases in Japanese knotweed biomass over time, whereas Herbicide was expected to reduce Japanese knotweed by the second year post-treatment (2015). Plots containing Leedy’s roseroot but no Japanese knotweed (hereafter, “Uninvaded”) were established along the basal 2 m of the cliff face. Uninvaded plots were selected from random GPS points known to contain Leedy’s roseroot, with the center of the 3 m wide x 2 m tall plot located at the random point. Different Uninvaded plots were randomly selected and measured in 2013, 2014, and 2015 to ensure this variable captured year-to-year variation in Leedy’s roseroot having never experienced effects of invasion. Pre-treatment (2012, 2013) and post-treatment (2014, 2015) sampling of Japanese knotweed and Leedy’s roseroot was performed in treatment blocks (Control, Cut, and Herbicide plots). Leedy’s roseroot in Uninvaded plots were sampled in 2013, 2014, and 2015. For every plot, total Leedy’s roseroot were counted. Functional traits quantified in each plot included: total

59 flowering Leedy’s roseroot, total Leedy’s roseroot stems, total flowering Leedy’s roseroot (measured 2013-2015 only), mean stem length of individuals in a plot (cm; 2013-2015), and mean leaves per stem of individuals in a plot (2013-2015). Seedlings (evidenced by presence of cotyledons) or individuals growing in the talus were recorded in 2015. For plots within treatment blocks, Japanese knotweed stand basal area (SBA, cm2/m2), stem density (stems/m2), mean stem diameter (cm), and percent cover (6 cover classes, Daubenmire 1959) were measured.

Consistent with the hypothesis of competition between Japanese knotweed and Leedy’s Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 roseroot, I expected Leedy’s roseroot totals to be higher in Uninvaded plots compared to invaded plots in the treatment blocks. Compared to Uninvaded plots, I predicted Leedy’s roseroot in invaded treatment block plots would show more resource allocation towards functional traits associated with growth and maintenance, such as longer stems and more leaves, rather than reproductive organs, flowers. Post-treatment, I expected Leedy’s roseroot counts and traits in Herbicide to be similar to Uninvaded and to diverge from Cut and Control.

Japanese knotweed plot abiotic sampling Photosynthetically active radiation (PAR, µmol/m2/s) was measured pre-treatment (2013) and post-treatment (2015) using a LiCor LI-250A light meter. A talus PAR recording and a cliff PAR recording were taken in each treatment plot. All PAR recordings took place on fair-weather days between 11:00 am and 4:00 pm in mid- to late summer. In general, measuring PAR for all plots at midday on the same day is best practice (Scanga 2009; Raney 2014) but was impossible due to the size of the area being measured. Pre-treatment (2013) PAR recordings occurred on two sequential days; eight PAR recordings were collected for a single location and I used the mean for analysis. For each post-treatment (2015) recording, one stabilized PAR reading was taken on two days one week apart and the mean was used for analysis. Temperature was measured pre-treatment (2012) using Thermochron iButtons waterproofed with silicone. In 10 out of 20 treatment plots, iButtons were placed in both the talus and in a crevice of the cliff face. In 12 Uninvaded plots, iButtons were placed on the cliff face. Temperature iButton readings were collected to the nearest 0.1 °C every hour for the period of August 30-September 27. For each iButton, max and min temperatures for the entire period were extracted. Post-treatment (2015) rock temperatures were taken (to the nearest 0.1 °C) at the talus and cliff for treatment blocks using an Extech dual-laser infrared thermometer. Infrared

60 thermometer recordings were taken between 11:00 am and 4:00 pm on two fair-weather days one week apart in mid- to late summer, and the mean was used for analysis.

Plant community sampling design In May 2015, 14 transects were established non-randomly at the New York Glenora Cliffs population (Fig. 2.1), each along a rappel line beginning on the forest slope above the cliff face (hereafter, “plateau”), and continuing vertically down the cliff into the talus in order to Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 capture environmental variability across the cliff face. Transects were approximately stratified across gradients of Leedy’s roseroot density (many, few, no individuals). Measurements were taken in five 1 m2 plots centered along a transect at ~3 m intervals, and included 1 plateau plot, 1 top cliff plot (mean distance from plateau plot=3.5 m), 1 mid-cliff plot (mean distance from plateau plot=5.5 m), and 1 lower cliff plot (mean distance from plateau plot=8.5 m). If the bottom of the cliff entered the lake, I established a base cliff plot (mean distance from plateau plot=9.2 m). If the cliff ended in the talus, I created a talus plot (mean distance from plateau plot=11.8 m).

Biotic and abiotic sampling In late June and early July 2015, I estimated percent cover (6 cover classes, Daubenmire 1959) for species within each 1 m2 sampling plot. Vascular plants were identified to genus or species, with the exception of some Asteraceae and Poaceae with flowers absent during the time of sampling. For nonvascular plants, one species of Marchantiophyta (liverworts) was identified to species, but bryophytes (mosses) and lichens were not identified past these broad classifications. Botanical nomenclature follows USDA Plants (USDA NRCS 2016). I also recorded Leedy’s roseroot presence (“Leedy’s plots”) or absence (“non-Leedy’s plots,” 3m+ from the nearest Leedy’s roseroot individual). Species richness for each plot was the number of species/m2. I measured PAR with a LiCor LI-250A light meter at the top-center of each plot, and rock temperature to the nearest 0.1 °C was measured with an Extech dual-laser infrared thermometer. PAR and rock temperature were measured for all transects in late June or early July 2015 on a fair-weather day between 11:00 am and 4:00 pm. Measuring PAR for all plots at midday on the same day (Scanga 2009; Raney 2014) was impossible due to the size of the area

61 being measured and time required to establish each transect. Long-term temperature readings were collected using Thermochron iButtons waterproofed with silicone and inserted into stable cliff crevices in each plot. Temperature readings from iButtons were collected to the nearest 0.5 °C every two hours May 25-October 2. I estimated cliff stability for cliff plots (not for talus or plateau) based on the protocol described by Benumof and Griggs (1999). Stability classes for cliff features (summarized in Appendix A) were estimated for cliff face plots (base cliff, lower cliff, mid-cliff, and top cliff) Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 and included crevice spacing (crevices per area), crevice dimensions (width, depth), crevice orientation (likelihood of rock-fall), weathering (from smooth edges to cliff rock appearing freshly cut and angular), amount of infill or loose rock debris within crevices, and seepage level/flow rate. My estimation of cliff stability features differed from the Benumof and Griggs (1999) protocol; I omitted measurement of rock strength with a Schmidt hammer and included a crevice depth measurement. Cliff feature measurements were compiled into a numeric variable called total cliff stability, on a scale between 28.5 and 107 (Appendix A). Each cliff feature was also considered individually as a factor for analysis. Seepage flow rate (mL/s) was intended to be measured quantitatively by placing sponges of consistent surface area against seeping faces/crevices for a standard time, then calculating relative seepage flow rates as the difference in sponge weight before and after sampling. This method was ineffective in the field, and seepage level/flow rates were instead classified as one of five categories from no seepage to point source flow as part of the cliff stability estimation protocol (Appendix A).

Statistical analysis Japanese knotweed interaction R version 3.2.2 (R Development Core Team 2016) was used for all statistical analyses. A significance level of α=0.05 was required to reject null hypotheses. Table 3.1 contains a summary of explanatory and response variables for analysis of the interaction between Japanese knotweed and Leedy’s roseroot. Leedy’s roseroot total and functional trait response variables were log-transformed for analysis and zeroes were removed. Counts were not converted to densities because all sampling plots were of equal area.

62 Table 3.1 - Summary of variables included in Japanese knotweed interaction analysis.

Variable type Name Data type Ordered factor (levels: Control, Explanatory Treatment Cut, Herbicide, Uninvaded) Ordered factor (levels: Pre, Post, Explanatory Time Post2, Uninvaded) Response Japanese knotweed SBA (cm2/m2) Continuous

Japanese knotweed mean stem diameter Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Response Continuous (mm) Japanese knotweed stem density Response Continuous (stems/m2) Response Japanese knotweed cover (%) Continuous Response Total Leedy’s roseroot Continuous Response Total flowering Leedy’s roseroot Continuous Response Total Leedy’s roseroot stems Continuous Response Total flowering Leedy’s roseroot stems Continuous Response Leedy’s roseroot mean stem length (cm) Continuous Response Leedy’s roseroot mean leaves per stem Continuous Response Talus max iButton temperature (°C) Continuous Response Talus min iButton temperature (°C) Continuous Response Cliff max iButton temperature (°C) Continuous Response Cliff min iButton temperature (°C) Continuous Response Talus rock temperature (°C) Continuous Response Cliff rock temperature (°C) Continuous Response Talus PAR (µmol/m2/s) Continuous Response Cliff PAR (µmol/m2/s) Continuous Random effect Block Unordered factor

I used linear mixed models (LMM) to test hypotheses about differences in response variables based on the interaction between treatment and time, and included a random intercept for block. An LMM was as follows,

63 Y = α + Xtrtβtrt + Xtimeβtime + Xtrtβtrt*Xtimeβtime + ai + ε Formula 3.1 - Linear mixed model (LMM)

where Y is a response variable, α is the unknown parameter for intercept, Xtrt is the explanatory variable treatment, Xtime is the explanatory variable time, βtrt and βtime are unknown parameters for slope, ai models the random intercept included to account for variance based sampling section, and ε captures the residuals (Zurr et al. 2009). For all LMMs, p-values reported for Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 model estimates and contrasts were calculated based on Satterthwaite’s approximation (SAS Institute 1978), which produces non-integer degrees of freedom (Kline 2016). To model changes in Japanese knotweed density and functional traits over time I used a univariate LMM. The Tukey method for computing multiple comparisons of least-squared means was used to identify sources of difference between groups; this method adjusts significance to minimize risk for type I error. I used Cohen’s d as a measure of effect size (Cohen 1988). Model assumptions were checked by examining residual plots, which suggested no serious violations of assumptions.

Plant community I used logistic regression to model the binomial response variable Leedy’s roseroot presence-absence as a function of the explanatory variable species richness. Logistic regression followed the general formula:

logit(π) = α + Xβ Formula 3.2 - Logistic regression where α, X, and β are as defined for Formula 3.1, the logit link function bounds fitted values between 0 and 1 as required for presence-absence data, and π is the probability term for a response variable following the binomial distribution. Odds ratios for logistic regression were computed as eβ, where e is the base of the natural logarithm. I used LMMs (Formula 3.1) with single explanatory variables to model the response variable species richness as a function of abiotic factors. Explanatory variables included in these analyses were daily mean temperature (°C), daily max temperature (°C), daily min temperature (°C), daily temperature range (°C), rock temperature (°C), PAR (µmol/m2/s), continuous distance from anchor (m), discrete location on

64 cliff, crevice infill, crevice depth, crevice width, crevice orientation, crevice spacing, weathering, and total stability. To analyze species assemblages, I used non-metric multidimensional scaling (NMS) ordination, a multivariate analysis method that uses a plot by species matrix to visualize species assemblages (metaMDS::vegan, Oksanen et al. 2015). NMS ordination was most appropriate analyzing species community data because it is a relatively assumption free multivariate analysis technique and is useful for analyzing matrices with many zeroes. The species matrix used for this Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 analysis included percent cover (midpoint for each cover class, e.g. 0%-5% was 2.5 in the matrix) for the 50 species found in the 69 sampling plots. Leedy’s roseroot was not included in the matrix to focus on species co-occurring with Leedy’s roseroot on the cliff face. Square-root transformation and Wisconsin double standardization were applied to the matrix prior to NMS ordination analysis to reduce final stress and improve visualization. Bray-Curtis distances were used as dissimilarity measures because they are often appropriate for ordering species community data along gradients (Faith et al. 1987). The NMS ordination solution was found using local monotone regression with weak treatment of ties (Kruskal 1964; Faith et al. 1987). I ran a maximum of 300 random starting iterations, and configurations were compared using the Procrustes method until a stable solution was reached. A two-axis solution and a three-axis solution were computed and compared. I used permutational multivariate analysis of variance (MANOVA, adonis::vegan, Oksanen et al. 2015) to test significance of explanatory variables in describing variation in the (untransformed, unstandardized) matrix. Permutational MANOVA partitions the matrix based on an explanatory variable, then performs pseudo-F-tests on permutations of the raw data (rather than the residuals) to obtain p-values (Anderson 2001). Permutational MANOVA can constrain permutations within blocking factors. I specified transect as a blocking factor to accommodate the experimental design. Explanatory variables tested via permutational MANOVA included Leedy’s roseroot presence-absence, continuous distance to the nearest Leedy’s roseroot individual (cm), and the abiotic factors listed above as explanatory variables for univariate LMMs describing species richness. Significant factor explanatory variables were overlaid as polygons on the NMS ordination solution, grouping plots that shared values for a given variable. Multivariate homogeneity of variance of factor explanatory variables, an assumption of permutational MANOVA, was tested using the multivariate dispersion test (vegan::betadisper, Oksanen et al. 2015) described by Anderson (2006). For significant

65 continuous explanatory variables, the NMS ordination solution was overlaid with smoothing surfaces defined with a generalized additive model using thin-plate regression splines (vegan::ordisurf, Oksanen et al. 2015). I used indicator power (IP) index (vegan::indpower, Oksanen et al. 2015) to identify indicator species commonly occurring with Leedy’s roseroot. This method is appropriate for identifying indicators for a target species of conservation value, and species with low IP predict the absence of the target species (Halme et al. 2009). IP represents an improvement on other Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 similar indices because it is relatively conservative, uses all information in the species matrix, and remains robust when indicator and target species differ in evenness (Halme et al. 2009). To calculate IP, the species matrix was converted to presence-absence, and Leedy’s roseroot was included in the matrix. For prediction of Leedy’s roseroot absence, I used a conservative threshold of IP=0.000 and p=1.00.

Results Japanese knotweed interaction In 2015, Japanese knotweed treatment blocks contained a total of 344 Leedy’s roseroot individuals, constituting about 7.6% of the 4,515 individuals at Glenora Cliffs (Chapter 2, this thesis). Number of stems per individual ranged from 1 to 83. Mean stem length was 11.4 cm, with a mean of 32 leaves per stem. A total of 161 individual plants were flowering, roughly half of all plants within treatment plots, and 674 total inflorescences were counted. Of the 344 total plants within treatment plots, 7 plants were growing in the talus and 100 plants were seedlings, meaning 29% of all plants counted were seedlings. The seedlings were concentrated mostly within a few treatment plots; one plot had 53 seedlings. Uninvaded plots in 2015 contained a total of 110 plants (2.4% of Glenora Cliffs) and 787 stems. Number of stems per individual ranged from 1 to 13 and mean stem length was 16.7 cm, with a mean of 39 leaves per stem. A total of 75 individual plants were flowering, 68% of all plants in Uninvaded, and 297 total inflorescences were counted. Of the total 110 plants within Uninvaded, 0 were growing in the talus and 3 were seedlings. Herbicide significantly reduced Japanese knotweed SBA (Appendix D1, LMM). Herbicide plots had lower Japanese knotweed SBA post-treatment compared to Cut (p<0.001, d=1.606) and Control (p<0.001, d=1.314), as well as after two years of treatment compared to

66 Cut (p<0.001, d=2.530) and Control (p<0.001, d=2.207) (Appendix E1) (Fig. 3.2). Similar trends were observed for Japanese knotweed mean stem diameter (Appendix D2, LMM), stem density (Appendix D3, LMM), and percent cover (Appendix D4, LMM). Across treatments, stem diameter decreased over time (Appendix D5) and stem density increased over time (Appendix D6). Total Leedy’s roseroot was higher in Uninvaded plots compared to pre-treatment Control plots in 2012 (p=0.020, d=1.103; Appendix D7, LMM), but no other comparisons were Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 significantly different from each other (Appendix E2). Total flowering Leedy’s roseroot differed significantly across treatments (Appendix D8, LMM; Fig. 3.3). Compared to Uninvaded plots, all pre-treatment blocks had decreased total flowering Leedy’s roseroot, including pre-treatment Herbicide plots (p<0.001, d=1.252), Cut plots (p=0.005, d=1.124), and Control plots (p<0.001, d=1.347). Other comparisons are summarized in Appendix E3. Mean Leedy’s roseroot stem length also revealed significant trends across treatments and time (Appendix D9, LMM; Fig. 3.4). After two years of treatment, Leedy’s roseroot in Herbicide plots had shorter mean stem length than Uninvaded plots (p<0.001, d=1.631), pre-treatment Control plots (p<0.001, d=2.402), Cut plots (p<0.001, d=2.578), and Herbicide plots (p<0.001, d=1.275), as well as post-treatment Control plots (p=0.003, d=1.697), Cut plots (p<0.001, d=1.824), and Herbicide plots (p=0.042, d=1.578). Other comparisons are summarized in Appendix E4. Pre-treatment, cliff max iButton temperatures in Uninvaded plots were significantly higher compared to plots with Japanese knotweed (Herbicide, p<0.001; Cut, p<0.001; Control, p<0.001; Appendix E5) (Appendix D10, LMM) (Fig. 3.5A). Post-treatment, cliff rock temperatures were significantly higher in Herbicide plots compared to Cut (p<0.001) and Control (p<0.001) (Appendix E6) (Appendix D11, LMM; Fig. 3.5B), with similar trends for talus rock temperatures (Appendix D12, LMM). Cliff PAR did not differ significantly across treatments or time. Talus PAR was higher in post-treatment Herbicide plots compared to pre-treatment PAR for all treatment plots (Herbicide, p<0.001; Cut, p<0.001; Control, p<0.001), and compared to post-treatment Cut (p<0.001) and Control (p<0.001) (Appendix E7) (Appendix D13, LMM) (Fig. 3.6).

67 * Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 *

Figure 3.3 - Boxplots comparing log-transformed total flowering Leedy’s roseroot count across treatments and time. Different letters represent significant differences, based on Tukey method for computing multiple comparisons of least-squared means performed as a post hoc test for LMM. Boxes contain values within first and third quartiles, and bold line shows the mean.

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A) * B)

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AB A AB BC ABC

70 Plant community Leedy’s plots had higher species richness (species/m2) than non-Leedy’s plots (df=68, β=0.458, odds ratio=1.580, p=0.002, logistic regression). Across the cliff face, different discrete locations on the cliff were associated with differences in species richness (Appendix D14) (Fig. 3.7), with some cliff locations having significantly higher species richness compared to plateau or talus (Appendix E8). Higher richness was also associated with more moderate weathering

(β=1.930, t=3.140, p=0.003, LMM) and thicker crevice infill (β=-1.020, t=-2.018, p=0.049, Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 LMM). Two-axis and three-axis NMS ordination solutions were found to describe species assemblages, giving final stresses of 0.152 and 0.115 respectively. I chose the two-axis solution for ease of interpretation and because the final stress of 0.152 was acceptably low (McCune and Grace 2002). Species assemblages based on Leedy’s roseroot presence-absence were significantly different (p<0.001, r2=0.051, permutational MANOVA; Fig. 3.8), and spread of multivariate variance was higher for non-Leedy’s plots compared to Leedy’s plots (df=1, dfresidual=67, F=21.293, p<0.001, multivariate dispersion test). Continuous distance to the nearest Leedy’s roseroot also significantly described variation in the ordination (p=0.002, r2=0.036, permutational MANOVA). Species with high indicator power for Leedy’s roseroot included Asplenium trichomanes (IP=0.806, p=0.05), Bidens sp. (IP=0.766, p=0.05), Poaceae spp. (IP=0.672, p<0.01), Sonchus asper (IP=0.672, p=0.03), and Geranium robertianum (IP=0.638, p<0.01). Species predictive of Leedy’s roseroot absence (IP=0.000, p=1.00) included Marchantia polymorpha, Bromus sp., Impatiens capensis, Campanula rotundifolia, Taxus canadensis, Verbascum thapsus, Calystegia sepium, Vaccinium sp., Draba arabisans, Myosotis scorpioides, Vitis riparia, Rubus odoratus, Maianthemum racemosum, Sambucus canadensis, Linaria vulgaris, Fraxinus americana, Epipactis helleborine, Carya ovata, and Carex spp. Location on cliff also significantly described variation in the the ordination (p<0.001, r2=0.123, permutational MANOVA). Comparison of species assemblages of only three locations (plateau, all cliff face plots, and talus) also indicated significant differences (p<0.001, r2=0.090, permutational MANOVA; Fig. 3.9). No abiotic variables measured significantly described variation in the NMS ordination. Minimum daily temperature may have been biologically significant (p=0.061, r2=0.026, permutational MANOVA). Figure 3.10 shows the same NMS

71 ordination as Figures 3.8 and 3.9 but points are sampling plots. Overlaid on Figure 3.10 is a smooth surface for daily minimum temperature. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

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plateau

talus cliff

Figure 3.9 - Species for NMS ordination of 69 plots, final stress=0.152. Polygons for three locations on cliff overlaid. Key to species codes in Appendix F.

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Figure 3.10 - NMS ordination of 69 plots, final stress=0.152. Daily min temperature (°C) smoother overlaid.

Discussion Japanese knotweed treatment Herbicide treatment effectively reduced Japanese knotweed. Japanese knotweed SBA, stem density, stem diameter, and percent cover were lower in post-treatment Herbicide plots compared to Control and Cut. Effective elimination of Japanese knotweed was important to allow for conclusions about how experimentally-imposed presence vs. absence of Japanese knotweed may impact Leedy’s roseroot totals and functional traits. An unexpected pattern was seen in Japanese knotweed stem diameter and density over time. Japanese knotweed appeared to change resource allocation post-treatment, shifting from fewer large stems to a greater number of smaller stems per m2. This change in allocation was seen across plots, not only in plots treated with herbicide. Differences in weather during the period of 2012-2015 may explain differential allocation in Japanese knotweed. The region experienced moderate drought June-August 2012 (NOAA 2016b), the first pre-treatment year of this study. Drought, or other environmental conditions, may have favored the fewer, larger Japanese knotweed stems per m2 seen in 2012 and 2013. However, lake water permeates the

74 talus where Japanese knotweed grows, and water levels at Seneca Lake are heavily monitored and regulated (NYSDOS 2014), likely providing Japanese knotweed with ample water even during droughts. I expect differential resource allocation of Japanese knotweed was attributable to translocation of herbicide among plots through Japanese knotweed rhizomes. Borders were established between treatment plots, but 1 m may have been insufficient to separate Japanese knotweed stands, which consist of clonal ramets joined by rhizomes extending up to 15-20 m Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 laterally and 1-2 m underground (Locandro 1973). Each block in this study was 12 m long, meaning Japanese knotweed in treatment plots may have been united by rhizomes causing treatment in one plot to result in effects across the block. Both Herbicide and Cut treatments may have affected Japanese knotweed resource allocation; results of this study indicated herbicide reduced aboveground Japanese knotweed biomass, but others have shown cutting alone can reduce belowground biomass (Seiger and Merchant 1997). In addition, the timing of treatment application in this study may have unintentionally favored Japanese knotweed regeneration from rhizomes. Both removals were performed in the fall, at which point Japanese knotweed would have already shifted carbon from aboveground to belowground for the winter (Price et al. 2002). Many have described Japanese knotweed’s potential to regenerate from rhizomes; less well- understood is how its rhizome physiology interacts with herbicide application, but future research in this area will help improve control of this invasive species (Bashtanova et al. 2009). Harnessing Japanese knotweed’s ability to translocate herbicide could help control this invasive species.

Japanese knotweed-Leedy’s roseroot interaction Results of this study allowed for an estimate of the extent to which Leedy’s roseroot interacts with Japanese knotweed on a population level. I estimated the treatment blocks of this study, which had 7.6% of all plants at Glenora Cliffs in 2015, spanned about half of the full invasion. Assuming Leedy’s roseroot was evenly distributed both across this length of cliff and vertically up the cliff (although Chapter 2 of this thesis suggested Leedy’s roseroot was concentrated on the lower portion of the cliff, potentially in the Japanese knotweed interaction zone), an estimated 15.2% of Leedy’s roseroot at Glenora Cliffs would have interacted with Japanese knotweed. Globally, this translates to approximately 10% of all Leedy’s roseroot

75 experiencing Japanese knotweed interaction, given that Glenora Cliffs had about two-thirds of all Leedy’s roseroot in the world in 2015 (Chapter 2, this thesis). Of all potential threats listed in the FRP (USFWS 1998, 2015), Japanese knotweed likely affects the largest proportion of Leedy’s roseroot individuals, making the results of this study highly relevant for conservation of this threatened subspecies. Many studies of interspecific interactions face the challenge of teasing apart aboveground and belowground competition (van der Putten et al. 2009), but quantifying the mechanism of Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 interaction in this study system was more straightforward. With the exception of a small number of Leedy’s roseroot found growing in the talus, Japanese knotweed and Leedy’s roseroot occupy different rooting spaces, Japanese knotweed in the talus and Leedy’s roseroot on the cliff face, meaning the two species do not compete for soil space, water, or nutrients. Results of this study suggested Japanese knotweed impacts Leedy’s roseroot through shade, which reduces the amount of light reaching the talus and at the cliff base (although my measurement of PAR on the cliff may have been inaccurate, see Matthes-Sears et al. 1997) and is associated with decreased talus temperature. Moisture was not measured, but may also be affected; the Japanese knotweed canopy could either increase humidity or decrease precipitation received by Leedy’s roseroot. However, Chapter 2 of this thesis found that Leedy’s roseroot is concentrated at areas of seepage in the cliff face--a water source that Japanese knotweed cannot impact. My results align with a meta-analysis finding shade to be the most common mechanism of interaction between invasive species and rare species (Roberts et al. 2015). My results suggest Japanese knotweed shade may affect Leedy’s roseroot functional traits, decreasing flowering in Leedy’s roseroot. I found Japanese knotweed shade was associated with decreased flowering Leedy’s roseroot individuals compared to Leedy’s roseroot in uninvaded areas. This trend was not observed consistently across years, indicating effects of Japanese knotweed shade on Leedy’s roseroot may be stronger in some years, perhaps due to annual variation in weather events. As previously mentioned, a moderate drought occurred in summer 2012, and Leedy’s roseroot may have still been in recovery in 2013. Alternatively, experimenter presence may have changed the interaction between Japanese knotweed and Leedy’s roseroot, even in Cut and Control, by repeatedly walking through plots over several years and potentially pushing Japanese knotweed away from the cliff face. Herbicide removal of Japanese knotweed was not associated with significant increases in flowering compared to Cut

76 and Control. This study included only two years of post-treatment observations, which may not be long enough to see effects on Leedy’s roseroot: a long-lived, stress-tolerant species with storage organ rhizomes (Clausen 1975). However, the observation that Leedy’s roseroot flowers readily only a few months after germinating in the greenhouse (pers. obs.) may indicate two years was a long enough period to detect any potential interaction between these two species. Truly neutral interactions between Japanese knotweed and Leedy’s roseroot flowering are another possibility; Leedy’s roseroot flowers in late spring, while Japanese knotweed stems have Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 not yet reached their maximum heights. Overall, this study’s finding of decreased flowering associated with invasion that did not persist across years and was not a result of the experimental manipulation imposed is weak evidence to support large-scale removal of Japanese knotweed at Glenora Cliffs. After applying herbicide for two years, Leedy’s roseroot in Herbicide plots had mean stem lengths about half as long as in Uninvaded, while Leedy’s roseroot experiencing Japanese knotweed shade (Cut and Control) had stems of intermediate length. This finding was unexpected because herbicide treatments were intended to restore Leedy’s roseroot to its invasion-free condition, in this case longer rather than shorter stems. In Chapter 2 of this thesis, I found increased stem length was correlated with higher moisture and lower temperature and PAR. The longer stems in Uninvaded plots may have been a result of abiotic differences; although cliff temperatures were higher in Uninvaded than in invaded plots (seeming to indicate shorter rather than longer stems), neither moisture nor PAR were measured in Uninvaded plots. However, Herbicide plots did experience increased PAR and increased temperature (thus more moisture evaporation), the likely mechanism behind shorter stems in Herbicide plots. Shorter stems are not necessarily detrimental to Leedy’s roseroot, but are evidence of individuals responding to the experimental manipulation of the abiotic conditions they experience. Leedy’s roseroot light, temperature, and moisture requirements are still not fully understood (Chapter 2, this thesis), but this rare subspecies may be restricted to open, high light areas similar to conditions along glacial margins during the Pleistocene (Olfelt and Freyman 2014). If high light conditions are a crucial aspect of Leedy’s roseroot niche, shortened stems in Herbicide plots would not indicate stress. Alternatively, increased light/temperature conditions are associated with decreased moisture that may stress Leedy’s roseroot, a species restricted to seeping areas (Chapter 2, this thesis). Leedy’s roseroot is restricted to north-facing cliffs throughout most of its

77 range and east-facing cliffs in New York (USFWS 1998). Leedy’s roseroot is likely restricted to these aspects because it requires the higher moisture linked with aspect (Warren 2010), and because it can tolerate extreme aspect-associated temperature conditions (Buffo et al. 1972) that exclude other species. If Leedy’s roseroot is sensitive to climate variables associated with aspect, increased PAR and temperature associated with removal of Japanese knotweed would be undesirable, especially in the only east-facing population of Leedy’s roseroot, which already receives more direct sunlight than north-facing populations (Buffo et al. 1972). Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Changes in Leedy’s roseroot functional traits suggest its interaction with Japanese knotweed may be a combination of competitive and facilitative effects. Decreased flowering suggests Japanese knotweed outcompetes Leedy’s roseroot for light. Increased stem length indicates Japanese knotweed shields Leedy’s roseroot from solar heat re-radiating off of open talus--a condition imposed by this study’s herbicide application that resulted in shorter stems. Such shielding is not necessarily facilitative; adult Leedy’s roseroot individuals tolerate some of the higher temperatures and PAR present on the cliff face (Chapter 2, this thesis). Leedy’s roseroot seedlings are almost certainly less drought-tolerant than adults due to their lack of a developed storage rhizome and may benefit from light and temperature mediating effects associated with Japanese knotweed presence. I was not able to measure abiotic or biotic gradients associated with seedlings. Results of this study also suggested effects of Japanese knotweed on Leedy’s roseroot may be stronger in certain years, perhaps oscillating between facilitation and competition depending on the environment (Choler et al. 2001), although an alternative explanation of year- to-year variability could be due to sampling effects. For example, presence of researchers around Japanese knotweed could have compacted the talus around the Japanese knotweed, forcing the canopy away from the cliff and thereby changing the interaction between Japanese knotweed and Leedy’s roseroot across all treatments. Indeed, post-treatment PAR on the cliff face did not differ among treatments, lending credence to the idea that results were impacted by sampling effects. If the variation across years detected was instead a result of macroclimate variability, this observation suggests Japanese knotweed shade in combination with harsh weather, increased extreme weather events as a result of climate change, or other stressors may amplify negative impacts on Leedy’s roseroot at Glenora Cliffs. Even periodic impacts on Leedy’s roseroot flowering could, over time, translate into depletion of genetic diversity, decreased population

78 size, and potentially extinction. Glenora Cliffs should continue to be censused in the future to monitor additional changes in fertility at the population level. Overall, one might generalize that Leedy’s roseroot responds to shade from other species or rock formations (associated with decreased light and temperature and, by extension, increased moisture) by decreasing flowering, and that open conditions (increased light and temperature, decreased moisture) are associated with shorter Leedy’s roseroot stems. For interspecific interactions involving shade alone, Leedy’s roseroot will probably respond similarly. Results Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 may be less generalizable for interactions with species that have different life forms or mechanisms of competition, perhaps for example the invasive vine Cynanchum rossicum (black swallow-wort) at Glenora Cliffs.

Leedy’s roseroot life history The data collected in this study allowed for limited conclusions about Leedy’s roseroot colonization and mortality. Many Leedy’s roseroot seedlings were counted, but they occurred in only a few treatment plots, aligning with the prediction of Larson et al. (2000) that cliff plant colonization should occur opportunistically, with colonization events being few and far-between but highly concentrated. Unfortunately for this study, such a distribution of seedlings resulted in sample sizes too low to compare seedling density across abiotic and biotic gradients, preventing detection of patterns associated with Leedy’s roseroot colonization. Abiotic and biotic factors composing the regeneration niche for Leedy’s roseroot may differ from the niche of adult Leedy’s roseroot (Grubb 1977), as has been described by this thesis. Mortality of Leedy’s roseroot is occasionally observed (Joel Olfelt, pers. comm.), but the length of time covered by this study was likely not long enough to see significant mortality in such a long-lived, stress-tolerant perennial (Clausen 1975), even if Japanese knotweed presence or absence truly was associated with Leedy’s roseroot mortality. The only potential Leedy’s roseroot cause-of-death observed during this study was associated with Leedy’s roseroot found in the talus. In this experiment, few Leedy’s roseroot individuals (2% in 2015) were observed in the talus. It is thought seed dispersal to the talus is not limited (Olfelt et al. 1998; Booth 1999), and cliff instability likely makes dislodgement of adult Leedy’s roseroot individuals fairly frequent, but it is uncertain which factors prevent Leedy’s roseroot from succeeding in the talus. Chapter 2 of this thesis discussed Leedy’s roseroot restriction to the cliff face on the basis of

79 abiotic factors, but biotic factors may also play a role. Competitors for light or soil resources-- including Japanese knotweed or other species like the vines Calystegia sepium, Vitis riparia, and Solanum dulcamara present at Glenora Cliffs--may decrease colonization or increase mortality of Leedy’s roseroot in the talus. Future work should test hypotheses regarding how abiotic and biotic factors impact Leedy’s roseroot throughout its life history (Grubb 1977; Stearns 1992). Increased understanding of important threats at different life stages would help estimate the long- term viability of this rare subspecies. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

Plant community Species richness was higher on the cliff face and in Leedy’s plots, seemingly indicating Leedy’s roseroot was concentrated in areas of more rather than less potential competition. This finding appears to contrast with my expectation that Leedy’s roseroot is a poor competitor, restricted to areas where other species cannot persist, and with predictions of others that cliffs should have less competition compared to adjacent areas (Karlsson 1973). However, Nekola (1999) described cliffs as having steeper species-area curves than other systems. Consistent with this idea, the present study captured more richness within the sampling area of 1 m2 on the cliff face compared to a 1 m2 plot on plateau or talus. High cliff face heterogeneity (Cooper 1997; Kuntz and Larson 2006) may explain the capacity of cliffs to allow species coexistence and niche partitioning within small areas (Matthes-Sears and Larson 1995). Results of Chapter 2 of this thesis indicated heterogeneity of temperature, PAR, and rock features across the cliff face, and between the cliff face, talus, and plateau, meaning a large number of niches for various species are probably present across this cliff face. The present study also found species richness was correlated with moderately stable to unstable measures of most aspects of cliff stability, in line with the intermediate disturbance hypothesis (Connell 1978) and studies of other stress- tolerant cliff species (Kuntz and Larson 2006; Matthes-Sears and Larson 1995). In this study I was unable to quantify many aspects of Leedy’s roseroot niche, or the degree of niche overlap between species. Future studies should test the prevalence of competition and niche partitioning on cliff faces. Despite making up <1% of land cover (Larson et al. 2004), cliffs house a disproportionate number of rare and endemic plants in many regions (Larson et al. 2000), including the eastern U.S. (Loehle 2006; Marcinko 2007). If cliffs, due to their heterogeneity and

80 lack of disturbance from humans and herbivores (Larson et al. 2004), can harbor higher richness than other ecosystems, conservation should prioritize cliff communities. Results of the present study also indicated different species assemblages between the cliff face, talus, and plateau, with differences in minimum daily temperature May 25-October 2 as a potential driver of species assembly, in line with the findings of others (e.g. Bunce 1968; Cooper 1997). Other abiotic factors were not significant drivers of assembly at Glenora Cliffs, which is surprising given that resource heterogeneity has been tied to niche partitioning across cliff faces Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 (Kuntz and Larson 2006). Species at Glenora Cliffs do not fit neatly within any community type. Based on site features, the redcedar-oak Finger Lakes community (Mohler et al. 2006) would appear a viable candidate. At Glenora Cliffs, characteristic species of the redcedar-oak community included Acer saccharum, Solidago juncea, and Maianthemum racemosum. The calcareous cliff community type of New York state (Edinger et al. 2014), characterized by Cystopteris bulbifera, Campanula rotundifolia, Geranium robertianum, may be a better classification for Glenora Cliffs, an outcrop of calcareous shale (USFWS 1998). Within the cliff face assemblage, a distinct species assemblage co-occurred with Leedy’s roseroot, supporting the idea that rare species inhabit a narrow niche (Brown 1984). Multivariate dispersion was lower for Leedy’s plots compared to non-Leedy’s plots, which could be interpreted as more species evenness among Leedy’s plots (Anderson et al. 2006), although it should be noted that differences in dispersion could alternatively indicate a violation of the assumption of homogeneity of variance [Anderson 2006]. Species were identified as indicators of Leedy’s roseroot, including Asplenium trichomanes, Sonchus asper, Geranium robertianum, a Bidens species, and Poaceae species. These species may co-occur with Leedy’s roseroot due to their similar cliff face niches. Rocky areas are listed as habitats for Asplenium trichomanes (Wagner et al. 1993) and Geranium robertianum (Weldy and Werier 2011). Sonchus asper and several species of Bidens occupy waste places (Weldy and Werier 2011), consistent with the observations of Marks (1983) and Larson et al. (2004) that species of anthropogenic waste places originated on cliffs. Indicator species could help identify new sites for Leedy’s roseroot establishment in New York. Future work might seek to understand interactions between Leedy’s roseroot and these co-occurring species. For example, some species, particularly bryophytes, can act as nurse plants to assist vascular plant establishment in harsh environments (Groeneveld et al. 2007; Ren et al. 2010), and could be an important part of the life history of Leedy’s roseroot and

81 other cliff plants. Mosses are abundant in two other Leedy’s roseroot populations: Whitewater WMA, Minnesota, and Harney Peak, South Dakota (pers. obs.). Two species characterizing Leedy’s plots were non-native, Geranium robertianum and Sonchus asper. Interactions between Leedy’s roseroot and these non-native weedy species may be of concern, although cliffs are hypothesized to be the original habitat of many weeds (Larson et al. 2004). Perhaps as important as common associates are species present in the cliff face assemblage with which Leedy’s roseroot does not co-occur. These species could be restricted to Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 niches diverging from that of Leedy’s roseroot, or may be competitively excluding Leedy’s roseroot. Species associated with Leedy’s roseroot absence included woody species such as Taxus canadensis, Vaccinium sp., Rubus odoratus, Maianthemum racemosum, Sambucus canadensis, Fraxinus americana, Vitis riparia, and Carya ovata. Because woody species often require higher resource conditions, these species may be restricted to areas of Glenora Cliffs where Leedy’s roseroot does not occur. Calystegia sepium, a species restricted to the talus, logically predicted absence of Leedy’s roseroot, which was restricted to the cliff face in the plots sampled. Species like Impatiens capensis and Marchantia polymorpha likely require more water than occurs at Leedy’s roseroot microsite. Other species including Myosotis scorpioides, Linaria vulgaris, Epipactis helleborine, Draba arabisans, Carex spp., Bromus sp., and Verbascum thapsus were rare within the matrix (four or fewer occurrences), and may have been absent from Leedy’s plots by chance. Campanula rotundifolia is a species of the cliff face that was not rare within the matrix, but this species predicted Leedy’s roseroot absence. Leedy’s roseroot and C. rotundifolia may be restricted to different niches along the highly heterogeneous cliff face (Kuntz and Larson 2006), or they may exclude one another from similar niches via competition; both species are stress tolerant, long-lived perennials. The FRP expressed high concern for competitive interactions between Leedy’s roseroot and invasive species at Glenora Cliffs (USFWS 1998, 2015). The species assemblages recorded in this study do not suggest cliffs are less invasible than other systems (Crawley 1987) or especially prone to colonization by invasive species with specific traits or strategies (Larson et al. 2004; Zedler and Kercher 2004). Invasive species of various habits including herbaceous (Alliaria petiolata), subshrub (Japanese knotweed), and vine (Cynanchum rossicum, Calystegia sepium) succeed in this system, in some cases dominating. Glenora Cliffs may be more invasible than other cliffs, perhaps due to disturbance regime; horizontally-bedded shale is presumably

82 less stable (more disturbed) than more massively bedded rock types. Continued invasive species monitoring at Glenora Cliffs is recommended.

Conservation implications Results of this study supported the idea that Japanese knotweed has extensive rhizomes extending several meters laterally (Locandro 1973), allowing this invasive species to translocate herbicide among aboveground and belowground parts (Bashtanova et al. 2009). To take Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 advantage of translocation patterns in Japanese knotweed, land managers might consider using xylem- and phloem-mobile herbicides in combination at different times in the growing season (Bashtanova et al. 2009). Glyphosate was the primary herbicide used in this study, which is phloem-mobile (Gougler and Geiger 1981), and we saw some evidence of translocation effects on Japanese knotweed stands. In this system, however, large-scale removal of Japanese knotweed for Leedy’s roseroot conservation appears unnecessary. Clear, consistent negative impacts of Japanese knotweed on Leedy’s roseroot were not a finding of this study. I found Japanese knotweed shade decreased temperature and perhaps light experienced by Leedy’s roseroot, and was associated with decreased flowering in some years compared to uninvaded areas. Herbicide removal of Japanese knotweed increased PAR and talus temperature, conditions which resulted in shorter Leedy’s roseroot stems. I recommend Glenora Cliffs continue to be censused in the future to monitor additional changes in fertility at the population level. Ex situ conservation of Leedy’s roseroot could be an additional conservation strategy to consider. Leedy’s roseroot could be out-planted into cliff crevices with appropriate features to establish new populations or bolster existing populations. Chapter 2 of this thesis suggested abiotic factors, including seepage, high light, and cliff rock features, associated with Leedy’s roseroot. The present study suggested indicator species, including species of cliffs and waste places such as Asplenium trichomanes, Sonchus asper, and Geranium robertianum. Observations of other populations (Table 1.1) indicate aspect is important, as previously mentioned, while rock type is not necessarily important.

Conclusion

In this study I explored interactions of Leedy’s roseroot with the biotic community at Glenora Cliffs, with a focus on invasive Japanese knotweed, which was experimentally removed

83 in a three-treatment block design. Species community analysis suggested Leedy’s roseroot at Glenora Cliffs co-occurs with an assemblage of other cliff-dwelling species that may share niche characteristics with Leedy’s roseroot, although none of the abiotic factors I analyzed were significantly associated with assembly. Species co-occurring with Leedy’s roseroot could serve as indicators of microsites appropriate for Leedy’s roseroot on other cliffs in the region. For the Japanese knotweed removal study, herbicide reduced Japanese knotweed and had unexpected stand-wide effects on Japanese knotweed resource allocation, perhaps evidence of herbicide Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 translocation through interconnected rhizome systems. Japanese knotweed presence was associated with decreased flowering in Leedy’s roseroot, although herbicide application did not significantly increase Leedy’s roseroot flowering. Two years of herbicide removal of Japanese knotweed allowed more light to reach the talus, increased temperature of the talus and cliff base, and was associated with shorter Leedy’s roseroot stems in 2015. The main goal of this study was to assist Leedy’s roseroot conservation by determining whether Japanese knotweed invasion at Glenora Cliffs posed a threat to Leedy’s roseroot. Japanese knotweed removal was not recommended because both presence and absence of Japanese knotweed were associated with changes in abiotic factors and Leedy’s roseroot traits compared to areas where Japanese knotweed had never grown, and because negative effects of Japanese knotweed shade on flowering were not consistent across the period of this study. To facilitate Leedy’s roseroot conservation, I recommended continued monitoring of Leedy’s roseroot fertility at Glenora Cliffs and identified indicator species for new sites for Leedy’s roseroot establishment. Better understanding of Leedy’s roseroot life history and mortality will be important for understanding how its populations change over time. Although adults are long-lived and appear currently secure despite changes in abiotic factors, lack of regeneration could be a concern. I had hoped this study would identify factors associated with Leedy’s roseroot seedling establishment and describe how Japanese knotweed or other species might either exclude or facilitate seedlings. I described observations regarding Leedy’s roseroot establishment and mortality, but additional study of Leedy’s roseroot life history is required. Future work should also seek to better understand life history and interspecific interactions of cliff species in general, an understudied area. To this aim, I found higher richness on the cliff compared to talus and plateau, and species assemblages unique to each of these locations.

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90 CHAPTER 4: CONCLUSION TO THESIS

This thesis aimed to assist conservation of Leedy’s roseroot by quantifying factors associated with its presence and by addressing recovery criteria and threats listed in the FRP (USFWS 1998) and Five-Year Review (USFWS 2015). This research took place at Glenora Cliffs, Glenora, New York, the largest population of this subspecies in the world. In Chapter 2, I presented results of censuses of Glenora Cliffs over a 12-year period and identified abiotic Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 factors associated with Leedy’s roseroot. From 2003 to 2015, Leedy’s roseroot at Glenora Cliffs declined in flowering rate and increased in sterility in some sections of the population. Abiotic factors correlated with Leedy’s roseroot included increased seepage and lower heights on the cliff (where I found seepage to be concentrated), increased light, lower weathering, and higher mean and maximum temperatures during summer. Increased Leedy’s roseroot stem length was correlated with lower light and wtih temperatures and features of the cliff face. In Chapter 3, I considered the plant species community co-occurring with Leedy’s roseroot at Glenora Cliffs, with a specific focus on impacts of the invasive species Japanese knotweed, which was experimentally removed in a three-treatment block design. Species community analysis suggested Leedy’s roseroot co-occurs with an assemblage of other cliff-dwelling species at Glenora Cliffs. Japanese knotweed was associated with decreased flowering in Leedy’s roseroot compared to areas never invaded by Japanese knotweed. Plots in which Japanese knotweed was removed with herbicide exhibited increased temperature and light, and shorter Leedy’s roseroot stems compared to both invaded areas and areas having never experienced invasion. The work presented in this thesis addresses delisting criteria listed in the FRP and provides additional information about several potential threats identified (USFWS 1998, 2015). First, this work facilitated identification, contact, and education of landowners at Glenora Cliffs. Agreeing on conservation goals among about 24 different landowners will be the biggest challenge for delisting of the Glenora Cliffs population (USFWS 2015). Therefore, establishing relationships between landowners, the U.S. Fish and Wildlife Service, and SUNY-ESF was an important outcome of this study. Many landowners at Glenora Cliffs seemed proud to have a rare species growing on their property and were interested in learning about its ecology. The main impetus for this thesis was to determine whether Japanese knotweed invasion at Glenora Cliffs posed a threat to Leedy’s roseroot, another potential threat outlined by the FRP (USFWS 1998, 2015). Results of our Japanese knotweed removal experiment suggest largescale

91 Japanese knotweed removal at Glenora Cliffs may not be worth the investment at this time. Although presence of Japanese knotweed was associated with changes in abiotic conditions of the talus and cliff, negative effects of Japanese knotweed shade on Leedy’s roseroot flowering were not consistent across the four years of this study, sampling effects may have influenced results, and removal of Japanese knotweed with herbicide was not associated with increases in Leedy’s roseroot total or flowering. Monitoring of Leedy’s roseroot over a longer period may be necessary to determine what effects, if any, Japanese knotweed has on Leedy’s roseroot. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 However, the observation that Leedy’s roseroot flowers readily after only a few months after germinating in the greenhouse (pers. obs.) may indicate two years was a long enough period to detect any significant interaction between these two species. Alternatively, it is possible that presence of a researcher in this system may have changed the interaction between Japanese knotweed and Leedy’s roseroot, obfuscating their interaction. Another delisting criterion refers to the correlation between Leedy’s roseroot presence and seepage, which had been hypothesized (Sather et al. 1993; USFWS 1998) but had not been demonstrated quantitatively prior to this study. Of all abiotic factors I measured, Leedy’s roseroot presence was most strongly associated with seepage. Some factors I identified as predictors of Leedy’s roseroot presence may be unique to Glenora Cliffs, but as others have suggested (USFWS 1998) I expect seepage presence controls Leedy’s roseroot distribution throughout its range. I was unable to relate seepage or other abiotic factors to Leedy’s roseroot life history or physiology, but future studies of these topics will be important for understanding mechanisms of change in Leedy’s roseroot populations. Future work should also investigate seepage at other populations of Leedy’s roseroot, and aim to identify seepage sources. Quantifying the underground residence time of seepage water represents another area for future work because long residence times may influence the nutrient regime experienced by Leedy’s roseroot. Identification of seepage as part of Leedy’s roseroot niche further validates its glacial relict status, which has specific conservation implications (Olfelt et al. 2001), and supports the suggestion in the FRP that potential disruption of seepage should be considered a major threat to Leedy’s roseroot (USFWS 1998, 2015). Depletion of groundwater or precipitation upslope of cliffs where Leedy’s roseroot grows--whether due to land use, climate change, or other factors-- could negatively impact this rare subspecies. For example, the extirpation of Tsuga canadensis, a dominant species upslope of Glenora Cliffs, by hemlock woolly adelgid (Adelges tsugae) could

92 impact water regimes or chemistry at this site. My results also clarify other potential threats to Leedy’s roseroot. Inherent cliff instability and erosion (USFWS 1998, 2015) may threaten Leedy’s roseroot, but some unstable cliff features I measured were correlated with Leedy’s roseroot and disturbance may facilitate its establishment by removing competitors. Climate change (USFWS 1998, 2015) may impact Leedy’s roseroot in complicated ways differing from impacts on other plant species.

Finally, this study contributed to the delisting criterion related to population monitoring. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Census methods relatively consistent with those used in Minnesota (pers. obs.) were established for Glenora Cliffs, and the results of four censuses over a 12-year period were presented as part of this thesis. Analysis of census data revealed reduced flowering and increased sterility in sections of the Glenora Cliffs population, and results of the Japanese knotweed removal experiment suggested shading of Leedy’s roseroot by Japanese knotweed may reduce flowering. Due to these potential impacts on Leedy’s roseroot population viability, I recommend continued monitoring of Glenora Cliffs by the New York Natural Heritage Program. Population size at Glenora Cliffs could be supplemented via ex situ conservation methods. However, such a project is probably unnecessary given that Leedy’s roseroot at Glenora Cliffs does not appear to be dispersal- or germination-limited. Rather, future work might focus on out-planting Leedy’s roseroot to new locations on Finger Lakes cliffs or elsewhere. Although Clausen (1975) discussed his difficulty growing Leedy’s roseroot in cold frames in Ithaca, NY, my experience suggests Leedy’s roseroot may grow well under many conditions, including non-cliff areas. For example, Leedy’s roseroot was successfully established in loose gravel with full sun on a green roof in Syracuse, NY; conditions perhaps similar to the level-ground, open habitat along glacial margins that Leedy’s roseroot is thought to have inhabited during the Pleistocene (Olfelt and Freyman 2014). One potential natural location for out-planting Leedy’s roseroot could be Watkins Glen State Park, 9 miles south of Glenora, where an isolated Leedy’s roseroot individual persists (pers. obs.; Clausen 1975). Future work would be required to assess Watkins Glen for appropriate establishment sites; the individual at this location occupies a south-facing aspect, but north-facing aspects may be more appropriate (Table 1.1). This thesis described other easily observable factors associated with Leedy’s roseroot: seepage, high light conditions, and presence of other cliff species such as Asplenium trichomanes and Geranium robertianum. Planting new individuals at the state-owned

93 historical population at Watkins Glen may be particularly desirable given the private ownership issue at Glenora Cliffs (USFWS 2015). Environmental interpretation opportunities might be another benefit to restoring the Watkins Glen population; the park is visited by nearly one million people each year, and it recently received a grant for a renovation project that will allow it to accommodate even more visitors (NYSGPO 2016).

Literature Cited Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

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New York State Governor’s Press Office. 2016. Governor Cuomo announces $6.5 million to transform Watkins Glen State Park entrance. Albany, NY. [Internet]. Accessed 25 May 2016. Available from: https://www.governor.ny.gov/news/governor-cuomo-announces-65-million- transform-watkins-glen-state-park-entrance/

Olfelt JP, Freyman WA. 2014. Relationships of North American members of Rhodiola (Crassulaceae). American Journal of Botany 92:901-910.

Sather N. 1993. Leedy’s roseroot: a cliffside glacial relict. Minnesota Department of Natural Resources, St. Paul. 10 p.

United States Fish and Wildlife Service. 1998. Sedum integrifolium ssp. leedyi (Leedy’s roseroot) federal recovery plan. Ft. Snelling, MN. 31 p.

United States Fish and Wildlife Service. 2015. Leedy’s roseroot (Rhodiola integrifolia ssp. leedyi) 5-Year Review: Summary and Evaluation. Bloomington, MN. 25 p.

94 Appendix A - Cliff stability estimation protocol, adapted from Benumof and Griggs (1999). The variable total stability was calculated for a plot by adding the numbers for each category.

k 18 14 10 Shale Roc Granite Strength Limestone

9 7 5 3 10 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 High None Slight Moderate Complete Weathering

8 30 28 21 15 5 cm 5 Talus - 30 cm 30 <5 cm <5 - Crevice Crevice Spacing >100 cm >100 “Infinite”/ 100 30cm

9 5 3 20 18 14 Dip Steep Dip In Dip Dip In, Dip Dip Out Dip Out Dip Crevice Crevice Rock fall Rock Moderate Moderate Horizontal Threatening Interlocking Orientation

7 6 5 4 2 1 5 cm 5 Max. Max. Talus - 10 cm 10 cm 20 <1 cm <1 Width 1 - - >20 cm >20 Crevice Crevice 5 10

7 6 5 4 2 1 5 cm 5 Max. Max. Talus - 10 cm 10 cm 15 cm 20 Depth 1 - - - >20 cm >20 Crevice Crevice 5 10 15

7 6 5 4 1 0.5

vice vice infill Talus Cre No Infill No Cemented Thin Infill Thin Thick Infill Thick Part Part Cemented

6 5 4 3 2 age age Point

Seep Moderate Trace Drip Trace Heavy Drip Heavy No Seep No Source Flow Source Wet Cliff Face Cliff Wet

Weak Strong Stability Ranking Moderate

Very Weak Very Very Strong Very

95 Appendix B - Chapter 2 results of Tukey’s multiple comparisons of least-squared means of LMMs describing: 1) Leedy’s roseroot total as a function of size (degrees of freedom asymptotic), 2) Leedy’s roseroot total as a function of the interaction between size and fertility (degrees of freedom asymptotic), 3) mean weekly temperature as a function of the interaction between week and Leedy’s roseroot presence-absence, 4) maximum weekly temperature as a function of the interaction between week and Leedy’s roseroot presence-absence. contrast β estimate SE df t ratio p-value 1) Leedy’s L - M -630.583 79.144 -7.968 <0.001 by size L - S -399.667 79.144 -5.050 <0.001 M - S 230.917 79.144 2.918 0.016 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 2) Leedy’s L,Fertile - M,Fertile -507.833 54.593 -9.302 <0.001 by size and L,Fertile - S,Fertile -61.083 54.593 -1.119 0.872 fertility L,Fertile - L,Sterile 142.000 54.593 2.601 0.110 L,Fertile - M,Sterile 19.250 54.593 0.353 0.999 L,Fertile - S,Sterile -196.583 54.593 -3.601 0.007 M,Fertile - S,Fertile 446.750 54.593 8.183 <0.001 M,Fertile - L,Sterile 649.833 54.593 11.903 <0.001 M,Fertile - M,Sterile 527.083 54.593 9.655 <0.001 M,Fertile - S,Sterile 311.250 54.593 5.701 <0.001 S,Fertile - L,Sterile 203.083 54.593 3.720 0.005 S,Fertile - M,Sterile 80.333 54.593 1.471 0.683 S,Fertile - S,Sterile -135.500 54.593 -2.482 0.143 L,Sterile - M,Sterile -122.750 54.593 -2.248 0.229 L,Sterile - S,Sterile -338.583 54.593 -6.202 <0.001 M,Sterile - S,Sterile -215.833 54.593 -3.953 0.002 3) Mean temp 1,presence - 1,absence 0.788 0.257 823.44 3.063 0.444 by week 2,presence - 2,absence 0.670 0.257 823.44 2.604 0.810 and Leedy’s 3,presence - 3,absence 0.511 0.257 823.44 1.989 0.994 presence- 4,presence - 4,absence -0.121 0.257 823.44 -0.471 1.000 absence 5,presence - 5,absence 0.304 0.257 823.44 1.184 1.000 6,presence - 6,absence 0.385 0.257 823.44 1.498 1.000 7,presence - 7,absence 0.788 0.257 823.44 3.066 0.441 8,presence - 8,absence 0.478 0.257 823.44 1.860 0.998 9,presence - 9,absence 0.853 0.257 823.44 3.318 0.258 10,presence - 10,absence 1.413 0.257 823.44 5.496 <0.001 11,presence - 11,absence 1.707 0.257 823.44 6.637 <0.001 12,presence - 12,absence 1.597 0.257 823.44 6.209 <0.001 13,presence - 13,absence 1.690 0.257 823.44 6.571 <0.001 14,presence - 14,absence 1.776 0.257 823.44 6.907 <0.001 15,presence - 15,absence 1.550 0.257 823.44 6.026 <0.001 16,presence - 16,absence 1.651 0.257 823.44 6.420 <0.001 17,presence - 17,absence 1.934 0.257 823.44 7.521 <0.001 18,presence - 18,absence 1.680 0.257 823.44 6.532 <0.001 19,presence - 19,absence 0.556 0.257 823.44 2.163 0.978 4) Max temp 1,presence - 1,absence 1.313 1.015 823.83 1.294 1.000 by week 2,presence - 2,absence 1.849 1.015 823.83 1.822 0.9988 and Leedy’s 3,presence - 3,absence 0.224 1.015 823.83 0.221 1.000 presence- 4,presence - 4,absence -0.026 1.015 823.83 -0.026 1.000 absence 5,presence - 5,absence 1.099 1.015 823.83 1.083 1.000 6,presence - 6,absence 1.295 1.015 823.83 1.276 1.000 7,presence - 7,absence 1.5631 1.015 823.83 1.540 1.000 8,presence - 8,absence 1.456 1.015 823.83 1.435 1.000 9,presence - 9,absence 1.920 1.015 823.83 1.892 0.998 10,presence - 10,absence 3.456 1.015 823.83 3.405 0.208 11,presence - 11,absence 3.527 1.015 823.83 3.476 0.173 12,presence - 12,absence 3.777 1.015 823.83 3.722 0.084 13,presence - 13,absence 3.688 1.015 823.83 3.634 0.110 14,presence - 14,absence 4.331 1.015 823.83 4.267 0.012 15,presence - 15,absence 4.242 1.015 823.83 4.179 0.017

96 contrast β estimate SE df t ratio p-value 16,presence - 16,absence 3.813 1.015 823.83 3.757 0.075 17,presence - 17,absence 4.795 1.015 823.83 4.725 0.002 18,presence - 18,absence 4.313 1.015 823.83 4.250 0.013 19,presence - 19,absence 1.134 1.015 823.83 1.118 1.000

Appendix C - Chapter 2 summaries of model coefficients for the fixed effects of the LMM describing: 1) mean weekly temperature as a function of the interaction between week and Leedy’s roseroot presence-absence, 2) max weekly temperature as a function of the interaction between week and Leedy’s roseroot presence-absence, 3) seepage level as a function of location on cliff, 4) crevice orientation as a function of location on cliff. Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Model term β estimate SE df t ratio p-value 1) Mean temp (Intercept) 17.60 0.321 38 54.853 <0.001 by week LeedysAbsence -0.787 0.251 784.6 -3.138 0.002 and Leedy’s week2 -3.336 0.287 784 -11.628 <0.001 presence- week3 -0.182 0.287 784 -0.633 0.527 absence week4 0.050 0.287 784 0.175 0.861 week5 0.110 0.287 784 0.384 0.702 week6 -0.505 0.287 784 -1.759 0.079 week7 1.570 0.287 784 5.473 <0.001 week8 1.483 0.287 784 5.170 <0.001 week9 2.419 0.287 784 8.433 <0.001 week10 4.603 0.287 784 16.047 <0.001 week11 3.738 0.287 784 13.031 <0.001 week12 4.026 0.287 784 14.034 <0.001 week13 4.994 0.287 784 17.407 <0.001 week14 3.017 0.287 784 10.515 <0.001 week15 6.087 0.287 784 21.219 <0.001 week16 4.870 0.287 784 16.976 <0.001 week17 2.825 0.287 784 9.848 <0.001 week18 0.636 0.287 784 2.216 0.027 week19 -1.600 0.287 784 -5.577 <0.001 LeedysAbsence:week2 0.118 0.351 784 0.336 0.737 LeedysAbsence:week3 0.276 0.351 784 0.786 0.432 LeedysAbsence:week4 0.909 0.351 784 2.586 0.010 LeedysAbsence:week5 0.483 0.351 784 1.375 0.170 LeedysAbsence:week6 0.402 0.351 784 1.145 0.252 LeedysAbsence:week7 -0.001 0.351 784 -0.002 0.998 LeedysAbsence:week8 0.309 0.351 784 0.880 0.379 LeedysAbsence:week9 -0.657 0.351 784 -0.187 0.852 LeedysAbsence:week10 -0.626 0.351 784 -1.781 0.075 LeedysAbsence:week11 -0.919 0.351 784 -2.616 0.009 LeedysAbsence:week12 -0.809 0.351 784 -2.303 0.022 LeedysAbsence:week13 -0.902 0.351 784 -2.568 0.010 LeedysAbsence:week14 -0.988 0.351 784 -2.814 0.005 LeedysAbsence:week15 -0.762 0.351 784 -2.169 0.030 LeedysAbsence:week16 -0.863 0.351 784 -2.457 0.014 LeedysAbsence:week17 -1.146 0.351 784 -3.263 0.001 LeedysAbsence:week18 -0.892 0.351 784 -2.539 0.011 LeedysAbsence:week19 0.231 0.351 784 0.658 0.511 2) Max temp (Intercept) 23.126 1.105 59.400 20.932 <0.001 by week LeedysAbsence -1.313 0.991 784.900 -1.326 0.185 and Leedy’s week2 -1.750 1.132 784 -1.545 0.123 presence- week3 -1.178 1.132 784 -1.041 0.298 absence week4 -2.036 1.132 784 -1.798 0.073 week5 0.571 1.132 784 0.505 0.614 week6 -1.464 1.132 784 -1.293 0.196 week7 1.107 1.132 784 0.978 0.328 week8 2.464 1.132 784 2.176 0.030

97 Model term β estimate SE df t ratio p-value week9 3.214 1.132 784 2.839 0.005 week10 5.393 1.132 784 4.763 <0.001 week11 4.821 1.132 784 4.258 <0.001 week12 5.428 1.132 784 4.794 <0.001 week13 6.643 1.132 784 5.866 <0.001 week14 5.107 1.132 784 4.510 <0.001 week15 7.607 1.132 784 6.718 <0.001 week16 9.000 1.132 784 7.948 <0.001 week17 5.893 1.132 784 5.204 <0.001 week18 2.714 1.132 784 2.397 0.017 week19 -1.929 1.132 784 -1.703 0.089 LeedysAbsence:week2 -0.536 1.387 784 -0.386 0.699 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 LeedysAbsence:week3 1.089 1.387 784 0.785 0.432 LeedysAbsence:week4 1.339 1.387 784 0.966 0.334 LeedysAbsence:week5 0.214 1.387 784 0.155 0.877 LeedysAbsence:week6 0.018 1.387 784 0.013 0.990 LeedysAbsence:week7 -0.250 1.387 784 -0.180 0.857 LeedysAbsence:week8 -0.143 1.387 784 -0.103 0.918 LeedysAbsence:week9 -0.607 1.387 784 -0.438 0.662 LeedysAbsence:week10 -2.143 1.387 784 -1.545 0.123 LeedysAbsence:week11 -2.214 1.387 784 -1.597 0.111 LeedysAbsence:week12 -2.464 1.387 784 -1.777 0.076. LeedysAbsence:week13 -2.375 1.387 784 -1.713 0.087 LeedysAbsence:week14 -3.018 1.387 784 -2.176 0.030 LeedysAbsence:week15 -2.928 1.387 784 -2.112 0.035 LeedysAbsence:week16 -2.500 1.387 784 -1.803 0.072 LeedysAbsence:week17 -3.482 1.387 784 -2.511 0.012 LeedysAbsence:week18 -3.000 1.387 784 -2.163 0.031 LeedysAbsence:week19 0.178 1.387 784 0.129 0.898 3) Seepage (Intercept) 3.500 0.239 30.07 14.618 <0.001 by location location_name:mid_cliff -0.643 0.243 33.76 -2.649 0.012 location_name:lower_cliff -1.071 0.243 33.76 -4.415 <0.001 location_name:base_cliff -0.804 0.328 35.88 -2.448 0.019 4) Crevice (Intercept) 3.071 0.274 48 11.196 <0.001 orientation location_name:mid_cliff 0.286 0.388 48 0.736 0.465 by location location_name:lower_cliff 0.857 0.388 48 2.209 0.032 location_name:base_cliff 1.429 0.501 48 2.852 0.006

98 Model term β estimate SE df t value p-value timepost -21.173 4.804 220.000 -4.408 <0.001 timepost2 -34.224 4.804 220.000 -7.125 <0.001 trt_nameCut:timepost 31.089 6.793 220.000 4.576 <0.001 trt_nameControl:timepost 30.700 6.793 220.000 4.519 <0.001 trt_nameCut:timepost2 31.212 6.793 220.000 4.595 <0.001 trt_nameControl:timepost2 32.391 6.793 220.000 4.768 <0.001 2) JKW diameter (Intercept) 17.452 0.580 54.280 30.087 <0.001 by treatment trt_nameCut 0.609 0.572 219.020 1.063 0.289 and time trt_nameControl -0.695 0.572 219.020 -1.214 0.226 timepost -11.027 0.701 219.020 -15.732 <0.001 timepost2 -13.452 0.714 219.210 -18.839 <0.001 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 trt_nameCut:timepost 3.435 0.991 219.020 3.465 <0.001 trt_nameControl:timepost 4.422 0.991 219.020 4.461 <0.001 trt_nameCut:timepost2 5.186 1.001 219.120 5.183 <0.001 trt_nameControl:timepost2 6.522 1.001 219.120 6.518 <0.001 3) JKW density (Intercept) 4.277 0.709 115.260 6.032 <0.001 by treatment trt_nameCut 0.375 0.871 220.000 0.430 0.668 and time trt_nameControl 0.654 0.871 220.000 0.750 0.454 timepost 2.015 1.067 220.000 1.888 0.060 timepost2 -3.072 1.067 220.000 -2.879 0.004 trt_nameCut:timepost 5.929 1.509 220.000 3.929 <0.001 trt_nameControl:timepost 7.302 1.509 220.000 4.839 <0.001 trt_nameCut:timepost2 9.413 1.509 220.000 6.238 <0.001 trt_nameControl:timepost2 9.116 1.509 220.000 6.041 <0.001 4) JKW cover (Intercept) 14.000 3.096 148.520 4.522 <0.001 by treatment trt_nameCut 62.000 3.742 135.720 16.571 <0.001 and time trt_nameControl 60.000 3.742 135.720 16.036 <0.001 trt_nameUninvaded -14.000 3.721 148.520 -3.762 <0.001 timepost 22.000 3.742 135.720 5.880 <0.001 trt_nameCut:timepost -8.000 5.291 135.720 -1.512 0.133 trt_nameControl:timepost -9.500 5.291 135.720 -1.795 0.075 5) JKW diameter (Intercept) 17.423 0.488 28.780 35.68 <0.001 by time timepost -8.408 0.490 219.030 -17.15 <0.001 timepost2 -9.478 0.493 219.120 -19.22 <0.001 6) JKW density (Intercept) 4.6197 0.5361 46.280 8.618 <0.001 by time timepost 6.4248 0.7782 220.00 8.256 <0.001 timepost2 3.1041 0.7782 220.00 3.989 <0.001 7) log(Leedy’s) (Intercept) 1.717 0.200 233.670 8.580 <0.001 by treatment trt_nameCut -0.169 0.290 214.370 -0.584 0.560 and time trt_nameControl 0.041 0.260 214.240 0.157 0.875 trt_nameUninvaded 0.228 0.221 233.410 1.032 0.303 timepre -0.175 0.233 214.580 -0.749 0.455 timepost 0.057 0.276 209.600 0.205 0.838 trt_nameCut:timepre 0.027 0.343 213.690 0.080 0.936 trt_nameControl:timepre -0.256 0.315 212.940 -0.812 0.418 trt_nameCut:timepost -0.175 0.397 212.620 -0.441 0.660 trt_nameControl:timepost -0.016 0.372 209.760 -0.043 0.966 8) log(flowering (Intercept) 1.164 0.188 232.220 6.180 <0.001 Leedy’s) by trt_nameCut -0.120 0.266 208.370 -0.453 0.651 treatment trt_nameControl 0.140 0.239 209.150 0.586 0.559 and time trt_nameUninvaded 0.484 0.208 231.090 2.329 0.021 timepre -0.289 0.214 209.470 -1.346 0.180 timepost 0.201 0.253 204.830 0.794 0.428 trt_nameCut:timepre 0.218 0.315 208.060 0.693 0.489 trt_nameControl:timepre -0.192 0.289 208.630 -0.666 0.506 trt_nameCut:timepost -0.304 0.364 207.210 -0.834 0.405 trt_nameControl:timepost -0.167 0.341 205.200 -0.489 0.625 9) log(length) (Intercept) 2.146 0.106 179.130 20.235 <0.001 by treatment trt_nameCut 0.333 0.146 98.720 2.275 0.025 and time trt_nameControl 0.224 0.132 102.890 1.699 0.092

99 Model term β estimate SE df t value p-value trt_nameUninvaded 0.685 0.117 174.250 5.844 <0.001 timepre 0.682 0.135 95.170 5.064 <0.001 timepost 0.510 0.139 92.250 3.664 <0.001 trt_nameCut:timepre -0.158 0.197 94.410 -0.803 0.424 trt_nameControl:timepre -0.255 0.183 94.050 -1.397 0.166 trt_nameCut:timepost -0.153 0.201 96.570 -0.762 0.448 trt_nameControl:timepost -0.130 0.188 93.130 -0.692 0.490 10) Pre-treatment (Intercept) 33.650 1.442 41.930 23.332 <0.001 cliff max temp trt_nameCut -0.450 2.001 25.640 -0.225 0.824 by treatment trt_nameControl -0.800 2.001 25.640 -0.400 0.693 trt_nameUninvaded 8.475 1.953 41.930 4.340 <0.001 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 11) Post-treatment (Intercept) 24.282 0.850 36.290 28.564 <0.001 cliff rock temp trt_nameCut -4.082 0.787 40.000 -5.187 <0.001 by treatment trt_nameControl -4.478 0.787 40.000 -5.690 <0.001 12) Post-treatment (Intercept) 33.567 1.294 47.640 25.936 <0.001 talus rock temp trt_nameCut -11.207 1.464 40.000 -7.655 <0.001 by treatment trt_nameControl -11.574 1.464 40.000 -7.906 <0.001 13) Talus PAR (Intercept) 84.648 50.418 99.250 1.679 0.096 by treatment trt_nameCut 2.648 63.561 100.330 0.042 0.967 and time trt_nameControl -65.179 63.561 100.330 -1.025 0.307 timepost2 384.643 63.561 100.330 6.052 <0.001 trt_nameCut:timepost2 -434.741 89.888 100.330 -4.836 <0.001 trt_nameControl:timepost2 -378.940 89.888 100.330 -4.216 <0.001 14) Species (Intercept) 4.108 0.865 69.000 4.749 <0.001 richness by location_name_lower_cliff 2.321 1.014 60.580 2.288 0.026 location location_name_mainland -0.036 1.014 60.580 -0.036 0.972 location_name_mid_cliff 2.678 1.014 60.580 2.640 0.010 location_name_talus -1.822 1.175 68.000 -1.550 0.126 location_name_top_cliff 1.749 1.014 60.580 1.724 0.090

Appendix E - Chapter 3 results of Tukey’s multiple comparisons of least-squared means of LMMs describing: 1) Japanese knotweed SBA as a function of treatment and time, 2) log- transformed total Leedy’s roseroot as a function of the interaction between treatment and time, 3) log-transformed total flowering Leedy’s roseroot as a function of the interaction between treatment and time, 4) log-transformed mean Leedy’s roseroot stem length as a function of the interaction between treatment and time, 5) pre-treatment cliff max temperature as a function of treatment, 6) post-treatment cliff rock temperature as a function of treatment, 7) talus PAR as a function of treatment and time, 8) species richness as a function of location on cliff. contrast β estimate SE df t ratio p-value 1) JKW SBA Herbicide,pre - Cut,pre -0.375 0.888 228.3 -0.422 1.000 by treatment Herbicide,pre - Control,pre -0.654 0.888 228.3 -0.736 0.998 and time Herbicide,pre - Herbicide,post -2.015 1.087 228.3 -1.853 0.646 Herbicide,pre - Cut,post -8.318 1.087 228.3 -7.652 <0.001 Herbicide,pre - Control,post -9.970 1.087 228.3 -9.172 <0.001 Herbicide,pre - Herbicide,post2 3.072 1.087 228.3 2.826 0.114 Herbicide,pre - Cut,post2 -6.716 1.087 228.3 -6.178 <0.001 Herbicide,pre - Control,post2 -6.697 1.087 228.3 -6.161 <0.001 Cut,pre - Control,pre -0.279 0.888 228.3 -0.314 1.000 Cut,pre - Herbicide,post -1.640 1.087 228.3 -1.509 0.851 Cut,pre - Cut,post -7.943 1.087 228.3 -7.307 <0.001 Cut,pre - Control,post -9.595 1.087 228.3 -8.827 <0.001 Cut,pre - Herbicide,post2 3.447 1.087 228.3 3.171 0.045 Cut,pre - Cut,post2 -6.341 1.087 228.3 -5.833 <0.001 Cut,pre - Control,post2 -6.322 1.087 228.3 -5.816 <0.001 Control,pre - Herbicide,post -1.361 1.087 228.3 -1.252 0.944 Control,pre - Cut,post -7.664 1.087 228.3 -7.051 <0.001 Control,pre - Control,post -9.317 1.087 228.3 -8.571 <0.001

100 contrast β estimate SE df t ratio p-value Control,pre - Herbicide,post2 3.726 1.087 228.3 3.427 0.020 Control,pre - Cut,post2 -6.062 1.087 228.3 -5.577 <0.001 Control,pre - Control,post2 -6.044 1.087 228.3 -5.560 <0.001 Herbicide,post - Cut,post -6.303 1.255 228.3 -5.022 <0.001 Herbicide,post - Control,post -7.956 1.255 228.3 -6.338 <0.001 Herbicide,post - Herbicide,post2 5.087 1.255 228.3 4.053 0.002 Herbicide,post - Cut,post2 -4.701 1.255 228.3 -3.745 0.007 Herbicide,post - Control,post2 -4.682 1.255 228.3 -3.730 0.007 Cut,post - Control,post -1.652 1.255 228.3 -1.316 0.926 Cut,post - Herbicide,post2 11.390 1.255 228.3 9.074 <0.001 Cut,post - Cut,post2 1.602 1.255 228.3 1.276 0.937 Cut,post - Control,post2 1.621 1.255 228.3 1.291 0.933 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Control,post - Herbicide,post2 13.042 1.255 228.3 10.391 <0.001 Control,post - Cut,post2 3.254 1.255 228.3 2.593 0.195 Control,post - Control,post2 3.273 1.255 228.3 2.608 0.189 Herbicide,post2 - Cut,post2 -9.788 1.255 228.3 -7.798 <0.001 Herbicide,post2 - Control,post2 -9.769 1.255 228.3 -7.783 <0.001 Cut,post2 - Control,post2 0.018 1.255 228.3 0.015 1.000 2) log(Leedy’s) Uninvd,Uninvd - Herbicide,pre 0.403 0.165 216.08 2.433 0.525 by treatment Uninvd,Uninvd - Cut,pre 0.544 0.171 219.76 3.180 0.113 and time Uninvd,Uninvd - Control,pre 0.617 0.164 208.93 3.758 0.020 Uninvd,Uninvd - Herbicide,post 0.171 0.226 243.54 0.757 1.000 Uninvd,Uninvd - Cut,post 0.515 0.219 242.99 2.347 0.589 Uninvd,Uninvd - Control,post 0.146 0.213 242.64 0.686 1.000 Uninvd,Uninvd - Herbicide,post2 0.228 0.226 243.49 1.008 1.000 Uninvd,Uninvd - Cut,post2 0.397 0.242 244.25 1.637 0.960 Uninvd,Uninvd - Control,post2 0.187 0.203 241.64 0.921 1.000 Herbicide,pre - Cut,pre 0.142 0.188 208.35 0.753 1.000 Herbicide,pre - Control,pre 0.215 0.182 212.50 1.180 0.998 Herbicide,pre - Herbicide,post -0.232 0.239 212.87 -0.969 1.000 Herbicide,pre - Cut,post 0.112 0.233 213.03 0.482 1.000 Herbicide,pre - Control,post -0.256 0.228 216.01 -1.125 0.999 Herbicide,pre - Herbicide,post2 -0.175 0.239 213.38 -0.732 1.000 Herbicide,pre - Cut,post2 -0.006 0.254 215.11 -0.023 1.000 Herbicide,pre - Control,post2 -0.216 0.218 213.54 -0.988 1.000 Cut,pre - Control,pre 0.0732 0.187 208.15 0.392 1.000 Cut,pre - Herbicide,post -0.373 0.243 212.63 -1.537 0.977 Cut,pre - Cut,post -0.029 0.236 208.46 -0.124 1.000 Cut,pre - Control,post -0.398 0.231 209.60 -1.723 0.938 Cut,pre - Herbicide,post2 -0.316 0.242 210.93 -1.305 0.995 Cut,pre - Cut,post2 -0.147 0.258 213.48 -0.573 1.000 Cut,pre - Control,post2 -0.357 0.222 210.05 -1.611 0.965 Control,pre - Herbicide,post -0.446 0.238 212.98 -1.876 0.884 Control,pre - Cut,post -0.102 0.232 211.38 -0.443 1.000 Control,pre - Control,post -0.471 0.226 208.37 -2.087 0.771 Control,pre - Herbicide,post2 -0.390 0.238 213.37 -1.637 0.960 Control,pre - Cut,post2 -0.221 0.253 215.00 -0.871 1.000 Control,pre - Control,post2 -0.430 0.216 209.46 -1.989 0.829 Herbicide,post - Cut,post 0.344 0.278 211.01 1.235 0.997 Herbicide,post - Control,post -0.025 0.275 215.44 -0.090 1.000 Herbicide,post - Herbicide,post2 0.057 0.282 205.90 0.201 1.000 Herbicide,post - Cut,post2 0.226 0.296 212.45 0.761 1.000 Herbicide,post - Control,post2 0.016 0.267 215.60 0.059 1.000 Cut,post - Control,post -0.369 0.268 210.72 -1.373 0.992 Cut,post - Herbicide,post2 -0.287 0.278 207.92 -1.034 1.000 Cut,post - Cut,post2 -0.118 0.291 211.17 -0.406 1.000 Cut,post - Control,post2 -0.328 0.261 211.24 -1.258 0.997 Control,post - Herbicide,post2 0.082 0.274 212.53 0.298 1.000 Control,post - Cut,post2 0.250 0.288 219.10 0.869 1.000 Control,post - Control,post2 0.041 0.255 206.06 0.160 1.000 Herbicide,post2 - Cut,post2 0.169 0.296 213.05 0.570 1.000

101 contrast β estimate SE df t ratio p-value Herbicide,post2 - Control,post2 -0.041 0.266 212.87 -0.153 1.000 Cut,post2 - Control,post2 -0.210 0.281 218.87 -0.747 1.000 3) log(flowering Uninvd,Uninvd - Herbicide,pre 0.773 0.159 187.19 4.873 <0.001 Leedy’s) by Uninvd,Uninvd - Cut,pre 0.675 0.164 193.64 4.124 0.005 treatment Uninvd,Uninvd - Control,pre 0.826 0.158 179.25 5.230 <0.001 and time Uninvd,Uninvd - Herbicide,post 0.284 0.213 239.40 1.332 0.994 Uninvd,Uninvd - Cut,post 0.708 0.207 237.30 3.421 0.057 Uninvd,Uninvd - Control,post 0.310 0.201 235.55 1.542 0.976 Uninvd,Uninvd - Herbicide,post2 0.484 0.213 239.30 2.275 0.643 Uninvd,Uninvd - Cut,post2 0.605 0.228 242.65 2.657 0.365 Uninvd,Uninvd - Control,post2 0.344 0.192 231.67 1.795 0.916 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Herbicide,pre - Cut,pre -0.098 0.172 199.48 -0.567 1.000 Herbicide,pre - Control,pre 0.0526 0.167 204.02 0.314 1.000 Herbicide,pre - Herbicide,post -0.489 0.219 203.38 -2.231 0.674 Herbicide,pre - Cut,post -0.065 0.214 203.78 -0.305 1.000 Herbicide,pre - Control,post -0.463 0.209 206.14 -2.209 0.690 Herbicide,pre - Herbicide,post2 -0.288 0.219 203.96 -1.315 0.995 Herbicide,pre - Cut,post2 -0.168 0.233 204.74 -0.720 1.000 Herbicide,pre - Control,post2 -0.428 0.200 204.25 -2.137 0.739 Cut,pre - Control,pre 0.1503 0.171 199.03 0.879 1.000 Cut,pre - Herbicide,post -0.392 0.223 202.32 -1.758 0.928 Cut,pre - Cut,post 0.032 0.216 199.30 0.150 1.000 Cut,pre - Control,post -0.365 0.212 200.05 -1.722 0.938 Cut,pre - Herbicide,post2 -0.191 0.222 201.13 -0.858 1.000 Cut,pre - Cut,post2 -0.070 0.236 202.81 -0.297 1.000 Cut,pre - Control,post2 -0.331 0.204 201.62 -1.624 0.962 Control,pre - Herbicide,post -0.542 0.218 202.64 -2.482 0.489 Control,pre - Cut,post -0.118 0.212 201.63 -0.555 1.000 Control,pre - Control,post -0.515 0.207 198.88 -2.489 0.484 Control,pre - Herbicide,post2 -0.341 0.218 203.09 -1.561 0.973 Control,pre - Cut,post2 -0.221 0.232 203.97 -0.949 1.000 Control,pre - Control,post2 -0.481 0.199 201.03 -2.422 0.534 Herbicide,post - Cut,post 0.424 0.255 201.04 1.661 0.954 Herbicide,post - Control,post 0.027 0.252 204.68 0.106 1.000 Herbicide,post - Herbicide,post2 0.201 0.259 196.91 0.776 1.000 Herbicide,post - Cut,post2 0.321 0.272 201.55 1.182 0.998 Herbicide,post - Control,post2 0.061 0.245 205.64 0.249 1.000 Cut,post - Control,post -0.397 0.246 200.95 -1.613 0.964 Cut,post - Herbicide,post2 -0.223 0.255 198.68 -0.877 1.000 Cut,post - Cut,post2 -0.103 0.267 200.97 -0.385 1.000 Cut,post - Control,post2 -0.363 0.239 202.22 -1.518 0.979 Control,post - Herbicide,post2 0.174 0.252 202.39 0.692 1.000 Control,post - Cut,post2 0.294 0.265 207.49 1.111 0.999 Control,post - Control,post2 0.034 0.234 197.86 0.146 1.000 Herbicide,post2 - Cut,post2 0.120 0.272 202.28 0.443 1.000 Herbicide,post2 - Control,post2 -0.140 0.245 203.47 -0.572 1.000 Cut,post2 - Control,post2 -0.260 0.258 208.05 -1.008 1.000 4) log(length Uninvd,Uninvd - Herbicide,pre 0.003 0.114 183.78 0.028 1.000 Leedy’s) by Uninvd,Uninvd - Cut,pre -0.172 0.117 185.06 -1.469 0.985 treatment Uninvd,Uninvd - Control,pre 0.034 0.111 180.88 0.309 1.000 and time Uninvd,Uninvd - Herbicide,post 0.175 0.120 188.49 1.451 0.986 Uninvd,Uninvd - Cut,post -0.005 0.117 185.53 -0.047 1.000 Uninvd,Uninvd - Control,post 0.081 0.114 184.23 0.710 1.000 Uninvd,Uninvd - Herbicide,post2 0.685 0.120 188.32 5.682 <0.001 Uninvd,Uninvd - Cut,post2 0.352 0.129 194.00 2.732 0.319 Uninvd,Uninvd - Control,post2 0.461 0.109 179.75 4.244 0.004 Herbicide,pre - Cut,pre -0.175 0.137 150.57 -1.281 0.996 Herbicide,pre - Control,pre 0.031 0.133 157.49 0.233 1.000 Herbicide,pre - Herbicide,post 0.172 0.139 148.69 1.233 0.997 Herbicide,pre - Cut,post -0.009 0.138 153.76 -0.064 1.000

102 contrast β estimate SE df t ratio p-value Herbicide,pre - Control,post 0.078 0.136 158.95 0.572 1.000 Herbicide,pre - Herbicide,post2 0.682 0.139 149.25 4.893 <0.001 Herbicide,pre - Cut,post2 0.349 0.147 153.56 2.370 0.573 Herbicide,pre - Control,post2 0.458 0.132 160.30 3.473 0.051 Cut,pre - Control,pre 0.206 0.135 151.64 1.528 0.977 Cut,pre - Herbicide,post 0.347 0.142 148.24 2.448 0.515 Cut,pre - Cut,post 0.167 0.139 147.75 1.199 0.998 Cut,pre - Control,post 0.253 0.138 155.95 1.834 0.900 Cut,pre - Herbicide,post2 0.857 0.142 148.79 6.043 <0.001 Cut,pre - Cut,post2 0.524 0.148 147.51 3.534 0.043 Cut,pre - Control,post2 0.633 0.134 157.27 4.724 <0.001 Control,pre - Herbicide,post 0.141 0.139 155.26 1.015 1.000 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 Control,pre - Cut,post -0.040 0.135 150.97 -0.295 1.000 Control,pre - Control,post 0.047 0.132 147.96 0.352 1.000 Control,pre - Herbicide,post2 0.651 0.139 155.50 4.694 <0.001 Control,pre - Cut,post2 0.317 0.145 154.62 2.183 0.707 Control,pre - Control,post2 0.427 0.128 150.02 3.327 0.079 Herbicide,post - Cut,post -0.180 0.142 151.64 -1.268 0.996 Herbicide,post - Control,post -0.094 0.141 156.88 -0.667 1.000 Herbicide,post - Herbicide,post2 0.510 0.144 146.98 3.539 0.043 Herbicide,post - Cut,post2 0.177 0.152 151.82 1.165 0.999 Herbicide,post - Control,post2 0.286 0.137 158.06 2.087 0.770 Cut,post - Control,post 0.086 0.137 152.43 0.629 1.000 Cut,post - Herbicide,post2 0.691 0.142 149.14 4.868 <0.001 Cut,post - Cut,post2 0.357 0.149 151.17 2.398 0.552 Cut,post - Control,post2 0.467 0.134 153.95 3.495 0.048 Control,post - Herbicide,post2 0.604 0.140 154.36 4.301 0.003 Control,post - Cut,post2 0.271 0.148 159.41 1.826 0.903 Control,post - Control,post2 0.380 0.130 148.21 2.921 0.218 Herbicide,post2 - Cut,post2 -0.333 0.152 152.62 -2.195 0.699 Herbicide,post2 - Control,post2 -0.224 0.137 155.65 -1.640 0.958 Cut,post2 - Control,post2 0.109 0.144 160.19 0.756 1.000 5) Pre-treatment Herbicide - Cut 0.450 2.103 26.78 0.214 0.996 cliff max temp Herbicide - Control 0.800 2.103 26.78 0.380 0.981 by treatment Herbicide - Uninvd -8.475 2.053 46.33 -4.128 <0.001 Cut - Control 0.350 2.103 26.78 0.166 0.998 Cut - Uninvd -8.925 2.053 46.33 -4.347 <0.001 Control - Uninvd -9.275 2.053 46.33 -4.518 <0.001 6) Post-treatment Herbicide - Cut 4.082 0.807 42.11 5.056 <0.001 cliff rock temp Herbicide - Control 4.478 0.807 42.11 5.546 <0.001 by treatment Cut - Control 0.396 0.807 42.11 0.490 0.876 7) Talus PAR Herbicide,pre - Cut,pre -2.648 65.212 105.26 -0.041 1.000 by treatment Herbicide,pre - Control,pre 65.179 65.212 105.26 0.999 0.912 and time Herbicide,pre - Herbicide,post2 -384.643 65.212 105.26 -5.898 <0.001 Herbicide,pre - Cut,post2 47.450 65.212 105.26 0.728 0.979 Herbicide,pre - Control,post2 59.476 65.212 105.26 0.912 0.943 Cut,pre - Control,pre 67.829 65.212 105.26 1.040 0.903 Cut,pre - Herbicide,post2 -381.995 65.212 105.26 -5.858 <0.001 Cut,pre - Cut,post2 50.098 65.212 105.26 0.768 0.972 Cut,pre - Control,post2 62.124 65.212 105.26 0.953 0.932 Control,pre - Herbicide,post2 -449.821 65.212 105.26 -6.898 <0.001 Control,pre - Cut,post2 -17.729 65.212 105.26 -0.272 1.000 Control,pre - Control,post2 -5.702 65.212 105.26 -0.087 1.000 Herbicide,post2 - Cut,post2 432.092 65.212 105.26 6.626 <0.001 Herbicide,post2 - Control,post2 444.119 65.212 105.26 6.810 <0.001 Cut,post2 - Control,post2 12.026 65.212 105.26 0.184 1.000 8) Species base_cliff - lower_cliff -2.321 1.074 67.64 -2.161 0.270 richness by base_cliff - mainland 0.036 1.074 67.64 0.034 1.000 location base_cliff - mid_cliff -2.678 1.074 67.64 -2.494 0.140 base_cliff - talus 1.822 1.262 74.68 1.443 0.701

103 contrast β estimate SE df t ratio p-value base_cliff - top_cliff -1.749 1.074 67.64 -1.629 0.583 lower_cliff - mainland 2.357 0.814 60.16 2.896 0.056 lower_cliff - mid_cliff -0.357 0.814 60.16 -0.439 0.998 lower_cliff - talus 4.143 1.015 66.51 4.081 0.002 lower_cliff - top_cliff 0.571 0.814 60.16 0.702 0.981 mainland - mid_cliff -2.714 0.814 60.16 -3.334 0.018 mainland - talus 1.786 1.015 66.51 1.759 0.499 mainland - top_cliff -1.786 0.814 60.16 -2.194 0.256 mid_cliff - talus 4.500 1.015 66.51 4.433 <0.001 mid_cliff - top_cliff 0.928 0.814 60.16 1.141 0.862 talus - top_cliff -3.571 1.015 66.51 -3.518 0.010 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021

Appendix F - Species codes associated with NMS ordination matrix. Code Scientific name Code Scientific name AceSac Acer saccharum MaiRac Maianthemum racemosum AgeAlt Ageratina altissima MarPol Marchantia polymorpha AllPet Alliaria petiolata MyoSco Myosotis scorpioides AspTri Asplenium trichomanes Pan Panicum sp. Ast1 Asteraceae sp. ParPen Parietaria pensylvanica Ast2 Asteraceae sp. ParQui Parthenocissus quinquefolia BerVul Berberis vulgaris PenHir Penstemon hirsutus Bid Bidens sp. Poa Poaceae spp. Bro Bromus sp. Pol Polygonum sp. Bry Bryophyta (mosses) PolVir Polypodium virginianum CalSep Calystegia sepium PruSer Prunus serotina CamRot Campanula rotundifolia RubOcc Rubus occidentalis Car Carex spp. RubOdo Rubus odoratus CarOva Carya ovata SamCan Sambucus canadensis CynRos Cynanchum rossicum SolDul Solanum dulcamara CysBul Cystopteris bulbifera Sol Solidago sp. DraAra Draba arabisans SolJun Solidago juncea EpiHel Epipactus helleborine SonAsp Sonchus asper Epi Epilobium sp. TarOff Taraxacum officinale FraAme Fraxinus americana TaxCan Taxus canadensis GerRob Geranium robertianum ToxRad Toxicodendron radicans HypPer Hypericum perforatum TriPer Triodanis perfoliata ImpCap Impatiens capensis Vac Vaccinium sp. Lic Lichen spp. VerTha Verbascum thapsus LinVul Linaria vulgaris VitRip Vitis riparia

104 KALI MATTINGLY 1038 Lancaster Avenue Email: [email protected] Syracuse, New York 13210 Phone: (606) 923-7521

Education Master of Science in Ecology, expected 2016 State University of New York College of Environmental Science and Forestry, Syracuse, New York, GPA 3.961 Bachelor of Arts in Biology, 2014 Transylvania University, Lexington, Kentucky, GPA 3.846

Graduate Record Examinations, 2013 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 84th Percentile Verbal, 72nd Percentile Quantitative, 78th Percentile Writing

Awards, Grants, and Training New York Flora Association Research Award, New York Flora Association, June 2015 Edna Bailey Sussman Scholarship, Edna Bailey Sussman Foundation, April 2015 Graduate Colloquium on Teaching, SUNY College of Environmental Science and Forestry, August 2014 B.A. Biology Magna Cum Laude, Transylvania University, May 2014 Nu Iota Scholarship, Alpha Omicron Pi Foundation, May 2014 Christine Conway Fellowship, Alpha Lambda Delta National Council, March 2014 Fern and Fern Allies Workshop, Floracliff Nature Sanctuary, June 2013 Kenan Jones Undergraduate Research Grant, Transylvania University, March 2013

Research Experience SUNY College of Environmental Science and Forestry, Dept. of Environmental and Forest Biology, Donald J. Leopold Lab • Curator of lab twitter account @LeopoldLabESF, posting updates and pictures of lab member active research. Research Project Assistant, May 2015-present • Field study measuring endangered cliff plant Rhodiola integrifolia subsp. leedyi in New York, Minnesota, and South Dakota. • Oversaw a paid undergraduate field assistant for summer 2015 field season. • Censused Minnesota R. integrifolia subsp. leedyi populations with an eight-member field crew from Northeastern Illinois University, and censused New York populations with a seven-member field crew from New York Natural Heritage Program. • Gained experience in vegetation and environmental sampling methods, basic GPS, and safe rappelling. • Grew from seed and successfully established a population of R. integrifolia subsp. leedyi on a green roof. • Collected seeds for Centers for Plant Conservation repository, following protocol and producing a report. • Performed tissue culture micropropagation pilot studies for ex situ conservation of R. integrifolia ssp. leedyi. • Communicated research to the public via an article for the New York Flora Association’s Spring 2016 Newsletter, which reaches scientists and amateur naturalists throughout New York State. Research Project Assistant, June 2014-present • Field study of the impact of invasive Fallopia japonica on R. integrifolia subsp. leedyi. • Supervised application of cutting and herbicide treatments to remove Fallopia japonica. • Analyzed and presented data in SAS and R, sharing findings in plant ecology lab group meetings. • Compiled written reports and a gave a presentation for the U.S. Fish and Wildlife Service office at Cortland, NY, which has overseen the execution of this project.

University of Kentucky, Dept. of Forestry, Mary A. Arthur Lab Undergraduate Research Assistant, September 2013-December 2013 • Assembled and monitored leaf collection apparatuses for an invasive species decomposition study. • Attended biweekly meetings of ecosystem ecology lab group, the only undergraduate student invited. National Science Foundation Research Experiences for Undergraduates Intern, May 2013-August 2013 • Field study of invasive Euonymus fortunei’s effects on plant community structure and characteristics of an urban eastern hardwood forest fragment.

105 • Collected soil cores and analyzed bulk density, pH, and moisture content of soil samples in the lab. • Performed vegetation sampling and developed plant identification skills using field guides and herbarium. • Analyzed data in SPSS and SAS, and interpreted data visualizations in PC-ORD, MS Excel, and SigmaPlot.

Teaching Experience SUNY College of Environmental Science and Forestry, Dept. of Environmental and Forest Biology Graduate Teaching Assistant, Ecology of Mosses and Indigenous Issues, Spring Semester 2016 • Upper-level lecture and lab course. • Set up labs, specimen quizzes, and field trips. • Assisted professor in teaching lab techniques, including compound and dissection microscopy. • Total number individual students worked with: 21 Downloaded from http://meridian.allenpress.com/jfwm/article-supplement/432994/pdf/10_3996022018-jfwm-010_s12 by guest on 01 October 2021 • Estimated total student contact hours: 1470 Graduate Teaching Assistant, Diversity of Life I Lab, Fall Semesters 2014 and 2015 • Sophomore-level plant and fungal taxonomy lab course. • Taught two 3-hour ~25-student lab sections per week. • Delegated responsibilities to undergraduate teaching assistance and helped them prepare lecture topics. • Kept students focused and safe during field trips. • Helped revise exams questions, developed new lab exercises, and created rubrics for instructional material. • Total number individual students worked with: 53 (2014), 51 (2015) • Estimated total student contact hours per semester: 2370 Graduate Teaching Assistant, General Biology II Lecture, Spring Semester 2015 • Introductory-level molecular and cellular biology lecture course. • Collaborated with other teaching assistants on four 1.5-hour review sessions each week (2-10 students per session), reviewing material learned during the primary lectures. • Gave one 30-minute lecture on biogeography to ~200 students. • Estimated total number individual students worked with: 50 • Estimated total student contact hours: 510 Transylvania University Writing Center Writing Consultant, January 2012-May 2013 • Consulted with science students about improving lab reports, scientific literature reviews, and proposals. • Led a series of writing workshops tailored to science students, training technical minds to write accessibly. • Estimated total number individual students worked with: 75 • Estimated total student contact hours: 320

Publication and Presentations Mattingly, K. Z., R. W. McEwan, R. D. Paratley, S. R. Bray, J. R. Lempke, and M. A. Arthur. 2016. Recovery of forest floor diversity after removal of the nonnative invasive plant Euonymus fortunei. J. Torrey Bot. Soc. 143(2):103-116. National Conference on Undergraduate Research, University of Kentucky, Lexington, Kentucky, April 2014 Midwest Ecology and Evolution Conference, University of Dayton, Dayton, Ohio, March 2014

Technical Reports Mattingly, K. Z., D. J. Leopold. 2015. Controlling of Japanese knotweed (Fallopia japonica var. japonica) on Leedy’s roseroot (Rhodiola integrifolia ssp. leedyi), a federally threatened plant. Final report to U.S. Fish and Wildlife Service Great Lakes Restoration Initiative, Endangered Species Program. State University of New York College of Environmental Science and Forestry, Syracuse, NY. 5 p. Mattingly, K. Z., D. J. Leopold. 2014. Controlling of Japanese knotweed (Fallopia japonica var. japonica) on Leedy’s roseroot (Rhodiola integrifolia ssp. leedyi), a federally threatened plant. Interim report to U.S. Fish and Wildlife Service Great Lakes Restoration Initiative, Endangered Species Program. State University of New York College of Environmental Science and Forestry, Syracuse, NY. 4 p.

106