Assessing Changes in Bog Turtle (Glyptemys Muhlenbergii) Population Abundance and Factors Influencing Nest Predation in Virginia

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Assessing Changes in Bog Turtle (Glyptemys muhlenbergii) Population Abundance and Factors Influencing Nest Predation in Virginia

Michael Thomas Holden

Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Master of Science

Fisheries and Wildlife Sciences

Carola A. Haas, Chair Emmanuel A. Frimpong, Co-Chair Sarah M. Karpanty Nicholas M. Caruso

11 May 2021 Blacksburg, Virginia

Keywords: bog turtle, wetlands, nest predation, subsidized predators, conservation

Assessing Changes in Bog Turtle (Glyptemys muhlenbergii) Population Abundance and Factors Influencing Nest Predation in Virginia

Michael T. Holden

ABSTRACT (ACADEMIC)

Across the globe, wildlife populations are facing increasing challenges, with many taxonomic groups significantly declining. Among endangered vertebrates (including birds, non- avian reptiles, fishes, mammals, and amphibians), turtles are one of the most threatened groups with over 60% of the 356 recognized species classified as threatened or worse. Bog turtles (Glyptemys muhlenbergii), are among the most imperiled of North American freshwater turtles. These small, secretive turtles have declined by up to 90% in parts of their range, which consists of the Northern Population and the Southern Population, and spans the eastern U.S. from New York to Georgia. These declines are mainly documented in the northern part of their range, but recent work in North Carolina suggests that turtles in the southern part of their range are similarly declining. Prior to this research, surveys aimed at estimating abundance had not been conducted in Virginia since the late 1990’s. The research described here was conducted as part of a state-wide population assessment of bog turtles in Virginia. For my first chapter, I conducted capture-mark-recapture surveys in six wetlands in Floyd County, Virginia during 2019 and 2020, and generated abundance estimates. These wetlands had been surveyed in the same manner in 1997, which provided me the opportunity to compare recent abundance estimates with those generated from the 1997 data. My analyses suggest that turtle abundance across these six sites has declined by approximately 50% since 1997. This decline appears to be driven by, but not wholly attributable to, the alteration and loss of habitat at 2-3 of the 6 sites. Habitat loss is acknowledged as one of the major drivers of population declines throughout the range of the bog turtle, in addition to illegal collection for the international pet trade. Due to the life history traits of this species (long life span and low fecundity), the loss of an individual from any life stage from the population can have detrimental effects. While many turtle populations are not heavily impacted from periods of low reproductive success, numerous subsequent years of complete nesting failure can negatively impact population-level survival. Recent studies have suggested that anthropogenically subsidized nest predators may be playing a role in continued nest failure at certain wetlands. My second chapter investigated the factors

associated with anthropogenic footprint (i.e., buildings) and infrastructure that may be driving nest predation by these subsidized predators. In 2019 and 2020, I conducted a field experiment in 35 wetlands which utilized artificial turtle nests to investigate variation in nest predation across Montgomery and Floyd Counties, Virginia. I found that increases in the percent of developed land-use and other metrics of anthropogenic disturbance significantly increased nest predation, while increases in the percent of land-use without roads or buildings significantly decreased nest predation. The findings from these two chapters are consistent with population trends documented in other parts of the bog turtle range, and build upon prior studies to investigate drivers of nest predation. These results provide information that can be used by managers to aid in the conservation of this state endangered species, and suggest further courses of research for future projects.

Assessing Changes in Bog Turtle (Glyptemys muhlenbergii) Population Abundance and Factors Influencing Nest Predation in Virginia

Michael T. Holden

ABSTRACT (GENERAL AUDIENCE)

Across the globe, wildlife populations are facing increasing challenges, with many taxonomic groups significantly declining. Turtles are one of the most threatened groups of vertebrates with over 60% of the 356 species of turtle classified as threatened or endangered. Bog turtles (Glyptemys muhlenbergii), are among the most imperiled of North American freshwater turtles. These small, secretive turtles have declined by up to 90% in parts of their range, which consists of the Northern Population and the Southern Population, and spans the eastern U.S. from New York to Georgia. Prior to this research, no information on population trends was available for Virginia. To address this knowledge gap, I conducted surveys for bog turtles in six wetlands in Floyd County, Virginia during 2019 and 2020, and used the data from those surveys to estimate how many turtles were present in the wetlands. These wetlands had been surveyed in the same manner in 1997, which provided me the opportunity to compare recent estimates with those generated from the 1997 data. My analyses suggest that the total number of bog turtles present across these six sites has declined by approximately 50% since 1997. This decline appears to be caused at least in part by the alteration and loss of habitat at 2 of the 6 sites. Habitat loss is thought to be one of the major drivers of population declines throughout the range of the bog turtle, in addition to illegal collection for the international pet trade. Recent studies have suggested an additional problem, that anthropogenically subsidized nest predators may be playing a role in continued nest failure at certain wetlands. Animals such as raccoons, skunks, and bears can persist in greater numbers around human habitation, as we provide extra food sources such as garbage, bird feeders, deer feeders, etc. I investigated the factors associated with human infrastructure that may be driving nest predation by these subsidized predators. In 2019 and 2020, I conducted a field experiment in 35 wetlands using artificial turtle nests to investigate variation in nest predation across Montgomery and Floyd Counties, Virginia. I found that nest predation was significantly higher in areas with a higher percent of developed land-use.

The findings from these two studies are consistent with population trends documented in other parts of the bog turtle range, and build upon prior studies to investigate drivers of nest predation. These results provide information that can be used by managers to aid in the conservation of this endangered species, and suggest further courses of research for future projects.

ACKNOWLEDGEMENTS

First off, I must admit how lucky I have been to have so many helpful and supportive people around me during this research. From the field technicians and volunteers, to my advisors and committee, to the graduate community in FiWGSA, to my friends and family back home. I am very proud of this research, but it was far from a solo venture. I guess I am just thankful that this is going to be such a long list. I would like to start by thanking my co-advisors for everything. Thank you to Dr. Carola Haas for the enduring support and encouragement over the past three years. I have learned so much about being both a good scientist, and a good person. While lessons on conducting rigorous and sound science are beneficial, lessons on how to be compassionate, caring, and putting the well-being of others first are invaluable. Thank you to Dr. Emmanuel Frimpong for all of your support. From helping me through stressful late night analytical re-runs a week before I had to finish this thesis, to walking me through Tom’s Creek to look for field sites. I will forever be grateful for you accepting me into this program without a strong statistical background, and then working with me to develop those skills. It’s amazing that I have been lucky enough to have two mentors throughout this process. Thank you to J.D. Kleopfer and the VADWR, as well as VT Fish and Wildlife for funding for research and assistantships. Thank you to my committee members, Dr. Sarah Karpanty and Dr. Nicholas Caruso. Thank you Sarah for providing invaluable support in terms of field equipment, expertise, and feedback. Thanks Nick for the ever-ending R knowledge, reassurance when I got stressed out, and mountain biking science chats. Thank you to my partner-in-bog-crime, Joe Barron. This has been a stressful but rewarding project to work on, and having you there to figure it out has been immensely helpful. I appreciate all the modeling explanations, 3:00am memes, and the variety of field work experiences we’ve shared. Thank you to Amy Roberts of VADWR for all of your expertise and assistance. It has been really great getting to know you, and I always enjoyed field days with you, talking about the farm, and learning about the turtles! Thank you to Mike Knoerr for giving me a chance when you were in a tight spot and had to find a field crew leader last minute. What a fortuitous series of events for me! I appreciate all that you’ve done for me, from giving me a chance to work with such an amazing species, to life advice, to continued job references, to tacos and beer at White Duck. Thank you to Tim Calhoun for being an all-around rock star. You started out helping in the field, but after taking on the lab manager role and responsibilities, you have been extremely helpful, from finding people to work on various tasks (and mainly just doing them yourself), to pulling data for me. Thank you to the rest of the Haas lab group, including Dr. George Brooks, Billy Moore, Houston Chandler, and Joe Buckwalter for all of the help. You all have helped me through field work, theoretical and quantitative issues, R coding problems, and just being there during the general stress of grad school. Thank you to the excellent undergraduate field technicians who helped on turtle surveys, trapping, and data entry/management: Ryan Moore, Taina McLeod, Allison Leipold, Sam Van

Noy, George Wenn III, Ty Stephenson, Logan Anderson, Allison Carlock, Jacob Berman, and Tori Nutt. Thank you to Ryan Moore and Bailey Chandler for your help in the field in 2020 on the nest predation experiment. I ran it almost entirely alone in 2019, and y’alls help in 2020 was immense. I know it isn’t the most thrilling field work, but y’all really helped me out. Thank you to all the other graduate students I have met in FiWGSA and VT--this is a great community of inspiring scientists and friends, and I am lucky to have met you all. Vance, Dave, Hunter, and everyone else, I’ve enjoyed getting to know you all through the socializing and conversational distractions in Cheatham. There are too many to name, but thank you so much. Thank you to all my hometown friends. While I haven’t been able to get home to Athens in the recent years as much as I would like, I appreciate all the love and support you’ve all shown me since we were kids: Chris, Zack, Gage, Garry, Osi, Jesse, and all the others. Thank you to the Odum School of Ecology community at UGA, including all of my supervisors and friends. Thank you to Misha Boyd for being the greatest academic advisor ever, I don’t know anyone from Odum who isn’t thankful for you. Thank you to Drs. Sonia Altizer, Andy Davis, Dara Satterfield, Alexa McKay, Daniel Harris, and Scott Connelly for all of your help and support as a young scientist. Thanks to my friends Kaleigh, Hayley, Chris, Sarah, Henry, and everyone else for giving me a community of young people to discuss nature and ecology with (and maybe a little partying…). Most importantly, I would like thank my family. You’ve always been supportive of my lifestyle and career, even though I’ve often been poor and would come home smelling like mud and swamp water. Life is never easy, and often times really hard, but we’ve all made it through and I love you. Mom, you instilled in me a love of nature and the desire to explore it at a young age. I’ll always remember that photo of me with a toad stuck in my belly-button from the creek behind Grandma and Gramps Ball’s house. Dad, you’ve been supportive of me in so many ways, even though I probably spent a little too much time bouncing around seasonal tech jobs. Thanks for encouraging me to pursue my passions and aspirations. Ryan, I am so happy you joined our family. I know we can be a lot at times, but having a brother has been such a great experience. I am truly happy and thankful to have you in my life, and I am looking forward to many years of adventures and memories. Jessica, I cannot begin to tell you how thankful I am that I have such an amazing and supportive big sister. You have literally been there for me through everything, and I will be forever grateful. Thanks to the rest of the family, Grandma Smith, Sammye, Uncle Michael, the Noonans, and everyone else for your love, support, and encouragement. And finally, I want to thank the bog turtles. I certainly enjoy finding them more than they enjoy being found, but without the drive to help conserve such a fascinating, beautiful, interesting, and (sometimes) frustrating species, this research would not have been possible.

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TABLE OF CONTENTS

Academic Abstract ...... ii General Audience Abstract ...... iv Acknowledgements ...... vi Table of Contents ...... viii List of Tables ...... x List of Figures ...... xi

Chapter I: Introduction

Introduction ...... 1 Literature Cited – Chapter I ...... 3

Chapter II: Population assessment of historically known Glyptemys muhlenbergii populations in Southwest VA

Abstract ...... 5 Introduction ...... 7 Methods ...... 9 Study Area ...... 9 Field Methods ...... 9 Data Collection ...... 11 Analytical Methods ...... 11 Results ...... 15 Survey Data ...... 15 Estimates of Abundance and Capture Probability ...... 16 Effect of Survey Method on Captures ...... 16 Influence of Temperature Covariates on Capture Rates ...... 17 Discussion...... 17 Management Recommendations...... 32 Tables – Chapter II ...... 40 Figures – Chapter II ...... 47 Literature Cited – Chapter II ...... 57

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Chapter III: More predators, more problems? Investigating the relationship between anthropogenically subsidized predators and nest depredation in southwest Virginia

Abstract ...... 63 Introduction ...... 65 Methods ...... 67 Shared Field Methods ...... 67 Field Methods – Nest Study ...... 70 Data Collection – Nest Study ...... 72 Field Methods – Rotten Egg Study ...... 74 Data Collection – Rotten Egg Study ...... 76 Analytical Methods – Nest Study Predation Analysis ...... 77 Analytical Methods – Nest Study Survival Analysis ...... 79 Analytical Methods – Rotten Egg Study ...... 80 Results ...... 81 Nest Study ...... 81 Rotten Egg Study ...... 84 Discussion...... 84 Management Implications ...... 91 Tables – Chapter III ...... 94 Figures – Chapter III ...... 102 Literature Cited – Chapter III ...... 112

Chapter IV: Conclusion

Conclusion ...... 118 Literature Cited – Chapter IV ...... 121

Appendix A ...... 122 Appendix B ...... 129

LIST OF TABLES

Chapter II: Population assessment of historically known Glyptemys muhlenbergii populations in Southwest VA

Table 1.1: Effort expended for mark-recapture surveys at 6 historic sites in Floyd County, Virginia in 1997, 2019, and 2020 ...... 40 Table 1.2: Summary data of total captures from mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020...... 41 Table 1.3: Summary data of unique individual capture numbers resulting from mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020 ...... 42 Table 1.4: Results of abundance and capture probability from the mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020...... 43 Table 1.5: Results of the ANOVA and Kruskal-Wallis tests comparing capture rates between probing and trapping surveys in 1997 and 2019 ...... 44 Table 1.6: Average air temperatures during bog turtle surveys in 1997, 2019, and 2020 ...... 45 Table 1.7: Captures, effort, and capture rates separated by method from the mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020 ...... 46

Chapter III: More predators, more problems? Investigating the impacts of anthropogenic activity on wetland nest predation rates

Table 2.1: List of covariates used and data sources for artificial nest predation and survival models for the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020 ...... 94 Table 2.2: Nest fates during the Nest Study conducted in 2019 and 2020 at 35 wetlands in Floyd and Montgomery counties, Virginia ...... 95 Table 2.3: Predator species identified from the Nest Study conducted in 2019 and 2020 at 35 wetlands in Floyd and Montgomery Counties, Virginia ...... 96 Table 2.4: Summary of model comparison metrics for binomial generalized linear mixed effects models predicting predation of artificial nests in the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020 ...... 97 Table 2.5: Summary of model comparison metrics for mixed effects Cox proportional hazards models for survival of artificial nest survival during the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020 ...... 98 Table 2.6: Summary of covariate hazard ratios and covariate evaluation metrics from the top Cox proportional hazards model ...... 99 Table 2.7: Summary of predation during the Rotten Egg Study ...... 100 Table 2.8: Results from the Grid Study analysis comparing predation on fresh vs. rotten eggs in Montgomery County, Virginia in August 2020 ...... 101

LIST OF FIGURES

Chapter II: Population assessment of historically known Glyptemys muhlenbergii populations in Southwest VA

Figures 1.1 – 1.4: Plots of abundance estimates with 95% confidence intervals, unique capture numbers, and total captures for different groupings of pooled mark-recapture data ...... 47 – 50 Figures 1.5 & 1.6: Scatterplots showing the relationship between thermal environmental variables and the number of trapping captures per day of effort from 1997, 2019, and 2020 with and without outliers...... 51 – 52 Figures 1.7 & 1.8: Piece-wise regression plots showing the relationship between thermal environmental variables and the number of trapping captures per day of effort from 1997, 2019, and 2020 with and without outliers ...... 53 – 54 Figure 1.9: Examples of variable importance plots from boosted regression tree models exploring the influence of thermal environmental variables on trapping capture rates ...... 55 Figure 1.10: Example of partial dependence plots from boosted regression tree models exploring the influence of thermal environmental variables on trapping capture rates ...... 56

Chapter III: More Predators, more problems? Investigating the impacts of anthropogenic activity on wetland nest predation rates

Figures 2.1 & 2.2: Maps of sites used in the Nest Study and the Rotten Egg Study during 2019 and 2020 in Montgomery and Floyd Counties, Virginia ...... 102 – 103 Figure 2.3: Diagram of the experimental grid set up used in the Rotten Egg Study ...... 104 Figure 2.4: Flowchart of experimental design and data partitioning in the Nest Study ...... 105 Figures 2.5 & 2.6: Histograms of predation events during the Nest Study ...... 106 – 107 Figure 2.7: Visualizaton of GLMER model predictions from the Nest Study ...... 108 Figure 2.8: Hazard ratios for model coefficients from the mixed Cox proportional hazards model from the Nest Study ...... 109 Figure 2.9: Kaplan-Meier survival curves of artificial nests set during the Nest Study ...... 110 Figure 2.10: Photos of predation events on artificial nests set during the Nest Study ...... 111

Chapter I: Introduction

Bog turtles (Glyptemys muhlenbergii) are one of the rarest turtle species in the United

States (Ernst and Lovich 2009), and considered one of the most endangered of North American turtle species (USFWS 2001, Rosenbaum et al 2007, van Dijk 2011). The primary drivers of decline for this species include the loss and alteration of suitable habitat, primarily through development, succession, altered hydrology, and the construction of roads (Groombridge 1982,

Noss et al. 1995, USFWS 2001), as well as illegal collection for the pet trade (Lee and Norden

1996). They are habitat specialists occurring in the eastern US in open canopy wet meadows and fens in the piedmont and montane regions (Morrow et al. 2001). Bog turtles occurs in 2 disjunct populations (Southern and Northern), and range from northern Georgia to New York, with a gap in distribution from southern Virginia to Maryland. They have experienced a severe reduction in distribution and abundance across their range, with some estimates of decline in range upward of

90% (USFWS 1997, USFWS 2001, van Dijk 2011, Tutterow at al. 2017).

Currently, the northern population is listed as threatened by the US Fish and Wildlife

Service (USFWS) and receives protection under the Endangered Species Act (USFWS 1997), while the southern population is protected due to similarity of appearance and only individuals are protected, not the habitats in which they occur. This is due to the fact that at the time of listing, the northern population was found to be threatened by a variety of factors, while in the southern portion of their range relatively less research had been done, but turtles seemed to be locally abundant in North Carolina and Virginia. The USFWS cited 3 main reasons not to grant the southern population the same protections as the northern population: discovery of new populations outside the prior known distribution in North Carolina, lack of information on threats to turtles in the southern population, and a lack of survey information (USFWS 1997). However,

1 collectively they are listed as critically endangered by the IUCN and are protected in all 12 states in which they occur. They are also listed among the top 40 tortoises and freshwater turtles considered to be at very high risk of extinction due to the highly fragmented nature of remaining habitat, and low reproductive output (Turtle Conservation Coalition 2011).

Bog turtles face a range of threats, including habitat alteration (degradation fragmentation and destruction), invasive species, and illegal collection for the pet trade (USFWS 1997, van

Dijk 2011, NCWRC 2018). In order to ensure the species can persist in Virginia, we need a better understanding of status of bog turtle populations in Virginia. Although it is listed as a state endangered species, it has been unclear whether populations are stable, declining, or increasing in the state.

While the threats listed above are the main drivers of decline recognized in many populations, it has been suggested that unsustainably low recruitment and survivorship of early life stages may contribute to population declines (Tutterow et al. 2017, Knoerr 2021). Though there are a number of causes for low recruitment, such as high hatchling predation and mortality or low hatch success due to infertility, freezing, or drowning (Congdon et al. 2000, Butler et al.

2004, Knoerr 2021), a factor in some areas may be high rates of egg predation by anthropogenically subsidized nest predators.

The overall goals of this research were to investigate whether populations of bog turtles at six historically known wetlands have declined over the past 20 years, and to try and understand what, if any, mechanisms may be responsible. To answer these questions, I conducted two seasons of mark-recapture surveys to compare to data collected during mark- recapture surveys conducted in 1997. To my knowledge, this is the longest time span between two abundance estimate comparisons for this species to date. I also conducted an artificial nest

2 experiment to test if any land-scape scale drivers associated with anthropogenic footprint (i.e., building density, building area, road density, etc.) and land-use effect rates of nest predation in wetlands suitable for bog turtles.

Literature Cited – Chapter I

Butler, J. A., C. Broadhurst, M. Green, and Z. Mullin. 2004. Nesting, nest predation and hatchling emergence of the Carolina Diamondback Terrapin, Malaclemys terrapin centrata, in Northeastern Florida. American Midland Naturalist 152:145 – 155. Congdon, J. D., R. D. Nagle, O. M. Kinney, M. Osenioski, H. W. Avery, R. C. van Loben Sels, and D. W. Tinkle. 2000. Nesting ecology and embryo mortality: Implications for hatchling success and demography of Blanding's Turtles (Emydoidea blandingii). Chelonian Conservation and Biology 3:569–579. Ernst, C. H., and J. E. Lovich. 2009. Turtles of the United States and Canada Second Edition. The John Hopkins University Press. Baltimore, Maryland, USA. Groombridge, B. 1982. The IUCN Amphibia-Reptilia Red Data Book: Testudines, Crocodylia, Rhynchocephalia. International Union for Conservation of Nature and Natural Resources, Gland, Switzerland. Knoerr, M. D., G. J. Graeter, K. Barrett. 2021. Hatch success and recruitment patterns of the bog turtle. The Journal of Wildlife Management 85:293-302. Lee, D. S., and A. W. Norden. 1996. The distribution, ecology and conservation needs of G. muhlenbergii, with special emphasis on Maryland. Midland Naturalist 40:7-46. Morrow, J. L., J. H. Howard, S. A. Smith, and D. K. Poppel. 2001. Home range and movements of the bog turtle (Clemmys muhlenbergii) in Maryland. Journal of Herpetology 35:68. North Carolina Wildlife Resources Commission. 2018. Conservation plan for the bog turtle (Glyptemys muhlenbergii) in North Carolina. North Carolina Wildlife Resources Commission, Raleigh, North Carolina, USA. Noss, R.F., E.T. LaRoe III, and J.M. Scott. 1995. Endangered Ecosystems of the United States: 1668 A Preliminary Assessment of Loss and Degradation. Biological Report 28. National 1669 Biological Service. United States Department of Interior. Washington, D.C.

Rosenbaum, P. A., J. M. Robertson, K. R. Zamudio. 2007. Unexpectedly low genetic divergences among populations of the threatened bog turtle (Glyptemys muhlenbergii). Conservation Genetics 8:331 – 342. Turtle Conservation Coalition [Rhodin, A.G.J., Walde, A.D., Horne, B.D., van Dijk, P.P., Blanck, T., and Hudson, R. (Eds.)]. 2011. Turtles in Trouble: The World’s 25+ Most Endangered Tortoises and Freshwater Turtles—2011. IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Turtle Conservation Fund, Turtle Survival Alliance, Turtle Conservancy, Chelonian Research Foundation, Conservation International, Wildlife Conservation Society, and San Diego Zoo Global, Lunenburg, Massachusetts, USA. 54 pp. Tutterow, A.M., G.J. Graeter, and S.E. Pittman. 2017. Bog turtle demographics within the southern population. Copeia 105:293-300. United States Fish and Wildlife Service. 1997. Endangered and threatened wildlife and plants, final rule to list the northern population of the bog turtle as threatened and the southern population as threatened due to similarity of appearance. U.S. Fish and Wildlife Service, Hadley, Massachusetts, USA. United States Fish and Wildlife Service (USFWS). 2001. Bog turtle (Clemmys muhlenbergii), northern population recovery plan. Hadley, USA. van Dijk, P. P. 2011. Glyptemys muhlenbergii (errata version published in 2016). The IUCN Red List of Threatened Species 1:e.T4967A97416755.

Chapter II: Population assessment of historically known Glyptemys muhlenbergii populations in Southwest VA

ABSTRACT

Northern Population and the Southern Population, and spans the eastern U.S. from New York to

Georgia. These declines are mainly documented in the northern part of their range, but recent work in North Carolina, South Carolina, and Georgia suggests that turtles in the southern range are similarly declining. Prior to this research, thorough surveys aimed at estimating abundance had not been conducted in Virginia since the late 1990’s. This research was conducted as part of a state-wide population assessment of bog turtles in Virginia. For my first chapter, I conducted

Capture-Mark-Recapture surveys in six wetlands in Floyd County, Virginia during 2019 and

2020, and generated abundance estimates using RMark. These wetlands had been surveyed in the same manner in 1997, which provided me the opportunity to compare modern abundance estimates with those generated from the 1997 data. My analyses suggest that turtle abundance across these six sites has declined by approximately 50% since 1997. This decline appears to be driven by, but now wholly attributed to, the alteration and loss of habitat at 2 of the 6 sites. This research provides evidence that bog turtle populations may be declining. Understanding the

5 cause, severity, and prevalence of declines, as well as investigating mitigation options are vital to conserving this species.

Introduction

Prior to this study, little was known about the state of bog turtle populations in Virginia.

Recent work has focused on ecology and population monitoring, but population parameters such as abundance had not been estimated many sites in over 20 years. This research was part of a population status assessment of Virginia bog turtles, being undertaken for the Virginia

Department of Game and Inland Fisheries (VDGIF, now the Department of Wildlife Resources,

VDWR) to compare to data from the mid-1990s. In the state of Virginia, G. muhlenbergii is currently listed as State Endangered (VDGIF 2018). However, a more thorough assessment of their status is needed. Declines have been documented across their range, with many populations in the north being extirpated or severely reduced in comparison to historic distributions (USFWS

2001). The southern population is less studied compared to the northern population, but research out of North Carolina, South Carolina, and Georgia suggests that these southern populations are declining as well (NCWRC 2018). However, there have been no estimates of population size conducted in Virginia in more than 2 decades which limits managers’ ability to infer their population status.

In 1997, Shawn Carter, working for Mike Pinder (of the then VDGIF), in coordination with Dr. Carola Haas, conducted bog turtle population surveys at 6 sites in Floyd County,

Virginia. These surveys used multiple capture methods, with the amount of survey effort recorded. These surveys allowed for the estimation of population abundance via Lincoln-

Petersen mark-recapture estimates and are the base line comparison for our modern surveys.

In order to determine if any changes in the populations at these sites have occurred, I reanalyzed the data collected in 1997 (C. A. Haas and S. L. Carter, Virginia Tech, unpublished data) and compared it with data collected in 2019 and 2020. By replicating the sampling methods

7 and effort, I hoped to be able to directly compare estimates. Previously, it was known that northern bog turtle populations have declined extensively (USFWS 2001), and recent declines have been documented in southern populations, particularly those in northwest North Carolina

(Tutterow et al. 2017, Knoerr 2018).

The goal of this study was to determine if significant changes in abundance have occurred in these 6 wetlands. While surveys have occurred at these sites over the last 20 years, there have not been any robust enough to estimate population parameters. Research across these six sites focused on other topics, such as wetland hydrology and movement ecology, and standardized surveys were not conducted at all six sites within a single year. To estimate changes in population abundance from 1997 to 2019 and 2020, I conducted 2 seasons (i.e., years) of surveys using a capture-mark-recapture framework. Mark-recapture is a sampling method that allows for the estimation of population abundance and capture probability by conducting multiple surveys at a single site, marking individuals that are captured, and analyzing the proportions of new and recaptured individuals over multiple surveys.

My results suggest that changes in population abundance have occurred, so I examine alternative hypothesis that may explain potential changes, including altered shifts in phenology, poaching, variation in surveyor detection, and the influences of environmental and site-specific variables such as temperature, heat accumulation, and the presence of grazing livestock. When there is no evidence that supports these alternative hypothesis, I explore factors that may contribute to declining abundances, such as altered habitat conditions. Finally, I propose management and future research recommendations based on my findings to aid in the conservation of this rare and endangered turtle.

Methods

Study Area

During the 2019 and 2020 field seasons, I resampled the same 6 sites that were originally sampled by Carter et al. (C. A. Haas and S. L. Carter, Virginia Tech, unpublished data 1997). To preserve site security due to the sensitive nature of this species, these sites will be referred to by their acronyms: DA, CW, ST, CG, RM, and NH. To directly compare population estimates from the 3 years, I replicated the amount of survey effort from the 1997 study as closely as possible. I conducted the trapping and probing surveys during the same time frame, for the same duration, and with a combination of the same surveyors and those with comparable levels of prior sampling experience with bog turtles.

In 1997, Carter and crew set traps on 21 May and ran them for 20 non-consecutive days, ending on 18 June. In 2019, I set traps on 19 May and ran them for 20 non-consecutive trap days, ending on 15 June. In 2020, I set traps on 23 May and ran them for 20 non-consecutive trap days, ending on 17 June. In concert with the trap surveys, probing/visual surveys were conducted during non-consecutive 4-day periods in early June. Additionally, in order to test the hypothesis that shifts in phenology may have influenced capture rates in 2019, I conducted 12 non- consecutive days of trapping surveys in April 2020 from 6 April until 23 April at the same level of effort as the other trapping surveys (Table 1.1). Traps were closed multiple times due to overnight freezing temperatures and flooding risk.

Field Methods

Trapping - Following Carter’s 1997 protocols, I placed differing numbers of traps at each site based on the area of the wetland (range: 15-25, total=122, Table 1.1). Traps were constructed from ½” galvanized hardware cloth to form a rectangular-shaped box with openings on either

9 end and each trap was identified via a metal tree tag with a unique number. Since our traps were passive (i.e., non-baited with a one-way door flap) and relied upon proper location placement to be effective, I placed traps by identifying likely paths used by G. muhlenbergii throughout the wetlands they inhabit (USFWS 2001). They use trails that are similar in appearance to rodent paths, consisting of travel lanes through wetland vegetation and open mucky patches. These corridors are the optimum locations for trap placement, especially if recent use is apparent (i.e.,

G. muhlenbergii tracks in the mud). In addition to the apparent movement corridors, traps can be placed in other areas that are likely to be used by G. muhlenbergii, such as spaces between tussocks, within rivulets, etc. To keep trapping effort consistent across years, Christian d’Orgeix

(Virginia State University) assisted with general trap location. This is important to note as Dr. d’Orgeix served as the technician in 1997 who checked the traps that season. Locations that had become unsuitable due to habitat change were not included in the 2019/2020 trapping surveys.

Once an appropriate location was identified, traps were placed in accordance with the trapping protocol (Appendix A). The total number of traps deployed at each wetland was taken from the

1997 study and was consistent across all 3 seasons (Table 1.1).

Probe/Visual sampling - To replicate the 1997 study, 6 or 7 surveyors searched the sites on each of the 4 days. In 2019, probing surveys occurred at all 6 of the original sites, whereas in

2020 only the 4 sites with captures in 2019 were surveyed. Time surveyed at each wetland was taken from the 1997 study, was consistent across all 3 seasons, and was dependent upon wetland size, with larger wetlands being surveyed for longer periods than smaller wetlands (Table 1.1).

The wetland area was measured in 1997, and only the seepage areas that the traps were placed in were measured, not the entire wetlands.

I attempted to replicate survey methods to reduce variation, in part by recruiting members of the original survey team to assist with surveys in 2019. Two surveyors (Dr. Carola Haas and

Shay Garriock) were able to assist with both the 2007 and the 2019 surveys. The other 5 surveyors consisted of VDGIF biologists with prior experience and members of the Virginia

Tech (VT) bog turtle crew. In 2020, I was unable to recruit any out-of-town help due to COVID-

19 and debated cancelling the 2020 probing surveys. However, we were able to conduct these surveys with members of the VT bog turtle crew, and while we had fewer people, we adjusted survey times in order to match the amount of person-effort from the 1997 and 2019 surveys.

Data Collection

I recorded site-specific data during or prior to probing surveys (depending on availability of technicians and surveyors). This information consisted of the time wetlands were entered and exited, the time the surveys started and ended, weather conditions during surveys, identity of surveyors, and site ID. Once surveys were finished, the total survey time was recorded and multiplied by number of surveyors to quantify person-hours surveyed in order to confirm the survey times matched the amount of effort expended during the 1997 study.

Analytical Methods

Estimates of Abundance and Capture Probability - I estimated abundance of turtles for each year using closed population models with captures both pooled across sites and separated by site. I used the package “RMark” (Laake 2013) in the statistical software R (R version 3.6.3), which acts as an R-based user interface to create population models through Program MARK

(White and Burnham 1999). I used the Huggins group of loglinear models (Huggins 1989, 1991,

Otis et al. 1978), which are conditional likelihood models that estimate 2 parameters: p (the probability of capture) and c (the probability of recapture) for closed populations. Using these

11 parameters, the derived estimated abundance (N̂ ) of a population can be modeled as a function of various inputs (sampling event, individual specific covariates, temperature, survey method, etc.).

Abundance models in RMark can incorporate different sources of heterogeneity in capture probabilities. However, due to the unequal effort between survey methods (trapping via probing, Table 1.1) but high number of captures via probing, it would not be sound to incorporate the effects of time and survey method into the models. Since probing was only done on 4 days, but accounted for 34-39% of the total captures (Appendix A), in order to include the probing captures in the analysis only a constant capture probability model (M0) was used to estimate abundance. The M0 model includes no heterogeneous parameters, thus holding capture probabilities constant between sampling periods and individuals. Since there is no parameterization based on survey period or method, I was able to include all captures in the models. This means the M0 model yields a single estimate for capture and recapture probability

(p̂ ). N̂ and p̂ are estimated via log likelihood maximization based on the number of unique captures, recaptures, sampling periods, and the provisional estimate of abundance (Otis et al.

1978, Krebs 2014).

Individual capture histories were generated for each turtle captured throughout the study

(only those captured within survey limits were used for statistical analyses). Capture histories were pooled across all sites for the overall population estimates and included both trapping and probing captures. In addition to the overall pooled estimates, I created capture histories of subsets of the 6 sites to investigate the relative influence of individual sites on the overall abundance, resulting in 7 datasets. I chose these groupings in order to examine the impacts that the apparent declines at RM and CW had on the overall abundance estimates, and if the remaining 4 sites had declined after accounting for RM and CW (see discussion section

“Abundance Estimates”). For 1997 I created 3 datasets: one with captures from all 6 sites pooled, one with captures from NH, CG, ST, and DA, and a third with captures from NH, CG, and DA. For 2019 and 2020, I created 2 datasets: one with captures from NH, CG, ST, and DA, and a second with captures from NH, CG, and DA, as there were no captures at RM and CW in

2019, and those sites were not surveyed in 2020. I used 24 hour periods as the individual sampling events, resulting in capture histories with 20 sampling units. I fit each capture history to M0 models which generated abundance and capture probability estimates.

Influence of Survey Method on Capture Rate – In order to test whether survey method

(trap vs. probe) had a significant effect on the numbers of turtles captured, I summarized the captures based on year, site, and method. First, I standardized capture rates by calculating the captures/day of effort (20 days for probing, 4 days for trapping) for each year/site/method combination. Next, I ran a Shapiro-Wilkes test on the standardized capture data and found that they were non-normally distributed (p < 0.001). I ran another Shapiro-Wilkes test on the log- transformed standardized capture data, which indicated that those data were normally distributed

(p = 0.344). Next, I used an ANOVA to test whether survey method or year significantly influenced the captures/day of effort. Another Shapiro-Wilkes test was run on the ANOVA residuals, and found those to be normally distributed as well (p = 0.423).

In addition to the ANOVA using the log-transformed data, I used a Kruskal-Wallis rank sum test, which is the non-parametric equivalent of a one-way ANOVA to test whether the non- transformed data showed similar results.

Influence of Temperature Covariates on Capture Rate – In order to test whether capture rates were influenced by average air temperature or Heating Degree Day (HDD), I obtained weather data from the National Oceanic and Atmospheric Administration’s Climate Data Online

13 tool (https://www.ncdc.noaa.gov/cdo-web/) for April-July of 1997, 2019, and 2020. I calculated the average temperature per day, as well as the single day HDD (Tmax + Tmin)/2-Tbase), and 2, 3, and 4-day cumulative HDD. Air temperature has been speculated to influence bog turtle activity

(Ernst and Lovich 2009), and thus was included in the analysis. HDD is a metric of environmental heat accumulation. While HDD, is typically used in agricultural research to predict crop growth (and is referred to as “Growing Degree Day” at longer temporal scales), it has wide applications and has been used in entomological, ichthyological, and herpetological research in the past (Obbard and Brooks 1987, Honsey et al. 2019, Jones et al. 2019, Murray

2020) to predict growth and phenological patterns. Tbase is a biologically derived temperature threshold that is organism-specific (e.g., the minimum temperature for plant growth). There are no published HDD Tbase values for bog turtles, so I used the minimum temperature recorded from a bog turtle capture in Virginia per the Virginia Department of Wildlife Resources bog turtle database (10⁰C).

I used the data from trapping captures in 1997, 2019, and 2020 as my response variable, as there were not enough days of probing surveys to analyze. Since I only included trapping captures, and trapping survey effort was standardized and consistent between years, I did not standardize the data per any unit of effort, as captures were recorded on a per day of effort basis.

In order to test the relationships between capture rates and the specified covariates, I first created scatter plots to visually analyze if any relationships were apparent. I tested for outliers in the capture data using boxplots and the “OutVals” function, and created a new dataset with the outliers removed. I next used piece-wise logistic regressions to examine potential relationships in the data without outliers. I used the R package “segmented” (Muggeo 2003, Muggeo 2008), which allows for break-point estimation when there is not a known break that was confirmed

14 visually via the scatterplots with outliers removed. The regressions were run on the data with, and without, outliers.

Finally, I used Boosted Regression Tree models (BRTs) to further explore the data.

BRTs are a combination method that utilize regression tree models and boosting, and are described as an additive regression method. BRTs are advantageous as they can handle a variety of predictor variables, are robust to outliers, and can fit complex non-linear relationships (Elith et al. 2008). The BRT analysis was conducted using the R package “dismo” (Hijmans et al. 2020).

I ran a series of models, varying the tree complexity between 3-10, the learning rate between

0.01 - 0.0001, and using a bag fraction of 0.5. I interpreted the model outputs using variable importance plots and partial dependency plots.

Results

Survey data

1997 survey results - During the surveys conducted by Carter et al. in 1997, there were a total of 107 captures of 68 unique turtles (Tables 1.2 & 1.3). Of the 107 total captures, 65 were caught via trapping (60.7%) and 42 were caught via probing (39.3%).

2019 survey results - During the 2019 surveys, I had a total of 41 captures of 28 unique turtles (Tables 1.2 &1.3). Of the 41 total captures, 27 were caught via trapping (65.9%) and 14 were caught via probing (34.1%).

2020 survey results - During trapping surveys in April 2020, I had a total of 4 captures of

3 unique turtles (Tables 1.2 & 1.3). During surveys in May and June 2020, I had a total of 39 captures of 24 unique turtles (Tables 1.2 & 1.3). Of the 39 total captures, 24 were caught via trapping (61.5%) and 15 were caught via probing (38.5%).

Estimates of Abundance and Capture Probability

The model results show that the mean estimated abundance of turtles was higher in 1997 than in 2019 and 2020, while the estimates from 2019 and 2020 were similar. The only significant difference based on non-overlapping 95% confidence intervals were the estimates from the capture histories incorporating all 6 sites from 1997 and 2019 (Table 1.4, Fig. 1.1), which suggests that the mean abundance estimate of turtles significantly decreased by more than

50%.

Figure 1.1 and Table 1.4 show the estimated abundances across years incorporating all 6 sites and show a decreasing trend in turtle abundance from 103.3 turtles in 1997 to 48.2 turtles in

2019, indicating a possible decline of 53.3%. The 95% CIs from the 1997 estimates do not overlap those from 2019, indicating that this is a statistically significant decline in estimated abundance. Additionally, the raw data is also indicative that a decline may have occurred. Not only was the estimated number of turtles lower across all 6 sites, but so were the unique number of individuals caught, as well as the total number of captures. Across all sites there was a 61.7% decrease in the number of total captures from 1997 to 2019, and a 58.8% decrease in the number of unique animals caught.

Capture probability estimates across models are similar, and none are significantly different based on the confidence intervals. Estimates ranged from 0.042-0.071 depending on the capture history used. Table 1.4 shows the model results for both abundance and capture probability.

Effect of Survey Method on Captures

The results of the ANOVA testing the effect of survey method and year found that both covariates significantly influenced the captures/day. Looking at the captures/effort, there were

16 higher capture rates from probing (average = 1.26 captures/day) than trapping (average = 0.41 captures/day) across all site/year combinations (p <0.001). Additionally, the influence of year was significant in the ANOVA (p = 0.010), thus allowing me to reject the null hypothesis that there was no difference between samples from probing vs. trapping (Table 1.5).

The results of the Kruskal-Wallis rank sum test showed the same trend, that method significantly affected the captures/day of effort (p = 0.003), again allowing me to reject the null hypothesis (Table 1.5).

Influence of Temperature Covariates on Capture Rates

The exploratory analyses suggest that environmental warming may have an effect on trapping capture rates. Visual analysis of the scatterplots with and without outliers (Figs. 1.5 &

1.6) showed possible linear and non-linear relationships. Both the piece-wise logistic regressions

(Figs. 1.7 & 1.8), and the BRT models show similar trends (Figs. 1.9 & 1.10). Across BRT models, HDD 2-4 consistently had the highest allocation of variable influence, while HDD and average daily air temperature ranked the lowest. Minimum temperature was occasionally ranked in the top 2-3 variables (Fig. 1.9). Additionally, the effect of the variables on capture rates via trapping appeared similar between the BRT models and the piece-wise regressions, in that capture rates were highest at low HDD 2-4, and decreased as those covariates increased. The partial dependence plots for daily maximum air temperature (TMAX) from the BRT models suggested that trapping captures were lowest below ~22⁰C, and as TMAX increased above 22⁰C so did capture rates (Fig. 1.10).

Discussion

Abundance estimates - Figure 1.1 and Table 1.4 summarize the estimated abundances across years incorporating all 6 sites and show a decreasing trend in turtle abundance. The raw

17 data also suggest that a decline may have occurred. Not only was the estimated number of turtles lower across all 6 sites, but so were the unique number of individuals caught, as well as the total number of captures. Given these changes in estimated abundances, I developed alternative hypotheses that may explain these observations and results. The hypotheses I investigated included the following: whether survey design or variation in surveyor experience may have played a role, potential effects of temperature and heat accumulation on capture rates, the possibility of a phenological shift in turtle behavior, and the impacts of grazing on turtle detection. Before discussing those potential sources of variation in capture numbers, I will discuss the most likely driver of these declines: habitat alterations at CW, RM, and ST.

During 2019 there were zero captures at 2 sites (CW and RM), likely caused by significant habitat alterations, and in 2020 those sites were not surveyed. In order to determine if the potential extirpation of these 2 sites caused the overall decline in abundance estimates, the same modeling process was conducted with data only from sites with captures during all 4 years:

NH, CG, ST, and DA. Results from these analyses show that even after accounting for the 2 potentially extirpated sites, a potential decrease in estimated abundance was found, decreasing from 86.8 in 1997 to 48.2 in 2019 and 35.4 in 2020 (Fig. 1.2, Table 1.4), indicating a possible decline of 44.5-59.2%. It is important to note that the 95% CIs did overlap between 1997 and

2019. The CIs from 1997 and 2020 did not overlap, however the 2019 and 2020 CIs did. Again, the raw captures suggest a decline occurred even after accounting for the loss of CW and RM.

There was a 51.7-54.1% decrease in the number of total captures from 1997 to 2019 and 2020, and a 49.1-56.4% decrease in the number of unique individuals at these 4 sites.

Recent anecdotal evidence suggests that ST may have suffered declines due to reduced habitat availability as a result from stream incision which can have detrimental impacts on

18 wetland hydrology (similar to ditching), so the modeling process was conducted on the 3 remaining sites to determine if the loss of habitat at CW and RM, along with suspected decreases in habitat at ST accounted for the decrease in overall estimated abundance. The estimated abundance for the 3 remaining sites (NH, CG, DA) decreased from 43.9 turtles in 1997 to 29.0 in

2019 and 24.0 in 2020, indicating a possible decline of 33.9 – 45.3% for these 3 sites. However, the 95% confidence intervals almost entirely overlap, indicating no significant difference (Fig.

1.3, Table 1.4). The raw captures, again, seem to indicate that a decline may have occurred.

There was a 47.6-63.5% decrease in the total number of captures from 1997 to 2019 and 2020, and a 41.2-55.9% decrease in the number of unique turtles caught at these 3 sites from 1997 to

2019 and 2020. It is important to note that site ST accounted for almost 20% of all captures, and

~30% of unique individuals captured in 1997. That may explain the large decrease in estimated abundance when ST is removed from the capture history and why the smaller sample size when the site is removed makes it harder to assess changes.

Exploring factors affecting low capture rates in modern surveys - Understanding what has happened at these sites is more complicated than looking at the abundance estimates and deciding that a decline has or has not occurred. True trend analysis requires substantial data, with the implementation of ecological time series models utilizing abundance estimates over multiple time steps as well as environmental and survey-specific covariates (Dennis and Otten 2000, Link and Sauer 1997, Humbert et al. 2009). For trend analyses to have a reasonable level of precision, consistent sampling and a suitable sample size are also important (Dunn 2002). However, any information about changes in populations can be useful to managers in understanding if and when to take action. For some species, short term declines in population may not merit conservation action. Life history traits such as high fecundity and short time to reproductive age

19 can reverse population declines (Dunn 2002). Unfortunately, bog turtles do not exhibit those life history traits. Female bog turtles lay 2-4 eggs per year, and the age of maturity is typically between 5-7 years old depending on location (Ernst and Lovich 2009). This means that bog turtle populations may take a relatively long time to rebound after declines.

Estimates of Capture Probability - In terms of trapping detection, one study focused on bog turtles found that over 9000 trap hours was required to detect an individual in an occupied, low-abundance site (Somers and Mansfield-Jones 2008) with an average trap density of 0.002 traps/m2, but capture probabilities were not reported. Another study on bog turtles found the average capture probability via trapping across 8 sites to be 0.19 (range: 0.03-0.41) with a higher trap density at 0.04 traps/m2, and total trap hours per site ranging from 17,400 hours to 62,976 hours (Stratmann 2015).

My results indicate the average capture probability across models to be 0.052 (range:

0.042 – 0.071, Table 1.4), with total trap hours per site ranging from 7,200 hours to 12,000 hours and trap density ranging from 0.02 – 0.24 traps/m2 (Table 1.1). While capture probabilities estimated from this study are lower compared to others reported, the question of interest is whether detection rates varied across the years of this survey. If capture probabilities are consistent, the decrease in abundance estimates may not be attributable to changes in detection.

The average capture probabilities generated from the models (which included captures from both trapping and probing), were not significantly different, as the 95% confidence intervals for all capture probability estimates overlapped. This suggests that the estimated decline in abundance was not a result of variation in capture rates across survey years.

Did variation in surveyor experience affect capture rates? - One explanation for the decrease in captures is that a lack of expertise or ability may have resulted in lower detections

20 and a failure to find turtles that were in the wetlands. That is not an unreasonable possibility when working with a species with such low detection rates. Current work out of the Haas lab led by J. C. Barron suggests that in optimal conditions probing detection rates are only ~0.25, with wetland area being the only factor found to influence detection rates (J. C. Barron, unpublished data), although only probing surveys were conducted for that study.

Looking at the 2 capture methods, similar proportions of turtles were caught via each method across all years (range: 60.7-65.9% trapping, 34.1-39.3% probing, Appendix A).

Trapping requires prior knowledge of bog turtle’s usage of wetland microhabitats. I personally placed most of the traps during the 2019 and 2020 seasons, and each trap’s placement was confirmed by other biologists with at least 5-10 years of experience trapping bog turtles in these wetlands. Thus, it is unlikely that trapping detections were significantly lower in 2019 and 2020 due to trap placement alone.

Probing, on the other hand, could involve more observer bias. Had there been a significant difference in the probing abilities of the teams in the 3 seasons which led to the low overall captures in 2019 and 2020 compared to 1997, I would expect to see a significantly lower percentage of overall captures via probing. If trapping detection was consistent across years, but probing detection was lower in 2019 and 2020, then I would expect to see a higher proportion of turtles caught via trapping compared to 1997. This is not the case as the percentage of total captures via probing was 60.7% in 1997, 65.8% in 2019, and 61.5% in 2020. The amount of prior bog turtle survey experience was relatively consistent across years as well, with most surveyors having 1-5 years of survey experience, with a few people in each year having <1-year experience. The number of person-hours spent probing was consistent across all years. However, in 2020, due to the COVID-19 pandemic, I was unable to have as many individuals help as

21 during the prior seasons. In 1997 and 2019, survey crews consisted of 7 people surveying for the appropriate amount of time (Table 1.1). In 2020, we were only able to bring 4-5 people per day, but we surveyed longer to have the correct number of person-hours. Due to the similarity in captures via each method, as well as the consistent level of survey effort and surveyor experience, it is unlikely that these factors biased the data and were the cause of the decrease in captures across survey years.

Did temperature affect capture rates? - The average air temperature during the 1997 surveys was 20.8⁰C, 23.4⁰C during the 2019 surveys, and 23.6⁰C during the 2020 surveys

(NOAA DCO). A summary of total average and weekly average temperatures can be found in

Table 1.6. The results of the piece-wise regression and BRT modelling suggest that there is some effect of temperature and cumulative heating (measured through HDD 1-4) on capture rates via trapping. However, this was an exploratory analysis. Without an experiment designed to test the effects of temperature on capture rates, I can only speculate that there is a relationship between bog turtle activity and cumulative environmental warming. This should be investigated in future work, either through field surveys across a gradient of temperature conditions, or potentially via an experimental ex-situ study. By creating a series of mesocosms/enclosures that vary in temperature, and housing bog turtles in the enclosures, it could be possible to monitor activity levels across an experimentally structured temperature gradient.

Additionally, recent analysis of detection probabilities in an occupancy style framework conducted by the Haas Lab during 2019 and 2020 in Floyd County, Virginia found no evidence that ambient air temperature during surveys influenced detection (J.C. Barron, unpublished data).

Prior studies on bog turtle activity have only looked at daily air temperatures, and no published literature has looked at the correlation between HDDs and activity in this species. Future work

22 should investigate this relationship, and could do so by comparing HDDs with long-term datasets of bog turtle captures. By using long-term datasets, the influence of heat accumulation on capture rates could be better tested, as each year would be treated as a data point. Teasing apart the influence of HDD from other environmental variables such as precipitation would be difficult if a small number of years were included.

Figures 1.5 and 1.6 appear to show that 1997 was cooler in both average air temperature and the cumulative heating covariates, as the distribution of data from 1997 was clustered on the left side of the graphs relative to 2019 and 2020. This, in addition to the results of the BRT models, may provide some evidence that cooler conditions in 1997 resulted in higher capture rates. In other species of turtles, surface activity and movement behavior are known to be related to temperature and longer-term weather patterns (e.g. Nieuwolt 1996, Converse and Savidge

2003, Tucker et al. 2015).

Did phenological shifts due affect capture rates? - Another alternative explanation for low capture rates in 2019 and 2020 is that a phenological shift in behavior resulted in changes to season-specific detection rates across years. After the 2019 season concluded, and I determined that raw captures were more than 50% lower than 1997, I tried to think of alternative explanations to a true decrease in abundance. I hypothesized that a potential increase in average monthly or daily temperatures may have resulted in a phenological shift in peak activity. Bog turtles are known to hibernate during the winter, and studies have shown that the timing of spring emergence is correlated to soil temperature, which is correlated to ambient air temperature, and emergence typically occurs around 23⁰C (Zappalorti 1976, Feaga and Haas 2015). They are typically found under the surface or cryptically basking under vegetation and brush during most of the year (USFWS 2001, Pittman and Dorcas 2009). However, in the springtime following the

23 emergence from overwintering hibernacula, they spend more time on the surface searching for mates, which may result in higher trap detection rates than other times of the year. Studies have shown that the while bog turtles can be active throughout the year, activity peaks in April-July, with 62% of Virginia bog turtle observations occurring in May (Mitchell 1994, Ernst and Lovich

2009, Smith and Cherry 2016). If average temperatures have risen enough due to climate change, shifting the timing of the spring emergence and peak activity, I may have failed to detect turtles that were in the wetlands due to this hypothesized shift in phenology.

To test this hypothesis, I conducted 12 non-consecutive days of trapping surveys in April

2020. Over the course of the 12 days I trapped 4 turtles, equaling in 0.33 captures/day. During

May and June, there were 24 trapping captures equaling 1.2 captures/day. This suggests that, in

2020 at least, capture rates were lower in April compared to May and June, which does not support the hypothesis that turtles were more active earlier in the year, causing lower capture rates during the survey period in 2020. However, it is important to note that April 2020 (and the rest of the winter that year) was especially cold, with lots of precipitation. Without conducting telemetry to monitor the timing of emergence from hibernacula during this period in 2019 and

2020, I cannot say whether spring emergence is correlated with cumulative environmental warming, but further research should investigate this possibility, and could do so in a similar manner as described in the section above on the impact of HDD on capture rates.

To understand how climate change may be altering bog turtle phenology, we may not need to track turtle’s emergence dates over multiple years, but rather we could make inferences using prior literature and climate data. The first step would be to investigate the relationships between ambient air temperature/HDD and soil temperature. Since soil temperature has been tied to emergence date, climate data over the past 20 years could be analyzed to determine if changes

24 in temperature may have resulted in changes in soil warming, which could result in shifts in the timing of emergence.

Impacts of Grazing on Captures – Another possible source of variability in detection between sites is grazing pressure. Because vegetation height and density decrease with grazing pressure, it may be easier to detect surface active turtles in wetlands that are grazed. In 1997, 4 of the 6 sites were grazed (DA, CW, ST, and CG). In 2019 and 2020, CG was not grazed at all, and

ST was partially grazed (but most of the survey effort took place on grazed sections). Without quantifying and comparing vegetation structure (which may be impacted by grazing), it is difficult to say how much this may have changed capture probability, but it is important to acknowledge. The only site that was grazed in 1997, and ungrazed in 2019 and 2020 was CG, which did have a decrease in raw capture data. One study did find that captures and bog turtle density were higher at grazed sites than ungrazed sites (Tesauro and Ehrenfeld 2007) but did not calculate and compare detection rates. While higher densities of turtles may result in higher site- specific detection rates (Stratmann 2015 reported the highest detection probability at the site with the most captures, which was also one of the smaller sites surveyed), the actual influence of grazing on detection is still unknown. It is also worth noting that grazing can have a wide variety of impacts on bog turtle wetlands, both positive (e.g., reduction of successional woody vegetation) and negative (e.g., turtle mortality via crushing) (Tesauro and Ehrenfeld 2007, Travis et al. 2018). Whether or not the impacts are positive or negative may be dependent on the level of grazing pressure.

Timing and Drivers of Decline - To understand what has happened at these 6 sites in the last 20 years, we have to think about the site level changes, as well as the landscape level changes. There are a variety of drivers that may be playing a role, including illegal poaching,

25 traffic-related mortality, and habitat loss and alteration. Unfortunately, I did not have the data to make inferences about the effects of these drivers, with the exception of habitat alteration. There are little to no data to inform how serious the first two drivers may be in this area, and further studies should investigate the impacts that poaching and road mortality, along with other potential sources of mortality may be having on these and other populations in Virginia.

However, we do know that significant habitat alterations have occurred at these six sites. Starting with the 2 sites presumed to be extirpated (CW and RM), both sites were impacted by alterations to the habitat which affected wetland hydrology and reduced the amount of available suitable habitat. At CW, the wetland was illegally ditched, resulting in a dramatic alteration to the hydrology of the wetland. This ditching drained the water and lowered the water table in order to create more grazing pasture but resulted in a reduction of available habitat for bog turtles.

Unfortunately, no legal action was taken against the responsible party, and no mitigation or restoration efforts have taken place. This site may still be periodically occupied depending on the level of grazing pressure, and dispersing turtles from adjacent sites, but is unlikely to sustain a population of turtles without conservation action.

The other site that is presumed extirpated, RM, was not impacted directly by anthropogenic influences, but rather by beavers. Historically this site sat above a mill dam, which flooded a creek basin creating a suitable wetland. In the early 2000s however, beaver activity resulted in the flooding of the site, turning the suitable habitat into a beaver pond. This in and of itself likely cause the inhabiting turtles to disperse. In the late 2000s, a series of dam failures (both beaver and human) resulted in the beaver pond draining completely. The site is now predominantly floodplain with an incised creek flowing through the middle, with very small pockets of suitable habitat dispersed throughout.

Of the 4 remaining sites, ST has also experienced a loss of habitat due to stream incision.

Numerous surveyors who have been to ST over the years have commented on how little suitable wetland remains even compared to 10 years ago (J. Feaga and A. Roberts, Pers. Comm.). Like many bog turtle wetlands, ST is an open, relatively flat wetland with large patches of soft emergent wetland, alder thickets, and a stream. Over the years, the stream at ST has become incised. Like CW, this stream incision appears to have caused an alteration to the hydrology of the site, decreasing the amount of soft saturated soils that are needed for turtles. The stream incision may be exacerbated by grazing practices at the site. Cattle have had access to the wetland at ST and are able to freely enter the entirety of the stream. Even over the 2.5 years that I have been surveying ST, I have seen an increase in bank erosion and incision resulting from cattle moving in and out of the stream. In order to mitigate, and preferably reverse this erosion, proper stream management practice should be implemented (e.g., fencing cattle out of the stream, planting of native riparian vegetation, installing Beaver Dam Analogs to collect sediment and raise the streambed and water table). Additionally, this site could prove to be a good candidate for federal and state programs aimed at habitat restoration on private lands, such as the

Natural Resources Conservation Service’s (NRCS) Working Lands for Wildlife program, which utilizes Farm Bill funding to work with private landowners of working lands to enhance and restore habitat.

Further evidence that these declines have occurred in the past ~15 years can be seen in a dataset collected by Kathy Fleming in 2004. Fleming trapped 4 of the 6 sites (RM, ST, CG, and

DA) from 14 May 2004 until 7 July 2004. Those trapping surveys resulted in 87 total captures of

53 individuals. Due to the greater amount of survey effort, the lack of quantified probing/hand searching survey effort, and the fact that CW and NH were not surveyed, abundance estimates

27 from the Fleming data cannot be directly compared to estimates from the 1997, 2019, and 2020 surveys. However, the abundance estimates generated from the 2004 data can inform our understanding of when and where declines may have occurred. In 2004, the abundance estimate indicates that there were 78.5 turtles in the 4 wetlands surveyed (CG, DA, RM, ST). This estimate was not found to be significantly different than the 1997 estimate of those 4 sites (80.4 turtles, Fig. 1.4). This, along with the fact that the raw captures across these four sites were higher in 2004 than in 2019 and 2020 suggested that the declines in abundance happen after the

2004 surveys.

Does the Decline in Abundance Found Here Match Other States? - While capture probability may have varied across years due to the aforementioned factors, I still have reason to believe that at least 3 of the 6 sites surveyed have either been locally extirpated or experienced declines in abundance. Looking to neighboring states, as well as the northern population, the trends are similar. In North Carolina, the closest and likely most similar state, it is estimated that up to 90% of historically occupied wetlands have been extirpated (NCWRC 2018). In Georgia and South Carolina, most if not all populations are presumed extinct except for a small number of sites in Georgia (Stratmann et al. 2020). Northern populations have also experienced drastic declines, including the extirpations of sites in every state they occur in (USFWS 2001, van Dijk

2011). The primary driver of declines across the range is the alteration and destruction of wetland habitat (Weakly and Schafale 1994, Noss et al. 1995, USFWS 2001, NCWRC 2018).

The types of habitat alterations vary based on location, but can include development of the site, ditching of the wetland, increased in impervious surface cover in the area, increases in wells, underground excavations for utilities like fiber optic cable, etc. A key issue in Virginia (and many other places) is the fact that the majority of bog turtle wetlands occur on private property,

28 and the lack of regulatory enforcement means these sites are harder to protect from anthropogenic habitat alterations than sites on protected land. While bog turtle wetlands in the southern range are not protected via the Endangered Species Act, they are protected via other regulations like the Clean Water Act. This should theoretically prevent them from being destroyed, especially on public or federal lands. However, CW is an example of the limitations of wetland regulation enforcement in southwest Virginia. While regulations protecting wetlands are important, there are better avenues to encourage landowners not to alter wetland habitat. By providing education on the ecological and economic benefits that wetlands provide, as well as financial incentives through leases or easements, and opportunities to enroll in cost-share programs to implement management practices that benefit bog turtles, positive incentives could be provided to not alter habitat. This approach may foster better relations with private landowners by creating beneficial relationships, rather than increasing mistrust and resentment towards agencies and biologists.

While the loss of a single wetland in and of itself is detrimental, it can have negative repercussions for the surrounding sites. Bog turtles disperse between wetlands and persist in a matrix of occupied patches across the landscape, rather than individual wetlands with no immigration or emigration (Shoemaker and Gibbs 2013). Losing patches results in increased fragmentation, which can negatively impact the overall population viability through increased road mortality, predation pressure, and ease of collection (Groombridge 1982, USFWS 2001) and was cited as a primary cause for species listing (USFWS 1997).

What does this mean for Virginia’s bog turtles? - While these results cannot be extrapolated to all occupied sites in Virginia, it is not an unreasonable assumption to believe that the factors that impacted abundance at these sites are acting on other sites. Most of, if not all of

29 the sites in Virginia face similar threats. In addition to habitat alteration and fragmentation, another main threat is the illegal collection of animals for the international pet trade (USFWS

2001, NCWRC 2018). Over the course of the 2019 and 2020 field seasons, I received anecdotal evidence that wetlands in Floyd County had been poached within the past 10-15 years. This is based on first-person accounts from private landowners who told us that people had taken small turtles out of the wetlands they owned. The removal of even a few individuals, especially adult females, can have long-term impacts on populations due to the life history traits of the species such as their low annual reproductive output (Ernst and Lovich 2009), long generation time

(Shoemaker 2011) and apparently small population sizes compared to other turtle species (Tryon

1990, Rosenbaum et al. 2007).

However, it is likely that there are still populations left in Floyd County (and the rest of the bog turtle’s range in Virginia) that have not yet been discovered. The colonization of previously unoccupied wetland patches or restored wetland patches within a network of sites is also possible. Occupancy surveys led by J.C. Barron in 2019 and 2020 resulted in the discovery of 9 occupied wetlands. While I did not find much evidence that turtles from CW, RM, or ST have dispersed to one of the other 3 sites, that does not mean that those animals died. There are several other occupied sites in the area that may have received turtles migrating out of CW, RM and ST. However, as mentioned before, the loss of any habitat fragments can impact the long- term viability of Virginia’s bog turtles.

I documented a decline of up ~50% in the abundance across these 6 sites. This was driven but not fully explained by the extirpation of CW and RM. Understanding the causes of the declines at CW and RM is important moving forward with management recommendations.

The situation at CW, while unfortunate, is relatively straightforward and with proper compliance and enforcement of wetland regulations is avoidable. Illegal ditching of wetlands and other drastic anthropogenic alterations to habitat that impact hydrology negatively affect habitat suitability for bog turtles. Preventing these actions should be a high priority for managers, as well as taking legal action to prevent the situations from reoccurring. Mitigation or wetland restoration can aid in improving the quality of habitat and allow extirpated sites to become recolonized naturally. If reintroduction is a consideration, restored sites that were previously occupied might serve as candidates to receive transplants or head started individuals.

The situation with RW is more complicated. The habitat alterations at this site were due to a combination of beaver and human activity, which have left the site largely unsuitable for bog turtles. However, it may still serve as an important movement corridor between adjacent occupied sites, and with management intervention could be restored. While beaver activity played a role in the alteration of RW, this should not be interpreted as cause to exclude beaver from the area. The relationship between beaver activity and bog turtle habitat is complex and requires more research to fully understand. Investigating these types of relationships can be challenging due to the long timespans they act on. Habitat alteration and creation by beavers can happen in a matter of months, but the processes involved with sediment deposition and hydrology that lead to suitable bog turtle habitat can take decades or longer. Due to their long lifespan and slow life history, it may be hard to study how bog turtle populations respond to beaver activity within a complex wetland mosaic at long timescales.

Whether impacts from beaver wetlands positively or negatively affect bog turtle populations can vary based on the time scale. Immediately after dam creation, wetland flooding by beavers can negatively impact populations by eliminating suitable habitat for all life stages

31 through inundation of nesting, foraging, and overwintering habitat. In the short term (+/- 10 years) the amount of available suitable habitat can decrease, while less suitable habitat can increase (Sirois et al 2014). One study comparing bog turtle population parameters before and after beaver activity impacted habitat suitability found significant decreases in both adult survival rates and adult abundance (Sirois et. al 2014).

Beaver activity can also have beneficial impacts on bog turtle habitat suitability. It is known that bog turtles prefer open canopied wetlands (Tryon 1990, USFWS 2001). Recent work aimed to understand the variation in drivers of occupancy and abundance in bog turtle populations suggests that abundance is negatively correlated to wetland forest cover (Stratmann et al. 2020). Periodic temporary disturbances like beaver activity can prevent the succession of bog turtle habitat and reduce the amount of wetland forest cover (Weakley and Schafale 1994).

Additionally, by creating new habitat that eventually becomes suitable for bog turtles, or altering wetland connectivity, beavers may play a role in maintaining population connectivity and dispersion/colonization opportunities at the landscape scale. Beaver activity can also raise the water table in the surrounding area, stabilizing or expanding the amount of wetland habitat

(Karran et al. 2018). It is possible that long term effects of beaver activity are beneficial to bog turtle populations by altering the accumulation of sediments in wetlands (Giriat et al. 2015,

Puttock et al. 2018), increasing landscape scale wetland connectivity (Hood and Larson 2014,

Nummi and Holopainen 2020), and aiding in wetland and riparian habitat restoration (Law et al.

2017, Nummi et al. 2019).

Management Recommendations

Effort expended on Mark-Recapture vs. Occupancy - To recommend future management actions, the cost of surveys needs to be considered. In addition to the CMR surveys discussed in

32 this chapter, our lab conducted an occupancy-framework survey of ~50 wetlands in Floyd

County led by J.C. Barron in 2019 and 2020. While the CMR surveys focused intensively on a small number of wetlands, the occupancy surveys were conducted an average of 4 times at each of the wetlands and consisted of only probing/visual surveys with no trapping effort. The difference in effort at each site between the 2 projects aligns with the varied objectives for each project, and here I provide approximate effort expended for these surveys.

Effort for CMR surveys was split between trapping and probing surveys. Trapping effort consisted of trap deployment, checking, and removal. With a crew of 4 people, on average it took

1.5 hours/site to set up traps (total: 36 person-hours), and ~1 hour/site (total: 24 person-hours) to remove them. Trap checks took between 0.5-2 hours/site (on average ~45 minutes/site, for an average of 4.5 hours total) each day. Trapping effort in total took 25 person-hours/site each year.

This amounted to 150 person-hours in 2019 and 100 person-hours 2020 for a total of 250 person- hours. Probing effort varied based on wetland size, with effort at each site ranging from 2.17-

4.88 person-hours/day, with total effort across the 4 days of probing each year ranging from 8.68

– 19.52 person-hours/site (Table 1.1). This amounted to 75.76 person-hours probing in 2019, and

49.84 person-hours in 2020 for a total of 125.6 person-hours probing between 2019 and 2020.

Combining trapping and probing effort for CMR surveys equated to 375.6 person-hours between

2019 and 2020.

Occupancy surveys consisted only of probing surveys, with effort equaling 4 person- hours/ha of core wetland with a minimum of 10 minutes of effort regardless of crew size. Crews typically consisted of 3-4 surveyors. 214 surveys were conducted across the 54 sites spanning 66 days. In total, 393 person-hours were spent on occupancy surveys, with an average of 7.63 person-hours per site.

While fewer person-hours were spent actively surveying on a per-site basis (but more total person-hours) during the occupancy study than what was spent during the CMR surveys, a few other factors should be noted. Material costs between the 2 studies were roughly equivalent, except for traps. I was able to mainly use traps previously built but spent ~$200 on supplies to build more, plus the person-hours it took to build them. Drive time and fuel/vehicle costs varied as well. On average, crews spent ~10 hours per day on occupancy surveys from start to finish.

For CMR surveys, we spent ~8 hours per day for probing (including trap checks), and ~6 hours per day of trapping. Future studies of both types should consider not only the person-hours expended during surveys, but also the additional time and monetary costs of travel. Finally, we did not include the time involved in obtaining landowner permission to survey a site, which can be substantial.

Benefits of CMR vs. Occupancy - Occupancy surveys during 2019 and 2020 required more labor overall, due to the large number of sites that were surveyed. Whether it makes sense to conduct an occupancy study with a relatively small number of sites should be considered in addition to the per-site labor required and goals of the research, if a decision has to be made between conducting occupancy and CMR studies in the future.

Regardless of the costs, both approaches have been important to understanding the state of Virginia’s bog turtle populations. Over the course of 2 seasons, 9 previously unknown populations were discovered, and results from the occupancy model produced from these surveys suggests many more populations are still left to find (J. C. Barron, unpublished data).

Additionally, model results such as detection estimates from the occupancy study can help inform our understanding of the effort required to determine occupancy at new or unknown sites.

Given that future funding for bog turtle research and monitoring is likely to be limited, it is important to weigh the costs and values each of these survey types provide. Occupancy style surveys can aid in the discovery of new populations, help us better understand the distribution of bog turtles in Virginia, and inform surveyors of required effort expenditures for detecting turtles.

These types of surveys can also aid in our understanding of land-scape scale trends in occupancy

(Chandler et al. 2020).

CMR surveys, which are more labor intensive on a per-site basis, can help scientists and managers understand changes in population demographics such as abundance, age distribution, and sex ratios (Thompson et. al 1998), survival rates (Armstrong and Ewen 2001, Lettink and

Armstrong 2003), and population viability analyses (Armstrong and Ewen 2001, Armstrong et al. 2002), but require historic data to make these comparisons. In the absence of historic survey data, it is never too late to establish baselines which can help aid in conservation management decisions, as we can make inferences about population viability by examining population demographics such as age structure and sex ratios. For example, bog turtle populations that consist solely of older turtles, with few to no juveniles may not be as viable as populations with individuals of all ages.

Recommendations on Survey Method – When future studies are being designed, the goals should be thoroughly considered when choosing a survey method. Trapping surveys over the 20 days required fewer person-hours and resulted in more total captures than probing surveys.

However, the analyses showed that significantly more turtles were caught per day of probing across years than per day of trapping. Thus, depending on the budget, site locations (i.e., how spread out the sites are), and goals of future projects, increasing the probing effort will likely result in higher total captures, which can improve model accuracy and aid in better estimates of

35 capture probability. If budgets or available surveyors are limited, increasing the trap density or duration could yield high numbers of captures. Additionally, estimating capture probability separately for trapping vs. probing would require sufficient samples sizes from each survey method, something that was not possible in this study. Accurately estimating capture probability for each survey method would be valuable information and should be an aim of future research.

Future Recommendations - My recommendations are separated into short term and long term and are as follows. Over the next few years (short term), effort should be focused on widespread occupancy style surveys of unknown sites guided by results from the modelling efforts of Virginia Tech researchers and habitat surveys of historically occupied wetlands. We know from the results of this and other studies that when habitat is destroyed or altered, bog turtle populations decline (Sirois et al. 2014). Therefore, by looking at habitat quality of historically known wetlands, both individually and as complexes, we can infer whether populations are likely to have become extirpated like RM and CW. This would also be less costly than full CMR surveys, as one person could theoretically check the conditions of numerous wetlands in a day.

Current occupancy models only include Floyd County, but by expanding to the rest of the bog turtle’s range in Virginia we can better understand where and how many occupied wetlands exist that we are currently unaware of. While it is important to protect the sites that are known to be occupied, identifying new sites may (and has) reveal healthy and growing populations (i.e., capture of all age classes, presence of suitable habitat for all life stages, etc.) which can serve as sources for conservation tools like translocation and head starting. Alternatively, we may find that the populations we already know of are the most stable and robust in the state, in which case conservation efforts should be focused on protecting those sites.

In the long term, managers should re-focus on CMR style surveys. Follow-up surveys at these 6 sites can provide useful insight as to the rates of declines following dramatic habitat alterations. If this were to occur, I would suggest that the survey protocol be modified.

Consistent low-effort trapping is less labor intensive than probing surveys but capture rates were significantly lower per both person-hour of labor, and days of survey effort for trapping compared to probing (Table 1.7, results of ANOVA and Kruskal-Wallis analysis on capture method). Increasing the trap density could help increase both detection and capture rates without drastically increasing effort. This would require more effort to set and remove traps and would take longer to conduct trap checks. Calculations to determine labor and monetary costs of increased trapping vs. probing effort would be necessary, but either would likely increase detection rates. Increasing trap density may increase trapping captures/person-hour of labor, which may be a benefit over increased probing effort, as captures/hour are not likely to change if probing hours are increased.

While capture rates may not change with increased probing effort, the overall number of captures and recaptures likely would, which may result in more precise and accurately estimated confidence intervals. While the estimates from this study show that a decline has occurred, the wide margins of error make it hard to tell how significant this decline was. With more captures and better data, the estimates will become more reliable and better able to inform conservation.

Additionally, future CMR surveys should be structured and planned in a way that allows for true trend analysis of these populations, as described earlier. Specifically, the effort allotted to trapping and surveying should be carefully considered with the analytical approach to estimating abundance and detection in mind.

CMR surveys can help us understand the success and impacts of various conservation strategies such as wetland restoration and turtle translocation. If habitat restoration efforts are undertaken, we can use CMR surveys to study recolonization rates. Specifically, improving our understanding of how turtles recolonize sections of restored wetland complexes within a mosaic of occupied patches can provide information on the benefit of habitat restoration. RM and CW could provide a starting place for this in Virginia.

Furthermore, CMR surveys at previously unsurveyed wetland complexes (or those without recent surveys) could improve our understanding of how abundant bog turtles are within the wetlands they occupy in Virginia. This study provided evidence that bog turtle abundance has decreased even at wetlands without significant habitat alteration. The causes of these declines are unknown, but CMR studies at other groups of wetlands may give insight to mechanisms of decline. It is possible that the extirpations of RM and CW are negatively impacting bog turtle populations at the surrounding sites, but site-specific drivers of decline could also be playing a role. Examples of site-specific drivers of decline are wetland reduction from stream incisions and deforestation (which may be happening at ST, as suggested via anecdotal evidence), illegal poaching of animals, and nest failure and depredation (Zappalorti et al. 2017, Knoerr et al. 2020).

Another reason to estimate abundances of turtle populations is so that if managers are considering tools such as translocation or augmentation, they can identify the number of animals available in potential source or recipient populations. Future CMR surveys for these purposes should focus more intensively on individual wetlands or wetland complexes, by increasing either trapping or probing effort (or both) as previously discussed, so that site-specific abundance estimates can be generated. Here I lacked the data sufficient to generate such estimates and thus

38 pooled capture histories across sites. Greater effort and higher numbers of recaptures may be able to provide better estimates.

My final recommendation focuses on landowner relationships and improving agricultural practices in the area. Most bog turtle wetlands occur on private property in Virginia, many of which are used and/or impacted by agriculture, mainly cattle. There should be a strong focus on cultivating healthy relations with landowners of newly-discovered populations and continuing to build on relations with existing landowners. Aiding their understanding of the importance of bog turtle wetlands can help conserve this species.

One potential route to aid in landowner relations may be helping these individuals become familiarized with and enroll in programs through agencies such as the United States

Departments of Agriculture (USDA) and Interior (USDI) as well as other state, and non- governmental agencies (such as Project Bog Turtle). Several programs exist to provide assistance to private landowners to restore and protect wetlands and wildlife habitat. Collaborating with biologists and policy makers in states who are familiar with these processes and are actively working with landowners in those areas to use these funds can help protect and restore critical habitat in Virginia.

In conclusion, this research identified a previously unknown decline in bog turtles at a set of 6 sites in Floyd County, Virginia. I explored potential causes for low numbers of captures in

2019 and 2020 and provided explanations where I could, proposed hypotheses where I lacked data to support my claims and gave recommendations on improvements to future Capture-Mark-

Recapture studies in Virginia. Finally, I have suggested management directions that should be considered by the appropriate agencies to help mitigate and reverse these declines.

Tables – Chapter II

Table 1.1. Effort expended for mark-recapture surveys of bog turtles at 6 sites in Floyd County, Virginia in 1997, 2019, and 2020. Total trap-days (surveys used for abundance estimates) can be calculated by multiplying number of traps by 20 days and probing person-hour-days can be calculated by multiplying number of person-hours by 4 days. Trap densities reported are values measured in 1997, and reflect the density of traps within the seeps that were being surveyed, not the entire wetland area. Site Number of Total trap Probing hours Total probing Trap Density traps per site hours per day hours (traps/m2) CG 21 10,080 3.25 12.96 0.06 CW* 25 12,000 3.25 12.96 0.02 DA 15 7,200 2.17 8.68 0.06 NH 21 10,080 2.17 8.68 0.18 RM* 16 7,680 3.25 12.96 0.09 ST 24 11,520 4.88 19.52 0.24 *Sites with 0 captures in 2019, and not surveyed in 2020

Table 1.2. Summary of total bog turtle captures during mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020. Numbers represent the number of total turtles captured at each of the 6 sites during the 3 survey years. Total turtle captures refer to the number of all turtles caught during surveys, and include the recaptures of individual turtles. Site 1997 2019 2020 (April) 2020 (May/June) CG 35 14 0 4 CW* 10 0 - - DA 21 6 1 9 NH 7 13 3 10 RM* 12 0 - - ST 22 8 0 16 TOTAL 107 41 4 39 *Sites with 0 captures in 2019, and not surveyed in 2020

Table 1.3. Summary of unique turtles captured during mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020. Numbers represent the number of unique individual turtles (regardless of how many times each turtle was captured) at each of the 6 sites during the 3 survey years. Site 1997 2019 2020 (April) 2020 (May/June) CG 15 9 0 3 CW* 6 0 - - DA 12 5 1 6 NH 7 6 2 6 RM* 7 0 - - ST 21 8 0 9 TOTAL 68 28 3 24 *Sites with 0 captures in 2019, and not surveyed in 2020

Table 1.4. Results of abundance and detection estimation from the mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020. Estimates were modeled for datasets consisting of capture histories from different subsets of sites. The “All Sites Pooled” category are the estimates across all 6 sites*. The “Four Sites with Captures” category are the estimates from NH, CG, ST, and DA during the 3 years (RM and CW excluded from 1997 data). The third category “Three Sites Without Major Habitat Changes” includes NH, CG, and DA. This category excludes CW and RM from the 1997 data, as well as the exclusion of ST from all 3 years. All Sites Pooled Year Abundance Lower Upper CI Detection Lower CI Upper CI CI 1997 103.3 87.2 132.8 0.052 0.039 0.069 2019 48.2 36.2 78.0 0.042 0.025 0.069

Four Sites with Captures Year Abundance Lower Upper CI Detection Lower CI Upper CI CI 1997 86.8 71.3 117.0 0.049 0.035 0.068 2019* 48.2 36.2 78.0 0.042 0.025 0.069

2020 35.4 28.2 54.6 0.055 0.035 0.086 Three Sites Without Major Habitat Changes Year Abundance Lower Upper CI Detection Lower CI Upper CI CI 1997 43.9 38.0 58.5 0.071 0.052 0.098 2019 29.0 23.1 46.3 0.057 0.035 0.092 2020 24.0 17.7 44.5 0.048 0.025 0.089 *Estimates for 2019 in the second category are the same as the first category, as those sites did not have captures at RM and CW in 2019 and were not sampled in 2020.

Table 1.5. Results of the ANOVA and Kruskal-Wallis tests comparing capture rates between probing and trapping surveys in 1997 and 2019. Survey effort was standardized as the number of person-hours of labor per capture. Both tests found a significant difference between capture rates via probing and trapping, and the ANOVA found a significant difference in capture rates across years.

ANOVA Results Covariate Df F-value P-value Site 5 0.727 0.611 Year 1 8.048 0.010 Method 1 15.98 0.001 Kruskal-Wallis Results Covariate Df Chi2 P-value Method 1 8.700 0.003

Table 1.6. Average air temperatures (⁰C) during bog turtle surveys in 1997, 2019, and 2020. The data are separated by week, as well as the overall average temperature during the 20-day trapping period. Data was downloaded through the National Oceanic and Atmospheric Administration’s Climate Data Online tool (https://www.ncdc.noaa.gov/cdo-web/). Survey Week 1997 2019 2020 1 20.5 24.2 21.6 2 19.0 27.1 26.6 3 17.9 22.7 25.3 4 24.6 20.8 22.9 Total 20.8 23.4 23.6

Table 1.7. Captures, effort, and capture rates separated by method from the mark-recapture surveys at 6 historically occupied sites in Floyd County, Virginia in 1997, 2019, and 2020. Values are the number of turtles captured per person-hour of labor for each of the 2 survey methods. Both raw captures and capture rates of probing were higher than trapping per person- hour of effort across all years. Captures Effort (hours) Capture Rates Year Trapping Probing Trapping Probing Trapping Probing 1997 65 42 150 75.76 0.433 0.555 2019 27 14 150 75.76 0.180 0.185 2020 24 15 100 49.84 0.240 0.301

Figures – Chapter II

Figure 1.1. Boxplot showing the abundance estimates from 1997, 2019 across all 6 sites sampled for bog turtles in Floyd County, Virginia. The y axis represents the number of turtles and the x axis represents the year. Blue squares show the estimated abundance from the selected model for that year, red circles show the number of unique turtles caught during that year, and yellow triangles show the total number of turtles caught. The whiskers show the 95% confidence interval of the abundance estimate. Overlapping CIs indicated a non-significant difference in abundance. This graph shows that the estimated abundance in 1997 was significantly higher than in 2019.

Figure 1.2. Boxplot showing the abundance estimates from 1997, 2019, and 2020 from the 4 sites in Floyd County, Virginia with captures of bog turtles in all 3 years (this excludes CW and RM from the 1997 data). The results from 2019 are the same as in Fig. 1.2, as there were zero captures at 2 sites in 2019. The y axis represents the number of turtles and the x axis represents the year. Blue squares show the estimated abundance from the selected model for that year, red circles show the number of unique turtles caught during that year, and yellow triangles show the total number of turtles caught. The whiskers show the 95% confidence interval of the abundance estimate. Overlapping CIs indicated a non-significant difference in abundance. This graph shows that the estimated abundance in 1997 was not significantly different to 2019, or in 2019 compared to 2020. However, the estimate from 1997 is significantly higher than in 2020.

Figure 1.3. Boxplot showing the abundance estimates for bog turtles from 1997, 2019, and 2020 from the 3 sites in Floyd County, Virginia, without known habitat alterations (CG, DA, NH). The y axis represents the number of turtles and the x axis represents the year. Blue squares show the estimated abundance from the selected model for that year, red circles show the number of unique turtles caught during that year, and yellow triangles show the total number of turtles caught. The whiskers show the 95% confidence interval of the abundance estimate. Overlapping CIs indicated a non-significant difference in abundance. This graph shows that the estimated abundance in 1997 was not significantly different than in 2019 and 2020.

Figure 1.4. Boxplot showing the abundance estimates from 1997and 2004 from the 4 sites in Floyd County, Virginia surveyed for bog turtles by Shawn Carter in 1997 and by Kathy Fleming in 2004. The y axis represents the number of turtles and the x axis represents the year. Blue squares show the estimated abundance from the selected model for that year, red circles show the number of unique turtles caught during that year, and yellow triangles show the total number of turtles caught. The whiskers show the 95% confidence interval of the abundance estimate. Overlapping CIs indicated a non-significant difference in abundance. This graph shows that the estimated abundance in 1997 was not significantly different than in 2004 at these sites.

Figure 1.5. Scatterplots showing the relationships between bog turtle trapping captures in 1997 (blue dots), 2019 (pink dots), and 2020 (yellow dots) and environmental conditions. Heating Degree Days (HDD) are a metric of thermal accumulation. HDD2/3/4 are the cumulative HDD values for 2, 3, and 4 days. The y axis on the scatter plots represents the number of turtles captured per day of trapping, and the x-axis represents the values of a given day for HDD and average temperature. The data in this figure includes outliers.

Figure 1.6. Scatterplots showing the relationships between bog turtle trapping captures in 1997 (blue dots), 2019 (pink dots), and 2020 (yellow dots) and environmental conditions. Heating Degree Days (HDD) are a metric of thermal accumulation. HDD2/3/4 are the cumulative HDD values for 2, 3, and 4 days. The y axis on the scatter plots represents the number of turtles captured per day of trapping, and the x-axis represents the values of a given day for HDD and average temperature. The data in this figure does not include outliers.

Figure 1.7. These graphs visualize the results of piece-wise regression analysis between environmental covariates and bog turtle trapping captures per day. Heating Degree Days (HDD) are a metric of thermal accumulation. HDD2/3/4 are the cumulative HDD values for 2, 3, and 4 days. The y axis on the scatter plots represents the number of turtles captured per day of trapping, and the x-axis represents the values of a given day for HDD and average temperature.

The data in this figure includes outliers.

Figure 1.8. These graphs visualize the results of piece-wise regression analysis between environmental covariates and bog turtle trapping captures per day. Heating Degree Days (HDD) are a metric of thermal accumulation. HDD2/3/4 are the cumulative HDD values for 2, 3, and 4 days. The y axis on the scatter plots represents the number of turtles captured per day of trapping, and the x-axis represents the values of a given day for HDD and average temperature.

The data in this figure does not include outliers.

Fig. 1.9. Relative importance plots from a subset of the Boosted Regression Tree models (BRTs) predicting the number of bog turtles captured via trapping per day of effort as a response to environmental variables. The labels of the bars correspond to the environmental variable, and the x-axis represents the proportion of variable influence. Heating Degree Days (HDD) are a metric of thermal accumulation. HDD2/3/4 are the cumulative HDD values for 2, 3, and 4 days. The y axis on the scatter plots represents the number of turtles captured per day of trapping, and the x- axis represents the values of a given day for HDD and average temperature. The data in this figure does not include outliers. These plots show that the average air temperature (TempAvg) and 1-day HDD were not as influential as the other variable, while the minimum temperature, and HDD2-4 consistently higher influence across models.

Fig. 1.10. Example of the partial dependence plots from the Boosted Regression Tree models (BRTs) predicting the number of bog turtles captured via trapping per day of effort as a response to environmental variables. Each graph represents the fitted function of trapping captures as a response to each variable (black line), while other variables are kept constant. The red lines show the smoothed function. The relative influence of each predictor is shown in the parenthesis next to the predictor name. Heating Degree Days (HDD) are a metric of thermal accumulation. HDD2/3/4 are the cumulative HDD values for 2, 3, and 4 days. The y axis on the scatter plots represents the number of turtles captured per day of trapping, and the x-axis represents the values of a given day for HDD and average temperature. These plots show a general trend of decreasing captures of turtles via trapping as cumulative HDD values increase. Additionally, turtle captures increase as temperature rises above ~22⁰C.

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Laake, J. L. 2013. RMark: An R interface for analysis of capture-recapture data with MARK. AFSC Processed Report 2013-01, 25p. Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service, 7600 Sand Point Way NE, Seattle WA 98115. Law, A., M. G. Gaywood, K. C. Jones, P. Ramsay, and N. J. Willby. 2017. Using ecosystem engineers as tools in habitat restoration and rewilding: Beaver and wetlands. Science of the Total Environment 605–606:1021–1030. Lee, D. S., and A. W. Norden. 1996. The distribution, ecology and conservation needs of G. muhlenbergii, with special emphasis on Maryland. Midland Naturalist 40:7–46. Lettink, M. and D. P. Armstrong. 2003. An introduction to using mark-recapture analysis for monitoring threatened species. Pp. 5.32 in: Department of Conservation 2003: Using mark-recapture analysis for monitoring threatened species: introduction and case study. Department of Conservation Technical Series 28, 63 p. Link, W. A. and J. R. Sauer. 1997. New approaches to the analysis of population trends in land birds. Ecology 78:2632–2634. Morrow, J. L., J. H. Howard, S. A. Smith, and D. K. Poppel. 2001. Home range and movements of the bog turtle (Clemmys muhlenbergii) in Maryland. Journal of Herpetology 35:68–73. Muggeo, V. M. 2003. Estimating regression models with unknown break-points. Statistics in Medicine 22:3055–3071. Muggeo, V. M. 2008. segmented: An R package to fit regression models with broken-line relationships. R News 8:20–25 Murray, M. S. 2020. Using degree days to time treatments for insect pests. Utah State University Extension. Logan, Utah, USA Nieuwolt, P. M. 1996. Movement, activity, and microhabitat selection in the western box turtle, Terrapene ornata luteola, in New Mexico. Herpetologica 54:487–495. North Carolina Wildlife Resources Commission. 2018. Conservation plan for the bog turtle (Glyptemys muhlenbergii) in North Carolina. North Carolina Wildlife Resources Commission, Raleigh, North Carolina, USA. Noss, R. F., E. T. LaRoe III, and J. M. Scott. 1995. Endangered ecosystems of the United States: A preliminary assessment of loss and degradation. Biological Report 28. National Biological Service. United States Department of Interior. Washington, D.C.

Nummi P., and S. Holopainen. 2019. Restoring wetland biodiversity using research: Whole- community facilitation by beaver as framework. Aquatic Conservation: Marine and Freshwater Ecosystems 30:1798–1802. Nummi, P., E. M. Suontakanen, S. Holopainen, and V. M. Väänänen. 2019. The effect of beaver facilitation on common teal: Pairs and broods respond differently at the patch and landscape scales. Ibis 161:301–309. Obbard, M. E., and R. J. Brooks. 1987. Prediction of the onset of the annual nesting season of the common snapping turtle, Chelydra serpentina. Herpetologica 43:324–328. Otis, D. L., K. P. Burnham, G. C. White, and D. R. Anderson. 1978. Statistical inference from capture data on closed animal populations. Wildlife Monographs 62:1–135. Pittman, S. E., and M. E Dorcas. 2009. Movements, habitat use, and thermal ecology of an isolated population of bog turtles (Glyptemys muhlenbergii). Copeia 4:781–790. Puttock A., H. A. Graham, D. C. Carless, and R. E. Brazier. 2018. Sediment and nutrient storage in a beaver engineered wetland. Earth Surface Processes and Landforms 42: 2358–2370. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/. Rosenbaum, P. A., J. M. Robertson, and K. R. Zamudio. 2007. Unexpectedly low genetic divergences among populations of the threatened bog turtle (Glyptemys muhlenbergii). Conservation Genetics 8:331–342. Shoemaker K. T. 2011. Demography and population genetics of the bog turtle (Glyptemys muhlenbergii): implications for regional conservation planning in New York State. Doctoral dissertation. Syracuse: State University of New York College of Environmental Science and Forestry. Shoemaker, K. T., and J. P. Gibbs. 2013. Genetic connectivity among populations of the threatened bog turtle (Glyptemys muhlenbergii) and the need for a regional approach to turtle conservation. Copeia 2013:324–331. Sirois, A. M., J. P. Gibbs, A. L. Whitlock, and L. A. Erb. 2014. Effects of habitat alterations on bog turtles (Glyptemys muhlenbergii): A comparison of two populations. Journal of Herpetology 48:455–460.

Smith, L. M., and R. P. Cherry. 2016. Movement, seasonal activity, and home range of an isolated population of Glyptemys muhlenbergii, Bog Turtle, in the Southern Appalachians. Southeastern Naturalist 15:207–219. Somers, A. B., and J. Mansfield-Jones. 2008. Role of trapping in detection of a small bog turtle (Glyptemys muhlenbergii) population. Chelonian Conservation and Biology 7:149–155. Stratmann, T. S. M. 2015. Finding the needle in the haystack: New insights into locating bog turtles (Glyptemys muhlenbergii) and their habitat in the southeastern United States. Thesis, Clemson University, Clemson, USA. Stratmann, T. S. M., T. M. Floyd, and K. Barrett. 2020. Habitat and history influence abundance of bog turtles. The Journal of Wildlife Management 84:331–343. Tesauro, J., and D. Ehrenfeld. 2007. The effects of livestock grazing on the bog turtle [Glyptemys (=Clemmys) muhlenbergii]. Herpetologica 63:293–300. Thompson, W. L., G. C. White, and C. Gowan. 1998. Monitoring vertebrate populations. Academic Press, Inc., San Diego. Travis, K. B., E. Kiviat, J. Tesauro, L. Stickle, M. Fadden, V. Steckler, and L. Lukas. 2018. Grazing for bog turtle (Glyptemys muhlenbergii) habitat management: Case study of a New York fen. Herpetological Conservation and Biology 13:726–742. Tryon, B. W. 1990. Bog turtles (Clemmys muhlenbergii) in the south - a question of survival. Bulletin of the Chicago Herpetological Society 25:57–66. Tucker, C. R., J. T. Strickland, B. S. Edmond, D. K. Delaney, and D. B. Ligon. 2015. Activity patterns of ornate box turtles (Terrapene ornata) in northwestern Illinois. Copeia 103:502–511. Turtle Conservation Coalition [Rhodin, A.G.J., Walde, A.D., Horne, B.D., van Dijk, P.P., Blanck, T., and Hudson, R. (Eds.)]. 2011. Turtles in Trouble: The World’s 25+ Most Endangered Tortoises and Freshwater Turtles—2011. IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Turtle Conservation Fund, Turtle Survival Alliance, Turtle Conservancy, Chelonian Research Foundation, Conservation International, Wildlife Conservation Society, and San Diego Zoo Global, Lunenburg, Massachusetts, USA. 54 pp. Tutterow, A. M., G. J. Graeter, and S. E. Pittman. 2017. Bog turtle demographics within the southern population. Copeia 105:293–300.

United States Fish and Wildlife Service. 1997. Endangered and threatened wildlife and plants, final rule to list the northern population of the bog turtle as threatened and the southern population as threatened due to similarity of appearance. U.S. Fish and Wildlife Service, Hadley, Massachusetts, USA. United States Fish and Wildlife Service. 2001. Bog turtle (Clemmys muhlenbergii), northern population recovery plan. Hadley, USA. van Dijk, P. P. 2011. Glyptemys muhlenbergii (errata version published in 2016). The IUCN Red List of Threatened Species 1:e.T4967A97416755. Virginia Department of Game and Inland Fisheries. 2018. Special status faunal species in Virginia. Bureau of Wildlife Resources, Statewide Resources, Henrico, Virginia, USA. Weakley, A. S., and M. P. Schafale. 1994. Non‐alluvial wetlands of the Southern Blue Ridge— diversity in a threatened ecosystem. Water, Air, and Soil Pollution 77:359–383. White, G., and K. P. Burnham. 1999. Program Mark: Survival estimation from populations of marked animals. Bird Study 46: (supplement) 120–138. Zappalorti, R. T. 1976. The amateur zoologist’s guide to turtles and crocodilians. Stackpole Books, Harrisburg, Pennsylvania, USA. Zappalorti, R. T., A. M. Tutterow, S. E. Pittman, and J. E. Lovich. 2017. Hatching success and predation of bog turtle (Glyptemys muhlenbergii) eggs in New Jersey and Pennsylvania. Chelonian Conservation Biology 16:194–202.

Chapter III: More predators, more problems? Investigating the relationship between anthropogenically subsidized predators and nest depredation in southwest Virginia

ABSTRACT

Habitat alteration and destruction are among the major drivers of population declines throughout the range of the bog turtle, in addition to illegal collection for the international pet trade. Due to the life history traits of this species (long life span and low fecundity), the loss of an individual from any life stage from the population can have detrimental effects. While many turtle populations are not heavily impacted from periods of low reproductive success, numerous subsequent years of complete nesting failure can negatively impact population-level survival.

Recent studies have suggested that anthropogenically subsidies nest predators may be playing a role in continued nest failure at certain wetlands. My second chapter investigated the factors associated with anthropogenic disturbance and infrastructure that may be driving nest predation by these subsidized predators. In 2019 and 2020, I conducted a field experiment in 35 wetlands which utilized artificial turtle nests to investigate variation in nest predation across Montgomery and Floyd Counties, Virginia. In addition to the main study, I conducted an experiment in 2020 investigating differences in predation on rotten vs. fresh eggs. I found that increases in the percent of developed land-use and other metrics of anthropogenic disturbance significantly increased nest predation, while increases in the percent of land-use without these disturbances significantly decreased nest predation. I found no significant difference in predation rates of rotten vs. fresh eggs. This research provides evidence that increases in anthropogenic disturbance at the landscape level may influence rates of wetland nest predation. These findings can be used

63 by managers in the allocation of resources for bog turtle conservation, especially in areas of continuous long-term nest failure.

Introduction

Nest mortality in bog turtles has recently been shown to be a contributing factor in overall population declines at some wetlands (Knoerr et al. 2021). Understanding what drives nest predation is important to informing the conservation of bog turtles, and other species that utilize similar habitats. If nest predation contributes to population declines of G. muhlenbergii and other species, it will be important to document not only the rates of predation, but the identities of the predators themselves for multiple reasons. Areas with higher predator diversity require greater effort in terms of management solutions (Rader et al. 2007) because simple measures to protect nests may not be as effective in certain areas. If predator diversity is low and mainly includes mid-sized mammals, traditional strategies such as nest caging are effective but may be ineffective against a diverse suite of nest predators (Nordberg et al. 2019), supporting the notion that identifying nest predators regardless of the outcome is vital. Additionally, bog turtles are vulnerable to certain predators such as raccoons at all life stages (Mitchell 1994), so predation rates of nests could serve as an index of predation pressures on adults and juveniles.

I hypothesized that as the amount of anthropogenic disturbance and infrastructure increases, so would nest predation from mid-sized mammals such as raccoons, opossums, and skunks. Specifically, I am referring to changes in the built environment at the landscape scale, including increases in impervious surface cover, building density, etc. (referred to as anthropogenic development/disturbance). To test this hypothesis, I conducted experimental field studies. The main study, which will be referred to as the “Nest Study,” consisted of placing artificial nests. I conducted these trials at three separate times. I placed artificial nests for a four- week period during each of the summers of 2019 and 2020, resulting in two month-long replicates. These 2 month-long periods will be referred to as the “month-replicates.” In addition,

I conducted a single week-long replicate period during the summer of 2020 prior to the 4-week study at the same sites, but at different locations within the wetlands. By adding this single week- long replicate in 2020 and using the first week of data from the 2019 and 2020 month-long periods, I was able to generate 3 week-long replicates for analysis, referred to as the “week- replicates.” Figure 2.4 shows a flowchart diagram of the study design.

The goal of the Nest Study was to test whether predation rates within wetlands varied and/or were influenced by several location-related covariates of interest (mainly landscape and local habitat variables). I measured two main responses: whether a nest was predated during the study and how long it took for nest predation to happen. I also documented predator ID when possible via camera trapping and the use of clay eggs to obtain dentition imprints (Donovan et al.

1997, Keyser et al. 1998, Borgmann et al. 2004). In order to investigate drivers of predation without the temporal component of survival (referred to as the “predation analysis”), I analyzed the week-long replicates with Least Absolute Shrinkage and Selection Operator (LASSO) regression variable selection and binomial generalized linear mixed effects models, as most nests were predated within the first week. To investigate the drivers of nest survival over time

(referred to as the “survival analysis”), I analyzed the month-long data using Cox proportional hazards models and an information theoretic approach of model selection via AICc.

After the 2019 study concluded, I hypothesized that the state of the eggs (how fresh or old/rotten they were, referred to as “egg type” – fresh or rotten) may have influenced predation rates, as there was a peak in predation during the final week. I was uncertain if predator activity happened to increase at this time of year or if experimental eggs left in the field for a long period of time were emitting an odor that could attract predators. To examine the second hypothesis, I conducted a separate experimental field study during the summer of 2020 to test whether rotten

66 eggs were more likely to be predated than fresh eggs. This study will be referred to as the

“Rotten Egg Study.” For the Rotten Egg Study, I placed grids rotten and fresh eggs at 5 sites in

Montgomery County, Virginia in order to determine whether predation rates on each egg type differed.

Methods

Shared Field Methods

Study Area – The Nest Study was conducted in 35 wetlands throughout Floyd and

Montgomery Counties (Fig. 2.1) during 2019 and 2020. In 2019 the experiment was conducted at 20 wetland sites. Wetlands that I selected in 2019 contained suitable habitat for G. muhlenbergii but were either not known to be occupied by bog turtles (no known records of captures during prior surveys) or were outside of the known species range. I gained access to these sites either through verbal permission from private landowners or through the appropriate agency personnel depending on property ownership. Sites were either assessed by me prior to the start of the experiment or had been surveyed in the past and were deemed to have suitable habitat but no detections of bog turtles. Seventeen out of the 20 sites were located in Floyd County,

Virginia, with the other 3 sites in Montgomery County. The surrounding landscape use consisted of a mix of agricultural, residential, and other types of development, and varied based on location. Many sites in Floyd County were surrounded by agricultural landscapes, whereas for many sites in Montgomery County (including those used in 2020), the surrounding landscape use was classified as “Developed” per the 2016 National Land Cover Database (referred to as NLCD

2016, Dewitz 2019).

In 2020, due to the restrictions of the pandemic, it was more difficult to contact private landowners in person. This, coupled with the desire to focus on wetlands in more heavily

67 disturbed landscapes to compare to the 2019 sites, resulted in the inclusion of several wetland sites that were unlikely to support bog turtle populations due to their location outside the known species’ range, as well as wetland types not typically inhabited by bog turtles (such as forested riparian flood plains of low order streams). However, all sites contained inundated soils and wetland vegetation. In 2020, I conducted my experiment in 15 wetlands, all of which were in

Montgomery County. There was no overlap in sites between the 2 years.

The Rotten Egg Study was conducted at 5 wetland sites during August 2020 after the

Nest Study had been concluded. These sites were distributed within Montgomery County,

Virginia (Fig. 2.2). These 5 sites consisted of a mix of public and private property, had all been used during the Nest Study in either 2019 or 2020, and were selected based on the following criteria. Each site had at least one predation event during the Nest Study, had enough space to set up the grid of eggs, and were easily accessible to check the grids daily. I mainly selected sites that had high rates of predation (both overall predation rate and short timing until predation) to increase the chances that eggs would be predated within the one-week time span of the Rotten

Egg Study. The 4 sites with high rates of predation were Coal Mining Heritage Park, East

Montgomery Park, Mid County Park, and Milton’s Marsh. I included one site with low predation rates in case each of those site’s grids was entirely predated after the first night (Campus 1 from

2019 Nest Study).

Egg Acquisition and Placement - To simulate G. muhlenbergii nests, I utilized commercially obtained quail eggs, and clay eggs that I made. The quail eggs were sourced from

Loudounberry Farm & Garden (www.loudounberryfarm.com) during both years. Eggs were shipped in protective cardboard layers and were immediately refrigerated upon arrival. While avian eggs and turtle eggs are different in terms of shell composition and texture, quail eggs have

68 been used in a number of studies and are confirmed as an adequate substitution in nest predation studies of other turtle species (Marchand et al. 2002, Marchand and Litvaitis 2004) and are approximately the same size as a bog turtle egg. The purpose of the clay egg was to obtain imprints of tooth marks left by nest predators to aid in identification. To make the clay egg, I used non-toxic modelling clay to create an object comparable in size and shape to the quail eggs.

To create a nest, I located suitable habitat within our study sites (grass-based tussocks, sphagnum patches, etc.). During the Nest Study, specific nest placement was prioritized such that microhabitat was consistent with natural bog turtle nests. During the Rotten Egg Study, microhabitat was considered, but location within the grid (details below) was prioritized. To reduce the impact that nest creation had on predation rates (either by attracting or repelling predators), I attempted to keep the time spent consistent as possible between sites. To reduce the amount of human scent left on the eggs, nest, and materials used in the field, I wore disposable latex/nitrile (or similar) gloves and replaced for each nest. I sterilized all equipment with 3-10 % bleach solution or ethanol wipes between wetlands. This is important to reduce the influence of human scent on predation rates on turtle nests (Burke et al. 2005), as well as to prevent the spread of pathogens between sites (Olson et al. 2021). I used a wandering path through the wetlands to reduce the chances of predation, as predators may cue in on where I walked and how long I stayed in one place.

For the nest study, I created 5 artificial bog turtle nests per wetland. Once a suitable nest site had been identified, I excavated a small hole (approximately 1-2” wide x 2-3” deep) using a small knife to simulate the nest excavation process of a G. muhlenbergii on a tussock or other suitable substrate (Herman 1981, Zappalorti 1976). Two quail eggs and one clay egg were inserted into the hole (approximating the number of eggs in a natural bog turtle nest, average

69 clutch size n=3.5, Ernst and Lovich 2009) during set up of the Nest Study, but only a single egg was inserted in the hole during the Rotten Egg Study. After insertion of the eggs, I replaced the grass/moss/nesting substrate in order to camouflage the nest, similar to the manner of covering the eggs that has been observed in some bog turtles (personal observation, 50+ nests observed in- situ). In areas where I expected that it would be difficult to locate the nests on subsequent checks

(due to homogenous habitat, dense vegetation, etc.), I marked nests with flagging placed away from the nest with distance and direction recorded. Prior studies suggest flagging does not affect depredation rates (Tuberville and Burke 1994, Burke et al. 2005), but the use was limited as much as possible.

Field Methods – Nest Study

Timeframe - The study period occurred within the natural nesting period of G. muhlenbergii. The typical egg-laying period occurs from May to July, with hatchlings emerging from their nests from August to September in most locations (Ernst and Lovich 2009). In 2019 I ran the experiment for approximately 30 days, starting once the nests were placed on 25 and 26

June. All non-predated nests were removed on 25 July. In 2020, I ran the single week-long replicated of the Nest Study from 12-18 July. On 18 July I pulled all materials, and reset the nests at different locations within the wetlands. I ran the 2020 month-long replicate from 18 July to 18 August. During all replicates, I monitored the nests until all eggs within a nest had been depredated or the study period ended.

Camera Trapping & Temperature loggers - To aid in the identification of nest predators and to ascertain the timing of predation events, I placed camera traps on 1-2 nests per site at the start of the study. (I was unable to use camera traps at every nest because of the limited availability of borrowed cameras.) I placed cameras at nests based on the microhabitat structure,

70 such that the nest location with the best possible view/able of the camera was selected. If a nest with a camera was predated, I moved the camera to a non-predated nest if there were still non- predated nests remaining within the site. I used a variety of camera trap models, including

Stealth Cam P series (Stealth Cam, Irving TX, USA) and multiple models of Moultrie brand cameras (PRADCO outdoor brands, Birmingham AL, USA). Cameras were set to trigger via movement utilizing IR flash sensors (if possible, varied based on the model of camera). Traps were set to burst mode with the shortest delay possible. Camera traps were placed 1-3 m away from nests atop metal fencing poles and 4’ wooden stakes, which were angled downward slightly to have the best view of the nests. Cameras were attached using webbed strapping and were secured with locks when on public property. At highly accessed sites on public property, cameras were either attached and locked to trees or other immovable objects or were not used to prevent theft.

In addition to the camera traps, iButtons (Maxim Integrated, Addison TX, USA) or Onset HOBO

Bluetooth Pendant (MicroDAQ LTD, Contoonook NH, USA) temperature loggers were placed within a subset of nests in 2019 and within each nest in 2020. These were used in the hope that temperature logs could inform the timing of predation events by showing a sudden change in temperature followed by increased diurnal fluctuations after loggers were exposed and/or removed from the nest depression. A small piece of floral wiring was wrapped around iButtons creating an exposed loop, which were then wrapped in cellophane and sealed with Plasti Dip®.

During placement, light weight clear monofilament fishing line was tied to the iButton or HOBO and secured to nearby vegetation to prevent loss. The iButton data from 2019 were not used in any analysis, due to user error. (When starting the iButtons, you have to choose to allow rolling- over of data, such that when the memory fills up, the iButton either stops recording, or starts

71 overwriting itself. I set the iButtons to overwrite by accident, and due to that setting all temperature data was lost.)

Data Collection – Nest Study

Nest Set-up - During the set-up of artificial nests, a variety of measurements were taken, including some that have been hypothesized to influence nest predation rates (see appendix B for datasheets): time set, ID of person who creates nest, GPS coordinates and location description, weather conditions, and nest ID.

Nest Depredation – During the Nest Study, after initial placement of nests, each site was checked every day for at least the first 7 days, and then less frequently for a total of 4 weeks for the month-long replicates (range = once every 2 – 4 days). During the week-long Rotten Egg

Study in 2020, the nests were checked every day. During nest checks, I recorded whether the nest had been partially depredated (one egg consumed), fully depredated (both quail eggs consumed), disturbed (no eggs consumed, but nest had been excavated or in some way disturbed), or undisturbed. If nests were found to be completely depredated, the entire nest set up was removed from the wetland. Egg fragments were placed in Ziploc bags and frozen. In addition to recording nest fate, I recorded field notes describing noticeable disturbance to vegetation, how much (if at all) the clay egg had been chewed, whether the temperature logger was disturbed, etc. When available, camera trap memory cards were analyzed to determine the identity of the predator. If a nest was found to be disturbed, but not depredated, it was left as found, but detailed observations (photos, written description, etc.) were recorded. If available, the camera trap memory card was replaced and analyzed to determine the cause of disturbance.

During review of the memory card images, I documented the species/identity of the predator (if applicable/possible), the time of predation as recorded on the camera trap, and whether the

72 photos were believed to be of the predation event (based on timing of photographs and of temperature changes recorded by the logger).

GIS Data - The following GIS analyses of anthropogenic disturbance were conducted using ArcGIS Pro version 2.3.2 (ESRI, Redlands, CA), and a summary of covariates used can be found in Table 2.1. Once nests were created and GPS points recorded, I found the averaged center point for the nests within each wetland, and created a series of buffers around these center points. I identified landscape level variables related to suburbanization or anthropogenic landscape disturbance that have been hypothesized to influence populations of net predators or vulnerability of nests to predation. These were as follows: density of buildings, density of roads, proportion of land in different cover types or land use categories, distance from a nest to the nearest road, and distance from nest to the nearest building. I obtained estimates for each of these variables at 3 different spatial scales, using a 100 m, 500 m, and 2.2 km buffer centered around each study site. I calculated the average center point for the 5 nests within each wetland and used that for the center of each study site when generating buffers. The 100 m buffer was chosen as the smallest scale as this was the maximum recommended buffer around wetlands that could be found (Castelle et al. 1992, USFWS 2001). The largest buffer of 2.2 km was chosen as this is the approximate radius needed to encompass the average home range of raccoons reported near the study area (Owen et al. 2015). Since I documented high rates of predation by raccoon via camera trap and clay egg data, this buffer was chosen as the large-scale buffer. Additionally, using this buffer prevented the overlap of measurements between most sites. The middle buffer, 500 m, was chosen as a midpoint between the two extremes.

I utilized 3 sources of data to measure the variables discussed below. Building information was measured from the open source dataset Microsoft U.S. Building Footprints layer

(Microsoft, https://github.com/microsoft/USBuildingFootprints). Land cover classifications were derived from the 2016 NLCD (Dewitz 2019), and road data were generated from the U.S. Census

Bureau TIGER/Line® shapefile database (US Census Bureau 2019). The distance from each nest to the nearest road and nearest building were measured. The following measurements were recorded for each of the 3 buffers across all sites. The total area of building footprints (m2), the total length of roads, and the percentage of land cover classified as Developed, Agricultural, or

Natural. Land cover categories from the 2016 NLCD layer were reclassified into the 3 categories mentioned as follows: All “Developed” subtypes (classes 21 – 24) were included in Developed;

“Pasture/Hay” and “Cultivated Crops” were included in Agricultural; and all “Water” (classes 11 and 12), “Barren” (class 31), “Forest” (classes 41-43), “Shrubland” (classes 51 and 52),

“Herbaceous” (classes 71-74) and “Wetlands” (classes 90 and 95) were included in Natural. I visually assessed the raster data and found that, predominantly, the areas around my field sites and known bog turtle wetlands that were reclassified as Agricultural consisted of “Pasture/Hay” in the original raster. While “Barren” was included in the Natural reclassification category, visual inspection of the NLCD raster layer did not show any “Barren” land cover near my study sites. A summary of all covariates used for analysis can be found in Table 2.1.

Field Methods – Rotten Egg Study

Timeframe – I conducted the Rotten Egg experiment in 2020 for one week. Grids were places on 8 August and all materials were pulled on 14 August.

Egg condition - To test differences in predation rates of fresh vs. older and presumably rotten eggs, I first attempted to spoil the eggs that would be in the “rotten” category. In an attempt to mimic the environmental conditions (mainly temperature) that the eggs during the

2019 Nest Study were exposed to, I placed half of the eggs (n=100) in a container and put it

74 outside in a location that receives both bright direct light as well as shade throughout the day, starting in mid-July 3 weeks prior to the start of the Rotten Egg Study. The container was solid opaque plastic with a secure lid and was used to prevent predation and/or mechanical damage to the eggs during this time. The remaining half of the eggs were left refrigerated until the morning of 8 August.

Grid set-up – To construct the grids of eggs, I determined the maximum area of a grid that could fit at the site with the least amount of habitat available and used this measurement at all sites to keep consistent spacing between eggs. The grids consisted of 5 rows of eggs with each row spaced 1m apart. Each row consisted of 8 eggs, each egg within a row spaced 2 m apart (Fig.

2.3). Eggs were systematically placed, alternating between fresh and rotten. I chose this systematic placement of eggs over a random distribution to prevent clustering of egg types and to control for any potential spatial patterns of predation influencing the results. With the assistance of a field technician, I used a 50 m measuring tape and a compass to mark off each row and column to have the most precise placement of eggs possible. I did not use any flagging to mark off each egg but did use flagging to mark off 2 diagonal corners of the grid. The flags were placed ~1m away from the egg on the corner and were used to prevent disorientation and aid in egg location during subsequent checks.

Eggs were kept in separate containers according to egg type during travel and nest placement and nitrile gloves were worn during all handling to prevent contamination of the eggs with human scent. Various other methods were used to reduce the amount of human scent left in the wetlands, including wiping tools used to dig eggs holes with ethanol wipes, and bleaching of waders and other equipment between sites. No eggs were fully covered to aid in subsequent location. If the placement location (as determined by measuring tape and compass) was

75 unsuitable for egg placement, the closest suitable spot was used. Examples of unsuitable spots included: flooded patches/standing water, downed woody debris, rocks, and tree roots.

In order to quickly and easily determine the type of egg post-predation, rather than relying entirely on location within the grid (because I worried it may become difficult to identify all spots on the grid), I utilized small wooden golf tees. Tees were non-painted and a mark was placed on the top of the tee. To indicate a rotten egg, an “X” was placed using a graphite pencil, while fresh eggs were indicated by a “O.” The tees were driven into the ground in the center of the hole and the egg was placed on top of them. Using this method, I was able to rapidly identify the egg type upon predation. By using the same pencil, only using the same type of tee (a bag of

~300 hundred was purchased) for each egg, and wearing nitrile gloves while marking tees, I hoped to control for any effect the tee may have had on predation rates. Tees were able to be recovered from all but one predated egg hole (141 tees recovered out of 142 eggs predated).

Data Collection – Rotten Egg Study

Grid checks – Each grid was checked daily for the duration of the study. During grid checks, I aimed to walk haphazardly through the wetland, and tried to take differing paths to avoid creating direct paths to each egg as much as possible. Each egg was located, and was reported as either predated, undisturbed, or disturbed. Once an egg was predated, all remnants of eggs shells and the golf tee were removed and subsequently discarded. Egg type was always confirmed both via tee, and placement within the grid. In situations where eggs were disturbed but not predated (still intact), they were carefully replaced. Disturbance was infrequent and attributed to potential flooding events (n = 2).

Camera Traps – Camera traps were placed within sites when possible. Like the Nest

Study, areas with high levels of public foot traffic were not camera trapped to avoid theft. I

76 placed cameras at 4/5 sites, mainly on tree trunks, and at such angles that as much of the grid was in view of the camera as possible. For this study, predator ID was not of as much concern, as the question was focused only on overall predation rates. I did expect high amounts of predation by raccoons due to the results of the Nest Study at the selected sites.

Analytical Methods – Nest Study Predation Analysis

To determine what factors influenced predation, I used the captures from the 3 week-long replicates (Fig. 2.4), as most predation events occurred during those periods (Figs. 2.5 & 2.6).

Models for this analysis used the binary response of predated or not predated. All analyses were done in R (R version 3.6.3). Prior to analysis, all continuous numerical covariates were scaled and centered.

Variable selection – Due to the high number of covariates, and the likelihood that collinearity and correlation were present, I chose to utilize LASSO regression as my variable selection process. LASSO regression (Least Absolute Shrinkage and Selection Operator) is a regularization method (regularization methods are types of penalized least squares regressions) similar to ridge regression, except it allows for the parameter estimates to be shrunk to 0 through the application of the L-1 penalty parameter λ (Tibshirani 1996, Tibshirani 2011).

After the application of the penalty parameter, variables with non-zero beta coefficients are selected for incorporation into the final model. Prior to the final LASSO model, a tuning process is necessary to find the optimal value of λ, and the tuning process varies based on the type of final model desired. LASSO regression has been shown to be a useful machine-learning variable selection process when a high number of predictor variables are present (Muthukrishnan and Rohini 2016). This method was particularly useful as opposed to an information theoretic approach to model selection which typically requires a priori hypotheses about the influence of

77 the measured covariates, and because I had a relatively large number of predictors included in this analysis.

LASSO Regressions – I used the “glmmLasso” package (Groll 2017) to incorporate the random effect of site in the LASSO regressions. This package utilizes a type of LASSO regression modified for use with generalized linear mixed-effects models (GLMMS, Groll and

Tutz 2014). I utilized 2 different approaches to tune the penalty parameter. The 2 methods are similar in that they use iterative modeling processes to find the optimal value of λ but differ in the evaluation metrics. The first method utilized n-fold cross-validation (Kohavi 1995) of 100 iterations of n=5 folds, with the optimal value of λ being that which resulted in the minimum deviance. Cross-validation methods of lambda tuning are the standard practice for LASSO regressions (Ranstam and Cook 2018). While useful, this method can be somewhat conservative in the variables included (which can be a good thing when avoiding overfitting).

Thus, Groll (2017) suggests using a similar approach, but instead of minimizing deviance, iteratively-run models are ranked via AIC (Akaike 1973) and the optimal lambda is that which produces the minimum AIC value (Groll 2017, personal communication). This method can result in overfitting, and the inclusion of more predictor variables than the cross- validation λ tuning process. Thus, I used both tuning processes, and used the variables selected from both in a final model selection process.

Final Models – The final model was a binomial generalized linear mixed effect model

(GLMM), constructed with the “glmer” function of the R package “lme4” (Bates et al. 2015) with site included as the random effect. Variables with significant p-values (0.1 for the CV method and 0.05 for the AIC method) were considered in final model selection. In addition to

2 2 2 AIC, I considered Nakagawa’s R values for GLMMs (R GLMM (M) and R GLMM (C), Nakagawa and

2 Schielzeth 2013, Nakagawa et al. 2017). R GLMM (M) is a measurement of the variance explained

2 2 only by the fixed effects (marginal R ), and R GLMM (C) is a measurement of the variance explained by the full model (conditional R2). I also considered the Intra-class Correlation

Coefficient (ICC, Nakagawa and Schielzeth 2013, Nakagawa et al. 2017), which is a statistic that quantifies the amount of variance explained by the random effect. R2 values were generated using the “tab_model” function of the R package “sjPlot” (Lüdecke 2021). Finally, I checked

Variance Inflation Factor scores/indications of multi-collinearity between selected variables and adjusted included variables accordingly. VIF scores were checked using the “vif” function of the

R package “car” (Fox and Weisberg 2019).

Analytical Methods – Nest Study Survival Analysis

To analyze the survival of nests over the course of the month-long replicates, I utilized

Cox proportional hazards models using the R packages “coxme” (Therneau 2020), “survival”

(Therneau 2021) and “survminer” (Kassambara et al. 2021). I used the same suite of predictor variables as the predation analysis. I reduced the number of predictors by examining correlation matrices and removing covariates with correlations > 0.7, and variance inflation factors were checked and confirmed to be < 3 in order to avoid multi-collinearity between variables in the final models (Zurr et al. 2010). Next, I created a set of candidate models incorporating the set of non-correlated variables, as well as variables that may have been influential as suggested by the

LASSO regressions in the predation analysis. Model comparison was done via AICc in the information theoretic framework of model selection (Burnham and Anderson 2002).

Final models consisted of mixed effects Cox proportional hazards models (CPHs), a type of survival analysis tool that differs from the Kaplan-Meier models (KMs) in that CPHs are non- parametric multivariate models, and allow for the inclusion of random effects using the coxme

79 package (Sargent 1998, Crawley 2013, Therneau 2020). Coxme fits a CPH with a random effect

(Therneau 2020).

In survival models, the hazard function is a representation of the instantaneous rate of occurrence of a given event (death, onset of disease, etc.), conditional that the individual has survived until time t (Austin 2017). In all models, site was included as the random effect. CPHs are useful when there are multiple predictor variables and the fate of the individuals is known throughout the study. The output from the top model includes the coefficients (known as log- hazard ratios) as well as the exponentiated model coefficients, known as hazard ratios. Hazard ratios provide information on the influence of one-unit changes in the predictor covariates (e.g.,

1 m, 1 % land cover change, etc.) on the response (survival probability).

Analytical Methods – Rotten Egg Study

To analyze the Rotten Egg Study results, I used 2 different approaches. First, I ran a paired t-test to determine if there was a significant difference between the sample means with the total number of each egg type predated each day within a site serving as the samples. I used the

R base function “t.test” (R version 3.6.3, R Core Team 2020).

Next, to test whether egg type influenced whether or not a given egg was predated, I utilized a Generalized Linear Mixed Effects Regression (GLMER) using the “glmer” function in the lme4 package (Bates et al. 2015). I used this method to test whether the egg type influenced whether or not an egg was predated and included site as a random effect. I used a binary response of predated or survived (1 = predated, 0 = survived) and specified the glmer family as binomial.

Results

Nest Study

Nest Fates - The week-long replicate dataset (Fig. 2.4) included 250 nests. These nests included those set during the first week of the 2019 and 2020 month-long replicates, as well as the single week-long replicate in 2020. Of the 250 nests overall, 80 were predated (~32%), 6 were destroyed (~2%), and 164 survived (~66%) after one week. Within replicates, predation rates varied from 22-45% (Table 2.2), with the third replicate having the highest percentage of nests predated. The timing of predation across replicates is shown in Fig. 2.5a. Overall, most nests were predated during the first 24 hours, and lower, but consistent, numbers of nests were predated during the rest of the week. However, the timing of predation varied between replicates, with predation in the first replicate occurring during the middle of the week, while in the second and third replicates there was high predation during the first 24 hours (Figs. 2.5b-d).

The month-long replicate dataset included 175 nests (Fig. 2.4). These nests included those set during the 2-month long replicates during 2019 and 2020. Of the 175 nests overall, 90 were predated (~51%), 14 were destroyed (~8%), and 76 survived (~43%) at the end of the month. Between the 2 replicates, the percentages of nests predated and survived were almost identical (Table 2.2). The timing of predation across the two replicates is shown in Fig. 2.6a.

Overall, most nests were predated during the first 24 hours, with lower rates of predation occurring throughout the rest of the month. However, the timing of predation varied between the two replicates (Figs. 2.6b & c) similar to the trend seen in the week-long replicates, with lower but more consistent rates of predation during 2019, and higher rates of predation earlier during

2020 which then tapered off.

Nest Predators – During the week-long replicates, I identified the following predator species: raccoon, black bear, white-tailed deer, striped skunk, and domestic dog (Table 2.3).

Additionally, through clay egg bite marks I documented nests being predated by unknown small mammals, unknown mid-sized mammals, and unknown predators when evidence was not documented through camera trapping or clay egg evidence. Overall, 58% of nests were predated by raccoons, 16% by unknown predators, 10% by black bear, and <10% by the other identified predators/predator types (Table 2.3). However, predation rates by species varied during each replicate, predominantly the percentage of nests predated by raccoon. During the first replicate

(in 2019), 18% of nests were predated by raccoon, 18% by striped skunk, 14% by black bear, 5% by unidentified small and mid-sized mammals, and 41% by unknown predators. During the second and third replicates (in 2020), raccoon predation accounted for a higher proportion of nests predated, with 83% and 65% respectively. The rest of the predation during the third replicate was mainly by black bear (15%), and deer (15%) (Table 2.3).

During the month-long replicates, the same suit of predators was identified with the addition of one instance of bird predation and no instances of domestic dog predation (Table

2.3). Overall, 36% of nests were predated by raccoon, 14% by black bear, <10% by skunk, deer, bird, unknown small mammal, unknown mesopredator, and 33% by unknown predators (Table

2.3). Like the week-long results, the percent of nests predated by raccoon were higher in 2020 than 2019 (58% and 18% respectively). Black bear predation was relatively similar among replicates (13% in 2020, 16% in 2019). Deer predation was higher in 2020 (13%) than in 2019

(2%), but only differed by 4 nests. The rest of the nest predators were mainly documented in only one of the 2 years and did not account for more than 6%. There was a higher percentage of unknown predators in 2019 (44%) than in 2020 (15%) (Table 2.3).

Predation Analysis - The final model predicting the probability of predation during the week-long replicates included the percentage of developed land cover w/in 500m (model 1.2,

Table 2.4, Fig. 2.7). This covariate was included as significant at the alpha = 0.1 level in the CV-

LASSO, as well as the AIC- LASSO method (p = 0.029). Building area within ~2.2km was also included in the CV- LASSO, and the model including both covariates would have been selected

(model 1.3, Table 2.4), however significant multi-collinearity was found. The VIF score was 6.3, and the estimate sign for building area switched when I ran a GLMER including only building area, indicating higher-order correlation. The AIC- LASSO included several additional covariates (model 1.4, Table 2.4), but high VIF scores were calculated and thus the covariate list was reduced (model 1.5, Table 2.4) until VIF scores were <2. The final model was selected since it had the lowest AIC (excluding model 1.3 due to multi-collinearity), relatively high ICC, and

2 only a marginally lower R GLMM (C) value.

The model results indicate that predation probability was found to significantly increase as the proportion of developed land-use within 500m increased (p = 0.006, Fig. 2.7, Table 2.4).

2 2 The R GLMM (M) and R GLMM (C) of the final model were 0.173 and 0.601 respectively, with ICC =

0.52.

Survival Analysis – The final selected model included the following covariates: percent developed land class within 500m, distance to the nearest road, percent pasture land class within

100m, distance to the nearest building, and the total building area in m2 within 100m (Table 2.5).

All covariates were found to be statistically significant (Table 2.6). The hazard ratios (Table 2.6,

Fig. 2.8) shows that percent developed land class and distance to the nearest road had hazard ratios > 1 (meaning increasing units of covariates = lower survival probabilities), while percent pasture, distance to the nearest building, and building area had hazard ratios < 1 (meaning

83 increasing units of covariates = higher survival probabilities). The individual covariate influences on survival can be seen in Fig. 2.9, which were generated using fixed effects CPHs as mixed effect CPHs are not easily visualized. However, the model coefficients were similar between the mixed and fixed CPHs, thus providing an accurate visual representation.

Rotten Egg Study

Three of the 5 sites (CMP, EMP, MCP) had almost 100% predation within the first 2 days and the percentage of fresh eggs predated almost equaled the percentage of rotten eggs predated (Table 2.7). At Milton’s Marsh, 57.5% of eggs were predated, again almost evenly split between egg types. The last site, Center Woods, only had 7 eggs predated, 5 of them fresh eggs and 2 of them rotten.

The result of the paired t-test failed to reject the null hypothesis that significant differences existed between the number of each egg type predated across the 5 sites (Table 2.8, p= 0.396). The results of the GLMER support the same conclusion, that there was no effect of egg type on whether an egg was predated (Table 2.8, p=0.648). The camera traps caught many of the predation events, and in every instance of a predation event being caught on camera, the predators were identified as raccoons.

Discussion

Nest Predation – The results of this study show that predation rates of the artificial nests were highest in the first few days. After that, predation was lower, but continued throughout the study. While neither year nor replicate were selected as significant predictors of predation probability, the raw data show that percent of nests predated varied between years and replicates

(Table 2.2) at the week-long time scale but was consistent at the month-long time scale.

Variation between years is likely not an effect of year, but rather site, as there was no overlap in

84 sites between 2019 and 2020. Year was included to account for possible variation, but without samples from the same site in multiple years it is unsurprising that no effect of year was detected.

There was noticeable variation in predation rates between sites which was supported by the model results (ICC scores, Table 2.4). Prior studies of bog turtle nest success have also shown that predation can vary significantly between sites (Whitlock 2002, Byer et al. 2018,

Knoerr et al. 2021). This may be driven by features of the surrounding landscape, such that sites in areas with relatively higher anthropogenic development had different rates of predation than those in less developed areas.

Looking at the week-long results, the percentage of nests predated ranged from 22-45%, with an average of 32%. After the first week, predation continued, with the final percentages of nests predated at the end of the month ranging from 50-53%, with an average of 51%. The amount of literature reporting nest predation of bog turtle nests with large sample sizes is small, but suggests that the percentages of predation found during this study would potentially be comparable to natural rates (range: ~50- ~75%, Whitlock 2002, Zappalorti et al. 2017, Byer et al.

2018, Knoerr 2018). It is important to acknowledge that the percent of nests predated after each month in this study cannot be directly compared to other studies, as those reported the natural rates of predation over the entire nesting period. It is probable that predation events would have continued in this study, but the rates of that potential predation are unknown. However, as ~30% of all nests were predated within the first week, and the final percentages of nests predated were

~50%, it is plausible that the final percentage of nests predated after 2-3 months would have been within the range of natural predation, albeit on the high side.

In other turtle species, reported nest predation rates vary. A study using artificial turtle nests around pond habitats (simulating Emydid/Chelydrid turtle nests) found the average

85 depredation across sites to be 42% (Marchand and Litvaitis 2004). One study that focused on

Kinosternon, Pseudemys, and Trachemys nests reported an average depredation of 84.2% (Burke et al. 1998). Similarly, a study focused on Malaclemys terrapin nests in Florida found depredation of 81.9% and 86.5% (Butler et al. 2004). Thus, the amount of depredation reported here may have been within the range of natural predation on a variety of turtle nests, including bog turtles, however there are differences between natural turtle nests and the artificial nests used in this study. Olfactory cues can be important in aiding predators in nest location, and some turtle species (such as Chrysemys picta) expel bladder water during nest excavation (Kinney et al.

1998), and prior studies have replicated this by holding turtles in containers with water, then using that water to wet the artificial nests after setting (Marchand and Litvatis 2003). This was not done in this study due to logistical constraints, but may be useful in future studies if bog turtles exhibit this behavior

Effects of Egg Age on Predation - I suspected after the 2019 season that the condition of the eggs may have influenced the predation rates at the end of the month, in that the quail eggs may have started to rot, and potentially increased predation via increased olfactory detection.

The observed pattern in predation was a decline in the number of predation events after the first week, but a spike in predation during the last week of the 2019 season. However, the results of the Rotten Egg Study appear to refute this. There was no detectable difference (either in the raw data or the statistical analysis) that predation rates on rotten eggs differed to fresh eggs, suggesting that the variation in predation timing in 2019 was not due to eggs becoming more detectable as the study progressed via olfactory cues.

Nest Survival – The majority of predation events happened within the first few days during all replicates. During the month-long replicates, 26.7% of predation occurred within the

86 first day, and 62.2% of predation occurred within the first seven days. This is consistent with other studies of turtle nest predation, which suggest that the majority of predation occurs within the first week, and even the first 48 hours in some studies (Tinkle et al. 1981, Congdon et al.

1983, 1987, Holcomb and Carr 2013, Riley and Litzgus 2014, Buzuleciu et al. 2015).

While most predation occurred early on, predation events continued throughout the study during both the month- and week-long replicates, particularly in 2019 (Figs. 2.5 and 2.6). Some studies on bog turtle nest predation have shown that predation events continue throughout the incubation period as found here (Byer 2015, Knoerr et al. 2021). Interestingly, the 2020 replicates had higher predation rates earlier in the study compared to the 2019 replicates. This may be due to higher predation pressures from anthropogenically subsidized mesopredators at the sites in 2020, which were selected for their more developed surroundings. This hypothesis is supported by the results of the models from this study, which found that increased predation probability and less time until predation were correlated with certain metrics of anthropogenic landscape use, as described below.

Nest Predators – I documented the following nest predators via camera trap: raccoon,

American black bear, striped skunk, white-tailed deer, and a single domestic dog (Canis lupis familiaris) (Figs. 2.10a-d). Raccoon and striped skunk are known predators of bog turtle nests

(USFWS 2001, Zappalorti et al. 2017, Knoerr et al. 2021). It is likely that black bears also predate bog turtle nests (as suggested by Zappalorti et al. 2017), but I could not find mention of confirmed black bear predation on natural bog turtle nests in the literature. Black bears and domestic dogs have been occasionally documented predating the nests of chelonians (Pignati et al. 2013, Bjorndal 2020). Ernst and Lovich also document black bear as predators of a variety of emydid turtles and their eggs. I have not found any cases of bog turtle nest predation via white-

87 tailed deer or dogs, although there are documented observations of dogs and deer predating bird eggs and hatchlings (Pietz and Granfors 2000, Rader et al. 2007, Ellis-Felege et al. 2008, Murray

2015, Chiavacci et al. 2018). As in other studies, I documented suspected predation by avian predators (through egg remnant evidence) and small mammals (through dentition patterns in clay eggs). I did not document the following mesopredators known to predate bog turtle nests: red fox

(Vulpes vulpes) (USFWS 2001, Zappalorti et al. 2017) and Virginia opossum (Knoerr et al.

2021), although they are present on the landscape (personal observations).

These results show that the nests were predated by a suite of predators, including the subsidized species I hypothesized would predate nests. Due to this variation in the species that depredate nests, it is likely that conservation efforts that focus on protecting nests and increasing recruitment in this area should consider diverse strategies and include methods of protection that are effective against all the species documented. Additionally, while this study focused on the egg life stage, there is likely predation of other bog turtle life stages occurring by species such as raccoons, skunks, foxes, etc. (Ernst and Lovich 2009).

Factors influencing Predation Probability – The model results suggest that as the percentage of developed land-use surrounding the wetlands increased, the chances of those nests being predated significantly increased. Because I do not have data on predator abundance or behavior, the cause of this increased rate of predation is unknown, however, I documented higher rates of predation from raccoons at the sites in the more heavily developed areas (most sites in

2020).

Factors influencing Survival Probability – The results of the Cox proportional hazards model support my hypothesis that increased anthropogenic disturbance results in increased predation (measured here in terms of survival probability). Like the predation analysis, the

88 survival analysis also found that increases in the percentage of developed land class significantly reduced survival probability (Table 2.6, Fig. 2.9). The percent of pastureland class also significantly influenced survival probability, and matches the trend seen in developed land class.

As the percentage of land cover classified as pasture within 100 meters of the nests increased, survival also increased (hazard ratio < 1). This makes sense, as most occupied bog turtle wetlands are classified as pasture in the 2016 NLCD dataset. This also supports my hypothesis, as this shows that in areas with lower anthropogenic disturbance (associated with built environments) survival was higher.

As the distance from the nests to the closest road increased, survival probability significantly decreased (Table 2.6, Fig. 2.9). This effect at first appears to be counter-intuitive in the context of my hypothesis, as one may expect increased amounts of development near roadways. However, this trend may not be the result of inflated subsidized mesopredator populations responding to anthropogenic influences, but rather mesopredator avoidance of roads.

It is well-known that roads are a large source of human-influenced mortality and demographic shifts in wildlife populations (Clarke et al. 1998, Forman and Alexander 1998, Fahrig and

Rytwinski 2009, Grilo et al. 2009). Most research has found that found that predators utilize roadways and other linear features created by humans (such as power line cuts), and that predation may be higher nearer to these features (Demars and Boutin 2018, Raiter et al. 2018,

Dickie et al. 2020, Wysong et al. 2020), with only a few studies suggesting that predators such as raccoons and black bears exhibit learned road-avoidance behaviors (Brody and Pelton 1989,

Gehrt 2002, Prange et al. 2004, Stillfried et al. 2015. It may be the case that the effect of road avoidance behaviors had a greater effect on predation rates over the course of the month than the effects of anthropogenic influence on mesopredator populations, but further research

89 investigating the mechanisms driving predation in this is needed, as this hypothesis conflicts with the majority of studies on predator-road interactions.

Like distance to the nearest road, the total building area within 100 meters of the nests seemed to have a conflicting relationship with my overarching hypothesis. This covariate had a hazard ratio < 1, indicating that as building area increased, so did survival probability. I hypothesize that this is due to a similar effect as the distance to the nearest road, in that predator avoidance behaviors near human structures (which may have domestic dogs, cats, and other mesopredator deterrents) may reduce predation. Alternatively, when there are greater amounts of buildings near a wetland, predators may be subsidized to the point of not needing to forage in wetlands via anthropogenic food sources such as trash, outdoor pet food, bird feeders, etc. More research into this relationship is needed though to understand the underlying mechanisms, as I currently lack the necessary data to investigate this.

The final predictor, distance to the nearest building, was also found to significantly influence survival probability. This covariate had a hazard ratio < 1, meaning that as nests got further from the closest building, survival probability increased. This last result seems to conflict with my other results, specifically the relationship between survival and building area within 100 meters. This trend could possibly be explained that, unrelated to how many buildings are in an area, mesopredators are foraging close to buildings (potentially more so in rural areas where food subsidies are scarce, thus the few buildings there are have mesopredators scavenging around them). Thus, when nests are closer to buildings, they are more likely to be encountered. This should be noted as speculation, and further research is needed to understand the drivers of this relationship.

Management Recommendations

This research provides support for the hypothesis that increased anthropogenic disturbance increases nest predation. The underlying mechanism may be that increased food subsidies artificially inflate predator populations, thus increasing the risk of predation. In order to test whether this is the driving mechanism, future work should survey for predator abundance and density in concert with an experimental study along a gradient of land-use and anthropogenic footprint. By testing whether the number of predators is correlated to landscape scale metrics that were found to influence predation rates in this study, we could learn whether higher predation is caused by inflated predator populations, or something else, such as variation in foraging behaviors. Whatever the mechanism, this variation in predation rates may have potentially detrimental impacts on bog turtle populations throughout their range, where anthropogenic development is increasing. There has been recent interest and research involving reproductive success and failure, which found that predation may play a key role in nest failure throughout the range of the bog turtle (Zappalorti 2017, Knoerr 2021). Due to the life history traits of this species, periodic recruitment may be enough to sustain populations, but continuous years of high levels of nest failure may lead to decreases in population-level survival rates and long term population viability (Tutterow et al. 2017). If the goal of management is to increase nest success

(as Tutterow et al. 2017 and Knoerr 2021 implicate as important for sites in the southern population), certain measures have been shown to be effective, such as nest caging and predator exclusion (Zappalorti 2017, Knoerr 2021).

Given these potential conservation goals, understanding the drivers of nest predation can inform decision making on the allocation of resources. This study provides evidence that landscape scale metrics (% developed/pastureland classes) influence the rates of nest predation.

These metrics are easy to measure using publicly available GIS data, and could be used to plan the allocation of resources when multiple sites are being considered.

In addition to nest predation, mesopredators are known to predate bog turtles of all life stages (Ernst and Lovich 2009). Thus, not only should nest protection be considered, but the potential predation of juvenile and adult bog turtles. Understanding the impacts that the surrounding landscape has on bog turtle populations may help managers decide which sites are worth considering for purchase or other long-term protection, and may encourage wildlife managers to have input into local planning and zoning decisions. If a wetland is experiencing high nest mortality due to predation in consecutive years, it is likely that juvenile and adult turtles are being predated too. Thus, efforts to increase recruitment may be futile in the long run, unless long term predator exclusion practices can be implemented to protect all life stages from predation. Prior studies on nest predation have not attempted to test whether rates of nest predation are correlated to rates of adult predation, but this would be valuable information. To test this, turtles across age groups (including nests) would need to be monitored long term in order to document predation events. However, collecting that data would likely be challenging.

Inferences could be made about predation rates based on the presences or absence of scars/predator inflicted wounds on adult turtles, but that may be an unreliable metric if actual mortality is unknown.

In conclusion, the goal of these 2 experiments was to investigate drivers of nest predation as influenced by anthropogenic activity. Understanding these relationships could impact the understanding of how human alterations to habitats and landscapes influences inter-specific relationships via predation, as well as inform management of species requiring conservation actions. My results support the hypothesis that predation is higher in response to increased levels

92 of anthropogenic development and infrastructure. Future research is needed to elucidate the mechanisms driving these relationships, as this work can improve ecological understanding and conservation efforts.

Tables – Chapter III

Table 2.1. List of covariates used and sources of the data for Generalized Linear Mixed-effects Models and Cox proportional hazards models of artificial nest predation and survival during the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020. Landscape-scale covariates were measured at three radii around the centroid of the nests placed at each site (100 m, 500 m, 2 km). Site-specific variables were measured directly at or from the centroid of the nests placed at each site.

Landscape Scale Covariates Covariate Source Land-use type 2016 NLCD Building area Microsoft U.S. Building Footprint Layer Road Length 2019 U.S. Census TIGER data Site Specific Covariates Covariate Source Year N/A Grazed Personal Observation Distance to Nearest Building 2019 U.S. Census TIGER data Distance to Nearest Road 2019 U.S. Census TIGER data Vegetation Density Personal Observation Vegetation Height Personal Observation

Table 2.2. Nest fates during the Nest Study conducted in 2019 and 2020 at 35 wetlands in Floyd and Montgomery Counties, Virginia. Data is reported for both the week-long replicates of the study, as well as the month-long replicates. The week-long replicates consist of the first week of each month, as well as a third individual week-long run of the study. The number of nests placed during each replicate are reported, as well as the fates of nests. # Pred is the number of nests that was predated, # Dest is the number of nests destroyed but not predated, and # Surv is the number of nests that survived.

Week-long Nest Fates Replicate # Nests # Pred # Dest # Surv 1 (2019) 100 22 (22%) 1(1%) 77 (77%) 2 (2020) 75 24 (32%) 0 (0%) 51 (68%) 3 (2020) 75 34 (45%) 5 (7%) 36 (48%) Total 250 80 (32%) 6 (2%) 164 (66%) Month-long Nest Fates Year # Nests # Pred # Dest # Surv 2019 100 50 (50%) 6 (6%) 44 (44%) 2020 75 40 (53%) 8 (11%) 32 (43%) Total 175 90 (51%) 14 (8%) 76 (43%)

Table 2.3. Predators that were identified to particular taxa from the Nest Study conducted in 2019 and 2020 at 35 wetlands in Floyd and Montgomery Counties, Virginia. Both the number of nests predated by each predator type, as well as the percent of total nests predated are reported. Predators ID’d were raccoons (Procyon lotor), black bear (Ursus americanus), striped skunk (Mephitis mephitis), white-tailed deer (Odocoileus virginianus), and domestic dog (Canis familiaris). Also included are unknown avian predators, small mammals, unknown mesopredators, and unknown predators.

Week-long Predator IDs Replicate Racc. Bear Skunk Deer Bird Smammal Unk. Meso. Dog Unk 1 (2019) 4 3 4 0 0 1 1 0 9 % Rep. 1 18 14 18 0 0 5 5 0 41 2 (2020) 20 0 0 0 0 0 0 1 3 % Rep. 2 83 0 0 0 0 0 0 4 13 3 (2020) 22 5 0 5 0 0 1 0 1 % Rep. 3 65 15 0 15 0 0 3 0 3 Total 46 8 4 5 0 1 2 1 13 % Total 58 10 5 6 0 1 3 1 16 Month-long Predator IDs Year Racc. Bear Skunk Deer Bird Smammal Unk. Meso. Dog Unk 2019 9 8 4 1 1 2 3 0 22 % 2019 18 16 8 2 2 4 6 0 44 2020 23 5 0 5 0 0 1 0 6 % 2020 58 13 0 13 0 0 3 0 15 Total 32 13 4 6 1 2 4 0 28 % Total 36 14 5 7 1 2 5 0 33

Table 2.4. Summary of model comparison metrics for binomial generalized linear mixed effects models predicting predation of artificial nests in the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020. Variables included are: DEV (% developed land cover), Build_area (total building area in m2), Road_length (total road length in m), Past (% pasture land cover), Dist_Road (distance to nearest road in m), and Grazed (whether or not the wetland was actively grazed by cattle, binary). Variables with numbers following the name indicate that variable with measured within a buffer (100 = 100 m buffer, 500 = 500 m buffer, RHR = 2.2 km buffer).

2 Model Model # AIC R GLMM (M) ICC 2 /R GLMM (C) Week-Replicate Predation Probability ~1 + (1 | Site) 1.1 252.7 0.000 /0.550 0.55 ~ DEV_500 + (1 | Site) 1.2 245.7 0.173 /0.601 0.52 ~ DEV_500 + Build_area_RHR + (1 | Site) 1.3 239.7 0.317 /0.651 0.49 ~ Road_Length_RHR + Road_Length_500 + DEV_500 + 1.4 246.2 0.440 /0.657 0.39 Past_100 + Dist_Road + Build_area_RHR + Build_area_500 + Grazed + (1 | Site) ~ Road_Length_500 + DEV_500 + AG_100 + Dist_Road + 1.5 251.6 0.207 /0.596 0.49 Grazed + (1 | Site)

Table 2.5. Summary of model comparison metrics for mixed effects Cox proportional hazards models for survival of artificial nests during the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020. Model df AICc ΔAICc weight ~ Dist_Build + Dist_Road + Build_area_100 + 6 762.9 0.000 0.656 DEV_500 + Past_100 ~ Dist_Build + Dist_Road + Build_area_100 + 8 765.5 2.548 0.183 DEV_500 + Past_100 + Grazed + Year ~ Dist_Road + Road_Length_500 + Past_100 4 766.4 3.396 0.120 ~ 1 1 768.5 5.551 0.041

Table 2.6. Summary of covariate hazard ratios and covariate evaluation metrics from the top Cox proportional hazards model of artificial nest survival during the Nest Study in Montgomery and Floyd Counties, Virginia in 2019 and 2020.

Covariate Hazard Ratio S.E. Z p-value Distance to Building 0.380 0.463 -2.09 0.037 Distance to Road 2.282 0.381 2.39 0.017 Building area (100m) 0.274 0.489 -2.65 0.008 %Development (500m) 2.773 0.449 2.27 0.023 %Pasture (100m) 0.459 0.387 -2.01 0.044

Table 2.7. Summary of predation during the Rotten Egg Grid Study conducted in August of 2020. Grids of rotten (n=20) and fresh (n=20) eggs were placed at 5 wetlands in Montgomery County, Virginia. The study lasted for 1 week with grids being checked daily. The number of eggs predated each day that grids were checked is shown for each site, as well as the total number of eggs of each type predated at each site. C.M. Park Center Woods E. M. Park Mid Co. Park Milton Marsh Day Fresh Rotten Fresh Rotten Fresh Rotten Fresh Rotten Fresh Rotten 1 7 8 0 0 18 17 7 7 11 10 2 7 10 0 0 1 1 8 11 0 0 3 2 0 2 0 0 0 0 1 1 0 4 1 0 0 1 0 0 2 0 0 0 5 0 0 1 1 1 0 0 0 0 0 6 3 1 2 0 0 0 1 0 0 1 Total 18 19 5 2 20 18 18 19 12 11

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Table 2.8. Results from the Rotten Egg Grid Study analysis comparing predation on fresh vs. rotten eggs in Montgomery County, Virginia in August 2020. The paired t-test indicates failure to reject the null hypothesis that egg type did effect whether an egg was predated. The binomial generalized linear mixed effect regression results indicate the same, that egg type was not influential on predation.

Paired t-test Test Mean Diff. Df t-stat p-value Fresh vs. Rotten 0.200 29 0.862 0.396 GLMER Covariate Estimate S.E. Z p-value Egg Type -0.150 0.328 -0.456 0.648

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Figures – Chapter III

2.1. The Nest Study area, including locations of wetlands in 2019 (blue circles) and 2020 (yellow circles). Montgomery County is outlined in black to the north, with Floyd County outlined to the south. The inset map shows the locations of the two counties relative to the state of Virginia (shown in purple).

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Fig. 2.2. The Rotten Egg Grid Study area, including locations of wetlands used for the study in 2020 (purple circles). Montgomery County in outlined in black. The inset map shows the locations of the two counties relative to the state of Virginia (shown in purple).

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Fig. 2.3. Diagram of the experimental grid set up used in the Rotten Egg Study conducted in Montgomery County, Virginia in 2020. Blank circles represent fresh eggs, and crossed circles represent rotten eggs. Rows were spaced 1 meter apart, while columns were spaced 2 meters apart, for a total grid dimension of 5 meters by 16 meters. Each grid contained 20 fresh eggs and 20 rotten eggs for a total of 40 eggs.

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Fig. 2.4. Flowchart of experimental design and data partitioning in the Nest Study. Month-long replicates were conducted during June and July of both 2019 (month 1) and 2020 (month 2). In addition, a single week-long replicate was conducted directly prior to the month-long replicate in 2020. Taking the data from the first week of each month-long replicate (outlined in red) yielded a final dataset of 2 month-long replicates and 3 week-long replicates.

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Figure 2.5a-d. Histograms showing the distribution of predation events during the week-long replicates of the Nest Study conducted in Montgomery County, Virginia in 2019 and 2020. The x-axis of each figure shows the number of nests predated, and the y-axes show the number of days since the nests were set. Fig. 2.5 a shows the total distribution of predation events, while figs. b-d show the distribution of predation in each of the week-long replicates.

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Figure 2.6a-c Histograms showing the distribution of predation events during the month-long replicates of the Nest Study conducted in Montgomery County, Virginia in 2019 and 2020. The x-axis of each figure shows the number of nests predated, and the y-axes show the number of days since the nests were set. Fig. 2.5 a shows the total distribution of predation events, while figs. b-c show the distribution of predation in each of the month-long replicates.

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Figure 2.7. Visualizaton of model predictions from the Nest Study. This figure represents the binomial generlized linear mixed effects model predicting predation of artificial nests set during the Nest Study in Floyd and Montgomery Counties, Virignia in 2019 and 2020. The y-axis represents the probability of predation, while the x-axis shows the percent of developed land cover within 500 meters of the nests. Blue dots represent nests that were predated, while the orange nests represent nests that were not predated. The figure shows that as the percent of land classified as developed within 500 meters of the nests increases, the probability of predation significantly increases.

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Figure 2.8. Hazard ratios for model coefficients from the mixed Cox proportional hazards model with the highest AICc support predicting survival probability of artificial nests set during the Nest Study in Floyd and Montgomery counties, Virignia in 2019 and 2020. The y-axis shows the five covarites. DEV_500 and Past_100 are the percent of land within 500 and 100 meters that were classified as developed and pasture, Dist_Road and Dist_Build are the distances to the nearest building, and Build_area_100 is the total building area in square meters within 100 meters. The x-axis shows the hazard ratios, with the dashed line representing a hazard ratio of 1 (or no influence on survival). Covariates to the right of the dashed line indicated increasing hazard with increasing units of the covariate. Covariates to the left of the dashed line indicate decreasing hazard.

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Figure 2.9. Kaplan-Meier survival curves of artificial nests set during the Nest Study in Floyd and Montgomery counties, Virignia in 2019 and 2020. Covariates presented were shown to have a significant effect on nest survival in the most supported Cox proportional hazards model. Y- axes represent the survival probability, while x-axes represent the number of days since the nests were set. Red lines indicate the mean of each covariate, while blue and yellow lines indicate the mean +/- 1 standard deviation. Covariates shown in the top three graphs were shown to have hazard ratios < 1, while those shown in the bottom two graphs had hazard ratios > 1. Each graph shows that survival probabilty dropped the sharpest in the first 10 days.

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Figure 2.10a-d. Camera trap photos of predation events on artificial nests set during the Nest Study in Floyd and Montgomery counties, Virignia in 2019 and 2020. A shows a raccoon (Procyon lotor) with and egg in its paws/mouth. B shows a black bear (Ursus americanus) uncovering an artificial nest (eggs visible in front of snout), the eggs were missing on the next photo the trap took. C shows a white-tailed deer (Odocoileus virignianus) predating a nest, as eggs were missing the following day. D shows a striped skunk (Mephitis mephitis) nest searching, 4 out of 5 nests at the site were predated the following day, with clay egg evidence supporting the predator ID being a skunk.

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Chapter IV: Conclusion

This work suggests that significant declines have occurred at six historically occupied wetlands. This is worrisome, as these were considered some of the better sites known in VA in terms of the number of marked turtles. Two of the six sites appear to now be extirpated, another is losing habitat, and the other three had relatively smaller numbers of turtles in 1997 making it hard to detect long-term demographic trends. We cannot assume that populations are healthy and stable just because we are continuing to catch turtles, but really we need longer term intensive monitoring surveys to fully understand population trends.

Typical bog turtle management strategies involved purchasing of occupied wetlands and habitat, which is a valid approach and should continue. However, informed decisions about managing existing populations should be made. If populations are declining on properties that we have known of for long periods of time, some of which are in public ownership, it might not be sufficient to only focus on habitat acquisitions. When we think about protecting bog turtle habitat, we should really be considering what it means to conserve a species that uses a network of habitat patches (see Buhlmann et al. 1997). Naturally, this species likely utilized a variety of wetland patches within a season and a variety of habitat types during different seasons. Many of the patches may be ecologically ephemeral wetlands that would have been influenced by populations of wildlife that have been extirpated or extremely reduced in Virginia including bison, elk, and beaver. Old beaver ponds that fill in, beaver meadows, and other shallower wetlands are constantly shifting across the landscape at larger timescales, and bog turtle populations likely would have been following the habitat as the hydrology changed, rather than staying in place in individual wetlands indefinitely. It’s still important to focus on individual

118 wetlands and populations of bog turtles, but if we lose sight of how this species interacts with the landscape we may not be providing the best management approaches.

In terms of management, further population surveys are needed. When assessing trends, including only initial populations with high abundances might prevent our ability to detect increasing trends, so the declining trend could be an artifact of the limited sampling. Assessing a larger number of sites, across more of the known range of bog turtles in Virginia, is an important next step. Conducting mark-recapture sampling at additional sites would not only provide baseline data for future trend analyses, but would allow managers to make inferences about populations from size structure and sex ratios. Previous work documenting the importance of hydrological characteristics (Feaga et al. 2012), and habitat structure especially for hibernacula

(Carter et al. 1999, Pittman and Dorcas 2009, Feaga and Haas 2015 ), combined with work suggesting the importance of the proximity of wetland complexes (Buhlmann et al. 1997,

Shoemaker and Gibbs 2013), and research in this document suggesting the importance of surrounding land use to nest survival (Chapter 3), suggest that reviewing local and landscape scale habitat factors across the state is also warranted.

Additionally, better understanding environmental conditions that influence detection could improve interpretation of capture rates and future survey design. Designing and implementing long term surveys to investigate shifts in phenology could also be really beneficial.

In this rapidly changing climate, bog turtle activity patterns might be changing. We should also focus more research on understanding the drivers of decline of this species, and how those drivers might vary across the landscape. While habitat alteration and destruction may be relatively easy to observe, other drivers are not as apparent, such as loss of individuals to poaching, or predation via subsidized predators.

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This study found that both the proportions and timing of predation varied across sites.

Additionally, I documented predation from a variety of predators, and found evidence that landscape scale indices of anthropogenic land use and infrastructure influenced predation. If data suggest that nest mortality may be an issue in certain wetlands with extremely high predation pressure, managers may decide to take action to reduce nest mortality. The logistics and implications of nest protection can be complicated so should not be attempted without clear long- term strategies to mitigate the problem. There are currently no demographic studies in Virginia suggesting that nest survival is responsible for declines. Although conducting intensive demographic studies through mark-recapture and/or survival estimates from long-term telemetry is expensive, important information could be gained even in the first year or two of such research. For example, the sites in North Carolina where nest failure was associated with population declines were all sites where biologists had noted a failure to capture young turtles or unmarked individuals in recent years. Although detection probability of hatchling turtles may be quite low, populations where the size/age structure of captures shows no individuals younger than 5-6 years could alert managers to a potential problem. Some of the conservation tools associated with mitigating low recruitment include nest protection via caging, or even the use of electric fences to exclude predators from entire nesting areas. This work can help inform the best practices when using these conservation tools. My results suggest that predation pressure is highest immediately after the nests were set. In practice, this could mean that, if nest protection practices are implemented, finding bog turtle nests, and caging them as soon as possible should be a priority. This work can provide insight into which sites may need more protection, or help in decision making if resources are limited and only a subset of sites can be protected.

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While these findings may have significant implications for bog turtle conservation, they

may also be applicable to a wider variety of species. Any species that nests in similar wetlands

may face this same variation in nest predation pressure. These trends may potentially be

applicable to other habitat types that these predators are foraging in as well, and future research

is needed to understand whether this is the case.

Literature Cited – Chapter IV

Buhlmann, K. A., J. C. Mitchell, and M. G. Rollins. 1997. New approaches for the conservation of bog turtles, Clemmys muhlenbergii, in Virginia. Proceedings: Conservation, Restoration, and Management of Tortoises and Turtles – An International Conference, pp. 359–363. Carter, S. L., C. A. Haas., and J. C. Mitchell. 1999. Home range and habitat selection of bog turtles in Southwestern Virginia. Journal of Wildlife Management 63:853–860. Feaga, J. B., C. A. Haas, and J. A. Burger. 2012. Water table depth, surface saturation, and drought response in bog turtle (Glyptemys muhlenbergii) wetlands. Wetlands 32:1001– 1021. Feaga, J. B., and C. A. Haas. 2015. Seasonal thermal ecology of bog turtles (Glyptemys muhlenbergii) in southwestern Virginia. Journal of Herpetology 49: 264–275. Pittman, S. E., and M. E Dorcas. 2009. Movements, habitat use, and thermal ecology of an isolated population of bog turtles (Glyptemys muhlenbergii). Copeia 4:781–790. Shoemaker, K. T., and J. P. Gibbs. 2013. Genetic connectivity among populations of the threatened bog turtle (Glyptemys muhlenbergii) and the need for a regional approach to turtle conservation. Copeia 2013:324–331.

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Appendix A: Chapter 1 Supplementary Materials

Trap Placement Protocol

Specific placement of the traps is critical, as G. muhlenbergii utilize travel corridors throughout the wetlands they inhabit (USFWS 2001). They utilize trails that are similar in appearance to rodent paths, consisting of corridors through wetland vegetation and open mucky patches. These corridors are the optimum locations for trap placement, especially if recent use is apparent (i.e., tracks in the mud). In addition to the apparent movement corridors, traps can be placed in other areas that are likely to be used by G. muhlenbergii, such as spaces between tussocks, within rivulets, etc. Once an appropriate location is identified, traps should be placed parallel to the apparent movement path, with the wings opened in order to funnel incoming turtles towards the mouth of the trap. Traps have wings on each side of the openings (for a total of 4 wings), as well as an inward-facing flap on either end that can be pushed up allowing animals to enter the traps but not leave. During placement, traps need to be sunk ~0.5-1” into the muck layer of the wetland so that the turtles have access to water and are not stranded above the water layer, but should not be sunk so deep that the trap would overflow in the case of a rain event. In the case of heavy rains, sites will be assessed to determine whether traps will need to be pulled from the wetlands. If flooding is possible, susceptible traps will be pulled.

Once a trap is set, it is imperative that the proper steps are taken. Traps should be covered with vegetation, mud, debris, etc. in order to both camouflage the traps themselves, as well as reduce the likelihood of a captured turtle over heating in the trap. If possible, the use of a wooden plank covered by mud and vegetation is ideal, however if enough mud and plant material are used to cover the trap, that will be sufficient. Traps will be marked with flagging with the trap # written on the flagging. Flagging should be placed in a location that is visible from a distance (to

122 minimize the loss of a trap), such as a tree branch, or other small scrub shrub, the tops of tall emergent vegetation, etc. GPS coordinates for each trap will also be taken.

Traps need to be checked at least once per 24-hour period, and generally should be checked at the same time each day such that a full trap day occurs between checks. For example, if trap #3 at site DA is checked at 13:30 on the first day, that trap should be check at roughly

13:30 every day of the trapping session. Once a turtle is captured in a trap, it should be carefully removed from the trap, making sure that no injury or undue stress occurs. Appropriate morphometric data should be collected, documentary photos taken (if needed) and the animal should be released. Turtles should be released close to - but not immediately adjacent to - the trap where they were captured as not to overly disturb that animal’s movement patterns, but minimizing the chances that the animal will go straight into the trap.

Probing Protocol

While conducting visual surveys, knowing where to look will increase the chances of finding turtles. As per the Northern Recovery Plan: “Bog turtles will bask on sedge tussocks and mossy hummocks, or be half-buried in shallow water or rivulets. Walking noisily through the wetland will often cause the turtles to submerge before they can be observed. Be sure to search areas where turtles may not be visible, including shallow pools, underground springs, open mud areas, vole runways and under tussocks.” (USFWS 2001).

During probing surveys, one or two wooden poles (4’x ½” wooden dowel rods are a good choice, but any equivalent tool can be used) will be used to probe into mud, water, root masses, and vegetation in order to feel for turtles. Experienced surveyors can generally tell turtle carapaces apart from wood and rocks (the common hard substances in wetlands) but all materials

123 that feel hard should be checked. Inexperienced surveyors should always check for a turtle any time a hard substance in encountered when probing.

Capture Breakdowns

The following table summarizes the number of turtle captures during each year of surveys. The number and percentage of total captures via each survey method are summarized for each site, as well as the total capture numbers, and number of unique individuals captured.

Site Number of Capture Number of Number of Number of Unique Events by Trapping Capture Events Total Turtles Turtles by Probing May & June 1997 CG 23 12 35 15 DA 11 10 21 12 NH 5 2 7 7 ST 11 11 22 21 CW 7 3 10 6 RM 8 4 12 7 1997 TOTALS 65 (60.7%) 42 107 68 (39.3%) May & June 2019 CG 7 6 14 9 DA 6 1 7 5 NH 10 3 13 6 ST 4 4 8 8 CW* 0 0 0 0 RM* 0 0 0 0 2019 TOTALS 27 (65.9%) 14 (34.1%) 41 28 April 2020 CG 0 NA 0 0 DA 1 NA 1 1 NH 3 NA 3 2 ST 0 NA 0 0 APRIL TOTALS 4 (100%) NA 4 3 May & June 2020 CG 2 2 4 3 DA 6 3 9 6 NH 9 1 10 6 ST 7 9 16 9 MAY/JUNE 24 15 39 24 TOTALS 2020 TOTALS 28 (65.1%) 15 (34.9%) 43 26

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Turtle Morphometrics

For all turtles captured, I recorded the following information: Turtle ID, sex, time found, collector, straight carapace length (SCL, mm), max carapace length (CLmax, mm), straight plastron length (SPL, mm), max plastron length (PLmax, mm), plastron width (PL width, mm), shell height (mm), weight (g), scute counts, injuries, and all other data located on the VDGIF G. muhlenbergii Data Sheet.

Datasheets

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Captures and Personnel Effort Expended During CMR Surveys

The following table presents the total number of bog turtles (including recaptures) captured via trapping and probing survey methods, the total approximate person-hours spent on each survey method, and the captures/hour of effort in 1997, 2019, and 2020 at 6 occupied wetlands in Floyd County, Virginia. Hours of labor include the time spent setting, pulling, and checking traps, as well as the time spent actively probing. Travel time, and time spent working up turtles (taking morphometrics, photographs, etc.) are not included. Captures/hour were calculated by dividing the number of captures and the total hours for each survey method (i.e., trapping captures/hour are not the number of captures per hour that traps were in the wetlands, but the number of hours of labor described above.

Captures Hours (total) Captures/Hour Year Trapping Probing Trapping Probing Trapping Probing 1997 65 42 150 75.76 0.433 0.555 2019 27 14 150 75.76 0.180 0.185 2020 24 15 100 49.84 0.240 0.301

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Appendix B: Chapter 2 Supplementary Materials

Nest Study Datasheets

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