QUANTIFYING THE EFFECTS OF HABITAT DISTURBANCE ON THE TIMBER RATTLESNAKE, Crotalus horridus, IN NORTHEASTERN PENNSYLVANIA By Jonathan M. Adamski, B.S. East Stroudsburg University of Pennsylvania A Thesis Submitted in Partial Fulfillment of The Requirements for the Degree of Master of Science in Biology To the Office of Graduate and Extended Studies of East Stroudsburg University of Pennsylvania May 10, 2019 SIGNATURE/APPROVAL PAGE The signed approval page for this thesis was intentionally removed from the online copy by an authorized administrator at Kemp Library. The final approved signature page for this thesis is on file with the Office of Graduate and Extended Studies. Please contact Theses@esu.edu with any questions. ABSTRACT A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Biology to the Office of Graduate and Extended Studies of East Stroudsburg University of Pennsylvania Student’s Name: Jonathan Adamski Title: Quantifying the Effects of Habitat Disturbance on the Timber Rattlesnake, Crotalus horridus, in Northeastern Pennsylvania Date of Graduation: May 10, 2019 Thesis Chairperson: Thomas C. LaDuke, Ph.D. Thesis Member: Shixiong Hu, Ph.D. Thesis Member: Terry Master, Ph.D. Thesis Member: Emily Rollinson, Ph.D. Abstract: This project examined the relationship between anthropogenic habitat disturbance and population levels in Crotalus horridus (Timber Rattlesnake). This study relied on population and habitat information collected by the Pennsylvania Fish and Boat Commission (PFBC) during a previous study known as the Timber Rattlesnake Assessment Project (TRAP). Geographic Information Science (GIS) was utilized to measure landscape features such as canopy coverage, trails, and road density through habitat utilized by Timber Rattlesnakes. Using the information from TRAP, in conjunction with GIS technology, quantitative results were produced and analyzed to construct a clear picture of how human habitat alterations affect Timber Rattlesnake populations. The results were primarily derived from two main models, (1) a linear regression with a normalize distribution and (2) a generalized linear model with a binomial distribution. An inverse relationship was found between rattlesnake populations and proximity and density of buildings at the large spatial scale. These findings suggest that anthropogenic disturbance impacts Timber Rattlesnakes negatively in the commonwealth. The weak relationships between the variables assessed may be, in part, attributable to the use of TRAP reports which were mostly based on one or two site visits and not intended to provide population estimates. Further work will be necessary to refine our models, including improved population estimates and expansion of our work to the entire commonwealth. Acknowledgments My time in graduate school has been an incredibly rewarding endeavor that has left me with memories and experiences that I won’t soon forget. I would like to thank my friends, both new and old, for always being there to bounce ideas off, to pick me up when I’m down, and for all the good times had. Specifically, I’d like to thank the many other graduate students in the department who have gone through the same cycle of many ups and many more downs with meAlexandra Machrone, Justin Clarke, Kristine Bentkowski, Joseph Schell, Brandon Swayser, and Sebastian Harris. Thank you for teaching me about new biological areas, helping with field work, and letting me join you in the field for your projects. Lastly, I’d like to thank Nikolai Kolba for helping with various GIS questions over the years, doing field work with me, and overall being a great friend. I’d like to extend thanks to the Fish and Boat Commission and to Chris Urban, specifically. This project would not have happened if not for the hard work put forth through TRAP, nor without the research grant awarded to East Stroudsburg University regarding the TRMP. Thank you to the many professors that have helped shaped me into the person I am today- Dr. Hu, Dr. Wilson, Dr. Smith, Dr. Brunkard, Dr. Whitford, and Dr. Hotz. Thank you to Heather Dominguez for her help with logistics in the bio department and ensuring the day to day mission was accomplished, as well as helping with travel grants. i Thank you to Dr. Whidden for the experiences in conservation, mammalian surveying techniques, vegetation studies, and for enjoying a bonfire on many camping trips, whether Stony Acres or various Bioblitz’s. Thank you to Dr. Wallace, who introduced me to a whole new area of biology and taxonomy and for allowing me to do independent research under him. Thank you to Dr. Master for the many experiences over the years- Christmas bird counts, field trips, adventures in Cape May, as well as the regular roasting. Thank you for your contributions to this project and lending your experience. Thank you to Dr. Rollinson: This project would not have been anywhere near what is has become without your help. I’m incredibly grateful that you chose to come work at ESU’s Biology Department and are available to answer questions on a regular basis (Sorry that I constantly chase you down the hallway to ask statistic questions). Additionally, thank you for all the hard work you’ve put into my project over the last year. I’m sincerely grateful for everything you’ve done for me and the other graduate students. Thank you to Dr. Thomas LaDuke for being my mentor, advisor, supervisor, and friend over the last 7 years. I’ve learned an incredible amount of knowledge from you over the last few years, not all of it related to academia. I’m grateful that I came to ESU when I did, and that I was able to work alongside someone as experienced as you in the field. Thank you for all of your help with classes, undergraduate research, problems with the animal labs, guiding me through my stressful thesis work, always lending an ear over ii a glass of whiskey, and for always giving a guiding hand when life became too overwhelming. Thank you to the many Pennsylvania GIS county coordinators for providing me with building data for their respective counties. Thank you to the Pennsylvania Spatial Data Access for providing public GIS layers for use in my research. Thank you to Jim and Lael Rutherford for their generous scholarship that helped make this project possible. Thank you to the many PARS, TRAP, and TRMP volunteers that assisted with field work for the many projects involving our study species, Crotalus horridus. Without your hard work this project would not have been possible. Thank you to my family, who has always been supportive of my interests and goals over the years and who have allowed me to pursue a career that I will always enjoy. Thank you to my mother, Patricia, for always putting her children before her and for always pushing us to pursue what we love. iii Table of Contents Acknowledgments................................................................................................................ i List of Figures .................................................................................................................... vi List of Tables .................................................................................................................... vii List of Appendices ............................................................................................................. ix CHAPTER 1: INTRODUCTION ....................................................................................... 1 Life History of the Timber Rattlesnake........................................................................... 2 Decline of Timber Rattlesnake ........................................................................................ 9 Timber Rattlesnake Assessment Project ....................................................................... 12 Geographic Information Science ................................................................................... 12 Objectives ...................................................................................................................... 13 CHAPTER 2: METHODS AND MATERIALS .............................................................. 14 Study Area ..................................................................................................................... 14 Data Collection .............................................................................................................. 16 Correlation Factors ........................................................................................................ 19 Analysis ......................................................................................................................... 22 Presence/pseudo-absence comparisons ......................................................................... 24 CHAPTER 3: RESULTS .................................................................................................. 26 Model 1: Abundance Model .......................................................................................... 26 Model 1: Fifty-Meters ............................................................................................... 27 Model 1: Four-Hundred-Meters ................................................................................ 29 Model 1: Five-Thousand-Meters ............................................................................... 31 Model 2: Non-Zero Abundance Model ......................................................................... 34 Model 2: Fifty-Meters ............................................................................................... 34 Model 2: Four-Hundred-Meters ................................................................................ 35 Model 2: Five-Thousand-Meters ............................................................................... 37 Model 3: Presence-Absence Model............................................................................... 39 Model 3: Fifty-Meters ............................................................................................... 39 Model 3: Four-Hundred-Meters ................................................................................ 41 Model 3: Five-Thousand-Meters ............................................................................... 43 Model 4: Abundance Model Without Pike County....................................................... 45 iv Model 4: Fifty-Meters ............................................................................................... 45 Model 4: Four-Hundred-Meters ................................................................................ 47 Model 4: Five-Thousand-Meters ............................................................................... 49 Model 5: Non-Zero Abundance Model Without Pike County ...................................... 50 Model 5: Fifty-Meters ............................................................................................... 50 Model 5: Four-Hundred-Meters ................................................................................ 52 Model 5: Five-Thousand-Meters ............................................................................... 54 Model 6: Presence-Absence Model Without Pike County............................................ 56 Model 6: Fifty-Meters ............................................................................................... 56 Model 6: Four-Hundred-Meters ................................................................................ 58 Model 6: Five-Thousand-Meters ............................................................................... 60 Presence/Pseudo-Absence Analyses ............................................................................. 62 CHAPTER 4: DISCUSSION ............................................................................................ 65 Model 1: Abundance Model .......................................................................................... 66 Model 2: Non-Zero Abundance Model ......................................................................... 67 Model 3: Presence-Absence Model............................................................................... 67 Model 4: Abundance Model Without Pike County....................................................... 68 Model 5: Non-Zero Abundance Model Without Pike County ...................................... 69 Model 6: Presence-Absence Model Without Pike County............................................ 70 Presence/Pseudo-Absence Comparisons ....................................................................... 71 Conclusion..................................................................................................................... 71 Future Goals .................................................................................................................. 75 WORKS CITED ............................................................................................................... 78 APPENDICES .................................................................................................................. 85 Appendix A: Raw Data of Models ................................................................................ 86 Appendix B: Descriptive Statistics ............................................................................. 159 Appendix C: Raw Data for Random Points ................................................................ 168 Appendix D: Descriptive Statistics for Random Points .............................................. 178 Appendix E: R-Squared and AIC Values .................................................................... 179 Appendix F: Significant Factors ................................................................................. 181 Appendix G: TRAP Sites ............................................................................................ 183 v List of Figures Figure 1. Several yellow phase C. horridus basking between boulders on a powerline right of way. ......................................................................................................... 4 Figure 2. A large, female black phase C. horridus where the keeled scales, forked tongue, and pit organs can be easily viewed. .................................................................... 4 Figure 3. A black phase C. horridus in defensive posturing with the rattle raised at one of the northeastern field sites. .................................................................................. 6 Figure 4. Map of the regional variations in C. horridus venom as described by Glenn et al., 1994. Notice that there is overlap between A and C in Georgia and Florida as well as overlap between all four types in South Carolina. .............................. 7 Figure 5. A large fungal lesion found on a juvenile black phase. ..................................... 11 Figure 6. Regional map of the northeastern counties including the sites found within this area. .................................................................................................................... 16 vi List of Tables Table 1. Correlation coefficients between each factor at 50m for Model 1. Moderate correlations are bolded. ...................................................................................... 20 Table 2. Correlation coefficients between each factor at 400m for Model 1. Moderate correlations are bolded. ...................................................................................... 21 Table 3. Correlation coefficients of all factors within the 5000m buffer zone. Moderate correlations are bolded while strong correlations are italicized......................... 22 Table 4. Results of Model 1 at the 50m buffer zone. A significant relationship was observed between population size and nearest building. ................................... 27 Table 5. Results of Model 1 at the 50m buffer zone after factors were centered. A significant relationship was observed between population size and nearest building. ............................................................................................................. 27 Table 6. Results of Model 1 at the 400m buffer zone. There were no significant relationships observed. ....................................................................................... 29 Table 7. Results of Model 1 at the 400m buffer zone with factors centered. There were no significant relationships observed. ..................................................................... 30 Table 8. Results of Model 1 at the 5000m buffer zone. A significant relationship was observed between nearest building and population size as well as between quantity of buildings and population size. ......................................................... 32 Table 9. Results of Model 1 at the 5000m buffer zone with factors centered. A significant relationship was observed between nearest building and population size as well as between quantity of buildings and population size. ................... 32 Table 10. Results of Model 2 at the 50m buffer zone with all factors included. There were no building measures at 50m. A significant relationship was observed between population size and nearest building. ................................................................. 34 Table 11. Results of Model 2 at the 400m buffer zone with all factors included. A significant relationship was observed between population size and nearest building. ............................................................................................................. 36 Table 12. Results of Model 2 at the 5000m buffer zone with all factors included. A significant relationship was observed between population size and nearest building. ............................................................................................................. 37 Table 13. Results of the GLM for Model 3 within the 50m buffer zone. There were no buildings measured within this buffer zone. There was no significant relationship observed at this spatial scale. ......................................................... 39 Table 14. Results of the GLM for Model 3 at the 400m buffer zone. A significant relationship was observed between quantity of buildings and occupancy. ....... 42 Table 15. Results of the GLM for the 5000m buffer zone in Model 3. There was no significant result observed between the factors and the response variable. ....... 43 vii Table 16. Results of Model 4 at 50m showing a significant relationship between trail density and population size. There were no buildings measured at this spatial scale.................................................................................................................... 45 Table 17. Results of Model 4 at the 400m buffer zone. There was no significant relationship observed between the factors and population size. ........................ 47 Table 18. Results of Model 4 at the 5000m buffer zone. A significant relationship was observed between quantity of buildings and population size as well as canopy cover and population size................................................................................... 49 Table 19. Results of Model 5 at the 50m buffer zone. A significant relationship was observed between population size and nearest building. There were no buildings measured at this spatial scale. ............................................................................ 51 Table 20. The results of Model 5 at the 400m buffer zone A significant relationship was found between nearest building and population size. ........................................ 52 Table 21. Results of Model 5 at the 5000m buffer zone. A significant relationship was observed between population size and road density, population size and quantity of buildings, and population size and canopy cover. ......................................... 55 Table 22. Results of the GLM at the 50m buffer zone for Model 6. There was no significant relationship observed between the factors and the response variable. There were no buildings measured at this spatial scale. .................................... 57 Table 23. Results of the GLM at the 400m buffer zone for Model 6. There was no significant relationship observed between the factors and the response variable. ............................................................................................................................ 58 Table 24. Results from the GLM at the 5000m buffer zone for Model 6. There were no significant relationships observed between the factors and population. ............ 60 viii List of Appendices Appendix I. Raw data for Model 1 at the 50m buffer zone. ............................................. 86 Appendix II. Raw data for Model 1 at the 400m buffer zone. .......................................... 91 Appendix III. Raw data for Model 1 at the 5000m buffer zone. ...................................... 96 Appendix IV. Raw data for Model 2 at the 50m buffer zone. ........................................ 100 Appendix V. Raw data for Model 2 at the 400m buffer zone......................................... 104 Appendix VI. Raw data for Model 2 at the 5000m buffer zone. .................................... 108 Appendix VII. Raw data for Model 3 at the 50m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. .................. 111 Appendix VIII. Raw data for Model 3 at the 400m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. .................. 116 Appendix IX. Raw data for Model 3 at the 5000m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. .................. 121 Appendix X. Raw data for Model 4 at the 50m buffer zone........................................... 126 Appendix XI. Raw data for Model 4 at the 400m buffer zone. ...................................... 130 Appendix XII. Raw data for Model 4 at the 5000m buffer zone. ................................... 134 Appendix XIII. Raw data for Model 5 at the 50m buffer zone. ...................................... 138 Appendix XIV. Raw data for Model 5 at the 400m buffer zone. ................................... 141 Appendix XV. Raw data for Model 5 at the 5000m buffer zone. ................................... 144 Appendix XVI. Raw data for Model 6 at the 50m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. .................. 147 Appendix XVII. Raw data for Model 6 at the 400m buffer zone where Number of Snakes (Population) has been changed to presence (1) – absence (0) data. ................. 151 Appendix XVIII. Raw data for Model 6 at the 5000m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence ................... 155 Appendix XIX. Descriptive statistics of factors at the 50m buffer zone for Model 1. ... 159 Appendix XX. Descriptive statistics of factors at the 400m buffer zone for Model 1. .. 159 Appendix XXI. Descriptive statistics of factors at the 5000m buffer zone for Model 1. 160 Appendix XXII. Descriptive statistics of factors at the 50m buffer zone for Model 2... 160 Appendix XXIII. Descriptive statistics of factors at the 400m buffer zone for Model 2. .......................................................................................................................... 161 Appendix XXIV. Descriptive statistics of factors at the 5000m buffer zone for Model 2. .......................................................................................................................... 161 Appendix XXV. Descriptive statistics of factors at the 50m buffer zone for Model 3. . 162 Appendix XXVI. Descriptive statistics of factors at the 400m buffer zone for Model 3. .......................................................................................................................... 162 Appendix XXVII. Descriptive statistics of factors at the 5000m buffer zone for Model 3. .......................................................................................................................... 163 Appendix XXVIII. Descriptive statistics of factors at the 50m buffer zone for Model 4. .......................................................................................................................... 163 ix Appendix XXIX. Descriptive statistics of factors at the 400m buffer zone for Model 4. .......................................................................................................................... 164 Appendix XXX. Descriptive statistics of factors at the 5000m buffer zone for Model 4. .......................................................................................................................... 164 Appendix XXXI. Descriptive statistics of factors at the 50m buffer zone for Model 5. 165 Appendix XXXII. Descriptive statistics of factors at the 400m buffer zone for Model 5. .......................................................................................................................... 165 Appendix XXXIII. Descriptive statistics of factors at the 5000m buffer zone for Model 5. .......................................................................................................................... 166 Appendix XXXIV. Descriptive statistics of factors at the 50m buffer zone for Model 6. .......................................................................................................................... 166 Appendix XXXV. Descriptive statistics of factors at the 400m buffer zone for Model 6. .......................................................................................................................... 167 Appendix XXXVI. Descriptive statistics of factors at the 5000m buffer zone for Model 6. .......................................................................................................................... 167 Appendix XXXVII. Raw data at the 50m buffer zone for the one-hundred random points. .......................................................................................................................... 168 Appendix XXXVIII. Raw data at the 400m buffer zone for the one-hundred random points. ............................................................................................................... 171 Appendix XXXIX. Raw data at the 5000m buffer zone for the one-hundred random points. ............................................................................................................... 175 Appendix XL. Descriptive statistics of factors at the 50m buffer zone for the random points. ............................................................................................................... 178 Appendix XLI. Descriptive statistics of factors at the 400m buffer zone for the random points. ............................................................................................................... 178 Appendix XLII. Descriptive statistics of factors at the 5000m buffer zone for the random points. ............................................................................................................... 178 Appendix XLIII. R-Squared and AIC values for all models, sorted by model number, then by spatial scale. Note that GLMs do not give an R-squared value. ......... 179 Appendix XLIV. R-Squared and AIC Values for all models sorted by spatial scale followed by model number. Note that GLMs do not give an R-squared value. .......................................................................................................................... 179 Appendix XLV. All models shown ordered by spatial scale with an 'X' indicating factors that showed a significant relationship with population size. ........................... 181 Appendix XLVI. All models shown ordered by model number with an 'X' indicating factors that showed a significant relationship with population size. ............... 182 Appendix XLVII. Rattlesnake sites in the Northeast produced by TRAP through the PFBC. Sites are randomly offset from actual locations by up to 5000m to minimize the potential of poaching activity from this work. ........................... 183 x CHAPTER 1: INTRODUCTION The timber rattlesnake, Crotalus horridus, has long been a species of interest and concern to herpetologists and conservationists in the northeastern United States. Over the years, C. horridus has seen severe declines in its northeastern range due to overharvesting, persecution, and habitat loss (Galligan and Dunson, 1979; Stechert, 1982; Reinert, 1990; Clark et al., 2010; Levin, 2016). As the conservation movement has gained momentum, many studies have been conducted on the long-term effects of these factors and changes in C. horridus populations (Martin, 1993; Andrews and Gibbons, 2005; Clark et al., 2010, 2011; Urban, 2012). Since C. horridus has been delisted as a candidate species in Pennsylvania, due to the relatively high numbers of snakes discovered by the Pennsylvania Fish and Boat Commission (PFBC) during the Timber Rattlesnake Assessment Project (TRAP), a monitoring program is needed to maintain confirmation of population integrity. East Stroudsburg University, under Dr. Thomas C. LaDuke, is creating the Timber Rattlesnake Monitoring Project (TRMP) through a series of integrated studies including: (1) mark and recapture study using passive integrated transponders (PIT) tags to assess population sizes; (2) assessing how anthropogenic habitat features affect population sizes; (3) measuring microhabitat use by gravid 1 females; and (4) measuring population recruitment by tracking neonate individuals returning to sites year after year. This specific project, number two under TRMP, will measure habitat features and relate changes in these features to snake populations as a means of assessing the relationship between specific habitat factors and timber rattlesnake population size. This will be critical to the monitoring process as the end goal is the ability to quantitatively assess population changes through disturbances in C. horridus habitat. Life History of the Timber Rattlesnake Crotalus horridus is a medium to large, venomous, heavy bodied snake in the family Crotalidae that can grow to four feet in length (Hulse et al., 2001). Males are longer in length with a larger girth than females, they can reach lengths of 180cm. This size difference can be attributed to the males’ combative nature (Sutherland, 1958; Gibbons, 1972). Males also have greater than 21 subcaudal scales, representing clear sexual dimorphism from females who generally have less than 21 sub caudal scales. This species has dark, chevron shaped markings on the dorsum with a yellow to black background, representing two distinct color morphs based on the color of the head (Figure 1, Figure 2, Figure 3). It was formerly believed that color was loosely linked to the sex of an individual, with more males being black and more females being yellow (Klauber, 1956). Subsequent studies in Pennsylvania disproved this hypothesis, color is independent of age or sex (Schaefer, 1969). Dark color morphs are commonly found in mountainous regions of the East coast, and it was thought that black coloration was not a result of genetics, but instead a result of ontogenetic changes in individuals (Gloyd, 1940). This, however, was also disproved by Schaefer (1969), as they found that light and 2 dark color phases could be distinguished at birth and the color only deepened into adulthood. Juveniles commonly present with an orange, median stripe on the dorsum while neonates are commonly light grey to beige in color with distinct crossbands (pers. obs.). The ventrum of C. horridus is typically cream colored with dense black speckling (Rubio, 2014). The scales of C. horridus are large and triangular with a distinct medial keel. The signature trait of the rattlesnake is the rattle found on the end of their tail. This structure is typically used to warn would be predators or large fauna of the rattlesnake’s presence by rapidly shaking the tail and allowing each bead to clack against the others (Rubio, 2014).The rattle is composed of keratinized beads that form at the tip of the tail after each molt (Hulse et al., 2001)(Figure 3). Neonates are born with only a single keratinous button at the tip of the tail, but should add 2 to 5 more segments by the end of their first year (Rubio, 2014). Some claim C. horridus can be somewhat accurately aged by dividing the number of rattle segments by the average number of sheds per year, but this only works on snakes with intact rattles still including the button (Furman, 2007). Compared to other vipers, C. horridus appears to have a relatively mild temperament, preferring to flee when humans are present (Gibbons, 2017). Until recently, this species, Crotalus horridus, was separated into two distinct subspecies, Crotalus horridus (Timber Rattlesnake) and Crotalus horridus atricaudatus (Canebrake Rattlesnake). The subspecies C. h. atricaudatus was recognized due to differences in dorsal scale row counts, a larger average adult size (30-60 inches in C. h. horridus and 42-65 inches in C. h. atricaudatus), and an orange dorsal stripe that bisected 3 the chevron pattern medially (Gloyd, 1935; Rubio, 2014). Additionally, the southern subspecies, C. h. atricaudatus has a stripe along the face extending from the eye to the rear of the mouth (Gloyd, 1935). However, variation in scale counts is now thought to be attributed to sexual dimorphism and not subspeciation, as the variation in morphological traits is equal between C. h. horridus and C. h. atricaudatus (Pisani et al., 1973). Additional genetic research has shown that there are distinct east-west populations of C. horridus but not along north-south gradients (Clark et al., 2003). Figure 1. Several yellow phase C. horridus basking between boulders on a powerline right of way. Figure 2. A large, female black phase C. horridus where the keeled scales, forked tongue, and pit organs can be easily viewed. 4 C. horridus ranges from New Hampshire to Florida on the east coast of the United States and extends westward to Texas and southeastern Minnesota (Conant and Collins, 1998). In the northern parts of its range, this species hibernates in the winter months at communal den sites in crevices on south facing slopes (Galligan and Dunson, 1979; Ernst, 1992; Gibbons, 2017). Of northern den sites examined, 70% faced south while the other 30% faced southwest and southeast (Galligan and Dunson, 1979). Likewise, members of the “canebrake” population in the south may hibernate in tree stumps or tree root systems for short periods of extreme cold (Gibbons, 2017). This species has differing habitat preferences across its range. In the northeast it prefers wooded areas for foraging with nearby rocky edges for basking, gestation, and denning. In the southeastern portion of the range it prefers lowland thickets, canebrakes, and swampy edges. Lastly, in the western portion of the range the species prefers dry, brushy flatlands and beech-maplebirch woodlands (Campbell and Lamar, 2004). In addition to having a rattle, C. horridus contains a trait common to all other pit vipers, the facial pit. This pit is found on the head between the eyes and nostrils and allows the snake to sense heat emanating from potential prey. The posterior portion of the pit contains a membrane stretched across it which is in contact with thermal receptors attached to the trigeminal nerve. Stimulation of these heat receptors travels along the trigeminal nerve to the optic tectum where it can be represented as visual stimuli (Goris, 2011). 5 Figure 3. A black phase C. horridus in defensive posturing with the rattle raised at one of the northeastern field sites. This species has a solenoglyphous dentition and therefore has two large venom glands at the posterior portion of the skull that lead to retractable fangs on the maxilla bone (Reinert et al., 1984). The maxillary teeth found in other tetrapods have been reduced to just these fangs. The venom of C. horridus varies geographically in its composition as well as its potency and can be divided into four variations including: A, B, A + B, and C. Type A venom is found in the southern portion of the range and contains the neurotoxin canebrake toxin. The type B venom is the most common throughout the range and consists of hemotoxins and polypeptides that cause hemorrhagic damage to potential prey and predators. The third venom type, A+B, is found in intergrade zones between A and B and has been noted in eastern South Carolina, southeastern Georgia, southwestern Arkansas, and northern Louisiana. The last venom, 6 Type C, had one of the lowest LD50’s the paper’s author had ever observed among snake taxa and lacks both the canebrake toxin of Type A as well as the peptides of Type B. Type C venom is found in Georgia, Florida, and South Carolina and seems to be, at least partially, sympatric with Type A (Glenn et al., 1994) (Figure 4). Figure 4. Map of the regional variations in C. horridus venom as described by Glenn et al., 1994. Notice that there is overlap between A and C in Georgia and Florida as well as overlap between all four types in South Carolina. C. horridus is an ambush predator that often relies on fallen trees to find prey items (Reinert et al., 1984). It has been shown that individuals will curl up next to fallen trees with a portion of the body and the lower jaw coming in contact with the fallen trees to feel for vibrations of incoming mammals, usually rodents, that compose a majority of their prey (Reinert et al., 1984; Hulse et al., 2001). However, other prey items make up at 7 least a portion of the diet including members of “Lacertilia”, Serpentes, Anura, Piciformes, Galliformes, Passeriformes, Chiroptera, and Eulipotyphla (Clark, 2002). Using the Jacobson’s organ the snake can sense if incoming individuals are prey (Rubio, 2014). Rattlesnakes are generalists, thus, the proportion of prey consumed appears to match the prey’s proportion in the environment with a majority of prey caught at night (Reinert et al., 1984). While C. horridus is often thought of as being near the top of the food chain, there are several predators that prey on them when the chance arises. In the northern parts of the range, Coluber constrictor is commonly found near den sites and is known to take young C. horridus (Klemens, 1993). Anecdotally, when C. constrictor is present C. horridus populations appear to be in flux. Additionally, hawks may prey even on adult individuals (Klauber, 1956; Ernst and Ernst, 2003). In the southern portion of the range, Drymarchon sp. and Lampropeltis sp. will commonly prey on large and small individuals of C. horridus, being immune to their venom (Gibbons, 2017). During the warmer months males, post-partum females, and non-breeding females disperse from den sites into surrounding forest for feeding opportunities. In addition to hunting, males will seek out receptive females to breed with throughout the late summer. There is at least some evidence that males will guard basking females or a highly suitable basking site for possible mating opportunities (Howey, 2017). Females start ovulation in late spring and reproduction occurs in mid to late summer (Martin, 1993). Females of C. horridus seem to aggregate in family groups of related females. This provides several benefits including group defense against predators as well as increasing the ability to 8 thermoregulate. It is theorized that not only does group basking deter predators, but females may be more likely to defend a site if they know that related members will indirectly benefit. Likewise, if adults are grouped together it increases the likelihood that neonates may scent trail an adult to den sites, increasing survivability of offspring (Clark et al., 2012). C. horridus is strongly K-selected; females do not mature until roughly six or seven years of age, they only breed once every 2-6 years depending on abiotic conditions, and they have relatively small litters of 3-16 young (Gibbons, 1972; Galligan and Dunson, 1979; Martin, 1993; Gibbons, 2017). This species is viviparous giving birth to live young with at least some transfer of nutrients from mother to offspring (Blackburn, 2000; Hulse et al., 2001). Mothers will stay with the young for up to two weeks, roughly timing their parting with the first molt of the neonates (Gibbons, 2017). During this time, mothers will typically become bolder and actively defend the young against would-be predators. Surprisingly, neonate individuals tend to act in an opposite way, being incredibly curious to the happenings around them and not readily avoiding danger as they should (pers. obs.). Decline of Timber Rattlesnake There are many biotic and abiotic factors that contribute to a population’s decline including habitat destruction, overharvesting, pollution, and disease (Wilcove et al., 1998). The factors described by Wilcove et al. (1998) all contribute to population changes in C. horridus. Habitat destruction is a pervasive problem that many species in the modern age are facing, imperiled or otherwise. Roads have become commonplace through many habitat types and have been shown to restrict gene flow and genetic diversity among populations (Forman 2000; Shine et al., 2004; Andrews and Gibbons, 9 2005; Coffin, 2007; Row et al., 2007; Eigenbrod et al., 2008; Fahrig and Rytwinski, 2009; Clark et al., 2010; Beebee, 2013). C. horridus, specifically, has been shown to be exceptionally susceptible to the impacts of roadways bisecting habitat because of their unwillingness to traverse open habitat (Andrews and Gibbons, 2005). Studies have also shown that C. horridus is already experiencing a decrease in genetic diversity in the south due to population fragmentation by roadways (Clark et al., 2010). Overharvesting occurred, until recently, in the form of rattlesnake roundups. In the modern era, these events are strictly educational and all snakes that are not being tagged with a hunting license are returned to the site that they were collected from. These events awarded prizes to participants in various categories such as largest snake and longest rattle (Reinert, 1990). Many individual snakes observed at hunts appeared to be injured, with several showing signs of damaged cervical vertebrae (Reinert, 1990). Of the snakes collected at hunts, a large number appeared to be gravid females, thought to have been collected in such numbers due to their preference for open areas with high amounts of sunlight (Reinert, 1990). While hunters were supposed to return captured snakes to the same area they were captured, several hunters explained they had no intention of doing so. It has been shown that C. horridus who have been relocated experience high mortality in the range of 50% (Reinert and Rupert, 1999). The relocation of snakes coupled with severe injury and handling of gravid individuals could potentially carry many unintended consequences. There are anecdotal accounts that repeated handling or excess stress may cause infections of Snake Fungal Disease (SFD) to become more severe. There are reports of O. ophiodiicola in Pennsylvania in Luzerne (LaDuke pers. comm., pers. obs.) and Lycoming 10 (Dunning pers. comm.) counties (Figure 5). With enough warmth and several molts it would seem that many individuals can overcome infections (Lorch et al., 2016). Fungal diseases have impacted many other reptile and amphibian taxa as well including Batrachochytrium dendrobatidis in Anura (Retallick et al., 2001), B. salamandrivorans in Caudata (Martel et al., 2013), Pseudogymnoascus destructans in Chiroptera (Blehert et al., 2009), and Ophidiomyces ophiodiicola in Serpentes (Allender et al., 2015; McBride et al., 2015; Guthrie et al., 2016).The full impacts of Snake Fungal Disease are unknown but are one more reason that a species with a cryptic lifestyle and low fecundity should be monitored. In addition to these factors, hiking trails have become more abundant throughout the commonwealth. One study revealed a negative correlation between species abundance and trail area in wood turtles (Garber and Burger, 1995). The average person is largely biased against rattlesnakes, owed to the sensationalized view that rattlesnakes are an aggressive species, and hiking trails through habitat increase the likelihood of human interaction with the species, leading to eventual mortality. Figure 5. A large fungal lesion found on a juvenile black phase. 11 Timber Rattlesnake Assessment Project Our understanding of the status of the timber rattlesnake in Pennsylvania has been improving gradually over the years. Until 2016 it was listed as a Candidate Species in Pennsylvania (Stauffer, 2016). The Pennsylvania Fish and Boat Commission conducted a study from 2003-2014 known as the Timber Rattlesnake Assessment Project (TRAP) whose purpose was confirming historical site occupancy and generally checking potential habitat for the presence of C. horridus (Urban, 2012). This study found C. horridus at more than 1000 sites in Pennsylvania, showing they are more numerous than previously thought. With the conclusion of this study, the species’ conservation status was reduced (Stauffer, 2016). However, the species’ hunting limits will remain in place and environmental impact studies will still be conducted on and near C. horridus habitat. Geographic Information Science Geographic Information Systems (GIS) reference geospatial data in the real world by overlaying various landscapes/ habitat features on a base map, thus providing a view of the spatial orientation of mapped features and the ability manipulate and analyze such data (Maguire, 1991). There are seemingly innumerable GIS applications but we are using it to track wildlife populations (Peterson, 2001) as well as monitor habitat loss and fragmentation (Vogelmann, 1995; Heilman et al., 2002). Studies involving herpetofauna and GIS have typically been limited to habitat suitability modeling for a given species or group (Raxworthy et al., 2003; Santos et al., 2006, 2009). Other projects, such as the Pennsylvania Amphibian and Reptile Survey, have used citizen science jointly with GIS technology to map out population ranges. There are also projects that have attempted to work out passages between territories over 12 roadways (Clevenger et al., 2002). This project will attempt a novel use, regarding timber rattlesnakes in Pennsylvania, of GIS technology in assessing population integrity of given sites in relationship to habitat features at different spatial scales. Objectives Due to the low fecundity of individual females, the risk of spreading pathogens, and the ever-increasing habitat destruction from human development, this project aims to accomplish two goals using GIS technology: 1. Use GIS technology to evaluate the relationship between anthropogenic habitat alterations and population status where data are available. 2. Use the relationships revealed in 1, above, to produce formulae that can estimate the impact of future changes of similar type on populations. 13 CHAPTER 2: METHODS AND MATERIALS This project used geographic information science (GIS) technology to quantify habitat features surrounding Timber Rattlesnake habitat within Pennsylvania. Data was collected from several major sources including the Pennsylvania Fish and Boat Commission (PFBC), Pennsylvania Spatial Data Access (PASDA), and local county GIS coordinators. This data was processed in ESRI’s GIS program ArcMap®. Data for certain counties was removed from consideration due to anomalies in structure. The data that was processed in ArcMap® was then transferred to R where the analyses were conducted. A set of six generalized linear models were produced to assess the relationships between environmental factors and snake populations. Study Area The study area consisted of the northeastern Pennsylvania counties including Monroe, Pike, Carbon, Luzerne, Lackawanna, Wayne, Wyoming, and Susquehanna (Figure 6). This portion of the state was chosen due to ease of access for regular visits during which additional population data could be collected. This area of the state has a high rate of development and diverse habitat types, many of which are unsuitable to the 14 life history of Crotalus horridus. As such, while several of the sites hold substantial colonies, many of the populations in this area have low population numbers. This wide range of population sizes likely leads to a more realistic model, as there is no bias towards large or small populations. However, there is bias in the overall methodology of how data was collected from sites. The goal of the Timber Rattlesnake Assessment Project (TRAP) was to confirm rattlesnake sites, not rattlesnake numbers. Due to these methods low site numbers are a result of low effort while absences may represent pseudoabsences. All sites used for the project were verified by the TRAP and historically held timber rattlesnake populations, or were new sites discovered by TRAP that contained a population of rattlesnakes. All the sites reported by TRAP within the northeast counties were used. The data from TRAP was imported into Microsoft Excel in the commaseparated values (CSV) format, as this is the only format that ArcMap® supports. Using the latitude and longitude from the TRAP surveys, the sites were imported into ArcMap®, creating the points for each rattlesnake site. Rattlesnake sites were clipped to the extent of the focal counties, using the Clip tool in ArcMap®, to remove any additional rattlesnake sites that were not included in the scope of this study. Each site was then isolated using the Select function and then made into its own layer to individually create buffers around each point for analysis. Buffers of interest (radii 50m, 400m, and 5,000m), were then added to each site using the buffer tool in ArcMap®. These buffer zones align with various life history components of C. horridus. The innermost buffer zone, 50m, represents immediate habitat at a site that has been identified as critical to individuals in a population, primarily basking and gestation habitat. The intermediate buffer zone (400m) would likely contain the den site as well as alternate gestating and basking habitat that is 15 vital to biological maintenance of the population, especially gravid females. The last buffer zone, 5000m, likely includes all other important habitat such as foraging habitat based on farthest traveling distances of males seeking mating opportunities. Figure 6. Regional map of the northeastern counties including the sites found within this area. Data Collection Rattlesnake population information was collected by TRAP teams that visited sites around the state. Population numbers at these sites varied widely, as many of the sites were only visited once, and the number of snakes seen was recorded as the population. There are a few exceptions to this, including PIT-tagging sites such as the 16 Hell Creek site that has a large population and has been visited numerous times over several years. Snakes at several such sites across the state have been marked with passive integrated transponder (PIT) tags. These tags are subdermal and can be checked with a handheld receiver. Additionally, Dr. LaDuke and his students have visited many sites around the northeast to mark snakes. This has helped to improve population estimates at several different sites, mostly in Luzerne and Monroe counties. If snakes have been marked at a site, the total number of marked snakes has been used as the population estimate as opposed to the number provided by the TRAP. If snakes are not being marked with PIT tags at a site, then the highest number observed at the site during a given visit is used as the population estimate. This was modified if obvious characteristics give away individuals as unique members, such as a yellow phase juvenile who hadn’t previously been recorded. Neonate snakes were not added to population estimates due to the wide mortality fluctuation in these individuals as well as the uncertainty that they would remain within the same natal subpopulation. Roadway data was collected from the Pennsylvania Spatial Data Access (PASDA). Three roadway layers were collected: state, local, and unpaved. These three layers were combined using the merge tool in ArcMap® to form one layer that could be easily manipulated. This joint layer was then projected into the North America Equidistant Conic projection, as were all the layers that were used. From here, a new column was created in the road attribute table and given the name Shape_Length. Using the calculate geometry tool within the attribute table the total length, in meters, of each line segment was found. The roads were clipped, using the Clip tool in ArcMap®, to each buffer zone and summed using the statistics tool within the attribute table. A density of 17 roads was then calculated for each site by taking the total distance of roads within the buffer zone and dividing by the total area of the buffer zone, 7,853.98m2 for the 50m, 502,654.82m2 for the 400m, and 78,539,816.34m2 for the 5,000m. This density (m/m2) relativized the measurements from each buffer zone. Additionally, the Near tool was used to calculate the distance from each site to the nearest road. Trail data was collected from PASDA and were processed using the same procedure described above. Recreational waterways were excluded from the trail data. Building data was collected for each county from its respective GIS county coordinator. Since there was no standard method regarding the form geographic data was in, each county represented buildings in different ways. Pike count only has data for land parcels, instead of buildings, and represents this with polygon data. Monroe and Wayne counties maintain building data as polygons. The remaining counties all use point data for buildings. Due to Pike County only having land parcel data, the description field of the attribute table was manually reviewed to identify all records that mentioned a building on the property. Of the 61,000 attributes in the Pike County land parcel data, 36,000 were identified as containing buildings. However, many of the land parcels with buildings contained more than one building. Additionally, the polygons were converted to points to represent the geographic location of the buildings in a more useful representation. This works well for small land parcels but not for parcels as they become larger. Thus, the buildings in Pike County may not be represented accurately for this model. The Near tool was used to calculate distance from each site to the nearest building. Using the Clip tool in ArcMap®, buildings were clipped to each buffer zone and a total count of buildings within the buffer zone was conducted for each site. A handful of the counties included 18 highway markers with the building data. These points were subsequently removed from the layer. Canopy data was collected from PASDA in the raster format. This file spans the entirety of Pennsylvania and has a resolution of 1x1m2. Due to its high resolution, this data layer was ideal for the project and was chosen over other more highly recommended data types such as a normalized difference vegetation index (NDVI), as the smallest usable resolution that could be located was 250x250m2. However, this raster file only contained values that held canopy cover (as a value of 1) and did not assign values to open canopy. Using the Raster Calculator tool, within the Map Algebra toolbox, null values were assigned a value of 0 within the raster layer. With the Raster Clip tool, within Raster Processing tools, the canopy raster layer was clipped to each buffer zone. These clipped raster files returned values of open and closed canopy within each buffer zone. The amount of closed canopy grids was divided by the total amount of canopy grids to find the percent canopy coverage for each buffer zone. Correlation Factors Correlation coefficients were examined to determine collinearity between predictor variables in Model 1 at each spatial scale to establish if autocorrelation was playing a role in each of the models. Moderate and high degrees of correlation were noted between variables. Ideally, if factors are shown to be strongly autocorrelated the overall factors used in the model can be reduced by eliminating one of the correlated factors. 19 At the fifty-meter buffer zone a moderate positive correlation was observed between Nearest Trail and Nearest Road (r= 0.474, Table 1) and between Nearest Building and Nearest Road (r= 0.547, Table 1). Table 1. Correlation coefficients between each factor at 50m for Model 1. Moderate correlations are bolded. Nearest Road Road Density Nearest Trail Trail Density Nearest Building Total Buildings Canopy Cover Road Density Nearest Trail Trail. Density Nearest Building Total Buildings -0.169 0.474 -0.059 -0.090 -0.019 -0.157 0.547 -0.005 0.339 -0.166 - - - - - 0.086 -0.070 0.152 -0.278 0.049 - At the four-hundred-meter buffer zone for Model 1 there was a moderate positive correlation between Nearest Trail and Nearest Road (r= 0.474, Table 2), Nearest Building and Nearest Road (r= 0.547, Table 2), and Road Density and Total Buildings (r= 0.461, Table 2). A moderate negative correlation was observed between Nearest Road and Road Density (r= -0.572, Table 2), Nearest Trail and Trail Density (-0.453, Table 2) and Nearest Building and Total Buildings (-0.450, Table 2). 20 Table 2. Correlation coefficients between each factor at 400m for Model 1. Moderate correlations are bolded. Nearest Road Road Density Nearest Trail Trail Density Nearest Building Total Buildings Canopy Cover Road Density Nearest Trail Trail Density Nearest Building Total Buildings -0.572 0.474 -0.149 -0.200 -0.033 -0.453 0.547 -0.198 0.339 -0.339 -0.332 0.461 -0.192 0.173 -0.450 0.369 -0.372 0.291 -0.153 0.347 -0.394 At the five-thousand-meter buffer zone there were moderate positive correlations between Nearest Road and Nearest Trail (r= 0.506, Table 3) and Nearest Road and Nearest Building (r=0.535, Table 3). There was a strong positive correlation between Road Density and Total Buildings (r= 0.873, Table 3). At this spatial scale there was a moderate negative correlation between Road Density and Nearest Building (r= -0.415, Table 3), Nearest Trail and Trail Density (r= -0.655, Table 3), and Road Density and Nearest Trail (-0.520, Table 3). There was a strong negative correlation between Total Buildings and Canopy Cover (r= -0.734, Table 3) as well as between canopy cover and road density. 21 Table 3. Correlation coefficients of all factors within the 5000m buffer zone. Moderate correlations are bolded while strong correlations are italicized. Nearest Road Road Density Nearest Trail Trail Density Nearest Building Total Buildings Canopy Cover Road Density Nearest Trail Trail Density Nearest Building Total Buildings -0.377 0.506 -0.520 -0.260 0.116 -0.655 0.535 -0.415 0.345 -0.349 -0.272 0.873 -0.343 0.037 -0.344 0.324 -0.758 0.329 0.038 0.379 -0.734 The correlations that were observed between factors at different spatial scales were ultimately deemed to be insignificant relative to our purposes. There were few correlations observed, most of which were relatively low in magnitude. The few factors that did show a higher degree of correlation were only found at the large spatial scale, 0.873 (total buildings and road density), -0.758 (Canopy Cover and Road Density) and 0.734 (total buildings and canopy cover) and were deemed to not have a strong influence on the results of our models. Due to these low correlation values and the fact that correlations were not replicated across spatial scales it was decided that all factors should be retained in the models. Analysis All analyses were conducted using the statistical programming language R. A table was made for each of the three buffer zones in a CSV format to be imported into R. Snake population size was modeled as a function of the following fixed factors: Nearest 22 Road, Road Density, Nearest Trail, Trail Density, Nearest Building, Total Buildings, and Canopy Cover (Model 1). These factors were chosen as they increase the likelihood of detrimental effects on rattlesnake populations, usually in the form of mortality. This model was treated as the base model against which others are compared, similarly to Vos and Chardon (1998). Two other models were used to separately address variation in snake population size within occupied sites (Model 2) and to focus on drivers of presence-absence rather than abundance of snakes (Model 3). Model 2 avoids possible issues of zero-inflation in Model 1, while Model 3 removes noise from variation in abundance to focus just on factors affecting presence. Model 2 used a normal linear regression model, while a generalized linear model (GLM) assuming a binomial error distribution was used for Model 3. These three core models were also run with a subset of the data that excluded Pike County, due to differences in GIS data resolution from Pike as compared to the other Pennsylvania counties and certain effects of its positioning on the state border. The spread of Pike County’s building data was irregular when compared to the other counties and did not align with actual building location. Additionally, many of Pike County’s rattlesnake sites occurred along the Delaware River near the New York border. Because of this, buffer zones around these sites included land in New York State, for which no GIS information was collected. Therefore, the models were re-evaluated after excluding the sites from this county. These models (Model 4, 5, and 6) were otherwise identical to 23 Models 1, 2, and 3, respectively. These models (Model 1-6) were then repeated for each spatial scale. Additionally, a linear model was run on each factor individually for each buffer zone to validate statistical significance in the aggregate models. The Bonferroni correction was used to adjust critical values for multiple comparisons. A Bonferroniadjusted critical value of 0.00833 was used for comparisons when 6 factors were used (Fifty-meter buffer zones) and a value of 0.0071 was used for the other two spatial scales. Most models were conducted using unscaled predictor variables. Model 1 was additionally tested with centered data (i.e., representing each predictor variable value as a deviation from that variable’s mean) to assess the effect of centering on the model outcome. Presence/pseudo-absence comparisons Finally, we wanted to account for the inherent sampling bias of the TRAP project. As mentioned previously, teams were sent out to verify historic sites for rattlesnake populations, but also looked for rattlesnake populations in suitable habitat. It is likely that TRAP participants used their knowledge of what constitutes good rattlesnake habitat in choosing where to search, thus biasing the location of sites. In addition to this factor, TRAP surveyors searching for new sites probably avoided many tracts of private property where permission to search could not be easily obtained. Therefore, we also compared the rattlesnake sites to “pseudo-absences” generated by sampling random background points in the northeast region of Pennsylvania using the Create Random Points Tool in ArcMap®. One-hundred random points were generated within the study area with the three spatial scale buffer zones added to each point. Canopy cover for pseudo-absences 24 was taken directly at the point, as opposed to the entire buffer zone. These values of 1’s (Closed canopy) and 0’s (Open canopy) were used for comparisons. In addition to this, nearest roads and nearest trails as well as road and trail densities were measured at each spatial scale around the pseudo-absences. Using this data, an analysis of variance (ANOVA) was conducted on each factor between the rattlesnake sites and random point data. If the random data is significantly different from that of the rattlesnake points, this demonstrates that the rattlesnake sites are not distributed randomly with respect to the locations and densities of the factors (roads, trails, buildings, etc.), indicating that they are either attracted to or repulsed by the presence of those factors. Comparing rattlesnake sites to pseudo-absences (e.g., background environmental conditions) provides an alternate method of testing whether these populations have specific habitat associations within the available environmental options in the region. Additionally, this can help account for possible bias introduced in the selection of the sites that generated the true absences (e.g., if those sites were chosen to sample because they appeared to be plausible rattlesnake habitat, rather than more broadly sampling habitat types within the region). 25 CHAPTER 3: RESULTS The results are presented in model order from one to six. Within each model the results are presented starting with the small spatial scale and then are presented in size order following this. Each spatial scale is presented first with all factors included and then with population size or occupancy as a function of each individual factor. Results from the statistical tests are represented in tables when all factors are included and are included in the text when just one factor was used. The predictor variables (Nearest Road, Road Density, Nearest Trail, Trail Density, Nearest Building, Total Buildings, and Canopy Cover) are the same across all models. Models one, two, four, and five are linear regressions assuming a normal distribution of residuals, with population size (abundance) as the response. Models 3 and Model 6 are generalized linear models (GLM) assuming binomially-distributed residuals, using presence-absence data as the response. Model 1: Abundance Model The first linear model (Model 1) included population size as a function of all factors and included all sites. This model examined how the factors affect snake abundance, including absences, at the different spatial scales. Model 1: Fifty-Meters 26 Model 1: Fifty-Meters At the fifty-meter buffer zone this model showed a significant relationship between population size and distance to nearest building (p = 0.04, Table 4). Another model was analyzed on this data set, for fifty meters, with the distribution of data centered. Centering the data did not change the outcome with nearest building still being the only significant factor (p= 0.04, Table 5). Table 4. Results of Model 1 at the 50m buffer zone. A significant relationship was observed between population size and nearest building. R2= 0.0234 AIC= 806.53 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Canopy Percent Residuals Coefficient -0.0023 -460.58 -0.00014 378.78 Df 1 1 1 1 Sum Sq 41.98 11.01 50.60 164.49 Mean Sq 41.98 11.01 50.60 164.49 F value 0.655 0.171 0.789 2.56 Pr(>F) 0.420 0.679 0.376 0.112 0.0032 -2.84 5.81 1 1 107 277.12 12.64 6855.46 277.12 12.64 64.06 4.32 0.197 0.0399* 0.657 Table 5. Results of Model 1 at the 50m buffer zone after factors were centered. A significant relationship was observed between population size and nearest building. R2= 0.0234 AIC= 806.53 Coefficient Nearest Road -1.230 Road Density -0.437 Nearest Trail -0.707 Trail Density 1.307 Nearest Building 2.025 Canopy Percent -0.348 Residuals 3.730 Df 1 1 1 1 1 1 107 Sum Sq 41.98 11.01 50.60 164.49 277.12 12.64 6855.46 27 Mean Sq 41.98 11.01 50.60 164.49 277.12 12.64 64.06 F value 0.655 0.171 0.789 2.567 4.325 0.197 Pr(>F) 0.420 0.679 0.376 0.112 0.0399* 0.657 Roadways varied in their distance to sites at the fifty-meter buffer from 29.9m to 2,445.7m with a mean distance of 720.8m (n=116). A linear model that was used with population size as a function of only nearest road did not yield significant results (ANOVA; df= (1, 115), F= 0.588, p= 0.444). The density of roads within the fifty-meter buffer zone (found by dividing the total length of roads by the area of the buffer zone) ranged from 0 to 0.009001m/m2 with a mean density of 0.0001196m/m2 (n=118). The linear model relating road density to population size did not produce a significant result (ANOVA; df= (1, 116), F= 0.0821, p= 0.775). The distance of trails varied from 3.4m to 14,548.48 at the fifty-meter buffer in model 1 with a mean distance of 5,043.22 meters (n=117). The linear model relating population size as a function of this factor did not show a significant relationship (ANOVA; df = (1, 115), F= 1.5519, p= 0.2154). The density of trails for this model at fifty meters ranged from 0m/m2 to 0.02849m/m2 with a mean density of 0.000517m/m2 (n=118). The linear model for this factor did not yield a significant result (ANOVA; df = (1,116), F= 3.0528, p= 0.08324). The distance of nearby buildings to sites ranged from 51.5 meters to 3,311.77 meters with a mean distance of 882.32 meters (n=115). Another linear model using population size as a function of distance to nearest building did not show a significant result (ANOVA; df= (1, 113), F= 0.7657, p= 0.3834). There were no buildings within the fifty-meter buffer zone for Model 1 (n=118). 28 The canopy cover of sites at fifty meters ranged from 28.64% to 100% with a mean cover of 94.72% (n=117). The linear model relating canopy cover to snake population size did not show a significant result (ANOVA; df= (1, 115), F= 0.9611, p= 0.329). Model 1: Four-Hundred-Meters The linear model at four-hundred-meters did not yield any significant results though Nearest Building was just outside of this threshold (Table 6). In a separate run, the four-hundred-meter buffer for model 1 was also scaled to center the factors used. A linear model was run using these scaled factors. This model did not yield results different from the original model (Table 7). Table 6. Results of Model 1 at the 400m buffer zone. There were no significant relationships observed. R2= -0.005034 AIC= 810.73 Coefficients Df Nearest Road -0.0036 1 Road Density -1507.50 1 Nearest Trail -0.00012 1 Trail Density 156.18 1 Nearest Building 0.0032 1 Buildings Within -0.035 1 Canopy Percent -4.16 1 Residuals 8.72 106 Sum Sq 41.98 84.61 32.76 3.59 253.48 0.19 7.59 6989.11 29 Mean Sq 41.98 84.61 32.76 3.59 253.48 0.19 7.59 65.93 F value 0.636 1.283 0.496 0.054 3.844 0.002 0.115 Pr(>F) 0.426 0.259 0.482 0.815 0.052 0.956 0.735 Table 7. Results of Model 1 at the 400m buffer zone with factors centered. There were no significant relationships observed. R2= -0.005034 AIC= 810.73 Coefficients Nearest Road -1.936 Road Density -1.230 Nearest Trail -0.624 Trail Density 0.228 Nearest Building 2.037 Buildings Within -0.106 Canopy Percent -0.299 Residuals 3.749 Df 1 1 1 1 1 1 1 106 Sum Sq Mean Sq 41.98 41.98 84.61 84.61 32.76 32.76 3.59 3.59 253.48 253.48 0.19 0.19 7.59 7.59 6989.11 65.93 F value 0.636 1.283 0.496 0.054 3.844 0.002 0.115 Pr(>F) 0.426 0.259 0.482 0.815 0.052 0.956 0.735 The four-hundred-meter buffer zone did not vary from the fifty-meter buffer zone regarding measurements of nearest road, nearest trail, or nearest building (Appendix XIX, Appendix XX). A linear model with nearest road alone at four-hundred-meters did not yield a significant result (ANOVA; df= (1, 114), F= 0.5884, p= 0.4446). The density of roads within the four-hundred-meter buffer zone for Model 1 ranged from 0 m/m2 to 0.003602 m/m2 with a mean density of 0.0004563 m/m2 (n=118). The linear model relating road density to population size did not show a significant result (ANOVA; df= (1, 116), F= 0.3702, p= 0.5441). The linear model with population size as a function of nearest trail did not show a significant result (ANOVA; df= (1, 115), F= 1.5519, p=0.2154). The density of trails in Model 1 at four-hundred-meters ranged from 0 m/m2 to 0.005346 m/m2 with a mean density of 0.0006586 m/m2 (n=118). The linear model did not show a significant 30 relationship between trail density and population size at four-hundred-meters (ANOVA; df= (1, 116), F= 0.1353, p= 0.7137). No significant relationship was observed between population size and nearest building (ANOVA; df= (1,113), F= 0.7657, p= 0.3834). The quantity of buildings within the four-hundred-meter buffer zone in Model 1 ranged from zero to sixteen with a mean quantity of 1.14 (n= 115). No significant relationship was discovered between quantity of buildings and population size (ANOVA; df= (1, 115), F= 0.7274, p= 0.3955). Canopy cover at four-hundred-meters in Model 1 ranged from 63.8% to 100% with a mean cover of 94.736% (n= 117). The linear model with population size as a function of canopy cover did not show a significant result (ANOVA; df= (1, 115), F= 0.001, p= 0.9744). Model 1: Five-Thousand-Meters The last buffer zone within Model 1, five-thousand-meters, showed a significant relationship between population size and nearest building (p= 0.02, Table 8), as well as a significant relationship between population size and quantity of buildings (p= 0.04, Table 8). Another model was run with the factors scaled to adjust for the distribution of the data, however, results did not change with this model. There was a significant relationship between nearest building (p= 0.02, Table 9) and population size as well as quantity of buildings and population size (p= 0.04, Table 9). 31 Table 8. Results of Model 1 at the 5000m buffer zone. A significant relationship was observed between nearest building and population size as well as between quantity of buildings and population size. R2= 0.0879 AIC= 677.67 Coefficient Nearest Road -0.0028 Road Density 8623.13 Nearest Trail 0.00025 Trail Density 2897.49 Nearest Building 0.0043 Buildings Within -0.0012 Canopy Percent 28.46 Residuals -34.71 Df 1 1 1 1 1 1 1 86 Sum Sq 69.73 167.42 6.75 0.33 425.08 308.23 163.52 6144.23 Mean Sq 69.73 167.42 6.75 0.33 425.08 308.23 163.52 71.44 F value 0.976 2.343 0.094 0.004 5.94 4.314 2.288 Pr (>F) 0.325 0.129 0.759 0.945 0.016* 0.040* 0.133 Table 9. Results of Model 1 at the 5000m buffer zone with factors centered. A significant relationship was observed between nearest building and population size as well as between quantity of buildings and population size. R2= 0.0879 AIC= 677.67 Coefficient Nearest Road -1.53 Road Density 7.04 Nearest Trail 1.32 Trail Density 1.02 Nearest Building 2.77 Buildings Within -3.23 Canopy Percent 2.14 Residuals 3.99 Df 1 1 1 1 1 1 1 86 Sum Sq 69.73 167.42 6.75 0.33 425.08 308.23 163.52 6144.23 Mean Sq 69.73 167.42 6.75 0.33 425.08 308.23 163.52 71.44 F value 0.976 2.343 0.094 0.004 5.949 4.314 2.288 Pr(>F) 0.325 0.129 0.759 0.945 0.016* 0.040* 0.133 The minimum and maximum distance from sites to nearest road did not change for the five-thousand-meter buffer zone, ranging from 29.9m to 2445.7m, however the mean, 764.4 (n=101), is slightly altered due to the lower sample size of this buffer zone. The linear model with population size as a function of nearest road alone did not yield a significant result (ANOVA; df= (1, 99), F= 0.775, p= 0.3808). The density of roads within the five-thousand-meter buffer for Model 1 ranged from 0.0003487m/m2 to 32 0.004001m/m2 with a mean density of 0.001383m/m2 (n=102). This linear model did not show a significant result between road density and population size (ANOVA; df= (1, 100), F= 3.0216, p= 0.08524). The distance of trails to sites in this buffer zone ranged from 3.4m to 14,548.48m with a mean of 5,177.062 (n=102). The linear model for this factor did not show a significant relationship (ANOVA; df= (1, 100), F= 1.4362, p=0.2336). The density of trails within this buffer zone ranged from 0m/m2 to 0.001597m/m2 with a mean density of 0.0002557m/m2 (n=102). The linear model for trail density at five-thousand-meters for Model 1 did not show a significant relationship between trail density and population size (ANOVA; df= (1, 100), F= 0.3271, p= 0.5687). Buildings had a mean distance of 936.45m (n= 96) with a minimum distance of 78.1m and a maximum distance of 15,706m. There was no significant relationship between nearest building and population size at 5 five-thousand-meters in Model 1 (ANOVA; df= (1, 94), F= 0.6195, p= 0.4332). The quantity of buildings within the fivethousand-meter buffer zone ranged from forty-two to fifteen-thousand-seven-hundredand-six with a mean quantity of buildings of 1,871.03 (n=95). The linear model did not show a significant relationship between number of buildings within the five-thousandmeter buffer zone and population size (ANOVA; df= (1, 93), F= 0.2731, p= 0.6025). Lastly, the amount of canopy cover in Model 1 for five-thousand-meters ranged from 58.44% to 97.76% with a mean cover of 88.56% (n= 101). There was no significant relationship between canopy cover and population size at five-thousand-meters for Model 1 (ANOVA; df= (1, 99), F= 0.0266, p= 0.8708). 33 Model 2: Non-Zero Abundance Model The second linear model (Model 2) included population size as a function of all factors but removed all sites that did not have snake populations. This was done to remove the bias associated with zero inflation. Model 2 assesses the relationship between the predictor variables and snake abundance at sites where at least some snakes are present. Model 2: Fifty-Meters The model that was run for the fifty-meter buffer zone showed a significant relationship between population size and nearest building (p= 0.04, Table 10). Table 10. Results of Model 2 at the 50m buffer zone with all factors included. There were no building measures at 50m. A significant relationship was observed between population size and nearest building. R2= 0.0302 AIC= 562.81 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Canopy Percent Residuals Coefficients -0.0045 -901.69 -0.00025 331.24 0.0050 1.37 4.37 Df 1 1 1 1 1 1 69 Sum Sq 93.21 50.20 120.20 65.38 385.89 1.90 5930.19 Mean Sq 93.21 50.20 120.20 65.38 385.89 1.90 85.94 F value 1.084 0.584 1.398 0.760 4.489 0.022 Pr (>F) 0.301 0.447 0.241 0.386 0.037* 0.882 At the fifty-meter buffer distances of roadways to sites ranged from 29.9m to 2,061.8m with a mean distance of 737.5m (n=77). The linear model between nearest road and population size did not yield a significant result at fifty-meters (ANOVA; df= (1, 75), F= 1.0863, p= 0.3006). The density of roads within the fifty-meter buffer zone ranged from 0m/m2 to 0.009m/m2 with a mean density of 0.00017m/m2 (n=79). A linear model 34 was used to assess the relationship between road density and snake populations but did not reveal a significant result (ANOVA; df= (1, 77), F= 0.2722, p= 0.6033). The distance of trails at the fifty-meter buffer zone for Model 2 ranged from 3.4m to 14,517.335m with a mean distance of 5,182.67 (n=78). There was no significant relationship found between nearest trail and population size at this spatial scale (ANOVA; df= (1, 76), F= 2.2363, p= 0.1389). The density of trails at the fifty-meter buffer zone ranged from 0m/m2 to 0.0284m/m2 with a mean density of 0.0007724m/m2 (n=79). The linear model did not show a significant relationship between population size and road density (ANOVA; df= (1, 77), F= 1.3973, p= 0.2408). Distances of buildings ranged from 51.5m to 3,311.77m at the fifty-meter buffer zone with a mean distance of 913.12m (n= 77). The linear model did not show a significant relationship between nearest building and population size (ANOVA; df= (1, 75), F= 0.4582, p= 0.5006). There were zero buildings measured within the fifty-meter buffer zone for all sites (n= 79). Canopy cover within the fifty-meter buffer zone ranged from 41.67% to 100% with a mean cover of 93.4% (n=78). The linear model that was used to compare the relationship between canopy cover and population size did not show a significant result (ANOVA; df= (1, 76), F= 0.2181, p= 0.6419). Model 2: Four-Hundred-Meters The linear model for the four-hundred-meter buffer zone yielded a significant result between population size and nearest building (p= 0.02, Table 11). 35 Table 11. Results of Model 2 at the 400m buffer zone with all factors included. A significant relationship was observed between population size and nearest building. R2= 0.0348 AIC= 563.34 Coefficients Nearest Road -0.0077 Road Density -3178.28 Nearest Trail -0.00014 Trail Density 557.45 Nearest Building 0.0063 Buildings Within 0.29 Canopy Percent -9.11 Residuals 15.99 Df 1 1 1 1 1 1 1 68 Sum Sq 93.21 142.95 90.83 0.63 465.36 15.85 21.66 5816.47 Mean Sq 93.21 142.95 90.83 0.63 465.36 15.85 21.66 85.53 F value 1.089 1.671 1.061 0.007 5.440 0.185 0.253 Pr(>F) 0.300 0.200 0.306 0.931 0.022* 0.668 0.616 The distances between the fifty-meter and four-hundred-meter buffer zones did not change regarding nearest road, nearest trail, and nearest building (Appendix XXII, Appendix XXIII). The linear model for nearest road at four-hundred-meters did not show a significant result (ANOVA; df= (1, 75), F= 1.0863, p= 0.3006). The density of roads ranged from 0m/m2 to 0.003107m/m2 with a mean density of 0.000427m/m2. The linear model that evaluated population size and road density did not yield a significant result (ANOVA; df= (1, 77), F= 0.207, p= 0.6504). There was no significant relationship between nearest trail and population size within the four-hundred-meter buffer zone (ANOVA; df= (1, 76), F= 2.2363, p= 0.1389). The density of trails within the four-hundred-meter buffer zone ranged from 0m/m2 to 0.00534m/m2 with a mean density of 0.000558m/m2 (n= 79). The linear model did not show a significant relationship between population size and trail density (ANOVA; df= (1, 77), F= 0.6022, p= 0.4401). 36 The linear model did not produce a significant result between nearest building and population size (ANOVA; df= (1, 75), F= 0.4582, p= 0.5006). The quantity of buildings within the four-hundred-meter buffer zone ranged from zero to twelve with a mean quantity of .66 (n= 78). There was no significant relationship between population size and total buildings shown by the linear model (ANOVA; df= (1, 76), F= 0.0069, p= 0.9339). Canopy cover at the four-hundred-meter buffer zone for Model 2 ranged from 67.7% to 100% with a mean cover value of 95.4% (n=78). The linear model did not show a significant result between canopy cover and population size at the four-hundred-meter buffer (ANOVA; df= (1, 76), F= 0.2982, p= 0.5866). Model 2: Five-Thousand-Meters A linear model was used at five-thousand-meters to assess the relationship between the measured factors and population size, however, only nearest building was significant (p= 0.03, Table 12). Table 12. Results of Model 2 at the 5000m buffer zone with all factors included. A significant relationship was observed between population size and nearest building. R2= 0.1097 AIC= 468.03 Coefficients Nearest Road -0.0053 Road Density 11848.70 Nearest Trail 0.00035 Trail Density 7101.51 Nearest Building 0.0061 Buildings Within -0.0025 Canopy Percent 44.66 Residuals -51.09 Df 1 1 1 1 1 1 1 54 Sum Sq 165.08 269.61 7.151 46.22 495.25 189.02 213.75 5155.62 37 Mean Sq 165.08 269.61 7.15 46.22 495.25 189.02 213.75 95.47 F value 1.729 2.823 0.074 0.484 5.187 1.979 2.238 Pr(>F) 0.194 0.098 0.785 0.489 0.026* 0.165 0.140 The distance of sites from roadways within the five-thousand-meter buffer zone ranged from 29.9m to 2,061.8m with a mean distance of 796.123m (n=64). A linear model did not show a significant result between population size and nearest road (ANOVA; df= (1, 62), F= 1.7025, p= 0.1968). The density of roads within this buffer zone ranged from 0.000419m/m2 to 0.00359m/m2 with a mean density of 0.00137m/m2 (n=65). The relationship between road density and population size was not significant (ANOVA; df= (1, 63), F= 3.6841, p= 0.05947). For the five-thousand-meter buffer zone the distance from sites to trails ranged from 3.4m to 14,517.33m with a mean distance of 5,390.93. The linear model used to assess the relationship between nearest trail and population size did not show a significant result (ANOVA; df= (1, 63), F= 2.1766, p= 0.1451). Trail density within this buffer zone ranged from 0m/m2 to 0.00133m/m2 with a mean density of 0.000223m/m2 (n=65). No relationship was found between trail density and population size seen for this buffer zone (ANOVA; df= (1, 63), F= 1.3572, p= 0.2484) The building distances recorded at the five-thousand-meter buffer zone for Model 2 ranged from 78.16m to 3,311.77m with a mean distance of 985.9m (n=64). There was no significant relationship between distance to buildings and population size within this buffer zone (ANOVA; df= (1, 62), F= 0.2266, p= 0.6357). The quantity of buildings within this buffer zone ranged from forty-two to seven-thousand-five-hundred-ninety-one with a mean quantity of 1,675.58 (n=63). The linear model did not yield a significant result when run with quantity of buildings and population size (ANOVA; df= (1, 61), F= 1.4683, p= 0.2303). 38 Canopy cover within this buffer zone ranged from 70.1% to 97.69% with a mean cover of 89.1% (n=64). There was no significant relationship discovered between canopy cover and population size (ANOVA; df=( 1, 62), F= 0.0578, p= 0.8108). Model 3: Presence-Absence Model The third model that was explored used a generalized linear model with a binomial distribution to relate occupancy data as a function of the factors previously mentioned. All factors were included in this model. If the site had a population size of zero, it was assigned a value of zero, for absent, while any sites with a population size greater than or equal to one had a value of one assigned to them, for present, indicating that the site was occupied at the time it was visited. The goal of Model 3 was to assess which factors determine whether snakes will be present or absent. Model 3: Fifty-Meters This model did not show a significant relationship between factors and site occupancy at fifty-meters (Table 13). Table 13. Results of the GLM for Model 3 within the 50m buffer zone. There were no buildings measured within this buffer zone. There was no significant relationship observed at this spatial scale. AIC= 128.75 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Canopy Percent Residuals Coefficient -0.0000091 2949.74 0.000045 1504.12 0.00014 -2.97 3.12 Df 1 1 1 1 1 1 107 39 Z- Value -0.018 0.007 0.965 0.012 0.339 -1.257 1.360 Pr (>|z|) 0.986 0.994 0.334 0.991 0.734 0.209 0.174 The distance of sites to nearest roadways varied from 29.9m to 2,061.8m with a mean distance of 737.5m (n=77) for occupied sites while distances at unoccupied sites varied from 88.5m to 2,445.7m with a mean distance of 687.69 (n=39). There was no significant relationship seen between nearest road and site occupancy for this spatial scale in Model 3 (GLM; df= (1, 114), Z= 0.484 p= 0.628). The density of roadways in occupied sites ranged from 0m.m2 to 0.009m/m2 with a mean density of 0.000178m/m2 (n=79) while no roadways were recorded within the fifty-meter buffer zone for unoccupied sites. The model did not show a significant relationship between road density and occupancy at fifty-meters for Model 3 (GLM; df=(1, 106), Z= 0.011, p= 0.990). Within this buffer zone (50m) the distances of trails to sites varied from 3.4m to 14,517.34 with a mean distance of 5,182.67 (n=78) for occupied sites while unoccupied sites ranged from 87.3 to 14,548.48m with a mean distance of 4,764.34m (n=39). The GLM did not show a significant relationship between population and trail density for this spatial scale (GLM; df= (1, 115), Z= 0. .435, p= 0. 663). The density of trails at this spatial scale ranged from 0m/m2 to 0.2849m/m2 for occupied sites while unoccupied sites did not have any trails within the buffer zone for Model 3. There was no significant relationship observed between trail density and occupancy at this spatial scale for Model 3 (GLM; df= (1, 116), Z= 0.012, p= 0.990). The distances from sites to buildings ranged from 51.5m to 3,311.77m with a mean distance of 913.12m (n=77) for occupied sites while nearest building ranged from 98.7m to 2,253.4m for unoccupied sites with a mean distance of 822.94m (n=33). The GLM did not show a significant relationship between site occupancy and nearest building 40 (GLM; df= (1, 113), Z= 0.733, p=0.263). There were no buildings present within the fifty-meter buffer zone for Model 3. Canopy cover ranged from 41.67% to 100% for occupied sites with a mean cover of 93.43% (n=78) while the canopy cover at unoccupied sites ranged from 28.6 to 100% with a mean cover of 97.29% (n= 39). There was no significant relationship between occupancy and canopy cover for Model 3 at fifty-meters (GLM; df= (1, 115), Z= -1.512, p= 0.1306). Model 3: Four-Hundred-Meters The GLM at the four-hundred-meter buffer zone showed a significant relationship between quantity of buildings and population (p=0.03, Table 14). There was no difference between measurements regarding nearest road, nearest trail, and nearest building between the fifty-meter and four-hundred-meter buffer zones (Appendix XXV, Appendix XXVI). The density of roadways within the four-hundred-meter buffer zone for occupied sites ranged from 0m/m2 to 0.0031m/m2 with a mean density of 0.000428m/m2 (n=79) while the density of roadways at the unoccupied sites ranged from 0m/m2 to 0.0036m/m2 with a mean density of 0.000514m/m2 (n=39). The GLM did not show a significant relationship between road density and site occupancy at this spatial scale in Model 3 (GLM; df= (1, 116), Z= -0.539, p= 0.589). 41 Table 14. Results of the GLM for Model 3 at the 400m buffer zone. A significant relationship was observed between quantity of buildings and occupancy. AIC= 152.33 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Residuals Coefficient 0.00013 388.76 0.0000034 -99.55 -0.00053 -0.21 3.07 -1.69 Df 1 1 1 1 1 1 1 106 Z- Value 0.210 0.959 0.062 -0.633 -1.083 -2.137 0.937 -0.552 Pr (>|z|) 0.833 0.337 0.950 0.526 0.278 0.032 0.580 .5306 At the four-hundred-meter buffer zone in Model 3 trail densities ranged from 0m/m2 to 0.00534m/m2 with a mean density of 0.000558m/m2 (n=79) for occupied sites while trail density in unoccupied sites ranged from 0m/m2 to 0.00528m/m2 with a mean density of 0.000862m/m2 (n=39). No significant relationship was observed between trail density and occupancy for this spatial scale in Model 3 (GLM; df= (1, 116), Z= -1.052, p= 0.2928). The quantity of buildings for occupied sites at four-hundred-meters ranged from zero to twelve with a mean quantity of 0.667 (n=78). For unoccupied sites the quantity of buildings ranged from zero to sixteen with a mean quantity of 2.102 (n=39). The GLM did not show a significant relationship between quantity of buildings and occupancy at the four-hundred-meter buffer zone for Model 3 after the Bonferroni correction accounting for multiple comparisons (GLM; df= (1, 115), Z= -2.203, p= 0.0276). Canopy cover at the four-hundred-meter buffer zone ranged from 67.7% to 100% with a mean cover of 95.43% at occupied sites while cover ranged from 63.8% to 100% with a mean cover of 93.33% (n= 39) at unoccupied sites. There was no significant 42 relationship observed between canopy cover and population for this spatial scale (GLM; df= (1, 115), Z= 1.461, p= 0.144). Model 3: Five-Thousand-Meters The GLM at the five-thousand-meter buffer zone in Model 3 did not show a significant relationship between the factors and site occupancy (Table 15). Table 15. Results of the GLM for the 5000m buffer zone in Model 3. There was no significant result observed between the factors and the response variable. AIC= 128.75 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Residuals Coefficient 0.000098 1438.44 -0.000024 -1240.32 0.000071 -0.00038 5.58 -5.20 Df 1 1 1 1 1 1 1 86 Z- Value 0.175 1.706 -0.296 -1.281 0.138 -1.682 1.108 -1.053 Pr (>|z|) 0.861 0.087 0.767 0.200 0.889 0.092 0.267 0.292 The distance of nearest roadways to occupied sites ranged from 29.9m to 2,061.8m with a mean distance of 796.12m (n=64) while the distance from unoccupied sites to nearest road ranged from 88.5m to 2,445.7m with a mean distance of 709.54m (n=37). The GLM did not show a significant relationship between site occupancy and nearest roadway (GLM; df= (1, 99), Z= 0.785, p= 0.433). The density of roadways at this spatial scale for occupied sites in Model 3 ranged from 0.000419m/m2 to 0.003599m/m2 with a mean density of 0.00137m/m2 (n=65) while unoccupied sites had a density range of 0.0003m/m2 to 0.004m/m2 with a mean density of 0.00139m/m2 (n= 37). The GLM did not show a significant relationship between site occupancy and road density within the five-thousand-meter buffer zone for Model 3 (GLM; df= (1, 100), Z= -0.0109, p= 43 0.913) The distance from trails to sites at five-thousand-meters ranged from 3.4m to 14,517.34m with a mean distance of 5,309.93 (n=65) for occupied sites while unoccupied sites ranged from 87.3m to 14, 548.48m with a mean distance of 4,801.33m (n=37). There was no significant relationship observed between distance to nearest trail and site occupancy for this spatial scale in Model 3 (GLM; df= (1, 100), Z= 0.556, p= 0.578). The density of trails within the five-thousand-meter buffer zone ranged from 0m/m2 to 0.00133m/m2 for occupied sites with a mean density of 0.00022m/m2 (n=65) while unoccupied sites ranged from 0m/m2 to 0.00159m/m2 with a mean density of 0.000312m/m2 (n=37). The GLM did not show a significant relationship between trail density and population size at this spatial scale for Model 3 (GLM; df= (1, 100), Z= 1.217, p= 0.2234). For this spatial scale, in Model 3, the distance to nearest building for occupied sites ranged from 78.16m to 3,311.77m with a mean distance of 985. 93m (n= 64) while the distance of nearest buildings to unoccupied sites ranged from 146.76m to 2,253.4m with a mean distance of 837.5m (n= 32). There was no significant relationship observed between nearest building and population at this spatial scale for Model 3 (GLM; df= (1, 94), Z= 1.074, p= 0.283). The quantity of buildings within the five-thousand-meter buffer zone for occupied sites ranged from forty-two to seven-thousand-five-hundred-ninety-one with a mean quantity of 1,675.58 (n= 63). For the unoccupied sites the quantity of buildings ranged from eighty-four to fifteen-thousand-seven-hundred-six with a mean quantity of 2,255.81 (n= 32). The GLM did not show a significant relationship between quantity of buildings and site occupancy at this spatial scale for model 3 (GLM; df= (1, 93), Z= -1.033, p= 0.3014). 44 The amount of canopy cover for occupied sites at the five-thousand-meter buffer zone in Model 3 ranged from 70.1% to 97.69% with a mean cover of 89.15% (n= 64) while unoccupied sites had 58.44% to 97.76% cover with a mean of 87.54% (n=37). The linear model did not show a significant relationship between canopy cover and site occupancy at the five-thousand-meter buffer zone for Model 3 (GLM; df= (1, 99), Z= 1.027, p= 0.304). Model 4: Abundance Model Without Pike County The fourth model (Model 4) was identical to Model 1, however, the data from Pike county is removed. This model included population size as a function of all measured factors. Model 4: Fifty-Meters At the fifty-meter buffer zone there was a significant relationship between trail density and population size (p= 0.01, Table 16). Table 16. Results of Model 4 at 50m showing a significant relationship between trail density and population size. There were no buildings measured at this spatial scale. R2= 0.0557 AIC= 648.38 Coefficients Nearest Road -0.0030 Road Density -630.29 Nearest Trail -0.00010 Trail Density 1031.66 Nearest Building 0.0037 Canopy Percent 0.91 Residuals 1.98 Df 1 1 1 1 1 1 80 Sum Sq 56.58 13.75 24.46 520.43 252.31 0.92 6274.92 Mean Sq 56.58 13.75 24.46 520.43 252.31 0.92 78.43 F value 0.721 0.175 0.311 6.635 3.216 0.011 Pr(>F) 0.398 0.676 0.578 0.011* 0.076 0.913 As with previous models, each factor was put into a linear model individually to assess the relationship between each factor and population size at each site. Distances 45 from roadways to sites varied from 29.9m to 2,445.7m with a mean distance of 767.91 (n=89). The model relating population size to nearest roadway at fifty-meters did not yield a significant result (ANOVA; df= (1, 87), F= 0.6256, p=0.4311). The density of roads within this buffer zone ranged from 0m/m2 to 0.009m/m2 with a mean density of 0.0001m/m2 (n= 90). The linear model for road density at fifty-meters did not reveal a significant result (ANOVA; df= (1, 87), F= 0.0799, p= 0.7782). The distance of trails to sites at fifty-meters varied from 21.6m to 14,548.48m with a mean distance of 5,676.42m (n= 90). There was no significant relationship observed between population size and distance to nearest trail for this spatial scale (ANOVA; df= (1, 88), F= 0.963, p= 0.3291). The density of trails at fifty-meters for Model 4 ranged from 0m/m2 to 0.0227m/m2 with a mean density of 0.000361m/m2 (n= 90). However, the linear model for this spatial scale showed a significant relationship between trail density and population size after correcting for multiple comparisons (Bonferroni, critical value= 0.0083) (ANOVA; df= (1, 88), F= 7.5827, p= 0.00716). The distance from buildings to sites at the fifty-meter buffer zone ranged from 78.16m to 3,311.77m with a mean distance of 913.342m (n=88). The linear model used at this spatial scale for nearest building and snake numbers did not show a significant relationship (ANOVA; df= (1, 86), F= 0.2152, p= 0.6439). There were no buildings measured within the fifty-meter buffer zone for Model 4. The canopy cover for Model 4 at fifty-meters ranged from 28.64% to 100% with a mean cover of 96.28% (n=89). The linear model did not show a significant relationship between canopy cover and population size (ANOVA; df= (1, 87), F= 0.5957, p= 0.4423). 46 Model 4: Four-Hundred-Meters Within the four-hundred-meter buffer zone for Model 4 there was no significant relationship discovered between the factors and population size (Table 17). The values of nearest factor did not differ between the fifty-meter and fourhundred-meter buffer zones for Model 4 (Appendix XXVIII, Appendix XXIX). The linear model for four-hundred-meters in Model 4 between nearest road and population size did not show a significant result (ANOVA; df= (1, 87), F= 0.625, p= 0.4311). The density of roads for this spatial scale in Model 4 ranged from 0m/m2 to 0.0031m/m2 with a mean density of 0.0004177m/m2 (n=90). There was no significant relationship found between road density and population size (ANOVA; df= (1, 88), F= 0.096, p= 0.7574). Table 17. Results of Model 4 at the 400m buffer zone. There was no significant relationship observed between the factors and population size. R2= -0.0337 AIC= 643.89 Coefficients Nearest Road -0.0040 Road Density -1587.84 Nearest Trail -0.00010 Trail Density 253.59 Nearest Building 0.0033 Buildings Within -0.14 Canopy Percent -4.56 Residuals 9.17 Df 1 1 1 1 1 1 1 79 Sum Sq 56.58 66.41 16.35 2.017 203.70 8.06 6.85 6783.40 Mean Sq 56.58 66.41 16.35 2.01 203.70 8.06 6.85 85.86 F value 0.659 0.773 0.190 0.023 2.37 0.093 0.079 Pr(>F) 0.419 0.381 0.663 0.878 0.127 0.760 0.778 The linear model relating nearest trail and population size did not show a significant result (ANOVA; df= (1, 88), F= 0.963, p= 0.3291). The densities of trails within the four-hundred-meter buffer zone ranged from 0m/m2 to 0.00534m/m2 with a mean density of 0.000789m/m2 (n=90). The linear model did not show a significant 47 relationship at this spatial scale between trail density and population size (ANOVA; df= (1, 88), F= 0.2414, p= 0.6244). Within this buffer zone for Model 4 there was no relationship found between nearest building and population size (ANOVA; df= (1,86), F= 0.2152, p= 0.6439). The quantity of buildings within this buffer zone ranged from zero to sixteen with a mean quantity of 0.9438 (n=89). There was no significant relationship seen between total buildings within the buffer zone and population size for Model 4 at four-hundred-meters (ANOVA; df= (1, 87), F= 0.3617, p=0.5492). Canopy cover within the four-hundred-meter buffer zone ranged from 67.7% to 100% with a mean cover of 94.44% (n=89). There was no significant relationship seen between canopy cover and population size at this buffer zone for Model 4 (ANOVA; df= (1, 87), F= 0.116, p= 0.7342). The last buffer zone for Model 4, five-thousand-meters, showed a significant relationship between quantity of buildings and population size (p=0.02, Table 18) as well as canopy cover and population size (p=0.048, Table 18). 48 Table 18. Results of Model 4 at the 5000m buffer zone. A significant relationship was observed between quantity of buildings and population size as well as canopy cover and population size. R2= 0.122 AIC= 592.01 Coefficients Nearest Road -0.0017 Road Density 11645.94 Nearest Trail 0.00016 Trail Density -491.08 Nearest Building 0.0028 Buildings Within -0.0018 Canopy Percent 43.21 Residuals -48.70 Df 1 1 1 1 1 1 1 73 Sum Sq 65.18 269.36 0.26 0.69 307.84 459.84 311.71 5671.62 Mean Sq 65.18 269.36 0.26 0.69 307.84 459.84 311.71 77.69 F value 0.839 3.467 0.003 0.008 3.96 5.918 4.012 Pr(>F) 0.362 0.066 0.953 0.924 0.050 0.017* 0.048* The distance between roadways and sites within this buffer zone for Model 4 varied from 29.9m to 2,445.7m with a mean distance of 770.84m (n=88). The linear model that tested population size as a function of distance to nearest roadways did not show a significant result (ANOVA; df= (1, 86), F= 0.6335, p= 0.4283). The density of roadways within this buffer zone in Model 4 ranged from 0.000348m/m2 to 0.004001m/m2 with a mean density of 0.0011354m/m2 (n=89). The linear model relating population size as a function of road density for this spatial scale did not show a significant result (ANOVA; df= (1 87), F= 3.9419, p= 0.05025). Model 4: Five-Thousand-Meters Within the five-thousand-meter buffer zone for Model 4 the distances from sites to nearest trail ranged from 21.6m to 12,548.48m with a mean distance of 530.453m (n=89). The linear model for nearest trail distance and population size did not show a significant result (ANOVA; df= (1, 87), F= 0.9948, p= 0.3213). Densities of trails within the five-thousand-meter buffer zone ranged from 0m/m2 to 0.00160m/m2 with a mean 49 density of 0.000249m/m2 (n= 89). There was no significant relationship seen between trail density and population size at five-thousand-meters for Model 4 (ANOVA; df= (1, 87), F= 0.0964, p= 0.7569). The distance of nearest building ranged from 78.16m to 3,311.77m with a mean distance of 914.548m (n=83). The linear model that tested population size as a function of distance to nearest building did not show a significant result (ANOVA; df = (1, 81), F= 0.2176, p= 0.6421). At this spatial scale, for Model 4, the quantity of buildings within the buffer zone ranged from forty-two to fifteen-thousand-seven-hundred-six with a mean quantity of 1,904.304 (n= 82). The linear model did not show a significant relationship between population size and quantity of buildings for this spatial scale (ANOVA; df= (1, 80), F= 0.4506, p= 0.504). Canopy cover within this buffer zone for Model 4 ranged from 58.44% to 97.7% with a mean cover of 88.44% (n=88). The linear model did not show a significant relationship between canopy cover and population size at this spatial scale (ANOVA; df= (1, 86), F= 0.0034, p= 0.9536). Model 5: Non-Zero Abundance Model Without Pike County The fifth model, Model 5, was identical to Model 2- that is, all sites that had a value of zero recorded for their population size were removed. Additionally, for this model Pike county was removed. Model 5: Fifty-Meters The linear model for the fifty-meter buffer zone showed a significant relationship between population size and nearest building (p=0.03, Table 19). 50 Table 19. Results of Model 5 at the 50m buffer zone. A significant relationship was observed between population size and nearest building. There were no buildings measured at this spatial scale. R2= 0.100 Coefficients Df AIC= 394.96 Nearest Road -0.0081 1 Road Density -1594.83 1 Nearest Trail -0.00032 1 Trail Density 1002.14 1 Nearest Building 0.0087 1 Canopy Percent 8.44 1 Residuals -1.55 44 Sum Sq Mean Sq F value Pr(>F) 180.47 66.73 150.96 281.53 604.67 42.43 5036.86 180.47 66.73 150.96 281.53 604.67 42.43 114.47 1.576 0.583 1.318 2.459 5.282 0.370 0.215 0.449 0.257 0.123 0.026* 0.545 Nearest distance of roadways to sites varied within the fifty-meter buffer zone for Model 5 from 29.9m to 2,061.8m with a mean distance of 809.45m (n=52). The linear model did not find a significant result relating nearest distance of roadways to site (ANOVA; df= (1, 50), F= 1.4744, p= 0.2304). The density of roadways at this spatial scale for fifty-meters ranged from 0m/m2 to 0.009m/m2 with a mean density of 0.0001m/m2 (n=53). A linear model was run relating population size as a function of road density and did not find a significant result (ANOVA; df= (1, 51), F= 0.2047, p= 0.6529). The distance of trails ranged from 21.6m to 14,517.33m with a mean distance of 6,287.33m (n=53). There was no significant result discovered between nearest trail and population size for this spatial scale in Model 5 (ANOVA; df= (1, 51), F= 2.3026, p= 0.1353). The density of trails for Model 5 ranged from 0m/m2 to 0.227m/m2 with a mean density of 0.000613m/m2 (n=53). The linear model relating population size as a function of trail density did not show a significant relationship for this spatial scale in Model 5 (ANOVA; df= ( 1, 51), F= 3.6766, p= 0.06079). 51 The distance of nearest building to sites ranged from 78.16m to 3,311.77m with a mean distance of 953.65m (n=52). The linear model relating nearest building to population size did not show a significant relationship (ANOVA; df= (1, 50), F= 0.0574, p= 0.8117). There were no buildings measured within the fifty-meter buffer zone in Model 5. The total amount of canopy cover in Model 5 at this spatial scale ranged from 41.67% to 100% with a mean cover of 95.39% (n=52). There was no significant relationship reported between canopy cover and population size at fifty-meters (ANOVA; df= (1, 50), F= 0.2676, p= 0.6072). Model 5: Four-Hundred-Meters Within the four-hundred-meter buffer zone for Model 5 there was a significant relationship seen between nearest building and population size (p=0.04, Table 20). Table 20. The results of Model 5 at the 400m buffer zone A significant relationship was found between nearest building and population size. R2= 0.03858 AIC= 399.18 Coefficient Nearest Road -0.011 Road Density -4191.94 Nearest Trail -0.00034 Trail Density -52.31 Nearest Building 0.0092 Buildings Within 1.51 Canopy Percent 0.77 Residuals 9.54 Df 1 1 1 1 1 1 1 43 Sum Sq 180.47 169.81 119.84 14.24 564.17 53.40 0.11 5261.61 Mean Sq 180.47 169.81 119.84 14.24 564.17 53.40 0.11 122.36 F value 1.474 1.387 0.979 0.116 4.610 0.436 0.000929 Pr(>F) 0.231 0.245 0.327 0.734 0.037* 0.512 0.975 The values of nearest roadway, trail, and building did not differ between the fiftymeter and four-hundred-meter buffer zones(Appendix XXXI, Appendix XXXII). The 52 linear model relating population size as a function of nearest road for the four-hundredmeter buffer did not show a significant result (ANOVA; df= (1, 50), F= 1.4744), p=0.2304). The density of roads at this spatial scale ranged from 0m/m2 to 0.003107m/m2 with a mean density of 0.000399m/m2 (n=53). There was not a significant relationship measured between road density and population size for Model 5 at this spatial scale (ANOVA; df= (1, 51), F= 0.0446, p= 0.8336). There was no significant relationship seen between nearest trail and population size at four-hundred-meters in Model 5 (ANOVA; df= (1, 51), F= 2.3026, p= 0.1353). The density of trails within this buffer zone for Model 5 ranged from 0m/m2 to 0.00534m/m2 with a mean density of 0.000706m/m2 (n= 53). The linear model did not show a significant relationship between trail density and population size (ANOVA; df= (1, 51), F= 0.508, p= 0.4792). The linear model relating population size as a function of nearest building at fourhundred-meters for Model 5 did not show a significant relationship (ANOVA; df= (1, 50), F= 0.0574, p=0.08117). The quantity of buildings within this buffer zone ranged from zero to six with a mean quantity of 0.3269 (n=52). The linear model did not show a significant relationship between quantity of buildings and population size (ANOVA; df= (1, 50), F= 0.4601, p= 0.5007). The amount of canopy cover within the four-hundred-meter buffer zone ranged from 67.7% to 100% with a mean cover of 94.78% (n= 52). The linear model did not show a significant relationship between canopy cover and population size for this buffer zone in Model 5 (ANOVA; df= (1, 50), F= 0.2912, p= 0.5918). 53 Model 5: Five-Thousand-Meters For the last buffer zone, five-thousand-meters, within Model 5 there was a significant relationship observed between road density and population size (p= 0.03, Table 21), quantity of buildings and population size (p= 0.03, Table 21), and canopy percent and population size (p= 0.03, Table 21). Additionally, the relationship between distance to nearest building and population size was nearly significant (p= 0.05, Table 21). Distances of nearest road to each site varied from 29.9m to 2,061.8m with a mean distance of 815.31m (n= 51). The linear model that related population size as a function of distance to nearest road did not show a significant result (ANOVA; df= (1, 49), F= 1.533, p= 0.2215). The density of roadways within the five-thousand-meter buffer zone for Model 5 ranged from 0.000419m/m2 to 0.003599m/m2 with a mean density of 0.00132m/m2 (n=52). The linear model relating population size as a function of road density did not show a significant result after Bonferroni corrections (ANOVA; df= (1, 50), F= 5.1955, p= 0.02695). 54 Table 21. Results of Model 5 at the 5000m buffer zone. A significant relationship was observed between population size and road density, population size and quantity of buildings, and population size and canopy cover. R2= 0.221 AIC= 375.3433 Coefficient Nearest Road -0.0030 Road Density 21705.37 Nearest Trail -0.00024 Trail Density -227.35 Nearest Building 0.0050 Buildings Within -0.0061 Canopy Percent 78.25 Residuals -82.51 Df 1 1 1 1 1 1 1 41 Sum Sq 180.96 501.09 1.76 1.80 411.80 510.68 512.32 4216.09 Mean Sq 180.96 501.09 1.76 1.809 411.80 510.68 512.32 102.83 F value 1.759 4.872 0.017 0.017 4.004 4.966 4.982 Pr(>F) 0.191 0.032* 0.896 0.895 0.052 0.031* 0.031* The distance of trails to sites for Model 5 within the five-thousand-meter buffer zone ranged from 21.6m to 14,517.33m with a mean distance of 6,391.553m (n= 52). The linear model relating nearest trail to population size did not show a significant relationship (ANOVA; df= (1, 50), F= 2.4856, p= 0.1212). The density of roadways within this buffer zone ranged from 0m/m2 to 0.00133m/m2 with a mean density of 0.000204m/m2 (n=52). There was no significant relationship observed between trail density and population size within this buffer zone for Model 5 (ANOVA; df= (1, 50), F= 0.8757, p= 0.3539). The distance of buildings to sites within the five-thousand-meter buffer zone ranged from 78.16m to 3,311.77m with a mean distance of 962.886m (n= 51). There was no significant relation seen between nearest building and population size for this spatial scale in Model 5 (ANOVA; df= (1, 49), F= 0.0405, p= 0.8414). The total amount of buildings within this buffer zone for Model 5 varied from forty-two to seven-thousand55 five-hundred-ninety-one with a mean quantity of 1,679.34 (n= 50). The linear model that related population size as a function of total buildings did not show a significant relationship (ANOVA; df= (1, 48), F= 1.8941, p= 0.1751). The amount canopy cover within this buffer zone for Model 5 ranged from 70.13% to 97.69% with a mean cover of 89.09% (n= 51). There was no significant relationship observed between canopy cover and population size within this buffer zone (ANOVA; df= (1, 49), F= 0.0972, p= 0.7566). Model 6: Presence-Absence Model Without Pike County The last model, Model 6, was derived from Model 3 and followed a similar structure to the previous two models with Pike County removed. Model 6: Fifty-Meters The first buffer zone, fifty-meters, did not show a significant relationship between the predictor and response variables (Table 22). The distance from nearest road to sites ranged from 29.9m to 2,061.8m with a mean distance of 809.45m (n=52) for occupied sites while the distances ranged from 88.5m to 2,445.7m for unoccupied sites with a mean distance of 709.54m (n=37). The GLM did not show a significant relationship between nearest roadway and population for this spatial scale in Model 6 (GLM; df= 1, 87), Z= 0.844, p= 0.399). The density of roadways for occupied sites ranged from 0m/m2 to 0.009m/m2 with a mean density of 0.000169m/m2 (n= 53). There were no roadways measured within the fifty-meter buffer zone for Model 6. The GLM did not show a significant relationship between population and road density (GLM; df= (1, 88), Z= 0.010, p= 0.992). 56 Table 22. Results of the GLM at the 50m buffer zone for Model 6. There was no significant relationship observed between the factors and the response variable. There were no buildings measured at this spatial scale. AIC= 124.15 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Canopy Percent Residuals Coefficient -0.000019 1958.22 0.000096 1643.22 -0.00015 -1.44 1.28 Df 1 1 1 1 1 1 80 Z- Value -0.036 0.004 1.806 0.009 -0.316 -0.689 0.620 Pr (>|z|) 0.972 0.996 0.071 0.993 0.752 0.491 0.535 The distance of nearest trail to occupied sites ranged from 21.6m to 14,517.34m with a mean distance of 6,287.33m (n=53) while distance of unoccupied sites to nearest trails ranged from 87.3m to 14,538.48m with a mean distance of 4,801.33m. There was no significant relationship observed between trail density and site occupation at this spatial scale for Model 6 (GLM; df= (1, 88), Z= 1.312, p=, 0.190). The density of trails within this buffer zone ranged from 0m/m2 to 0.0227m/m2 for occupied sites with a mean density of 0.000613m/m2 (n=53). There were no trails measured within the fifty-meter buffer zone for Model 6. The GLM did not show a significant relationship between trail density and population at this spatial scale (GLM; df= (1, 88), Z= 0.014, p= 0.989). At this spatial scale, fifty-meters, distance from occupied sites to nearest building ranged from 78.16m to 3,311.77m with a mean distance of 953.65m (n=53) while the distance of unoccupied sites to nearest sites ranged from 146.76m to 2,253.4m with a mean distance of 855.11m (n=37). There was no significant relationship observed between nearest building and site occupancy at this spatial scale for Model 6 (GLM; df= 57 (1, 86), Z= 0.720, p= 0.472). There were no buildings measured within the fifty-meter buffer zone. Canopy cover for the fifty-meter buffer zone ranged from 41.6% to 100% for occupied sites with a mean cover of 95.39% (n= 52) while canopy cover ranged from 28.6% to 100% for unoccupied sites with a mean cover of 97.52% (n=37). The GLM did not show a significant relationship between canopy cover and site occupancy at this spatial scale for Model 6 (GLM; df= (1, 87), Z= -0.820, p= 0.412). Model 6: Four-Hundred-Meters At the four-hundred-meter buffer zone there was no significant relationship observed between the predictors and population, though total buildings were nearly significant (p=0.06, Table 23). There was no difference between the fifty-meter buffer zone and four-hundred-meter buffer zone regarding nearest road, nearest trail, and nearest building (Appendix XXXIV, Appendix XXXV). Table 23. Results of the GLM at the 400m buffer zone for Model 6. There was no significant relationship observed between the factors and the response variable. AIC= 121.16 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Residuals Coefficient 0.00027 556.60 0.00010 194.76 -0.00061 -0.49 -2.47 2.44 Df 1 1 1 1 1 1 1 79 58 Z- Value 0.408 1.209 1.575 0.898 -1.085 -1.919 -0.620 0.680 Pr (>|z|) 0.683 0.227 0.115 0.369 0.278 0.055 0.535 0.496 The density of roads within the four-hundred-meter buffer zone for occupied sites ranged from 0m/m2 to 0.0031m/m2 with a mean density of 0.000399m/m2 (n= 53) while the density of unoccupied sites ranged from 0m/m2 to 0.00224m/m2 with a mean density of 0.000444m/m2 (n=37). The GLM did not show a significant relationship between population and road density at this spatial scale (GLM; df= (1, 88), Z= -0.278, p= 0.781). The density of trails for occupied sites within the four-hundred-meter buffer zone ranged from 0m/m2 to 0.00534m/m2 with a mean density of 0.000706m/m2 (n= 53) while the density of trails at unoccupied sites ranged from 0m/m2 to 0.00528m/m2 with a mean density of 0.000908m/m2 (n= 37). There was no significant relationship observed between trail density and occupancy at this spatial scale for Model 6 (GLM; df= (1, 88), Z= -0.593, p= 0.5530). The quantity of buildings within the four-hundred-meter buffer zone ranged from zero to six for occupied sites with a mean quantity of 0.3269 (n= 52) while the quantity at unoccupied sites ranged from zero to sixteen with a mean quantity of 1.81 (n=37). There was no significant relationship observed between number of buildings within the buffer zone and population for Model 6 at this spatial scale after using the Bonferroni correction to account for multiple comparisons (GLM; df= (1, 87), Z= -1.981, p= 0.0476). The amount of canopy cover for occupied sites ranged from 67.7% to 100% with a mean cover of 94.78% (n= 52) while canopy cover at unoccupied sites ranged from 74.8% to 100% with a mean cover of 93.96% (n= 37). There was no significant relationship observed between canopy cover and population at this spatial scale (GLM; df= (1, 87), Z= 0.531, p= 0.595). 59 Model 6: Five-Thousand-Meters There were no significant relationships observed between the predictor variables and occupancy at the five-thousand-meter buffer zone (Table 24). The distance of nearest roadway to occupied sites varied from 29.9m to 2,061.8m with a mean distance of 815.31m (n=51) while the distance from nearest road to unoccupied sites ranged from 88.5m to 2,445.7m with a mean distance of 709.54m (n= 37). There was no significant relationship observed between nearest road and occupancy at this spatial scale for Model 6 (GLM; df= ( 1, 86), Z= 0.886, p= 0.376). The density of roadways at occupied sites ranged from 0.000419m/m2 to 0.00359m/m2 with a mean density of 0.00132m/m2 (n= 52) while the density of roadways at unoccupied sites ranged from 0.000348m/m2 to 0.004m/m2 with a mean density of 0.00139m/m2 (n=37). There was no significant relationship observed between road density and occupancy at this spatial scale for Model (GLM; df= (1, 87), Z= -0.381, p= 0.704). Table 24. Results from the GLM at the 5000m buffer zone for Model 6. There were no significant relationships observed between the factors and population. AIC= 118.15 Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Residuals Coefficient 0.00015 1408.76 0.000028 -1249.63 -0.00036 -0.00034 5.68 -5.47 Df 1 1 1 1 1 1 1 73 60 Z- Value 0.270 1.620 0.323 -1.084 -0.612 -1.507 1.059 -1.073 Pr (>|z|) 0.787 0.105 0.747 0.278 0.541 0.132 0.290 0.283 The distance of occupied sites to nearest trail ranged from 21.6m to 14,517.34m with a mean distance of 6,391.55m (n=52) while the distance from nearest trail to unoccupied sites ranged from 87.3m to 14,548.48m with a mean distance of 4,801.339m (n= 37). There was no significant relationship observed between nearest trail and occupancy at this spatial scale for Model 6 (GLM; df= (1, 87), Z= 1.396, p= 0.163). The density of trails within the five-thousand-meter buffer zone for occupied sites ranged from 0m/m2 to 0.00133m/m2 with a mean density of 0.000204m/m2 (n=52) while the density at unoccupied sites ranged from 0m/m2 to 0.00159m/m2 with a mean density of 0.000312m/m2 (n=37). There was no significant relationship observed between trail density and population at the five-thousand-meter buffer zone for Model 6 (GLM; df= (1, 87), Z= -1.374, p= 0.1695). The distance of nearest building to occupied sites ranged from 78.16m to 3,311.77m with a mean distance of 962.88m (n=51) while the distance from nearest building to unoccupied sites ranged from 146.76m to 2,253.4m with a mean distance of 837.5m (n= 32). There was no significant relationship observed between nearest building and population size at this spatial scale for Model 6. (GLM; df= (1, 81), Z= 0.864, p= 0.387). The quantity of buildings within the five-thousand-meter buffer zone for occupied sites ranged from forty-two to seven-thousand-five-hundred-ninety-one with a mean quantity of 1,679.34m (n=50). The amount of buildings within the five-thousand-meter buffer zone for unoccupied sites ranged from eighty-four to fifteen-thousand-sevenhundred-six with a mean quantity of 2,255.81 (n=32). There was no significant relationship observed between quantity of buildings and population at this spatial scale for Model 6 (GLM; df= (1, 80), Z= -0.935, p= 0.350). 61 Canopy cover within the five-thousand-meter buffer zone for Model 6 ranged from 70.13% to 97.69% for occupied sites with a mean cover of 89.09% (n=51) while the canopy cover of unoccupied sites ranged from 58.44% to 97.76% with a mean cover of 87.54% (n=37). There was no significant relationship observed between occupancy and canopy cover for this spatial scale in Model 6 (GLM; df= (1, 86), Z= 0.892, p= 0.373). Presence/Pseudo-Absence Analyses There were one-hundred total points added to the Northeast region in ArcMap®. Two points were removed from Nearest Trail as the distance from the site to the nearest trail was greater than the distance from the site to the state border. Due to the nature of how canopy was measured for the random points, the values of canopy did not change between buffer zones for random points. (Appendix XL). The distance of random points to nearest road varied from 0.8484m to 1,266.63m with a mean distance of 269.03m (n=100). A significant difference was found between nearest roads to rattlesnake sites and nearest roads to random points (ANOVA; df= (1, 214), F= 59.33, p< 0.0001). The distance of nearest trail to the random points ranged from 6.779m to 13,125.97m with a mean distance of 3,917.05m (n=98). There was not a significant difference observed between nearest trail to rattlesnake sites and nearest trail to random points (ANOVA; df= (1, 213), F= 3.874, p= 0.0503). Within the fifty-meter buffer zone the density of roadways ranged from 0m/m2 to 0.026m/m2 with a mean density of 0.0029m/m2 (n=100). There was a significant difference between the density of roadways within fifty-meters of a rattlesnake site and the density of roads within fifty-meters of the random points (ANOVA; df= (1, 216), F= 62 22.23, p< 0.0001). The density of trails at this spatial scale for random points ranged from 0m/m2 to 0.0125m/m2 with a mean density of 0.000341m/m2 (n=100). There was not a significant difference observed between the trail density within fifty-meters of a rattlesnake site and the trail density within fifty-meters of a random point (ANOVA; df= (1, 216), F= 0.204, p= 0.652). The amount of canopy cover for the fifty-meter buffer zone ranged from 0% to 100% with a mean cover of 64% (n= 100). There was a significant difference between the canopy cover of rattlesnake sites at fifty-meters and the canopy cover of random points (ANOVA; df= (1, 215), F= 44.15, p< 0.0001). The density of roadways within the four-hundred-meter buffer zone for random points ranged from 0m/m2 to 0.0213m/m2 with a mean density of 0.00285m/m2 (n=100). There was a significant difference observed between road density around rattlesnake sites at four-hundred meters and road density around the random points at four-hundredmeters (ANOVA; df= (1, 216), F= 58.33, p< 0.0001). The density of trails at this spatial scale ranged from 0m/m2 to 0.00347m/m2 with a mean density of 0.00009468m/m2 (n=100). There was a significant difference observed between trail density around rattlesnake sites and trail density around the random points (ANOVA; df= (1, 216), F= 13.74, p< 0.001). There was a significant difference observed between the canopy cover around rattlesnake sites at four-hundred-meters and canopy cover at the random points (ANOVA; df= (1, 215), F= 46.32, p< 0.0001). The density of roadways within the five-thousand-meter buffer zone for random points ranged from 0.000398m/m2 to 0.0142m/m2 with a mean density of 0.00441m/m2 (n=100). There was a significant difference observed between the road density around 63 rattlesnake sites at five-thousand-meters and the road density around random points at five-thousand meters (ANOVA; df= (1, 200), F= 129.2, p<0.0001). The density of trails at this spatial scale ranged from 0m/m2 to 0.00173m/m2 with a mean density of 0.000253m/m2 (n=100). The was no significant difference observed between trail density around rattlesnake sites at five-thousand-meters and trail density around random points at five-thousand-meters (ANOVA; df= (1, 200), F= 0.003, p= 0.954). There was a significant difference observed between canopy cover around rattlesnake sites at fivethousand-meters and canopy cover of random points (ANOVA; df= (1, 199), F= 25.56, p< 0.0001). 64 CHAPTER 4: DISCUSSION Another recently published work relating habitat to C. horridus populations was limited to using habitat suitability models to predict where populations of this species should be (Kolba, 2016). Our work on this project represents a novel method of measuring the effects of disturbance on this species at various spatial scales. Likewise, whereas the previous work explored presence data as well as habitat features, both natural and abiotic, to predict where timber rattlesnakes should be, our approach explores where members of this species have been observed and relates anthropogenic impacts to these real-world observations to assess how population estimates are impacted. This project can have real-world implications in the management of this species at large spatial scales. The following section will describe in detail the significance of the results produced by the project. The layout of the discussion will follow the same order as the results, covering each model in detail before moving on to overall conclusions and future directions. 65 Model 1: Abundance Model This model, which examined snake populations as a function of all factors in a linear model with all counties, showed a significant positive relationship between population size and distance to nearest building at the small and large spatial scales (50mand 5000m, respectively), with an additional significant negative relationship to total buildings at the large spatial scale. This relationship suggests snake populations do better at greater distances from buildings. As with all the models, R-Squared values are much higher at the large spatial scale while Akaike Information Criterion (AIC) values are lowest, suggesting that our data explains more of the variation in population size at this spatial scale. There are low quantities of trails and roads found within fifty-meters of a handful of sites with all sites in close proximity to these features containing a non-zero population of snakes (Appendix XIX). While canopy cover was not shown to be significant at any spatial scale, the pattern of the data fits the biology of the rattlesnake with canopy cover at small spatial scales having lower values than at the intermediate and large spatial scale, though the average amount of cover between the small and intermediate spatial scale did not differ very much. This suggests that snakes utilize small amounts of open habitat within this buffer zone that they would use primarily for maintenance behaviors including gestation, digestion, and ecdysis. The higher values of canopy cover at the large spatial scale suggest that forested habitat is required, likely for foraging and mate seeking behavior. There were no buildings measured at the small spatial scale, making their effect impossible to interpret. However, a negative relationship was observed between quantity of buildings and snake populations at the large spatial 66 scale, suggesting that developing land with associated structures may have a detrimental effect on populations. Model 2: Non-Zero Abundance Model Model 2 included snake abundance only within presence-sites. Within this model, all spatial scales showed a significant positive relationship between population size and distance to nearest building, again suggesting that as buildings encroach on snake habitats, snake populations become smaller. This model showed a similar pattern of higher R-squared values and lower AIC values at the large spatial scale, similarly suggesting that the observations better explain variation in snake populations at the large spatial scale. Removing the variance applied to sites with population numbers of zero does not seem to greatly change model results, with significant relationships and coefficients not differing by much. This model shows that small spatial scales have a wider range of canopy cover values with some sites having very low values at this spatial scale. Additionally, the large spatial scale seems to have much higher overall canopy cover with a smaller range of values. This reinforces the idea that forest cover seems to be more important at large spatial scales while the small spatial scale is reliant on patches of open canopy for maintenance habitat. Additionally, this model again shows that roads and trails are present close to rattlesnake populations. There was a negative relationship observed between buildings and populations at the large spatial scale, further suggesting that buildings are detrimental to snake populations at the landscape level. Model 3: Presence-Absence Model The third model used a generalized linear model with a binomial distribution and converted population size to occupancy data as a function of all factors. This model 67 showed a significant negative relationship between quantity of buildings and occupancy at the intermediate spatial scale. This model represented an interesting result in that it is the only model showing a significant relationship between these factors at this spatial scale, though it does reinforce the idea that buildings have a negative impact at larger spatial scales relative to the snake site. However, the small and large spatial scales within this model did not reveal a significant relationship and both had lower AIC values than the intermediate spatial scale, suggesting that this model better fits the data at these scales. This model also reveals that the factors are in line with what would be expected relative to presence-absence data; that is, sites where snakes are absent show higher densities of roads and trails, and have roads, trails, and buildings closer to them than sites where rattlesnakes are present (Appendix XXV, Appendix XXVI, Appendix XXVII, Appendix XXXIV, Appendix XXXV, Appendix XXXVI). This is further evidence that as human development encroaches on rattlesnake habitat, sites may become extirpated. Model 4: Abundance Model Without Pike County As mentioned previously, the next three models are mirrors of Model 1, Model 2, and Model 3 with Pike County removed. Model 4 showed a significant positive relationship between trail density and population size at the small spatial scale, which was corroborated when trail density was run individually in relation to population size. This was the only model to show a relationship with trail density and was the only model to show a significant relationship between an individual factor and population size after the critical value was corrected using the Bonferonni correction. This represents an unusual outcome in the otherwise consistent results. It may represent a Type I error in the results, incorrectly rejecting the null hypothesis. However, given the small p-value that 68 was observed here, it seems unlikely that this is true. Additionally, a significant negative relationship was observed between quantity of buildings and population size as well as a significant positive relationship between canopy cover and population size. This species, C. horridus, utilizes larger spatial scales for mate-seeking and foraging behaviors, and thus it would make sense that increased canopy and reduced quantity of buildings are favored by timber rattlesnakes. The R-squared values again suggest that the factors at the large spatial scale better explain the variation in population size. Model 5: Non-Zero Abundance Model Without Pike County This model, a repeat of Model 2 except that Pike County data was removed, showed a significant positive relationship between distance to nearest building and population size at the small and intermediate spatial scales. Additionally, the large spatial scale showed a significant positive relationship between canopy cover and population size as well as road density and population size as well as a significant negative relationship between total buildings and population size. The findings from this model corroborate observations from other models in that snake populations have an inverse relationship with distance to nearest building as well as building density around sites. Likewise, the canopy data reinforces the idea that this species needs forest habitat to disperse into. The road data here represents an interesting result in that it suggests that snake populations are higher when road density is higher. This is likely another type I error, as no other model showed a significant relationship between this factor and population size, nor is high road density congruent with the life history of timber rattlesnakes (Andrews and Gibbons, 2005). Once again, the R-squared values were much 69 higher at the large spatial scale while AIC values were lowest suggesting that more of the variation in population size can be explained by the predictors at this spatial scale. Model 6: Presence-Absence Model Without Pike County The last model was a mirror of Model 3 with Pike County removed. This model did not show any significant relationships between predictors and population size at any spatial scale, whether in one model or separate models for individual factors. Despite this absence of statistically significant relationships, some trends were present in the data. The roadway and trail data agreed with what would be expected for this species, with trails and roads being in lower densities around occupied sites and distances to these factors being higher around occupied sites. Building data for this model was in line with the other models in that occupied sites had less buildings within the buffer zones and distance to nearest building was farther for occupied sites, suggesting that building presence influences C. horridus populations. Lastly, canopy cover was higher around occupied sites at larger spatial scales while being lower at small spatial scales. This is in line with the known life history of C. horridus where habitats needed for maintenance and gestating represent small openings within largely forested regions and would be mostly visible at smaller spatial scales while at large spatial scales the habitat needed for dispersing, either for mating or feeding behavior, would dominate. Additionally, this model repeats what was shown in Model 3: roadways and trails are denser at unoccupied sites with more buildings within buffer zones, and lower overall canopy cover at unoccupied sites. 70 Presence/Pseudo-Absence Comparisons The random points were added to the map to explore whether the sites represented truly random points where snakes were observed or if there was a bias associated with locating the rattlesnake sites. Whereas the six models examined previously compare designated rattlesnake sites to one another, the present comparison asks whether they have a preference for a specific habitat type compared to random samples of habitat within the region. The random background points differed significantly from the known rattlesnake locations in terms of their factors at nearly every spatial scale except for trail density at small and large spatial scales as well as distance to nearest trail. These results suggest sampling bias associated with the rattlesnake sites which is in line with the habitat requirements of this species, specifically at the small spatial scale where open canopy is necessary for maintenance behaviors associated with basking. Individuals of this species can be easily located in springtime after exiting a den site or in the fall just before ingress for hibernation. Additionally, these results show us that C. horridus has specific habitat requirements and these sites that were explored likely represent the realized niche of the species. Due to this preference for habitat during various parts of the year this species can be more readily located if the habitat requirements are understood. Our results show that this is likely the case with our sites, at least for sites that were newly discovered during the TRAP. The bias in our data likely contributes to the low Rsquared values in our models as well as the low degree of significance that was observed. Conclusion These results present an interesting look into the forces that affect rattlesnake populations through various degrees of disturbance. The main conclusions are that, 71 among the factors examined, building density and canopy cover seem to be the main forces affecting population size at the large spatial scale. This information is reflective of the biology of the timber rattlesnake (Reinert, 1984a, 1984b) as well as the idea that buildings create disturbance zones that affect wildlife populations (Theobald et al., 1997). In nearly all models examined, buildings had a significant effect, although with a low effect size, whether it was distance to the nearest building or total quantity of buildings within the buffer zone. This information suggests that encroaching development may be detrimental to timber rattlesnake populations at the large scale level and may affect foraging habitat quality in some way, possibly by reducing the number of prey items, either through habitat loss or through the increase of meso-predators that are commensal with human settlements (Theobald et al., 1997). Additionally, anthropogenic impacts may isolate populations and reduce genetic diversity (Clark et al., 2010) while also increasing human-snake interactions, causing mortality (sensu Garber and Burger, 1995). The reduced canopy cover caused by development at the large spatial scale also likely reduces the quantity of prey items for individuals. It’s strange that few significant effects were observed between roads and population size in any model. Studies have shown that roadways have a major effect on individual snakes with high rates of mortality (Shine et al., 2004; Andrews and Gibbons, 2005; Frazer, 2005; Row et al., 2007; Clark et al., 2010). However, this could be related to one of the flaws represented in this work in that road area and road use were not accounted for. There is at least some difference in the effect of a large highway relative to that of a country dirt road. Additionally, some work has shown that hiking trails should have a measurable effect on herpetofauna populations by increasing the interactions of 72 humans and wildlife. This can result in direct mortality or collection for the pet trade, a problem which seems pervasive throughout all herpetofauna taxa (Garber and Burger, 1995; pers. obs.). As mentioned previously, timber rattlesnakes in particular are susceptible to the hazards of roadways due to their habit of relying on cryptic coloration (Andrews and Gibbons, 2005). However, the lack of significant results arising from these two predictor categories likely stems from the initial selection of survey sites, as illustrated by the comparison with pseudo-absence or background points. Specifically, rattlesnakes seem to have a preference for specific habitat types and surveyors have likely become attuned to this habitat specificity. Roadways and trails are commonly used to scout for rattlesnake habitat and this could be affecting the results of the model, since most sites will theoretically have trails or roads associated with them. Even though roads and trails did not show up as significant factors, it’s assuring to see that measurements of these features fall in line with what would be expected: roadways and trails are less dense at occupied sites, there is higher canopy cover at occupied sites, there are fewer buildings at occupied sites, and lastly buildings, trails, and roads were farther away from occupied. One explanation of the results seen here is that roadways and trails may represent a transient threat to individuals while buildings represent a more persistent threat. While interactions with vehicles undoubtedly cause mortality to wildlife crossing them, there are a number of factors that must combine to cause this mortality: snakes must encounter a road and make a conscious decision to cross, a vehicle must be traveling down the same roadway, the driver may or may not see the snake crossing the road, and finally there is a chance that drivers may decide to safely stop and allow a snake to cross a roadway if they observe one in the road. This logic follows for trailways where a snake must make the 73 decision to cross, a person must be coming down the trail at the same time, and the person must be aware of the snake’s presence on the trail which may or may not occur depending on how cryptic a snake is and the degree to which a person is searching. Alternatively, buildings, and all things they include, represent a permanent human presence in a given area. The building presence comes with a more regular human presence, along with pets that may or may not interact with wildlife, increased vehicle presence, increased impermeable surfaces, increased habitat loss, destruction, and alteration. Based on my results, these factors seem to combine to be a larger threat to snake populations, overall. Finally, due to the higher R-squared values and low AIC values seen at the large spatial scale versus the small and intermediate scales, it can be suggested that anthropogenic habitat features better explain the variation in population size at the large spatial scale. One of the drawbacks of this project was that it did not include natural abiotic factors. Due to the low R-squared values shown at the small spatial scale it seems likely that other factors are having the greatest influence on population size such as slope and slope aspect, cover objects, elevation, or temperature. Additionally, the fourhundred-meter buffer relationships do not usually agree with many of the results of the other two spatial scales. This buffer zone consistently had lower R-squared values that were reinforced by higher AIC values than either of the other spatial scales. There were several cases where the R-squared values for this spatial scale were adjusted below zero. It is likely that this spatial scale is too close in size to the small spatial scale and not large enough to capture intermediate differences where the effects of natural habitat factors drop off and the effects of anthropogenic factors pick up. 74 Running the models without Pike County seems to have improved them. In addition to high R-squared values in the last three models, AIC values also seem to be lower suggesting that the predictor variables better explain the range of response variables seen. Overall, these two indicators of how well a model fits followed a consistent pattern among models. In both model series (1-3 and 4-6) the fifty-meter buffer zone had higher AIC values and lower R-squared values while the five-thousandmeter buffer zone had lower AIC values and higher R-squared values, showing that the variance in population size and occupancy is better explained by our variables at the large spatial scales. Overall, all three models tell us different things. However, the R-squared values are highest for Model 5 while the AIC values were lowest for Model 6. This suggests that these two models explain more of the variation in population size and occupancy. However, Model 4 may be more informative when a model that accounts for zero-inflation is used. Future Goals There are several factors that should be considered in terms of future work with this project. First, anyone undertaking a follow-up study should consider adding in natural factors, such as slope, slope aspect, rock cover, elevation, etc., to see how their interactions are impacted by anthropogenic factors. Likewise, these features may contribute more to the models and may be more strongly predictive of the rattlesnake abundance. Additionally, the project should be expanded to more of the state to bolster the sample size and amplify any factors that are significant. One of the major flaws in this study is that population estimates were low at nearly every site that did not have long term monitoring, primarily due to the nature of 75 TRAP where the goal was to locate populations and not to assess their size. With better populations estimates the models shown here would have better predictive ability and would give better insight into the effect our factors are having on the population. The other TRAMP efforts, most notably mark and recapture, can likely improve population estimates and help refine our models. Additionally, it may be worth adding other buffer zones to assess just where the overlap between anthropogenic and natural factors lies. Both categories clearly influence rattlesnake populations, though our model lacks the ability to assess just where one ends and one picks up. It may be worth adding several buffers of differing size such as 750m, 1500m, and 2250m. These smaller changes in spatial scale could likely give a better picture of what is happening at the intermediate spatial scale and what may be the driving force behind population size at this level. Lastly, it may be worth modifying how the road data is used in the model. One possible change would be to use road area within a buffer zone to assess how roads relate to population size at given spatial scales. This would require road layers to include width of the roadway in addition to length. Additionally, road substrate and road use could be included as random factors in a generalized linear model. Road substrate, whether asphalt, dirt, or gravel, may affect the role that the roadways play. The amount of time that a road is used on a given day would also affect the overall impact of a roadway. A road that is traversed once or twice a day will have a strongly different impact than a road that sees constant traffic throughout the day. 76 In conclusion, our project is a stepping stone into the study of how habitat affects populations of the timber rattlesnake at the landscape level. Anthropogenic factors have a larger impact at the large spatial scale with buildings being the main driving force. Likewise, natural factors are likely the driving force behind population size at the small spatial scale. There are many ways that our work can be improved upon to narrow down how habitat features specifically affect population levels, but this project gives a glimpse into the nature of how anthropogenic features are changing rattlesnake populations. 77 WORKS CITED 78 Allender M.C., Raudabaugh D.B., Gleason F.H. and Miller A.N. 2015. 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Raw data for Model 1 at the 50m buffer zone. 86 Site County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.000 2124.90 0.000 451.1 0 0.584 Christman 1 Carbon 0 279.10 0.000 197.70 0.000 222.6 0 0.990 Christman 10 Carbon 0 151.90 0.000 1062.20 0.000 146.8 0 1.000 Christman 11 Carbon 29 397.10 0.000 21.60 0.023 78.2 0 0.662 Christman 12 Carbon 10 408.70 0.000 58.00 0.000 195.0 0 1.000 Christman 1N Carbon 2 165.70 0.000 569.10 0.000 614.9 0 1.000 Christman 2 Carbon 4 186.80 0.000 227.90 0.000 414.2 0 1.000 Christman 3 Carbon 0 854.40 0.000 88.20 0.000 158.5 0 0.931 Christman 4 Carbon 0 316.90 0.000 252.60 0.000 315.3 0 1.000 Christman 5 Carbon 0 1224.10 0.000 595.70 0.000 624.2 0 1.000 Christman 6 Carbon 1 1126.70 0.000 411.50 0.000 423.0 0 1.000 Christman 7 Carbon 0 436.60 0.000 302.80 0.000 458.6 0 1.000 Christman 8 Carbon 0 401.50 0.000 192.05 0.000 350.9 0 1.000 Christman 9 Carbon 0 588.30 0.000 393.40 0.000 489.8 0 1.000 Hell Creek Carbon 73 145.00 0.000 3037.30 0.000 1582.9 0 1.000 Hickory Run 4 Carbon 11 1544.20 0.000 552.50 0.000 2021.8 0 1.000 Hickory Run 5 Carbon 16 1101.90 0.000 86.40 0.000 89.2 0 1.000 Lehighton 1N Carbon 2 475.30 0.000 240.90 0.000 244.5 0 0.932 Nesquehoning 1 Carbon 0 297.40 0.000 2154.20 0.000 319.8 0 0.956 Tamaqua 1 Carbon 0 247.57 0.000 3289.00 0.000 978.9 0 1.000 Tamaqua 1N Carbon 0 415.50 0.000 2830.20 0.000 600.6 0 1.000 87 Weatherly 1 Carbon 0 1437.60 0.000 854.90 0.000 884.3 0 1.000 Weatherly 1N-Ribello Carbon 2 348.80 0.000 2379.90 0.000 1126.1 0 0.973 Weatherly 1N-Stan Carbon 1 219.30 0.000 162.60 0.000 322.7 0 1.000 Weatherly 2 Carbon 0 398.30 0.000 229.10 0.000 387.5 0 1.000 Weatherly 3 Carbon 1 544.20 0.000 316.30 0.000 440.4 0 1.000 Weatherly 4 Carbon 1 29.90 0.009 2393.90 0.000 1101.3 0 0.980 Weatherly 5 Carbon 0 217.40 0.000 162.80 0.000 320.7 0 1.000 Weatherly 6 Carbon 1 732.90 0.000 3411.40 0.000 660.9 0 0.998 Weatherly 7 Carbon 2 1466.50 0.000 4823.60 0.000 2034.3 0 1.000 Avoca 7 Luzerne 6 265.90 0.000 4099.60 0.000 463.7 0 1.000 Dutch Mountain 6 Luzerne 0 320.70 0.000 6494.40 0.000 2013.9 0 1.000 Hickory Run 1-Koval Luzerne 0 238.80 0.000 1199.50 0.000 606.1 0 1.000 Hickory Run 2- Koval Luzerne 31 966.60 0.000 1260.40 0.000 1114.2 0 1.000 Hickory Run 3- Koval Luzerne 2 583.00 0.000 944.50 0.000 525.8 0 0.912 Nanticoke 1N Luzerne 4 1110.80 0.000 2009.10 0.000 1253.6 0 1.000 Pittston 1 Luzerne 0 363.80 0.000 4098.00 0.000 151.6 0 1.000 Pittston 2 Luzerne 0 258.00 0.000 3557.40 0.000 158.4 0 1.000 Pittston 3 Luzerne 0 632.70 0.000 5916.00 0.000 858.0 0 0.960 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.000 3289.90 0.000 1312.7 0 0.286 Red Rock 2 Luzerne 0 1116.90 0.000 194.80 0.000 1637.0 0 1.000 Red Rock 3 Luzerne 0 766.10 0.000 89.30 0.000 767.4 0 0.985 Sweet Valley 1 Luzerne 0 430.60 0.000 6257.30 0.000 2253.4 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.000 87.30 0.000 1945.0 0 1.000 Wilkes Barre East 1 Luzerne 0 811.20 0.000 5878.10 0.000 733.1 0 1.000 Mount Pocono 1N Monroe 1 620.80 0.000 64.80 0.000 694.1 0 1.000 Mount Pocono 2N Monroe 4 827.40 0.000 25.00 0.010 700.7 0 0.880 Pocono Pines 1N Monroe 3 Stroudsburg 2N Monroe 5 Lake Maskenozha 1N Pike 3 Milford 1N Pike 3 Narrowsburg 1N Pike 21 907.56 0.000 3311.77 0.000 0.000 51.20 0.000 504.80 0.000 2424.60 0.000 377.60 0.000 377.60 0.000 0.000 3311.8 0 0 1.000 472.5 0 0.912 491.5 0 1.000 0 1.000 0.000 88 Narrowsburg 2 Pike 3 390.80 0.000 6085.00 0.000 269.9 0 0.925 Narrowsburg 2N Pike 5 367.30 0.000 8573.00 0.000 1115.1 0 1.000 Narrowsburg 3 Pike 1 59.10 0.000 6611.90 0.000 1522.1 0 0.864 Pecks Pond 1N Pike 2 286.10 0.000 3.40 0.028 195.0 0 0.715 Pond Eddy 1N Pike 8 764.00 0.000 5170.00 0.000 703.8 0 0.961 Pond Eddy 2N Pike 2 174.50 0.000 5259.00 0.000 179.7 0 0.898 Pond Eddy 3N Pike 3 554.20 0.000 5595.00 0.000 369.5 0 0.898 Port Jervis North 1 Pike 2 507.00 0.000 6698.00 0.000 421.1 0 1.000 Promised Land 1 Pike 9 108.20 0.000 109.10 0.000 2184.0 0 1.000 Promised Land 2N Pike 16 546.60 0.000 547.90 0.000 1350.9 0 0.513 Promised Land 3N Pike 5 1275.80 0.000 691.70 0.000 1973.0 0 1.000 Rowland 1 Pike 4 122.60 0.000 257.40 0.000 95.2 0 1.000 Rowland 1N Pike 5 959.07 0.000 959.07 0.000 876.2 0 0.815 Rowland 2N Pike 3 1181.50 0.000 1600.20 0.000 745.2 0 0.838 Rowland 3N Pike 6 928.30 0.000 1657.50 0.000 1367.7 0 0.916 Rowland 4N Pike 6 1078.90 0.000 1759.40 0.000 1319.4 0 1.000 Shohola 1N Pike 5 212.60 0.000 2718.50 0.000 51.5 0 0.945 Shohola 2 Pike 0 409.60 0.000 1788.40 0.000 389.0 0 1.000 Shohola 2N Pike 4 32.60 0.005 3442.60 0.000 379.2 0 0.700 Shohola 3 Pike 0 157.60 0.000 6371.50 0.000 98.7 0 0.860 Shohola 4 Pike 3 1319.30 0.000 1319.30 0.000 1019.5 0 1.000 Shohola 4N Pike 3 589.60 0.000 2076.00 0.000 1166.5 0 0.869 Shohola 5 Pike 3 1244.30 0.000 1246.50 0.000 1322.7 0 1.000 Shohola 3N Pike 2 604.20 0.000 4604.30 0.000 623.9 0 0.883 Twelvemile Pond 1N Pike 2 507.30 0.000 1232.70 0.000 505.9 0 0.621 Great Bend 1 Susquehanna 0 640.70 0.000 2560.10 0.000 800.1 0 1.000 89 Starrucca 1 Susquehanna 0 251.40 0.000 251.40 0.000 273.1 0 1.000 Susquehanna 1N Susquehanna 2 510.90 0.000 867.80 0.000 482.5 0 0.999 White Mills 1 Wayne 3 252.20 0.000 4747.60 0.000 889.6 0 1.000 White Mills 1N Wayne 3 106.70 0.000 4904.10 0.000 1117.4 0 0.417 White Mills 2 Wayne 3 573.20 0.000 6118.60 0.000 607.3 0 1.000 Dutch Mountain 1 Wyoming 3 534.50 0.000 12447.34 0.000 561.0 0 1.000 Dutch Mountain 1N Wyoming 1 1508.54 0.000 14033.38 0.000 1430.0 0 0.997 Dutch Mountain 2 Wyoming 0 1231.58 0.000 14548.48 0.000 1018.2 0 1.000 Dutch Mountain 2N Wyoming 7 618.80 0.000 14517.34 0.000 557.8 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.000 12580.00 0.000 353.6 0 1.000 Dutch Mountain 3N Wyoming 3 1119.80 0.000 13554.38 0.000 1157.6 0 1.000 Dutch Mountain 4 Wyoming 3 444.90 0.000 7862.37 0.000 1932.7 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.000 8452.92 0.000 946.9 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.000 13997.25 0.000 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.000 12393.23 0.000 2026.7 0 0.878 Jenningsville 2N Wyoming 3 1502.40 0.000 12400.73 0.000 1335.3 0 0.741 Meshoppen 1 Wyoming 0 414.20 0.000 8617.82 0.000 426.6 0 1.000 Meshoppen 1N Wyoming 2 831.80 0.000 8763.22 0.000 414.5 0 1.000 Meshoppen 2N Wyoming 6 202.60 0.000 10486.80 0.000 261.4 0 1.000 Noxen Wyoming 0 2203.10 0.000 13030.57 0.000 2108.5 0 0.976 Noxen 1 Wyoming 4 1328.50 0.000 9656.14 0.000 1428.6 0 1.000 Noxen 10 Wyoming 6 1025.90 0.000 10649.31 0.000 911.2 0 1.000 Noxen 10N Wyoming 4 1702.40 0.000 11205.63 0.000 1716.0 0 1.000 Noxen 1N Wyoming 3 1273.90 0.000 12692.77 0.000 1318.2 0 1.000 Noxen 2 Wyoming 4 911.60 0.000 13087.59 0.000 558.0 0 1.000 Noxen 2N Wyoming 3 1317.10 0.000 12992.57 0.000 1312.3 0 1.000 90 Noxen 3 Wyoming 0 1320.80 0.000 13011.71 0.000 1328.3 0 1.000 Noxen 3N Wyoming 2 1335.40 0.000 12391.78 0.000 1343.5 0 1.000 Noxen 4 Wyoming 2 711.20 0.000 9559.28 0.000 593.5 0 1.000 Noxen 4N Wyoming 1 1882.00 0.000 14165.07 0.000 1704.5 0 1.000 Noxen 5 Wyoming 0 2445.70 0.000 14364.49 0.000 1497.4 0 1.000 Noxen 5N Wyoming 4 955.30 0.000 12570.92 0.000 925.9 0 1.000 Noxen 6N Wyoming 2 934.80 0.000 10707.81 0.000 521.0 0 1.000 Noxen 7 Wyoming 0 1649.60 0.000 12814.95 0.000 1535.8 0 1.000 Noxen 7N Wyoming 3 1625.90 0.000 11145.25 0.000 1575.3 0 1.000 Noxen 8 Wyoming 8 1280.70 0.000 12783.07 0.000 1249.5 0 0.934 Noxen 8N Wyoming 4 299.40 0.000 8074.57 0.000 1071.0 0 1.000 Noxen 9 Wyoming 0 1746.90 0.000 13753.02 0.000 1800.8 0 1.000 Noxen 9N Wyoming 2 296.00 0.000 12346.09 0.000 358.8 0 1.000 Tunkannock 1N Wyoming 2 244.80 0.000 3519.70 0.000 364.5 0 0.721 Appendix II. Raw data for Model 1 at the 400m buffer zone. 91 Site County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.929 Christman 1 Carbon 0 279.10 0.0006 197.70 0.0047 222.55 2 0.819 Christman 10 Carbon 0 151.90 0.0019 1062.20 0.0000 146.76 10 0.975 Christman 11 Carbon 29 397.10 0.0001 21.60 0.0048 78.16 3 0.902 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0047 194.96 2 0.915 Christman 1N Carbon 2 165.70 0.0028 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0015 227.90 0.0011 414.20 0 0.932 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0045 158.52 2 0.925 Christman 4 Carbon 0 316.90 0.0003 252.60 0.0028 315.33 2 0.929 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 0.994 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0023 458.56 0 0.951 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0035 350.89 2 0.896 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0006 489.78 0 0.929 Hell Creek Carbon 73 145.00 0.0006 3037.30 0.0000 1582.90 0 0.991 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 0.951 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0052 89.18 2 0.908 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0039 244.48 1 0.932 Nesquehoning 1 Carbon 0 297.40 0.0016 2154.20 0.0000 319.82 13 0.799 Tamaqua 1 Carbon 0 247.57 0.0003 3289.00 0.0000 978.90 0 0.930 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 0.741 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 0.996 Weatherly 1N-Ribello Carbon 2 348.80 0.0006 2379.90 0.0000 1126.11 0 0.997 Weatherly 1N-Stan Carbon 1 219.30 0.0013 162.60 0.0053 322.65 6 0.869 92 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0031 387.50 1 0.898 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0019 440.40 0 0.926 Weatherly 4 Carbon 1 29.90 0.0018 2393.90 0.0000 1101.32 0 0.988 Weatherly 5 Carbon 0 217.40 0.0013 162.80 0.0053 320.73 6 0.868 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.999 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 0.999 Avoca 7 Luzerne 6 265.90 0.0014 4099.60 0.0000 463.70 0 0.883 Dutch Mountain 6 Luzerne 0 320.70 0.0009 6494.40 0.0000 2013.90 0 0.997 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0000 606.10 0 0.889 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 0.796 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.677 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 0.888 Pittston 1 Luzerne 0 363.80 0.0007 4098.00 0.0000 151.60 16 0.927 Pittston 2 Luzerne 0 258.00 0.0016 3557.40 0.0000 158.40 9 0.791 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.829 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.902 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0024 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0018 767.40 0 1.000 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0022 87.30 0.0016 1945.00 0 0.994 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 0.938 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0053 694.10 0 0.998 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0035 700.70 0 0.995 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0017 Lake Maskenozha 1N Pike 3 0.0000 2424.60 0.0000 504.80 0.984 472.50 0 0.990 Milford 1N Pike 3 377.60 0.0003 377.60 93 0 0.990 Narrowsburg 1N Pike 21 0 1.000 Narrowsburg 2 Pike 3 390.80 0.0002 6085.00 0.0000 269.90 1 0.961 Narrowsburg 2N Pike 5 367.30 0.0009 8573.00 0.0000 1115.10 0 0.971 Narrowsburg 3 Pike 1 59.10 0.0026 6611.90 0.0000 1522.10 0 0.825 Pecks Pond 1N Pike 2 286.10 Pond Eddy 1N Pike 8 764.00 0.0006 3.40 0.0042 195.00 8 0.940 0.0000 5170.00 0.0000 703.80 0 0.998 Pond Eddy 2N Pike 2 174.50 0.0012 5259.00 0.0000 179.70 5 0.937 Pond Eddy 3N Pike 3 554.20 0.0000 5595.00 0.0000 369.50 1 0.991 Port Jervis North 1 Pike 2 507.00 0.0000 6698.00 0.0000 421.10 0 1.000 Promised Land 1 Pike 9 108.20 0.0012 109.10 0.0013 2184.00 0 0.956 Promised Land 2N Pike 16 546.60 0.0000 547.90 0.0000 1350.90 0 0.927 Promised Land 3N Pike 5 1275.80 0.0000 691.70 0.0000 1973.00 0 0.999 0.0000 0.0003 491.50 0.0000 Rowland 1 Pike 4 122.60 0.0024 257.40 0.0008 95.20 6 0.937 Rowland 1N Pike 5 959.07 0.0000 959.07 0.0000 876.20 0 0.978 Rowland 2N Pike 3 1181.50 0.0000 1600.20 0.0000 745.20 0 0.976 Rowland 3N Pike 6 928.30 0.0000 1657.50 0.0000 1367.70 0 0.997 Rowland 4N Pike 6 1078.90 0.0000 1759.40 0.0000 1319.40 0 0.998 Shohola 1N Pike 5 212.60 0.0019 2718.50 0.0000 51.50 12 0.928 Shohola 2 Pike 0 409.60 0.0000 1788.40 0.0000 389.00 1 0.995 Shohola 2N Pike 4 32.60 0.0011 3442.60 0.0000 379.20 2 0.953 Shohola 3 Pike 0 157.60 0.0036 6371.50 0.0000 98.70 14 0.638 Shohola 4 Pike 3 1319.30 0.0000 1319.30 0.0000 1019.50 0 0.984 Shohola 4N Pike 3 589.60 0.0000 2076.00 0.0000 1166.50 0 0.995 Shohola 5 Pike 3 1244.30 0.0000 1246.50 0.0000 1322.70 0 0.998 Shohola 3N Pike 2 604.20 0.0000 4604.30 0.0000 623.90 0 0.981 94 Twelvemile Pond 1N Pike 2 507.30 0.0000 1232.70 0.0000 505.90 0 0.938 Great Bend 1 Susquehanna 0 640.70 0.0000 2560.10 0.0000 800.10 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0010 251.40 0.0010 273.10 3 0.900 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.735 White Mills 1 Wayne 3 252.20 0.0027 4747.60 0.0000 889.60 0 0.923 White Mills 1N Wayne 3 106.70 0.0031 4904.10 0.0000 1117.40 0 0.772 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 0.993 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 0.964 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.976 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0014 12580.00 0.0000 353.60 1 0.981 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 0.992 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0004 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0010 13997.25 0.0000 0 0.995 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.983 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.987 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 0.989 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 0.968 Meshoppen 2N Wyoming 6 202.60 0.0027 10486.80 0.0000 261.40 1 0.988 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.992 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 0.990 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 0.998 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 0.999 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 0.988 95 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 0.998 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 0.996 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 0.998 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 0.984 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.911 Noxen 8N Wyoming 4 299.40 0.0011 8074.57 0.0000 1071.00 0 0.901 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 0.996 Noxen 9N Wyoming 2 296.00 0.0002 12346.09 0.0000 358.80 1 0.907 Tunkannock 1N Wyoming 2 244.80 0.0011 3519.70 0.0000 364.50 1 0.878 Appendix III. Raw data for Model 1 at the 5000m buffer zone. 96 Site County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0026 2124.90 0.0005 451.14 4034 0.872 Christman 1 Carbon 0 279.10 0.0013 197.70 0.0014 222.50 512 0.882 Christman 10 Carbon 0 151.90 0.0022 1062.20 0.0002 146.76 2601 0.833 Christman 11 Carbon 29 397.10 0.0018 21.60 0.0007 78.16 3167 0.921 Christman 12 Carbon 10 408.70 0.0011 58.00 0.0008 194.96 581 0.890 Christman 1N Carbon 2 165.70 0.0009 569.10 0.0009 614.90 787 0.893 Christman 2 Carbon 4 186.80 0.0013 227.90 0.0006 414.20 726 0.887 Christman 3 Carbon 0 854.40 0.0010 88.20 0.0009 158.50 471 0.862 Christman 4 Carbon 0 316.90 0.0016 252.60 0.0008 315.33 1238 0.884 Christman 5 Carbon 0 1224.10 0.0009 595.70 0.0008 624.19 606 0.930 Christman 6 Carbon 1 1126.70 0.0016 411.50 0.0007 423.00 2195 0.918 Christman 7 Carbon 0 436.60 0.0016 302.80 0.0006 458.56 2298 0.928 Christman 8 Carbon 0 401.50 0.0016 192.05 0.0016 350.89 2683 0.933 Christman 9 Carbon 0 588.30 0.0016 393.40 0.0007 489.78 2738 0.929 Hell Creek Carbon 73 145.00 0.0036 3037.30 0.0001 1582.90 5239 0.850 Hickory Run 4 Carbon 11 1544.20 0.0007 552.50 0.0003 2021.80 554 0.901 Hickory Run 5 Carbon 16 1101.90 0.0013 86.40 0.0013 89.10 424 0.884 Lehighton 1N Carbon 2 475.30 0.0032 240.90 0.0008 244.48 7373 0.741 Nesquehoning 1 Carbon 0 297.40 0.0021 2154.20 0.0001 319.82 5193 0.818 Tamaqua 1 Carbon 0 247.57 0.0021 3289.00 0.0000 0.802 Tamaqua 1N Carbon 0 415.50 0.0021 2830.20 0.0000 0.808 Weatherly 1 Carbon 0 1437.60 0.0009 854.90 0.0007 884.28 518 0.927 Weatherly 1N-Ribello Carbon 2 348.80 0.0016 2379.90 0.0005 1126.11 2764 0.922 Weatherly 1N-Stan Carbon 1 219.30 0.0020 162.60 0.0007 322.65 3921 0.908 97 Weatherly 2 Carbon 0 398.30 0.0016 229.10 0.0006 387.50 2682 0.933 Weatherly 3 Carbon 1 544.20 0.0016 316.30 0.0007 440.40 2723 0.930 Weatherly 4 Carbon 1 29.90 0.0017 2393.90 0.0005 1101.32 2734 0.909 Weatherly 5 Carbon 0 217.40 0.0020 162.80 0.0007 320.73 3917 0.908 Weatherly 6 Carbon 1 732.90 0.0015 3411.40 0.0002 660.86 1585 0.893 Weatherly 7 Carbon 2 1466.50 0.0014 4823.60 0.0000 2034.26 1701 0.853 Avoca 7 Luzerne 6 265.90 0.0006 4099.60 0.0001 463.70 483 0.905 Dutch Mountain 6 Luzerne 0 320.70 0.0006 6494.40 0.0000 2013.90 121 0.963 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0004 606.10 1021 0.875 Hickory Run 2- Koval Luzerne 31 966.60 0.0008 1260.40 0.0002 1114.20 812 0.909 Hickory Run 3- Koval Luzerne 2 583.00 0.0008 944.50 0.0002 525.80 574 0.908 Nanticoke 1N Luzerne 4 1110.80 0.0023 2009.10 0.0001 1253.60 5986 0.726 Pittston 1 Luzerne 0 363.80 0.0040 4098.00 0.0000 151.60 15706 0.584 Pittston 2 Luzerne 0 258.00 0.0030 3557.40 0.0001 158.40 9643 0.621 Pittston 3 Luzerne 0 632.70 0.0025 5916.00 0.0000 858.00 3735 0.704 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0014 3289.90 0.0001 1312.70 934 0.902 Red Rock 2 Luzerne 0 1116.90 0.0008 194.80 0.0005 0.920 Red Rock 3 Luzerne 0 766.10 0.0011 89.30 0.0004 0.905 Sweet Valley 1 Luzerne 0 430.60 0.0006 6257.30 0.0000 2253.40 118 0.964 Sweet Valley 2 Luzerne 0 88.50 0.0006 87.30 0.0004 1945.00 84 0.944 Wilkes Barre East 1 Luzerne 0 811.20 0.0030 5878.10 0.0000 733.10 9805 0.711 Mount Pocono 1N Monroe 1 620.80 0.0034 64.80 0.0001 694.10 7057 0.820 Mount Pocono 2N Monroe 4 827.40 0.0035 25.00 0.0001 700.70 7591 0.818 Pocono Pines 1N Monroe 3 907.56 0.0027 3311.77 0.0000 3311.77 4727 Stroudsburg 2N Monroe 5 0.0018 51.20 0.0003 Lake Maskenozha 1N Pike 3 0.0027 2424.60 0.0001 504.80 0.795 472.50 3967 0.874 98 Milford 1N Pike 3 377.60 0.0023 377.60 0.0001 491.50 2105 0.879 Pecks Pond 1N Pike 2 286.10 0.0021 3.40 0.0005 195.00 3395 0.870 Promised Land 1 Pike 9 108.20 0.0007 109.10 0.0006 2184.00 397 0.921 Promised Land 2N Pike 16 546.60 0.0007 547.90 0.0007 1350.90 427 0.919 Promised Land 3N Pike 5 1275.80 0.0010 691.70 0.0011 1973.00 561 0.886 Rowland 3N Pike 6 928.30 0.0011 1657.50 0.0001 1367.70 683 0.903 Rowland 4N Pike 6 1078.90 0.0011 1759.40 0.0001 1319.40 675 0.902 Shohola 4 Pike 3 1319.30 0.0018 1319.30 0.0001 1019.50 1314 0.879 Shohola 4N Pike 3 589.60 0.0018 2076.00 0.0001 1166.50 1309 0.892 Shohola 5 Pike 3 1244.30 0.0018 1246.50 0.0001 1322.70 1378 0.878 Shohola 3N Pike 2 604.20 0.0016 4604.30 0.0000 623.90 1217 0.917 Twelvemile Pond 1N Pike 2 507.30 0.0018 1232.70 0.0002 505.90 4167 0.898 Great Bend 1 Susquehanna 0 640.70 0.0021 2560.10 0.0001 800.10 1850 0.807 Starrucca 1 Susquehanna 0 251.40 0.0014 251.40 0.0003 273.10 588 0.770 White Mills 1 Wayne 3 252.20 0.0016 4747.60 0.0000 889.60 1654 0.798 White Mills 1N Wayne 3 106.70 0.0016 4904.10 0.0000 1117.40 1554 0.814 White Mills 2 Wayne 3 573.20 0.0015 6118.60 0.0000 607.30 1469 0.849 Dutch Mountain 1 Wyoming 3 534.50 0.0006 12447.34 0.0000 561.00 271 0.934 Dutch Mountain 1N Wyoming 1 1508.54 0.0004 14033.38 0.0000 1430.01 142 0.968 Dutch Mountain 2 Wyoming 0 1231.58 0.0004 14548.48 0.0000 1018.17 151 0.976 Dutch Mountain 2N Wyoming 7 618.80 0.0004 14517.34 0.0000 557.80 156 0.973 Dutch Mountain 3 Wyoming 0 163.60 0.0003 12580.00 0.0000 353.60 94 0.978 Dutch Mountain 3N Wyoming 3 1119.80 0.0005 13554.38 0.0000 1157.60 218 0.956 Dutch Mountain 4 Wyoming 3 444.90 0.0005 7862.37 0.0000 1932.70 42 0.964 Dutch Mountain 5 Wyoming 0 385.50 0.0006 8452.92 0.0000 946.90 88 0.965 Jenningsville 1 Wyoming 0 259.10 0.0007 13997.25 0.0000 0.904 Jenningsville 1N Wyoming 1 2061.80 0.0007 12393.23 0.0000 2026.70 318 0.925 Jenningsville 2N Wyoming 3 1502.40 0.0006 12400.73 0.0000 1335.30 Meshoppen 1 Wyoming 0 414.20 0.0013 8617.82 0.0000 426.60 402 0.798 Meshoppen 1N Wyoming 2 831.80 0.0013 8763.22 0.0000 414.50 455 0.778 Meshoppen 2N Wyoming 6 202.60 0.0009 10486.80 0.0000 261.40 334 0.870 0.913 99 Noxen Wyoming 0 2203.10 0.0007 13030.57 0.0000 2108.50 655 0.937 Noxen 1 Wyoming 4 1328.50 0.0007 9656.14 0.0000 1428.60 247 0.925 Noxen 10 Wyoming 6 1025.90 0.0008 10649.31 0.0000 911.20 268 0.958 Noxen 10N Wyoming 4 1702.40 0.0008 11205.63 0.0000 1716.00 344 0.961 Noxen 1N Wyoming 3 1273.90 0.0008 12692.77 0.0000 1318.20 439 0.955 Noxen 2 Wyoming 4 911.60 0.0005 13087.59 0.0000 558.00 243 0.949 Noxen 2N Wyoming 3 1317.10 0.0008 12992.57 0.0000 1312.30 624 0.929 Noxen 3 Wyoming 0 1320.80 0.0005 13011.71 0.0000 1328.30 216 0.961 Noxen 3N Wyoming 2 1335.40 0.0005 12391.78 0.0000 1343.50 208 0.968 Noxen 4 Wyoming 2 711.20 0.0008 9559.28 0.0000 593.50 477 0.914 Noxen 4N Wyoming 1 1882.00 0.0004 14165.07 0.0000 1704.50 154 0.977 Noxen 5 Wyoming 0 2445.70 0.0004 14364.49 0.0000 1497.40 183 0.976 Noxen 5N Wyoming 4 955.30 0.0012 12570.92 0.0000 925.90 912 0.881 Noxen 6N Wyoming 2 934.80 0.0005 10707.81 0.0000 521.00 224 0.947 Noxen 7 Wyoming 0 1649.60 0.0008 12814.95 0.0000 1535.80 738 0.914 Noxen 7N Wyoming 3 1625.90 0.0008 11145.25 0.0000 1575.30 363 0.959 Noxen 8 Wyoming 8 1280.70 0.0010 12783.07 0.0000 1249.50 865 0.897 Noxen 8N Wyoming 4 299.40 0.0010 8074.57 0.0000 1071.00 427 0.894 Noxen 9 Wyoming 0 1746.90 0.0007 13753.02 0.0000 1800.80 597 0.936 Noxen 9N Wyoming 2 296.00 0.0014 12346.09 0.0000 358.80 1152 0.832 Tunkannock 1N Wyoming 2 244.80 0.0024 3519.70 0.0001 364.50 2669 0.701 Appendix IV. Raw data for Model 2 at the 50m buffer zone. 100 Site County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.584 Christman 11 Carbon 29 397.10 0.0000 21.60 0.0228 78.16 0 0.662 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0000 194.96 0 1.000 Christman 1N Carbon 2 165.70 0.0000 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0000 227.90 0.0000 414.20 0 1.000 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Hell Creek Carbon 73 145.00 0.0000 3037.30 0.0000 1582.90 0 1.000 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 1.000 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0000 89.18 0 1.000 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0000 244.48 0 0.932 Weatherly 1N-Ribello Carbon 2 348.80 0.0000 2379.90 0.0000 1126.11 0 0.973 Weatherly 1N-Stan Carbon 1 219.30 0.0000 162.60 0.0000 322.65 0 1.000 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0000 440.40 0 1.000 Weatherly 4 Carbon 1 29.90 0.0090 2393.90 0.0000 1101.32 0 0.980 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.998 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 1.000 Avoca 7 Luzerne 6 265.90 0.0000 4099.60 0.0000 463.70 0 1.000 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 1.000 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.912 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 1.000 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0000 694.10 0 1.000 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0097 700.70 0 0.880 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0000 0 1.000 Lake Maskenozha 1N Pike 3 504.80 0.0000 2424.60 0.0000 472.50 0 0.912 Milford 1N Pike 3 377.60 0.0000 377.60 0.0000 491.50 0 1.000 Narrowsburg 1N Pike 21 0 1.000 Narrowsburg 2 Pike 3 390.80 0.0000 6085.00 0.0000 269.90 0 0.925 Narrowsburg 2N Pike 5 367.30 0.0000 8573.00 0.0000 1115.10 0 1.000 0.0000 0.0000 101 Narrowsburg 3 Pike 1 59.10 0.0000 6611.90 0.0000 1522.10 0 0.864 Pecks Pond 1N Pike 2 286.10 0.0000 3.40 0.0285 195.00 0 0.715 Pond Eddy 1N Pike 8 764.00 0.0000 5170.00 0.0000 703.80 0 0.961 Pond Eddy 2N Pike 2 174.50 0.0000 5259.00 0.0000 179.70 0 0.898 Pond Eddy 3N Pike 3 554.20 0.0000 5595.00 0.0000 369.50 0 0.898 Port Jervis North 1 Pike 2 507.00 0.0000 6698.00 0.0000 421.10 0 1.000 Promised Land 1 Pike 9 108.20 0.0000 109.10 0.0000 2184.00 0 1.000 Promised Land 2N Pike 16 546.60 0.0000 547.90 0.0000 1350.90 0 0.513 Promised Land 3N Pike 5 1275.80 0.0000 691.70 0.0000 1973.00 0 1.000 Rowland 1 Pike 4 122.60 0.0000 257.40 0.0000 95.20 0 1.000 Rowland 1N Pike 5 959.07 0.0000 959.07 0.0000 876.20 0 0.815 Rowland 2N Pike 3 1181.50 0.0000 1600.20 0.0000 745.20 0 0.838 Rowland 3N Pike 6 928.30 0.0000 1657.50 0.0000 1367.70 0 0.916 Rowland 4N Pike 6 1078.90 0.0000 1759.40 0.0000 1319.40 0 1.000 Shohola 1N Pike 5 212.60 0.0000 2718.50 0.0000 51.50 0 0.945 Shohola 2N Pike 4 32.60 0.0051 3442.60 0.0000 379.20 0 0.700 Shohola 4 Pike 3 1319.30 0.0000 1319.30 0.0000 1019.50 0 1.000 Shohola 4N Pike 3 589.60 0.0000 2076.00 0.0000 1166.50 0 0.869 Shohola 5 Pike 3 1244.30 0.0000 1246.50 0.0000 1322.70 0 1.000 Shohola 3N Pike 2 604.20 0.0000 4604.30 0.0000 623.90 0 0.883 Twelvemile Pond 1N Pike 2 507.30 0.0000 1232.70 0.0000 505.90 0 0.621 102 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.999 White Mills 1 Wayne 3 252.20 0.0000 4747.60 0.0000 889.60 0 1.000 White Mills 1N Wayne 3 106.70 0.0000 4904.10 0.0000 1117.40 0 0.417 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 1.000 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 1.000 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.997 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 1.000 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.878 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.741 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 1.000 Meshoppen 2N Wyoming 6 202.60 0.0000 10486.80 0.0000 261.40 0 1.000 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 1.000 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 1.000 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 1.000 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 1.000 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 1.000 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 1.000 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 1.000 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 1.000 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.934 Noxen 8N Wyoming 4 299.40 0.0000 8074.57 0.0000 1071.00 0 1.000 Noxen 9N Wyoming 2 296.00 0.0000 12346.09 0.0000 358.80 0 1.000 Tunkannock 1N Wyoming 2 244.80 0.0000 3519.70 0.0000 364.50 0 0.721 103 Appendix V. Raw data for Model 2 at the 400m buffer zone. 104 Site County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.929 Christman 11 Carbon 29 397.10 0.0001 21.60 0.0048 78.16 3 0.902 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0047 194.96 2 0.915 Christman 1N Carbon 2 165.70 0.0028 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0015 227.90 0.0011 414.20 0 0.932 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Hell Creek Carbon 73 145.00 0.0006 3037.30 0.0000 1582.90 0 0.991 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 0.951 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0052 89.18 2 0.908 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0039 244.48 1 0.932 Weatherly 1N-Ribello Carbon 2 348.80 0.0006 2379.90 0.0000 1126.11 0 0.997 Weatherly 1N-Stan Carbon 1 219.30 0.0013 162.60 0.0053 322.65 6 0.869 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0019 440.40 0 0.926 Weatherly 4 Carbon 1 29.90 0.0018 2393.90 0.0000 1101.32 0 0.988 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.999 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 0.999 Avoca 7 Luzerne 6 265.90 0.0014 4099.60 0.0000 463.70 0 0.883 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 0.796 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.677 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 0.888 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0053 694.10 0 0.998 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0035 700.70 0 0.995 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0017 0.984 Lake Maskenozha 1N Pike 3 504.80 0.0000 2424.60 0.0000 472.50 0 0.990 Milford 1N Pike 3 377.60 0.0003 377.60 0.0003 491.50 0 0.990 Narrowsburg 1N Pike 21 0 1.000 Narrowsburg 2 Pike 3 390.80 0.0002 6085.00 0.0000 269.90 1 0.961 Narrowsburg 2N Pike 5 367.30 0.0009 8573.00 0.0000 1115.10 0 0.971 0.0000 0.0000 105 Narrowsburg 3 Pike 1 59.10 0.0026 6611.90 0.0000 1522.10 0 0.825 Pecks Pond 1N Pike 2 286.10 0.0006 3.40 0.0042 195.00 8 0.940 Pond Eddy 1N Pike 8 764.00 0.0000 5170.00 0.0000 703.80 0 0.998 Pond Eddy 2N Pike 2 174.50 0.0012 5259.00 0.0000 179.70 5 0.937 Pond Eddy 3N Pike 3 554.20 0.0000 5595.00 0.0000 369.50 1 0.991 Port Jervis North 1 Pike 2 507.00 0.0000 6698.00 0.0000 421.10 0 1.000 Promised Land 1 Pike 9 108.20 0.0012 109.10 0.0013 2184.00 0 0.956 Promised Land 2N Pike 16 546.60 0.0000 547.90 0.0000 1350.90 0 0.927 Promised Land 3N Pike 5 1275.80 0.0000 691.70 0.0000 1973.00 0 0.999 Rowland 1 Pike 4 122.60 0.0024 257.40 0.0008 95.20 6 0.937 Rowland 1N Pike 5 959.07 0.0000 959.07 0.0000 876.20 0 0.978 Rowland 2N Pike 3 1181.50 0.0000 1600.20 0.0000 745.20 0 0.976 Rowland 3N Pike 6 928.30 0.0000 1657.50 0.0000 1367.70 0 0.997 Rowland 4N Pike 6 1078.90 0.0000 1759.40 0.0000 1319.40 0 0.998 Shohola 1N Pike 5 212.60 0.0019 2718.50 0.0000 51.50 12 0.928 Shohola 2N Pike 4 32.60 0.0011 3442.60 0.0000 379.20 2 0.953 Shohola 4 Pike 3 1319.30 0.0000 1319.30 0.0000 1019.50 0 0.984 Shohola 4N Pike 3 589.60 0.0000 2076.00 0.0000 1166.50 0 0.995 Shohola 5 Pike 3 1244.30 0.0000 1246.50 0.0000 1322.70 0 0.998 Shohola 3N Pike 2 604.20 0.0000 4604.30 0.0000 623.90 0 0.981 Twelvemile Pond 1N Pike 2 507.30 0.0000 1232.70 0.0000 505.90 0 0.938 106 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.735 White Mills 1 Wayne 3 252.20 0.0027 4747.60 0.0000 889.60 0 0.923 White Mills 1N Wayne 3 106.70 0.0031 4904.10 0.0000 1117.40 0 0.772 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 0.993 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 0.964 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.976 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 0.992 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.983 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.987 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 0.968 Meshoppen 2N Wyoming 6 202.60 0.0027 10486.80 0.0000 261.40 1 0.988 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 0.990 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 0.998 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 0.999 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 0.988 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 0.998 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 0.996 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 0.998 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 0.984 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.911 Noxen 8N Wyoming 4 299.40 0.0011 8074.57 0.0000 1071.00 0 0.901 Noxen 9N Wyoming 2 296.00 0.0002 12346.09 0.0000 358.80 1 0.907 Tunkannock 1N Wyoming 2 244.80 0.0011 3519.70 0.0000 364.50 1 0.878 107 Appendix VI. Raw data for Model 2 at the 5000m buffer zone. 108 Site County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0026 2124.90 0.0005 451.14 4034 0.872 Christman 11 Carbon 29 397.10 0.0018 21.60 0.0007 78.16 3167 0.921 Christman 12 Carbon 10 408.70 0.0011 58.00 0.0008 194.96 581 0.890 Christman 1N Carbon 2 165.70 0.0009 569.10 0.0009 614.90 787 0.893 Christman 2 Carbon 4 186.80 0.0013 227.90 0.0006 414.20 726 0.887 Christman 6 Carbon 1 1126.70 0.0016 411.50 0.0007 423.00 2195 0.918 Hell Creek Carbon 73 145.00 0.0036 3037.30 0.0001 1582.90 5239 0.850 Hickory Run 4 Carbon 11 1544.20 0.0007 552.50 0.0003 2021.80 554 0.901 Hickory Run 5 Carbon 16 1101.90 0.0013 86.40 0.0013 89.10 424 0.884 Lehighton 1N Carbon 2 475.30 0.0032 240.90 0.0008 244.48 7373 0.741 Weatherly 1N-Ribello Carbon 2 348.80 0.0016 2379.90 0.0005 1126.11 2764 0.922 Weatherly 1N-Stan Carbon 1 219.30 0.0020 162.60 0.0007 322.65 3921 0.908 Weatherly 3 Carbon 1 544.20 0.0016 316.30 0.0007 440.40 2723 0.930 Weatherly 4 Carbon 1 29.90 0.0017 2393.90 0.0005 1101.32 2734 0.909 Weatherly 6 Carbon 1 732.90 0.0015 3411.40 0.0002 660.86 1585 0.893 Weatherly 7 Carbon 2 1466.50 0.0014 4823.60 0.0000 2034.26 1701 0.853 Avoca 7 Luzerne 6 265.90 0.0006 4099.60 0.0001 463.70 483 0.905 Hickory Run 2- Koval Luzerne 31 966.60 0.0008 1260.40 0.0002 1114.20 812 0.909 Hickory Run 3- Koval Luzerne 2 583.00 0.0008 944.50 0.0002 525.80 574 0.908 Nanticoke 1N Luzerne 4 1110.80 0.0023 2009.10 0.0001 1253.60 5986 0.726 Mount Pocono 1N Monroe 1 620.80 0.0034 64.80 0.0001 694.10 7057 0.820 Mount Pocono 2N Monroe 4 827.40 0.0035 25.00 0.0001 700.70 7591 0.818 Pocono Pines 1N Monroe 3 907.56 0.0027 3311.77 0.0000 3311.77 4727 Stroudsburg 2N Monroe 5 0.0018 51.20 0.0003 0.795 109 Lake Maskenozha 1N Pike 3 504.80 0.0027 2424.60 0.0001 472.50 3967 0.874 Milford 1N Pike 3 377.60 0.0023 377.60 0.0001 491.50 2105 0.879 Pecks Pond 1N Pike 2 286.10 0.0021 3.40 0.0005 195.00 3395 0.870 Promised Land 1 Pike 9 108.20 0.0007 109.10 0.0006 2184.00 397 0.921 Promised Land 2N Pike 16 546.60 0.0007 547.90 0.0007 1350.90 427 0.919 Promised Land 3N Pike 5 1275.80 0.0010 691.70 0.0011 1973.00 561 0.886 Rowland 3N Pike 6 928.30 0.0011 1657.50 0.0001 1367.70 683 0.903 Rowland 4N Pike 6 1078.90 0.0011 1759.40 0.0001 1319.40 675 0.902 Shohola 4 Pike 3 1319.30 0.0018 1319.30 0.0001 1019.50 1314 0.879 Shohola 4N Pike 3 589.60 0.0018 2076.00 0.0001 1166.50 1309 0.892 Shohola 5 Pike 3 1244.30 0.0018 1246.50 0.0001 1322.70 1378 0.878 Shohola 3N Pike 2 604.20 0.0016 4604.30 0.0000 623.90 1217 0.917 Twelvemile Pond 1N Pike 2 507.30 0.0018 1232.70 0.0002 505.90 4167 0.898 White Mills 1 Wayne 3 252.20 0.0016 4747.60 0.0000 889.60 1654 0.798 White Mills 1N Wayne 3 106.70 0.0016 4904.10 0.0000 1117.40 1554 0.814 White Mills 2 Wayne 3 573.20 0.0015 6118.60 0.0000 607.30 1469 0.849 Dutch Mountain 1 Wyoming 3 534.50 0.0006 12447.34 0.0000 561.00 271 0.934 Dutch Mountain 1N Wyoming 1 1508.54 0.0004 14033.38 0.0000 1430.01 142 0.968 Dutch Mountain 2N Wyoming 7 618.80 0.0004 14517.34 0.0000 557.80 156 0.973 Dutch Mountain 3N Wyoming 3 1119.80 0.0005 13554.38 0.0000 1157.60 218 0.956 Dutch Mountain 4 Wyoming 3 444.90 0.0005 7862.37 0.0000 1932.70 42 0.964 Jenningsville 1N Wyoming 1 2061.80 0.0007 12393.23 0.0000 2026.70 318 0.925 Jenningsville 2N Wyoming 3 1502.40 0.0006 12400.73 0.0000 1335.30 Meshoppen 1N Wyoming 2 831.80 0.0013 8763.22 0.0000 414.50 455 0.778 Meshoppen 2N Wyoming 6 202.60 0.0009 10486.80 0.0000 261.40 334 0.870 Noxen 1 Wyoming 4 1328.50 0.0007 9656.14 0.0000 1428.60 247 0.925 0.913 110 Noxen 10 Wyoming 6 1025.90 0.0008 10649.31 0.0000 911.20 268 0.958 Noxen 10N Wyoming 4 1702.40 0.0008 11205.63 0.0000 1716.00 344 0.961 Noxen 1N Wyoming 3 1273.90 0.0008 12692.77 0.0000 1318.20 439 0.955 Noxen 2 Wyoming 4 911.60 0.0005 13087.59 0.0000 558.00 243 0.949 Noxen 2N Wyoming 3 1317.10 0.0008 12992.57 0.0000 1312.30 624 0.929 Noxen 3N Wyoming 2 1335.40 0.0005 12391.78 0.0000 1343.50 208 0.968 Noxen 4 Wyoming 2 711.20 0.0008 9559.28 0.0000 593.50 477 0.914 Noxen 4N Wyoming 1 1882.00 0.0004 14165.07 0.0000 1704.50 154 0.977 Noxen 5N Wyoming 4 955.30 0.0012 12570.92 0.0000 925.90 912 0.881 Noxen 6N Wyoming 2 934.80 0.0005 10707.81 0.0000 521.00 224 0.947 Noxen 7N Wyoming 3 1625.90 0.0008 11145.25 0.0000 1575.30 363 0.959 Noxen 8 Wyoming 8 1280.70 0.0010 12783.07 0.0000 1249.50 865 0.897 Noxen 8N Wyoming 4 299.40 0.0010 8074.57 0.0000 1071.00 427 0.894 Noxen 9N Wyoming 2 296.00 0.0014 12346.09 0.0000 358.80 1152 0.832 Tunkannock 1N Wyoming 2 244.80 0.0024 3519.70 0.0001 364.50 2669 0.701 Appendix VII. Raw data for Model 3 at the 50m buffer zone where Number of Snakes (Population) has been changed to presence (1) absence(0) data. 111 Site County Population Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 1 523.70 0.0000 2124.90 0.0000 451.14 0 0.584 Christman 1 Carbon 0 279.10 0.0000 197.70 0.0000 222.55 0 0.990 Christman 10 Carbon 0 151.90 0.0000 1062.20 0.0000 146.76 0 1.000 Christman 11 Carbon 1 397.10 0.0000 21.60 0.0228 78.16 0 0.662 Christman 12 Carbon 1 408.70 0.0000 58.00 0.0000 194.96 0 1.000 Christman 1N Carbon 1 165.70 0.0000 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 1 186.80 0.0000 227.90 0.0000 414.20 0 1.000 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0000 158.52 0 0.931 Christman 4 Carbon 0 316.90 0.0000 252.60 0.0000 315.33 0 1.000 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 1.000 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0000 458.56 0 1.000 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0000 350.89 0 1.000 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0000 489.78 0 1.000 Hell Creek Carbon 1 145.00 0.0000 3037.30 0.0000 1582.90 0 1.000 Hickory Run 4 Carbon 1 1544.20 0.0000 552.50 0.0000 2021.82 0 1.000 Hickory Run 5 Carbon 1 1101.90 0.0000 86.40 0.0000 89.18 0 1.000 Lehighton 1N Carbon 1 475.30 0.0000 240.90 0.0000 244.48 0 0.932 Nesquehoning 1 Carbon 0 297.40 0.0000 2154.20 0.0000 319.82 0 0.956 Tamaqua 1 Carbon 0 247.57 0.0000 3289.00 0.0000 978.90 0 1.000 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 1.000 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 1.000 Weatherly 1N-Ribello Carbon 1 348.80 0.0000 2379.90 0.0000 1126.11 0 0.973 112 Weatherly 1N-Stan Carbon 1 219.30 0.0000 162.60 0.0000 322.65 0 1.000 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0000 387.50 0 1.000 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0000 440.40 0 1.000 Weatherly 4 Carbon 1 29.90 0.0090 2393.90 0.0000 1101.32 0 0.980 Weatherly 5 Carbon 0 217.40 0.0000 162.80 0.0000 320.73 0 1.000 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.998 Weatherly 7 Carbon 1 1466.50 0.0000 4823.60 0.0000 2034.26 0 1.000 Avoca 7 Luzerne 1 265.90 0.0000 4099.60 0.0000 463.70 0 1.000 Dutch Mountain 6 Luzerne 0 320.70 0.0000 6494.40 0.0000 2013.90 0 1.000 Hickory Run 1-Koval Luzerne 0 238.80 0.0000 1199.50 0.0000 606.10 0 1.000 Hickory Run 2- Koval Luzerne 1 966.60 0.0000 1260.40 0.0000 1114.20 0 1.000 Hickory Run 3- Koval Luzerne 1 583.00 0.0000 944.50 0.0000 525.80 0 0.912 Nanticoke 1N Luzerne 1 1110.80 0.0000 2009.10 0.0000 1253.60 0 1.000 Pittston 1 Luzerne 0 363.80 0.0000 4098.00 0.0000 151.60 0 1.000 Pittston 2 Luzerne 0 258.00 0.0000 3557.40 0.0000 158.40 0 1.000 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.960 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.286 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0000 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0000 767.40 0 0.985 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0000 87.30 0.0000 1945.00 0 1.000 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 1.000 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0000 694.10 0 1.000 Mount Pocono 2N Monroe 1 827.40 0.0000 25.00 0.0097 700.70 0 0.880 Pocono Pines 1N Monroe 1 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 1 0.0000 51.20 0.0000 0 1.000 Lake Maskenozha 1N Pike 1 504.80 0.0000 2424.60 0.0000 472.50 0 0.912 Milford 1N Pike 1 377.60 0.0000 377.60 0.0000 491.50 0 1.000 Narrowsburg 1N Pike 1 0 1.000 Narrowsburg 2 Pike 1 390.80 0.0000 6085.00 0.0000 269.90 0 0.925 Narrowsburg 2N Pike 1 367.30 0.0000 8573.00 0.0000 1115.10 0 1.000 0.0000 0.0000 113 Narrowsburg 3 Pike 1 59.10 0.0000 6611.90 0.0000 1522.10 0 0.864 Pecks Pond 1N Pike 1 286.10 0.0000 3.40 0.0285 195.00 0 0.715 Pond Eddy 1N Pike 1 764.00 0.0000 5170.00 0.0000 703.80 0 0.961 Pond Eddy 2N Pike 1 174.50 0.0000 5259.00 0.0000 179.70 0 0.898 Pond Eddy 3N Pike 1 554.20 0.0000 5595.00 0.0000 369.50 0 0.898 Port Jervis North 1 Pike 1 507.00 0.0000 6698.00 0.0000 421.10 0 1.000 Promised Land 1 Pike 1 108.20 0.0000 109.10 0.0000 2184.00 0 1.000 Promised Land 2N Pike 1 546.60 0.0000 547.90 0.0000 1350.90 0 0.513 Promised Land 3N Pike 1 1275.80 0.0000 691.70 0.0000 1973.00 0 1.000 Rowland 1 Pike 1 122.60 0.0000 257.40 0.0000 95.20 0 1.000 Rowland 1N Pike 1 959.07 0.0000 959.07 0.0000 876.20 0 0.815 Rowland 2N Pike 1 1181.50 0.0000 1600.20 0.0000 745.20 0 0.838 Rowland 3N Pike 1 928.30 0.0000 1657.50 0.0000 1367.70 0 0.916 Rowland 4N Pike 1 1078.90 0.0000 1759.40 0.0000 1319.40 0 1.000 Shohola 1N Pike 1 212.60 0.0000 2718.50 0.0000 51.50 0 0.945 Shohola 2 Pike 0 409.60 0.0000 1788.40 0.0000 389.00 0 1.000 Shohola 2N Pike 1 32.60 0.0051 3442.60 0.0000 379.20 0 0.700 Shohola 3 Pike 0 157.60 0.0000 6371.50 0.0000 98.70 0 0.860 Shohola 4 Pike 1 1319.30 0.0000 1319.30 0.0000 1019.50 0 1.000 Shohola 4N Pike 1 589.60 0.0000 2076.00 0.0000 1166.50 0 0.869 Shohola 5 Pike 1 1244.30 0.0000 1246.50 0.0000 1322.70 0 1.000 Shohola 3N Pike 1 604.20 0.0000 4604.30 0.0000 623.90 0 0.883 Twelvemile Pond 1N Pike 1 507.30 0.0000 1232.70 0.0000 505.90 0 0.621 Great Bend 1 Susquehanna 0 640.70 0.0000 2560.10 0.0000 800.10 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0000 251.40 0.0000 273.10 0 1.000 Susquehanna 1N Susquehanna 1 510.90 0.0000 867.80 0.0000 482.50 0 0.999 114 White Mills 1 Wayne 1 252.20 0.0000 4747.60 0.0000 889.60 0 1.000 White Mills 1N Wayne 1 106.70 0.0000 4904.10 0.0000 1117.40 0 0.417 White Mills 2 Wayne 1 573.20 0.0000 6118.60 0.0000 607.30 0 1.000 Dutch Mountain 1 Wyoming 1 534.50 0.0000 12447.34 0.0000 561.00 0 1.000 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.997 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 1 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0000 12580.00 0.0000 353.60 0 1.000 Dutch Mountain 3N Wyoming 1 1119.80 0.0000 13554.38 0.0000 1157.60 0 1.000 Dutch Mountain 4 Wyoming 1 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0000 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0000 13997.25 0.0000 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.878 Jenningsville 2N Wyoming 1 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.741 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 1.000 Meshoppen 1N Wyoming 1 831.80 0.0000 8763.22 0.0000 414.50 0 1.000 Meshoppen 2N Wyoming 1 202.60 0.0000 10486.80 0.0000 261.40 0 1.000 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.976 Noxen 1 Wyoming 1 1328.50 0.0000 9656.14 0.0000 1428.60 0 1.000 Noxen 10 Wyoming 1 1025.90 0.0000 10649.31 0.0000 911.20 0 1.000 Noxen 10N Wyoming 1 1702.40 0.0000 11205.63 0.0000 1716.00 0 1.000 Noxen 1N Wyoming 1 1273.90 0.0000 12692.77 0.0000 1318.20 0 1.000 Noxen 2 Wyoming 1 911.60 0.0000 13087.59 0.0000 558.00 0 1.000 Noxen 2N Wyoming 1 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 1 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 115 Noxen 4 Wyoming 1 711.20 0.0000 9559.28 0.0000 593.50 0 1.000 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 1.000 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 1 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 1 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 1 1625.90 0.0000 11145.25 0.0000 1575.30 0 1.000 Noxen 8 Wyoming 1 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.934 Noxen 8N Wyoming 1 299.40 0.0000 8074.57 0.0000 1071.00 0 1.000 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 1.000 Noxen 9N Wyoming 1 296.00 0.0000 12346.09 0.0000 358.80 0 1.000 Tunkannock 1N Wyoming 1 244.80 0.0000 3519.70 0.0000 364.50 0 0.721 Appendix VIII. Raw data for Model 3 at the 400m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. 116 Site County Population Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 1 523.70 0.0000 2124.90 0.0000 451.14 0 0.929 Christman 1 Carbon 0 279.10 0.0006 197.70 0.0047 222.55 2 0.819 Christman 10 Carbon 0 151.90 0.0019 1062.20 0.0000 146.76 10 0.975 Christman 11 Carbon 1 397.10 0.0001 21.60 0.0048 78.16 3 0.902 Christman 12 Carbon 1 408.70 0.0000 58.00 0.0047 194.96 2 0.915 Christman 1N Carbon 1 165.70 0.0028 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 1 186.80 0.0015 227.90 0.0011 414.20 0 0.932 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0045 158.52 2 0.925 Christman 4 Carbon 0 316.90 0.0003 252.60 0.0028 315.33 2 0.929 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 0.994 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0023 458.56 0 0.951 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0035 350.89 2 0.896 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0006 489.78 0 0.929 Hell Creek Carbon 1 145.00 0.0006 3037.30 0.0000 1582.90 0 0.991 Hickory Run 4 Carbon 1 1544.20 0.0000 552.50 0.0000 2021.82 0 0.951 Hickory Run 5 Carbon 1 1101.90 0.0000 86.40 0.0052 89.18 2 0.908 Lehighton 1N Carbon 1 475.30 0.0000 240.90 0.0039 244.48 1 0.932 Nesquehoning 1 Carbon 0 297.40 0.0016 2154.20 0.0000 319.82 13 0.799 Tamaqua 1 Carbon 0 247.57 0.0003 3289.00 0.0000 978.90 0 0.930 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 0.741 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 0.996 Weatherly 1N-Ribello Carbon 1 348.80 0.0006 2379.90 0.0000 1126.11 0 0.997 117 Weatherly 1N-Stan Carbon 1 219.30 0.0013 162.60 0.0053 322.65 6 0.869 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0031 387.50 1 0.898 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0019 440.40 0 0.926 Weatherly 4 Carbon 1 29.90 0.0018 2393.90 0.0000 1101.32 0 0.988 Weatherly 5 Carbon 0 217.40 0.0013 162.80 0.0053 320.73 6 0.868 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.999 Weatherly 7 Carbon 1 1466.50 0.0000 4823.60 0.0000 2034.26 0 0.999 Avoca 7 Luzerne 1 265.90 0.0014 4099.60 0.0000 463.70 0 0.883 Dutch Mountain 6 Luzerne 0 320.70 0.0009 6494.40 0.0000 2013.90 0 0.997 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0000 606.10 0 0.889 Hickory Run 2- Koval Luzerne 1 966.60 0.0000 1260.40 0.0000 1114.20 0 0.796 Hickory Run 3- Koval Luzerne 1 583.00 0.0000 944.50 0.0000 525.80 0 0.677 Nanticoke 1N Luzerne 1 1110.80 0.0000 2009.10 0.0000 1253.60 0 0.888 Pittston 1 Luzerne 0 363.80 0.0007 4098.00 0.0000 151.60 16 0.927 Pittston 2 Luzerne 0 258.00 0.0016 3557.40 0.0000 158.40 9 0.791 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.829 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.902 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0024 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0018 767.40 0 1.000 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0022 87.30 0.0016 1945.00 0 0.994 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 0.938 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0053 694.10 0 0.998 Mount Pocono 2N Monroe 1 827.40 0.0000 25.00 0.0035 700.70 0 0.995 Pocono Pines 1N Monroe 1 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 1 0.0000 51.20 0.0017 0.984 Lake Maskenozha 1N Pike 1 504.80 0.0000 2424.60 0.0000 472.50 0 0.990 Milford 1N Pike 1 377.60 0.0003 377.60 0.0003 491.50 0 0.990 Narrowsburg 1N Pike 1 0 1.000 Narrowsburg 2 Pike 1 390.80 0.0002 6085.00 0.0000 269.90 1 0.961 Narrowsburg 2N Pike 1 367.30 0.0009 8573.00 0.0000 1115.10 0 0.971 0.0000 0.0000 118 Narrowsburg 3 Pike 1 59.10 0.0026 6611.90 0.0000 1522.10 0 0.825 Pecks Pond 1N Pike 1 286.10 0.0006 3.40 0.0042 195.00 8 0.940 Pond Eddy 1N Pike 1 764.00 0.0000 5170.00 0.0000 703.80 0 0.998 Pond Eddy 2N Pike 1 174.50 0.0012 5259.00 0.0000 179.70 5 0.937 Pond Eddy 3N Pike 1 554.20 0.0000 5595.00 0.0000 369.50 1 0.991 Port Jervis North 1 Pike 1 507.00 0.0000 6698.00 0.0000 421.10 0 1.000 Promised Land 1 Pike 1 108.20 0.0012 109.10 0.0013 2184.00 0 0.956 Promised Land 2N Pike 1 546.60 0.0000 547.90 0.0000 1350.90 0 0.927 Promised Land 3N Pike 1 1275.80 0.0000 691.70 0.0000 1973.00 0 0.999 Rowland 1 Pike 1 122.60 0.0024 257.40 0.0008 95.20 6 0.937 Rowland 1N Pike 1 959.07 0.0000 959.07 0.0000 876.20 0 0.978 Rowland 2N Pike 1 1181.50 0.0000 1600.20 0.0000 745.20 0 0.976 Rowland 3N Pike 1 928.30 0.0000 1657.50 0.0000 1367.70 0 0.997 Rowland 4N Pike 1 1078.90 0.0000 1759.40 0.0000 1319.40 0 0.998 Shohola 1N Pike 1 212.60 0.0019 2718.50 0.0000 51.50 12 0.928 Shohola 2 Pike 0 409.60 0.0000 1788.40 0.0000 389.00 1 0.995 Shohola 2N Pike 1 32.60 0.0011 3442.60 0.0000 379.20 2 0.953 Shohola 3 Pike 0 157.60 0.0036 6371.50 0.0000 98.70 14 0.638 Shohola 4 Pike 1 1319.30 0.0000 1319.30 0.0000 1019.50 0 0.984 Shohola 4N Pike 1 589.60 0.0000 2076.00 0.0000 1166.50 0 0.995 Shohola 5 Pike 1 1244.30 0.0000 1246.50 0.0000 1322.70 0 0.998 Shohola 3N Pike 1 604.20 0.0000 4604.30 0.0000 623.90 0 0.981 Twelvemile Pond 1N Pike 1 507.30 0.0000 1232.70 0.0000 505.90 0 0.938 Great Bend 1 Susquehanna 0 640.70 0.0000 2560.10 0.0000 800.10 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0010 251.40 0.0010 273.10 3 0.900 Susquehanna 1N Susquehanna 1 510.90 0.0000 867.80 0.0000 482.50 0 0.735 119 White Mills 1 Wayne 1 252.20 0.0027 4747.60 0.0000 889.60 0 0.923 White Mills 1N Wayne 1 106.70 0.0031 4904.10 0.0000 1117.40 0 0.772 White Mills 2 Wayne 1 573.20 0.0000 6118.60 0.0000 607.30 0 0.993 Dutch Mountain 1 Wyoming 1 534.50 0.0000 12447.34 0.0000 561.00 0 0.964 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.976 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 1 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0014 12580.00 0.0000 353.60 1 0.981 Dutch Mountain 3N Wyoming 1 1119.80 0.0000 13554.38 0.0000 1157.60 0 0.992 Dutch Mountain 4 Wyoming 1 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0004 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0010 13997.25 0.0000 0 0.995 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.983 Jenningsville 2N Wyoming 1 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.987 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 0.989 Meshoppen 1N Wyoming 1 831.80 0.0000 8763.22 0.0000 414.50 0 0.968 Meshoppen 2N Wyoming 1 202.60 0.0027 10486.80 0.0000 261.40 1 0.988 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.992 Noxen 1 Wyoming 1 1328.50 0.0000 9656.14 0.0000 1428.60 0 0.990 Noxen 10 Wyoming 1 1025.90 0.0000 10649.31 0.0000 911.20 0 0.998 Noxen 10N Wyoming 1 1702.40 0.0000 11205.63 0.0000 1716.00 0 0.999 Noxen 1N Wyoming 1 1273.90 0.0000 12692.77 0.0000 1318.20 0 0.988 Noxen 2 Wyoming 1 911.60 0.0000 13087.59 0.0000 558.00 0 0.998 Noxen 2N Wyoming 1 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 1 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 120 Noxen 4 Wyoming 1 711.20 0.0000 9559.28 0.0000 593.50 0 0.996 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 0.998 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 1 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 1 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 1 1625.90 0.0000 11145.25 0.0000 1575.30 0 0.984 Noxen 8 Wyoming 1 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.911 Noxen 8N Wyoming 1 299.40 0.0011 8074.57 0.0000 1071.00 0 0.901 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 0.996 Noxen 9N Wyoming 1 296.00 0.0002 12346.09 0.0000 358.80 1 0.907 Tunkannock 1N Wyoming 1 244.80 0.0011 3519.70 0.0000 364.50 1 0.878 Appendix IX. Raw data for Model 3 at the 5000m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. 121 Site County Population Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 1 523.70 0.0026 2124.90 0.0005 451.14 4034 0.872 Christman 1 Carbon 0 279.10 0.0013 197.70 0.0014 222.50 512 0.882 Christman 10 Carbon 0 151.90 0.0022 1062.20 0.0002 146.76 2601 0.833 Christman 11 Carbon 1 397.10 0.0018 21.60 0.0007 78.16 3167 0.921 Christman 12 Carbon 1 408.70 0.0011 58.00 0.0008 194.96 581 0.890 Christman 1N Carbon 1 165.70 0.0009 569.10 0.0009 614.90 787 0.893 Christman 2 Carbon 1 186.80 0.0013 227.90 0.0006 414.20 726 0.887 Christman 3 Carbon 0 854.40 0.0010 88.20 0.0009 158.50 471 0.862 Christman 4 Carbon 0 316.90 0.0016 252.60 0.0008 315.33 1238 0.884 Christman 5 Carbon 0 1224.10 0.0009 595.70 0.0008 624.19 606 0.930 Christman 6 Carbon 1 1126.70 0.0016 411.50 0.0007 423.00 2195 0.918 Christman 7 Carbon 0 436.60 0.0016 302.80 0.0006 458.56 2298 0.928 Christman 8 Carbon 0 401.50 0.0016 192.05 0.0016 350.89 2683 0.933 Christman 9 Carbon 0 588.30 0.0016 393.40 0.0007 489.78 2738 0.929 Hell Creek Carbon 1 145.00 0.0036 3037.30 0.0001 1582.90 5239 0.850 Hickory Run 4 Carbon 1 1544.20 0.0007 552.50 0.0003 2021.80 554 0.901 Hickory Run 5 Carbon 1 1101.90 0.0013 86.40 0.0013 89.10 424 0.884 Lehighton 1N Carbon 1 475.30 0.0032 240.90 0.0008 244.48 7373 0.741 Nesquehoning 1 Carbon 0 297.40 0.0021 2154.20 0.0001 319.82 5193 0.818 Tamaqua 1 Carbon 0 247.57 0.0021 3289.00 0.0000 0.802 Tamaqua 1N Carbon 0 415.50 0.0021 2830.20 0.0000 0.808 Weatherly 1 Carbon 0 1437.60 0.0009 854.90 0.0007 884.28 518 0.927 Weatherly 1N-Ribello Carbon 1 348.80 0.0016 2379.90 0.0005 1126.11 2764 0.922 122 Weatherly 1N-Stan Carbon 1 219.30 0.0020 162.60 0.0007 322.65 3921 0.908 Weatherly 2 Carbon 0 398.30 0.0016 229.10 0.0006 387.50 2682 0.933 Weatherly 3 Carbon 1 544.20 0.0016 316.30 0.0007 440.40 2723 0.930 Weatherly 4 Carbon 1 29.90 0.0017 2393.90 0.0005 1101.32 2734 0.909 Weatherly 5 Carbon 0 217.40 0.0020 162.80 0.0007 320.73 3917 0.908 Weatherly 6 Carbon 1 732.90 0.0015 3411.40 0.0002 660.86 1585 0.893 Weatherly 7 Carbon 1 1466.50 0.0014 4823.60 0.0000 2034.26 1701 0.853 Avoca 7 Luzerne 1 265.90 0.0006 4099.60 0.0001 463.70 483 0.905 Dutch Mountain 6 Luzerne 0 320.70 0.0006 6494.40 0.0000 2013.90 121 0.963 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0004 606.10 1021 0.875 Hickory Run 2- Koval Luzerne 1 966.60 0.0008 1260.40 0.0002 1114.20 812 0.909 Hickory Run 3- Koval Luzerne 1 583.00 0.0008 944.50 0.0002 525.80 574 0.908 Nanticoke 1N Luzerne 1 1110.80 0.0023 2009.10 0.0001 1253.60 5986 0.726 Pittston 1 Luzerne 0 363.80 0.0040 4098.00 0.0000 151.60 15706 0.584 Pittston 2 Luzerne 0 258.00 0.0030 3557.40 0.0001 158.40 9643 0.621 Pittston 3 Luzerne 0 632.70 0.0025 5916.00 0.0000 858.00 3735 0.704 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0014 3289.90 0.0001 1312.70 934 0.902 Red Rock 2 Luzerne 0 1116.90 0.0008 194.80 0.0005 0.920 Red Rock 3 Luzerne 0 766.10 0.0011 89.30 0.0004 0.905 Sweet Valley 1 Luzerne 0 430.60 0.0006 6257.30 0.0000 2253.40 118 0.964 Sweet Valley 2 Luzerne 0 88.50 0.0006 87.30 0.0004 1945.00 84 0.944 Wilkes Barre East 1 Luzerne 0 811.20 0.0030 5878.10 0.0000 733.10 9805 0.711 Mount Pocono 1N Monroe 1 620.80 0.0034 64.80 0.0001 694.10 7057 0.820 Mount Pocono 2N Monroe 1 827.40 0.0035 25.00 0.0001 700.70 7591 0.818 Pocono Pines 1N Monroe 1 907.56 0.0027 3311.77 0.0000 3311.77 4727 Stroudsburg 2N Monroe 1 0.0018 51.20 0.0003 0.795 123 Lake Maskenozha 1N Pike 1 504.80 0.0027 2424.60 0.0001 472.50 3967 0.874 Milford 1N Pike 1 377.60 0.0023 377.60 0.0001 491.50 2105 0.879 Pecks Pond 1N Pike 1 286.10 0.0021 3.40 0.0005 195.00 3395 0.870 Promised Land 1 Pike 1 108.20 0.0007 109.10 0.0006 2184.00 397 0.921 Promised Land 2N Pike 1 546.60 0.0007 547.90 0.0007 1350.90 427 0.919 Promised Land 3N Pike 1 1275.80 0.0010 691.70 0.0011 1973.00 561 0.886 Rowland 3N Pike 1 928.30 0.0011 1657.50 0.0001 1367.70 683 0.903 Rowland 4N Pike 1 1078.90 0.0011 1759.40 0.0001 1319.40 675 0.902 Shohola 4 Pike 1 1319.30 0.0018 1319.30 0.0001 1019.50 1314 0.879 Shohola 4N Pike 1 589.60 0.0018 2076.00 0.0001 1166.50 1309 0.892 Shohola 5 Pike 1 1244.30 0.0018 1246.50 0.0001 1322.70 1378 0.878 Shohola 3N Pike 1 604.20 0.0016 4604.30 0.0000 623.90 1217 0.917 Twelvemile Pond 1N Pike 1 507.30 0.0018 1232.70 0.0002 505.90 4167 0.898 Great Bend 1 Susquehanna 0 640.70 0.0021 2560.10 0.0001 800.10 1850 0.807 Starrucca 1 Susquehanna 0 251.40 0.0014 251.40 0.0003 273.10 588 0.770 White Mills 1 Wayne 1 252.20 0.0016 4747.60 0.0000 889.60 1654 0.798 White Mills 1N Wayne 1 106.70 0.0016 4904.10 0.0000 1117.40 1554 0.814 White Mills 2 Wayne 1 573.20 0.0015 6118.60 0.0000 607.30 1469 0.849 Dutch Mountain 1 Wyoming 1 534.50 0.0006 12447.34 0.0000 561.00 271 0.934 Dutch Mountain 1N Wyoming 1 1508.54 0.0004 14033.38 0.0000 1430.01 142 0.968 Dutch Mountain 2 Wyoming 0 1231.58 0.0004 14548.48 0.0000 1018.17 151 0.976 Dutch Mountain 2N Wyoming 1 618.80 0.0004 14517.34 0.0000 557.80 156 0.973 Dutch Mountain 3 Wyoming 0 163.60 0.0003 12580.00 0.0000 353.60 94 0.978 Dutch Mountain 3N Wyoming 1 1119.80 0.0005 13554.38 0.0000 1157.60 218 0.956 Dutch Mountain 4 Wyoming 1 444.90 0.0005 7862.37 0.0000 1932.70 42 0.964 Dutch Mountain 5 Wyoming 0 385.50 0.0006 8452.92 0.0000 946.90 88 0.965 124 Jenningsville 1 Wyoming 0 259.10 0.0007 13997.25 0.0000 0.904 Jenningsville 1N Wyoming 1 2061.80 0.0007 12393.23 0.0000 2026.70 Jenningsville 2N Wyoming 1 1502.40 0.0006 12400.73 0.0000 1335.30 Meshoppen 1 Wyoming 0 414.20 0.0013 8617.82 0.0000 426.60 402 0.798 Meshoppen 1N Wyoming 1 831.80 0.0013 8763.22 0.0000 414.50 455 0.778 Meshoppen 2N Wyoming 1 202.60 0.0009 10486.80 0.0000 261.40 334 0.870 Noxen Wyoming 0 2203.10 0.0007 13030.57 0.0000 2108.50 655 0.937 Noxen 1 Wyoming 1 1328.50 0.0007 9656.14 0.0000 1428.60 247 0.925 Noxen 10 Wyoming 1 1025.90 0.0008 10649.31 0.0000 911.20 268 0.958 Noxen 10N Wyoming 1 1702.40 0.0008 11205.63 0.0000 1716.00 344 0.961 Noxen 1N Wyoming 1 1273.90 0.0008 12692.77 0.0000 1318.20 439 0.955 Noxen 2 Wyoming 1 911.60 0.0005 13087.59 0.0000 558.00 243 0.949 Noxen 2N Wyoming 1 1317.10 0.0008 12992.57 0.0000 1312.30 624 0.929 318 0.925 0.913 Noxen 3 Wyoming 0 1320.80 0.0005 13011.71 0.0000 1328.30 216 0.961 Noxen 3N Wyoming 1 1335.40 0.0005 12391.78 0.0000 1343.50 208 0.968 Noxen 4 Wyoming 1 711.20 0.0008 9559.28 0.0000 593.50 477 0.914 Noxen 4N Wyoming 1 1882.00 0.0004 14165.07 0.0000 1704.50 154 0.977 Noxen 5 Wyoming 0 2445.70 0.0004 14364.49 0.0000 1497.40 183 0.976 Noxen 5N Wyoming 1 955.30 0.0012 12570.92 0.0000 925.90 912 0.881 Noxen 6N Wyoming 1 934.80 0.0005 10707.81 0.0000 521.00 224 0.947 Noxen 7 Wyoming 0 1649.60 0.0008 12814.95 0.0000 1535.80 738 0.914 Noxen 7N Wyoming 1 1625.90 0.0008 11145.25 0.0000 1575.30 363 0.959 Noxen 8 Wyoming 1 1280.70 0.0010 12783.07 0.0000 1249.50 865 0.897 Noxen 8N Wyoming 1 299.40 0.0010 8074.57 0.0000 1071.00 427 0.894 Noxen 9 Wyoming 0 1746.90 0.0007 13753.02 0.0000 1800.80 597 0.936 Noxen 9N Wyoming 1 296.00 0.0014 12346.09 0.0000 358.80 1152 0.832 Tunkannock 1N Wyoming 1 244.80 0.0024 3519.70 0.0001 364.50 2669 0.701 125 Appendix X. Raw data for Model 4 at the 50m buffer zone. Site 126 County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.584 Christman 1 Carbon 0 279.10 0.0000 197.70 0.0000 222.55 0 0.990 Christman 10 Carbon 0 151.90 0.0000 1062.20 0.0000 146.76 0 1.000 Christman 11 Carbon 29 397.10 0.0000 21.60 0.0228 78.16 0 0.662 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0000 194.96 0 1.000 Christman 1N Carbon 2 165.70 0.0000 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0000 227.90 0.0000 414.20 0 1.000 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0000 158.52 0 0.931 Christman 4 Carbon 0 316.90 0.0000 252.60 0.0000 315.33 0 1.000 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 1.000 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0000 458.56 0 1.000 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0000 350.89 0 1.000 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0000 489.78 0 1.000 Hell Creek Carbon 73 145.00 0.0000 3037.30 0.0000 1582.90 0 1.000 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 1.000 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0000 89.18 0 1.000 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0000 244.48 0 0.932 Nesquehoning 1 Carbon 0 297.40 0.0000 2154.20 0.0000 319.82 0 0.956 Tamaqua 1 Carbon 0 247.57 0.0000 3289.00 0.0000 978.90 0 1.000 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 1.000 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 1.000 Weatherly 1N-Ribello Carbon 2 348.80 0.0000 2379.90 0.0000 1126.11 0 0.973 Weatherly 1N-Stan Carbon 1 219.30 0.0000 162.60 0.0000 322.65 0 1.000 127 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0000 387.50 0 1.000 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0000 440.40 0 1.000 Weatherly 4 Carbon 1 29.90 0.0090 2393.90 0.0000 1101.32 0 0.980 Weatherly 5 Carbon 0 217.40 0.0000 162.80 0.0000 320.73 0 1.000 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.998 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 1.000 Avoca 7 Luzerne 6 265.90 0.0000 4099.60 0.0000 463.70 0 1.000 Dutch Mountain 6 Luzerne 0 320.70 0.0000 6494.40 0.0000 2013.90 0 1.000 Hickory Run 1-Koval Luzerne 0 238.80 0.0000 1199.50 0.0000 606.10 0 1.000 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 1.000 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.912 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 1.000 Pittston 1 Luzerne 0 363.80 0.0000 4098.00 0.0000 151.60 0 1.000 Pittston 2 Luzerne 0 258.00 0.0000 3557.40 0.0000 158.40 0 1.000 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.960 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.286 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0000 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0000 767.40 0 0.985 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0000 87.30 0.0000 1945.00 0 1.000 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 1.000 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0000 694.10 0 1.000 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0097 700.70 0 0.880 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0000 Great Bend 1 Susquehanna 0 0.0000 2560.10 0.0000 640.70 800.10 0 1.000 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0000 251.40 0.0000 273.10 0 1.000 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.999 White Mills 1 Wayne 3 252.20 0.0000 4747.60 0.0000 889.60 0 1.000 White Mills 1N Wayne 3 106.70 0.0000 4904.10 0.0000 1117.40 0 0.417 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 1.000 128 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 1.000 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.997 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0000 12580.00 0.0000 353.60 0 1.000 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 1.000 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0000 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0000 13997.25 0.0000 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.878 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.741 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 1.000 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 1.000 Meshoppen 2N Wyoming 6 202.60 0.0000 10486.80 0.0000 261.40 0 1.000 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.976 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 1.000 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 1.000 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 1.000 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 1.000 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 1.000 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 129 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 1.000 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 1.000 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 1.000 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.934 Noxen 8N Wyoming 4 299.40 0.0000 8074.57 0.0000 1071.00 0 1.000 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 1.000 Noxen 9N Wyoming 2 296.00 0.0000 12346.09 0.0000 358.80 0 1.000 Tunkannock 1N Wyoming 2 244.80 0.0000 3519.70 0.0000 364.50 0 0.721 Appendix XI. Raw data for Model 4 at the 400m buffer zone. Site 130 County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.929 Christman 1 Carbon 0 279.10 0.0006 197.70 0.0047 222.55 2 0.819 Christman 10 Carbon 0 151.90 0.0019 1062.20 0.0000 146.76 10 0.975 Christman 11 Carbon 29 397.10 0.0001 21.60 0.0048 78.16 3 0.902 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0047 194.96 2 0.915 Christman 1N Carbon 2 165.70 0.0028 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0015 227.90 0.0011 414.20 0 0.932 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0045 158.52 2 0.925 Christman 4 Carbon 0 316.90 0.0003 252.60 0.0028 315.33 2 0.929 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 0.994 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0023 458.56 0 0.951 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0035 350.89 2 0.896 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0006 489.78 0 0.929 Hell Creek Carbon 73 145.00 0.0006 3037.30 0.0000 1582.90 0 0.991 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 0.951 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0052 89.18 2 0.908 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0039 244.48 1 0.932 Nesquehoning 1 Carbon 0 297.40 0.0016 2154.20 0.0000 319.82 13 0.799 Tamaqua 1 Carbon 0 247.57 0.0003 3289.00 0.0000 978.90 0 0.930 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 0.741 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 0.996 Weatherly 1N-Ribello Carbon 2 348.80 0.0006 2379.90 0.0000 1126.11 0 0.997 Weatherly 1N-Stan Carbon 1 219.30 0.0013 162.60 0.0053 322.65 6 0.869 131 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0031 387.50 1 0.898 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0019 440.40 0 0.926 Weatherly 4 Carbon 1 29.90 0.0018 2393.90 0.0000 1101.32 0 0.988 Weatherly 5 Carbon 0 217.40 0.0013 162.80 0.0053 320.73 6 0.868 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.999 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 0.999 Avoca 7 Luzerne 6 265.90 0.0014 4099.60 0.0000 463.70 0 0.883 Dutch Mountain 6 Luzerne 0 320.70 0.0009 6494.40 0.0000 2013.90 0 0.997 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0000 606.10 0 0.889 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 0.796 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.677 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 0.888 Pittston 1 Luzerne 0 363.80 0.0007 4098.00 0.0000 151.60 16 0.927 Pittston 2 Luzerne 0 258.00 0.0016 3557.40 0.0000 158.40 9 0.791 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.829 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.902 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0024 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0018 767.40 0 1.000 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0022 87.30 0.0016 1945.00 0 0.994 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 0.938 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0053 694.10 0 0.998 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0035 700.70 0 0.995 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0017 Great Bend 1 Susquehanna 0 0.0000 2560.10 0.0000 640.70 0.984 800.10 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0010 251.40 0.0010 273.10 3 0.900 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.735 White Mills 1 Wayne 3 252.20 0.0027 4747.60 0.0000 889.60 0 0.923 White Mills 1N Wayne 3 106.70 0.0031 4904.10 0.0000 1117.40 0 0.772 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 0.993 132 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 0.964 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.976 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0014 12580.00 0.0000 353.60 1 0.981 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 0.992 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0004 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0010 13997.25 0.0000 0 0.995 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.983 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.987 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 0.989 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 0.968 Meshoppen 2N Wyoming 6 202.60 0.0027 10486.80 0.0000 261.40 1 0.988 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.992 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 0.990 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 0.998 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 0.999 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 0.988 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 0.998 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 133 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 0.996 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 0.998 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 0.984 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.911 Noxen 8N Wyoming 4 299.40 0.0011 8074.57 0.0000 1071.00 0 0.901 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 0.996 Noxen 9N Wyoming 2 296.00 0.0002 12346.09 0.0000 358.80 1 0.907 Tunkannock 1N Wyoming 2 244.80 0.0011 3519.70 0.0000 364.50 1 0.878 Appendix XII. Raw data for Model 4 at the 5000m buffer zone. Site 134 County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0026 2124.90 0.0005 451.14 4034 0.872 Christman 1 Carbon 0 279.10 0.0013 197.70 0.0014 222.50 512 0.882 Christman 10 Carbon 0 151.90 0.0022 1062.20 0.0002 146.76 2601 0.833 Christman 11 Carbon 29 397.10 0.0018 21.60 0.0007 78.16 3167 0.921 Christman 12 Carbon 10 408.70 0.0011 58.00 0.0008 194.96 581 0.890 Christman 1N Carbon 2 165.70 0.0009 569.10 0.0009 614.90 787 0.893 Christman 2 Carbon 4 186.80 0.0013 227.90 0.0006 414.20 726 0.887 Christman 3 Carbon 0 854.40 0.0010 88.20 0.0009 158.50 471 0.862 Christman 4 Carbon 0 316.90 0.0016 252.60 0.0008 315.33 1238 0.884 Christman 5 Carbon 0 1224.10 0.0009 595.70 0.0008 624.19 606 0.930 Christman 6 Carbon 1 1126.70 0.0016 411.50 0.0007 423.00 2195 0.918 Christman 7 Carbon 0 436.60 0.0016 302.80 0.0006 458.56 2298 0.928 Christman 8 Carbon 0 401.50 0.0016 192.05 0.0016 350.89 2683 0.933 Christman 9 Carbon 0 588.30 0.0016 393.40 0.0007 489.78 2738 0.929 Hell Creek Carbon 73 145.00 0.0036 3037.30 0.0001 1582.90 5239 0.850 Hickory Run 4 Carbon 11 1544.20 0.0007 552.50 0.0003 2021.80 554 0.901 Hickory Run 5 Carbon 16 1101.90 0.0013 86.40 0.0013 89.10 424 0.884 Lehighton 1N Carbon 2 475.30 0.0032 240.90 0.0008 244.48 7373 0.741 Nesquehoning 1 Carbon 0 297.40 0.0021 2154.20 0.0001 319.82 5193 0.818 Tamaqua 1 Carbon 0 247.57 0.0021 3289.00 0.0000 0.802 Tamaqua 1N Carbon 0 415.50 0.0021 2830.20 0.0000 0.808 Weatherly 1 Carbon 0 1437.60 0.0009 854.90 0.0007 884.28 518 0.927 Weatherly 1N-Ribello Carbon 2 348.80 0.0016 2379.90 0.0005 1126.11 2764 0.922 Weatherly 1N-Stan Carbon 1 219.30 0.0020 162.60 0.0007 322.65 3921 0.908 135 Weatherly 2 Carbon 0 398.30 0.0016 229.10 0.0006 387.50 2682 0.933 Weatherly 3 Carbon 1 544.20 0.0016 316.30 0.0007 440.40 2723 0.930 Weatherly 4 Carbon 1 29.90 0.0017 2393.90 0.0005 1101.32 2734 0.909 Weatherly 5 Carbon 0 217.40 0.0020 162.80 0.0007 320.73 3917 0.908 Weatherly 6 Carbon 1 732.90 0.0015 3411.40 0.0002 660.86 1585 0.893 Weatherly 7 Carbon 2 1466.50 0.0014 4823.60 0.0000 2034.26 1701 0.853 Avoca 7 Luzerne 6 265.90 0.0006 4099.60 0.0001 463.70 483 0.905 Dutch Mountain 6 Luzerne 0 320.70 0.0006 6494.40 0.0000 2013.90 121 0.963 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0004 606.10 1021 0.875 Hickory Run 2- Koval Luzerne 31 966.60 0.0008 1260.40 0.0002 1114.20 812 0.909 Hickory Run 3- Koval Luzerne 2 583.00 0.0008 944.50 0.0002 525.80 574 0.908 Nanticoke 1N Luzerne 4 1110.80 0.0023 2009.10 0.0001 1253.60 5986 0.726 Pittston 1 Luzerne 0 363.80 0.0040 4098.00 0.0000 151.60 15706 0.584 Pittston 2 Luzerne 0 258.00 0.0030 3557.40 0.0001 158.40 9643 0.621 Pittston 3 Luzerne 0 632.70 0.0025 5916.00 0.0000 858.00 3735 0.704 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0014 3289.90 0.0001 1312.70 934 0.902 Red Rock 2 Luzerne 0 1116.90 0.0008 194.80 0.0005 0.920 Red Rock 3 Luzerne 0 766.10 0.0011 89.30 0.0004 0.905 Sweet Valley 1 Luzerne 0 430.60 0.0006 6257.30 0.0000 2253.40 118 0.964 Sweet Valley 2 Luzerne 0 88.50 0.0006 87.30 0.0004 1945.00 84 0.944 Wilkes Barre East 1 Luzerne 0 811.20 0.0030 5878.10 0.0000 733.10 9805 0.711 Mount Pocono 1N Monroe 1 620.80 0.0034 64.80 0.0001 694.10 7057 0.820 Mount Pocono 2N Monroe 4 827.40 0.0035 25.00 0.0001 700.70 7591 0.818 Pocono Pines 1N Monroe 3 907.56 0.0027 3311.77 0.0000 3311.77 4727 Stroudsburg 2N Monroe 5 0.0018 51.20 0.0003 Great Bend 1 Susquehanna 0 0.0021 2560.10 0.0001 640.70 0.795 800.10 1850 0.807 136 Starrucca 1 Susquehanna 0 251.40 0.0014 251.40 0.0003 273.10 588 0.770 White Mills 1 Wayne 3 252.20 0.0016 4747.60 0.0000 889.60 1654 0.798 White Mills 1N Wayne 3 106.70 0.0016 4904.10 0.0000 1117.40 1554 0.814 White Mills 2 Wayne 3 573.20 0.0015 6118.60 0.0000 607.30 1469 0.849 Dutch Mountain 1 Wyoming 3 534.50 0.0006 12447.34 0.0000 561.00 271 0.934 Dutch Mountain 1N Wyoming 1 1508.54 0.0004 14033.38 0.0000 1430.01 142 0.968 Dutch Mountain 2 Wyoming 0 1231.58 0.0004 14548.48 0.0000 1018.17 151 0.976 Dutch Mountain 2N Wyoming 7 618.80 0.0004 14517.34 0.0000 557.80 156 0.973 Dutch Mountain 3 Wyoming 0 163.60 0.0003 12580.00 0.0000 353.60 94 0.978 Dutch Mountain 3N Wyoming 3 1119.80 0.0005 13554.38 0.0000 1157.60 218 0.956 Dutch Mountain 4 Wyoming 3 444.90 0.0005 7862.37 0.0000 1932.70 42 0.964 Dutch Mountain 5 Wyoming 0 385.50 0.0006 8452.92 0.0000 946.90 88 0.965 Jenningsville 1 Wyoming 0 259.10 0.0007 13997.25 0.0000 Jenningsville 1N Wyoming 1 2061.80 0.0007 12393.23 0.0000 2026.70 Jenningsville 2N Wyoming 3 1502.40 0.0006 12400.73 0.0000 1335.30 Meshoppen 1 Wyoming 0 414.20 0.0013 8617.82 0.0000 426.60 402 0.798 Meshoppen 1N Wyoming 2 831.80 0.0013 8763.22 0.0000 414.50 455 0.778 Meshoppen 2N Wyoming 6 202.60 0.0009 10486.80 0.0000 261.40 334 0.870 Noxen Wyoming 0 2203.10 0.0007 13030.57 0.0000 2108.50 655 0.937 Noxen 1 Wyoming 4 1328.50 0.0007 9656.14 0.0000 1428.60 247 0.925 Noxen 10 Wyoming 6 1025.90 0.0008 10649.31 0.0000 911.20 268 0.958 Noxen 10N Wyoming 4 1702.40 0.0008 11205.63 0.0000 1716.00 344 0.961 Noxen 1N Wyoming 3 1273.90 0.0008 12692.77 0.0000 1318.20 439 0.955 Noxen 2 Wyoming 4 911.60 0.0005 13087.59 0.0000 558.00 243 0.949 Noxen 2N Wyoming 3 1317.10 0.0008 12992.57 0.0000 1312.30 624 0.929 Noxen 3 Wyoming 0 1320.80 0.0005 13011.71 0.0000 1328.30 216 0.961 0.904 318 0.925 0.913 137 Noxen 3N Wyoming 2 1335.40 0.0005 12391.78 0.0000 1343.50 208 0.968 Noxen 4 Wyoming 2 711.20 0.0008 9559.28 0.0000 593.50 477 0.914 Noxen 4N Wyoming 1 1882.00 0.0004 14165.07 0.0000 1704.50 154 0.977 Noxen 5 Wyoming 0 2445.70 0.0004 14364.49 0.0000 1497.40 183 0.976 Noxen 5N Wyoming 4 955.30 0.0012 12570.92 0.0000 925.90 912 0.881 Noxen 6N Wyoming 2 934.80 0.0005 10707.81 0.0000 521.00 224 0.947 Noxen 7 Wyoming 0 1649.60 0.0008 12814.95 0.0000 1535.80 738 0.914 Noxen 7N Wyoming 3 1625.90 0.0008 11145.25 0.0000 1575.30 363 0.959 Noxen 8 Wyoming 8 1280.70 0.0010 12783.07 0.0000 1249.50 865 0.897 Noxen 8N Wyoming 4 299.40 0.0010 8074.57 0.0000 1071.00 427 0.894 Noxen 9 Wyoming 0 1746.90 0.0007 13753.02 0.0000 1800.80 597 0.936 Noxen 9N Wyoming 2 296.00 0.0014 12346.09 0.0000 358.80 1152 0.832 Tunkannock 1N Wyoming 2 244.80 0.0024 3519.70 0.0001 364.50 2669 0.701 Appendix XIII. Raw data for Model 5 at the 50m buffer zone. Site 138 County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.584 Christman 11 Carbon 29 397.10 0.0000 21.60 0.0228 78.16 0 0.662 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0000 194.96 0 1.000 Christman 1N Carbon 2 165.70 0.0000 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0000 227.90 0.0000 414.20 0 1.000 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Hell Creek Carbon 73 145.00 0.0000 3037.30 0.0000 1582.90 0 1.000 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 1.000 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0000 89.18 0 1.000 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0000 244.48 0 0.932 Weatherly 1N-Ribello Carbon 2 348.80 0.0000 2379.90 0.0000 1126.11 0 0.973 Weatherly 1N-Stan Carbon 1 219.30 0.0000 162.60 0.0000 322.65 0 1.000 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0000 440.40 0 1.000 Weatherly 4 Carbon 1 29.90 0.0090 2393.90 0.0000 1101.32 0 0.980 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.998 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 1.000 Avoca 7 Luzerne 6 265.90 0.0000 4099.60 0.0000 463.70 0 1.000 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 1.000 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.912 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 1.000 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0000 694.10 0 1.000 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0097 700.70 0 0.880 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0000 0 1.000 139 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.999 White Mills 1 Wayne 3 252.20 0.0000 4747.60 0.0000 889.60 0 1.000 White Mills 1N Wayne 3 106.70 0.0000 4904.10 0.0000 1117.40 0 0.417 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 1.000 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 1.000 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.997 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 1.000 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.878 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.741 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 1.000 Meshoppen 2N Wyoming 6 202.60 0.0000 10486.80 0.0000 261.40 0 1.000 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 1.000 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 1.000 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 1.000 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 1.000 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 1.000 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 1.000 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 1.000 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 1.000 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.934 Noxen 8N Wyoming 4 299.40 0.0000 8074.57 0.0000 1071.00 0 1.000 Noxen 9N Wyoming 2 296.00 0.0000 12346.09 0.0000 358.80 0 1.000 Tunkannock 1N Wyoming 2 244.80 0.0000 3519.70 0.0000 364.50 0 0.721 140 Appendix XIV. Raw data for Model 5 at the 400m buffer zone. Site 141 County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0000 2124.90 0.0000 451.14 0 0.929 Christman 11 Carbon 29 397.10 0.0001 21.60 0.0048 78.16 3 0.902 Christman 12 Carbon 10 408.70 0.0000 58.00 0.0047 194.96 2 0.915 Christman 1N Carbon 2 165.70 0.0028 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 4 186.80 0.0015 227.90 0.0011 414.20 0 0.932 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Hell Creek Carbon 73 145.00 0.0006 3037.30 0.0000 1582.90 0 0.991 Hickory Run 4 Carbon 11 1544.20 0.0000 552.50 0.0000 2021.82 0 0.951 Hickory Run 5 Carbon 16 1101.90 0.0000 86.40 0.0052 89.18 2 0.908 Lehighton 1N Carbon 2 475.30 0.0000 240.90 0.0039 244.48 1 0.932 Weatherly 1N-Ribello Carbon 2 348.80 0.0006 2379.90 0.0000 1126.11 0 0.997 Weatherly 1N-Stan Carbon 1 219.30 0.0013 162.60 0.0053 322.65 6 0.869 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0019 440.40 0 0.926 Weatherly 4 Carbon 1 29.90 0.0018 2393.90 0.0000 1101.32 0 0.988 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.999 Weatherly 7 Carbon 2 1466.50 0.0000 4823.60 0.0000 2034.26 0 0.999 Avoca 7 Luzerne 6 265.90 0.0014 4099.60 0.0000 463.70 0 0.883 Hickory Run 2- Koval Luzerne 31 966.60 0.0000 1260.40 0.0000 1114.20 0 0.796 Hickory Run 3- Koval Luzerne 2 583.00 0.0000 944.50 0.0000 525.80 0 0.677 Nanticoke 1N Luzerne 4 1110.80 0.0000 2009.10 0.0000 1253.60 0 0.888 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0053 694.10 0 0.998 Mount Pocono 2N Monroe 4 827.40 0.0000 25.00 0.0035 700.70 0 0.995 Pocono Pines 1N Monroe 3 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 5 0.0000 51.20 0.0017 0.984 142 Susquehanna 1N Susquehanna 2 510.90 0.0000 867.80 0.0000 482.50 0 0.735 White Mills 1 Wayne 3 252.20 0.0027 4747.60 0.0000 889.60 0 0.923 White Mills 1N Wayne 3 106.70 0.0031 4904.10 0.0000 1117.40 0 0.772 White Mills 2 Wayne 3 573.20 0.0000 6118.60 0.0000 607.30 0 0.993 Dutch Mountain 1 Wyoming 3 534.50 0.0000 12447.34 0.0000 561.00 0 0.964 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.976 Dutch Mountain 2N Wyoming 7 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3N Wyoming 3 1119.80 0.0000 13554.38 0.0000 1157.60 0 0.992 Dutch Mountain 4 Wyoming 3 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.983 Jenningsville 2N Wyoming 3 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.987 Meshoppen 1N Wyoming 2 831.80 0.0000 8763.22 0.0000 414.50 0 0.968 Meshoppen 2N Wyoming 6 202.60 0.0027 10486.80 0.0000 261.40 1 0.988 Noxen 1 Wyoming 4 1328.50 0.0000 9656.14 0.0000 1428.60 0 0.990 Noxen 10 Wyoming 6 1025.90 0.0000 10649.31 0.0000 911.20 0 0.998 Noxen 10N Wyoming 4 1702.40 0.0000 11205.63 0.0000 1716.00 0 0.999 Noxen 1N Wyoming 3 1273.90 0.0000 12692.77 0.0000 1318.20 0 0.988 Noxen 2 Wyoming 4 911.60 0.0000 13087.59 0.0000 558.00 0 0.998 Noxen 2N Wyoming 3 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3N Wyoming 2 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 2 711.20 0.0000 9559.28 0.0000 593.50 0 0.996 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 0.998 Noxen 5N Wyoming 4 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 2 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7N Wyoming 3 1625.90 0.0000 11145.25 0.0000 1575.30 0 0.984 Noxen 8 Wyoming 8 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.911 Noxen 8N Wyoming 4 299.40 0.0011 8074.57 0.0000 1071.00 0 0.901 Noxen 9N Wyoming 2 296.00 0.0002 12346.09 0.0000 358.80 1 0.907 Tunkannock 1N Wyoming 2 244.80 0.0011 3519.70 0.0000 364.50 1 0.878 143 Appendix XV. Raw data for Model 5 at the 5000m buffer zone. Site 144 County Num. of Snakes Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 12 523.70 0.0026 2124.90 0.0005 451.14 4034 0.872 Christman 11 Carbon 29 397.10 0.0018 21.60 0.0007 78.16 3167 0.921 Christman 12 Carbon 10 408.70 0.0011 58.00 0.0008 194.96 581 0.890 Christman 1N Carbon 2 165.70 0.0009 569.10 0.0009 614.90 787 0.893 Christman 2 Carbon 4 186.80 0.0013 227.90 0.0006 414.20 726 0.887 Christman 6 Carbon 1 1126.70 0.0016 411.50 0.0007 423.00 2195 0.918 Hell Creek Carbon 73 145.00 0.0036 3037.30 0.0001 1582.90 5239 0.850 Hickory Run 4 Carbon 11 1544.20 0.0007 552.50 0.0003 2021.80 554 0.901 Hickory Run 5 Carbon 16 1101.90 0.0013 86.40 0.0013 89.10 424 0.884 Lehighton 1N Carbon 2 475.30 0.0032 240.90 0.0008 244.48 7373 0.741 Weatherly 1N-Ribello Carbon 2 348.80 0.0016 2379.90 0.0005 1126.11 2764 0.922 Weatherly 1N-Stan Carbon 1 219.30 0.0020 162.60 0.0007 322.65 3921 0.908 Weatherly 3 Carbon 1 544.20 0.0016 316.30 0.0007 440.40 2723 0.930 Weatherly 4 Carbon 1 29.90 0.0017 2393.90 0.0005 1101.32 2734 0.909 Weatherly 6 Carbon 1 732.90 0.0015 3411.40 0.0002 660.86 1585 0.893 Weatherly 7 Carbon 2 1466.50 0.0014 4823.60 0.0000 2034.26 1701 0.853 Avoca 7 Luzerne 6 265.90 0.0006 4099.60 0.0001 463.70 483 0.905 Hickory Run 2- Koval Luzerne 31 966.60 0.0008 1260.40 0.0002 1114.20 812 0.909 Hickory Run 3- Koval Luzerne 2 583.00 0.0008 944.50 0.0002 525.80 574 0.908 Nanticoke 1N Luzerne 4 1110.80 0.0023 2009.10 0.0001 1253.60 5986 0.726 Mount Pocono 1N Monroe 1 620.80 0.0034 64.80 0.0001 694.10 7057 0.820 Mount Pocono 2N Monroe 4 827.40 0.0035 25.00 0.0001 700.70 7591 0.818 Pocono Pines 1N Monroe 3 907.56 0.0027 3311.77 0.0000 3311.77 4727 Stroudsburg 2N Monroe 5 0.0018 51.20 0.0003 0.795 145 White Mills 1 Wayne 3 252.20 0.0016 4747.60 0.0000 889.60 1654 0.798 White Mills 1N Wayne 3 106.70 0.0016 4904.10 0.0000 1117.40 1554 0.814 White Mills 2 Wayne 3 573.20 0.0015 6118.60 0.0000 607.30 1469 0.849 Dutch Mountain 1 Wyoming 3 534.50 0.0006 12447.34 0.0000 561.00 271 0.934 Dutch Mountain 1N Wyoming 1 1508.54 0.0004 14033.38 0.0000 1430.01 142 0.968 Dutch Mountain 2N Wyoming 7 618.80 0.0004 14517.34 0.0000 557.80 156 0.973 Dutch Mountain 3N Wyoming 3 1119.80 0.0005 13554.38 0.0000 1157.60 218 0.956 Dutch Mountain 4 Wyoming 3 444.90 0.0005 7862.37 0.0000 1932.70 42 0.964 Jenningsville 1N Wyoming 1 2061.80 0.0007 12393.23 0.0000 2026.70 318 0.925 Jenningsville 2N Wyoming 3 1502.40 0.0006 12400.73 0.0000 1335.30 Meshoppen 1N Wyoming 2 831.80 0.0013 8763.22 0.0000 414.50 455 0.778 Meshoppen 2N Wyoming 6 202.60 0.0009 10486.80 0.0000 261.40 334 0.870 Noxen 1 Wyoming 4 1328.50 0.0007 9656.14 0.0000 1428.60 247 0.925 0.913 Noxen 10 Wyoming 6 1025.90 0.0008 10649.31 0.0000 911.20 268 0.958 Noxen 10N Wyoming 4 1702.40 0.0008 11205.63 0.0000 1716.00 344 0.961 Noxen 1N Wyoming 3 1273.90 0.0008 12692.77 0.0000 1318.20 439 0.955 Noxen 2 Wyoming 4 911.60 0.0005 13087.59 0.0000 558.00 243 0.949 Noxen 2N Wyoming 3 1317.10 0.0008 12992.57 0.0000 1312.30 624 0.929 Noxen 3N Wyoming 2 1335.40 0.0005 12391.78 0.0000 1343.50 208 0.968 Noxen 4 Wyoming 2 711.20 0.0008 9559.28 0.0000 593.50 477 0.914 Noxen 4N Wyoming 1 1882.00 0.0004 14165.07 0.0000 1704.50 154 0.977 Noxen 5N Wyoming 4 955.30 0.0012 12570.92 0.0000 925.90 912 0.881 Noxen 6N Wyoming 2 934.80 0.0005 10707.81 0.0000 521.00 224 0.947 Noxen 7N Wyoming 3 1625.90 0.0008 11145.25 0.0000 1575.30 363 0.959 Noxen 8 Wyoming 8 1280.70 0.0010 12783.07 0.0000 1249.50 865 0.897 Noxen 8N Wyoming 4 299.40 0.0010 8074.57 0.0000 1071.00 427 0.894 Noxen 9N Wyoming 2 296.00 0.0014 12346.09 0.0000 358.80 1152 0.832 Tunkannock 1N Wyoming 2 244.80 0.0024 3519.70 0.0001 364.50 2669 0.701 146 Appendix XVI. Raw data for Model 6 at the 50m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence(0) data. 147 Site County Population Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 1 523.70 0.0000 2124.90 0.0000 451.14 0 0.584 Christman 1 Carbon 0 279.10 0.0000 197.70 0.0000 222.55 0 0.990 Christman 10 Carbon 0 151.90 0.0000 1062.20 0.0000 146.76 0 1.000 Christman 11 Carbon 1 397.10 0.0000 21.60 0.0228 78.16 0 0.662 Christman 12 Carbon 1 408.70 0.0000 58.00 0.0000 194.96 0 1.000 Christman 1N Carbon 1 165.70 0.0000 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 1 186.80 0.0000 227.90 0.0000 414.20 0 1.000 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0000 158.52 0 0.931 Christman 4 Carbon 0 316.90 0.0000 252.60 0.0000 315.33 0 1.000 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 1.000 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0000 458.56 0 1.000 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0000 350.89 0 1.000 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0000 489.78 0 1.000 Hell Creek Carbon 1 145.00 0.0000 3037.30 0.0000 1582.90 0 1.000 Hickory Run 4 Carbon 1 1544.20 0.0000 552.50 0.0000 2021.82 0 1.000 Hickory Run 5 Carbon 1 1101.90 0.0000 86.40 0.0000 89.18 0 1.000 Lehighton 1N Carbon 1 475.30 0.0000 240.90 0.0000 244.48 0 0.932 Nesquehoning 1 Carbon 0 297.40 0.0000 2154.20 0.0000 319.82 0 0.956 Tamaqua 1 Carbon 0 247.57 0.0000 3289.00 0.0000 978.90 0 1.000 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 1.000 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 1.000 Weatherly 1N-Ribello Carbon 1 348.80 0.0000 2379.90 0.0000 1126.11 0 0.973 148 Weatherly 1N-Stan Carbon 1 219.30 0.0000 162.60 0.0000 322.65 0 1.000 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0000 387.50 0 1.000 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0000 440.40 0 1.000 Weatherly 4 Carbon 1 29.90 0.0090 2393.90 0.0000 1101.32 0 0.980 Weatherly 5 Carbon 0 217.40 0.0000 162.80 0.0000 320.73 0 1.000 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.998 Weatherly 7 Carbon 1 1466.50 0.0000 4823.60 0.0000 2034.26 0 1.000 Avoca 7 Luzerne 1 265.90 0.0000 4099.60 0.0000 463.70 0 1.000 Dutch Mountain 6 Luzerne 0 320.70 0.0000 6494.40 0.0000 2013.90 0 1.000 Hickory Run 1-Koval Luzerne 0 238.80 0.0000 1199.50 0.0000 606.10 0 1.000 Hickory Run 2- Koval Luzerne 1 966.60 0.0000 1260.40 0.0000 1114.20 0 1.000 Hickory Run 3- Koval Luzerne 1 583.00 0.0000 944.50 0.0000 525.80 0 0.912 Nanticoke 1N Luzerne 1 1110.80 0.0000 2009.10 0.0000 1253.60 0 1.000 Pittston 1 Luzerne 0 363.80 0.0000 4098.00 0.0000 151.60 0 1.000 Pittston 2 Luzerne 0 258.00 0.0000 3557.40 0.0000 158.40 0 1.000 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.960 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.286 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0000 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0000 767.40 0 0.985 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0000 87.30 0.0000 1945.00 0 1.000 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 1.000 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0000 694.10 0 1.000 Mount Pocono 2N Monroe 1 827.40 0.0000 25.00 0.0097 700.70 0 0.880 Pocono Pines 1N Monroe 1 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 1 0.0000 51.20 0.0000 0 1.000 Great Bend 1 Susquehanna 0 640.70 0.0000 2560.10 0.0000 800.10 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0000 251.40 0.0000 273.10 0 1.000 Susquehanna 1N Susquehanna 1 510.90 0.0000 867.80 0.0000 482.50 0 0.999 White Mills 1 Wayne 1 252.20 0.0000 4747.60 0.0000 889.60 0 1.000 White Mills 1N Wayne 1 106.70 0.0000 4904.10 0.0000 1117.40 0 0.417 149 White Mills 2 Wayne 1 573.20 0.0000 6118.60 0.0000 607.30 0 1.000 Dutch Mountain 1 Wyoming 1 534.50 0.0000 12447.34 0.0000 561.00 0 1.000 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.997 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 1 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0000 12580.00 0.0000 353.60 0 1.000 Dutch Mountain 3N Wyoming 1 1119.80 0.0000 13554.38 0.0000 1157.60 0 1.000 Dutch Mountain 4 Wyoming 1 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0000 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0000 13997.25 0.0000 0 1.000 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.878 Jenningsville 2N Wyoming 1 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.741 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 1.000 Meshoppen 1N Wyoming 1 831.80 0.0000 8763.22 0.0000 414.50 0 1.000 Meshoppen 2N Wyoming 1 202.60 0.0000 10486.80 0.0000 261.40 0 1.000 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.976 Noxen 1 Wyoming 1 1328.50 0.0000 9656.14 0.0000 1428.60 0 1.000 Noxen 10 Wyoming 1 1025.90 0.0000 10649.31 0.0000 911.20 0 1.000 Noxen 10N Wyoming 1 1702.40 0.0000 11205.63 0.0000 1716.00 0 1.000 Noxen 1N Wyoming 1 1273.90 0.0000 12692.77 0.0000 1318.20 0 1.000 Noxen 2 Wyoming 1 911.60 0.0000 13087.59 0.0000 558.00 0 1.000 Noxen 2N Wyoming 1 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 1 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 1 711.20 0.0000 9559.28 0.0000 593.50 0 1.000 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 1.000 150 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 1 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 1 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 1 1625.90 0.0000 11145.25 0.0000 1575.30 0 1.000 Noxen 8 Wyoming 1 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.934 Noxen 8N Wyoming 1 299.40 0.0000 8074.57 0.0000 1071.00 0 1.000 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 1.000 Noxen 9N Wyoming 1 296.00 0.0000 12346.09 0.0000 358.80 0 1.000 Tunkannock 1N Wyoming 1 244.80 0.0000 3519.70 0.0000 364.50 0 0.721 Appendix XVII. Raw data for Model 6 at the 400m buffer zone where Number of Snakes (Population) has been changed to presence (1) – absence (0) data. 151 Site County Population Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 1 523.70 0.0000 2124.90 0.0000 451.14 0 0.929 Christman 1 Carbon 0 279.10 0.0006 197.70 0.0047 222.55 2 0.819 Christman 10 Carbon 0 151.90 0.0019 1062.20 0.0000 146.76 10 0.975 Christman 11 Carbon 1 397.10 0.0001 21.60 0.0048 78.16 3 0.902 Christman 12 Carbon 1 408.70 0.0000 58.00 0.0047 194.96 2 0.915 Christman 1N Carbon 1 165.70 0.0028 569.10 0.0000 614.94 0 1.000 Christman 2 Carbon 1 186.80 0.0015 227.90 0.0011 414.20 0 0.932 Christman 3 Carbon 0 854.40 0.0000 88.20 0.0045 158.52 2 0.925 Christman 4 Carbon 0 316.90 0.0003 252.60 0.0028 315.33 2 0.929 Christman 5 Carbon 0 1224.10 0.0000 595.70 0.0000 624.19 0 0.994 Christman 6 Carbon 1 1126.70 0.0000 411.50 0.0000 423.00 0 1.000 Christman 7 Carbon 0 436.60 0.0000 302.80 0.0023 458.56 0 0.951 Christman 8 Carbon 0 401.50 0.0000 192.05 0.0035 350.89 2 0.896 Christman 9 Carbon 0 588.30 0.0000 393.40 0.0006 489.78 0 0.929 Hell Creek Carbon 1 145.00 0.0006 3037.30 0.0000 1582.90 0 0.991 Hickory Run 4 Carbon 1 1544.20 0.0000 552.50 0.0000 2021.82 0 0.951 Hickory Run 5 Carbon 1 1101.90 0.0000 86.40 0.0052 89.18 2 0.908 Lehighton 1N Carbon 1 475.30 0.0000 240.90 0.0039 244.48 1 0.932 Nesquehoning 1 Carbon 0 297.40 0.0016 2154.20 0.0000 319.82 13 0.799 Tamaqua 1 Carbon 0 247.57 0.0003 3289.00 0.0000 978.90 0 0.930 Tamaqua 1N Carbon 0 415.50 0.0000 2830.20 0.0000 600.60 0 0.741 Weatherly 1 Carbon 0 1437.60 0.0000 854.90 0.0000 884.28 0 0.996 Weatherly 1N-Ribello Carbon 1 348.80 0.0006 2379.90 0.0000 1126.11 0 0.997 152 Weatherly 1N-Stan Carbon 1 219.30 0.0013 162.60 0.0053 322.65 6 0.869 Weatherly 2 Carbon 0 398.30 0.0000 229.10 0.0031 387.50 1 0.898 Weatherly 3 Carbon 1 544.20 0.0000 316.30 0.0019 440.40 0 0.926 Weatherly 4 Carbon 1 29.90 0.0018 2393.90 0.0000 1101.32 0 0.988 Weatherly 5 Carbon 0 217.40 0.0013 162.80 0.0053 320.73 6 0.868 Weatherly 6 Carbon 1 732.90 0.0000 3411.40 0.0000 660.86 0 0.999 Weatherly 7 Carbon 1 1466.50 0.0000 4823.60 0.0000 2034.26 0 0.999 Avoca 7 Luzerne 1 265.90 0.0014 4099.60 0.0000 463.70 0 0.883 Dutch Mountain 6 Luzerne 0 320.70 0.0009 6494.40 0.0000 2013.90 0 0.997 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0000 606.10 0 0.889 Hickory Run 2- Koval Luzerne 1 966.60 0.0000 1260.40 0.0000 1114.20 0 0.796 Hickory Run 3- Koval Luzerne 1 583.00 0.0000 944.50 0.0000 525.80 0 0.677 Nanticoke 1N Luzerne 1 1110.80 0.0000 2009.10 0.0000 1253.60 0 0.888 Pittston 1 Luzerne 0 363.80 0.0007 4098.00 0.0000 151.60 16 0.927 Pittston 2 Luzerne 0 258.00 0.0016 3557.40 0.0000 158.40 9 0.791 Pittston 3 Luzerne 0 632.70 0.0000 5916.00 0.0000 858.00 0 0.829 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0000 3289.90 0.0000 1312.70 0 0.902 Red Rock 2 Luzerne 0 1116.90 0.0000 194.80 0.0024 1637.00 0 1.000 Red Rock 3 Luzerne 0 766.10 0.0000 89.30 0.0018 767.40 0 1.000 Sweet Valley 1 Luzerne 0 430.60 0.0000 6257.30 0.0000 2253.40 0 1.000 Sweet Valley 2 Luzerne 0 88.50 0.0022 87.30 0.0016 1945.00 0 0.994 Wilkes Barre East 1 Luzerne 0 811.20 0.0000 5878.10 0.0000 733.10 0 0.938 Mount Pocono 1N Monroe 1 620.80 0.0000 64.80 0.0053 694.10 0 0.998 Mount Pocono 2N Monroe 1 827.40 0.0000 25.00 0.0035 700.70 0 0.995 Pocono Pines 1N Monroe 1 907.56 0.0000 3311.77 0.0000 3311.77 0 Stroudsburg 2N Monroe 1 0.0000 51.20 0.0017 0.984 Great Bend 1 Susquehanna 0 640.70 0.0000 2560.10 0.0000 800.10 0 1.000 Starrucca 1 Susquehanna 0 251.40 0.0010 251.40 0.0010 273.10 3 0.900 Susquehanna 1N Susquehanna 1 510.90 0.0000 867.80 0.0000 482.50 0 0.735 White Mills 1 Wayne 1 252.20 0.0027 4747.60 0.0000 889.60 0 0.923 White Mills 1N Wayne 1 106.70 0.0031 4904.10 0.0000 1117.40 0 0.772 153 White Mills 2 Wayne 1 573.20 0.0000 6118.60 0.0000 607.30 0 0.993 Dutch Mountain 1 Wyoming 1 534.50 0.0000 12447.34 0.0000 561.00 0 0.964 Dutch Mountain 1N Wyoming 1 1508.54 0.0000 14033.38 0.0000 1430.01 0 0.976 Dutch Mountain 2 Wyoming 0 1231.58 0.0000 14548.48 0.0000 1018.17 0 1.000 Dutch Mountain 2N Wyoming 1 618.80 0.0000 14517.34 0.0000 557.80 0 1.000 Dutch Mountain 3 Wyoming 0 163.60 0.0014 12580.00 0.0000 353.60 1 0.981 Dutch Mountain 3N Wyoming 1 1119.80 0.0000 13554.38 0.0000 1157.60 0 0.992 Dutch Mountain 4 Wyoming 1 444.90 0.0000 7862.37 0.0000 1932.70 0 1.000 Dutch Mountain 5 Wyoming 0 385.50 0.0004 8452.92 0.0000 946.90 0 1.000 Jenningsville 1 Wyoming 0 259.10 0.0010 13997.25 0.0000 0 0.995 Jenningsville 1N Wyoming 1 2061.80 0.0000 12393.23 0.0000 2026.70 0 0.983 Jenningsville 2N Wyoming 1 1502.40 0.0000 12400.73 0.0000 1335.30 0 0.987 Meshoppen 1 Wyoming 0 414.20 0.0000 8617.82 0.0000 426.60 0 0.989 Meshoppen 1N Wyoming 1 831.80 0.0000 8763.22 0.0000 414.50 0 0.968 Meshoppen 2N Wyoming 1 202.60 0.0027 10486.80 0.0000 261.40 1 0.988 Noxen Wyoming 0 2203.10 0.0000 13030.57 0.0000 2108.50 0 0.992 Noxen 1 Wyoming 1 1328.50 0.0000 9656.14 0.0000 1428.60 0 0.990 Noxen 10 Wyoming 1 1025.90 0.0000 10649.31 0.0000 911.20 0 0.998 Noxen 10N Wyoming 1 1702.40 0.0000 11205.63 0.0000 1716.00 0 0.999 Noxen 1N Wyoming 1 1273.90 0.0000 12692.77 0.0000 1318.20 0 0.988 Noxen 2 Wyoming 1 911.60 0.0000 13087.59 0.0000 558.00 0 0.998 Noxen 2N Wyoming 1 1317.10 0.0000 12992.57 0.0000 1312.30 0 1.000 Noxen 3 Wyoming 0 1320.80 0.0000 13011.71 0.0000 1328.30 0 1.000 Noxen 3N Wyoming 1 1335.40 0.0000 12391.78 0.0000 1343.50 0 1.000 Noxen 4 Wyoming 1 711.20 0.0000 9559.28 0.0000 593.50 0 0.996 Noxen 4N Wyoming 1 1882.00 0.0000 14165.07 0.0000 1704.50 0 0.998 154 Noxen 5 Wyoming 0 2445.70 0.0000 14364.49 0.0000 1497.40 0 1.000 Noxen 5N Wyoming 1 955.30 0.0000 12570.92 0.0000 925.90 0 1.000 Noxen 6N Wyoming 1 934.80 0.0000 10707.81 0.0000 521.00 0 1.000 Noxen 7 Wyoming 0 1649.60 0.0000 12814.95 0.0000 1535.80 0 1.000 Noxen 7N Wyoming 1 1625.90 0.0000 11145.25 0.0000 1575.30 0 0.984 Noxen 8 Wyoming 1 1280.70 0.0000 12783.07 0.0000 1249.50 0 0.911 Noxen 8N Wyoming 1 299.40 0.0011 8074.57 0.0000 1071.00 0 0.901 Noxen 9 Wyoming 0 1746.90 0.0000 13753.02 0.0000 1800.80 0 0.996 Noxen 9N Wyoming 1 296.00 0.0002 12346.09 0.0000 358.80 1 0.907 Tunkannock 1N Wyoming 1 244.80 0.0011 3519.70 0.0000 364.50 1 0.878 Appendix XVIII. Raw data for Model 6 at the 5000m buffer zone where Number of Snakes (Population) has been changed to presence (1) - absence 155 Site County Population Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Canopy Percent Blakeslee 1N Carbon 1 523.70 0.0026 2124.90 0.0005 451.14 4034 0.872 Christman 1 Carbon 0 279.10 0.0013 197.70 0.0014 222.50 512 0.882 Christman 10 Carbon 0 151.90 0.0022 1062.20 0.0002 146.76 2601 0.833 Christman 11 Carbon 1 397.10 0.0018 21.60 0.0007 78.16 3167 0.921 Christman 12 Carbon 1 408.70 0.0011 58.00 0.0008 194.96 581 0.890 Christman 1N Carbon 1 165.70 0.0009 569.10 0.0009 614.90 787 0.893 Christman 2 Carbon 1 186.80 0.0013 227.90 0.0006 414.20 726 0.887 Christman 3 Carbon 0 854.40 0.0010 88.20 0.0009 158.50 471 0.862 Christman 4 Carbon 0 316.90 0.0016 252.60 0.0008 315.33 1238 0.884 Christman 5 Carbon 0 1224.10 0.0009 595.70 0.0008 624.19 606 0.930 Christman 6 Carbon 1 1126.70 0.0016 411.50 0.0007 423.00 2195 0.918 Christman 7 Carbon 0 436.60 0.0016 302.80 0.0006 458.56 2298 0.928 Christman 8 Carbon 0 401.50 0.0016 192.05 0.0016 350.89 2683 0.933 Christman 9 Carbon 0 588.30 0.0016 393.40 0.0007 489.78 2738 0.929 Hell Creek Carbon 1 145.00 0.0036 3037.30 0.0001 1582.90 5239 0.850 Hickory Run 4 Carbon 1 1544.20 0.0007 552.50 0.0003 2021.80 554 0.901 Hickory Run 5 Carbon 1 1101.90 0.0013 86.40 0.0013 89.10 424 0.884 Lehighton 1N Carbon 1 475.30 0.0032 240.90 0.0008 244.48 7373 0.741 Nesquehoning 1 Carbon 0 297.40 0.0021 2154.20 0.0001 319.82 5193 0.818 Tamaqua 1 Carbon 0 247.57 0.0021 3289.00 0.0000 0.802 Tamaqua 1N Carbon 0 415.50 0.0021 2830.20 0.0000 0.808 Weatherly 1 Carbon 0 1437.60 0.0009 854.90 0.0007 884.28 518 0.927 Weatherly 1N-Ribello Carbon 1 348.80 0.0016 2379.90 0.0005 1126.11 2764 0.922 156 Weatherly 1N-Stan Carbon 1 219.30 0.0020 162.60 0.0007 322.65 3921 0.908 Weatherly 2 Carbon 0 398.30 0.0016 229.10 0.0006 387.50 2682 0.933 Weatherly 3 Carbon 1 544.20 0.0016 316.30 0.0007 440.40 2723 0.930 Weatherly 4 Carbon 1 29.90 0.0017 2393.90 0.0005 1101.32 2734 0.909 Weatherly 5 Carbon 0 217.40 0.0020 162.80 0.0007 320.73 3917 0.908 Weatherly 6 Carbon 1 732.90 0.0015 3411.40 0.0002 660.86 1585 0.893 Weatherly 7 Carbon 1 1466.50 0.0014 4823.60 0.0000 2034.26 1701 0.853 Avoca 7 Luzerne 1 265.90 0.0006 4099.60 0.0001 463.70 483 0.905 Dutch Mountain 6 Luzerne 0 320.70 0.0006 6494.40 0.0000 2013.90 121 0.963 Hickory Run 1-Koval Luzerne 0 238.80 0.0013 1199.50 0.0004 606.10 1021 0.875 Hickory Run 2- Koval Luzerne 1 966.60 0.0008 1260.40 0.0002 1114.20 812 0.909 Hickory Run 3- Koval Luzerne 1 583.00 0.0008 944.50 0.0002 525.80 574 0.908 Nanticoke 1N Luzerne 1 1110.80 0.0023 2009.10 0.0001 1253.60 5986 0.726 Pittston 1 Luzerne 0 363.80 0.0040 4098.00 0.0000 151.60 15706 0.584 Pittston 2 Luzerne 0 258.00 0.0030 3557.40 0.0001 158.40 9643 0.621 Pittston 3 Luzerne 0 632.70 0.0025 5916.00 0.0000 858.00 3735 0.704 Pleasant View Summit 1- Koval Luzerne 0 1247.00 0.0014 3289.90 0.0001 1312.70 934 0.902 Red Rock 2 Luzerne 0 1116.90 0.0008 194.80 0.0005 0.920 Red Rock 3 Luzerne 0 766.10 0.0011 89.30 0.0004 0.905 Sweet Valley 1 Luzerne 0 430.60 0.0006 6257.30 0.0000 2253.40 118 0.964 Sweet Valley 2 Luzerne 0 88.50 0.0006 87.30 0.0004 1945.00 84 0.944 Wilkes Barre East 1 Luzerne 0 811.20 0.0030 5878.10 0.0000 733.10 9805 0.711 Mount Pocono 1N Monroe 1 620.80 0.0034 64.80 0.0001 694.10 7057 0.820 Mount Pocono 2N Monroe 1 827.40 0.0035 25.00 0.0001 700.70 7591 0.818 Pocono Pines 1N Monroe 1 907.56 0.0027 3311.77 0.0000 3311.77 4727 Stroudsburg 2N Monroe 1 0.0018 51.20 0.0003 0.795 Great Bend 1 Susquehanna 0 640.70 0.0021 2560.10 0.0001 800.10 1850 0.807 Starrucca 1 Susquehanna 0 251.40 0.0014 251.40 0.0003 273.10 588 0.770 White Mills 1 Wayne 1 252.20 0.0016 4747.60 0.0000 889.60 1654 0.798 White Mills 1N Wayne 1 106.70 0.0016 4904.10 0.0000 1117.40 1554 0.814 White Mills 2 Wayne 1 573.20 0.0015 6118.60 0.0000 607.30 1469 0.849 157 Dutch Mountain 1 Wyoming 1 534.50 0.0006 12447.34 0.0000 561.00 271 0.934 Dutch Mountain 1N Wyoming 1 1508.54 0.0004 14033.38 0.0000 1430.01 142 0.968 Dutch Mountain 2 Wyoming 0 1231.58 0.0004 14548.48 0.0000 1018.17 151 0.976 Dutch Mountain 2N Wyoming 1 618.80 0.0004 14517.34 0.0000 557.80 156 0.973 Dutch Mountain 3 Wyoming 0 163.60 0.0003 12580.00 0.0000 353.60 94 0.978 Dutch Mountain 3N Wyoming 1 1119.80 0.0005 13554.38 0.0000 1157.60 218 0.956 Dutch Mountain 4 Wyoming 1 444.90 0.0005 7862.37 0.0000 1932.70 42 0.964 Dutch Mountain 5 Wyoming 0 385.50 0.0006 8452.92 0.0000 946.90 88 0.965 318 Jenningsville 1 Wyoming 0 259.10 0.0007 13997.25 0.0000 Jenningsville 1N Wyoming 1 2061.80 0.0007 12393.23 0.0000 2026.70 0.904 Jenningsville 2N Wyoming 1 1502.40 0.0006 12400.73 0.0000 1335.30 Meshoppen 1 Wyoming 0 414.20 0.0013 8617.82 0.0000 426.60 402 0.798 Meshoppen 1N Wyoming 1 831.80 0.0013 8763.22 0.0000 414.50 455 0.778 Meshoppen 2N Wyoming 1 202.60 0.0009 10486.80 0.0000 261.40 334 0.870 Noxen Wyoming 0 2203.10 0.0007 13030.57 0.0000 2108.50 655 0.937 Noxen 1 Wyoming 1 1328.50 0.0007 9656.14 0.0000 1428.60 247 0.925 Noxen 10 Wyoming 1 1025.90 0.0008 10649.31 0.0000 911.20 268 0.958 Noxen 10N Wyoming 1 1702.40 0.0008 11205.63 0.0000 1716.00 344 0.961 Noxen 1N Wyoming 1 1273.90 0.0008 12692.77 0.0000 1318.20 439 0.955 Noxen 2 Wyoming 1 911.60 0.0005 13087.59 0.0000 558.00 243 0.949 Noxen 2N Wyoming 1 1317.10 0.0008 12992.57 0.0000 1312.30 624 0.929 0.925 0.913 158 Noxen 3 Wyoming 0 1320.80 0.0005 13011.71 0.0000 1328.30 216 0.961 Noxen 3N Wyoming 1 1335.40 0.0005 12391.78 0.0000 1343.50 208 0.968 Noxen 4 Wyoming 1 711.20 0.0008 9559.28 0.0000 593.50 477 0.914 Noxen 4N Wyoming 1 1882.00 0.0004 14165.07 0.0000 1704.50 154 0.977 Noxen 5 Wyoming 0 2445.70 0.0004 14364.49 0.0000 1497.40 183 0.976 Noxen 5N Wyoming 1 955.30 0.0012 12570.92 0.0000 925.90 912 0.881 Noxen 6N Wyoming 1 934.80 0.0005 10707.81 0.0000 521.00 224 0.947 Noxen 7 Wyoming 0 1649.60 0.0008 12814.95 0.0000 1535.80 738 0.914 Noxen 7N Wyoming 1 1625.90 0.0008 11145.25 0.0000 1575.30 363 0.959 Noxen 8 Wyoming 1 1280.70 0.0010 12783.07 0.0000 1249.50 865 0.897 Noxen 8N Wyoming 1 299.40 0.0010 8074.57 0.0000 1071.00 427 0.894 Noxen 9 Wyoming 0 1746.90 0.0007 13753.02 0.0000 1800.80 597 0.936 Noxen 9N Wyoming 1 296.00 0.0014 12346.09 0.0000 358.80 1152 0.832 Tunkannock 1N Wyoming 1 244.80 0.0024 3519.70 0.0001 364.50 2669 0.701 Appendix B: Descriptive Statistics Appendix XIX. Descriptive statistics of factors at the 50m buffer zone for Model 1. Min. Max. Mean Count 29.9 2445.7 720.7 116 Road Density (m/m ) 0.000 0.0090 0.0001 118 Nearest Trail (m) 3.40 14548.48 5043.22 117 Trail Density (m/m ) 0.0000 0.0284 0.0005 118 Nearest Building (m) 51.50 3311.77 883.32 115 Total Buildings 0 0 0 118 Canopy Cover 0.286 1 0.947 117 Nearest Road (m) 2 2 Appendix XX. Descriptive statistics of factors at the 400m buffer zone for Model 1. Min. Max. Mean Count 29.90 2445.70 720.76 116 Road Density (m/m ) 0.0000 0.0036 0.0004 118 Nearest Trail (m) 3.40 14548.48 5043.22 117 Trail Density (m/m ) 0.0000 0.0053 0.0006 118 Nearest Building (m) 51.50 3311.77 883.32 115 Total Buildings 0 16 1.14 115 Canopy Cover 0.638 1.000 0.947 117 Nearest Road (m) 2 2 159 Appendix XXI. Descriptive statistics of factors at the 5000m buffer zone for Model 1. Min. Max. Mean Count 29.9 2445.7 764.4 101 Road Density (m/m ) 0.0003 0.0040 0.0013 102 Nearest Trail (m) 3.40 14548.48 5177.06 102 Trail Density (m/m2) 0.0000 0.0015 0.0002 102 Nearest Building (m) 78.10 3311.77 936.45 96 Total Buildings 42 15706 1871.03 95 Canopy Cover 0.584 0.977 0.885 101 Nearest Road (m) 2 Appendix XXII. Descriptive statistics of factors at the 50m buffer zone for Model 2. Min. Max. Mean Count 29.9 2061.8 737.5 77 Road Density (m/m ) 0.0000 0.0090 0.0001 79 Nearest Trail (m) 3.40 14517.33 5182.67 78 Trail Density (m/m2) 0.0000 0.0284 0.0007 79 Nearest Building (m) 51.50 3311.77 913.12 77 Nearest Road (m) 2 Total Buildings 0 0 0 79 Canopy Cover 0.4167 1.000 0.934 78 160 Appendix XXIII. Descriptive statistics of factors at the 400m buffer zone for Model 2. Min. Max. Mean Count 29.9 2061.8 737.5 77 Road Density (m/m ) 0.0000 0.0031 0.0004 79 Nearest Trail (m) 3.40 14517.33 5182.67 78 Trail Density (m/m2) 0.0000 0.0053 0.0005 79 Nearest Building (m) 51.50 3311.77 913.12 77 Nearest Road (m) 2 Total Buildings 0 12 0.666 78 Canopy Cover 0.677 1.000 0.954 78 Appendix XXIV. Descriptive statistics of factors at the 5000m buffer zone for Model 2. Min. Max. Mean Count 29.9 2061.8 796.1 64 Road Density (m/m ) 0.0004 0.0035 0.0013 65 Nearest Trail (m) 3.40 14517.33 5390.93 65 Trail Density (m/m2) 0.0000 0.0013 0.0002 65 Nearest Building (m) 78.16 3311.77 985.90 64 Nearest Road (m) 2 Total Buildings 42 7591 1675.58 63 Canopy Cover 0.701 0.976 0.891 64 161 Appendix XXV. Descriptive statistics of factors at the 50m buffer zone for Model 3. Present Absent Min Max Mean Count Min Max Mean Count Nearest Road 29.9 2061.8 737.5 77 88.5 2445.7 687.69 39 Road Density 0.0000 0.0090 0.0001 79 0.0000 0.0000 0.0000 39 Nearest Trail 3.4 14517.34 5182.67 78 87.3 14548.48 4764.34 39 Trail Density 0.0000 0.2849 0.0007 79 0.0000 0.0000 0.0000 39 Nearest Building 51.50 3311.77 913.12 77 98.70 2253.40 822.94 38 Total Buildings 0 0 0 79 0 0 0 39 Canopy Cover 0.416 1.000 0.934 78 0.286 1.000 0.972 39 Appendix XXVI. Descriptive statistics of factors at the 400m buffer zone for Model 3. Present Absent Min Max Mean Count Min Max Mean Count Nearest Road 29.9 2061.8 737.5 77 88.5 2445.7 687.6 39 Road Density 0.0000 0.0031 0.0004 79 0.0000 0.0036 0.0005 39 Nearest Trail 3.40 14517.34 5182.67 78 87.30 14548.48 4764.34 39 Trail Density 0.0000 0.0053 0.0005 79 0.0000 0.0052 0.0008 39 Nearest Building 51.50 3311.77 913.12 77 98.70 2253.40 822.94 38 Total Buildings 0 12 0.667 78 0 16 2.102 39 Canopy Cover 0.677 1.000 0.954 78 0.638 1.000 0.933 39 162 Appendix XXVII. Descriptive statistics of factors at the 5000m buffer zone for Model 3. Present Absent Min Max Mean Count Min Max Mean Count Nearest Road 29.9 2061.8 796.12 64 88.5 2445.7 709.54 37 Road Density 0.0004 0.0035 0.0013 65 0.0003 0.0040 0.0013 37 Nearest Trail 3.40 14517.34 5390.93 65 87.30 14548.48 4801.33 37 Trail Density 0.0000 0.0013 0.0002 65 0.0000 0.0015 0.0003 37 Nearest Building 78.16 3311.77 985.93 64 146.76 2253.40 837.50 32 Total Buildings 42 7591 1675.58 63 84 15706 2255.81 32 Canopy Cover 0.701 0.976 0.891 64 0.584 0.977 0.875 37 Appendix XXVIII. Descriptive statistics of factors at the 50m buffer zone for Model 4. Min. Max. Mean Count 29.9 2445.7 767.9 89 Road Density (m/m ) 0.0000 0.0090 0.0001 90 Nearest Trail (m) 21.60 14548.47 5676.42 90 Trail Density (m/m2) 0.0000 0.0227 0.0003 90 Nearest Building (m) 78.16 3311.77 913.34 88 Total Buildings 0 0 0 90 Canopy Cover 0.286 1.000 0.962 89 Nearest Road (m) 2 163 Appendix XXIX. Descriptive statistics of factors at the 400m buffer zone for Model 4. Min. Max. Mean Count 29.9 2445.7 767.9 89 Road Density (m/m ) 0.0000 0.0031 0.0004 90 Nearest Trail (m) 21.6 14548.4756 5676.423 90 Trail Density (m/m2) 0.0000 0.0053 0.0007 90 Nearest Building (m) 78.16 3311.77 913.34 88 Nearest Road (m) 2 Total Buildings 0 16 0.9438 89 Canopy Cover 0.677 1.000 0.944 89 Appendix XXX. Descriptive statistics of factors at the 5000m buffer zone for Model 4. Min. Max. Mean Count 29.9 2445.7 770.8 88 Road Density (m/m ) 0.0003 0.0040 0.0013 89 Nearest Trail (m) 21.60 14548.47 530.45 89 Trail Density (m/m2) 0.0000 0.0015 0.0002 89 Nearest Building (m) 78.16 3311.77 914.54 83 Total Buildings 42 15706 1904.3048 82 Canopy Cover 0.584 0.977 0.884 88 Nearest Road (m) 2 164 Appendix XXXI. Descriptive statistics of factors at the 50m buffer zone for Model 5. Min. Max. Mean Count 29.9 2061.8 809.4 52 Road Density (m/m ) 0.000 0.0090 0.0001 53 Nearest Trail (m) 21.60 14517.33 6287.33 53 Trail Density (m/m2) 0.0000 0.0227 0.0006 53 Nearest Building (m) 78.16 3311.77 953.65 52 Nearest Road (m) 2 Total Buildings 0 0 0 53 Canopy Cover 0.416 1.000 0.953 52 Appendix XXXII. Descriptive statistics of factors at the 400m buffer zone for Model 5. Min. Max. Mean Count 29.9 2061.8 809.4 52 Road Density (m/m ) 0.0000 0.0031 0.0003 53 Nearest Trail (m) 21.60 14517.33 6287.33 53 Trail Density (m/m2) 0.0000 0.0053 0.0007 53 Nearest Building (m) 78.16 3311.77 953.65 52 Nearest Road (m) 2 Total Buildings 0 6 0.3269 52 Canopy Cover 0.677 1.000 0.947 52 165 Appendix XXXIII. Descriptive statistics of factors at the 5000m buffer zone for Model 5. Min. Max. Mean Count 29.9 2061.8 815.3 51 Road Density (m/m ) 0.0004 0.0035 0.0013 52 Nearest Trail (m) 21.60 14517.33 6391.55 52 Trail Density (m/m2) 0.0000 0.0013 0.0002 52 Nearest Building (m) 78.16 3311.77 962.88 51 Nearest Road (m) 2 Total Buildings 42 7591 1679.34 50 Canopy Cover 0.701 0.976 0.890 51 Appendix XXXIV. Descriptive statistics of factors at the 50m buffer zone for Model 6. Present Absent Min Max Mean Count Min Max Mean Count Nearest Road 29.9 2061.8 809.4 52 88.5 2445.7 709.5 37 Road Density 0.0000 0.0090 0.0001 53 0.0000 0.0000 0.0000 37 Nearest Trail 21.60 14517.34 6287.33 53 87.30 14548.48 4801.33 37 Trail Density 0.0000 0.0227 0.0006 53 0.0000 0.0000 0.0000 37 Nearest Building 78.16 3311.77 953.65 52 146.76 2253.40 855.11 36 Total Buildings 0 0 0 53 0 0 0 37 Canopy Cover 0.416 1.000 0.953 52 0.286 1.000 0.975 37 166 Appendix XXXV. Descriptive statistics of factors at the 400m buffer zone for Model 6. Present Absent Min Max Mean Count Min Max Mean Count Nearest Road 29.9 2061.8 809.4 52 88.5 2445.7 709.5 37 Road Density 0.0000 0.0031 0.0003 53 0.0000 0.0022 0.0004 37 Nearest Trail 21.60 14517.34 6287.33 53 87.30 14548.48 4801.33 37 Trail Density 0.0000 0.0053 0.0007 53 0.0000 0.0052 0.0009 37 Nearest Building 78.16 3311.77 953.65 52 146.76 2253.40 885.11 36 Total Buildings 0 6 0.3269 52 0 16 1.8108 37 Canopy Cover 0.677 1.000 0.947 52 0.740 1.000 0.939 37 Appendix XXXVI. Descriptive statistics of factors at the 5000m buffer zone for Model 6. Present Absent Min Max Mean Count Min Max Mean Count Nearest Road 29.9 2061.8 815.3 51 88.5 2445.7 709.5 37 Road Density 0.0004 0.0035 0.0013 52 0.0003 0.0040 0.0013 37 Nearest Trail 21.60 14517.34 6391.55 52 87.30 14548.48 4801.33 37 Trail Density 0.0000 0.0013 0.0002 52 0.0000 0.0015 0.0003 37 Nearest Building 78.16 3311.77 962.88 51 146.76 2253.40 837.50 32 Total Buildings 42 7591 1679.34 50 84 15706 2255.81 32 Canopy Cover 0.701 0.976 0.890 51 0.584 0.977 0.875 37 167 Appendix C: Raw Data for Random Points Appendix XXXVII. Raw data at the 50m buffer zone for the one-hundred random points. OID Near Road (m) Road Density (m/m2) Near Trail (m) Trail Density (m/m2) Canopy 1 317.611 0.0000 7002.931 0.0000 1 2 281.638 0.0000 2318.486 0.0000 0 3 200.433 0.0000 1465.068 0.0000 0 4 208.680 0.0000 864.769 0.0000 1 5 362.259 0.0000 6441.596 0.0000 1 6 6.572 0.0260 5170.337 0.0000 0 7 609.840 0.0000 3475.755 0.0000 1 8 65.323 0.0000 688.438 0.0000 1 9 262.126 0.0000 5682.931 0.0000 1 10 191.663 0.0000 5230.337 0.0000 0 11 164.866 0.0000 1609.948 0.0000 1 12 19.987 0.0103 2119.352 0.0000 1 13 807.379 0.0000 851.974 0.0000 1 14 250.808 0.0000 4806.914 0.0000 1 15 955.744 0.0000 2162.231 0.0000 1 16 230.234 0.0000 3270.166 0.0000 1 17 231.544 0.0000 4674.978 0.0000 1 18 134.333 0.0000 7106.927 0.0000 1 19 8.777 0.0169 610.497 0.0000 0 20 224.096 0.0000 2229.744 0.0000 1 21 309.330 0.0000 2038.842 0.0000 1 22 165.948 0.0000 4816.098 0.0000 0 23 247.073 0.0000 4266.310 0.0000 0 24 611.067 0.0000 3021.257 0.0000 1 25 287.561 0.0000 8414.501 0.0000 1 26 23.488 0.0121 2923.158 0.0000 1 27 152.254 0.0000 1015.181 0.0000 1 28 192.126 0.0000 9418.221 0.0000 0 29 389.770 0.0000 8962.951 0.0000 1 30 369.355 0.0000 948.005 0.0000 0 31 77.358 0.0000 2180.727 0.0000 0 32 224.258 0.0000 6500.430 0.0000 0 33 160.333 0.0000 0.0000 0 34 1266.637 0.0000 3477.072 0.0000 1 35 376.449 0.0000 1894.005 0.0000 1 36 13.570 0.0209 977.777 0.0000 1 168 37 279.946 0.0000 7562.989 0.0000 1 38 183.525 0.0000 4203.417 0.0000 0 39 103.438 0.0000 2775.681 0.0000 1 40 29.224 0.0096 5991.501 0.0000 1 41 16.358 0.0118 5765.601 0.0000 1 42 1214.028 0.0000 1214.028 0.0000 1 43 573.185 0.0000 6396.252 0.0000 0 44 0.848 0.0127 4740.591 0.0000 0 45 1.449 0.0127 11844.386 0.0000 1 46 509.851 0.0000 7186.755 0.0000 1 47 521.752 0.0000 1517.770 0.0000 1 48 58.684 0.0000 1656.206 0.0000 1 49 10.742 0.0124 8.401 0.0124 1 50 111.226 0.0000 4457.615 0.0000 0 51 382.102 0.0000 3502.248 0.0000 0 52 898.659 0.0000 1803.675 0.0000 1 53 121.305 0.0000 3947.865 0.0000 0 54 1209.785 0.0000 344.456 0.0000 0 55 24.228 0.0111 746.917 0.0000 1 56 335.662 0.0000 0.0000 1 57 419.114 0.0000 4345.209 0.0000 1 58 32.037 0.0097 9483.457 0.0000 0 59 46.982 0.0044 182.839 0.0000 0 60 16.420 0.0238 1478.742 0.0000 0 61 40.983 0.0073 976.086 0.0000 1 62 642.378 0.0000 2178.282 0.0000 1 63 324.034 0.0000 2161.443 0.0000 0 64 194.685 0.0000 1936.630 0.0000 0 65 51.827 0.0000 1912.481 0.0000 0 66 5.480 0.0085 6491.087 0.0000 1 67 1021.497 0.0000 3439.948 0.0000 1 68 64.451 0.0000 2748.798 0.0000 1 69 226.200 0.0000 12526.513 0.0000 0 70 37.215 0.0056 9499.331 0.0000 1 71 67.434 0.0000 1969.470 0.0000 1 72 125.081 0.0000 478.536 0.0000 0 73 280.816 0.0000 656.508 0.0000 1 74 370.852 0.0000 3611.278 0.0000 1 75 65.894 0.0000 11529.944 0.0000 1 76 313.173 0.0000 4014.770 0.0000 0 169 77 382.552 0.0000 6297.893 0.0000 0 78 8.830 0.0231 2213.728 0.0000 0 79 159.744 0.0000 13125.969 0.0000 0 80 883.685 0.0000 46.622 0.0091 1 81 305.096 0.0000 3053.198 0.0000 0 82 11.684 0.0122 9710.952 0.0000 0 83 31.891 0.0170 1318.368 0.0000 1 84 357.126 0.0000 3688.058 0.0000 1 85 178.547 0.0000 4806.953 0.0000 1 86 619.812 0.0000 2527.002 0.0000 1 87 421.248 0.0000 548.340 0.0000 1 88 6.779 0.0247 6.779 0.0126 0 89 325.472 0.0000 7321.144 0.0000 1 90 54.077 0.0000 4441.603 0.0000 1 91 105.965 0.0000 6302.265 0.0000 0 92 134.047 0.0000 1814.767 0.0000 1 93 361.517 0.0000 3344.854 0.0000 1 94 264.575 0.0000 5818.811 0.0000 1 95 205.869 0.0000 935.916 0.0000 1 96 172.915 0.0000 2059.987 0.0000 0 97 56.489 0.0000 167.100 0.0000 1 98 78.242 0.0000 4876.436 0.0000 1 99 379.066 0.0000 4619.210 0.0000 1 100 65.529 0.0000 8899.716 0.0000 1 170 Appendix XXXVIII. Raw data at the 400m buffer zone for the one-hundred random points. OID Near Road (m) Road Density (m/m2) Near Trail (m) Trail Density (m/m2) Canopy 1 317.611 0.00193 7002.931 0.00000 1 2 281.638 0.00035 2318.486 0.00000 0 3 200.433 0.00399 1465.068 0.00000 0 4 208.680 0.00156 864.769 0.00000 1 5 362.259 0.00076 6441.596 0.00000 1 6 6.572 0.00325 5170.337 0.00000 0 7 609.840 0.00000 3475.755 0.00000 1 8 65.323 0.00779 688.438 0.00000 1 9 262.126 0.00201 5682.931 0.00000 1 10 191.663 0.00220 5230.337 0.00000 0 11 164.866 0.00452 1609.948 0.00000 1 12 19.987 0.00902 2119.352 0.00000 1 13 807.379 0.00000 851.974 0.00000 1 14 250.808 0.00230 4806.914 0.00000 1 15 955.744 0.00000 2162.231 0.00000 1 16 230.234 0.00076 3270.166 0.00000 1 17 231.544 0.00131 4674.978 0.00000 1 18 134.333 0.00306 7106.927 0.00000 1 19 8.777 0.01183 610.497 0.00000 0 20 224.096 0.00151 2229.744 0.00000 1 21 309.330 0.00100 2038.842 0.00000 1 22 165.948 0.00220 4816.098 0.00000 0 23 247.073 0.00422 4266.310 0.00000 0 24 611.067 0.00000 3021.257 0.00000 1 25 287.561 0.00084 8414.501 0.00000 1 26 23.488 0.00410 2923.158 0.00000 1 27 152.254 0.00158 1015.181 0.00000 1 28 192.126 0.00176 9418.221 0.00000 0 171 29 389.770 0.00037 8962.951 0.00000 1 30 369.355 0.00056 948.005 0.00000 0 31 77.358 0.00501 2180.727 0.00000 0 32 224.258 0.00243 6500.430 0.00000 0 33 160.333 0.00333 0.00000 0 34 1266.637 0.00000 3477.072 0.00000 1 35 376.449 0.00093 1894.005 0.00000 1 36 13.570 0.01188 977.777 0.00000 1 37 279.946 0.00229 7562.989 0.00000 1 38 183.525 0.00206 4203.417 0.00000 0 39 103.438 0.00195 2775.681 0.00000 1 40 29.224 0.00414 5991.501 0.00000 1 41 16.358 0.00626 5765.601 0.00000 1 42 1214.028 0.00000 1214.028 0.00000 1 43 573.185 0.00000 6396.252 0.00000 0 44 0.848 0.00259 4740.591 0.00000 0 45 1.449 0.00165 11844.386 0.00000 1 46 509.851 0.00000 7186.755 0.00000 1 47 521.752 0.00000 1517.770 0.00000 1 48 58.684 0.00387 1656.206 0.00000 1 49 10.742 0.00486 8.401 0.00171 1 50 111.226 0.00436 4457.615 0.00000 0 51 382.102 0.00047 3502.248 0.00000 0 52 898.659 0.00000 1803.675 0.00000 1 53 121.305 0.00241 3947.865 0.00000 0 54 1209.785 0.00000 344.456 0.00033 0 55 24.228 0.00496 746.917 0.00000 1 56 335.662 0.00086 0.00000 1 57 419.114 0.00000 4345.209 0.00000 1 58 32.037 0.00654 9483.457 0.00000 0 59 46.982 0.00646 182.839 0.00111 0 172 60 16.420 0.01007 1478.742 0.00000 0 61 40.983 0.01008 976.086 0.00000 1 62 642.378 0.00000 2178.282 0.00000 1 63 324.034 0.00121 2161.443 0.00000 0 64 194.685 0.00461 1936.630 0.00000 0 65 51.827 0.00724 1912.481 0.00000 0 66 5.480 0.00424 6491.087 0.00000 1 67 1021.497 0.00000 3439.948 0.00000 1 68 64.451 0.00732 2748.798 0.00000 1 69 226.200 0.00174 12526.513 0.00000 0 70 37.215 0.00081 9499.331 0.00000 1 71 67.434 0.00290 1969.470 0.00000 1 72 125.081 0.00145 478.536 0.00000 0 73 280.816 0.00122 656.508 0.00000 1 74 370.852 0.00041 3611.278 0.00000 1 75 65.894 0.00312 11529.944 0.00000 1 76 313.173 0.00140 4014.770 0.00000 0 77 382.552 0.00007 6297.893 0.00000 0 78 8.830 0.02131 2213.728 0.00000 0 79 159.744 0.00176 13125.969 0.00000 0 80 883.685 0.00000 46.622 0.00347 1 81 305.096 0.00199 3053.198 0.00000 0 82 11.684 0.00265 9710.952 0.00000 0 83 31.891 0.00452 1318.368 0.00000 1 84 357.126 0.00060 3688.058 0.00000 1 85 178.547 0.00260 4806.953 0.00000 1 86 619.812 0.00000 2527.002 0.00000 1 87 421.248 0.00000 548.340 0.00000 1 88 6.779 0.00507 6.779 0.00159 0 89 325.472 0.00015 7321.144 0.00000 1 90 54.077 0.00356 4441.603 0.00000 1 173 91 105.965 0.00211 6302.265 0.00000 0 92 134.047 0.00156 1814.767 0.00000 1 93 361.517 0.00011 3344.854 0.00000 1 94 264.575 0.00289 5818.811 0.00000 1 95 205.869 0.00547 935.916 0.00000 1 96 172.915 0.00642 2059.987 0.00000 0 97 56.489 0.00500 167.100 0.00126 1 98 78.242 0.00224 4876.436 0.00000 1 99 379.066 0.00005 4619.210 0.00000 1 100 65.529 0.00311 8899.716 0.00000 1 174 Appendix XXXIX. Raw data at the 5000m buffer zone for the one-hundred random points. OID Near Road (m) Road Density (m/m2) Near Trail (m) Trail Density (m/m2) Canopy 1 317.611 0.00306 7002.931 0.00000 1 2 281.638 0.00367 2318.486 0.00017 0 3 200.433 0.01199 1465.068 0.00017 0 4 208.680 0.00498 864.769 0.00048 1 5 362.259 0.00459 6441.596 0.00000 1 6 6.572 0.00390 5170.337 0.00000 0 7 609.840 0.00746 3475.755 0.00040 1 8 65.323 0.00163 688.438 0.00017 1 9 262.126 0.00745 5682.931 0.00000 1 10 191.663 0.00388 5230.337 0.00000 0 11 164.866 0.00455 1609.948 0.00014 1 12 19.987 0.00484 2119.352 0.00011 1 13 807.379 0.00256 851.974 0.00112 1 14 250.808 0.00414 4806.914 0.00004 1 15 955.744 0.00874 2162.231 0.00045 1 16 230.234 0.00220 3270.166 0.00018 1 17 231.544 0.00677 4674.978 0.00026 1 18 134.333 0.00245 7106.927 0.00000 1 19 8.777 0.00656 610.497 0.00016 0 20 224.096 0.00319 2229.744 0.00173 1 21 309.330 0.00233 2038.842 0.00010 1 22 165.948 0.00368 4816.098 0.00001 0 23 247.073 0.00341 4266.310 0.00019 0 24 611.067 0.00444 3021.257 0.00017 1 25 287.561 0.00277 8414.501 0.00000 1 26 23.488 0.00488 2923.158 0.00031 1 27 152.254 0.00117 1015.181 0.00115 1 28 192.126 0.00529 9418.221 0.00000 0 29 389.770 0.00433 8962.951 0.00000 1 30 369.355 0.00454 948.005 0.00038 0 31 77.358 0.00283 2180.727 0.00011 0 32 224.258 0.00640 6500.430 0.00000 0 33 160.333 0.00215 0.00000 0 34 1266.637 0.00103 3477.072 0.00007 1 35 376.449 0.00617 1894.005 0.00093 1 36 13.570 0.00662 977.777 0.00019 1 37 279.946 0.00282 7562.989 0.00000 1 175 38 183.525 0.00873 4203.417 0.00003 0 39 103.438 0.00503 2775.681 0.00030 1 40 29.224 0.00744 5991.501 0.00000 1 41 16.358 0.01018 5765.601 0.00000 1 42 1214.028 0.00172 1214.028 0.00039 1 43 573.185 0.00772 6396.252 0.00000 0 44 0.848 0.00351 4740.591 0.00007 0 45 1.449 0.00040 11844.386 0.00000 1 46 509.851 0.00225 7186.755 0.00000 1 47 521.752 0.00733 1517.770 0.00071 1 48 58.684 0.00329 1656.206 0.00034 1 49 10.742 0.00312 8.401 0.00018 1 50 111.226 0.00280 4457.615 0.00005 0 51 382.102 0.00411 3502.248 0.00012 0 52 898.659 0.00310 1803.675 0.00165 1 53 121.305 0.00426 3947.865 0.00025 0 54 1209.785 0.00474 344.456 0.00117 0 55 24.228 0.00588 746.917 0.00093 1 56 335.662 0.00292 0.00000 1 57 419.114 0.00107 4345.209 0.00007 1 58 32.037 0.00335 9483.457 0.00000 0 59 46.982 0.00613 182.839 0.00042 0 60 16.420 0.01428 1478.742 0.00017 0 61 40.983 0.00404 976.086 0.00106 1 62 642.378 0.00277 2178.282 0.00038 1 63 324.034 0.00214 2161.443 0.00024 0 64 194.685 0.01295 1936.630 0.00020 0 65 51.827 0.00662 1912.481 0.00003 0 66 5.480 0.00388 6491.087 0.00000 1 67 1021.497 0.00219 3439.948 0.00005 1 68 64.451 0.00237 2748.798 0.00017 1 69 226.200 0.00296 12526.513 0.00000 0 70 37.215 0.00177 9499.331 0.00000 1 71 67.434 0.00359 1969.470 0.00043 1 72 125.081 0.00404 478.536 0.00025 0 73 280.816 0.00689 656.508 0.00088 1 74 370.852 0.01000 3611.278 0.00012 1 75 65.894 0.00433 11529.944 0.00000 1 76 313.173 0.00445 4014.770 0.00022 0 77 382.552 0.00339 6297.893 0.00000 0 176 78 8.830 0.00551 2213.728 0.00050 0 79 159.744 0.00291 13125.969 0.00000 0 80 883.685 0.00336 46.622 0.00093 1 81 305.096 0.00341 3053.198 0.00048 0 82 11.684 0.00266 9710.952 0.00000 0 83 31.891 0.00564 1318.368 0.00013 1 84 357.126 0.00350 3688.058 0.00022 1 85 178.547 0.00330 4806.953 0.00001 1 86 619.812 0.00202 2527.002 0.00026 1 87 421.248 0.00134 548.340 0.00033 1 88 6.779 0.00238 6.779 0.00016 0 89 325.472 0.00225 7321.144 0.00000 1 90 54.077 0.00306 4441.603 0.00022 1 91 105.965 0.00190 6302.265 0.00000 0 92 134.047 0.00435 1814.767 0.00008 1 93 361.517 0.00945 3344.854 0.00056 1 94 264.575 0.00750 5818.811 0.00000 1 95 205.869 0.00397 935.916 0.00049 1 96 172.915 0.00547 2059.987 0.00016 0 97 56.489 0.00309 167.100 0.00046 1 98 78.242 0.00286 4876.436 0.00005 1 99 379.066 0.00283 4619.210 0.00019 1 100 65.529 0.00323 8899.716 0.00000 1 177 Appendix D: Descriptive Statistics for Random Points Appendix XL. Descriptive statistics of factors at the 50m buffer zone for the random points. Min Max Mean Count Nearest Road 0.8484 1266.63 269.03 100 Road Density 0 0.026 0.0029 100 Nearest Trail 6.779 13125.97 3917.05 98 Trail Density 0 0.0125 0.000341 100 Canopy 0 1 0.64 100 Appendix XLI. Descriptive statistics of factors at the 400m buffer zone for the random points. Min Max Mean Count Nearest Road 0.8484 1266.63 269.03 100 Road Density 0 0.0213 0.00285 100 Nearest Trail 6.779 13125.97 3917.05 98 Trail Density 0 0.00347 0.00009468 100 Canopy 0 1 0.64 100 Appendix XLII. Descriptive statistics of factors at the 5000m buffer zone for the random points. Min Max Mean Count Nearest Road 0.8484 1266.63 269.03 100 Road Density 0.000398 0.0142 0.00441 100 Nearest Trail 6.779 13125.97 3917.05 98 Trail Density 0 0.00173 0.000253 100 Canopy 0 1 0.64 100 178 Appendix E: R-Squared and AIC Values Appendix XLIII. R-Squared and AIC values for all models, sorted by model number, then by spatial scale. Note that GLMs do not give an R-squared value. Model 1 50m Model 1 400m Model 1 5000m Model 2 50m Model 2 400m Model 2 5000m Model 3 50m Model 3 400m Model 3 5000m Model 4 50m Model 4 400m Model 4 5000m Model 5 50m Model 5 400m Model 5 5000m Model 6 50m Model 6 400m Model 6 5000m R- Squared 0.0234 -0.0050 0.0879 0.0302 0.0348 0.1097 0.0557 -0.0337 0.1220 0.1000 0.0385 0.2210 - AIC 806.53 810.73 677.678 562.81 563.34 468.03 128.75 152.33 128.75 648.38 643.89 592.02 394.96 399.19 375.34 124.15 121.16 118.15 Appendix XLIV. R-Squared and AIC Values for all models sorted by spatial scale followed by model number. Note that GLMs do not give an R-squared value. Model 1 50m Model 2 50m Model 3 50m Model 4 50m Model 5 50m Model 6 50m Model 1 400m Model 2 400m Model 3 400m Model 4 400m Model 5 400m Model 6 400m Model 1 5000m Model 2 5000m Model 3 5000m Model 4 5000m Model 5 5000m Model 6 5000m R- Squared 0.0234 0.0302 0.0557 0.1000 -0.0050 0.0348 -0.0337 0.0385 0.0879 0.1097 0.1220 0.2210 - 179 AIC 806.53 562.81 128.75 648.38 394.96 124.15 810.73 563.34 152.33 643.89 399.19 121.16 677.67 468.03 128.75 592.02 375.34 118.15 180 Appendix F: Significant Factors Appendix XLV. All models shown ordered by spatial scale with an 'X' indicating factors that showed a significant relationship with population size. Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within X - Model 1 5000m X X Model 2 50m X - Model 2 400m X Model 2 5000m X Model 1 50m Canopy Percent Model 1 400m Model 3 50m - Model 3 400m X Model 3 5000m Model 4 50m X - Model 4 400m Model 4 5000m X Model 5 50m X Model 5 400m X Model 5 5000m X - X Model 6 50m - Model 6 400m Model 6 5000m 181 X X Appendix XLVI. All models shown ordered by model number with an 'X' indicating factors that showed a significant relationship with population size. Nearest Road Road Density Nearest Trail Trail Density Nearest Building Buildings Within Model 1 50m X - Model 2 50m X - Model 3 50m Canopy Percent - Model 4 50m X Model 5 50m X Model 6 50m - Model 1 400m Model 2 400m X Model 3 400m X Model 4 400m Model 5 400m X Model 6 400m Model 1 5000m X Model 2 5000m X X Model 3 5000m Model 4 5000m Model 5 5000m X Model 6 5000m 182 X X X X Appendix G: TRAP Sites Appendix XLVII. Rattlesnake sites in the Northeast produced by TRAP through the PFBC. Sites are randomly offset from actual locations by up to 5000m to minimize the potential of poaching activity from this work. 183