The Pennsylvania State University

The Graduate School

COARSE AND FINE-SCALE DETERMINANTS OF SPECIES RANGE BOUNDARIES

A Dissertation in

Ecology

by

Staci M. Amburgey

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

May 2019 ii

The dissertation of Staci M. Amburgey was reviewed and approved* by the following:

David Miller Assistant Professor of Wildlife Population Ecology Dissertation Advisor Chair of Committee

Tracy Langkilde Professor and Head of Biology

Tyler Wagner Adjunct Professor of Fisheries Ecology Assistant Unit Leader, PA Cooperative Fish and Wildlife Research Unit

Matt Marshall Adjunct Assistant Professor of Wildlife Conservation National Park Service Eastern Rivers and Mountains Network Program Manager

Evan Grant Special Member U.S. Geological Survey Principle Investigator of the Amphibian Research and Monitoring Initiative

Jason Kaye Chair of the Intercollege Graduate Degree Program in Ecology Professor of Soil Biogeochemistry

*Signatures are on file in the Graduate School

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ABSTRACT

Species’ distributions change over time, contracting (e.g., through habitat loss, declining suitability from changing climate, negative interspecific interactions) or expanding (e.g., through restoration efforts, colonization of newly suitable habitats from changing climate, newly available niches due to turnover in species communities). A pressing need in conservation ecology is the identification of factors contributing to patterns of species occurrence and range limits. Conservation decisions rely on understanding ecological patterns and relationships between species and their environment. My dissertation aims to improve capacity for conservation by elucidating factors contributing to the formation of species distributions.

Our understanding of the causes of species range limits is complicated by scale-dependency in species’ responses to natural or anthropogenic pressures. For example, climate, species interactions, and habitat fragmentation can act at different scales to explain patterns of species occurrence across space and time. Climate is considered one of the most important, broad-scale filters of species occurrence as it acts on all components of an ecosystem. On the other hand, species interactions are considered more important in explaining finer, local-scale occurrence patterns. Additionally, the impact of anthropogenic changes such as urbanization can alter the distribution of species and their biological communities and interact with broad- and fine-scale pressures to explain species occurrence. Robust methods, such as quantitative population modeling, are necessary to understand the role of these factors in shaping species distributions while accounting for sampling error and process variation. In the following dissertation chapters, I advance our understanding of species distributions and the formation of range limits at multiple scales using three different systems and a suite of quantitative population modeling approaches.

In my first chapter, I assessed the importance of range position in explaining the climate sensitivity of wood frog (Lithobates sylvaticus) populations. The wood frog is a widely distributed, pond- breeding amphibian species that occurs across much of the northeastern United States and into Canada and Alaska. I developed a state-space model to quantify the effect that annual climate anomalies have on iv population growth rates relative to the regional climate of a site at its position in the range. I hypothesized that climate sensitivity would be highest at the climate extremes of the wood frog range. Increased sensitivity would translate into reduced population growth rates during years with climate anomalies approaching those extremes (i.e., climate envelope hypothesis). By understanding the influence of climate on demographic rates in different regional climates, I was better able to understand how climate contributes to the formation of range limits and hindcast wood frog population trajectories over the last thirty years. Some models supported the climate envelope hypothesis, where decreased population growth rates were associated with hotter years in warmer regions and more water availability was associated with increased recruitment in drier regions. Spatial and temporal variation in responses to climate highlight the importance of using dynamic models that focus on demographic responses to understand population trajectories.

In my second chapter, I focused on species interactions between the Shenandoah salamander

(Plethodon shenandoah) and the red-backed salamander (P. cinereus) to understand fine-scale drivers of species’ range limits. In sympatry, the widely distributed red-backed salamander is thought to competitively exclude the range-restricted Shenandoah salamander from deeper soil habitats, and it is largely considered responsible for setting the lower range limit of Shenandoah salamanders. Using repeat surveys to account for imperfect detection, I fit a suite of occupancy models to see if single-species or two-species conditional models better estimated the range boundary. Two-species models condition the occupancy and detection probabilities of one species on the occupancy and detection probabilities of a second species, accounting for potential species interactions to explain patterns in occupancy. I also included conditional autoregressive random effects that incorporate spatial information that may improve estimation of the range boundary. I found that accounting for imperfect detection prevented underestimation of the range boundary and area of co-occurrence of these two species. Spatially explicit single-species models also performed the best given the data, indicating that species interactions were not necessary for predicting the range boundary at this scale. I found that the co-occurrence zone of these two v species is wider than previously thought, highlighting the importance of methods that incorporate detection probability and spatial autocorrelation in understanding range boundaries.

For my third chapter, I build off the second chapter’s findings to investigate trait, behavior, and population process differences between Shenandoah and red-backed salamanders that may facilitate this broader than expected area of co-occurrence. When species with strong competitive interactions co-occur, theory predicts that co-occurrence can be facilitated by the differentiation of traits or behaviors in a way that reduces the strength of competition and therefore also limits the niche overlap of the species. In the case of the Shenandoah salamander, I predicted that if strong competitive exclusion was the primary cause of the range boundary between these two species, individuals in sympatry would have different physical characteristics, behaviors, or demography than in areas of allopatry. Using range boundaries identified in Chapter Two, I found little support for character displacement in individual trait or behavioral measures. While differentiation existed, traits showed species-level differences or similarly varied in both species by location on the transect. Differentiation in demography in the co-occurrence zone did support competitive processes potentially altering recruitment or dispersal in the co-occurrence zone. Additionally, microhabitat behavioral selection indicated some level of differentiation in cover object use that may help facilitate broader areas of co-occurrence. Species interactions may help structure species range limits through their influence on population-level processes, but traits may not show morphological differentiation rather than demographic differences when range limits occur over such a fine-scale gradient.

Lastly, my fourth chapter investigates the way intermediate-scale pressures such as habitat fragmentation can shape the occurrence of species across a landscape. Using multispecies occupancy models, I was able to estimate occupancy probabilities of 45 different species in small vertebrate communities across the California Floristic Province. This landscape was largely modified by urbanization in the last several decades, impacting the patterns of species richness in remaining habitat fragments. I investigated if species-specific variation in occupancy at the level of the overall patch and at sites within the patch could be explained by taxonomy, species life history, and range information. vi

Specifically, I estimated species commonness (relative prevalence) and sensitivity (response to patch size) at the patch and site. Taxonomic differences in species commonness explained patterns of biogeography in this region while species differences in fecundity and responses to extreme climate were correlated to sensitivity to fragmentation. Amphibian and mammalian sensitivity to patch size depended on whether patches occurred in more extreme portions of their climate range while reptiles did not differ in their response. By understanding biogeographic patterns in species occurrence and how fragmentation may impact species persistence in habitat patches, conservation measures can be targeted to those species most sensitive to the compounding effects of these broad-scale pressures.

Conservation decisions are based on an understanding of ecological patterns and relationships that are scale-dependent. I investigated broad-scale, intermediate, and local-level factors that contribute to population responses and shape species distributions. I identified key findings in each system that can be used by practitioners in conservation and management efforts. I also provided methodologies that allow for the quantification of these processes on species distributions, improving our understanding of species distribution modeling.

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TABLE OF CONTENTS

List of Figures ...... x

List of Tables ...... xv

Acknowledgements ...... xvii

Chapter 1 Introduction ...... 1

Literature Cited ...... 5

Chapter 2 Range position and climate sensitivity: the structure of among-population demographic responses to climatic variation ...... 8

Abstract ...... 10 Introduction ...... 11 Study System and Methods ...... 15 Study System and Life History ...... 15 Field sampling ...... 16 Climate covariates ...... 17 Data analysis ...... 18 Results ...... 23 Discussion ...... 25 Acknowledgements ...... 31 References ...... 31 Supplementary Material ...... 56 Table S1. Mean latitude (Lat) and longitude (Lon) of wood frog study areas (n = 27) included in the analysis. Precip and Hydro columns are the 3-month periods used to calculate Precip and Hydro annual climate values...... 56 Appendix S1. JAGS code for state space model implemented in program R via the R2jags package...... 68

Chapter 3 Knowing your limits: estimating range boundaries and co-occurrence zones for two competing plethodontid salamanders ...... 70

Abstract: ...... 71 Introduction ...... 73 Methods ...... 77 Study system ...... 77 Field sampling ...... 78 Data analysis ...... 80 Model comparisons ...... 83 Results ...... 84 Model comparison ...... 85 Discussion ...... 87 Conclusions ...... 92 Acknowledgments ...... 93 Literature Cited ...... 93 Data Availability ...... 102 viii

Appendix S1. WinBUGS code for occupancy models implemented in program R via the R2WinBUGS pacskage...... 116 Supplementary Material ...... 122 Figure S1. Based on raw detection data, Shenandoah (Plethodon shenandoah, PSHE) and red-backed (Plethodon cinereus, PCIN) salamanders did not frequently co- occur at a site (5x4m square) during a single survey but did overlap across a larger area when summarizing information from all surveys (Overall). Species non- detections (None) show the importance of accounting for detection probabilities. Data shown are for all transects surveyed at each study area. Meters surveyed varied by visit, and not all transects were surveyed both years...... 122 Figure S2. Number of detected animals over all surveys of each species across each transect for each study area. Animals were summed by each 5x4m grid of each transect with distance measured in meters. Panels a-c) are transects 1-3 of study area one, d-f) are transects 1-3 of study area two, g-i) are transects 1-3 of study area three, and j-k) are transects 1-2 of study area four. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 127 Figure S3. Proportion of each substrate type at each site. Panels a-c) are transects 1-3 of study area one, d-f) are transects 1-3 of study area two, g-i) are transects 1-3 of study area three, and j-k) are transects 1-2 of study area four. Substrate was simplified into four categories: rock (exposed expanses of talus, boulder, and cobble), soil/wood (fallen woody debris and open stretches of ground), moss/leaf (thick, moist ground cover), and other (none of the above). Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 129 Figure S4. Comparison of occupancy probabilities for all study areas across the four model permutations. Occupancy probability at each site for each species (green line = Shenandoah salamander, orange line = red-backed salamander) for each transect (top row = 1st transect, middle row = 2nd transect, and bottom row = 3rd transect) with proportion of each substrate type at each site. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 131 Figure S5. Comparison of true occupancy for all study areas across the four model permutations. True occupancy at each site for each species [orange = red-backed salamander (zA), green = Shenandoah salamander (zB)] for each transect (top row = 1st transect, middle row = 2nd transect, and bottom row = 3rd transect). Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 136 Figure S6. Overlap zone of both species based on the best supported single species occupancy model with CAR random effect. Co-occurrence probability was calculated by multiplying ψA * ψB. These values were then summed across each site and the next two immediate sites (three 4m x 5m sites or 60m2 area used; Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. Highlighted ix

areas are those with summed probabilities over 1 (i.e., at least 2 of the 3 sites in a row have co-occurrence probabilities of 0.5 or greater)...... 141 Table S1. Beta coefficients for Rock, SoilWood, MossLeaf, and Other substrates for each species, red-backed salamanders (PCIN; Plethodon cinereus) and for Shenandoah salamanders (PSHE; Plethodon shenandoah). Standard deviation and 2.5, 50, and 97.5% percentiles included from each of our four models. The single species occupancy model with CAR random effect was best supported in cross- fold validation...... 143

Chapter 4 Factors Facilitating Co-occurrence at the Range Boundary of Shenandoah and Red- backed Salamanders ...... 146

MATERIALS AND METHODS ...... 153 RESULTS ...... 157 DISCUSSION ...... 161 LITERATURE CITED ...... 168 APPENDIX 1. Model selection results and type of regression model for all trait and behavioral analyses. All models fit were logistic regressions except for SVL, which was a linear regression...... 189 APPENDIX 2. Microhabitat temperature by species detected in each zone of the transect (PSHE = Shenandoah salamander; PCIN = Red-backed salamander). Co-occur zone indicates the area of highest probability species co-occurrence based on Amburgey et al. (in review)...... 194 APPENDIX 3. Mean microhabitat temperature by date of survey. Error bars represent standard error...... 195 APPENDIX 4. Microhabitat temperature by species detected and microhabitat type in each zone of the transect (PSHE = Shenandoah salamander; PCIN = Red-backed salamander). Co-occur zone indicates the area of highest probability species co- occurrence based on Amburgey et al. (in review). Microhabitat types were (A) rock, (B) log, and (C) leaf litter...... 196

Chapter 5 Taxonomy, life history, range size, and climate envelope position determine species responses to habitat fragmentation in southern California ...... 197

Abstract ...... 198 Introduction ...... 200 Materials and Methods ...... 204 Pitfall Sampling and Covariate Measuring ...... 204 Life History and Range Size Information ...... 205 Climate Covariates ...... 205 Data analysis ...... 206 Results ...... 212 Taxonomic differences ...... 212 Life history and range covariates ...... 214 Discussion ...... 216 Acknowledgements ...... 221 Literature Cited ...... 222 Appendices ...... 243 Appendix 3. Beta coefficients for all models. Standard deviation (SD) and 2.5 and 97.5th quartile values provided...... 268

x

LIST OF FIGURES

Figure 2-1. The wood frog is a broadly distributed species that spans most of the northeastern United States into Canada and Alaska. Red dots indicate sites where egg mass counts were obtained. Thirty-year annual (a) precipitation and (b) temperature (Hijmans et al. 2005) maps show the broad range of climate conditions this species experiences across its range (IUCN 2014)...... 49

Figure 2-2. (a) The wood frog range (light grey) with an example of a northern (blue), central (black), and southern (red) population. (b) These populations come from different long-term climate normals (e.g., colder to warmer represented by mean 30-year temperature). If wood frog responses are consistent with bioclimatic envelope predictions, the probability of occurrence of wood frogs peaks at some optimal temperature and declines in more extreme conditions. (c) Sensitivity of wood frog population growth rates to annual climate variation is predicted to vary by long-term climate (shaded regions are 95% credible intervals). Sensitivity is the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions. We predict 1) populations in colder areas (blue) will be sensitive to warmer than average annual temperatures, leading to higher population growth rates (positive values); 2) populations in hotter areas (red) will be sensitive to warmer than average annual temperatures, leading to lower population growth rates (negative values); and 3) populations in areas far from climate extremes (black) will not be strongly affected by year to year deviations in temperature, leading to fairly consistent population growth rates (values around zero)...... 50

Figure 2-3. The climate space [based on 30-year mean annual temperature (oC*10) and precipitation values (mm)] that encompasses North America (dark blue), the wood frog range (light green), and our sites (red). Points on the scatterplot represent all temperature by precipitation raster cell values where wood frogs occur (light green) and do not occur (dark blue), with our sites in red. Precipitation values were truncated at 3500 mm for visualization purposes. Histograms represent frequencies of these same 30-year annual precipitation (top) and temperature (right) values in just the wood frog range. Boxplots of precipitation and temperature values from our sites show the minimum, median, maximum and 25th and 75th quartiles (box)...... 51

Figure 2-4. We estimated how sensitivity of wood frog population growth rates to annual climate variation changed with respect to long-term climate differences (shaded regions are 95% credible intervals). Sensitivity is the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions (y-axis). Long-term differences in mean climate are calculated using 30-year climate normals for conditions during the same portion of the year that annual covariates are measured (x-axis; see Tables 1 and 2) at our sampled sites. (a) Annual wood frog population growth rate two years later responded negatively to spring precipitation (PRECIP lag) across all areas, (b) annual wood frog population growth rate responded positively to years with more summer water availability (HYDRO) in areas where long-term average summer precipitation was higher (>50 mm), (c) annual wood frog population growth rate two years later responded negatively to years with more summer water availability (HYDRO lag) in areas where long-term average summer precipitation was higher (>105 mm) and positively in years where long-term averages were lower (<105 mm), (d) annual wood frog population growth rate responded negatively to extreme summer temperatures (HEAT) in areas where long-term average extreme temperature was higher (>24°C) and positively where long-term averages were lower xi

(<24°C), (e) annual wood frog population growth rate responded positively to increased winter severity (COLD) in areas where long-term average minimum temperature was milder (>-6.25°C) and negatively where long-term averages were colder (<-6.25°C)...... 52

Figure 2-5. We estimated how sensitivity of wood frog population growth rates to annual climate variation changed with respect to long-term climate differences (shaded regions are 95% credible intervals). Sensitivity is the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions (y-axis). Long-term differences in mean climate are calculated using 30-year climate normals for conditions during the same portion of the year that annual covariates are measured (x-axis; see Tables 1 and 2) at our sampled sites. (a) Annual wood frog population growth rate two years later did not significantly respond to spring precipitation (PRECIP lag) regardless of long-term winter severity, (b) annual wood frog population growth rate did not significantly respond to winter severity (COLD) regardless of long-term spring precipitation, (c) annual wood frog population growth rate did not significantly respond to summer water availability (HYDRO) regardless of long-term extreme summer heat, (d) annual wood frog population growth rate responded positively to years with more extreme summer temperatures (HYDRO lag) in areas where long-term average summer precipitation was higher (>20 mm), (e) annual wood frog population growth rate two years later responded negatively to increased summer water availability (HYDRO lag) in areas where long-term average extreme temperature was higher (>26.25°C) and positively where long-term averages were lower (<26.25°C)...... 54

FIGURE 3-1. Based on raw detection data, Shenandoah (Plethodon shenandoah, PSHE) and red- backed (Plethodon cinereus, PCIN) salamanders did not frequently co-occur at a site (5-m by 4-m square) during a single survey but did overlap across a larger area when summarizing information from all surveys (Overall). Species non-detections (None) show the importance of accounting for detection probabilities. Data shown are for a single transect at study area one. Meters surveyed varied by visit...... 107

FIGURE 3-2. Model comparison of occupancy probabilities for study area two. Occupancy probability at each site for each species (green = Shenandoah salamander, orange = red- backed salamander) for each transect (row) and model type (column). Distance was measured in meters. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 108

FIGURE 3-3. Detection probabilities across all transects from (a) single-species and (b-c) two species conditional occupancy models with conditional autoregressive (CAR) random effects. Models without a CAR random effect had similar probabilities to those shown. Detection probabilities were estimated for each survey but were either (a) estimated separately for each species or (b-c) were conditional upon the other species. Conditional probabilities include the probability of (b) detecting PCIN given that either PSHE is absent (pA) or PSHE and PCIN are both present (rA) and (c) detecting PSHE given either PCIN is absent (pB), both species are present and PCIN is also detected (rBA), or PCIN is not also detected (rBa)...... 109

FIGURE 3-4. Logit-transformed parameter coefficients for substrate covariates for red-backed salamanders (PCIN, Plethodon cinereus) and Shenandoah salamanders (PSHE, Plethodon shenandoah). ‘Other’ substrate not shown. Substrate coefficients and 2.5 and 97.5% credible intervals are from single-species occupancy model with conditional autoregressive (CAR) random effect...... 110 xii

FIGURE 3-5. Mean posterior occupancy probability at each site for each species with proportion of each substrate type at each site from best supported single-species occupancy models with conditional autoregressive (CAR) random effect. Values for the first transect for every study area shown. Distance was measured in meters. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 111

FIGURE 3-6. Primary (and secondary) contact zone(s) (dark green) of both species for a single transect of a single study area based on each of the four models. For single-species models, co-occurrence probability was calculated by multiplying ψiA*ψiB. For two species models, co-occurrence probability was calculated from our conditional Shenandoah model using only surveys where zA was estimated to be 1. These values were then summed across each site and the next two immediate sites (three 5-m by 4-m sites or 60m2 area used, Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. Single-species and two species conditional occupancy models with only substrate covariates do not show a clear peak area of overlap but rather have low levels of co-occurrence across much of their length. Origins of transects were marked as 0 m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 112

FIGURE 3-7. Primary and secondary contact zone(s) (dark green) of both species based on the single-species occupancy model with CAR random effect for the first transect for every study area. Co-occurrence probabilities (ψiA*ψiB) were summed across each site and the next two immediate sites (three 5-m by 4-m sites or 60m2 area used, Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. We defined the co-occurrence zone as the area of overlap at the range edge of resident animals. Summarizing nearby sites by their occupancy allowed for the identification of core areas of overlap rather than including potentially dispersing or outlier animals. Distance was measured in meters. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters...... 114

FIGURE 4-1. Hypotheses to test for measured traits and behaviors of Shenandoah (grey) and Red- backed (dark grey) Salamanders across the Shenandoah Salamander (PSHE), Co-occurrence, and Red-backed Salamander (PCIN) zones. For the pictured example of salamander body size (SVL), larger Shenandoah and smaller Red-backed Salamanders in only the Co- occurrence zone would support H1. Species differentiation (H2) indicates species are consistently different across all zones. Character convergence by zone (H3) occurs when both species are similar in only certain zones. Zonal differences (H4) indicate size differs by zone but similarly for both species. Lastly, complete convergence (H5) indicates no difference across zones or species...... 176

FIGURE 4-2. Morphological traits and population parameters differed by species [Shenandoah (PSHE, triangles) and Red-backed Salamanders (PCIN, circles)], transect zone (diamonds), or both. The Co-occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones. Shown are proportions with standard errors of animals (A) with a striped dorsum, indicating differences by species, (B) with damaged tails, indicating differences by species by zone, (C) that are female, indicating differences by species, and (D) that are adults, indicating differences by zone...... 177 xiii

FIGURE 4-3. Morphological stripe traits and microhabitat selection behavior differed either by species [Shenandoah (PSHE) and Red-backed Salamanders (PCIN)] or species by transect zone. The Co-occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones. Percentages of animals (A) with different stripe colors, indicating differences by species (except in the case of yellow stripes where differences were non-significant) and (B) using microhabitat types, indicating differences by zone by species...... 178

FIGURE 4-4. Mean snout-vent length (SVL) of captured animals. Bars represent standard errors. An interaction between species [Shenandoah (PSHE) and Red-backed (PCIN) Salamander] and zone best explained differences in body size for both metrics. The Co-occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones...... 179

FIGURE 4-5. General linear model results from our best supported microhabitat temperature-use model indicated support for an interaction of species [Shenandoah (PSHE) and Red-backed (PCIN) Salamander] and zone. The Co-occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones. 95% confidence intervals are shown in grey. Each salamander species used microhabitats more frequently in each species’ respective home range, but microhabitat use similarly declined as temperature increased for both species...... 180

Figure 5-1. The southwest United States (A, box) where pitfall sampling occurred is one of the world’s biodiversity hotspots. Red dots indicate pitfall arrays (i.e., sites) spanning six counties in southern California (B). Species ranges differ in their extent that overlaps with the pitfall sampling area and the long term (30-year) total winter precipitation and mean summer maximum temperatures experienced (C). Levelplots show the distribution of climate values across the latitude and longitude of species ranges...... 233

Figure 5-2. Data collection occurred through pitfall array sampling [each array consisting of three arms, seven pitfall traps (PFT), and three funnel traps (FT), A]. Every array (i.e., site i) was nested within a patch separated from other habitat by roads or other development (i.e., patch j). Multiple arrays in each patch allowed us to sample and estimate patch (Ωsj) and site (ψsj) occupancy probabilities as part of a hierarchical nested multispecies occupancy model (B). Species that occur at sites are a subset of those that occur at the overall patch. We predicted that variation in species responses to patch size could be associated with taxonomic group (C), where mammalian species might be most robust to changes in patch size based on reproductive and endothermic evolutionary strategies. Species life history traits within these taxonomic groups might explain additional variation in occupancy, e.g., species with larger body sizes might be more sensitive to the effects of fragmentation (D). Responses to fragmentation might also vary within species, e.g., species might be more sensitive to fragmentation at patches in hotter areas of their range (E). We examined patterns in two variables: relative commonness across the landscape (intercept) as well as sensitivity to patch size (slope) for individual species (C). We measured these variables at two scales: the probability of occupancy at the patch and, given the patch is occupied, the proportion of sites within a patch at which a species can be found (F)...... 234

Figure 5-3. Species commonness and sensitivity at the patch (A and C) and site (B and D) levels by taxonomic group. We transformed commonness values from the logit to probability scale to show the occupancy of species at the average patch and site within a patch. Mean taxonomic group values are bolded and indicate that mammals were most common in xiv

patches (A) and sites (B) while all groups are more sensitive to patch size at the patch (C) rather than site (D) levels. However, species-specific values vary within taxonomic group, and species that were sensitive to patch size (as judged by whether the 95% credible interval overlapped 0) are highlighted in grey. Species of conservation concern denoted as EN = endangered, NT = near threatened, or SC = California species of special concern...... 236

Figure 5-4. Scatterplots showing (A) a correlation between how common a species is at the patch and at sites within a patch, (B) no correlation between species sensitivity to patch size at the patch or sites within a patch, (C) a weak negative relationship between how common and sensitive to patch size a species is at the patch, and (D) no correlation between how common and sensitive to patch size a species is at the site. Shaded area is 95% credible region. . 238

Figure 5-5. Patch and site occupancy probabilities across patch sizes for example species from each taxonomic group (amphibians = teal, A-D; mammals = yellow, E-H; reptiles = purple, I-L). Probabilities are transformed from the logit to probability scale and plotted against loge standardized patch sizes (though unstandardized sizes are shown on the x-axis to highlight the spectrum of patch sizes). Species generally responded positively to increasing patch size at the patch level but had the opposite response at the site level. Some species have very clear plateaus in occupancy probabilities once a certain patch size is reached (e.g., E and F) while others increase only as some of the largest patch sizes are reached (e.g., A). Solid lines are posterior means and shaded areas are 95% credible regions...... 239

Figure 5-6. Estimated covariate effect sizes from models for A) body size (cm), B) fecundity (average young per year), C) age at sexual maturity (years), and D) range size (ha). Most covariates did not significantly influence species commonness and sensitivity at either the patch or site levels (as judged by whether 95% credible intervals overlapped 0). However, species sensitivity to patch size was greater for more fecund species at the patch level (B) and species with larger range sizes were more common at both the patch and site levels (D).241

Figure 5-7. Effect sizes for climate model coefficients estimating the effects of maximum summer temperature (ºC) (A) and total winter precipitation (mm) (B) by taxonomic group by patch size on patch and site commonness and sensitivity. Significance was judged by whether 95% credible intervals overlap 0. Effect sizes for commonness above 0 indicate the taxonomic group occurred more frequently in patches in hotter areas of the range. Effects sizes for sensitivity above 0 indicate the taxonomic group was less sensitive in larger patches in hotter areas of the range while being more sensitive in smaller patches...... 242

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LIST OF TABLES

Table 2-1. Annual climate covariates selected for state space models based on their potential importance in wood frog breeding and survival. Annual values at each site were used in modeling the effect of annual climate variation on wood frog population growth rates. 43

Table 2-2. Thirty-year normal climate covariates selected for state space models to account for long-term effects of climate at a site (i.e., values are constant over time). Their interaction with annual climate covariate values (Table 1) indicated if population growth rates differ in sensitivity across the range. The predicted relationship of the interaction between annual and long-term climate covariates to population growth rates across the wood frog range represent hypotheses from the bioclimatic envelope model...... 44

Table 2-3. All candidate state space models investigated for modeling wood frog egg mass counts. Each main model consists of the annual climate covariate (Precip, Hydro, Heat, Cold), the respective long-term climate normal (nmPrecip, nmHydro, nmHeat, nmCold), and the interaction between each annual and long-term covariate. Combination models are those with additional crossed interactions between annual climate covariates and long-term climate normals representing a different climate component (e.g., Hydro*nmHeat investigates the interaction between annual summer precipitation by long-term late summer maximum temperatures). The random effects of site (δi) and observation error (εti) were included in all models...... 45

Table 2-4. Parameter estimates from the four main climate covariate models. Precip, Hydro, Heat, and Cold represent annual climate values. nmPrecip, nmHydro, nmHeat, and nmCold are the long-term (~30 year) climate normal values. Interaction terms of annual and normal values (e.g., Precip*nmPrecip) represent the effect of an annual climate value by different long- term climate. SD is the standard deviation of a parameter estimate, and q0.025-0.975 represent 2.5th, 50th, and 97.5th quartile values...... 46

Table 2-5. Parameter estimates from the interaction model of spring precipitation and winter severity. Precip and Cold represent annual climate values. nmPrecip and nmCold are the long-term (~30 year) climate normal values. Interaction terms of annual and normal values (e.g., Precip*nmPrecip) represent the effect of an annual climate value by different long- term climate. SD is the standard deviation of a parameter estimate, and q0.025-0.975 represent 2.5th, 50th, and 97.5th quartile values...... 47

Table 2-6. Parameter estimates from the interaction model of summer water availability and extreme heat. Hydro and Heat represent annual climate values. nmHydro and nmHeat are the long-term (~30 year) climate normal values. Interaction terms of annual and normal values (e.g., Hydro*nmHydro) represent the effect of an annual climate value by different long- term climate. SD is the standard deviation of a parameter estimate, and q0.025-0.975 represent 2.5th, 50th, and 97.5th quartile values...... 48

TABLE 3-1. Model likelihood results from five-fold Bayesian cross validation with 20% data removed. Model likelihoods were calculated separately for each species and combined for additional comparison of model performance. Model likelihood was assessed using deviance calculated as -2[Σ(ln(L(test data))]. Three deviances were calculated based on the test data used: Overall (using all test data), both (using test data where both species information was xvi

removed), and one species removed (using test data where only one or the other species’ detection histories remained)...... 103

TABLE 3-2. Detection probabilities from single-species occupancy model with CAR random effect, the top supported model, for each species (A = red-backed salamander, B = Shenandoah salamander) for each survey occasion (t = 9). Standard deviation (SD) and 2.5, 50, and 97.5% percentiles are included...... 104

TABLE 3-3. Beta estimates for the effects of substrate type on occupancy from the top-supported single-species occupancy models with CAR random effect. Substrate categories including standard deviation (SD) and 2.5, 50, and 97.5% percentiles are shown for each species. 105

TABLE 3-4. Length of primary and secondary contact zones between red-backed and Shenandoah salamanders. We summed co-occurrence probabilities (ψiA*ψiB) across each site and the next two immediate sites (three 5-m by 4-m sites or 60m2 area used, Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. Primary zones represent the largest area of consistent overlap of both species ranges while secondary zones were smaller, additional areas of overlap. All lengths are in meters...... 106

TABLE 4-1. Sample size for all physical characteristic analyses for Shenandoah (PSHE) and Red- backed (PCIN) Salamanders. Not all traits were recorded for all animals thus excluding some animals from different analyses. Dorsum delineates if animals were striped or leadbacked. Color represents stripe color for animals with stripes. Percentages indicate percent of total animals with that trait...... 182

TABLE 4-2. Sample sizes for all behavioral characteristic analyses for Shenandoah (PSHE) and Red-backed (PCIN) Salamanders. Not all microhabitat types or temperatures were recorded for all animals thus excluding some animals from different analyses. Percentages indicate percent of total animals using that microhabitat category...... 184

TABLE 4-3. Logistic and linear regression results from top supported models for Shenandoah (PSHE) and Red-backed (PCIN) Salamander traits. Reference levels (PCIN, PSHE zone) were held constant while comparing other levels. Asterisks indicate significance as measured at the 0.05 level except for stripe color (Bonferroni correction; 0.013 level). Snout-vent length (SVL) is a measure of body size...... 185

TABLE 4-4. Logistic regression results for Shenandoah (PSHE) and Red-backed (PCIN) Salamander behavioral data (microhabitat type and temperature-influenced habitat use). Reference levels (PSHE and PSHE zone) were held constant while comparing other levels. Asterisks indicate significance as measured at the 0.05 level except for microhabitat type (0.017 level)...... 187

Table 5-1. Multispecies occupancy models for patch occupancy (Ωsj). Average site within patch occupancy (ψsj) models were similar and therefore not shown. The base model contained patch size and taxonomic group to account for phylogenetic differences between mean occupancy and sensitivity to patch size by each species. This model was further modified with the inclusion of the terms listed as Additional Models...... 231

xvii

ACKNOWLEDGEMENTS

I would like to thank the sources of funding that made these projects possible, chiefly the United

States Geological Survey’s John Wesley Powell Center for Analysis and Synthesis and the Amphibian

Research and Monitoring Initiative (ARMI). These folks do a lot of vital and useful research and are great groups with which to work. Additionally, I would like to thank the National Parks Service and Northeast

ARMI for funding and assisting with the fieldwork conducted in Shenandoah National Park. The National

Aeronautics and Space Administration (NASA) Pennsylvania Space Grant Graduate Fellowship,

Achievement Rewards for College Scientists (ARCS) Foundation, and Penn State University Ecosystem

Science and Management Department and Intercollege Graduate Degree Program in Ecology all provided personal funding. I would have had a lot fewer groceries and networking opportunities without their kindness in helping to fund me during my PhD.

I would like to thank my committee members, Evan Grant, Ty Wagner, Tracy Langkilde, Matt

Marshall, and Eric Post (while our paths crossed). The insight, advice, and time you gave to help me is very much appreciated. Additionally, thank you to Erin Muths who served as a mentor and friend when I only thought I was getting a boss. I would like to thank the additional contributions of all the coauthors listed on the enclosed chapters, especially Robert Fisher. I appreciate your input and time to get these papers out into the world. When one predominantly works on another’s previously collected data, one learns to appreciate the hard work and dedication of numerous principle investigators, field technicians, and research assistants. Thank you to everyone who contributed blood and sweat so we could all contribute tears and get these amazing projects completed. There are several people who went above and beyond the call of friendship to listen to me fumble my way through learning coding in R and Bayesian analyses (not to mention the natural history of the east coast, how to properly pronounce bagel and crayon, and how to appreciate new music every Friday). Most (but not all) these people are part of the

Applied Population Ecology lab or the fourth floor Forest Resources entourage, also known as some of the friendliest folks in scientific pursuit. I would also like to thank my advisor, Dave Miller, for being not xviii only a model data modeler but also a model human being. I am a better researcher but also a more thoughtful human because of Dave’s insistence on having well-supported conclusions in research and life.

I’m sad to be missing the formation of the family band but expect you all to tour the U.S. eventually.

Last of all, I would also like to thank my family for their patience as I disappeared into an academic blackhole yet again for several years. You all have been very supportive of my lack of free time and very convincing in your excitement when I send you photos of salamanders and snakes or make you hold frogs and newts. A particularly big, heartfelt thank you goes to Robert Newton and Carlton

Rochester for being supportive, loving, and helping me survive the process. Robert, you know when the shower pizza and beer needs to happen. Carlton, you know when I need to go find horned lizards. You both put up with a lot these last few years, and I really appreciate your patience and assistance. And as I promised Robert that the cat would make it into my acknowledgements, thank you to MC Cat

Commander. You’ve given me just enough sleep over the years to be able to complete this entire crazy process.

1

Chapter 1

Introduction

The drive to increase our fundamental understanding of factors that shape species distributions and contribute to range limits is an integral pursuit in ecology. Identifying the factors contributing to range limits can lead to insight in ecological and evolutionary processes in addition to conservation and management (Soberón 2007, Sexton et al. 2009). Though a knowledge of what limits or facilitates species occurrence is topical to modern ecology, it is fundamentally rooted in niche theory concepts developed decades to a century ago (Grinnell 1917, Elton 1927, Hutchinson 1957, MacArthur 1972). Species niche theory is linked to understanding the formation of and constraints on species occurrence (Grinnell 1917), and range limits are the manifestation of niche theory across space (Sexton et al. 2009). The fundamental niche, all possible conditions where a species can occur (Hutchinson 1957), results in a broad definition of the species range and does not include limitations such as historical barriers to dispersal (e.g. mountain ranges) or species interactions (e.g. competition, predation, and mutualisms). The realized niche is more restricted can be thought of as the fundamental niche limited to a specific space and time; this definition acknowledges potentially suitable conditions exist and yet still are unoccupied by a species (Hutchinson

1957). Both these niche definitions are important as they allow for predictions of range expansions and contractions (Soberón 2007). In the case of a range boundary created by negative interspecific interactions, if a competing species goes locally extinct in an area still within the physiological tolerances of the other species, a range expansion may occur (e.g. enemy release hypothesis, Keane and Crawley

2002; release from competitive exclusion, HilleRisLambers et al. 2013). Similarly, if shifting climate regimes alter the phenology of precipitation and reduce the availability of suitable breeding habitats for a species, a range contraction may occur (Keith et al. 2008).

Temporal and spatial variation in species characteristics related to the fundamental niche (e.g., genotype, phenotype, plasticity) translate into differences in population processes and help explain the formation of range limits (Sexton et al. 2009). Genetic differentiation is expected to increase while 2 genetic diversity decreases from the center to margin of a species’ range (Vucetich and Waite 2003).

Population stability is similarly expected to decrease from the center of a range to the margin, either through changes in abundance and/or through the availability of re-colonization sources if local extinction should occur (Sagarin and Gaiens 2002). Temporal and spatial variation also exist in the factors limiting species occurrence, e.g., the phenology and intensity of climate across a species’ range, the availability of microhabitats to be used by a species, or the severing of connectivity between habitats in a fragmented landscape (chapters within). Static models of species ranges assume species responses are in equilibrium within the environment and homogenous across widely varying habitats, species communities, and climatic conditions (Zurell et al. 2009). This may be a reasonable assumption in some systems or at a subset of areas within a species’ range, but for many species there is population heterogeneity in resiliency, adaptability, dispersal dynamics and growth across space (Kokko and López-Sepulcre 2006,

Eckert et al. 2008, Sexton et al. 2009). This translates into increasing or decreasing population growth rates that can explain population persistence or local extinction patterns (Kirkpatrick and Barton 1997) that help form range limits. Understanding this variation allows for more robust models of species distributions.

An additional difficulty in identifying factors creating species range limits comes from the various scales at which these pressures act and interact (Wiens 1989, Levin 1992). Accounting for this scale-dependency in population modeling increases the reliability of results and can lead to a better understanding of system dynamics (Zurell et al. 2009). Modeling species distributions previously relied heavily on occurrence-only data, limiting the understanding of what conditions exclude species and at what level they act (Phillips et al. 2009). However, more recent approaches consider not only species presences but absences, population growth rates, and mechanistic responses. The correlation of these responses to habitat suitability (Keith et al. 2008), interspecific competition (Yackulic et al. 2014), detection probability (MacKenzie and Kendall 2002; MacKenzie et al. 2006), and life history information

(Guisan and Thuiller 2005) can better contextualize scale-dependent processes. By including temporally- and spatially-varying measures and correcting for variation in sampling or process variables, higher 3 resolution of these complicated relationships can be achieved. Expansion of the types of models available to estimate these parameters has allowed researchers to create ecologically-grounded species distribution models and forecast range shifts and contractions (Guisan and Thuiller 2005, Keith et al. 2008, Elith and

Leathwick 2009, Zurrell et al. 2016).

The work presented within this dissertation emphasizes the importance of scale in species distribution modeling. Scale is important to all ecological processes and questions (Wiens 1989, Levin

1992), and the chapters herein highlight the relevancy of scale to each question and the resulting conclusions made. Study systems span from single species to two species to regional species communities and investigate finer-scale local responses to microhabitat conditions to the intermediate effects of urbanization and broader continental-wide effects of climate. Each chapter also emphasizes the importance of understanding ecological processes across the scales of time and space. The first chapter uses a dynamic state-space model that allows for population growth rates to vary across years and by the range position of sites, acknowledging that species (especially those that are broadly distributed) respond to climate depending on regional conditions. The influence of climate on populations over time and its variation across space can help explain range limits. The second and third chapters use spatially explicit locations of individuals to delineate the range limits of two interacting species, resulting in better estimation of the range boundary and identification of areas of sympatry and allopatry. Further investigation of the morphological, behavioral, and demographic responses to such interactions can then be put into the proper spatial context. Hierarchical multispecies occupancy models used in the fourth chapter are conditioned on the regional species pool available during sampling, helping to model patterns of species biogeography and understand the effect of fragmentation on shaping biological communities at both the scale of habitat patches but also at sites within occupied patches.

Another important theme emphasized in these chapters is that studying population processes can increase the utility of species distribution models. Population growth rates are a broad measure of differences in reproduction over time. Modeling population growth rates can then allow for predictions of population trajectories, feeding into forecasting species range shifts. Understanding the factors associated 4 with species occupancy patterns can inform us as to the processes influencing local extinction and colonization or resiliency of populations that can persist over time. Individual species traits can also explain variation in population processes, better connecting species presence and absence with life history and range information. Each chapter details the way a population process is used to advance our knowledge of range modeling. I use a suite of methods, from innovative dynamic state space modeling to modified multispecies hierarchical occupancy modeling to individual trait regression analyses, to connect these population processes to the distribution of species.

Finally, the overarching theme of this dissertation is expanding our analytical and ecological knowledge of species range limits and distribution modeling to better inform conservation and management. Much of the work discussed in the following chapters focuses on species of high conservation concern such as endangered or endemic organisms that are particularly threatened by the effects of climate change, urbanization, and other habitat disturbances (Stuart et al. 2004; Böhm et al.

2013). For instance, amphibians and reptiles are going extinct at rates much higher than background rates

(Alroy 2015, Ceballos et al. 2015), and small vertebrates in general play an integral role in ecosystem functioning and nutrient cycling (Hocking and Babbitt 2014, Ripple et al. 2017).

By providing case studies and innovative methods for modeling and understanding species distributions, these chapters provide insight into ecological questions based in niche theory and species biogeography. With anthropogenic stressors such as urban development and climate change providing novel management challenges to conservation practitioners, novel approaches will be required to understand and forecast species range. I advance the methodologies available to ask questions about range dynamics and provide insights into management opportunities of populations that are of pressing importance.

5

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MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey, and J.E. Hines. 2006. Occupancy

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8

Chapter 2

Range position and climate sensitivity: the structure of among-population

demographic responses to climatic variation

This chapter has been published by the journal Global Change Biology and is formatted

consistent with their requirements

Authors: STACI M. AMBURGEY1,2, DAVID A.W. MILLER1, EVAN H. CAMPBELL GRANT3,

TRACY A.G. RITTENHOUSE4, MICHAEL F. BENARD5, JONATHAN L. RICHARDSON6, MARK C.

URBAN7, WARD HUGHSON8, ADRIANNE B. BRAND3, CHRISTOPHER J. DAVIS9, CARMEN R.

HARDIN10, PETER W.C. PATON11, CHRISTOPHER J. RAITHEL12, RICK A. RELYEA13, A.

FLOYD SCOTT14, DAVID K. SKELLY15, DENNIS E. SKIDDS16, CHARLES K. SMITH17, EARL E.

WERNER18

1The Pennsylvania State University, Department of Ecosystem Sciences and Management,

University Park, PA, USA, 16802

2The Pennsylvania State University, Intercollege Graduate Ecology Program, University Park,

PA, USA, 16802

3 USGS Patuxent Wildlife Research Center, SO Conte Anadromous Fish Research Center,

Turners Falls, MA, USA, 01376

4University of Connecticut, Department of Natural Resources and the Environment, Storrs, CT,

USA, 06269

5Case Western Reserve University, Department of Biology, Cleveland, OH, USA, 44106

6Providence College, Department of Biology, Providence, RI, USA, 02918 9

7University of Connecticut, Department of Ecology and Evolutionary Biology, Storrs, CT, USA,

06269

8Parks Canada, Jasper, AB, Canada, T0E 1E0

9University of Pittsburgh, Department of Biological Sciences, Pittsburgh, PA, USA, 16424

10Wisconsin Department of Natural Resources, Forestry Division, Madison, WI, USA, 53707

11University of Rhode Island, Department of Natural Resources Science, Kingston, RI, USA,

02881

12Rhode Island Department of Environmental Management, Division of Fish and Wildlife, West

Kingston, RI, USA, 02892

13Rensselaer Polytechnic Institute, Department of Biological Sciences, Troy, New York, USA,

12180

14 Austin Peay State University, Department of Biology, Clarksville, TN, USA, 37040

15Yale University, School of Forestry and Environmental Studies, New Haven, CT, USA, 06511

16National Parks Service, Northeast Coastal and Barrier Network, Kingston, RI, USA, 02881

17 High Point University, Department of Biology, High Point, NC, USA, 27262

18University of Michigan, Department of Ecology and Evolutionary Biology, Ann Arbor, MI,

USA, 48109

Correspondence: Staci M. Amburgey, tel. +1 814 865 4183, fax +1 814 863 4710, e-mail: [email protected]

Keywords: bioclimatic envelope model, climate change, Lithobates sylvaticus, range shifts, species distribution model, state space model, wood frog

10

Abstract

Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North

America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position.

Processes such as species-interactions, local adaptation, and interactions with the physical 11 landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate.

Introduction

A persistent theme in ecology is the need to understand the factors that shape and describe species distributions (Grinnell, 1917; MacArthur, 1972; Gaston, 2009; Sexton et al., 2009). These factors have been touted as a means to understand the conditions that facilitate sustainable populations currently and in the future (Sexton et al., 2009). Species distributions are determined by a spectrum of biotic and abiotic factors that act across varying spatial and temporal scales (Anders & Post, 2006; Sexton et al., 2009). Among abiotic factors, climate is thought to be one of the most important determinants of species occurrence and key to the formation, maintenance and evolution of species distributions

(Darwin, 1859; Sexton et al., 2009; Araújo & Peterson, 2012). Climate may affect species directly via constraints in physiological tolerances, indirectly via its influence on community assemblages and habitats, or by complex interactions of both (Menge &

Olson, 1990). Understanding when and where climate constrains species’ occurrence is useful in predicting future responses, conserving and managing species in the face of ongoing global climate change (Pearson & Dawson, 2003; Araújo & Peterson, 2012) and identifying areas where other factors aside from climate are more strongly influencing distributions (e.g., biotic interactions; Urban et al., 2013).

Attempts to quantify the role climate plays in shaping species distributions frequently rely on the correlation between species occurrence and climate (i.e., a species’ bioclimatic envelope; Araújo & Peterson, 2012) to characterize current and to predict 12 future range dynamics. These static distribution modeling approaches are used to identify broad-scale patterns contributing to range limits (Pearson & Dawson, 2003; Hijmans &

Graham, 2006) and to predict range-wide effects of climate change on species distributions (Pearson & Dawson, 2003; Araújo et al., 2005; Thuiller et al., 2005). Under bioclimatic envelope models, climatic conditions where a species is not observed are assumed to prevent establishment of viable populations and thus are the environmental conditions that set range limits (Araújo & Peterson, 2012). However, these phenomenological models assume (1) species’ ranges are in equilibrium with climate conditions and (2) species responses are static across the range (Hijmans & Graham,

2006; Franklin, 2010). These assumptions do not realistically represent the dynamic nature of the physical environment and the species themselves, especially for broadly distributed species (Zurell et al., 2009). Static models of species responses to climate are insufficient to understand the effect annual climate variation can have on population persistence (Zurell et al., 2009; Franklin, 2010). Furthermore, the focus on species occurrence data ignores the temporal variation in species responses and the demographic processes that determine how a species will respond to climatic shifts (Merow et al.,

2014; Thuiller et al., 2014).

If climate shapes species distributions, changes in climate should have the greatest effect on populations occurring near the climatic extremes (e.g., increased temperature will have the greatest effect on populations in the warmest part of the range; MacArthur,

1972; Hoffman & Parsons, 1997; Parmesan et al., 2000). We test this by measuring sensitivity of demographic responses to climatic variation across the range of the wood frog (Lithobates sylvaticus). Specifically, we define sensitivity as the expected change in 13 annual population growth rate (r) with respect to change in an annual climatic measure

(e.g., summer extreme heat). We expect range contractions to occur when populations are lost because growth rate is negative for an extended period of time. Similarly, range expansions may occur when populations are gained because growth rate is positive for an extended period of time. Measuring sensitivity tells us how much growth rate is expected to change with a change in average annual conditions and thus how likely population declines (or expansions) are to occur. We test whether sensitivity of population growth rates to year-to-year variation in climate is stronger at the climatic extremes than at the climatic center of a species range (i.e., the bioclimatic envelope prediction; MacArthur,

1972; Hoffman & Parsons, 1997; Parmesan et al., 2000). Failure to find evidence to support this hypothesis could result from processes such as local adaptation, biotic interactions, and other abiotic variables leading to different patterns in sensitivity to change. Testing this hypothesis requires an understanding of how life history is impacted and thus how demographic rates respond to climatic variation (e.g., the relationship between population growth rate and temporal variation in environmental conditions;

Normand et al., 2014; Ross et al., 2015). This approach captures more of the process underlying range shifts rather than simply the observed pattern that previous correlative approaches have used to predict range shifts.

Amphibians make an interesting focal taxon to test the importance of population- level sensitivity to climate variation in range dynamics. Amphibians are expected to be particularly sensitive to the effects of climate due to their physiology and life history

(Hutchinson & Dupré, 1992; Duellman, 1999), generally limited dispersal abilities

(Beebee, 1996, but see Smith & Green, 2005), and reliance on seasonal precipitation and 14 temperature patterns to create breeding habitats and facilitate movement (Pechmann et al., 1989; Rittenhouse & Semlitsch, 2007; Urban et al., 2014). Their ecological importance as a link between terrestrial and aquatic systems (Ranvestel et al., 2004; Earl

& Semlitsch, 2012) and the decline of even common species (Stuart et al., 2004; Adams et al., 2013; Grant et al., 2016) make understanding the importance of climate in influencing population level dynamics important for forecasting future extinction risk.

Here we focus on the demographic responses to climate for a species of pond-breeding frog, the wood frog, whose range extends across much of northern North America (Fig.

1).

Using a spatially and temporally rich dataset, we tested the prediction that wood frog populations are most sensitive to annual climatic variation at sites near the climatic extremes of their distribution (Fig. 2) and that the species distribution is shaped by the interaction of long-term and annual climate conditions on population growth rates.

Population growth rates at sites may have three predicted responses based on their range position. For example, if sensitivity of wood frog populations to variation in temperature differs across the range, we predicted that 1) populations in the colder portion of the range (blue; Fig. 2a) would be positively affected by warmer than average annual temperatures, meaning that if warming occurred this could lead to more frequent years of high population growth rates and potential range expansion; 2) populations in the warmer portion of the range (red; Fig. 2a) would be negatively affected by warmer than average annual temperatures, meaning that if warming occurred this would lead to more frequent years of low population growth rates and potential range contraction; and 3) populations in the middle of the range (black; Fig. 2a) are far from climate extremes (Fig. 2b) and 15 annual temperatures would not strongly affect population growth rates. By fitting dynamic models that estimate annual changes in abundance in relation to long-term climate, we can better understand which populations within a species’ range are most likely to respond to changing climate.

Study System and Methods

We build on previous static approaches to model bioclimatic determinants of species distributions (e.g., Guisan & Zimmerman, 2000; Hijmans & Graham, 2006) by measuring local demographic responses of populations using a dynamic species distribution model (DSDM). The DSDM approach allowed us to test the importance of range position in determining responses to climate by measuring local sensitivity of population growth rate to annual variation in climate covariates. Our model takes the form of a hierarchical state-space model (SSM; De Valpine & Hastings, 2002; Buckland et al., 2004; Kéry & Schaub, 2012; Ross et al. 2015), allowing us to link annual population dynamics across different sites and study areas to annual variation in climatic variables. The results provide a measure of climate sensitivity (i.e., the expected change in mean population growth rates in response to changes in mean climate; Thuiller et al.,

2005; Thomas, 2010; Burrows et al., 2014).

Study System and Life History

Wood frogs occupy an extensive range, occurring from northern Alaska to

Canada and south to the south central United States (USGS National Amphibian Atlas,

2014; Fig. 1), spanning a large gradient of climatic conditions (Fig. 1). Specific elements of the wood frog life history potentially make them sensitive to changes in local climate.

Breeding normally occurs in early spring when rising temperatures rouse animals and 16 warm spring rains facilitate movement into breeding ponds. Adult frogs show high fidelity to breeding sites (Berven & Grudzien, 1990; Green & Bailey 2015). Breeding generally occurs in a short window of time, anywhere from a few consecutive evenings to a few weeks in length depending on location (Authors, pers. observations; Crouch &

Paton, 2000). Female wood frogs become sexually mature between two and four years of age and males between one and three years of age (Berven, 1982a; Berven, 2009; Green

& Bailey 2015), and both can live up to six years (Redmer & Trauth, 2005). Females typically lay one egg mass during each breeding season, and these egg masses are visually distinct and easy to locate and count (Crouch & Paton, 2000; Grant et al., 2005;

Green et al., 2013). Comparison of census methods show that counts of total egg masses seen per season serves as a suitable proxy for total breeding females per season in a pond

(Crouch & Paton, 2000).

Field sampling

We used egg mass counts from 746 sites within 27 study areas across the wood frog range (Fig. 1; Table S1). A site consisted of a pond or wetland (area ≤ 0.10 ha to

5.24 ha) that was visually sampled for wood frog egg masses during the peak of each breeding season and where wood frog egg masses were observed at least once during years when surveys occurred. Study areas designate geographic clusters of sites that occurred within relatively close proximity (e.g., within a single national park). Sites were surveyed in multiple years (range = 3-22 years, mean = 10 years; Table S1) with most, but not all, sites being surveyed multiple times within each year. Surveys occurred during or right after peak breeding based on the lack of calling adults and/or no additional egg masses during subsequent surveys, and a maximum count at a site was recorded each 17 breeding season and used as the response variable in analyses. Wood frog egg masses are conspicuous and detection probability is high (p = 0.96 ± 0.02 to 0.95 ± 0.01; Grant et al.,

2005).

Climate covariates

We tested specific predictions with each model about the variation in sensitivity of population growth rates to four climate covariates (Table 1, 2; Fig. S1): 1) spring precipitation (Precip), 2) summer water availability (Hydro), 3) summer extreme heat

(Heat), and 4) winter severity (Cold; Table 1). As our sites cover a broad geographic space, wood frog breeding was not synchronous across all study areas. Months used to calculate Precip and Hydro were benchmarked to average breeding dates in each study area, reflecting differences in seasonality across the wood frog range (Table S1). We obtained global climate normal (~1960-1990, 2.5 arc-minutes resolution) rasters from

WorldClim (Hijmans et al., 2005) and created 30-year climate normal maps of North

America in program R (R Core Team, 2016). We determined 30-year mean annual temperature and precipitation values across North America and within the recorded range of wood frog occurrence (IUCN, 2015) to determine where the species occurs within the broader North American climate space (Fig. 3). These values were used to depict the climate space of wood frogs and our sampled populations in Figure 3 but were not used in SSMs. Using PRISM (Daly et al., 2002) model output for the US and weather station data for Canada (Environment Canada, 2015), we calculated annual climate values for

Precip, Hydro, Heat, and Cold at every site every year for SSMs (Table 1). To model differences in long-term climate, we determined 30-year climate normal (average) values

(Hijmans et al., 2005) at every site over the same seasonal periods as our annual climate 18 covariates for SSMs (Table 2; nmPrecip, nmHydro, nmHeat, and nmCold). For example, at northern sites we calculated total precipitation values each year for February, March, and April, due to their importance in timing wood frog migrations and pond filling, and averaged them for an annual spring precipitation value (Precip). We then averaged total precipitation values over the same months across 30 years to get a long-term climate normal value (nmPrecip) that varied across but not within sites. Annual climate values were standardized by 33-year (1981-2013) mean and standard deviations at a site.

Climate normals were standardized using the mean and standard deviation from the entire extent of the wood frog range.

Data analysis

We used SSMs to estimate the effect of annual variation on population growth rate (De Valpine & Hastings, 2002; Buckland et al., 2004; Kéry & Schaub, 2011; Ross et al. 2015). Models were fit in JAGS (Plummer 2003) and implemented in program R via the R2jags package (Su & Yajima, 2012; see Appendix S1 for JAGS code). The hierarchical model allows for estimation of latent state and observation processes characterizing sampled populations while simultaneously accounting for process variation and observation error (Buckland et al., 2004; Kéry & Schaub, 2011). We were interested in understanding how these latent processes were affected by annual climate variation across the range. At the same time, the modeling framework allowed us to account for observation error in counts (e.g., through variable detection, field conditions, variable observer expertise) that was unrelated to the underlying population processes

(MacKenzie et al., 2006). 19

We described changes in wood frog population size (as based on egg mass counts that serve as a proxy for number of breeding females in a season) using an exponential population growth model

r Nt+1 = Nt * e (1) where population size Nt+1 is a function of the previous population size Nt (from the previous year) and the per capita annual growth rate (r, the exponent of the instantaneous growth rate). Using this as a starting point, we estimated regression coefficients characterizing the relationship between annual weather and the realized growth rate (rti) for a given year (t) and a given site (i) for each climate hypothesis.

To fit the model, we reformulated Eq. 1 by taking the natural logarithm of each side of the equation and indexing all parameters by year (t) and site (i) to capture annual and site-specific variation in the climate covariates and population responses. We added one to all observations to accommodate zeros in the data prior to log transformation.

log(Nt+1, i) = log(Nti) + rti (2)

The now additive growth rate rti was modified to include the effects of climate covariates and unexplained annual variation captured using random-error terms. Our goal was to estimate the effect of annual variation in each of our four climate covariates and how those effects differed across the range. We estimated these relationships using a linear model that included the main effects of annual climate values and the climate normal along with the interaction of the two (Table 3). The model took the form of:

rti = β1 * Annual Climateti + β2 * Climate Normali + (3)

β3 * Annual Climateti * Climate Normali + δi + εti 20

The model allowed us to determine sensitivity, defined as the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions, to long-term climate normal conditions. Specifically, the interaction term allowed us to quantify the amount of change in population growth rates given an annual shift in climate in respect to the climatic range position in which a population exists. We included a

2 random effect for site level differences, δi ~ Normal (0 , σ Site). This effect served as the local site-level intercept for growth rate, which we expected to vary around a mean value of 0. We included a second random error component for additional annual variation in

2 growth rate not explained by the climate covariates, εti ~ Normal (0 , σ proc). To account for observation error in counts that was not explained by the population level state processes, we assumed that the log observed count of egg masses for that site and year,

2 yti, is given by yti ~ Normal (log[Nti] , σ obs). It was also necessary to estimate a starting population size for each site. We used a prior value of log (N1i) ~ Normal (0, 100).

2 2 We used vague priors for random effect variance components (σ obs, σ proc) with

2 uniform distributions bounded between 0 and 5. For σ Site we used a uniform prior bounded between 0 and 0.2 to facilitate convergence. Priors for all regression coefficients were βk ~ Normal(0 , 100). We ran three parallel chains for 50,000 iterations each and discarded the first 1000 iterations as burn-in to allow for model convergence. Model convergence was determined visually from traceplots and Gelman Rubin statistics ( <

1.05; Gelman & Rubin 1992).

We predicted that climate covariates could have both immediate and lagged effects on annual growth rate (rti; Fig. S1). We predicted that covariates that disproportionately impact adult survival and season-to-season variation in breeding 21 would lead to changes in growth rate in the same year. In the case where we expected a covariate to impact the survival of eggs and tadpoles in a wetland and thus the number of potential recruits from a cohort, these were predicted to lead to changes in growth rates after a 2-year lag. Female wood frogs take approximately two years to reach sexual maturity in our study sites (e.g., lowland populations; Berven, 1982a, 2009; Green &

Bailey 2015). Therefore the effects of reproductive failure (e.g., desiccation of tadpoles in a dry year) on growth rates would not be evident in counts of egg masses in the year immediately following these suboptimal conditions. We hypothesized that annual Precip values affect movement of adult animals and the opportunity for successful oviposition

(i.e., pond filling), with low Precip values resulting in fewer egg masses laid and thus reduced recruitment two years later. Hydro values reflect desiccation risk for developing tadpoles (realized as altered recruitment two years later) and also drier summer conditions that can decrease adult survival during foraging or return to overwintering sites. Heat and Cold values reflect late summer dryness and overwintering cold stress expected to impact adults. While any number of time lag combinations and effects are possible, we fit the model (Eq. 3) focusing on these key periods due to their biological importance and support in the literature (Table 1).

We were also interested in how water availability and temperature may interact to explain variation in climate sensitivity. We expected that years of low precipitation

(Precip) would have a greater negative effect in sites with higher mean annual summer temperatures (e.g., hotter areas; nmHeat) as increased water on the landscape may help keep permeable amphibian skin moist and lessen desiccation risk (Rittenhouse et al.,

2009; Köhler et al., 2011). Similarly, we expected reduced winter severity (Cold) and its 22 indirect effect on water availability and pond filling in the spring to be greater in areas that receive less spring precipitation (e.g., drier areas; nmPrecip). We tested for these effects by including the interaction of different annual and long-term climate covariates

(e.g., Precip*nmCold, Hydro*nmHeat; Table 3). Annual covariates included the same time lags as previously discussed. Models with both temperature and precipitation included all annual and long-term covariates for each climate measure and an additional two interaction terms allowing annual and long-term covariates to interact (Table 3). This means a total of eight models testing climate hypotheses (Table 1 and 2) were run. None of the selected climate covariates were strongly correlated (|r| < 0.4).

When fitting models, we tested for goodness of fit using a posterior predictive check to test whether observed variability in counts was consistent with expected variation. We calculated observed variance in our data for each of the sites and determined if on average variance was less than or greater than the predicted variance of simulated data based on our model. We report the proportion of the time that the observed variance was greater than the predicted variance, with the expectation that if the model fits the data well we expect this proportion to be 0.5.

Additionally, we were interested in estimating the overall expected rate of change in wood frog population growth rates, dr/dt, based on our estimated climate relationships.

Expected change is a function of the local sensitivity to each of our climate covariates, dr/dX, as measured in our models as well as the rate of change in mean climate over that time period, dX/dt, where:

= 23

We calculated rate of change in each of our climate variables at each of our sites using linear regression where year was the predictor variable and annual values of each of our climate variables over a 30-year period from 1984 to 2013 were our response variables. We mapped these to geographic and climate space to highlight areas where climate may currently be altering the wood frog distribution.

Results

Sites spanned a >23 degree range in latitude and >50 degree range in longitude from North Carolina to Jasper National Park, Alberta, Canada. Study areas fell into 16 different states, one administrative subdivision (Washington D.C.) and one Canadian province (Alberta) (Fig.1). Our data show good geographic coverage along the wood frog’s southern and easternmost range limit but are restricted in geographic coverage in the northern and westernmost portions of the wood frog range. This was reflected in our coverage in climate space (Fig. 3), with best coverage in the portion of the range with warmer temperatures and higher precipitation. Therefore, we limit the presentation of results and their interpretation to only the sampled portion of the wood frog range.

Additionally, support for models was judged by whether or not credible intervals of parameter estimates overlapped zero, and we have limited our presentation of results to those models with the strongest support and thus credible intervals for interaction terms that did not overlap. Our posterior-predictive check values for each of our models were between 0.493 and 0.541, indicating that our models did a good job of capturing actual variation in growth rates.

Our first three models tested the effect of moisture on population growth rates, with the first focused on spring precipitation and the second on late summer water 24 availability. Contrary to our predictions, we found a negative relationship between Precip and wood frog population growth rates across all areas two years later (Table 4; Fig. 4a;

Fig. S2). The relationship of increased annual Hydro values to wood frog population growth rates differed depending on if a time lag was incorporated (Table 4; Fig. 4b,c; Fig.

S2). The same-year effect of Hydro was dependent on long-term climate, with populations in wetter areas responding most positively to wetter annual conditions as compared to those in drier areas (Fig. 4b). When incorporating a two-year time lag, increased values of Hydro were positively associated with growth rates only in drier areas

(Fig. 4c; Table 4), agreeing with our bioclimatic envelope predictions of increased sensitivity to water availability in drier portions of the range.

Our next two models focused on the effect of extreme heat and cold severity on population growth rates. The relationship between increased values of Heat and wood frog population growth rates (Table 4) depended upon long-term climate. Years with hotter summer temperatures had higher population growth rates in areas with cooler summer climates. However, there was a negative association between warmer summers and population growth in areas with hotter summer climates (Fig. 4d; Fig. S2). This agrees with our bioclimatic envelope prediction, where we expect population growth rate to be most sensitive to warming in the warmest portion of the range. The relationship of

Cold to population growth rates showed increased growth rates associated with milder winters across all areas (Table 4) with the most positive association in areas with milder winter climates (Fig 4e; Fig. S2).

Finally, we examined how precipitation and temperature interacted to affect population growth rates. We found that the two-year lag effect of annual variation in 25 spring precipitation did not depend on long-term winter climate (Precip*nmCold; Table

5; Fig. 5a; Fig. S2), and the effect of annual variation in winter severity did not vary significantly by long-term spring precipitation (Cold*nmPrecip; Table 5; Fig. S2). We found that the effect of summer water availability in the current year did not differ by long-term summer heat (Hydro*nmHeat; Table 6; Fig. 5c; Fig. S2). However, the effect of warmer summers differed between drier and wetter areas. Hotter summer temperatures had a positive relationship to population growth rates in wetter areas but negatively impacted growth rates in drier areas (Heat*nmHydro; Fig. 5d; Fig. S2). We found a positive relationship between increased summer water availability and wood frog growth rates two years later in areas with cooler summer temperatures but a negative relationship in areas with hotter summer temperatures (Table 6; Fig. 5e; Fig. S2). The interaction of increased summer temperatures had a similar impact on population growth rates as the summer water availability model with no time lag, with a positive effect of increased summer heat in wetter versus drier areas (Table 6).

Expected rate of change in population growth rates over the previous 30 years that could be attributed to changes in climate showed few major increases or decreases across the wood frog range (Fig. S3, S4). The biggest changes in population growth rates were estimated to have occurred for variables related to temperature. These suggest some reductions in growth rates in the southern portion of the wood frog range due to changes in heat and cold (Fig. S3d,e; S4d,e).

Discussion

We tested the prediction that the effect of climate on population growth rates varies in a predictable pattern based on local, long-term climate (i.e., bioclimatic 26 envelope prediction; MacArthur, 1972; Hoffman & Parsons, 1997; Parmesan et al.,

2000). Populations near the climatic extremes of the species range were predicted to be the most sensitive to annual variation in climate. Our use of hierarchical SSMs (De

Valpine & Hastings, 2002; Kéry & Schaub, 2012) allowed us test this broad-scale prediction by simultaneously linking climate directly to demographic rates at the temporal- (short-term variation in weather) and spatial- (individual populations) scales at which climate acts to affect species distributions. We acknowledge that our sampled sites are only a portion of the wood frog range and thus limit the interpretation of our results to conditions represented in this study. Our results provided mixed evidence to support this prediction, with differences in climate sensitivity often occurring in the opposite direction of this prediction. For example, the effect of summer temperature was consistent with our prediction – warmer summers had a more detrimental effect in the warmest part of the range. The effect of summer water availability was also consistent with this prediction, where increased moisture had a positive effect two years later in drier areas. On the other hand, variation in spring precipitation, summer water availability in the current year, and winter severity did not conform to predictions based on position within the range.

We also tested the climate sensitivity of populations to interactions of temperature and precipitation. We again predicted that population growth rates would be most sensitive to annual variation in one factor (e.g., increased summer heat) as they approached climate extremes of the other (e.g., drier areas). Again, we found mixed support for this prediction. Hotter summers had a positive effect on wood frog growth rates in wetter areas but a negative effect in drier areas as predicted. However, we found a contradictory positive effect of increased summer water availability two years later in 27 cooler areas and no significant association of spring precipitation and winter severity to wood frog population growth rates. This suggests that expected shifts due to changing climate for wood frogs may not be strongest at the climatic extremes of the range or easily predicted solely by climate, which is surprising given the expected sensitivity of amphibians to abiotic conditions.

Many of the metabolic, reproductive and phenological processes in amphibians are strongly linked to temperature (Berven, 1982a,b; Beebee, 1996; Gibbs & Breisch,

2001) and can be of key importance in structuring species distributions (Tingley et al.,

2009; Cahill et al., 2014). This may explain why bioclimatic envelope model predictions regarding temperature, specifically heat, were better supported in our models.

Temperature may have a more uniform effect across the landscape and may be better represented by coarse measures. Alternatively, precipitation largely acts through its effect on hydrological processes during the reproductive phase and interactions between water, soil, and vegetation during non-breeding periods (Drexler et al., 2004; Bauder, 2005;

Davis et al., in prep). Hydrologic deficits (Brooks, 2004), landscape topography (Boswell

& Olyphant, 2007), pond-selection by breeding animals (Pechmann et al., 1989; Skidds et al., 2007; Amburgey et al., 2014), and plasticity in development (Relyea, 2002;

Amburgey et al., 2012) are among the many factors that may attenuate the relationships between water availability and amphibian population growth rates. Our inferences are also limited to the study area that we were able to sample. Limited sampling of the colder and drier edge of climate space (Fig. 3) may restrict our ability to detect relationships occurring at those extremes. Our study did, however, provide good coverage at the warm 28 and wet edge of the wood frog range, which is most susceptible to the effects of climate change (Corn 2005; Meehl et al., 2007).

A multitude of other factors (e.g., local adaptation, biotic interactions, and other abiotic variables) can affect populations and lead to patterns contradictory to bioclimatic envelope predictions of climate sensitivity (HilleRisLambers et al., 2013; Urban et al.,

2013). The effect of moisture on the landscape likely depends on the form and the timing of precipitation and can also impact biotic factors that likewise contribute to heterogeneity in population growth rates. Increased spring precipitation may come as early spring snow and ice storms that can increase adult mortality through reduced freeze tolerance (Costanzo & Lee Jr., 1992) or truncate the breeding season (Berven, 1982b).

Increased moisture on the landscape may increase the probability of egg mass or tadpole stranding in temporary flooded areas or facilitate colonization or persistence of predators in ponds (Werner et al., 2009). Local adaptation to annual climate variation may alter climate sensitivity, with populations nearer to climate extremes accustomed to increased annual variation while populations farther away from extremes are not (e.g., Berven,

1982a, Laugen et al., 2003; Amburgey et al., 2012), though we cannot test this directly with our approach. Local dynamics may also vary spatially, where populations near climate extremes are at low enough densities that they are unable to respond to the benefits of years with more suitable climate conditions.

Species biology may additionally structure population responses to climate and result in deviations from bioclimatic envelope predictions. Wood frogs are freeze tolerant

(Storey & Storey, 1986; Costanzo & Lee Jr., 1992) though extended or extreme periods of freezing temperatures can impact overwintering survival (Costanzo et al., 1991; 29

O’Connor & Rittenhouse, 2016). In a portion of the range that encompassed our study areas, no differentiation in wood frog thermal tolerance was found (Manis & Claussen,

1986); however, far northern populations in Alaska have shown increased cold tolerance

(Larson et al., 2014). However, mild winters in colder areas may result in freeze-thaw cycles that rouse animals from torpor, resulting in increased energetic demands (Storey,

1987), mating behavior impairment (Costanzo et al., 1997), and reduced fecundity

(Benard, 2015). Additionally, the life stage on which climate most strongly acts may influence the population response. In amphibians, the aquatic larval stage already experiences heightened mortality, and climate conditions that affects tadpole survival may not lead to differential climate sensitivity at the population level as much as those factors that influence terrestrial juvenile and adult survival (Biek et al., 2002; Harper et al., 2008).

Currently, species distributions and range dynamics are frequently modeled using static approaches that treat climate and species responses as fixed across space and time

(Hijmans & Graham, 2006; Franklin, 2010). However, species responses to climate are spatially complex, especially for those with multistage life histories. Climate shifts will likely alter species distributions by acting on demographic processes where sensitivity to change is greatest. Combining estimates of climate sensitivity with data about observed or predicted changes in climate allows for predictions about local changes in population growth rate to be made. We did this for the last 30-year period, highlighting the variability in population response across the range (Fig. S3, S4). Demographic response for some climate variables fit predictions (e.g., negative responses to warming in the warmest regions). However, estimated demographic changes related to water availability 30 and interactions with temperature follow much less clear patterns, which would not easily be predicted using static modeling approaches. Our results demonstrate that focusing on demographic processes provides insights for understanding how species distributions may respond to change not possible with presence-absence correlative models focused on pattern (Normand et al., 2014; Ross et al., 2015). Correlative approaches based on a static snapshot of species distribution do not measure the actual mechanistic processes impacting populations (Dormann et al., 2012; Cahill et al., 2014) and do not estimate rates of change that demographic models can incorporate (Normand et al., 2014). Thus, correlations may break down with no-analogue climates (Williams & Jackson, 2007) and lack the predictive power explicit estimates of climate-demography relationships can offer (Normand et al., 2014). While our model is still correlative in relating demographic rates to climatic variation, it provides a finer scale approach that provides insights to potential mechanisms while also explaining broader patterns. Bioclimatic envelope modeling does not include other potentially important factors (e.g., biotic interactions, genetic differentiation, and geographical barriers) that may set species range limits alone or in concert with climate (HilleRisLambers et al., 2013; Urban et al., 2013). However, such demographic models can be modified to include such information and better inform our understanding of species range dynamics.

A demographic understanding of species distributions is essential to evaluating and understanding range limits, forecasting range shifts and stability, and managing species and conserving habitats. These aims will be critical in the context of changing climate. By pairing large-scale modeling studies with targeted experimental or demographic studies, we can better understand the way these broad-scale measures are 31 realized on the landscape and influence local populations (Merow et al., 2014; Normand et al., 2014). In the future, all species are likely to experience some change to their current distributions, whether through range contractions (via altered habitat suitability through changing climate) or expansions (via altered climate facilitating colonization of new habitats; Thuiller et al., 2008). With increasingly limited conservation resources, identification and prioritization of critical areas where species are most sensitive to changing climate (Beissinger & Westphal, 1998; Keith et al., 2008) and where range shifts may occur (Thuiller et al., 2008) will allow for more efficient and effective conservation management.

Acknowledgements

We are thankful to J. Petranka for contributed data. This work was conducted as part of the Amphibian Decline Working Group supported by the USGS John Wesley Powell

Center for Analysis and Synthesis, funded by the US Geological Survey. This manuscript is contribution #592 of the USGS Amphibian Research and Monitoring Initiative. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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Table 2-1. Annual climate covariates selected for state space models based on their potential importance in wood frog breeding and survival. Annual values at each site were used in modeling the effect of annual climate variation on wood frog population growth rates.

Covariate Definition Ecological Importance Precip = Deviation of the observed precipitation Values represent the wetness of an area during the start of spring Standardized Precipitation value from the estimated median for an breeding (e.g., Feb-Apr) such that a more positive value indicates Index 3-month (SPI3)1 area calculated over a 3-month period, more precipitation than predicted. Spring precipitation is uses only precipitation values (only inputs important as a cue for breeding adults to migrate to ponds and for to the system) filling ephemeral ponds2,3 Hydro = Deviation of the observed precipitation Similar to SPI3 but includes the effect of temperature on Standardized Precipitation value from the estimated median for an evapotranspiration rates, considers the way these rates will Evapotranspiration Index 3- area calculated over a 3-month period, influence drought severity and can be used as a measure of water month (SPEI3)4 uses precipitation and evapotranspiration available on the landscape, calculated during summer (e.g., May- values (inputs and outputs to the system) July) to get at pond drying. Hydroperiod impacts desiccation risk of tadpoles and can approximate dry summers that increase desiccation risk of adults 5 Heat = Hottest 10-day average temperature, falls Periods of intense heat increase the risk of heat stress and Extreme Heat Index (EHI)4 in the late summer for North America desiccation while moving between sites3,5 Cold = Cumulative index of freeze severity and Though freeze tolerance has been demonstrated in this species7, Air Freezing Index (AFI)4 frost depth that factors in magnitude and extreme cold temperatures and long durations of cold duration of below freezing air temperatures may reduce overwinter survival of juveniles and temperatures6 adults8 1 National Climatic Data Center, NOAA (2015) 7 Storey and Storey (1986) 2 Rittenhouse et al. (2009) 8 O’Connor and Rittenhouse (2016) 3 Davis et al. (in prep) 4 Daly et al. (2002) 5 Brooks (2004) 6 Bilotta et al. (2015) 44

Table 2-2. Thirty-year normal climate covariates selected for state space models to account for long-term effects of climate at a site

(i.e., values are constant over time). Their interaction with annual climate covariate values (Table 1) indicated if population growth rates differ in sensitivity across the range. The predicted relationship of the interaction between annual and long-term climate covariates to population growth rates across the wood frog range represent hypotheses from the bioclimatic envelope model.

Predicted Annual and Long-term Covariate Definition Ecological Importance Interaction (Bioclimatic Envelope Model) nmPrecip = 30-year mean monthly Measure of precipitation and water Precip * nmPrecip Precip Normal8 precipitation over same availability during spring breeding, long Negative impact of drier years in 3-month period as SPI3 term moisture dynamics of areas drier areas nmHydro = 30-year mean monthly Measure of precipitation and water Hydro * nmHydro Hydroperiod precipitation over same availability during tadpole development, Negative impact of drier years in Normal8 3-month period as SPEI3 long term moisture dynamics of areas drier areas nmHeat = 30-year maximum Measure of extreme heat patterns occurring Heat * nmHeat Heat Normal8 monthly temperature during the late summer, long term heat Negative impact of hotter years in over similar late summer regime hotter areas period as EHI nmCold = 30-year minimum Measure of winter severity patterns, long Cold * nmCold Cold Normal8 monthly temperature term cold regime Negative impact of colder years in over similar mid-winter colder areas period as AFI 8 WorldClim; Hijmans et al. (2005) 45

Table 2-3. All candidate state space models investigated for modeling wood frog egg mass counts. Each main model consists of the annual climate covariate (Precip, Hydro, Heat, Cold), the respective long-term climate normal (nmPrecip, nmHydro, nmHeat, nmCold), and the interaction between each annual and long-term covariate. Combination models are those with additional crossed interactions between annual climate covariates and long-term climate normals representing a different climate component (e.g.,

Hydro*nmHeat investigates the interaction between annual summer precipitation by long-term late summer maximum temperatures).

The random effects of site (δi) and observation error (εti) were included in all models.

Model Name Parameters

Precip (2-yr lag) β1(Precip2yr) + β2(nmPrecip) + β3(Precip2yr * nmPrecip) + δi + εti

Hydro β1(Hydro) + β2(nmHydro) + β3(Hydro * nmHydro) + δi + εti

Hydro (2-yr lag) β1(Hydro2yr) + β2(nmHydro) + β3(Hydro2yr * nmHydro) + δi + εti

Heat β1(Heat) + β2(nmHeat) + β3(Heat * nmHeat) + δi + εti

Cold β1(Cold) + β2(nmCold) + β3(Cold * nmCold) + δi + εti

Precip (2-yr lag) and β1(Precip2yr) + β2(nmPrecip) + β3(Precip2yr * nmPrecip) + β4(Cold) + β5(nmCold) + β6(Cold * Cold by long-term nmCold) + β7(Precip2yr* nmCold) + β8(Cold * nmPrecip) + δi + εti climate Hydro and Heat by long- β1(Hydro) + β2(nmHydro) + β3(Hydro * nmHydro) + β4(Heat) + β5(nmHeat) + β6(Heat * term climate nmHeat) + β7(Hydro * nmHeat) + β8(Heat * nmHydro) + δi + εti

Hydro (2-yr lag) and β1(Hydro2yr) + β2(nmHydro) + β3(Hydro2yr * nmHydro) + β4(Heat) + β5(nmHeat) + β6(Heat * Heat by long-term nmHeat) + β7(Hydro2yr * nmHeat) + β8(Heat * nmHydro) + δi + εti climate

46

Table 2-4. Parameter estimates from the four main climate covariate models. Precip, Hydro,

Heat, and Cold represent annual climate values. nmPrecip, nmHydro, nmHeat, and nmCold are the long-term (~30 year) climate normal values. Interaction terms of annual and normal values

(e.g., Precip*nmPrecip) represent the effect of an annual climate value by different long-term

th th climate. SD is the standard deviation of a parameter estimate, and q0.025-0.975 represent 2.5 , 50 , and 97.5th quartile values.

Model: Precip

Parameter Mean SD q0.025 q0.500 q0.975 Precip (2-yr lag) -0.0840 0.0370 -0.155 -0.0840 -0.0120 nmPrecip 4.00e-03 4.00e-03 -4.00e-03 4.00e-03 0.0130 Precip (2-yr lag)* 4.00e-03 0.0140 -0.0240 4.00e-04 0.0320 nmPrecip Model: Hydro Hydro 0.0533 0.0387 -0.0229 0.0534 0.129 nmHydro -0.0137 5.44e-03 -0.0243 -0.0137 -2.98e-03 Hydro* nmHydro 0.0732 0.0244 0.0255 0.0732 0.121 Hydro (2-yr lag) 0.0925 0.0415 0.0111 0.0924 0.174 nmHydro -7.21e-03 5.47e-03 -0.0179 -7.22e-03 3.57e-03 Hydro (2-yr lag)* -0.0605 0.0253 -0.110 -0.0605 -0.0109 nmHydro Model: Heat Heat 0.340 0.0670 0.208 0.340 0.471 nmHeat 7.50e-03 5.46e-03 -3.12e-03 7.51e-03 0.0183 Heat* nmHeat -0.266 0.0383 -0.341 -0.266 -0.191 Model: Cold Cold -0.438 0.117 -0.666 -0.438 -0.209 nmCold 0.0115 4.81e-03 2.09e-03 0.0115 0.0210 Cold* nmCold 0.258 0.0580 0.145 0.259 0.372

47

Table 2-5. Parameter estimates from the interaction model of spring precipitation and winter

severity. Precip and Cold represent annual climate values. nmPrecip and nmCold are the long-

term (~30 year) climate normal values. Interaction terms of annual and normal values (e.g.,

Precip*nmPrecip) represent the effect of an annual climate value by different long-term climate.

th th SD is the standard deviation of a parameter estimate, and q0.025-0.975 represent 2.5 , 50 , and

97.5th quartile values.

Model: Precip (2-yr lag) and Cold by long-term climate conditions

Parameter Mean SD q0.025 q0.500 q0.975 Precip (2-yr lag) -0.192 0.126 -0.439 -0.192 0.055 nmPrecip -8.00e-03 0.0110 -0.0300 -8.00e-03 0.0140 Precip (2-yr lag)* 9.00e-03 0.0170 -0.0240 9.00e-03 0.0420 nmPrecip Cold -0.398 0.125 -0.644 -0.398 -0.152 nmCold 0.0210 0.0130 -4.00e-03 0.0210 0.0460 Cold* nmCold 0.220 0.0630 0.0970 0.220 0.344 Precip (2-yr lag)* 0.0620 0.0700 -0.0760 0.0620 0.200 nmCold Cold*nmPrecip 0.0150 0.0240 -0.0320 0.0150 0.0630

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Table 2-6. Parameter estimates from the interaction model of summer water availability and extreme heat. Hydro and Heat represent annual climate values. nmHydro and nmHeat are the long-term (~30 year) climate normal values. Interaction terms of annual and normal values (e.g.,

Hydro*nmHydro) represent the effect of an annual climate value by different long-term climate.

th th SD is the standard deviation of a parameter estimate, and q0.025-0.975 represent 2.5 , 50 , and

97.5th quartile values.

Model: Hydro and Heat by long-term climate conditions

Parameter Mean SD q0.025 q0.500 q0.975 Hydro -0.0153 0.0701 -0.152 -0.0155 1.22e-01 nmHydro -0.0404 0.0117 -0.0634 -0.0405 -1.74e-02 Hydro*nmHydro 0.0736 0.0267 0.0210 0.0736 1.26e-01 Heat 0.202 0.0741 0.0562 0.202 3.47e-01 nmHeat 0.0383 0.0117 0.0153 0.0383 6.12e-02 Heat*nmHeat -0.259 0.0415 -0.340 -0.259 -1.77e-01 Hydro*nmHeat 0.0191 0.0474 -0.0742 0.0191 1.11e-01 Heat*nmHydro 0.0913 0.0269 0.0384 0.0914 1.44e-01

Model: Hydro (2-yr lag) and Heat by long-term climate conditions Hydro (2-yr lag) 0.333 0.0787 0.179 0.333 0.487 nmHydro -0.0354 0.0118 -0.0585 -0.0354 -0.0122 Hydro (2-yr lag)* -0.0120 0.0263 -0.0635 -0.0120 0.0393 nmHydro Heat 0.252 0.0722 0.111 0.252 0.393 nmHeat 0.0399 0.0117 0.0169 0.0399 0.0628 Heat*nmHeat -0.300 0.0390 -0.377 -0.300 -0.224 Hydro (2-yr lag)* -0.198 0.0475 -0.291 -0.198 -0.106 nmHeat Heat*nmHydro 0.0913 0.0267 0.0389 0.0914 0.144

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Figure 2-1. The wood frog is a broadly distributed species that spans most of the northeastern United States into Canada and Alaska.

Red dots indicate sites where egg mass counts were obtained. Thirty-year annual (a) precipitation and (b) temperature (Hijmans et al.

2005) maps show the broad range of climate conditions this species experiences across its range (IUCN 2014).

50

Figure 2-2. (a) The wood frog range (light grey) with an example of a northern (blue), central

(black), and southern (red) population. (b) These populations come from different long-term climate normals (e.g., colder to warmer represented by mean 30-year temperature). If wood frog responses are consistent with bioclimatic envelope predictions, the probability of occurrence of wood frogs peaks at some optimal temperature and declines in more extreme conditions. (c)

Sensitivity of wood frog population growth rates to annual climate variation is predicted to vary by long-term climate (shaded regions are 95% credible intervals). Sensitivity is the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions. We predict 1) populations in colder areas (blue) will be sensitive to warmer than average annual temperatures, leading to higher population growth rates (positive values); 2) populations in hotter areas (red) will be sensitive to warmer than average annual temperatures, leading to lower population growth rates (negative values); and 3) populations in areas far from climate extremes (black) will not be strongly affected by year to year deviations in temperature, leading to fairly consistent population growth rates (values around zero).

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Figure 2-3. The climate space [based on 30-year mean annual temperature (oC*10) and precipitation values (mm)] that encompasses North America (dark blue), the wood frog range

(light green), and our sites (red). Points on the scatterplot represent all temperature by precipitation raster cell values where wood frogs occur (light green) and do not occur (dark blue), with our sites in red. Precipitation values were truncated at 3500 mm for visualization purposes. Histograms represent frequencies of these same 30-year annual precipitation (top) and temperature (right) values in just the wood frog range. Boxplots of precipitation and temperature values from our sites show the minimum, median, maximum and 25th and 75th quartiles (box).

52

Figure 2-4. We estimated how sensitivity of wood frog population growth rates to annual climate variation changed with respect to long-term climate differences (shaded regions are 95% credible intervals). Sensitivity is the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions (y-axis). Long-term differences in mean climate are calculated using 30-year climate normals for conditions during the same portion of the year that annual covariates are measured (x- axis; see Tables 1 and 2) at our sampled sites. (a) Annual wood frog population growth rate two years later responded negatively to spring precipitation (PRECIP lag) across all areas, (b) annual wood frog population growth rate responded positively to years with more summer water availability (HYDRO) in areas where long-term average summer precipitation was higher (>50 mm), (c) annual wood frog population growth rate two years later responded negatively to years with more summer water availability (HYDRO lag) in areas where long-term average summer precipitation was higher (>105 mm) and positively in years where long-term averages were lower (<105 mm), (d) annual wood frog population growth rate responded negatively to extreme summer temperatures (HEAT) in areas where long-term average extreme temperature was higher (>24°C) and positively where long-term averages were lower (<24°C),

(e) annual wood frog population growth rate responded positively to increased winter severity (COLD) in areas where long-term average minimum temperature was milder (>-6.25°C) and negatively where long-term averages were colder (<-6.25°C). 53

54

Figure 2-5. We estimated how sensitivity of wood frog population growth rates to annual climate variation changed with respect to long-term climate differences (shaded regions are 95% credible intervals). Sensitivity is the expected change in annual population growth rate (r) for a one standard deviation increase in annual conditions (y-axis). Long-term differences in mean climate are calculated using 30-year climate normals for conditions during the same portion of the year that annual covariates are measured (x- axis; see Tables 1 and 2) at our sampled sites. (a) Annual wood frog population growth rate two years later did not significantly respond to spring precipitation (PRECIP lag) regardless of long-term winter severity, (b) annual wood frog population growth rate did not significantly respond to winter severity (COLD) regardless of long-term spring precipitation, (c) annual wood frog population growth rate did not significantly respond to summer water availability (HYDRO) regardless of long-term extreme summer heat, (d) annual wood frog population growth rate responded positively to years with more extreme summer temperatures (HYDRO lag) in areas where long-term average summer precipitation was higher (>20 mm), (e) annual wood frog population growth rate two years later responded negatively to increased summer water availability (HYDRO lag) in areas where long-term average extreme temperature was higher (>26.25°C) and positively where long-term averages were lower (<26.25°C). 55 56

Supplementary Material

Table S1. Mean latitude (Lat) and longitude (Lon) of wood frog study areas (n = 27) included in the analysis. Precip and Hydro columns are the 3-month periods used to calculate Precip and Hydro annual climate values.

Study Area State/ Lat Lon Years Precip Hydro Collaborator(s) Province Jasper National Park Alberta 52.8 - 2010-2012 Mar- Jun- Ward Hughson 9 118.04 May Aug Blue Ridge Parkway North 36.2 -81.75 1994-2005 Feb-Apr May- Jim Petranka, Chuck Smith Carolina 2 Jul Cumberland Gap National Kentucky 36.6 -83.69 1993-2005 Jan-Mar Apr- Jim Petranka, Chuck Smith Historical Park 0 Jun Great Smoky Tennessee 35.6 -83.77 1993-2005 Jan-Mar Apr- Jim Petranka, Chuck Smith Mountains National Park 1 Jun Tulula Restoration Wetlands North 35.2 -83.68 1995-2006 Feb-Apr May- Jim Petranka Carolina 8 Jul Mammoth Cave National Park Kentucky 37.1 -86.08 1994-1998 Jan-Mar Apr- Floyd Scott, Jim Petranka 6 Jun Daniel Boone Conservation Missouri 38.7 -91.39 2006-2014 Feb-Apr May- Tracy Rittenhouse,William Area 8 Jul Peterman Chequamegon National Forest Wisconsin 45.7 -90.07 2011-2013 Mar- Jun- Carmen Hardin 0 May Aug Yale Myers Forest and Connecticut 41.5 -72.76 2008-2012 Feb-Apr May- Mark Urban, Jonathan associated ponds 4 Jul Richardson Pawcatuck River watershed Rhode Island 41.5 -71.65 2001-2005 Feb-Apr May- Peter Paton, Dennis Skidds 0 Jul Rhode Island ponds Rhode Island 41.5 -71.72 1993-2014 Feb-Apr May- Peter Paton, Christopher Raithel 9 Jul

57

Table S1. continued

Study Area State/ Lat Lon Years Precip Hydro Collaborator(s) Province Chesapeake and Ohio Maryland 38.9 -77.10 2005-2012 Mar- Jun- Evan Grant National and Historical Park 3 May Aug Acadia National Park Maine 44.3 -68.37 2004-2012 Mar- Jun- Evan Grant 3 May Aug Cape Cod National Seashore Massachusetts 41.8 -70.06 2004-2013 Feb-Apr May- Evan Grant 5 Jul Patuxent Research Refuge Maryland 39.0 -76.77 2004-2014 Feb-Apr May- Evan Grant 8 Jul Eastern Massachusetts Massachusetts 42.3 -71.47 2004-2014 Feb-Apr May- Evan Grant National Wildlife Refuge 9 Jul Gettysburg National Military Pennsylvania 39.8 -77.24 2005- Feb-Apr May- Evan Grant Park 0 2007, Jul 2012 Rachel Carson National Maine 43.3 -70.55 2004-2014 Mar- Jun- Evan Grant Wildlife Refuge 6 May Aug Canaan Valley National West Virginia 39.0 -79.38 2004-2014 Feb-Apr May- Evan Grant Wildlife Refuge and State 8 Jul Park Erie National Wildlife Refuge Pennsylvania 41.7 -79.95 2004-2014 Mar- Jun- Evan Grant 7 May Aug Moosehorn National Wildlife Maine 45.0 -67.31 2005-2014 Mar- Jun- Evan Grant Refuge 8 May Aug Great Swamp National New Jersey 40.7 -74.50 2005-2014 Feb-Apr May- Evan Grant Wildlife Refuge 1 Jul Iroquois National Wildlife New York 42.5 -78.50 2004- Mar- Jun- Evan Grant Refuge 0 2007, May Aug 2009-2014 58

Table S1. continued

Study Area State/ Lat Lon Years Precip Hydro Collaborator(s) Province Rock Creek Park District of 38.9 -77.05 2005-2014 Feb-Apr May- Evan Grant Colombia 8 Jul Wallkill River National New Jersey 41.2 -74.56 2004-2014 Feb-Apr May- Evan Grant Wildlife Refuge 4 Jul Yale Myers Forest Connecticut 41.9 -72.12 2000-2013 Feb-Apr May- David Skelly 5 Jul E.S. George Reserve Michigan 42.4 -84.02 1998-2011 Feb-Apr May- Chris Davis, Rick Relyea, 6 Jul Mike Benard, Earl Werner, David Skelly 59

Figure S1. Life history diagram of the wood frog (Lithobates sylvaticus) shown over three breeding seasons (SPR t, t+1, and t+2). The four climate covariates impact the life cycle at different times. Spring precipitation (Precip) was hypothesized to impact the likelihood of adult breeding and egg mass deposition in a season and thus the likelihood of later recruitment of young (2-year lag). Summer water availability (Hydro) was hypothesized to immediately impact adult survival from one season to the next due to desiccation risk. Additionally, this covariate provided a measure of pond drying and was hypothesized to impact larval desiccation (2-year lag). Both extreme heat (Heat) and winter severity (Cold) were hypothesized to have an immediate effect on adult survival through late summer desiccation risk and overwintering stress respectively.

60

Figure S2. Average wood frog population growth rates for all sites (n = 746) over all years (t = 22). Anomalies (deviations from the standard climate) for each annual climate covariate are on the y-axis. Long-term differences in mean climate (calculated using 30-year climate normals for conditions during the same portion of the year that annual covariates are measured; Table 1, Table 2) are on the x-axis. Growth rates from a)

Precip (2-yr lag), b) Hydro, c) Hydro (2-yr lag), d) Heat, and e) Cold model output.

61

Figure S2. continued. Growth rates from f) Precip (2-yr lag)*nmCold, g) Cold*nmPrecip, h) Hydro*nmHeat, i) Heat*nmHydro, j) Hydro

(2-yr lag)*nmHeat, and k) Heat*nmHydro (2-yr lag) model output. Parameter estimates for i) and k) differ minimally (i.e, annual coefficient, Hydro, was modeled with and without a 2-yr lag).

62

Figure S3. Change in wood frog population growth rates over 30 years of change in annual climate. Population growth rates for a) Precip (2-yr lag), b) Hydro, c) Hydro (2-yr lag), d) Heat, and e) Cold were calculated using a linear model to estimate change in climate over 30 years multiplied by the effect of climate on growth rate based on climate sensitivities estimated from models. The sensitivity of wood frog populations differing by annual climate with respect to long-term climate was strongly supported in b), c), d), and e).

63

Figure S3. continued. Population growth rates for f) Precip (2-yr lag)*nmCold, g)

Cold*nmPrecip, h) Hydro*nmHeat, i) Heat*nmHydro, and j) Hydro (2-yr lag)*nmHeat.

Sensitivity of wood frog populations differing by annual climate with respect to long-term climate was strongly supported in i) and j). Parameter estimates for Heat*nmHydro including

Hydro (2-yr lag) differs minimally from i) and is not shown here.

64

Figure S4. Change in wood frog population growth rates over 30 years of change in annual climate in climate space. Population growth rates for a) Precip (2-yr lag), b) Hydro, c) Hydro (2-yr lag) were calculated using a linear model to estimate change in climate over 30 years multiplied by the effect of climate on growth rate based on climate sensitivities estimated from models. Long-term (30-year) mean annual temperature (x-axis) and annual precipitation (y-axis) are used.

65

Figure S4. continued. Population growth rates for d) Heat and e) Cold were calculated using a linear model to estimate change in climate over 30 years multiplied by the effect of climate on growth rate based on climate sensitivities estimated from models. Long-term (30-year) mean annual temperature (x-axis) and annual precipitation (y-axis) are used.

66

Figure S4. continued. Population growth rates for f) Precip (2-yr lag)*nmCold and g) Cold*nmPrecip were calculated using a linear model to estimate change in climate over 30 years multiplied by the effect of climate on growth rate based on climate sensitivities estimated from models.

Long-term (30-year) mean annual temperature (x-axis) and annual precipitation (y-axis) are used.

67

Figure S4. continued. Population growth rates for h) Hydro*nmHeat, i) Heat*nmHydro and j) Hydro (2-yr lag)*nmHeat were calculated using a linear model to estimate change in climate over 30 years multiplied by the effect of climate on growth rate based on climate sensitivities estimated from models. Long-term (30-year) mean annual temperature (x-axis) and annual precipitation (y-axis) are used. Parameter estimates for

Heat*nmHydro including Hydro (2-yr lag) differs minimally from i) and is not shown here.

68

Appendix S1. JAGS code for state space model implemented in program R via the R2jags package. model { # Likelihood # State process for (i in 1:S){ #loop over sites delta[i] ~ dnorm(0,tau.delta)T(-1,1) #random effect of site #truncated between -1 and 1 to increase mixing } for (t in 1:(T-1)){ #loop over year for (i in 1:S){ #loop over site r.proc[t,i] ~ dnorm(0 , tau.proc) #process error r[t,i] <- delta[i] + r.proc[t,i] + beta[1]*Annual[t,i] + beta[2]*Regional[i] + beta[3]*Annual[t,i]*Regional[i] logN[(t+1),i] <- logN[t,i] + r[t,i] #population growth model } }

# Observation process for (t in 1:T) { #loop over year for (i in 1:S){ #loop over site y[t,i] ~ dnorm(logN[t,i], tau.obs) y.new[t,i] ~ dnorm(logN[t,i], tau.obs) #simulate data for goodness of fit check } }

# Population sizes on real scale for (t in 1:T){ #loop over year for (i in 1:S){ #loop over site N[t,i] <- exp(logN[t,i]) } }

# Priors and constraints for (i in 1:S){ #loop over site logN[1,i] ~ dnorm(0,0.01) #Prior for initial population size at every site

} for (a in 1:3){ #loop across all betas beta[a] ~ dnorm(0, 0.01) #Prior for climate covariates }

sigma.proc ~ dunif(0, 5) #Prior for standard deviation of state process 69

sigma2.proc <- pow(sigma.proc, 2) tau.proc <- pow(sigma.proc, -2) #Precision process

sigma.obs ~ dunif(0, 5) #Prior for standard deviation of observation process sigma2.obs <- pow(sigma.obs, 2) tau.obs <- pow(sigma.obs, -2) #Precision observation

sigma.delta ~ dunif(0, 0.2) #Prior for standard deviation of site random error sigma2.delta <- pow(sigma.delta, 2) tau.delta <- pow(sigma.delta, -2) #Precision site random error } 70

Chapter 3

Knowing your limits: estimating range boundaries and co-occurrence zones for two

competing plethodontid salamanders

This chapter has been submitted to the journal Ecosphere and is formatted consistent with their

requirements

S.M. Amburgey1,2*, D.A.W. Miller1, A. Brand3, A. Dietrich4, E.H. Campbell Grant3

1The Pennsylvania State University, Department of Ecosystem Sciences and Management, University

Park, PA, USA, 16802

2The Pennsylvania State University, Intercollege Graduate Ecology Program, University Park, PA, USA,

16802

3USGS Patuxent Wildlife Research Center, SO Conte Anadromous Fish Research Center, Turners Falls,

MA, USA, 01376

4USGS Patuxent Wildlife Research Center, Laurel, MD, USA 20708

*Corresponding Author: [email protected]

71

Abstract:

Understanding threats to species persistence requires knowledge of where species currently occur. We explore methods for estimating two important facets of species distributions, namely where the range limit occurs and how species interactions structure distributions.

Accurate understanding of range limits is crucial for predicting range dynamics and shifts in response to interspecific interactions and climate change. Additionally, species interactions are increasingly recognized as an important, but not well understood, predictor of range shifts. Our objective was to predict range limits and contact zones for two plethodontid salamanders, the highly range restricted Shenandoah salamander (Plethodon shenandoah) and the wide-ranging red-backed salamander (Plethodon cinereus). Using detection/non-detection data, we assess four methodological decisions when estimating species’ distributions: 1) accounting for imperfect detection, 2) covariates to predict species occurrences, 3) accounting for species interactions, and

4) the inclusion of spatial autocorrelation. We found that Shenandoah salamander and red- backed salamander co-occurrence would have been underestimated and the range edge misidentified had we not accounted for incomplete detection. Covariates related to habitat were not sufficient to explain species’ range boundaries. Models that included spatial autocorrelation

(i.e., a conditional autoregressive random effect) performed better than models that included just species interactions (i.e., detection and occurrence were conditional on the other species being present) and models that included both spatial autocorrelation and species interactions. Further, we found that the breadth of primary contact zones was typically 60 to 170 m, which is greater on average than previous estimates. In addition, we frequently observed secondary, disjunct contact zones along the range boundary. Understanding the extent to which species co-occur and how the range boundaries are shaped is crucial to conservation efforts. Our work indicates that 72 accounting for detection is crucial for accurately characterizing range edges and that spatial models may be especially effective in modeling distributions at the boundary.

Keywords: conditional occupancy, conditional autoregressive models, detection probability, joint species distribution model, range limit, red-backed salamander, Shenandoah salamander

73

Introduction

Knowledge of current species range limits is important for measuring current limits, forecasting future range shifts, predicting future conservation needs, and prioritizing spatial allocation of management (Hulme 2005, Elith and Leathwick 2009). Understanding factors determining species range limits is also necessary to assess broad threats to species persistence and monitor shifts in local dynamics (Anderson et al. 2009). Despite the far-reaching importance of identifying species’ range limits, there are unique challenges that occur when estimating these boundaries.

An underlying basic yet critical assumption for determining higher resolution range limits is that the presence and absence of a species is known without error. Given this is rarely the case, the need to estimate detection errors are dependent on methodologies used to survey and model species distributions (Sastre and Lobo 2009, Rocchini et al. 2011, Stolar and Nielsen 2015) in addition to species’ characteristics that impact their detectability by observers (Pellet and

Schmidt 2005, Comte and Grenouillet 2013). Researchers often aggregate data from multiple studies, each with their own survey methodologies (and potential biases), to inform species range models. These data may lead to estimates of species distributions that are not as statistically robust due to inconsistent historical sampling or analytical approaches (Bailey et al. 2004,

Tingley and Beissinger 2009), variation in detection of target taxa (Grant 2014), or because ranges have shifted over time with changing environmental pressures or altered population dynamics. While identifying the factors underlying species occurrence is inherently challenging, these difficulties are exacerbated at range edges where species presence fades to absence and abundance is assumed to be lower than at the core of a species’ distribution (Hengeveld and

Haeck 1982, Sagarin et al. 2006). Occupancy modeling is an important advancement in species 74 range modeling as it corrects for variability in latent (unobservable) processes and accounts for imperfect detection of species (MacKenzie et al. 2006). By using a robust sampling design that allows us to account for imperfect species detection during surveys, a finer resolution of species occupancy at difficult to map areas (e.g., range edges) can be achieved.

In addition to accommodating imperfect detection, understanding distributional patterns at the range edge requires accurately estimating the spatial configuration of where species occur

(Johnson et al. 2013, Ver Hoef et al. 2018). As with all species distribution models, covariates related to climate, topography, geology, and vegetative habitat can be used to model the spatial configuration of species occurrence (MacKenzie et al. 2006, Tingley and Beissinger 2009).

However, variables that do well in explaining large-scale geographic patterns of occurrence may be less informative when predicting the fine-scale extent of a range limit. Spatially explicit models are another tool that may be used to better estimate the range boundaries of species’ distributions. Spatial models can capture similarities in species occupancy driven by the proximity of spatial units to one another (i.e., neighboring units are more likely to be similarly occupied or not occupied as compared to those that are far apart; Dormann 2007). By incorporating autocorrelated spatial random effects that may explain variation in species occupancy, variables driving patterns at the range boundary may be identified and fewer spurious associations may be made (Lichstein et al. 2002, Dormann 2007). Spatial autocorrelation may also better capture abiotic and biotic characteristics that co-vary over space and contribute to patterns in species occupancy (Legendre 1993, Lichstein et al. 2002).

Understanding species range limits, especially on a local scale, may also require accounting for species interactions (Louthan et al. 2015). Range models have historically focused on the role of climate as a primary ecological filter determining species distributions (Grinnell 75

1917, Menge and Olson 1990), but an increasing number of studies (Gilman et al. 2010, Pigot and Tobias 2012, HilleRisLambers et al. 2013, Yackulic et al. 2014) highlight the importance of species interactions in delimiting species ranges and influencing population dynamics. Several studies indicate these interactions provide a mechanistic explanation of patterns observed at the local scale (Pearson and Dawson 2003, Soberón 2007) and should be considered when creating conservation plans (Gilman et al. 2010). Parsing out the importance of species interactions and using them in modeling distributions may be of particular importance with rare, endangered, or specialist species when predicting range shifts (Preston et al. 2008, Bateman et al. 2012).

Competitive interactions can shape ranges (Sexton et al. 2009), alter or exacerbate responses to climate (Gilman et al. 2010), and lead to local extinctions of species (Bengtsson 1989), complicating range models. Species can actively exclude one another, thus altering occupancy, but behavioral interactions can also alter species’ detectability and therefore bias conclusions about occurrence (Bailey et al. 2009, Miller et al. 2012). Joint species distribution models

(JSDMs) are a broad category of models that account for relationships among species and aim to better attribute variance in patterns to abiotic or biotic factors (Ovaskainen et al. 2010, Richmond et al. 2010, Pollock et al. 2014). For one specific type of JSDM, occupancy models, species interactions can be estimated via their influence on the probabilities of species detection and occupancy (Richmond et al. 2010) and can thus better inform range-modeling efforts.

We used occupancy models to estimate the range boundary of a high-elevation, range- restricted plethodontid salamander, the Shenandoah salamander (Plethodon shenandoah;

Highton and Worthington 1967), and the more widely distributed red-backed salamander (P. cinereus; Green 1818) at the intersection of their ranges. Plethodontid salamanders are a diverse group with complex patterns of speciation and hybridization across space, and competition is 76 frequently invoked in explaining their distributions (Hairston 1987). We aimed to determine the value of habitat covariate information, spatial autocorrelation, and species interactions in generating accurate estimates of species occupancy at the range boundary for these two species.

Incorporating estimates of species interactions in explaining range limits in this system is especially interesting due to previous research suggesting competitive exclusion defines range boundaries for these two species and other plethodontid salamanders where their range boundaries intersect (Hairston 1987). Research on this system has purported that red-backed salamanders exert strong competitive exclusion on Shenandoah salamanders in locations where the two meet (Jaeger 1970, Griffis and Jaeger 1998), but, despite decades of research, the specific mechanism leading to competitive exclusion at the range edge is unknown (Jaeger et al.

2016). In addition, surface activity and thus detectability is known to vary with environmental conditions for terrestrial salamanders (Hairston 1987, Petranka and Murray 2001), and occupancy modeling can provide a valuable tool in this system where detection is imperfect

(Bailey et al. 2004).

We compared estimates from single-species occupancy models that independently calculate species occupancy and detection probabilities (MacKenzie et al. 2004) to two-species conditional occupancy models that model dependency between occupancy and detection probabilities of potentially interacting species (e.g., dependencies that may result from competition, avoidance, or from distinct habitat preferences; Richmond et al. 2010). We also tested for improvement in modeling the range edge when incorporating a spatial element to single- and two-species conditional occupancy models via the inclusion of a conditional autoregressive (CAR) random effect to explain spatial patterns in occupancy unrelated to measured biotic or abiotic covariates (Bled et al. 2013, Johnson et al. 2013, Ver Hoef et al. 77

2018). All models included substrate covariates to assess if occupancy patterns could be largely modeled using only metrics of local microhabitat (as these were originally identified as critical to the competition-driven distribution of P. shenandoah; Jaeger 1970). Cross-validation of these different model permutations allowed us to test the importance of species interactions and spatial structure in setting the species range limit. Understanding how changing climate and competition are affecting the range boundaries for these species requires a precise understanding of where the current as well as future range limits occur. With management plans relying on such “basic” information as knowing the current distribution of species and what factors contribute to their range limits, accounting for species detection in combination with choices about how to model distributions can greatly affect our ability to create accurate species distribution models.

Methods

Study system

The Shenandoah salamander is a small-bodied, terrestrial salamander endemic to

Shenandoah National Park, VA, USA (Highton and Worthington 1967). The species occurs on only three mountaintops at elevations over 850m (Highton and Worthington 1967, Jaeger 1970) and is federally listed as an endangered species (54 CFR 34464; US Fish and Wildlife Service

1994). As such, we maintained all geo-spatial relationships in analyses and results but exact geographic locations and location names are not published herein. The species range is restricted, and little is known about the abundance and population dynamics of the species (National Park

Service 2015). In contrast, the red-backed salamander is more broadly distributed, occurring across most of the northeastern United States and Canada (Green 1818) in the deeper soil and leaf litter of forested habitats (Jaeger 1970). Shenandoah salamanders occur in rocky, “talus” 78 habitats on north facing slopes where it is hypothesized that more extreme moisture and temperature regimes exclude the red-backed salamander (Jaeger 1971a). Red-backed salamanders surround these talus slopes (Jaeger 1970) and are posited to be competitively superior (Jaeger 1970, Griffis and Jaeger 1998), leading to little overlap in the observed species’ ranges. Lab-based and in-situ manipulative experiments suggest red-backed salamanders are capable of outcompeting Shenandoah salamanders for space in deeper soil habitats (Griffis and

Jaeger 1998) that are less physiologically stressful for both species (Jaeger 1971a,b). Laboratory experiments and species removal studies have shown that red-backed salamanders may exclude

Shenandoah salamanders by outcompeting them for microhabitat (Jaeger 1971b, Griffis and

Jaeger 1998) and food (Jaeger 1972, Dallalio et al. 2017). Previous research suggests interactions between the two species are responsible for the formation of the lower elevational range limit of the Shenandoah salamander post glaciation (Jaeger, 1970, 1971b, Highton, 1995, Griffis and

Jaeger 1998).

Field sampling

We surveyed four study areas along the lateral range boundaries for both Shenandoah and red-backed salamanders using parallel transects spaced 50m apart. We established transects based on recent observations of both species near suspected co-occurrence zones, i.e., areas of expected intersection of the species’ ranges. In establishing transects, we aimed to begin surveying in one species’ territory, pass through the co-occurrence zone, and continue into the other species’ territory while maintaining a constant aspect and elevation (e.g., moving from north-facing to south-facing slopes). Transects were surveyed in consecutive 50m segments using transect tape and a compass, and tree tags were placed every 50-100m on initial surveys to 79 mark transects for subsequent surveys. Total transect lengths surveyed during any individual visit varied due to physical obstacles (e.g., cliffs), time constraints, and a lack of surface-active animals or suitable habitat (e.g., surveying stopped if no animals were found in 100m). Two to three transects were established at each of the four study areas, with most transects surveyed in

2015 and 2016 (t = 6-9 surveys across spring, early summer, and fall). We surveyed approximately 270 m per transect on average (but transect length varied between 200 m and 400 m).

During the first survey, we surveyed transects starting in the Shenandoah salamander range and working toward the red-backed salamander range. Subsequent surveys were randomized to start in either species territory. For each transect, observers checked all cover objects within 2m of each side of the transect tape. Detected salamanders were captured by hand and placed in new plastic sandwich bags for immediate species identification (based on Highton and Worthington 1967) and measurement. Any uncertainty in species identification required verification by a second observer, and any individual that could not be definitively identified was noted and subsequently removed from our analyses (<10 animals removed). Animals were immediately returned to cover objects where captured, and we recorded the ‘site’ of capture (5 m by 4 m segment of the transect) for use in subsequent analyses. We categorized percent substrate type for each site for all transects during spring 2017 using four categories: rock (exposed expanses of talus, boulder, and cobble), soil/wood (fallen woody debris and open stretches of ground), moss/leaf (thick, moist ground cover), and other (none of the above).

80

Data analysis

We estimated patterns of occurrence for both species at all sites (i.e., 5 m by 4 m sections of the overall transect). Detection histories for each site, i, were analyzed using four parameterizations of single and two-species occupancy models (MacKenzie et al. 2004, 2006) fit in a Bayesian framework. By comparing parameterizations, we assessed the relative importance of different modeling choices in generating accurate predictions. We used a static occupancy formulation that assumes occupancy (i.e., presence or absence) was constant at sites across all surveys. Thus, we estimated the probability each site was occupied at some point during our study.

For our first model, we simultaneously fit single-species occupancy models for both red- backed (species A) and Shenandoah (species B) salamanders where species occupancy and detection probabilities were independent of the other species (MacKenzie et al. 2004, 2006). We

A defined the latent true site occupancy for species A at site i, zi , to be 1 when species A was

A present and 0 when absent. The probability that the site was occupied (i.e., zi = 1) was denoted

A A A as i , where zi ~ Bernoulli (i ). The probability of detecting a salamander at site i during visit t was conditional on true species occupancy at a site as well as the detection probability at that survey such that

A A 0 if zi = 0 Probyit = 1 = A A (1) pt if zi = 1

A Where yit = 1 if the species is detected and 0 if it is not detected during a visit. We used a prior

A of pt ~ Uniform (0, 1).

Our base model included only covariates for the fixed effects of substrate type at each site. 81

A logit(i ) = β1*Rocki + β2*SoilWoodi + β3*MossLeafi + β4*Otheri (2)

Substrate values were the proportion of the total area of each site that consisted of that substrate and sum to 1 for each site. We set the priors for substrate regression coefficients to be approximated by βk ~ Normal (0, 100). Boundary conditions (i.e., estimates of ψ at 0 or 1) became an issue later when we added the spatial random effect so we made two additional constraints to priors for all models. First, we bounded the β values between -7 and 7. Second, we

A bounded i between 0.0001 and 0.9999. Occupancy and detection probabilities for species B,

B B i and pt , were estimated in the same manner as species A.

For our second model, we further modified the substrate covariate-only model to fit a two-species conditional occupancy model that calculated probabilities of detection and occurrence for species B conditional upon the occupancy and detection of species A (Richmond et al. 2010). Similar to the single-species model, we estimated the effect of substrate covariates

A B B on i and i using Eq. (2). However, for the two-species model, i was also conditional on

A BA whether i = 1 (Richmond et al. 2010), resulting in two estimates of occurrence probability, i

Ba (occupancy of B given A is also present at a site) and i (occupancy of B given A is absent at

B BA A a site). We modified Eq. (2) to estimate i by including the intercept term α *i , which

Ba BA BA adjusts for the difference between i and i . We used a prior of α ~ Normal (0, 100).

In the two species conditional model, the probability of detecting a salamander was conditional on true species occupancy of both species at a site as well as whether the other species was detected during the survey (sensu Richmond et al. 2010) such that

0 if zA = 0 and zB = 0 ⎧ i i A A B ⎪pt if zi = 1 and zi = 0 A A B Probyit = 1 = 0 if zi = 0 and zi = 1 (3) ⎨ A A B ⎪rt if zi = 1 and zi = 1 ⎩ 82

0 if zA = 0 and zB = 0 ⎧ i i A B ⎪ 0 if zi = 1 and zi = 0 ⎪ pB if zA = 0 and zB = 1 Prob(yB = 1) = t i i (4) it BA A A B ⎨rt if zi = 1, yi = 0, and zi = 1 ⎪ Ba A A B ⎪ rt if zi = 1, yi = 1, and zi = 1 ⎩

We used p to denote detection probabilities when the second species was not present and r when the second species was present (Richmond et al. 2010). For species B, we separately estimated

BA the probability of detecting species B given that species A was present and also detected (rt )

Ba versus when species A was present but not detected (rt ). We similarly allowed all detection parameters to vary among visits using a uniform prior from 0 to 1. Priors for all other parameters follow single-species model specifications.

The third and fourth models we fit further modified the single-species (model 1) and two- species conditional occupancy models (model 2), respectively, by adding a site and species-

A B specific CAR random effect (i and i ) to Eq. (2). The CAR random effect assumes that the probability of site occupancy along the transect is correlated to neighboring sites and allows this information to be shared through a random effect structure (Johnson et al. 2013). Neighboring sites were defined as sites immediately preceding or following each current site along the transect. Terminal sites of each transect possessed only one neighbor. All neighbors were

A weighted equally. We approximated i using an intrinsic Gaussian CAR prior distribution

(car.normal in WinBUGS) with a precision term, τA. We set the prior for τA = 1/ (σA)2 where σA

B A ~ Uniform (0, 5). We estimated i using the same parameterization as for i .

All four models were fit using WinBUGS via the R2WinBUGS package (Sturtz et al.

2005). We used the spdep package (Bivand and Piras 2015) to create neighborhood matrices for the CAR analyses. All model code can be found in Appendix S1 in Supplemental Information. 83

Model convergence was determined visually from traceplots and Gelman Rubin statistics ( <

1.05, Gelman and Rubin 1992).

Model comparisons

Using five-fold Bayesian cross-validation to test the predictive accuracy of our models, we randomly removed 20% of the data prior to model fitting and then calculated model likelihood and deviance based on values predicted by each model (Hooten and Hobbs 2015,

Miller and Grant 2015). We repeated this for each of the remaining 20% segments of data and summed calculated deviances for the leave-out data across all five model fits. To better isolate where each model did better or worse, we calculated model likelihoods for each species individually and for both species together using 1) all test data, 2) test data where information on both species was removed, and 3) test data where only one or the other species information was removed. This allowed us to determine overall fit but also isolate parts of the dataset where one model did measurably better than another. Deviance was calculated as -2[Σ(ln(ℒ(test data))].

We also compared predicted co-occurrence zones of all models to determine how model

A B choice affected conclusions about range limits. Occupancy probabilities (i *i for single

BA species models assuming independence or i for two species models assuming conditional dependence) were mapped to each site. These values were then summed across each site and the next two immediate sites (or three 5 m by 4 m sites = 60m2 area used) of a transect to identify areas of consistent species occurrence. Male red-backed salamanders use a maximum area of

62.1m2 (averaged spring and autumnal space-use estimates; Muñoz et al. 2016) surrounding a resident territory, and we therefore used this area to infer resident animals versus potential

A B BA dispersers (assuming the same territory size for both species). Summed values of i *i or i 84 could equal a maximum value of 3. We considered a high probability of co-occurrence to be any site where the likelihood of co-occurrence across 15m (3 sites) was equal to or greater than one

(e.g., at least two of the three sites had a 0.5 or greater co-occurrence probability). For defining zones of co-occurrence, we considered two types of co-occurrence. Primary and secondary contact zones were both areas with a high probability of resident animals of both species co- occurring, but we defined primary zones as the longest stretch of continuous co-occurrence in a given transect. Secondary contact zones were narrower than primary zones, potentially representing dispersing or transient individuals.

Results

We used data from 1,954 red-backed salamander and 654 Shenandoah salamander detections for all analyses and tests. The ‘naïve’ (i.e., unadjusted for detection probability) spatial overlap of detected salamanders varied among surveys. Observations of co-occurrences at individual sites were relatively uncommon during a single survey, but, when summarized across all surveys, we observed a larger area of overlap between the two species (Fig. 1, Appendix S2:

Fig. S1). Number of detected salamanders of each species peaked toward the center of each species’ respective territory but was not consistently highest where only a single-species was detected (Appendix S2: Fig. S2). Substrate maps showed a trend of higher proportion of rock in

Shenandoah salamander territory and a higher proportion of soil/wood in red-backed salamander territory (Appendix S2: Fig. S3). Moss/leaf had large variation among transects in no consistent pattern while ‘other’ substrate types occurred sporadically. 85

Model comparison

We calculated model likelihoods and deviances using multiple criteria to compare how each model performed in modeling the range limit between red-backed and Shenandoah salamanders (Table 1). The best-supported overall model for all data and for both species was our third model, the single-species occupancy model with a CAR random effect (deviance =

486.20). When we considered different subsets of data, in most cases this model was the best supported model except where test data were restricted to only cases where both species detection histories were not included in the dataset used to fit the model. When test data where information on both species was removed, the two species conditional occupancy model with

CAR random effect was best supported (deviance = 96.94) as compared to the next best supported model (single-species occupancy model with CAR random effect, deviance = 98.67).

As expected, models with CAR random effects resulted in occupancy estimates that were smoothed across space with peak areas of species occupancy, showing similar spatial patterns between the single-species and the two-species conditional occupancy models with CAR random effects (Fig. 2). Models without CAR random effects and only substrate covariates showed no clear spatial pattern.

We calculated model deviance separately for each species for each model, and all models had a lower deviance for Shenandoah salamander parameters than red-backed salamander parameters (Table 1). For the best supported species occupancy model with CAR random effect, model deviance calculated for red-backed salamander estimates was 243.90 as compared to

143.64 for Shenandoah salamander estimates. When data were removed for only one species or the other, we obtained smaller deviances from models when only red-backed salamander data were present rather than when only Shenandoah salamander data were available. For the top 86 supported single-species occupancy model with CAR random effect, deviance using only red- backed salamander data was 143.64 while deviance was 243.90 when using only Shenandoah salamander data.

Detection probabilities for red-backed salamanders were higher than those for

Shenandoah salamanders for all models and all surveys (Fig. 3). From the single-species occupancy model with the CAR random effect, mean detection probability of red-backed salamanders was over twice that of Shenandoah salamanders (pA = 0.564 compared to pB =

0.241, Table 2). For the two-species conditional model with the CAR random effect, mean detection probability of red-backed salamanders was higher where Shenandoah salamanders did not co-occur (pA = 0.763 compared to rA = 0.415). Shenandoah salamanders were detected fairly similarly with or without the co-occurrence or detection of red-backed salamanders (pB = 0.259, rBA = 0.212, rBa = 0.200). Occupancy models with and without CAR random effects predicted similar detection probabilities.

Based on our top-ranked model, the single-species occupancy model with CAR random effect, substrate-use differed between the two species (Fig. 4, Table 3). Red-backed salamander occupancy was high in all substrates categories, with rock and soil/wood being the most important [mean = 3.606 (credible intervals = 2.129, 5.006) and 3.380 (1.432, 5.465) respectively]. Shenandoah salamander occupancy increased most strongly with greater rock cover [2.597 (0.879, 4.401)] and increased with all other substrates except soil/wood [-1.085

(0.757, 0.462)]. Occupancy estimates from both models with a CAR random effect showed a trend of higher occupancy for red-backed salamanders in areas with increased amounts of soil and wood substrate and higher Shenandoah salamander occupancy in areas with increased rock substrate (Fig. 5, Appendix S2: Fig. S4). Substrate patterns were similar in less supported models 87 except that occupancy probabilities were lower in rock than other substrates for the red-backed salamander when the CAR random effect was not included in the model (Fig. 4, Appendix S2:

Table S1). True occupancy (zA and zB) varied little across the different models except towards the ends of each transect (Appendix S2: Fig. S5).

When mapping overlap of each species’ range, extent of primary and secondary contact zones varied by model (Fig. 6). Contact zones from models incorporating a CAR random effect corresponded to peaks in predicted occupancy probabilities rather than the low-level

‘background’ co-occurrence rate estimated from models using only substrate covariates (Fig. 6).

For the top supported single-species occupancy model with CAR random effect, primary zones of co-occurrence varied by study area and transect (e.g., Fig. 7), with the extent varying between

60-170m (mean = 101.82m, SD = 37.51m, Table 4, Appendix S2: Fig. S6). Secondary contact zones were present for five of the eleven transects and varied between 5-60m (mean = 32m, SD

= 24.14m, Table 4, Appendix S2: Fig. S6). However, the smallest secondary contact zone of 5m was at the very end of a surveyed transect, indicating that co-occurrence may have extended past the end of the transect. The next smallest secondary contact zone was 10m in length.

Discussion

An understanding of species range limits and the factors establishing these constraints is germane to critical ecological and management questions. Here, we tested the value of including substrate information, species interactions, and spatial autocorrelation in occupancy models to best predict the range limits of an endangered plethodontid salamander. We found that correcting for detection probability was essential to understand both Shenandoah and red-backed salamander range boundaries and the breadth of their co-occurrence. If we had relied on only a 88 single or limited number of surveys and assumed we had observed all occurrences along transects without error, we would have underestimated the breadth of contact zones in every transect (Appendix S2: Fig. S1).

After correcting for false absences, we were better able to evaluate the potential influence of local-level substrate information and species interactions on species occupancy in order to understand the intersection of these two species’ ranges. The single-species occupancy model with CAR random effect was almost always the top-ranked for modeling red-backed and

Shenandoah salamander range limits, suggesting that explicitly including a conditional relationship between the two species did not improve estimation of the range limits in our study.

Additionally, spatially explicit, autoregressive models better captured patterns of occupancy across the range boundary than models solely relying on substrate covariates. CAR and other spatial models can provide a spatially smoothing approach to estimating range boundaries by sharing information among neighboring sites, thus improving model fit due to the spatially structured nature of most ecological data (Dormann 2007, Ver Hoef et al. 2018). Gradients in environmental characteristics along the range boundary such as temperature and humidity may help pattern species occupancy due to known differences in physiological tolerances between these salamander species (Jaeger 1971a, Dallalio et al. 2017). These autoregressive effects that share information from neighboring sites can better incorporate such patterns of spatial variation in abiotic and biotic gradients (Dormann 2007).

Previous research on the competitive interactions of Shenandoah and red-backed salamanders have focused on laboratory-based experiments or in-situ manipulations (Jaeger

1970, 1971a,b, Griffis and Jaeger 1998), but an understanding of the current, natural dynamics of populations in their native range are required for creating targeted management plans. Past 89 studies have suggested that red-backed salamanders exhibit a near perfect competitive ability to exclude Shenandoah salamanders and create sharply delineated range boundaries (Jaeger 1970,

Griffis and Jaeger 1998), corresponding to abrupt changes in microhabitat variables that comprise discrete ‘talus’ habitats. The fact we do not observe a hard transition zone with little overlap between species may be a product of three factors. First, range overlap may be increasing as a result of pressures such as climate change or the effects of invasive species within the system that may have changed in the course of three decades (Webb et al. 1995, Young et al.

2000, Grant et al. 2012), altering competitive dynamics in this system. Second, the past assumptions of near perfect competitive exclusion in the wild may be an artifact of historical survey methods or those not correcting for imperfect species detection (Tingley and Beissinger

2009, Comte and Grenouillet 2013). Whether or not each species was observed at a given site varied greatly from survey-to-survey (Fig. 1, Appendix S2: Fig. S1), and both species were rarely detected under the same cover object. This confirms research elsewhere regarding the inconsistency of plethodontid surface activity (Bailey et al. 2004) and also the importance of designing survey methods to account for imperfect detection, especially for those species that are rare, cryptic, or temporally unavailable for detection (Gu and Swihart 2004, Durso et al. 2011,

Kéry et al. 2013). Lastly, the importance of species interactions in species distribution modeling may be scale-dependent (Huston 1999, Pearson and Dawson 2003, Guisan and Thuiller 2005).

At the scale of the individual, behaviors such as microhabitat selection or foraging may be impacted by the presence of the other species (Jaeger 1971b, Jaeger 1972, Levin 1992, Griffis and Jaeger 1998, Dallalio et al. 2017). Interspecific aggression may influence cover object selection, surface activity, or occupancy at a smaller resolution but not manifest itself as differences in occupancy at a larger spatial scale. 90

We found consistently higher detection probabilities for red-backed salamanders in single-species models. When species interactions were considered in models, Shenandoah salamander detection probabilities did not differ from when red-backed salamanders did and did not co-occur, yet red-backed salamander detection probabilities were higher when Shenandoah salamanders were not also present. Differences in detection probability may be a product of differential surface activity between the two species or differences in abundance. Red-backed salamanders potentially are more abundant than Shenandoah salamanders, helping to explain their higher detection probability. Disentangling these explanations requires further insight that could be obtained from using a mark-recapture study to better understand differences in density versus individual capture rates. Jaeger (1974) found behavioral avoidance by red-backed salamanders in burrows when Shenandoah salamanders were also present, supporting the idea of interference competition between the two species that might also alter surface activity. This could contribute to the limited number of occasions where both species were detected under the same cover object, but this difference in detectability does not appear to manifest itself in distributional patterns at the transect level.

When mapping the range limit, the shape and extent of the co-occurrence zone varied based on our model (Fig. 6), highlighting the importance of accounting for and testing these hypothesized relationships when modeling species distributions. From the single-species occupancy model with CAR random effect, we found range overlap between the Shenandoah and red-backed salamander is currently wider than previously reported (< 100m, Jaeger 1972).

Jaeger (1970) found coexistence was possible (though rare) within 20m of the talus edge in soil but did not conduct surveys > 30m from the talus edge. Our results support an overlap of > 100m in about half of our transects. In addition, multiple transects showed secondary contact zones that 91 do not necessarily coincide with talus slopes and may be proximate to primary zones (i.e., may not represent distinct metapopulations; Griffis and Jaeger 1998), highlighting that the range boundary is not as sharply delineated as previously suspected and that the red-backed salamander may not be able to completely exclude Shenandoah salamanders from its range.

Red-backed salamanders did not demonstrate a preference for any measured substrate, but Shenandoah salamanders occupied areas with rocky substrate more often than those with soil and wood substrates (Jaeger 1971a,b). ‘Talus’ slopes have been used as a proxy for moisture and temperature regimes in which Shenandoah salamanders persist (on the three mountaintops at suitable elevations; Jaeger 1970, Grant et al. 2012), potentially explaining increasing

Shenandoah salamander occupancy with rock substrate. Jaeger (1971b) indicated that experimental evidence supported the finding that red-backed salamanders were unable to persist in talus slopes and would thus not co-occur with Shenandoah salamanders, yet we observed no difference in substrate use for red-backed salamanders. Substrate alone did not explain patterns in occupancy between the two species, though CAR models may also account for spatial patterns in habitat characteristics such as substrate and thus lessen the importance of substrate covariates alone in explaining occupancy patterns (Dormann 2007). Additionally, substrate measured at the

5 m by 4 m spatial scale may not accurately reflect the importance of microhabitat refugia used by each species or broad-scale associations that occur when considering larger scales.

Shenandoah salamanders are a range-restricted, endangered species that is currently threatened by the influence of changing climate on their montane ecosystems (Grant et al. 2012).

The current conservation status of the salamander and vulnerability to range contractions under future climate change makes understanding the elements contributing to their range limits especially pressing. This is one of the first studies in this system to estimate these range 92 boundaries using robust methodologies and to test the importance of different occupancy model structures and assumptions on co-occurrence conclusions. The occupancy models we employed are amenable to the inclusion of abiotic and biotic covariates and variations in occupancy and random effect structures, emphasizing their strength in such range modeling questions (Kéry et al. 2013). Future analyses with additional seasons of data could fit dynamic occupancy models to better characterize temporal variation in range boundaries (Anderson et al. 2009, Kéry et al.

2013, Amburgey et al. 2018). Additional abiotic and biotic gradients that vary over space might influence occupancy of both species, and future work in this system could include the role of measured climate along these transects in order to fit climate by space models, allowing for estimates of turnover for both species and furthering our understanding of the ecology of these range boundaries.

Conclusions

Delineation of species’ range edges using robust modeling methodologies is important for management of a species (Rondinini et al. 2006), particularly those that are threatened, endangered or range-restricted (Engler et al. 2004). Survey methods and modeling approaches that can accommodate uncertainty in species distribution models are important for conservation planning where biases can result in drastically different forecasts of range shifts (Wilson et al.

2005) or identification of management threats (Gu and Swihart 2004). Failure to incorporate spatial autocorrelation in models where it plays a part in species occupancy can lead to over- estimation and inaccurate prediction of species distributions (Dormann 2007). Similarly, failure to test for conditional species relationships that integrate biotic interactions can lead to erroneous species distribution models (Guisan and Thuiller 2005) or conclusions about species co- 93 occurrence (Bailey et al. 2009, Miller et al. 2012). We highlight the importance of testing the fit of a suite of range models in order to select reliable parameterizations for management and encourage validation of models to assess goodness of fit. Goals from a structured decision making workshop for the Shenandoah salamander indicated a favorable attitude of managers toward active management for this species, as long as uncertainty of the cause of declines and responses to management can be quantified (Grant et al. 2012). In considering potential future initiatives, an approach such as occupancy modeling to understand range dynamics better addresses uncertainty and informs managers about how best to target management efforts.

Acknowledgments

We would like to thank the Northeast Amphibian Research and Monitoring field crews and volunteers for their assistance with data collection. This work was permitted by the Virginia

Department of Game and Inland Fisheries (#053142 and #056283) and Shenandoah National

Park (#SHEN-2014-SCI-0022) and was partly funded by the USGS John Wesley Powell Center for Analysis and Synthesis, National Park Service, and the USGS Amphibian Research and

Monitoring Initiative (ARMI). This manuscript is contribution #XXX of the USGS ARMI program. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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102

Data Availability

All data belongs to the National Park Service, and public release of information on endangered species is not permitted. All R code for running occupancy models is available in Supplementary

Information Appendix S1. Additional Supporting Information may be found online. 103

TABLE 3-1. Model likelihood results from five-fold Bayesian cross validation with 20% data

removed. Model likelihoods were calculated separately for each species and combined for

additional comparison of model performance. Model likelihood was assessed using deviance

calculated as -2[Σ(ln(ℒ(test data))]. Three deviances were calculated based on the test data used:

Overall (using all test data), both (using test data where both species information was removed),

and one species removed (using test data where only one or the other species’ detection histories

remained).

Model Overall Both One species removed

Red-backed salamander (P. cinereus)

Single-species 386.18 88.95 297.23

Single-species CAR 312.22 68.32 243.90

Two-species 348.94 72.49 276.46

Two-species CAR 366.66 66.28 300.38

Shenandoah salamander (P. shenandoah)

Single-species 204.55 36.65 167.91

Single-species CAR 173.99 30.35 143.64

Two-species 196.20 38.05 158.15

Two-species CAR 183.15 33.81 149.34

Both species

Single-species 590.73 125.60 465.14

Single-species CAR 486.20 98.67 387.54

Two-species 545.15 110.54 434.61

Two-species CAR 549.81 100.09 449.72

104

TABLE 3-2. Detection probabilities from single-species occupancy model with CAR random

effect, the top supported model, for each species (A = red-backed salamander, B = Shenandoah

salamander) for each survey occasion (t = 9). Standard deviation (SD) and 2.5, 50, and 97.5%

percentiles are included.

Parameter Mean SD 2.5% 50% 97.5% pA[1] 0.565 0.033 0.500 0.565 0.625 pA[2] 0.398 0.031 0.336 0.397 0.459 pA[3] 0.464 0.026 0.414 0.464 0.516 pA[4] 0.727 0.022 0.682 0.727 0.766 pA[5] 0.644 0.024 0.599 0.645 0.690 pA[6] 0.613 0.023 0.567 0.613 0.660 pA[7] 0.466 0.024 0.419 0.466 0.515 pA[8] 0.525 0.024 0.476 0.525 0.572 pA[9] 0.678 0.022 0.635 0.679 0.722 pB[1] 0.234 0.033 0.170 0.233 0.302 pB[2] 0.164 0.024 0.120 0.164 0.213 pB[3] 0.198 0.025 0.151 0.197 0.248 pB[4] 0.395 0.028 0.342 0.395 0.449 pB[5] 0.371 0.027 0.319 0.371 0.426 pB[6] 0.192 0.022 0.152 0.191 0.234 pB[7] 0.161 0.019 0.124 0.161 0.201 pB[8] 0.213 0.021 0.171 0.212 0.255 pB[9] 0.242 0.024 0.196 0.242 0.289

105

TABLE 3-3. Beta estimates for the effects of substrate type on occupancy from the top-supported

single-species occupancy models with CAR random effect. Substrate categories including

standard deviation (SD) and 2.5, 50, and 97.5% percentiles are shown for each species.

Parameter Mean SD 2.5% 50% 97.5%

Red-backed salamander (P. cinereus)

Rock 3.606 0.734 2.129 3.635 5.006

SoilWood 3.380 1.015 1.432 3.353 5.465

MossLeaf 1.956 0.457 1.095 1.951 2.868

Other 2.822 2.449 -2.351 2.944 6.751

Shenandoah salamander (P. shenandoah)

Rock 2.597 0.911 0.879 2.570 4.401

SoilWood -1.085 0.757 -2.503 -1.087 0.462

MossLeaf 0.944 0.424 0.126 0.940 1.759

Other 1.388 2.317 -2.815 1.283 6.136

106

TABLE 3-4. Length of primary and secondary contact zones between red-backed and Shenandoah

A B salamanders. We summed co-occurrence probabilities (i *i ) across each site and the next two

immediate sites (three 5-m by 4-m sites or 60m2 area used, Muñoz et al. 2016) of a transect to

identify areas of consistent species occurrence. Primary zones represent the largest area of

consistent overlap of both species ranges while secondary zones were smaller, additional areas of

overlap. All lengths are in meters.

Primary zone Secondary zone Distance between Total length of

length length zones transect

Study Area One

Transect 1 170 10 10 345

Transect 2 75 35 95 295

Transect 3 110 395

Study Area Two

Transect 1 140 245

Transect 2 135 245

Transect 3 135 60 15 245

Study Area Three

Transect 1 60 50 45 245

Transect 2 70 5* 70 195

Transect 3 80 195

Study Area Four

Transect 1 70 195

Transect 2 75 295

*5m secondary contact zone was right at the end of transect sampling, indicating is likely continues outside of the area surveyed 107

FIGURE 3-1. Based on raw detection data, Shenandoah (Plethodon shenandoah, PSHE) and red-backed (Plethodon cinereus, PCIN) salamanders did not frequently co-occur at a site (5-m by 4-m square) during a single survey but did overlap across a larger area when summarizing information from all surveys (Overall). Species non-detections (None) show the importance of accounting for detection probabilities. Data shown are for a single transect at study area one. Meters surveyed varied by visit. 108

FIGURE 3-2. Model comparison of occupancy probabilities for study area two. Occupancy probability at each site for each species

(green = Shenandoah salamander, orange = red-backed salamander) for each transect (row) and model type (column). Distance was measured in meters. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. 109

FIGURE 3-3. Detection probabilities across all transects from (a) single-species and (b-c) two species conditional occupancy models with conditional autoregressive (CAR) random effects. Models without a CAR random effect had similar probabilities to those shown.

Detection probabilities were estimated for each survey but were either (a) estimated separately for each species or (b-c) were conditional upon the other species. Conditional probabilities include the probability of (b) detecting PCIN given that either PSHE is absent (pA) or PSHE and PCIN are both present (rA) and (c) detecting PSHE given either PCIN is absent (pB), both species are present and PCIN is also detected (rBA), or PCIN is not also detected (rBa). 110

FIGURE 3-4. Logit-transformed parameter coefficients for substrate covariates for red-backed salamanders (PCIN, Plethodon cinereus) and Shenandoah salamanders (PSHE, Plethodon shenandoah). ‘Other’ substrate not shown. Substrate coefficients and 2.5 and 97.5% credible intervals are from single-species occupancy model with conditional autoregressive (CAR) random effect. 111

FIGURE 3-5. Mean posterior occupancy probability at each site for each species with proportion of each substrate type at each site from best supported single-species occupancy models with conditional autoregressive (CAR) random effect. Values for the first transect for every study area shown. Distance was measured in meters. Origins of transects were marked as 0m with added segments towards red- backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. 112

FIGURE 3-6. Primary (and secondary) contact zone(s) (dark green) of both species for a single transect of a single study area based on

A B each of the four models. For single-species models, co-occurrence probability was calculated by multiplying i *i . For two species models, co-occurrence probability was calculated from our conditional Shenandoah model using only surveys where zA was estimated to be 1. These values were then summed across each site and the next two immediate sites (three 5-m by 4-m sites or 60m2 area used,

Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. Single-species and two species conditional occupancy models with only substrate covariates do not show a clear peak area of overlap but rather have low levels of co-occurrence across much of their length. Origins of transects were marked as 0 m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. 113 114

FIGURE 3-7. Primary and secondary contact zone(s) (dark green) of both species based on the single-species occupancy model with

A B CAR random effect for the first transect for every study area. Co-occurrence probabilities (i *i ) were summed across each site and the next two immediate sites (three 5-m by 4-m sites or 60m2 area used, Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. We defined the co-occurrence zone as the area of overlap at the range edge of resident animals. Summarizing nearby sites by their occupancy allowed for the identification of core areas of overlap rather than including potentially dispersing or outlier animals. Distance was measured in meters. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. 115

116

Appendix S1. WinBUGS code for occupancy models implemented in program R via the

R2WinBUGS pacskage.

### Single Species Occupancy Model ### model {

##Priors and constraints for (t in 1:T) { pA[t] ~ dunif(0,1) pB[t] ~ dunif(0,1)

##States for observation model PA[1,t] <- 0 PA[2,t] <- pA[t] PB[1,t] <- 0 PB[2,t] <- pB[t] }

## Fixed Effect of Substrate for (a in 1:8){ beta[a] ~ dnorm(0,0.01)I(-7,7) }

##Ecological model for partially observed true state, species A for (i in 1:n) { logit(PSIA[i]) <- beta[1]*zrock[i] + beta[2]*zsoilwood[i] + beta[3]*zmossleaf[i] + beta[4]*zother[i]

##True state species A zA[i] ~ dbern(PSIA.bound[i]) PSIA.bound[i] <- max(0.0001, min(0.9999, PSIA[i]))

##Ecological model for partially observed true state, species A logit(PSIB[i]) <- beta[5]*zrock[i] + beta[6]*zsoilwood[i] + beta[7]*zmossleaf[i] + beta[8]*zother[i]

##True state species B zB[i] ~ dbern(PSIB.bound[i]) PSIB.bound[i] <- max(0.0001, min(0.9999, PSIB[i]))

##Defining states for observation model ZA[i] <- 1 + zA[i] ## makes detection conditional on true occupancy of A ZB[i] <- 1 + zB[i] ## makes detection conditional on true occupancy of B } 117

##Observation model for (i in 1:n) { for (t in 1:T) { yA[i,t] ~ dbern(PA[ZA[i],t]) ##observed data for A yB[i,t] ~ dbern(PB[ZB[i],t]) ##observed data for B } } }

### Two Species Conditional Occupancy Model ### model {

##Priors and constraints for (t in 1:T) { pA[t] ~ dunif(0,1) rA[t] ~ dunif(0,1) pB[t] ~ dunif(0,1) rBA[t] ~ dunif(0,1) rBa[t] ~ dunif(0,1)

## States for observation model PA[1,t] <- 0 PA[2,t] <- pA[t] PA[3,t] <- 0 PA[4,t] <- rA[t]

PB[1,t] <- 0 PB[2,t] <- 0 PB[3,t] <- pB[t] PB[4,t] <- rBa[t] PB[5,t] <- rBA[t] } alphaBA ~ dnorm(0,0.01)

## Fixed Effect of Substrate for (a in 1:8){ beta[a] ~ dnorm(0,0.01)I(-7,7) }

##Ecological model for partially observed true state, species A for (i in 1:n) { 118

logit(PSIA[i]) <- beta[1]*zrock[i] + beta[2]*zsoilwood[i] + beta[3]*zmossleaf[i] + beta[4]*zother[i]

##True state species A zA[i] ~ dbern(PSIA.bound[i]) PSIA.bound[i] <- max(0.0001, min(0.9999, PSIA[i]))

##Ecological model for partially observed true state, species A logit(PSIB[i]) <- alphaBA*zA[i] + beta[5]*zrock[i] + beta[6]*zsoilwood[i] + beta[7]*zmossleaf[i] + beta[8]*zother[i] ##occupancy of B conditional on A

##True state species B zB[i] ~ dbern(PSIB.bound[i]) PSIB.bound[i] <- max(0.0001, min(0.9999, PSIB[i]))

##Defining states for observation model Z[i] <- 1 + zA[i] + 2*zB[i] ## makes detection conditional on true occupancy of A and B }

##Observation model for (i in 1:n) { for (t in 1:T) { yA[i,t] ~ dbern(PA[Z[i],t]) ##observed data for A ZZ[i,t] <- Z[i] + zA[i]*zB[i]*yA[i,t] ## makes detection conditional on true occupancy of A and B yB[i,t] ~ dbern(PB[ZZ[i,t],t]) ##observed data for B } } }

### Single Species Occupancy Model with CAR Random Effect ### model {

##CAR RE for species A rhoA[1:n] ~ car.normal(adj[],weights[],num[],spacetauA) spacetauA <- 1/(spacesigmaA*spacesigmaA) spacesigmaA ~ dunif(0,5)

##CAR RE for species B rhoB[1:n] ~ car.normal(adj[],weights[],num[],spacetauB) spacetauB <- 1/(spacesigmaB*spacesigmaB) 119

spacesigmaB ~ dunif(0,5)

##Priors and constraints for (t in 1:T) { pA[t] ~ dunif(0,1) pB[t] ~ dunif(0,1)

## States for observation model PA[1,t] <- 0 PA[2,t] <- pA[t]

PB[1,t] <- 0 PB[2,t] <- pB[t] }

## Fixed Effect of Substrate for (a in 1:8){ beta[a] ~ dnorm(0,0.01)I(-7,7) }

##Ecological model for partially observed true state, species A for (i in 1:n) { logit(PSIA[i]) <- beta[1]*zrock[i] + beta[2]*zsoilwood[i] + beta[3]*zmossleaf[i] + beta[4]*zother[i] + rhoA[i]

##True state species A zA[i] ~ dbern(PSIA.bound[i]) PSIA.bound[i] <- max(0.0001, min(0.9999, PSIA[i]))

##Ecological model for partially observed true state, species B logit(PSIB[i]) <- beta[5]*zrock[i] + beta[6]*zsoilwood[i] + beta[7]*zmossleaf[i] + beta[8]*zother[i] + rhoB[i]

##True state species B zB[i] ~ dbern(PSIB.bound[i]) PSIB.bound[i] <- max(0.0001, min(0.9999, PSIB[i]))

##Defining states for observation model ZA[i] <- 1 + zA[i] ## makes detection conditional on true occupancy of A ZB[i] <- 1 + zB[i] ## makes detection conditional on true occupancy of B }

##Observation model for (i in 1:n) { 120

for (t in 1:T) { yA[i,t] ~ dbern(PA[ZA[i],t]) ##observed data for A yB[i,t] ~ dbern(PB[ZB[i],t]) ##observed data for B

} } }

### Two Species Conditional Occupancy Model with CAR Random Effect ### model {

##CAR RE for PCIN

rhoA[1:n] ~ car.normal(adj[],weights[],num[],spacetauA) spacetauA <- 1/(spacesigmaA*spacesigmaA) spacesigmaA ~ dunif(0,5)

##CAR RE for PSHE

rhoB[1:n] ~ car.normal(adj[],weights[],num[],spacetauB) spacetauB <- 1/(spacesigmaB*spacesigmaB) spacesigmaB ~ dunif(0,5)

##Priors and constraints for (t in 1:T) { pA[t] ~ dunif(0,1) rA[t] ~ dunif(0,1) pB[t] ~ dunif(0,1) rBA[t] ~ dunif(0,1) rBa[t] ~ dunif(0,1)

## States for observation model PA[1,t] <- 0 PA[2,t] <- pA[t] PA[3,t] <- 0 PA[4,t] <- rA[t]

PB[1,t] <- 0 PB[2,t] <- 0 PB[3,t] <- pB[t] PB[4,t] <- rBa[t] 121

PB[5,t] <- rBA[t] } alphaBA ~ dnorm(0,0.01)

## Fixed Effect of Substrate for (a in 1:8){ beta[a] ~ dnorm(0,0.01)I(-7,7) #Substrate and Species specific intercepts }

##Ecological model for partially observed true state, species A for (i in 1:n) { logit(PSIA[i]) <- rhoA[i] + beta[1]*zrock[i] + beta[2]*zsoilwood[i] + beta[3]*zmossleaf[i] + beta[4]*zother[i]

##True state species A zA[i] ~ dbern(PSIA.bound[i]) PSIA.bound[i] <- max(0.0001, min(0.9999, PSIA[i]))

##Ecological model for partially observed true state, species B logit(PSIB[i]) <- alphaBA*zA[i] + rhoB[i] + beta[5]*zrock[i] + beta[6]*zsoilwood[i] + beta[7]*zmossleaf[i] + beta[8]*zother[i] ##occupancy of B conditional on A

##True state species B zB[i] ~ dbern(PSIB.bound[i]) PSIB.bound[i] <- max(0.0001, min(0.9999, PSIB[i]))

##Defining states for observation model Z[i] <- 1 + zA[i] + 2*zB[i] ## makes detection conditional on true occupancy of A and B }

##Observation model for (i in 1:n) { for (t in 1:T) { yA[i,t] ~ dbern(PA[Z[i],t]) ##observed data for A ZZ[i,t] <- Z[i] + zA[i]*zB[i]*yA[i,t] ## makes detection conditional on true occupancy of A and B yB[i,t] ~ dbern(PB[ZZ[i,t],t]) ##observed data for B } } }

122

Supplementary Material

Figure S1. Based on raw detection data, Shenandoah (Plethodon shenandoah, PSHE) and red-backed (Plethodon cinereus, PCIN) salamanders did not frequently co-occur at a site (5x4m square) during a single survey but did overlap across a larger area when summarizing information from all surveys (Overall). Species non-detections (None) show the importance of accounting for detection probabilities. Data shown are for all transects surveyed at each study area. Meters surveyed varied by visit, and not all transects were surveyed both years.

123

Study Area One

124

Study Area Two

125

Study Area Three

126

Study Area Four

127

Figure S2. Number of detected animals over all surveys of each species across each transect for each study area. Animals were summed by each 5x4m grid of each transect with distance measured in meters. Panels a-c) are transects 1-3 of study area one, d-f) are transects 1-3 of study area two, g-i) are transects 1-3 of study area three, and j-k) are transects 1-2 of study area four. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. 128

129

Figure S3. Proportion of each substrate type at each site. Panels a-c) are transects 1-3 of study area one, d-f) are transects 1-3 of study area two, g-i) are transects 1-3 of study area three, and j-k) are transects 1-2 of study area four. Substrate was simplified into four categories: rock (exposed expanses of talus, boulder, and cobble), soil/wood (fallen woody debris and open stretches of ground), moss/leaf (thick, moist ground cover), and other (none of the above). Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. 130

131

Figure S4. Comparison of occupancy probabilities for all study areas across the four model permutations. Occupancy probability at each site for each species (green line = Shenandoah salamander, orange line = red-backed salamander) for each transect (top row = 1st transect, middle row = 2nd transect, and bottom row = 3rd transect) with proportion of each substrate type at each site. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters.

132

Study Area One

133

Study Area Two

134

Study Area Three

135

Study Area Four

136

Figure S5. Comparison of true occupancy for all study areas across the four model permutations. True occupancy at each site for each species [orange = red-backed salamander (zA), green = Shenandoah salamander (zB)] for each transect (top row = 1st transect, middle row = 2nd transect, and bottom row = 3rd transect). Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters.

137

Study Area One

138

Study Area Two

139

Study Area Three

140

Study Area Four

141

Figure S6. Overlap zone of both species based on the best supported single species occupancy model with CAR random effect. Co- occurrence probability was calculated by multiplying ψA * ψB. These values were then summed across each site and the next two immediate sites (three 4m x 5m sites or 60m2 area used; Muñoz et al. 2016) of a transect to identify areas of consistent species occurrence. Origins of transects were marked as 0m with added segments towards red-backed salamander territory being marked as positive meters and added segments towards Shenandoah salamander territory being marked as negative meters. Highlighted areas are those with summed probabilities over 1 (i.e., at least 2 of the 3 sites in a row have co-occurrence probabilities of 0.5 or greater). 142

143

Table S1. Beta coefficients for Rock, SoilWood, MossLeaf, and Other substrates for each species, red-backed salamanders (PCIN;

Plethodon cinereus) and for Shenandoah salamanders (PSHE; Plethodon shenandoah). Standard deviation and 2.5, 50, and 97.5% percentiles included from each of our four models. The single species occupancy model with CAR random effect was best supported in cross-fold validation.

Parameter Mean SD 2.5% 50% 97.5%

Single species occupancy model

Red-backed salamander (P. cinereus)

Rock -0.878 0.355 -1.578 -0.872 -0.188

SoilWood 3.670 0.591 2.561 3.663 4.892

MossLeaf 1.015 0.238 0.559 1.015 1.487

Other 0.946 1.584 -1.985 0.880 4.235

Shenandoah salamander (P. shenandoah)

Rock 3.301 0.747 2.028 3.241 4.947

SoilWood -2.087 0.470 -3.020 -2.081 -1.173

MossLeaf 0.407 0.268 -0.117 0.406 0.941

Other 1.380 2.011 -1.988 1.153 6.058 144

Single species occupancy model with CAR random effect

Red-backed salamander (P. cinereus)

Rock 3.606 0.734 2.129 3.635 5.006

SoilWood 3.380 1.015 1.432 3.353 5.465

MossLeaf 1.956 0.457 1.095 1.951 2.868

Other 2.822 2.449 -2.351 2.944 6.751

Shenandoah salamander (P. shenandoah)

Rock 2.597 0.911 0.879 2.570 4.401

SoilWood -1.085 0.757 -2.503 -1.087 0.462

MossLeaf 0.944 0.424 0.126 0.940 1.759

Other 1.388 2.317 -2.815 1.283 6.136

Two species conditional occupancy model

Red-backed salamander (P. cinereus)

Rock -0.850 0.365 -1.565 -0.854 -0.143

SoilWood 3.783 0.638 2.583 3.755 5.107

MossLeaf 1.047 0.241 0.587 1.045 1.527

Other 0.956 1.695 -2.029 0.843 4.820 145

Shenandoah salamander (P. shenandoah)

Rock 4.625 0.814 3.117 4.603 6.246

SoilWood 0.574 0.705 -0.759 0.548 2.032

MossLeaf 2.280 0.642 1.204 2.204 3.629

Other 1.388 2.317 -2.815 1.283 6.136

Two species conditional occupancy model with CAR random effect

Red-backed salamander (P. cinereus)

Rock 5.462 1.130 2.860 5.676 6.924

SoilWood 5.138 1.291 2.239 5.359 6.921

MossLeaf 5.509 1.000 3.410 5.616 6.937

Other 4.040 2.283 -1.504 4.569 6.857

Shenandoah salamander (P. shenandoah)

Rock 3.511 2.198 -1.188 3.759 6.806

SoilWood -0.468 2.532 -5.590 -0.424 4.472

MossLeaf 1.865 2.212 -2.329 1.757 6.236

Other 2.327 3.205 -5.132 2.918 6.756 146

Chapter 4

Factors Facilitating Co-occurrence at the Range Boundary of Shenandoah

and Red-backed Salamanders

This chapter has been submitted to the Journal of Herpetology and is formatted consistent with

their requirements

STACI M. AMBURGEY1,2, 5, DAVID A.W. MILLER1, ADRIANNE BRAND3, ANDREW

DIETRICH4, AND EVAN H. CAMPBELL GRANT3

1The Pennsylvania State University, Department of Ecosystem Sciences and

Management, University Park, Pennsylvania, USA, 16802

2The Pennsylvania State University, Intercollege Graduate Ecology Program, University

Park, Pennsylvania, USA, 16802

3USGS Patuxent Wildlife Research Center, SO Conte Anadromous Fish Research Center,

Turners Falls, Massachusetts, USA, 01376

4USGS Patuxent Wildlife Research Center, Laurel, Maryland, USA 20708

5Corresponding author. Email: [email protected]

LRH: S.M. Amburgey et al.

RRH: Factors Facilitating Co-occurrence

147

ABSTRACT.—The transition from allopatry to sympatry, i.e., the co-occurrence zone of competing species, allows for investigation of the forces structuring range limits and provides evidence of the evolutionary and population responses of species to such interactions, including mechanisms facilitating co-occurrence (e.g., character displacement). The Shenandoah Salamander (Plethodon shenandoah), an endangered plethodontid, is limited to three mountaintops in Shenandoah National Park, Virginia,

USA. This species’ distributional limits are attributed to competitive exclusion by the

Red-backed Salamander (P. cinereus). Recent work showed range overlap between these species is greater than previously thought, requiring investigation of species morphology, behavior, and demographic measures in single-species and co-occurrence zones that might facilitate this overlap. We analyzed individual characteristics such as life stage, size, color, and microhabitat-use data from two years of transect surveys to see if traits differed within and outside co-occurrence zones. Several measures showed species- and zonal-specific differences, but we found limited support for character displacement. Both species were larger when co-occurring, indicating larger animals might better compete for resources or that symmetric competition restricts dispersal or recruitment processes at the co-occurrence zone. Microhabitat use also differed by species across the transects, with Red-backed Salamanders using more rock microhabitats in the co-occurrence zone, potentially due to competition for microclimates that minimize physiological stress. The lack of strong evidence for morphologic, behavioral, or demographic differentiation in situ at the range edge suggests that competition may not be as strong as previously thought and that other factors contribute to the range limits of Shenandoah Salamanders.

148

Keywords: Competitive Exclusion, Plethodon shenandoah, Plethodon cinereus,

Shenandoah Salamander, Red-backed Salamander, Character Displacement,

Shenandoah National Park, Interspecific competition

149

INTRODUCTION

Understanding the factors shaping current species’ range limits is important to identifying conditions that may lead to local extirpation or range expansion in response to shifting abiotic and biotic conditions (Thomas et al. 2004; Hijmans and Graham 2006).

Biotic interactions are frequently overlooked in species distribution modeling in favor of understanding abiotic controls (Pearson and Dawson 2003; Amburgey et al. 2017).

However, species interactions can significantly shape species distributions (Araújo and

Luoto 2007; Gilman et al. 2010; Pigot and Tobias 2012) and may provide a mechanistic explanation of distributional patterns (Pearson and Dawson 2003; Soberón 2007) and the formation of range boundaries (HilleRisLambers et al. 2013; Yackulic et al. 2014).

In particular, antagonistic interactions such as species competition have long been suspected to be important in influencing species distributions (MacArthur 1958; Strong

1980; Sexton et al. 2009) through their modification of local species occurrence and population demographic processes that create and maintain such range limits (Bengtsson

1989; Gilman et al. 2010). Variation in species traits and microhabitat use in co- occurrence zones, the location where two allopatric species shift to sympatry, are key in understanding the role of species interactions in the creation and maintenance of range boundaries. The gradient from allopatry to sympatry allows for study of the altered evolutionary and population processes that may structure range limits at the junction of species ranges (Grether et al. 2009). Co-occurrence zones provide opportunities to see how competition may manifest itself as changes in species morphology, behavior, and population dynamics (Adams and Rohlf 2000; Grether et al. 2009). 150

Species interactions may limit species occurrence through a number of mechanisms, but antagonism resulting from competition for similar habitat or food resources is thought to be the most prevalent (Jaeger 1970; Price and Kirkpatrick 2009;

Gifford and Kozak 2012). The principle of competitive exclusion states that species with similar ecological niches will not be able to coexist due to competition for resources

(Gause 1934), and this antagonism can result in formation of a range boundary for one or both species over a gradient in the limiting resources. The width of the co-occurrence zone between species with similar niches can be related to the presence and strength of competitive exclusion (Tobias et al. 2014). Co-occurrence of ecologically similar species is possible if individuals in overlapping portions of the range develop differentiated physical traits or behaviors (i.e., character displacement, Brown and Wilson 1956; Adams and Rohlf 2000; Adams 2004; Adams 2010). Population processes such as dispersal or demographic rates may also change (Carrete et al. 2005) in response to competitive interactions with the other species. Alternatively, co-occurrence may result if competition is not the strongest pressure constraining species occurrence. If other shared environmental or predatory selective pressures exist, this may instead lead to trait convergence in overlapping areas of the range (Cavender-Bares et al. 2009; Tobias et al.

2014).

To better understand the role of species interactions in setting species’ range limits, we focused on the co-occurrence zone of two Plethodon salamanders in a montane ecosystem with a range boundary largely attributed to competitive interactions (but see

Grant et al. 2018b). The Shenandoah Salamander (Plethodon shenandoah, Highton and

Worthington 1967) is a federally endangered terrestrial salamander (54 CFR 34464, US 151

Fish and Wildlife Service 1994) endemic to Shenandoah National Park, VA, USA. This species occurs on only three mountaintops over 850m in elevation (Jaeger 1970).

Competitive exclusion by the broadly distributed Red-backed Salamander (P. cinereus,

Green 1818) has been considered the primary driving force shaping the occurrence of this species (Jaeger 1970, 1971b; Griffis and Jaeger 1998). The Red-backed Salamander occurs across most of the northeastern United States (Green 1818), preferring moist soil and leaf litter in forested habitats (Jaeger 1970). Shenandoah Salamanders are limited to more sparsely vegetated, rocky habitats with thinner soils on northwestern and northeastern facing slopes where moisture and temperature regimes are less tolerable for the Red-backed Salamander (Jaeger 1971a). In field and lab-based experiments, Red- backed Salamanders excluded Shenandoah Salamanders in habitats with deeper soil

(Griffis and Jaeger 1998) that both species appeared to prefer (Jaeger 1971a,b).

Competition for microhabitat (Jaeger 1971b; Griffis and Jaeger 1998) and food (Jaeger

1972) were cited as potential limiting factors structuring these interspecific interactions.

However, laboratory and field manipulations do not satisfactorily explain the observed range boundary for these two species (Jaeger et al. 2016), and further clarification as to the role of species competition in this system is required.

Recent work using modern statistical methods that account for imperfect species detection indicated the range boundary of these two species is characterized by a 60-170 m zone of overlap (Amburgey et al. in review). This is wider than previously characterized (Jaeger 1970, 1972), but still relatively narrow compared to the range extent of the Shenandoah salamander. Results of Amburgey et al. (in review) defined areas of predominantly single-species occurrence (PSHE and PCIN zones) and areas 152 where both species could be found (Co-occurrence zone). To understand what mechanisms facilitated these broader than expected areas of species co-occurrence, we investigated how 1) morphological traits, 2) behaviors, and 3) demographic metrics of the two species differ among these three zones. For example, larger adult males may be better able to compete for resources (Jaeger 1972; Mathis 1991), demonstrating morphological trait (size) and demographic metric (sex ratio) differentiation by zone.

Other morphological traits such as variation in dorsal stripe and coloration may result in differential predation rates (Fitzpatrick et al. 2009; Grant et al. 2018a), which can facilitate co-occurrence by reducing abundances and decreasing competition.

Additionally, microhabitat selection behaviors may alter antagonistic interactions between species (Jaeger 1971; Griffiths and Mylotte 1987; Hagey et al. 2015).

Plethodontids are lungless salamanders that must breathe via diffusion across the skin, making microhabitat temperature and moisture important to survival, surface activity, and foraging (Jaeger 1971a,b; Feder 1983). Differences in physiological tolerances between the Shenandoah and Red-backed Salamanders exist (Griffis and Jaeger 1998), and use of cover objects to escape suboptimal conditions may make these important resources over which competition likely occurs (Griffis and Jaeger 1998). We also investigated tail damage (i.e., partial or complete tail autotomy), not as a sign of character displacement but as an indicator of interspecific aggressive behaviors (Matthis 1991) that might corroborate any observed differences in microhabitat use.

We investigated this suite of morphological, behavioral, and population-level characteristics (Table 1 and 2) to evaluate several hypotheses (H1-H5; Fig. 1). While trait differentiation could occur only in zones of species co-occurrence (H1: character 153 displacement) or between species across the entire area surveyed (H2: species differentiation), trait convergence could also occur but only in certain zones along the range boundary (H3: character convergence by zone). Traits could also be similar between the two species but vary across the area surveyed (H4: character convergence with zonal differences) or be similar between both species across the entire area surveyed

(H5: complete character convergence). We predicted that if interspecific competition is important in structuring range limits of the Shenandoah Salamander (as posited by Jaeger

1970, 1972), co-occurrence should be explained by some differentiation in species traits and behaviors in order to alter competitive interactions between these species (e.g.,

Jaeger 1972; Mougi and Nishimura 2005).

MATERIALS AND METHODS

Field Sampling.—We established and repeatedly surveyed eleven transects at the confluence of P. shenandoah and P. cinereus ranges at four study areas (n = 6-9 surveys per transect). As P. shenandoah is federally protected, location names and geographic coordinates are omitted in this paper. Methods used in establishing and surveying transects are detailed in Amburgey et al. (in review); in general, observers checked all cover objects within 2 m of either side of transect tape extending laterally from one species’ territory through an area of predicted co-occurrence and into the other species’ territory. Transect surveys were conducted in 50m segments. We assigned each captured animal to a 5 m by 4 m (2 m on each side of the transect tape) ‘site’ on the transect.

Captured salamanders were placed in plastic sandwich bags for species identification and measurement. 154

To test for differences in morphological traits related to our hypotheses, we recorded data on every captured animal including dorsum (striped or leadbacked), stripe color (if stripe was present; red-orange, yellow, brown, or white), and snout-vent length

(SVL). Population parameter measures included life stage (adult or juvenile) and sex

(male or female). All animals less than 35mm in total length were considered juveniles

(Sayler 1966) and not sexed as identification of sex was considered unreliable at this stage. We identified each animal to species using a combination of stripe width and shape

(when present) and ventral surface pigmentation (sensu Highton and Worthington 1967).

The two species are similar in physical appearance (Highton and Worthington 1967).

Any uncertainty in species identification required verification by a second observer, and subsequent uncertainty resulted in elimination of this animal from subsequent analysis

(<10 animals removed).

To test for differences in behaviors, we recorded microhabitat type (rock, log, or leaf litter) and measured the microhabitat temperature under all microhabitats found in the first 15m of the lower side of each 50m transect segment via Fluke 62 MAX infrared thermometer. Outside of the first 15m, we measured the microhabitat type and temperature where each detected animal was found along with five additional surrounding microhabitats to account for temporal fluctuations in temperature while surveying and to sample microhabitats unused by animals. To avoid altering temperatures when displacing microhabitat, temperatures were taken immediately after flipping objects or objects were replaced immediately until a temperature could be taken. This preserved the original temperature with little to no change (Amburgey, pers. obs.). Animals found wandering on the surface of leaf litter had temperatures taken directly beneath where they 155 were captured. Additionally, we noted tail damage on every captured individual as a proxy for behavioral aggression. Animals were processed immediately after capture and returned to the location at which they were detected. We collected individual trait data in

2015 and 2016 while microhabitat type and temperature data were collected in 2015.

Data Analysis.—In a previous paper (Amburgey et al. in review), we estimated site-specific occupancy probabilities for Shenandoah and Red-backed Salamanders. We corrected for heterogeneity in salamander surface activity (Feder 1983) and other factors resulting in imperfect detection (MacKenzie et al. 2006) using repeat surveys to get robust estimates of species occupancy (i.e., the probability that a species was present at a given site). Using our best supported model, we calculated co-occurrence probabilities

(i.e., the probability of occupancy of resident animals of both species) for every site (5 m by 4 m section) in each transect, allowing for delineation of the co-occurrence zone versus single-species zones. Co-occurrence zones were defined as areas with a high probability of co-occurrence where the likelihood of co-occurrence summed across 15m was equal to or greater than one. In Amburgey et al. (in review), animals of both species can occur in all transect zones; however, the core areas of occurrence for each species

(and thus those with the hypothesized highest abundances) correlate to their single- species zone. We expected that peak interspecific competitive interactions occur in the

Co-occurrence zone and animals in the opposite species’ zone are either non-resident animals or at low enough densities that intraspecific interactions may be more important in resource disputes. We use these transect zones (PSHE zone, Co-occurrence zone, and

PCIN zone) to test our hypotheses regarding the differentiation of measures across the range edge. 156

We indicated life stage, sex, dorsum, and tail damage as a binary measure (1/0) and analyzed each of these traits via logistic regression in Program R (R Core Team

2018). We analyzed stripe color and microhabitat type by splitting each category into a binary response (e.g., red stripe vs. not red stripe) and fitting logistic regression models on each category with a Bonferroni correction for multiple comparisons (Bonferroni

1936). This is similar statistically to a multinomial logistic regression with the added benefit of increased interpretability for all zone and species trait combinations. We checked SVL data for normality and log transformed all measurements to correct for skewedness prior to fitting a linear regression model. For each of these physical and behavior trait analyses, we fitted four models using the trait or behavior as the response variable and an additive effect of the explanatory variable of species, zone, an additive effect of species and zone, or an interaction of the two.

Lastly, we analyzed microhabitat temperature using logistic regression to investigate if each species differentially used microhabitats at different temperatures in co-occurrence versus single species zones. We analyzed the binomial response of ‘habitat use’, indicated as a 0 (no animal found under object) and 1 (animal found). We standardized temperature by the mean and standard deviation across all sites and study areas. We fitted five models with the response variable of microhabitat temperature use and an additive effect of the explanatory variable of temperature with species, zone, an additive effect of species and zone, an interaction of the two, or a three-way interaction of temperature, species, and zone. As we expected salamanders to prefer some optimal temperature with fewer salamanders found at temperatures outside this optimum 157

(Sutherland et al. 2016), microhabitat temperature was modeled as a quadratic relationship.

We performed model selection to choose a top-ranked model for each measure based on smallest AIC value (Akaike 1973; Appendix 1). For models with similar AIC values (ΔAIC < 1), we selected the top model based on which had the smallest AIC value in addition to minimizing the number of parameters in the model (Arnold 2010) and that also changed the -2log-likelihood value (Anderson 2008). Significance (α) was judged at the 0.05 level for all regression analyses except stripe color and microhabitat type analyses that required Bonferroni correction for multiple comparisons due to the splitting of trait categories. Instead, stripe color was judged at the 0.013 level (4 logistic regressions) and microhabitat type at the 0.017 level (3 logistic regressions). To better understand differences in measures by pairwise comparison, we conducted post hoc chi- square tests for our logistic regressions and Tukey HSD tests for our linear regression where needed. We again used a Bonferroni correction when conducting multiple chi- square tests on a binomial trait, but Tukey HSD already corrects for a family-wise error rate.

RESULTS

We detected 858 Shenandoah Salamanders and 4470 Red-backed Salamanders.

Sample size differed by analysis as observers occasionally did not record all trait information for every salamander (Table 1, 2). For Shenandoah Salamanders, we captured 36% and 62.4% of animals in the PSHE and Co-occurrence zones respectively and only 1.6% in the PCIN zone. We found 61.5% and 37.5% of Red-backed 158

Salamanders in PCIN and Co-occurrence zones with only 1% found in the PSHE zone.

On average, transect length was 263 m with 116 m of Co-occurrence zone.

Morphological Traits.—For dorsum differences, our top model included only species as an explanatory variable (w = 0.56; Appendix 1), where we were more likely to find striped Red-backed Salamanders as compared to Shenandoah Salamanders (p <

0.001; Table 3, Fig. 2A). When stripe was present, all top supported models for stripe colors contained only species (w ≥ 0.40; Appendix 1). Red-backed Salamanders were more likely to be red-orange striped than Shenandoah salamanders (p < 0.001; Table 3,

Fig. 3A), while Shenandoah Salamanders were more likely to have a white or brown colored stripe (p < 0.001). Both species were equally likely to have a yellow color stripe

(p = 0.17).

The best supported model for differences in log transformed SVL was the full model including species, zone, and their interaction (w = 0.85; Appendix 1). Shenandoah

Salamanders were larger than Red-backed Salamanders in the Co-occurrence zone (p <

0.001; Table 3, Fig. 4). Red-backed Salamanders in the Co-occurrence zone were similar in size to Red-backed Salamanders in the PSHE zone (p > 0.09) but were larger than those in PCIN zone (p < 0.001). Shenandoah Salamanders in the PSHE zone were similar in size to Red-backed Salamanders in the Co-occurrence zone (p > 0.50) but larger than

Red-backed Salamanders in the PCIN zone (p < 0.01). Post hoc Tukey HSD tests showed that Shenandoah Salamanders were smallest in the PSHE zone and largest in the PCIN zone (p < 0.05). Red-backed and Shenandoah Salamanders in the PSHE zone were not significantly different in size (p > 0.80), but Shenandoah Salamanders were larger than

Red-backed Salamanders in the PCIN zone (p < 0.01). 159

Behaviors.—For tail damage, we found the additive model containing species and zone was best supported (w = 0.82; Appendix 1). Fewer Shenandoah Salamanders possessed damaged tails (i.e., our competitive aggression proxy) as compared to Red- backed Salamanders in the Co-occurrence zone (p < 0.01; Table 3, Fig. 2B). Red-backed

Salamanders in the Co-occurrence zones had less tail damage as compared to those in the

PCIN zones (p < 0.05). We ran three post-hoc chi-square tests (α = 0.017) comparing tail damage in Shenandoah Salamanders in each zone and found no differences (p > 0.04).

The top supported model for all microhabitat types included the interaction of species and zone (w ≥ 0.50; Appendix 1). For the first logistic regression on leaf litter microhabitat, we found Red-backed Salamanders used a similar proportion of leaf litter in all transect zones (p > 0.10; Table 4, Fig. 3B), but Red-backed Salamanders were less often on leaf litter than Shenandoah Salamanders in the Co-occurrence zone (p < 0.001).

We ran four post-hoc chi-square tests (α = 0.015) to clarify zonal differences in species use of leaf litter. We found Shenandoah salamanders more often on leaf litter in the

PSHE zone as compared to the Co-occurrence zone (p < 0.001) but not compared to the

PCIN zone (p = 0.04). We also found Shenandoah Salamanders more often on leaf litter than Red-backed Salamanders in the PSHE zone (p < 0.001) but not in the PCIN zone (p

= 1.0). Warnings indicated chi-square test comparisons of Shenandoah Salamanders in the PCIN zone may be unreliable due to small sample size.

For the second logistic regression on log microhabitat, we found Shenandoah and

Red-backed Salamanders used a similar proportion of logs in the Co-occurrence zone (p

= 0.55; Table 4, Fig. 3B). Compared to Red-backed Salamanders in the Co-occurrence zone, we found more Red-backed Salamanders under logs in the PSHE and PCIN zones 160

(p < 0.001) and fewer Shenandoah Salamanders under logs in the PSHE zone (p < 0.001).

There was a similar amount of Shenandoah Salamanders under logs in the PCIN zone as compared to Red-backed Salamanders in the Co-occurrence zone (p = 0.75).

Lastly, in the Co-occurrence zone, Red-backed Salamanders used more rock microhabitats than Shenandoah Salamanders (p < 0.001). Red-backed Salamanders in the

Co-occurrence zone also used more rock microhabitats than Red-backed salamanders in either of the PSHE and PCIN zones (p < 0.001; Table 4, Fig. 3B). Shenandoah

Salamanders in the PSHE and PCIN zones used rock microhabitats as frequently as Red- backed Salamanders in the Co-occurrence zone (p = 0.04 and p = 0.81 respectively at α=

0.017). We ran one post-hoc chi-square test (α= 0.05), which indicated Red-backed

Salamanders used fewer rocks in the PSHE zone as compared to the PCIN zone (p <

0.001). For microhabitats where salamanders were detected, leaf litter was warmer (17.3

± 0.63°C) than log (15.6 ± 0.16°C) and rock (14.6 ± 0.11°C) on average.

The best supported model for temperature differences in habitat use included the additive effect of the interaction between species and zone (w = 0.97; Appendix 1). We found cooler temperatures were significantly associated with increased salamander habitat use by both species (p < 0.05; Table 4, Fig. 5). In the Co-occurrence zone, we detected more Red-backed Salamanders than Shenandoah Salamanders (p < 0.001).

Unsurprisingly, Shenandoah Salamanders were detected most in the PSHE zone and Red- backed Salamanders were detected most in the PCIN zone (p < 0.001). There was no support for the interaction of temperature by Species (w = 0.03; Appendix 1), indicating no differences in microhabitat temperature use by species (Appendix 2). Red-backed

Salamanders appeared to use microhabitats at similar temperatures to Shenandoah 161

Salamanders except for warmer leaf litter in the Co-occurrence zone (Appendix 4).

Average microhabitat temperature on days surveyed did not show a seasonal warming from spring into summer (Appendix 3) but differed randomly by day surveyed.

Demographic traits.—The top model for life stage included only zone (w = 0.43;

Appendix 1), with adults of both species found more often in the Co-occurrence zone as compared to the PSHE (p = 0.02) and PCIN (p < 0.001) zones (Table 3; Fig. 2D). A single post-hoc chi-square test showed the frequency of adults in the PSHE zone did not differ from the PCIN zone (p > 0.20) indicating differences in life stage were confined to the Co-occurrence zone. We found support for species but not zone in explaining the probability of male versus female salamanders (w = 0.58; Appendix 1), with significantly fewer female Shenandoah Salamanders as compared to Red-backed Salamanders (p =

0.02, Table 3; Fig. 2C).

DISCUSSION

We expected to find physical and behavioral differences between Shenandoah and

Red-backed Salamanders that might explain the ability of these ecologically similar species to co-occur within and near the range edge. We predicted that differentiation in morphology, behaviors, and demographic traits in the Co-occurrence zone (i.e., character displacement; Brown and Wilson, 1956) would allow for niche differentiation between the species and thus present a mechanism to reduce competitive interactions (Adams and

Ruhlf 2000; Adams 2004, 2010). However, for most Shenandoah and Red-backed

Salamander traits and behaviors, none of the characteristics investigated demonstrated distinct character displacement (H1; Fig. 1) for either or both species in the Co- 162 occurrence zone. We observed several important species-specific and zonal differences in characteristics with moderate support for an interaction between species and zone.

Differences in microhabitat use could indicate behavioral differentiation as a potential means to reduce competitive interactions. Body size differences by species and zone could be the result of altered population processes such as dispersal or recruitment driven by competition, but further investigation is required to understand current population demography.

Both species were larger (in SVL and TL) in the Co-occurrence zone as compared to their home range, though Shenandoah Salamanders were the largest in the PCIN-zone.

We suggest this could be due to two manifestations of how species respond to competition. First, differences in body size could be explained by the increased presence of adult animals in the Co-occurrence zone (Fig. 2D), indicating that these larger animals are either dispersing individuals at the range edge or that recruitment by smaller adults and juveniles is prohibited in the overlapping portion of the range edge. Second, increased body size can be an important determinant of competitive species interactions such as resource competition (Jaeger 1972; Mathis 1991). Shenandoah Salamanders in the Co-occurrence and PCIN-only zones may encounter increasingly more interspecific competition as Red-backed Salamander abundance increases into their home range; however, Red-backed Salamanders in the PSHE-only range may not encounter as many of the less abundant Shenandoah Salamanders (Jaeger 1970). In the Co-occurrence zone, abundance of both species may cause increased inter- and intraspecific competition that exclude all but the largest Red-backed Salamanders. While body size differentiation did not occur for both species in only the Co-occurrence zone, this differentiation in size 163 across the range boundary could indicate character displacement via differences in size- based prey consumption ability that may lessen resource-competition between species

(e.g., Adams and Rohlf 2000).

We observed additional differentiation in microhabitat use. Shenandoah

Salamanders used more rock microhabitats in the PSHE zone compared to the Co- occurrence zone, where Red-backed Salamanders used more rock microhabitats. This reversal of rock microhabitat use in the Co-occurrence zone could be indicative of an exclusion of Shenandoah Salamanders by Red-backed Salamanders from potentially cooler, wetter microhabitats (those which were on average one degree cooler than log microhabitats and generally had increased contact with the ground to retain soil moisture). Moving into the PSHE-zone where these warmer, drier talus slopes are less physiologically suitable for Red-backed Salamanders (Griffis and Jaeger 1998), competition for these cooler microhabitats may increase until either the abundance of

Shenandoah Salamanders or the climate excludes Red-backed Salamanders. Red-backed

Salamanders used more log microhabitats than Shenandoah Salamanders except in the

Co-occurrence zone where log microhabitat use converged, potentially due to competition over rock microhabitats forcing Shenandoah Salamanders to find alternate cover. We also detected more Shenandoah Salamanders on the leaf litter in all areas of the transect, possibly resulting from their resilience to drier and hotter conditions as compared to Red-backed Salamanders (Griffis and Jaeger 1998). Surface activity on leaf litter may also have occurred soon after rain events, though Red-backed salamanders were still not as active during these times as Shenandoah Salamanders. Microhabitat availability along the transect may explain some differences in microhabitat use as the 164 lateral range boundary goes from rockier to more deep-soil habitats. Amburgey et al. (in review) observed rock microhabitats were generally more available in Shenandoah

Salamander territory while soil and log microhabitats generally increased in Red-Backed

Salamander territory. Salamander use of microhabitat temperatures did not differ by species overall but instead showed temperature-dependent surface activity characteristic of plethodontid salamanders (Feder 1983), with warmer temperatures resulting in fewer detected salamanders under microhabitats. While these results suggest potential species- specific responses to antagonistic interactions, a field-based exclusion or removal study is needed to further investigate this dynamic (e.g., Griffis and Jaeger 1998) at the range boundary.

Species-specific differences in sex ratios, presence of dorsal stripe, and stripe color when present (except in the instance of yellow coloration) indicate potential interesting life history differences between these two species. The assumption that these two taxonomically, geographically, and behaviorally similar species would display similar sex-ratios (and thus mating behaviors, reproductive strategies, and population dynamics) may not be supported. For coloration, Red-backed Salamanders sported the red stripe more often than Shenandoah Salamanders, which more frequently had the rarer white and brown stripes. Coloration may be related to immune system functioning

(Venesky et al. 2015), aggressive behaviors, (Ducrest et al. 2008) and physiological tolerances (Petruzzi et al. 2006), and more would be required to test if these colors align with differences in these species’ thermal and moisture constraints (Griffis and Jaeger

1998). Coloration can also influence predation (Fitzpatrick et al. 2009; Grant et al.

2018a), which can ease competition and facilitate co-occurrence by reducing abundances. 165

Additionally, we found tail condition, our measure for interspecific aggression, indicated fewer missing or damaged Red-backed Salamander tails in the Co-occurrence zone and no differences in Shenandoah Salamander tail condition across the range edge.

This was contrary to our hypothesis that antagonistic species interactions in the Co- occurrence zone would result in damage to both species, potentially more so in the theorized competitively inferior Shenandoah Salamander (Griffis and Jaeger 1998).

Increased tail damage in the Red-backed Salamander in its home range could be due to increased intraspecific territorial defense as conspecific densities increase, while

Shenandoah Salamanders may avoid combative interactions.

Jaeger (1970, 1972) predicted minimal overlap of these two species through competitive exclusion over food and microhabitat resources (Jaeger 1971b; Griffis and

Jaeger 1998). Amburgey et al. (in review) found a wider area of co-occurrence at the range boundary than expected, though the co-occurrence zone between these two species is still minimal compared to their respective ranges. We expected that individual physical and behavioral traits and population parameters might reveal insights as to how these species co-occur at the junction of their ranges. As this is an observational study and does not directly test for competition between these two species, we had specific hypotheses for what would be observed given competition was important in structuring this range limit (Fig. 1). Our results do not conclusively support competitive exclusion in structuring the co-occurrence zone, and we found little support for potential alternative strategies by which species avoid interspecific competition. This may indicate that additional stressors outside of interspecific competition are important in setting this range boundary. Grant et al. (2018b) found that the presence of Red-backed Salamanders was 166 not as important in explaining the lower range limit of Shenandoah Salamanders as compared to climate. Resources may vary spatially across the range edge and may structure competitive relationships, facilitating co-existence of the two species with no observable character differentiation (MacArthur and Levins 1964). Similarities in species characteristics may be driven by similar abiotic (e.g., environmental) or biotic (e.g., predation) pressures that result in trait convergence (Abrams and Chen 2002), reducing abundances and facilitating co-occurrence of these species. Lastly, similarities in certain traits may evolve so that species may recognize another species and respond with territorial defense (e.g., similar bird songs; Cody 1969). Further sampling of individual animals farther away from the range edge, measures of additional morphology (e.g., jaw structure to investigate resource acquisition; Adams and Rohlf 2000) and measuring climate gradients across the range boundary and through time will help clarify the role of antagonism and environmental conditions in maintaining these two species’ range limits.

Endemic species are especially vulnerable to shifting conditions due to the prevalence of range-restricted and threatened species (Thuiller et al. 2005; Isaac et al.

2009). Limited variation in conditions across a small range reduce the potential adaptive capacity of a species (e.g., through limited thermal tolerance; Thuiller et al. 2005, Shah et al. 2017), and limited geographic space can physically hinder movement necessary for distributional shifts (Dirnböck et al. 2011). Montane species (and therefore doubly montane endemics such as the Shenandoah Salamander) are at particular risk from shifting biotic and abiotic conditions due to an upward limit on geographic space in which to respond (e.g., Raxworthy et al. 2008; but see Elsen and Tingley 2015). For example, Jaeger (1980) predicted a range contraction for Shenandoah Salamanders due to 167 extreme climate events exacerbated by competition with Red-backed Salamanders. If competition is weaker than previously expected or is altered with changing climate

(Dallalio et al. 2017), this will change projections of Shenandoah Salamander range shifts and risk.

Gradients where asympatric species transition into sympatry (i.e., co-occurrence zones) offer a chance to study the processes by which species interactions shape species distributions. By studying species in situ, we can attain a better understanding of the role interspecific interactions such as competition play in limiting species occurrence or altering morphological, behavioral, or population processes. Shifts in frequencies of morphological traits (i.e., character displacement) or altered behaviors or population parameters such as dispersal can indicate the presence and strength of competition, which characteristics are under selection pressure, and means by which these traits facilitate spatially overlapping ranges of ecologically similar species. A lack of support for interspecific competition can lead to a better understanding of additional biotic and abiotic factors driving species range limits.

Acknowledgments.— We would like to thank the Northeast Amphibian Research and Monitoring field crews and volunteers for their assistance with data collection. This work was permitted by the Virginia Department of Game and Inland Fisheries (#053142 and #056283) and Shenandoah National Park (#SHEN-2014-SCI-0022) and was partly funded by the USGS John Wesley Powell Center for Analysis and Synthesis, National

Park Service, and the USGS Amphibian Research and Monitoring Initiative (ARMI).

This manuscript is contribution #6XX of the USGS ARMI. Any use of trade, firm, or 168 product names is for descriptive purposes only and does not imply endorsement by the

U.S. Government.

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176

FIGURE 4-1. Hypotheses to test for measured traits and behaviors of Shenandoah (grey) and Red-backed (dark grey) Salamanders across the Shenandoah Salamander (PSHE),

Co-occurrence, and Red-backed Salamander (PCIN) zones. For the pictured example of salamander body size (SVL), larger Shenandoah and smaller Red-backed Salamanders in only the Co-occurrence zone would support H1. Species differentiation (H2) indicates species are consistently different across all zones. Character convergence by zone (H3) occurs when both species are similar in only certain zones. Zonal differences (H4) indicate size differs by zone but similarly for both species. Lastly, complete convergence

(H5) indicates no difference across zones or species.

177

FIGURE 4-2. Morphological traits and population parameters differed by species [Shenandoah (PSHE, triangles) and Red-backed

Salamanders (PCIN, circles)], transect zone (diamonds), or both. The Co-occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones. Shown are proportions with standard errors of animals (A) with a striped dorsum, indicating differences by species, (B) with damaged tails, indicating differences by species by zone, (C) that are female, indicating differences by species, and (D) that are adults, indicating differences by zone.

178

FIGURE 4-3. Morphological stripe traits and microhabitat selection behavior differed either by species [Shenandoah (PSHE) and Red- backed Salamanders (PCIN)] or species by transect zone. The Co-occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones. Percentages of animals (A) with different stripe colors, indicating differences by species (except in the case of yellow stripes where differences were non-significant) and (B) using microhabitat types, indicating differences by zone by species.

179

FIGURE 4-4. Mean snout-vent length (SVL) of captured animals. Bars represent standard errors. An interaction between species [Shenandoah (PSHE) and Red-backed (PCIN)

Salamander] and zone best explained differences in body size for both metrics. The Co- occur zone indicates the Co-occurrence zone where there was a high probability of both species versus the single-species zones.

180

FIGURE 4-5. General linear model results from our best supported microhabitat temperature-use model indicated support for an interaction of species [Shenandoah (PSHE) and Red-backed (PCIN) Salamander] and zone. The Co-occur zone indicates the Co- occurrence zone where there was a high probability of both species versus the single-species zones. 95% confidence intervals are shown in grey. Each salamander species used microhabitats more frequently in each species’ respective home range, but microhabitat use similarly declined as temperature increased for both species. 181

182

TABLE 4-1. Sample size for all physical characteristic analyses for Shenandoah (PSHE) and Red-backed (PCIN) Salamanders. Not all traits were recorded for all animals thus excluding some animals from different analyses. Dorsum delineates if animals were striped or leadbacked. Color represents stripe color for animals with stripes. Percentages indicate percent of total animals with that trait.

Trait Single-species zone (PSHE) Co-occurrence zone Single-species zone (PCIN) Stage Adult Juvenile Adult Juvenile Adult Juvenile PSHE 176 132 346 189 11 3 (57.1%) (42.9%) (64.7%) (35.3%) (78.6%) (21.4%) PCIN 25 22 1052 623 1455 1289 (53.2%) (46.8%) (62.8%) (37.2%) (53%) (47%) Sex Female Male Female Male Female Male PSHE 69 88 120 199 5 6 (43.9%) (56.1%) (37.6%) (62.4%) (45.5%) (54.5%) PCIN 8 16 438 548 637 733 (33.3%) (66.7%) (44.4%) (55.6%) (46.5%) (53.5%) Dorsum Stripe Lead Stripe Lead Stripe Lead PSHE 216 93 408 127 9 5 (69.9%) (30.1%) (76.3%) (23.7%) (64.3%) (35.7%) PCIN 43 5 1460 215 2389 358 (89.6%) (10.4%) (87.2%) (12.8%) (87%) (13%) Color Rd-O Y Wh Br Rd-O Y Wh Br Rd-O Y Wh Br 183

PSHE 150 32 13 19 261 69 44 32 4 3 0 3 (70.1%) (15%) (6.1%) (8.9%) (64.3%) (17%) (10.8%) (7.9%) (40%) (30%) (0%) (30%) PCIN 32 9 0 1 1119 256 10 53 1773 451 18 83 (76.2%) (21.4%) (0%) (2.4%) (77.8%) (17.8%) (0.7%) (3.7%) (76.3%) (19.4%) (0.8%) (3.6%) Tail Whole Damaged Whole Damaged Whole Damaged PSHE 238 71 444 91 11 3 (77%) (23%) (83%) (17%) (78.6%) (21.4%) PCIN 36 12 1294 381 2008 739 (75%) (24%) (77.3%) (22.7%) (73.1%) (26.9%) SVL PSHE 307 525 14 PCIN 47 1636 2710 184

TABLE 4-2. Sample sizes for all behavioral characteristic analyses for Shenandoah (PSHE) and Red-backed (PCIN) Salamanders. Not all microhabitat types or temperatures were recorded for all animals thus excluding some animals from different analyses. Percentages indicate percent of total animals using that microhabitat category.

Measure Single-species zone (PSHE) Co-occurrence zone Single-species zone (PCIN) Microhabitat type Leaf litter Log Rock Leaf litter Log Rock Leaf litter Log Rock PSHE 115 96 96 83 200 241 1 7 6 (37.5%) (31.3%) (31.3%) (15.8%) (38.2%) (46%) (7.1%) (50%) (42.9%) PCIN 3 32 12 92 599 940 125 1204 1395 (6.4%) 68.1%) (25.5%) (5.6%) (36.7%) (57.6%) (4.6%) (44.2%) (51.2%) Microhabitat temp Leaf litter Log Rock Leaf litter Log Rock Leaf litter Log Rock PSHE 2 12 14 2 40 47 0 3 0 PCIN 0 13 2 2 113 229 6 117 206 None 0 127 70 1 738 922 0 525 633 185

TABLE 4-3. Logistic and linear regression results from top supported models for

Shenandoah (PSHE) and Red-backed (PCIN) Salamander traits. Reference levels (PCIN,

PSHE zone) were held constant while comparing other levels. Asterisks indicate significance as measured at the 0.05 level except for stripe color (Bonferroni correction;

0.013 level). Snout-vent length (SVL) is a measure of body size.

Logistic Regression Coefficients Estimate SE z value P-value Stage (1=adult) ~ Zone (Intercept) 0.54 0.04 12.31 <0.001* PSHE zone -0.28 0.12 -2.39 0.02* PCIN zone -0.42 0.06 -7.15 <0.001* Sex (1=male) ~ Species (Intercept) 0.18 0.04 4.38 <0.001* PSHE 0.23 0.10 2.29 0.02* Dorsum (1=striped) ~ Species (Intercept) 1.91 0.04 42.78 <0.001* PSHE -0.87 0.09 -9.75 <0.001* Stripe (1=Red-Orange) ~ Species (Intercept) 1.20 0.04 31.21 <0.001* PSHE -0.54 0.09 -5.87 <0.001* Stripe (1=Yellow) ~ Species (Intercept) -1.46 0.04 -35.25 <0.001* PSHE -0.16 0.12 -1.38 0.17 Stripe (1=White) ~ Species (Intercept) -4.90 0.19 -25.86 <0.001* PSHE 2.60 0.24 11.05 <0.001* Stripe (1=Brown) ~ Species (Intercept) -3.29 0.09 -37.78 <0.001* 186

PSHE 0.92 0.17 5.52 <0.001* Tail Condition (1=damaged) ~ Species + Zone (Intercept) -1.23 0.06 -21.38 <0.001* PSHE -0.33 0.12 -2.80 <0.01* PSHE zone 0.32 0.16 2.05 <0.05* PCIN zone 0.23 0.07 3.21 <0.01* Linear Regression Coefficients Estimate SE t value P-value Log (SVL) ~ Species * Zone (Intercept) 3.54 0.01 515.53 <0.001* PSHE 0.05 0.01 3.48 <0.001* Co-occurrence zone -0.07 0.04 -1.70 0.09 PCIN zone -0.06 0.01 -6.54 <0.001* PSHE: Co-occurrence zone -0.01 0.05 -0.16 0.87 PSHE: PCIN zone 0.21 0.08 2.73 <0.01*

187

TABLE 4-4. Logistic regression results for Shenandoah (PSHE) and Red-backed (PCIN)

Salamander behavioral data (microhabitat type and temperature-influenced habitat use).

Reference levels (PSHE and PSHE zone) were held constant while comparing other levels.

Asterisks indicate significance as measured at the 0.05 level except for microhabitat type (0.017 level).

Coefficients Estimate SE z value P-value Microhabitat Type (leaf litter) ~ Species * Zone (Intercept) -2.82 0.11 -26.25 <0.001* PSHE 1.15 0.16 7.14 <0.001* PSHE zone 0.13 0.61 0.22 0.83 PCIN zone -0.22 0.14 -1.54 0.12 PSHE: PSHE zone 1.03 0.63 1.63 0.10 PSHE: PCIN zone -0.68 1.05 -0.64 0.52 Microhabitat Type (log) ~ Species * Zone (Intercept) -0.54 0.05 -10.59 <0.001* PSHE 0.06 0.10 0.59 0.55 PSHE zone 1.30 0.32 4.11 <0.001* PCIN zone 0.31 0.06 4.84 <0.001* PSHE: PSHE zone -1.61 0.35 -4.57 <0.001* PSHE: PCIN zone 0.17 0.55 0.31 0.75 Microhabitat Type (rock) ~ Species * Zone (Intercept) 0.31 0.05 6.14 <0.001* PSHE -0.47 0.10 -4.64 <0.001* PSHE zone -1.38 0.34 -4.07 <0.001* PCIN zone -0.26 0.06 -4.11 <0.001* PSHE: PSHE zone 0.75 0.37 2.03 0.04 PSHE: PCIN zone 0.13 0.55 0.24 0.81 Habitat Use ~ Temp + Temp2 + Species * Zone (Intercept) -1.66 0.06 -27.51 <0.001* 188

Temp -0.21 0.05 -4.58 <0.001* Temp2 -0.11 0.05 -2.27 <0.05* PSHE -1.49 0.12 -12.08 <0.001* PSHE zone -1.05 0.27 -3.83 <0.001* PCIN zone 0.41 0.09 4.66 <0.001* PSHE: PSHE zone 2.18 0.36 6.11 <0.001* PSHE: PCIN zone -3.46 0.59 -5.83 <0.001* 189

APPENDIX 1. Model selection results and type of regression model for all trait and behavioral analyses. All models fit were logistic regressions except for SVL, which was a linear regression.

Model ΔAIC Residual Residual w -2L

df Deviance

Life Stage (adult vs. juvenile)

~ Species 40.7 5321 7247.4 0.00 -3623.7

~ Zone 0.0 5320 7204.8 0.43 -3602.4

~ Species + Zone 0.5 5319 7203.2 0.34 -3601.6

~ Species * Zone 1.2 5317 7200 0.23 -3600.0

Sex (male vs. female)

~ Species 0.0 2865 3935.0 0.58 -1967.5

~ Zone 3.2 2864 3936.2 0.12 1968.1

~ Species + Zone 2.1 2863 3933.1 0.20 -1966.5

~ Species * Zone 3.6 2861 3930.6 0.10 -1965.3

Dorsum (striped vs. leadback)

~ Species 0.0 5326 4429.9 0.56 -2214.9 190

~ Zone 45.2 5325 4473.1 0.00 -2236.5

~ Species + Zone 1.2 5324 4427.0 0.31 -2213.5

~ Species * Zone 3.0 5322 4424.8 0.13 -2212.4

Stripe Color (red-orange vs. not red-orange)

~ Species 0.0 4433 4926.7 0.47 -2463.4

~ Zone 31.6 4432 4956.3 0.00 -2478.2

~ Species + Zone 0.6 4431 4923.3 0.34 -2461.7

~ Species * Zone 1.8 4429 4920.5 0.19 -2460.3

Stripe Color (yellow vs. not yellow)

~ Species 0.0 4433 4244.4 0.46 -2122.2

~ Zone 0.6 4432 4243.0 0.35 -2121.5

~ Species + Zone 2.3 4431 4242.7 0.15 -2121.3

~ Species * Zone 4.8 4429 4241.2 0.04 -2120.6

Stripe Color (white vs. not white)

~ Species 0.0 4433 713.4 0.40 -356.7

~ Zone 90.1 4432 801.5 0.00 -400.8 191

~ Species + Zone 7.5e-3 4431 709.4 0.40 -354.7

~ Species * Zone 1.3 4429 706.8 0.20 -353.4

Stripe Color (brown vs. not brown)

~ Species 0.0 4433 1548.4 0.77 -774.2

~ Zone 19.8 4432 1566.1 0.00 -783.1

~ Species + Zone 3.9 4431 1548.3 0.11 -774.2

~ Species * Zone 3.7 4429 1544.1 0.12 -772.0

Tail Condition (whole vs. damaged)

~ Species 9.6 5326 5898.0 0.01 -2949.5

~ Zone 6.1 5325 5893.5 0.04 -2946.7

~ Species + Zone 0.0 5324 5885.3 0.82 -2942.7

~ Species * Zone 3.6 5322 5884.9 0.14 -2942.4

Log(SVL)

~ Species 55.7 5237 409.0 0.00 -753.6

~ Zone 18.7 5236 406.0 0.00 -734.1

~ Species + Zone 3.5 5235 404.6 0.15 -723.6 192

~ Species * Zone 0.0 5233 404.1 0.85 -721.8

Microhabitat Type (leaf litter vs. not leaf litter)

~ Species 45.7 5245 2669.6 0.00 -1334.8

~ Zone 64.9 5244 2686.7 0.00 -1343.4

~ Species + Zone 0.02 5243 2619.9 0.50 -1309.9

~ Species * Zone 0.0 5241 2615.9 0.50 -1307.9

Microhabitat Type (log vs. not log)

~ Species 34.3 5245 7083.1 0.00 -3541.5

~ Zone 17.5 5244 7064.2 0.00 -3532.1

~ Species + Zone 18.8 5243 7063.5 0.00 -3531.8

~ Species * Zone 0.0 5241 7040.7 1.00 -3520.4

Microhabitat Type (rock vs. not rock)

~ Species 41.8 5245 7224.4 0.00 -3612.2

~ Zone 16.7 5244 7197.2 0.00 -3598.6

~ Species + Zone 0.4 5243 7179.0 0.45 -3589.5

~ Species * Zone 0.0 5241 7174.6 0.55 -3587.3 193

Microhabitat Use (occupied objects vs. unoccupied objects)

~ Temp + Temp^2 + Species 155.9 7632 4646.5 0.00 -2323.2

~ Temp + Temp^2 + Zone 644.0 7631 5132.5 0.00 -2566.3

~ Temp + Temp^2 + Species + Zone 156.0 7630 4642.6 0.00 -2321.3

~ Temp + Temp^2 + Species * Zone 0.0 7628 4482.6 0.97 -2241.3

~ Temp + Temp^2 * Species * Zone 7.0 7623 4479.6 0.03 -2239.8

194

APPENDIX 2. Microhabitat temperature by species detected in each zone of the transect (PSHE = Shenandoah salamander; PCIN =

Red-backed salamander). Co-occur zone indicates the area of highest probability species co-occurrence based on Amburgey et al. (in review).

195

APPENDIX 3. Mean microhabitat temperature by date of survey. Error bars represent standard error.

196

APPENDIX 4. Microhabitat temperature by species detected and microhabitat type in each zone of the transect (PSHE = Shenandoah salamander; PCIN = Red-backed salamander). Co-occur zone indicates the area of highest probability species co-occurrence based on

Amburgey et al. (in review). Microhabitat types were (A) rock, (B) log, and (C) leaf litter.

197

Chapter 5

Taxonomy, life history, range size, and climate envelope position determine species

responses to habitat fragmentation in southern California

This chapter is formatted for submission to the Proceedings of the National Academy of the

Sciences and is consistent with their requirements

S.M. Amburgey1,2*, D.A.W. Miller2, C.J. Rochester3, K. Delaney4, S. Riley4, R.N. Fisher3

1 Intercollege Graduate Degree Program in Ecology, Pennsylvania State University, University

Park, PA 16802

2 Ecosystem Sciences and Management, Pennsylvania State University, University Park, PA

16802

3 U.S. Geological Survey, Western Ecological Research Center, San Diego, CA, 92101, United

States of America

4 National Park Service – Santa Monica Mountains Recreation Area, Thousand Oaks, CA 91360

*Corresponding author: [email protected] 198

Abstract

Urbanization decreases native species diversity, but variation exists in individual species’ responses. We investigate responses to fragmentation in southern Californian small vertebrate communities by estimating species commonness and sensitivity (responsiveness to patch size) at patches and sites within patches. We investigated if taxonomic differences, life history traits, range size, and climate envelope position could explain patterns in species’ biogeography and responses to fragmentation. Biogeographic differences existed as mammals were most common at patches and sites within occupied patches, while larger patches had higher occupancy but sparser occurrence of amphibians. Species with larger range sizes were also more common across and within patches. We found sensitivity to fragmentation was higher for more fecund species and dependent on position within a species’ climate envelope. While all taxa were more common in warmer portions of their ranges, amphibians and mammals were less likely to occur in smaller patches within these warmer areas. In larger, warmer patches, amphibians occurred at fewer sites within patches. Similarly, amphibians were rarer at sites within patches in drier portions of their ranges, potentially due to the lack of suitable microhabitats within patches.

Mammals occurred at more sites within occupied patches that were also in these drier areas while reptiles did not differ in their sensitivity to patch size by where they occurred in the climate envelope. While larger patches can better support species and provide necessary microhabitats, continued habitat fragmentation and climate change may limit the ability of species to shift their distributions and persist.

199

Keywords: multispecies occupancy model, amphibian, mammal, reptile, urbanization, drought, climate warming, density compensation 200

Introduction

Urbanization is a multifaceted global conservation threat, altering the assemblage of species communities directly and indirectly (Fahrig 2003, McKinney 2008). How individual species respond to this disturbance depends on the ways in which fragmentation acts on the landscape. Human development leads to the direct removal of available habitat for species use as well as alteration of the remaining habitat. Fragmentation disrupts historic dispersal corridors and creates “islands” of potentially suitable habitat with limited movement in between (Davis and Glick 1978, Husté et al. 2014, Olejniczak et al. 2018). These remaining patches may have increased prevalence of invasive species (Riley et al. 2005, McKinney 2006), changes to predation pressure, and altered microclimates associated with edge effects (Forman and

Alexander 1998, Kremsater and Bunnell 1999, Fahrig 2003). Post fragmentation, species that are sensitive to disturbance may become locally extinct in remaining patches; however, other resilient species may take their place if they are able to capitalize on emptied niches (e.g., excess density compensation; Case 1975, Crooks and Soule 1999, Rodda and Dean-Bradley 2002). To better understand and forecast changes to urban species communities, research is needed to understand variation in species-specific responses to disturbed landscapes (Henle et al. 2004,

Grant et al. 2011, Rytwinski and Fahrig 2013, Brehme et al. 2018).

Life history traits may help explain the increased sensitivity or resiliency of some species to fragmentation (McKinney and Lockwood 1999, McKinney 2008, Grant et al. 2011).

Determining species’ sensitivity to fragmentation depends on an understanding of which species’ traits are associated with increased risk in an urbanized landscape (e.g., Andrews and Gibbons

2005, Brehme et al. 2018). Differences in life history traits and sensitivity to fragmentation may 201

vary broadly by taxonomy. For example, amphibians with complex life cycles or those that use different overwintering and breeding habitats could lose connectivity to sites or increase their risk of direct mortality from road crossings (Andrews et al. 2008). Life history traits within taxonomic groups can also contribute to species sensitivity to disturbance by affecting reproductive potential, dispersal loads, and potential population growth rates (Henle et al. 2004,

Öckinger et al. 2010). We expect reproductive potential to vary with respect to the slow-fast continuum of life history variation (e.g., longevity, age of first reproduction) or the low-high fecundity continuum (i.e., number of young produced versus early life reproduction). These can be measured by traits such as fecundity, body size, and age at sexual maturation (Table 1; Purvis et al. 2000, Grant et al. 2011, Rytwinski and Fahrig 2012, 2013). For example, organisms with higher reproductive output in a given year may be better able to compensate for indirect and direct mortality resulting from fragmentation without detrimental changes to the overall population (Rytwinski and Fahrig 2012, 2013; Brehme et al. 2018). Increased body size may also indicate species with greater spatial or resource requirements and could result in smaller patches being unable to sustain populations (Henle et al. 2004, Rytwinski and Fahrig 2012, 2013).

Additionally, species’ range size and position within their climate envelope may determine how sensitive populations of a species are to fragmentation (Kendle and Forbes 1997,

Ripley et al. 2017). A species’ geographic spread (i.e., range size) may be positively correlated with the colonization potential for locally extirpated populations (Sexton et al. 2009). Among species, larger range sizes may also be associated with species tolerance to a wider spectrum of conditions that could contribute to the robustness of the species to disturbance (Gaston and

Spicer 2008). Range position within a species may also contribute to species sensitivity to 202

fragmentation, where populations that occur in more extreme portions of a species’ climate envelope may be more sensitive to disturbance (Oliver et al. 2015). Fragmentation can provide an additive physiological stress on species in these more extreme climatic conditions and increases in impervious surfaces and solar radiation associated with urbanization (i.e., heat islands; Shochat et al. 2006) can further alter the local climate within a habitat patch and may push species beyond their physiological tolerances. This may be especially true for ectothermic species reliant on climate-driven breeding habitats (Walther et al. 2002, Paajimans et al. 2013).

Alternatively, populations at the extreme of a species’ climate envelope may also be adapted to stressful, changing habitat conditions (Rehm et al. 2015) and thus more resistant to the effects of fragmentation.

Broad-scale studies on fragmentation effects exist for birds, insects, and plants

(McDonnell and Hahs 2008, McKinney 2008, Aronson et al. 2014, Palma et al. 2017), with less focus on species such as reptiles, amphibians, and small mammals (but see Bolger et al. 1997,

Scheffers and Paszkowski 2012, Fisher et al. in prep among others). Responses of motile species or those that benefit from human-assisted colonization likely differ from responses of native terrestrial vertebrate communities. Reptiles and amphibians are some of the most at-risk taxa, with current rates of extinction much higher than background rates (Alroy 2015, Ceballos et al.

2015). Loss of small vertebrate communities can alter ecosystem function as these species are the foundation of trophic networks and the bulk of nutrient biomass, serving as integral prey and predator species within communities (Ranvestel et al. 2004, Ims et al. 2008, Hocking and Babbitt

2014, Ripple et al. 2017). 203

We investigated how the interplay of life history, range-wide characteristics, and habitat fragmentation helped shape communities in a heavily disturbed landscape in southern California.

We analyze data from across the California Floristic Province (Fig. 1A, B), one of the globe’s biodiversity hotspots (Myers et al. 2000; Calsbeek et al. 2003). The landscape has undergone significant development in the last eight decades (Mattoni and Longcore 1997, Bauder and

McMillan 1998), resulting in an altered habitat matrix with effects across all taxonomic groups

(e.g., Riley et al. 2006, Barr et al. 2015, Hung et al. 2017). Fisher et al. (in prep) found species richness-area relationships strongly related to patch size in this system, with communities increasing by 3.5 times as many species from smallest to largest patch sizes. However, individual species varied in their response to patch size, prompting further work to better understand what traits might explain these differences. Our goal was to identify species-specific traits associated with increased sensitivity or resiliency in an altered landscape (Sousa 1980; Haddad et al. 2008;

Williams et al. 2008). We examined patterns in two variables: relative commonness across the landscape as well as sensitivity to patch size for individual species (Fig. 2). We measured these variables at two scales: the probability a patch is occupied by a species and, given the patch is occupied, the proportion of sites within a patch at which a species can be found. We show that differences among taxonomic groups, life-history traits, and among and within species range traits have important effects on both commonness and sensitivity to patch size in this highly fragmented landscape.

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Materials and Methods

Pitfall Sampling and Covariate Measuring

We sampled across southern California using pitfall arrays (Fig. 1, 2A) to survey small vertebrate communities. Pitfall arrays provide a lower cost method to detect even cryptic species

(Fisher et al. 2008) and were installed in different years as part of multiple projects and funding agencies (Appendix 1). We synthesized data across these individual projects for this analysis, and in order to maximize the overlap of years sampled in each study we limited our analyses to the earliest two to seven years of sampling at all pitfall arrays (Appendix 1). Crews generally opened and monitored pitfall arrays for multiple days (1-17 days per sampling period) during multiple seasons across multiple years, checking traps daily to catalog species detected. We used only native species in our analysis. Counts of species were simplified to detected (1) and undetected (0) for all sampling occasions. Certain groups of species are difficult to distinguish in the field, and a composite classification was given (e.g., Peromyscus species were noted as

“Mouse”; Appendix 2).

We calculated the size (ha) of each habitat patch containing each array using paved roads

(Brehme et al. 2013) and human structures as boundaries on available habitat (as described in

Fisher et al. in prep). Due to unreliable data automation and incompatible geospatial data layers, we quantified patch size manually using historical, aerial imagery along with geospatial information systems layers on roads, development, and land-use classification (Fisher et al. in prep). For our analysis, we used a single patch size representing the area of available habitat at the beginning of pitfall sampling for each site. Overall, subsequent fragmentation was limited during the two to seven years of data used in this analysis. Additional development most 205

frequently occurred through the gradual erosion of habitat edges rather than the drastic splitting of core habitat. Additionally, we assumed that subsequent fragmentation would not be immediately realized as changes in species occupancy rather than shifts in abundance.

Life History and Range Size Information

Species information was aggregated by Brehme et al. (2018) and supplemented by additional literature search and solicitation of expert opinion (Appendix 2). Life history traits included average adult body size (cm; snout-vent-length for amphibians and reptiles and body length for mammals), average fecundity per year, and average years to sexual maturity. Other species information included taxonomic group, total range size (ha), and conservation status through the International Union for Conservation of Nature and Natural Resources (IUCN),

California Department of Fish and Wildlife (CDFW), and U.S. Fish and Wildlife Service

(USFWS). Range maps for each species were found through either the IUCN Red List (IUCN

2000), Amphibian and Reptile Atlas of Peninsular California (Biodiversity Research Center of the Californias and San Diego Natural History Museum 2018), field guides (Stebbins 2003,

Nafis 2018), or modified from one of these sources using expert opinion (Appendix 2).

Composite species (e.g., “Mice”) range maps consisted of a merged version of all included species’ geographic extents.

Climate Covariates

We further hypothesized that differences in climate across the species range may interact with patch size to help explain patterns in species occupancy. To investigate whether species are less likely to occur in smaller patches that also occur at an extreme of their climate envelope, we downloaded 30-year (1970-2000) climate normal data for North America from WorldClim at a 206

~1 km2 spatial resolution (Hijmans et al. 2005). We expected long-term winter precipitation

(mean total precipitation over January, February, and March) and maximum summer temperature

(mean maximum temperature over July, August, and September) to represent important climatic conditions for species. In the face of increasingly frequent and persistent drought associated with changing climate (Westerling et al. 2006), we predicted that changes in occupancy associated with reduced winter precipitation (when southern California gets the bulk of its rainfall) and hot temperatures (that can exacerbate drought and fire conditions; Westerling et al. 2006) are important for predicting future changes to species communities. Precipitation and temperature climate rasters were clipped to each species range and then standardized using the mean and standard deviation. For species with skewed distributions of precipitation values across their ranges, values were loge transformed prior to standardization. We defined areas in each climatic extreme as patches in the driest (≤15th percentile) or warmest (≥ 85th percentile) portion of each species range. All other values were considered part of the core set of climate conditions experienced by each species. We denoted these conditions as either a one (extreme) or zero

(core) for each patch in each species’ range.

Data analysis

We investigated species-specific variation in prevalence at an average patch size

(“commonness”) and the strength of the relationship between patch size and prevalence

(“sensitivity”) at the patch and site levels (Fig. 2). We estimated commonness to understand how frequently a species occurred at an average patch and an average site within a patch. Sensitivity instead estimates the responsiveness of a species to change in patch size. By measuring these processes at the patch and sites level, we were able to differentiate processes at different scales. 207

Species could respond at the patch level by dropping out of entire habitat patches and/or could respond at the site level through changes in their site prevalence within occupied patches. Site level responses better capture the homogeneity of occurrence within a patch while patch level responses capture broader trends in presence and absence across the landscape.

We started with a modified hierarchical multispecies occupancy model (Kéry and Royle

2008, Zipkin et al. 2009). The model accounts for imperfect species detection probabilities through repeat surveys (t) while also allowing for simultaneous estimation of individual species

(s) occupancy probabilities from a shared community distribution (Dorazio and Royle 2005,

Kéry and Royle 2008). This allows multispecies occupancy models to borrow strength from more abundant species of the community to allow for better estimates of parameters for rare or harder to detect species. Models were fit in JAGS (Plummer 2003) via the jagsUI package

(Kellner 2018) in program R (R Core Team 2016).

We estimated species occupancy at habitat patches and at pitfall arrays (i.e., sites) nested within patches (Fig. 2). To correct for differences in the potential pool of species s that could occur in a patch j, we added an additional nested level to our multispecies occupancy model based on the regional pool of species (). = 1 if the species could occur in an area (i.e., the patch is within the species’ range) or 0 if it was not possible for the species to occur there. We then calculated patch occupancy for every species s at every patch j () as a function of the occupancy probability of each species within a patch () multiplied by whether that species could be found in the regional species pool (). Patch occupancy was estimated as

~ ( ∗ ) where = 1 if a species was present or 0 if it was absent. To understand how patch-level covariates influenced average site occupancy probability, we used 208

the function of the average patch occupancy times the average site occupancy probability (), estimated as ~ ( ∗ ) where = 1 if a species was present at the average site within an average patch. We similarly calculated individual site occupancy for each species within a patch (), based on if each species was available in a patch to occupy a site ().

Finally, whether we observed a species at each site within a patch at each survey () was a product of the site occupancy and detection probability (), estimated as

~ ( ∗ ).

We then allowed , , and to vary as a function of covariates fit using generalized linear mixed models with a logit link function within the occupancy model.

Variation in species responses to patch size (Fisher et al. in prep) may be due to specific life history, range size, or range-position climate characteristics. To control for broad phylogenetic differences that may be correlated with life history strategies or range characteristics, we included taxonomic group in all models. Thus, we estimated the effect of traits on variation in occupancy probability within each taxon. We included three taxonomic groups (Amphibia,

Mammalia, and Reptilia) in our models (Eq. 1 and 2). Thus, our starting model for patch and site occupancy probabilities as a function of patch size was

logit = + ∗ Patch Size + ω[Taxa] + ω[Taxa] ∗ Patch Size (Eq. 1)

logit = + ∗ Patch Size + π[Taxa] + π[Taxa] ∗ Patch Size (Eq. 2)

209

where and served as species-specific random intercepts and and served as the random effects of patch size on species occupancy. Random effects were modeled as coming from a normal distribution, where ~ (0, ) and ~ (0, ). Taxonomic group terms functioned to set a mean intercept and slope for each group and account for differences in response by group to patch size. We treated taxonomic differences in intercepts and patch size effects as fixed parameters with priors ω~(0,100 ) and

π~(0,100 ). We used vague priors with uniform distributions bounded between 0 and 4

for all random effect variance components ( ). The intercept terms (e.g., + ω[Taxa]) capture the commonness of each species at the average patch (Fig. 2C). Similarly, the slope terms (e.g., + ω[Taxa]) capture the sensitivity of each species in response to changes in patch size. This allowed us to calculate commonness and sensitivity at both the patch and site levels.

Detection probability for each species at each patch was similarly calculated as a function of patch size where

logit() = + ∗ Patch Size (Eq. 3)

with species-specific random intercepts and effects for patch size assumed to be

~ (0, ). We used vague priors with uniform distributions bounded between 0 and

4 for all random effect variance components ( ). 210

Next, we fit covariates by further modifying the patch and site occupancy models from

Eq. 1 and 2 to include fixed effects for species-specific or patch-specific information. For example, we modeled the influence of average adult body size as

logit = + ∗ Patch Size + ∗ Body Size + ∗ Body Size ∗ Patch Size +

ω[Taxa] + ω[Taxa] ∗ Patch Size (Eq. 4) logit = + ∗ Patch Size + β ∗ Body Size + β ∗ Body Size ∗ Patch Size +

π[Taxa] + π[Taxa] ∗ Patch Size (Eq. 5)

where and β function as the fixed effects of body size within each taxonomic group on species patch and average site occupancy. Additionally, and β are the fixed effects of body size within each taxonomic group on species-specific responses to patch size. We treated taxonomic differences in intercepts and patch size effects as fixed parameters with priors

ω~(0,100 ) and π~(0,100 ). Similarly, we treated differences in traits in intercepts and patch size effects as fixed parameters with priors ~(0,100 ) and

π~(0,100 ). We used vague priors with uniform distributions bounded between 0 and

4 for all random effect variance components ( ). The intercept terms (e.g., + ω[Taxa] +

[Body]) capture the commonness of each species at an average patch and body size.

Similarly, the slope terms (e.g., + ω[Taxa] + [Body]) capture the sensitivity of each species of an average body size in response to changes in patch size. We ran models for each trait by replacing Body Size in Eq. 4 and 5. 211

We similarly estimated climate models except that we quantified the effect of climate conditions at each patch a for each species s by taxonomic group where

logit = + ∗ Patch Size + [Taxa] ∗ Climate + [Taxa] ∗ Climate ∗

Patch Size + ω[Taxa] + ω[Taxa] ∗ Patch Size (Eq. 6)

logit = + ∗ Patch Size + β[Taxa] ∗ Climate + β[Taxa] ∗ Climate ∗

Patch Size + π[Taxa] + π[Taxa] ∗ Patch Size (Eq. 7)

All fixed and random effects were estimated similarly to Eq. 4 and 5. We then calculated species commonness and sensitivity in extreme versus core climate conditions for each taxonomic group.

To see if species differences in commonness and sensitivity to patch size were correlated to conservation status, we ran a model similar parameterized as Eq. 6 and Eq. 7, where instead of extreme versus core climate denoted as a zero or one, we used any conservation status listing

(IUCN, California SSC, or CDFWS; 1) versus no listing (0). Again, all fixed and random effects were similarly estimated to Eq. 4 and 5.

We standardized all continuous covariates prior to model fitting. For measures with large and skewed ranges of values (i.e., patch size, total fecundity per year, and range size), we log transformed values prior to standardization. We ran a separate model for each covariate (Table 1) and judged the importance of each trait in explaining variation in patch or site occupancy based on whether 95% credible intervals for each fixed effect overlapped 0. For each model, we ran three parallel chains for 25,000 iterations each and discarded the first 10,000 as a burn-in prior to 212

model convergence. We assessed model convergence via traceplots and Gelman Rubin statistics

( < 1.1; Gelman and Rubin 1992, Brooks and Gelman 1997).

Results

We captured 45 species of native small vertebrates consisting of seven mammals, seven amphibians, and 31 reptiles (Appendix 2) at 698 pitfall arrays in 97 habitat patches (patch size =

0.42-81,402.48 ha; Appendix 1). Overall, we recorded 47, 156 individual species detections. Of the 45 species, the arroyo toad and California pocket mouse are federally endangered while 15 species are California Species of Special Concern (SSC). Patches were distributed randomly by size and climate regime across southern California; not all large patches or small patches fell into hotter or drier climate extremes, and a spectrum of patch sizes occurred along the coast of

California inland towards the desert (Appendix 1).

Taxonomic differences

We found differences in patch size effects among the three taxonomic groups surveyed.

Mammals were the most common, being most likely to occur at patches and, when present at a patch, at the highest proportion of sites within patches (Fig. 3A, B; Appendix 3). Compared to mammals, we found that amphibians and reptiles were less common for an average sized patch

(effect size on logit scale: -2.55 [credible interval: -4.27, -0.77] and -1.85 [-3.27, -0.70] respectively). Additionally, amphibians and reptiles were less common at sites within occupied patches (-2.00 [-3.43, -0.59] and -1.65 [-2.90, -0.44] respectively). Credible intervals for the differences between commonness of amphibians and reptiles at patches and sites overlapped zero. Sensitivity to patch size did not differ among taxonomic groups at the patch or site level in 213

most cases (Fig. 3) except amphibians were more sensitive to patch size at the patch level than reptiles [0.59 (0.01, 1.20)]. At the overall patch, all taxonomic group means and 20 of the 45 individual species were estimated to have a positive relationship to patch size as their credible intervals for the effect of patch size did not include zero. At sites within occupied patches, amphibians and reptile group means had a negative estimated effect with changing patch size while mammals were the only taxonomic group for which the group mean estimated effect included 0. For 35 of the 45 species, there was no change in the proportion of sites at which they occurred as patch size increased as the credible interval for the estimated effect size contained zero. However, the estimated effect was negative for nine species and positive for two species.

The probability individual species occurred at an average sized patch varied widely (12-

98%; Fig. 3A) with only amphibians of conservation concern less likely to occur as compared to unlisted amphibians (Appendix 3). The three least common species in patches (arroyo toad, ensatina, and spadefoot toad) are all CSSC or IUCN-recognized endangered or declining species.

The thirteen most commonly found species are all considered least concern and stable by the

IUCN except for the alligator lizard, which is declining. Mammalian and reptilian species of conservation concern occurred along the spectrum of commonness at the patch. Mean species commonness at sites within occupied patches also varied widely (18-98%; Fig. 3B).

Conservation status did not show a clear relationship to species commonness at sites, though the endangered arroyo toad was once again one of the least common species.

Sensitivity to patch size at the patch and sites within patches also did not differ predictably by conservation status (Fig. 3C, D; Appendix 3). Approximately 50% of species that responded positively to increasing patch size at the average patch were species of conservation 214

concern whereas slightly less than 50% of species that were not affected also are of concern.

However, arroyo toads (IUCN = endangered) and spadefoot and western toads (IUCN = near threatened) were some of the most sensitive species to patch size. At sites within patches, fewer species responded significantly to patch size. Five of the nine species that were less sensitive to increasing patch size also were species of conservation concern as were both species that were more sensitive to increasing patch size.

Commonness at the patch was correlated with commonness at sites within occupied patches (Fig. 4A). Species sensitivity at the patch and at sites within patches was not strongly correlated, indicating species were not similarly sensitive at both scales (Fig. 4B). Amphibians were particularly sensitive to patch size at the patch but not at sites within patches. Additionally, many reptile species were sensitive to patch size at the patch but not at sites within patches.

There was a weak negative correlation between commonness and sensitivity at the patch (Fig.

4C), with the general trend that rarer species (particularly amphibians) responded more to increasing patch size. Species commonness and sensitivity to patch size at sites within occupied patches was not correlated (Fig. 4D), though mammals were some of the most common and least sensitive taxa. Species within and between taxonomic groups still varied greatly in their response to fragmentation at the patch and site within patch level (e.g., Fig. 5).

Life history and range covariates

We tested whether species life history, range size, and position in their climate envelope explained additional variation in relative commonness and sensitivity to patch size. In most cases, effect sizes for life history covariates were small and included 0 in the credible interval

(Fig. 6; Appendix 3). However, more fecund species were more sensitive to patch size at the 215

average patch (Fig. 6B), and anuran amphibians and mammals were among the most sensitive

(Appendix 4). We also found that species with larger range sizes were more common at patches and at sites within occupied patches (Fig. 6D), but that range size did not help explain species sensitivity to patch size. Many of the most common and broadly-distributed species at the patch and the site were not species of conservation concern. Most, but not all, of the least common species with smaller range sizes were those of conservation concern (Appendix 5).

Whether a patch occurred at a climate extreme or in the core of a species’ range explained patterns of both commonness and sensitivity to fragmentation at the patch and site scales (Appendix 3). Mammals and reptiles were more common in patches that occurred in the warmest portion of a species’ range at both patches and sites within patches while amphibians were positively associated at only the site level (Fig. 7A). Despite being more common, we found that both mammals and amphibians were also more sensitive to patch size when the patch occurred in the warmest portion of the species’ range. As a result, mammals and amphibians were less likely to occur in small patches if that patch was near the warm extreme of the species’ range (Fig. 7A).We did not observe the same extent of relationships at the site level, where mammals did not differ in sensitivity to patch size by temperature while amphibians were less sensitive to patch size at sites within occupied patches at the climate extreme (Fig. 7A). Reptiles did not differ in sensitivity to patch size by climate at the average patch or sites within occupied patches.

For winter precipitation, none of the taxonomic groups were more or less common when in the driest portion of a species’ range (Appendix 3, Fig. 7B). At sites within occupied patches, amphibians were less common when in the drier areas while mammals and reptiles were more 216

common. There were no significant differences in taxonomic group sensitivity to patch size by precipitation at the patch level. At the site level, mammals were the only group more sensitive to patch size in patches at the drier extreme of the climate envelope.

Discussion

Fragmentation often decreases native species richness (McKinney and Lockwood 1999,

Riley et al. 2006, Barr et al. 2015, Hung et al. 2017). Identifying individual species’ traits that heighten or lessen species’ sensitivity to the impacts of urbanization can improve predictions of community change and management decisions (Henle et al. 2004, Grant et al. 2011, Rytwinski and Fahrig 2013, Brehme et al. 2018). We demonstrated that losses in species richness in

Southern California small vertebrate communities with fragmentation can in part be explained by life history and among and within-species range information. While taxonomic group explained differences in patterns of species biogeography, all three taxonomic groups were similarly sensitive to changes in patch size across the fragmentation gradient. We found that only higher fecundity was associated with increased species’ sensitivity to patch size at the patch level. Other factors that relate to slow-fast life history continuum such as adult body size and age at sexual maturity did not explain species commonness or sensitivity to fragmentation. Species’ range sizes also contributed to biogeographical patterns of species commonness at the patch and sites within occupied patches, where species occurred more often in patches and sites if they were already geographically widespread. Additionally, we found species sensitivity to patch size was dependent on where the patch occurred within a species’ climate envelope. Both amphibians and mammals were more sensitive to patch size when patches occurred in the warmest portions of 217

the species range. In the case of mammals, they were also more sensitive to patch size effects in patches in the driest portion of their range.

We observed key differences in the biogeography of amphibians, reptiles, and mammals and in their responses to fragmentation at the patch and site levels. Mammals were more common than amphibians and reptiles, potentially due to their ability to propagate in human structures and benefit from the removal of native predators that may dampen their populations

(Crooks 2002, Fischer et al. 2012). Urban development may not be as hard a boundary for them as for other groups (e.g., Parsons et al. 2018 but see Bolger et al. 1997). Amphibians were more sensitive to patch size than reptiles at the overall patch, which could be driven by frogs and toads requiring rarer, patchily-distributed aquatic or moist breeding habitats in which to lay their eggs.

When fragmentation occurs, a larger patch has a higher chance of containing these specific microhabitats (Ewers and Didham 2006). In the absence of breeding habitats, amphibians either risk road mortality from moving to patches with breeding habitat (Gibbs and Shriver 2005,

Andrews et al. 2008, Brehme et al. 2018) or are likely to become locally extinct.

In general, we observed a reduced sensitivity of species to patch size at sites within occupied patches. Density compensation, where species resilient to fragmentation fill empty niches vacated by more sensitive species (Case 1975, Crooks and Soule 1999, Rodda and Dean-

Bradley 2002), may explain this reduced sensitivity for some species. As the number of species that occur in a patch decreases with patch size, the remaining species may occur at a greater portion of the overall sites within that patch. The lack of correlation between sensitivity to patch size at the average patch and sites within occupied patches supports density compensation within these communities (Fig. 4B). Reptiles were particularly reduced in their sensitivity to patch size 218

at both scales, indicating that these species may be the ones filling empty niches vacated by more sensitive species. Reduced sensitivity to patch size at sites within patches may also be due to the distribution of habitat types across and within patches. Smaller patches may be more likely to be more uniform in their composition while larger patches likely are more varied (Hof et al. 2011).

Amphibians and species of conservation concern had the strongest positive relationship to patch size in whether they occurred at the overall patch. However, once present in a patch, the proportion of sites within a patch that were occupied by these species decreased as patch size increased (Fig. 4B), perhaps due to this habitat specificity (Fig. 3D).

We hypothesized that species that produce more offspring would be buffered against increased mortality rates associated with fragmentation and altered predation (Rytwinski and

Fahrig 2013, Brehme et al. 2018). Instead, highly fecund species were more sensitive to patch size at the patch, potentially due to the high variability in reproductive success of these species

(e.g., Alford and Richards 1999, Inchausti and Halley 2003). With the added risk of the effects of fragmentation, species living a high risk-high reward lifestyle may be limited in their opportunities to recover from poor reproductive years. We also found that species that had a wider geographic range were also most common at patches and sites within patches. Through source-sink dynamics or due to an increased breadth of conditions tolerated by these species, geographic spread can help predict species commonness and patterns in biogeography (Ripley et al. 2017). While we saw that only amphibians of conservation concern were less likely to occur in patches as compared to all at-risk taxa, southern California does have a high concentration of endangered, rare, and endemic species that are generally associated with more restricted ranges

(Dobson et al. 1997, Myers et al. 2000). 219

For medium to large sized patches, amphibian and mammalian occurrence was greater in the warmest portion of the species’ climate envelope. In larger patches, the abundance of microhabitats that can serve as refugia in extreme climate conditions is likely higher (e.g.,

Magoulick and Kobza 2003, Hof et al. 2011, Lehikoinen et al. 2018), potentially buffering species in these large patches to the effects of climate and fragmentation. However, species occupancy decreased faster in these hotter areas as patches became smaller. As patch size decreases, increased solar radiation and impervious surfaces in urban landscapes can compound negative effects on species in hotter portions of the climate envelope (i.e., heat islands, Shochat et al. 2006). Smaller patches also likely do not possess the heterogeneity in habitat types to provide climate refugia necessary for species to persist. Amphibians, where heat is related to the rapidity at which desiccation occurs (Köhler et al. 2011), may be particularly sensitive to this interaction at multiple scales (Fig. 7). Reptilian occupancy did not differ between extreme and core climate envelope conditions, potentially due to behavioral temperature regulation that reduces their exposure to extreme conditions (Kearney et al. 2009). Mammals and reptiles were more common at sites within drier patches, potentially due to a reduced need for wet microhabitats allowing them to be more widespread across patches in extreme portions of the climate envelope. On the other hand, amphibians were less common at sites that occurred in the driest portion of their climate envelope. Mammals were more sensitive to patch size at sites within patches in the drier portion of their climate range and were able to spread more homogenously across these patches. The effects of climate change may compound climate-patch relationships, with warming temperatures and more frequent and prolonged drought interacting with the effects of fragmentation to threaten at-risk species in smaller patches (Oliver et al. 2015, 220

Lehikoinen et al. 2018). Even in larger patches that do have higher habitat heterogeneity, species may persist but be unable to ultimately shift their distributions as climate continues to change and even microhabitats become islands of decreasing suitability within the overall patch (Hof et al. 2011).

We present the results of broad-scale surveying from across multiple studies in southern

Californian small vertebrate communities. Habitats were selected to answer a variety of conservation questions but were those that would allow for the continued monitoring of pitfall arrays on generally conserved lands, resulting in species communities and habitats that were likely more buffered than average locations from the direct effects of landscape development. As we see species responding to fragmentation even in these locations, species communities in other portions of the remaining habitat matrix in southern California are likely to be even more impacted. While this study focused on understanding the way fragmentation has changed species occupancy, this does not provide information on species that may yet drop out of communities due to declining growth rates (i.e., extinction debt; Kuussaari et al. 2009). Further research should focus on estimating demographic rates of remaining species within patches that set population trajectories on the path of local extinction or persistence.

We find that while some species were able to persist in smaller habitat patches, this does not necessarily translate into population persistence. Species that are habitat specialists may persist in smaller patches that contain specific habitat types but are at heightened risk from shifts in climate or further erosion of their patches through development (Hof et al. 2011, Lehikoinen et al. 2018). Additionally, some currently secure species that are both less common on the landscape and more sensitive to fragmentation may become imperiled as development continues 221

(Fig. 3, e.g., arboreal salamander, threadsnake; Crooks et al. 2017). Several species of current conservation concern (e.g., arroyo toad, spadefoot toad, ensatina) also are rarer and more sensitive to fragmentation, highlighting these species as potential conservation priorities. While large patches appear to buffer species to some extent from the negative impacts associated with habitat destruction (Fahrig 2003, McKinney 2008) or altered climate (Shochat et al. 2006), as development continues and climate changes further, species may be unable to shift their distributions in this urban matrix to respond (Hof et al. 2011, Oliver et al. 2015). Our improved understanding of the biogeography of these taxa and the influence of fragmentation and its interaction with climate on community composition allows for targeted research on at-risk species and the conservation of remaining habitat patches.

Acknowledgements

We are thankful to the numerous researchers from the USGS and NPS who collected these data, particularly Adam Backlin, Gary Buusted, Denise Clark, Stacie Hathaway, J.P.

Montagne, Drew Stokes, and Samantha Weber. This work was conducted as part of the

Amphibian Decline Working Group supported by the USGS John Wesley Powell Center for

Analysis and Synthesis, funded by the US Geological Survey. Additional funded was provided by the NASA Pennsylvania Space Grant Graduate Fellowship. This manuscript is contribution

#XXX of the USGS Amphibian Research and Monitoring Initiative. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S.

Government. 222

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Table 5-1. Multispecies occupancy models for patch occupancy (). Average site within patch occupancy () models were similar and therefore not shown. The base model contained patch size and taxonomic group to account for phylogenetic differences between mean occupancy and sensitivity to patch size by each species. This model was further modified with the inclusion of the terms listed as Additional Models.

Base Model Prediction logit Phylogenetic differences may explain variation in mean occupancy and sensitivity to patch size = + ∗ Patch Size + ω[Taxa] + ω[Taxa] ∗

Patch Size Additional Models Prediction

= ⋯ + ∗ Body Size + ∗ Body Size ∗ Patch Size Smaller bodied amphibians may be more sensitive to fragmentation while larger bodied snakes and mammals may be more negatively impacted (Rytwinski and Fahrig 2012, 2013)

= ⋯ + ∗ Fecundity/Year + ∗ Fecundity/Year ∗ Patch Size Lower fecundity species are more susceptible to negative impacts of fragmentation (Rytwinski and Fahrig 2012, 2013; Brehme et al. 2018)

= ⋯ + ∗ Age at Maturity + ∗ Age at Maturity ∗ Patch Size Slower sexual maturation correlates to slower life history that is more sensitive to fragmentation effects (Purvis et al. 2000; Rytwinski and Fahrig 2012) 232

= ⋯ + ∗ Range Size + ∗ Range Size ∗ Patch Size Species with smaller geographic extents should be more sensitive to fragmentation (Purvis et al. 2000)

= ⋯ + [Taxa] ∗ Dry + [Taxa] ∗ Dry ∗ Patch Size Total winter precipitation will influence the ability of species to occur at smaller patches based on altered microclimate caused by urbanization (cite) and will particularly influence the occupancy of sensitive amphibian species (cite)

= ⋯ + [Taxa] ∗ Heat + [Taxa] ∗ Heat ∗ Patch Size Maximum summer temperature will influence the ability of species to occur at smaller patches as temperatures increase in urban settings (cite) and species have a limited number of microhabitat refuges (cite)

= ⋯ + [Taxa] ∗ Cons + [Taxa] ∗ Cons ∗ Patch Size Conservation status may influence the commonness or sensitivity of species with imperiled species being rarer or more impacted by patch size 233

Figure 5-1. The southwest United States (A, box) where pitfall sampling occurred is one of the world’s biodiversity hotspots. Red dots indicate pitfall arrays (i.e., sites) spanning six counties in southern California (B). Species ranges differ in their extent that overlaps with the pitfall sampling area and the long term (30-year) total winter precipitation and mean summer maximum temperatures experienced (C). Levelplots show the distribution of climate values across the latitude and longitude of species ranges. 234

Figure 5-2. Data collection occurred through pitfall array sampling [each array consisting of three arms, seven pitfall traps (PFT), and three funnel traps (FT), A]. Every array (i.e., site i) was nested within a patch separated from other habitat by roads or other development (i.e., patch j).

Multiple arrays in each patch allowed us to sample and estimate patch () and site () occupancy probabilities as part of a hierarchical nested multispecies occupancy model (B).

Species that occur at sites are a subset of those that occur at the overall patch. We predicted that variation in species responses to patch size could be associated with taxonomic group (C), where mammalian species might be most robust to changes in patch size based on reproductive and endothermic evolutionary strategies. Species life history traits within these taxonomic groups might explain additional variation in occupancy, e.g., species with larger body sizes might be more sensitive to the effects of fragmentation (D). Responses to fragmentation might also vary within species, e.g., species might be more sensitive to fragmentation at patches in hotter areas of their range (E). We examined patterns in two variables: relative commonness across the landscape (intercept) as well as sensitivity to patch size (slope) for individual species (C). We measured these variables at two scales: the probability of occupancy at the patch and, given the patch is occupied, the proportion of sites within a patch at which a species can be found (F). 235

236

Figure 5-3. Species commonness and sensitivity at the patch (A and C) and site (B and D) levels by taxonomic group. We transformed commonness values from the logit to probability scale to show the occupancy of species at the average patch and site within a patch. Mean taxonomic group values are bolded and indicate that mammals were most common in patches (A) and sites (B) while all groups are more sensitive to patch size at the patch (C) rather than site (D) levels. However, species-specific values vary within taxonomic group, and species that were sensitive to patch size (as judged by whether the 95% credible interval overlapped 0) are highlighted in grey. Species of conservation concern denoted as EN = endangered, NT = near threatened, or California species of special concern (SSC, asterisk). 237

238

Figure 5-4. Scatterplots showing (A) a correlation between how common a species is at the patch and at sites within a patch, (B) no correlation between species sensitivity to patch size at the patch or sites within a patch, (C) a weak negative relationship between how common and sensitive to patch size a species is at the patch, and (D) no correlation between how common and sensitive to patch size a species is at the site. Shaded area is 95% credible region.

239

Figure 5-5. Patch and site occupancy probabilities across patch sizes for example species from each taxonomic group (amphibians = teal, A-D; mammals = yellow, E-H; reptiles = purple, I-L). Probabilities are transformed from the logit to probability scale and plotted against loge standardized patch sizes (though unstandardized sizes are shown on the x-axis to highlight the spectrum of patch sizes). Species generally responded positively to increasing patch size at the patch level but had the opposite response at the site level.

Some species have very clear plateaus in occupancy probabilities once a certain patch size is reached (e.g., E and F) while others increase only as some of the largest patch sizes are reached (e.g., A). Solid lines are posterior means and shaded areas are 95% credible regions. 240

241

Figure 5-6. Estimated covariate effect sizes from models for A) body size (cm), B) fecundity

(average young per year), C) age at sexual maturity (years), and D) range size (ha). Most covariates did not significantly influence species commonness and sensitivity at either the patch or site levels (as judged by whether 95% credible intervals overlapped 0). However, species sensitivity to patch size was greater for more fecund species at the patch level (B) and species with larger range sizes were more common at both the patch and site levels (D).

242

Figure 5-7. Effect sizes for climate model coefficients estimating the effects of maximum summer temperature (ºC) (A) and total winter precipitation (mm) (B) by taxonomic group by patch size on patch and site commonness and sensitivity. Significance was judged by whether

95% credible intervals overlap 0. Effect sizes for commonness above 0 indicate the taxonomic group occurred more frequently in patches in hotter areas of the range. Effects sizes for sensitivity above 0 indicate the taxonomic group was less sensitive in larger patches in hotter areas of the range while being more sensitive in smaller patches. 243

Appendices

Appendix 1. Sites (designated by array name and number) used in multispecies occupancy models. We identified 97 unique patches, which could contain multiple arrays from the same naming scheme or across naming schemes (e.g., Agua Chinon 1-7 and Limestone Canyon 18-19 were both in the same 4533.42 ha patch). SAMO stands for studies conducted in the Santa Monica Mountains. Patch age could be less than ten, ten to thirty, or greater than thirty years since fragmentation. Sampling period indicates to which the data were truncated for the analysis.

Array Patch Age Array Name Sampling Period Patch Size (ha) Latitude Longitude No. (years) Agua Chinon 1-7 1999-2003 4533.42 >30 33.70 -117.69 Aliso - Wood Canyon 1-17 2000-2003 2137.63 >30 33.55 -117.74 Camp Pendleton 1-2, 7-10 1996-2001 7448.58 >30 33.35 -117.47 Camp Pendleton 3-4 1996-2001 295.92 >30 33.24 -117.42 Camp Pendleton 5-6 1996-2001 691.2 >30 33.33 -117.49 Camp Pendleton 11-12 1996-2001 1718.28 >30 33.39 -117.56 Camp Pendleton 13-15 1996-2001 81402.48 >30 33.42 -117.39 Camp Pendleton 16-20 1996-2001 7111.71 >30 33.34 -117.37 Camp Pendleton 21-28 1996-1999 81402.48 >30 33.42 -117.39 Camp Pendleton 29-30 1996-1999 755.24 >30 33.41 -117.29 Camp Pendleton 31-32 2004-2009 72028.13 >30 33.34 -117.32 Carmel Mountain 1-5 2001-2002 216.71 <10 32.93 -117.22 Chino Hills 1-3, 7-16 1998-2002 10228.14 >30 33.91 -117.74 Chino Hills 4-6 1998-2003 10228.14 >30 33.91 -117.74 Chino Hills 17-19 1998-2002 4790.43 >30 33.94 -117.82 Chino Hills 20, 22-25 2001-2003 60901.85 >30 33.86 -117.69 Chino Hills 21, 28-32 2001-2003 10228.14 >30 33.91 -117.74 Chino Hills 26-27 2001-2003 226.62 >30 33.87 -117.67 Chino Hills 33-34 2002-2003 226.62 >30 33.87 -117.67 Chula Vista I 1-7 1995-2000 42.89 >30 32.65 -117.04 Chula Vista II 1 1995-2000 0.42 <10 32.66 -116.98 Chula Vista II 2-5 1995-2000 10805.28 >30 32.66 -116.98 Chula Vista II 6-9 1995-2000 55.64 <10 32.65 -116.99 244

Debs Park 1-7 2000, 2003 131.53 >30 34.10 -118.19 Del Mar Mesa 1-5 2001-2002 1950.87 <10 32.94 -117.17 Edison Easement 1-5 1998-2002 189.23 <10 33.67 -117.64 Elliott Reserve 1-17 1995-2000 11961.72 >30 32.89 -117.10 Hollenbeck Canyon 1-7 2003-2004 4811.76 >30 32.68 -116.82 Wildlife Area Hollenbeck Canyon 9-10 2003-2004 50458.60 >30 32.67 -116.80 Wildlife Area La Cresta 1-4 1999-2002 2176.42 >30 32.84 -116.87 Lake Perris State Rec Area, 1-10 1995-2000 761.87 >30 33.87 -117.19 San Jacinto Wildlife Area Lake Perris State Rec Area, 11-17 1995-2000 5320.02 >30 33.87 -117.14 San Jacinto Wildlife Area Lake Skinner 1-17 1995-2000 13402.67 >30 33.59 -117.03 Limestone Canyon 1-17 1995-2000 7346.70 >30 33.73 -117.67 Limestone Canyon 18-19 1999-2002 4533.42 >30 33.72 -117.69 Little Cedar Ridge 1-9 1995-2000 24388.15 >30 32.62 -116.86 Marron Valley 1-9 1995-2000 24388.15 >30 32.61 -116.77 Mission Trails 1-5 1999-2002 775.34 >30 32.82 -117.04 Motte Rimrock Reserve 1-10 1995-2000 1259.46 >30 33.81 -117.26 North Hills 1-10 1995-2000 13402.67 >30 33.70 -117.01 Orange Hills 1-5 1998-2001 59.53 <10 33.80 -117.79 Peter's Canyon 1-5 1998-2002 309.17 <10 33.77 -117.77 Point Loma 1, 10-12 1995-2000 59.67 >30 32.70 -117.24 3-4, 13- Point Loma 1995-2000 78.03 >30 32.67 -117.24 17 Point Loma 2 1995-2000 9.37 >30 32.67 -117.24 Point Loma 5-7 1995-2000 56.27 >30 32.70 -117.24 Point Loma 8-9 1995-2000 93.41 >30 32.70 -117.25 Pregill/Japatul 1-3 1995-1996 13448.37 >30 32.76 -116.69 Puente Hills 1-4 1998-2000, 2002 4790.43 >30 33.93 -117.87 Puente Hills 5-7 1998-2000, 2002 66.6 <10 33.98 -117.84 Puente Hills 8-11 1998-2000, 2002 631.38 10-30 33.97 -117.93 245

Puente Hills 12-19 1998-2000, 2002 870.45 >30 34.00 -118.04 1-4, 6-7, 2001-2002, Rancho Jamul 9-16, 19, 10400.79 >30 32.69 -116.86 2005-2008 21 5, 8, 17- Rancho Jamul 2001-2002 10400.79 >30 32.69 -116.86 18 Rancho Jamul 20 2001-2002, 2005 10400.79 >30 32.69 -116.86 Rancho Jamul 22 2005-2010 9746.00 >30 32.66 -116.87 Rattlesnake Reservoir 1-5 1998-2001 1882.91 >30 33.73 -117.73 Rawson Canyon 1-10 1995-2000 13402.67 >30 33.63 -117.01 SAMO – Simi Region 1 1 2000-2005 19.62 >30 34.19 -118.88 SAMO – Simi Region 1 2 2000-2005 38.92 >30 34.19 -118.89 SAMO – Simi Region 1 3 2000-2005 10.17 >30 34.20 -118.87 SAMO – Simi Region 1 4-6 2000-2005 2610.53 >30 34.22 -118.92 SAMO – Simi Region 1 7 2000-2005 23.58 >30 34.22 -118.89 SAMO – Simi Region 1 8 2000-2005 43.22 >30 34.22 -118.85 SAMO – Simi Region 1 9 2000-2005 9.41 10-30 34.21 -118.83 SAMO – Simi Region 1 10 2000-2005 21.30 >30 34.20 -118.85 SAMO – Simi Region 2 11-13 2000-2004 478.01 <10 34.19 -118.83 SAMO – Simi Region 2 14-16 2000-2004 413.18 10-30 34.18 -118.80 SAMO – Simi Region 2 17-19 2000-2004 293.60 10-30 34.17 -118.78 SAMO – Simi Region 2 20-22 2000-2004 13668.28 >30 34.18 -118.74 SAMO – Western Region 1 23, 27-32 2002-2007 12436.20 >30 34.12 -119.02 SAMO – Western Region 1 24-26 2002-2008 12436.20 >30 34.12 -119.02 SAMO – Western Region 2 33-41 2002-2007 12436.20 >30 34.12 -119.02 SAMO – Malibu Region 1 42-43 2005-2010 294.72 >30 34.14 -118.85 SAMO – Malibu Region 1 44-51 2005-2010 6156.72 >30 34.10 -118.78 SAMO – Malibu Region 2 52 2005-2010 428.49 >30 34.12 -118.75 53-54, SAMO – Malibu Region 2 2005-2010 1266.12 >30 34.12 -118.72 56-61 SAMO – Malibu Region 2 55 2005-2010 440.37 >30 34.10 -118.70 SAMO – Simi West 62-63 2012-2014 2569.62 >30 34.21 -118.90 SAMO – Simi West 64 2012-2014 68.07 10-30 34.17 -118.81 246

SAMO – Simi Mid 65-71, 73 2012-2014 13540.46 >30 34.18 -118.72 SAMO – Simi Mid 72 2012-2014 15.96 >30 34.15 -118.73 SAMO – Simi East 74-79 2012-2014 13540.46 >30 34.18 -118.72 SAMO – Simi East 80-81 2012-2015 13540.46 >30 34.18 -118.72 SAMO – Mugu North 82 2012-2015 48.65 10-30 34.18 -118.96 SAMO – Mugu North 83 2012-2015 1944.64 >30 34.17 -118.99 SAMO – Mugu North 84-91 2012-2015 12104.45 >30 34.12 -119 SAMO – Mugu South 92-96 2012-2015 12104.45 >30 34.12 -119 SAMO – Westlake 97-98 2012-2015 12104.45 >30 34.12 -119 SAMO – Westlake 100 2012-2015 479.07 >30 34.09 -118.87 SAMO – Westlake 99, 101 2012-2015 2473.83 >30 34.08 -118.94 SAMO – Westlake 102 2012-2015 2057.24 >30 34.16 -118.88 SAMO – Westlake 103 2012-2015 5.84 >30 34.04 -118.87 SAMO – Westlake 104 2012-2015 3594.33 >30 34.03 -118.80 SAMO – Malibu 105 2012-2015 2337.94 >30 34.10 -118.81 SAMO – Malibu 106-109 2012-2015 6118.68 >30 34.07 -118.75 SAMO – Malibu 110 2012-2015 57.51 >30 34.03 -118.71 SAMO – Malibu 111-112 2012-2015 1245.22 >30 34.12 -118.72 SAMO – Topanga 113, 115 2012-2015 1945.26 >30 34.09 -118.64 114, 116- SAMO – Topanga 2012-2015 8684.82 >30 34.10 -118.58 120 SAMO – Topanga 121 2012-2014 8684.82 >30 34.10 -118.58 San Diego National Wildlife 1-10 1995-2000 10805.28 >30 32.72 -116.95 Refuge San Diego National Wildlife 11-14 2008-2010 9746.00 >30 32.72 -116.90 Refuge San Joaquin Hills West 1-21 2000-2003 2913.69 <10 33.58 -117.79 Santa Margarita Ecological 1-5 1996-2000 6165.69 >30 33.44 -117.18 Reserve Santa Margarita Ecological 6-15 1999-2000 6165.69 >30 33.44 -117.18 Reserve Santa Ysabel Ecological 1, 3-6 2002-2003 54814.79 >30 33.12 -116.71 Reserve 247

Santa Ysabel Ecological 2002-2003, 2, 7-8 54814.79 >30 33.12 -116.71 Reserve 2005-2008 Santa Ysabel Ecological 9-15, 17- 2002-2003, 19608.00 >30 33.12 -116.64 Reserve 22, 24 2005-2008 Santa Ysabel Ecological 16, 23 2002-2003 19608.00 >30 33.12 -116.64 Reserve Santa Ysabel Ecological 25-31 2005-2008 19608.00 >30 33.12 -116.64 Reserve Santa Ysabel Ecological 32-33 2008-2012 54814.79 >30 33.12 -116.71 Reserve Spring Canyon 1-7 1999-2003 891.76 >30 32.56 -117.00 Starr Ranch 1-17 1995-2000 63115.92 >30 33.61 -117.55 Tenaja Corridor 1-40 1999-2000 81402.48 >30 33.51 -117.33 Tijuana Estuary 1-11 1997-2002 1088.91 >30 32.54 -117.12 Tijuana Estuary 12-15 1997-2002 151.80 >30 32.56 -117.13 1-2, 4-5, Torrey Pines 1 1995-1998 288.85 >30 32.91 -117.25 8-9 Torrey Pines 1 3, 6-7, 10 1995-2000 288.85 >30 32.91 -117.25 1, 4-5, 7- Torrey Pines 2 1995-1998 79.45 >30 32.94 -117.25 8 2-3, 6, 9- Torrey Pines 2 1995-2000 79.45 >30 32.94 -117.25 10 Torrey Pines 3 1-3, 9-10 1995-1998 288.85 >30 32.92 -117.26 Torrey Pines 3 4-8 1995-2000 288.85 >30 32.92 -117.26 Torrey Pines 3 11, 13 1995-1998 288.39 >30 32.93 -117.25 Torrey Pines 3 12, 14-15 1995-2000 288.39 >30 32.93 -117.25 University of California, 1-5 1996-2001 238.21 >30 33.64 -117.85 Irvine Unocal 1-3 1998-2002 87.69 <10 33.89 -117.90 Weir Canyon 1-12 1998-2002 2134.19 >30 33.82 -117.74 Wild Animal Park 1-20 1995-2000 54814.79 >30 33.10 -116.98

248

Appendix 2. Species taxonomy, life history, and range information used for analyses with main citations. Amphibia (A), Mammalia (M), and Reptilia (R) were taxonomic groups. Species can be a habitat specialist (Yes), a moderate specialist (Mod), or not show any specialization (No). IUCN status can be least concern (LC), near threatened (NT), or endangered (EN). California Department of Wildlife (CDW) status can be either species of state concern (SSC) or no state listing (No). U.S. Fish and Wildlife (USFWS) status can endangered (EN) or no status (No). Literature cited includes life history resources as well as range maps and modifications used for delineation of species’ ranges. Yrs to Sexual Fecundity/Yr IUCN CDW Status Range Size Body Size Body Size Literature No. Obs. Maturity USFWS USFWS Group Status Status Cited (cm) Species (ha)

Arboreal 31 A 15.5 3.0 8.1 LC No No 19926573.83 Staub & Wake 2005, NatureServe Salamander 2017, Lee et al. 2012, SDNHM 2014 (Aneides lugubris) Arroyo Toad 26 A 4177.5 2.5 6.6 EN SS EN 6704570.70 Holland and Sisk 2001, Hammerson C & Santos-Barrera 2004, Sweet & (Anaxyrus Sullivan 2005, SDNHM 2014, californicus) NatureServe 2017, Gallegos pers. comm. Baja California 476 A 575 0.5 3.45 LC No No 292701578.20 Schaub & Larsen 1978, Hammerson Treefrog & Santos-Barrera 2004, Rorabough and Lannoo 2005, SDNHM 2014, (Pseudacris NatureServe 2017 hypochondriaca) Blainville's 593 R 18 2.5 8.0 LC SS No 21293280.81 Hollingsworth & Hammerson 2007, Horned Lizard C Stebbins & McGinnis 2012, SDNHM 2014, Hult & Germano (Phrynosoma 2015, NatureServe 2017 blainvillii) 249

Botta's Pocket 1132 M 12 0.5 14.3 LC No No 228606795.10 Daly & Patton 1986, Jones & Baxter Gopher 6 2004, IUCN 2012, NatureServe 2017, Tremor 2017 (Thomomys bottae) Broad-Footed 6 M 4 0.8 12.0 LC No No 53464195.45 IUCN 2012, Cassola 2016, Matson et Mole 9 al. 2016, NatureServe 2017, Tremor 2017 (Scapanus latimanus) Brush/Cactus/Cali 3852 M 16 0.07 8.82 LC No No 2875627929.0 IUCN 2012, Álvarez-Castañeda & fornia/ North 0 Lacher 2016, Cassola 2016, Lacher American Mouse et al. 2016, Duggan 2017, NatureServe 2017 (Peromyscus boylii/eremicus/ca lifornicus/manicul atus) California Black- 119 R 2 1.5 25.8 LC No No 17465640.95 Ernst & Ernst 2003, Hollingsworth et Headed Snake 5 al. 2007, IUCN 2012, Stebbins & McGinnis 2012, NatureServe 2017 (Tantilla planiceps) California Glossy 1 R 8 3.5 81.5 LC SS No 14304392.75 Hammerson et al. 2007, Stebbins Snake C 2003, Stebbins & McGinnis 2012, (Arizona elegans NatureServe 2017 occidentalis) California 497 R 9 3.5 146 LC No No 157438860.00 Ernst & Ernst 2003, Hammerson et Kingsnake al. 2007, Stebbins & McGinnis 2012, 250

(Lampropeltis SDNHM 2014, NatureServe 2017 californiae) California 4 R 13.5 3.5 83.5 LC No No 28303138.74 Stebbins & McGinnis 2012, T.J. Lyresnake LaDuc pers. comm., SDNHM 2014, NatureServe 2017 (Trimorphodon lyrophanes) California 6 R 6 3.5 87.0 LC No No 27818244.72 Hollingsworth and Hammerson Mountain 2007, Ernst & Ernst 2003, IUCN Kingsnake 2012, Stebbins & McGinnis 2012, NatureServe 201, used fecundity L. (Lampropeltis californiae zonata) California Vole 2495 M 25 0.09 12.3 LC No No 45591694.55 IUCN 2012, NatureServe 2017, 9 Mellink et al. 2017 (Microtus californicus) California 979 R 8.5 3 114. LC No No 35753426.57 Mitrovich 2006, Hollingsworth et al. Whipsnake 0 2007, Stebbins & McGinnis 2012, SDNHM 2014, NatureServe 2017, (Masticophis Mitrovich et al. 2018 lateralis) Desert gray/ornate 3765 M 8 0.5 5.82 LC No No 313493437.90 IUCN 2012, Laakkonen 2017, shrew NatureServe 2017 (Notiosorex crawfordi/Sorex ornatus) Gilbert's Skink 148 R 6 2.5 8.85 LC No No 33049653.47 Hollingsworth & Hammerson 2007, Stebbins & McGinnis 2012, 251

(Plestiodon SDNHM 2014, NatureServe 2017 gilberti) Gopher Snake 482 R 13 4 177. LC No No 886582340.70 Rodríguez-Robles 2003, Hammerson 5 2007, IUCN 2012, Stebbins & (Pituophis McGinnis 2012, NatureServe 2017 catenifer) Granite Night 25 R 2 2.5 6.0 LC SS No 3716190.40 Hollingsworth & Hammerson 2007, Lizard C IUCN 2012, Stebbins & McGinnis 2012, NatureServe 2017 (Xantusia henshawi) Granite Spiny 262 R 10.5 2.0 9.95 LC No No 13580433.00 Mayhew 1963, Hollingsworth & Lizard Hammerson 2007, IUCN 2012, Stebbins & McGinnis 2012, (Sceloporus NatureServe 2017 orcutti) Long-Nosed 69 R 11 3.5 101. LC No No 331257038.00 Stebbins 2003 modified by R.N. Snake 5 Fisher, pers. comm., Hammerson et al. 2007, Stebbins & McGinnis 2012, (Rhinocheilus NatureServe 2017 lecontei) Monterey/Large- 97 A 14 3.5 6.85 LC SS No 54921836.05 Staub et al. 1995 (E. eschscholtzii Blotched Ensatina C platensi), Kuchta et al. 2005, SDNHM 2014, IUCN SSC (Ensatina Amphibian Specialist Group 2015, eschscholtzii NatureServe 2017 eschscholtzii/ klauberi) Night Snake 70 R 5 1.0 45.0 LC No No 16031687.47 Diller &Wallace 1986 (H. torquata deserticola), SDNHM 2014 modified 252

(Hypsiglena by R.N. Fisher, Ernst & Ernst 2003, ochroryhncha Hammerson et al. 2007, NatureServe klauberi) 2017 Orange-throated 3020 R 3.75 1.0 6.05 LC SS No 15877107.20 Hollingsworth & Hammerson 2007, Whiptail C IUCN 2012, Stebbins & McGinnis (Aspidoscelis 2012, NatureServe 2017 hyperythra) Pocket Mouse 1249 M 8.75 0.5 6.96 LC SS EN/ 95010957.84 IUCN 2012, Linzey et al. 2016, (Chaetodipus C No Miller et al. 2017, NatureServe 2017, californicus/fallax Cassola 2016, Tremor 2017 /longimembris) Red/Baja 249 R 12 3.0 114. LC No No 101756877.50 Hammerson et al. 2007, Stebbins & Coachwhip 5 McGinnis 2012, NatureServe 2017 (Masticophis flagellum/Coluber fuliginosus) Red Diamond 61 R 9 3.0 120. LC SS No 20389766.37 Hammerson et al. 2007, Dugan et al. Rattlesnake 5 C 2008, Stebbins & McGinnis 2012, SDNHM 2014, NatureServe 2017 (Crotalus ruber) Ringneck Snake 199 R 6 2.0 41.9 LC No No 854981442.80 Fitch 1975, Hammerson & Frost (Diadophis 2007, IUCN 2012, Stebbins & punctatus) McGinnis 2012, NatureServe 2017 Rosy Boa 19 R 5 2.5 77.5 LC No No 23679104.71 Rossi & Rossi 1995, Hammerson et al. 2007, Stebbins & McGinnis 2012, (Lichanura NatureServe 2017, IUCN 2012 roseofusca) modified by D. Woods, pers. comm. 253

San Diegan Tiger 1860 R 9 1.5 9.35 LC SS No 10635678.23 Stebbins 2003, Hammerson et al. Whiptail C 2007, Stebbins & McGinnis 2012, (Aspidoscelis NatureServe 2017 tigris stejnegeri) Side-Blotched 3463 R 13 0.5 5.05 LC No No 323722267.90 Tanner & Hopkins 1972, Lizard Hammerson et al. 2007, Stebbins & McGinnis 2012, SDNHM 2014, (Uta NatureServe 2017 stansburiana) Slender 1292 A 12.5 3.0 4.75 LC No No 9626890.00 Hammerson 2004, Parra-Olea et al. Salamander 2008, IUCN 2012, Stebbins & (Batrachoseps McGinnis 2012, NatureServe 2017 nigriventris/major major) Southern Alligator 2740 R 25 1.0 12.5 LC No No 58928235.34 Hammerson and Hollingsworth Lizard 5 2007, Stebbins & McGinnis 2012, SDNHM 2014, NatureServe 2017 (Elgaria multicarinata) Southern 41 R 2.5 2.0 14.4 LC No No 6365455.08 Kuhnz et al. 2005, Hollingsworth & California Legless 5 Hammerson 2007, Stebbins & Lizard McGinnis 2012, Papenfuss & Parham 2013, SDNHM 2014, (Anniella NatureServe 2017 stebbinsi) Southern Pacific 273 R 8 4.5 81.3 LC No No 13916976.36 Hammerson et al. 2007, Parker & Rattlesnake Anderson 2007 (C. oreganus concolor), Dugan et al. 2008, (Crotalus Stebbins & McGinnis 2012, SDNHM 2014 modified by Davis et 254 oreganus helleri) al. 2016, NatureServe 2017

Southwestern 8 R 6 2.5 95 LC No No 35017250.46 Ernst & Ernst 2003, Stebbins 2003, Speckled Frost et al. 2007, Glaudus & Rattlesnake Rodríguez-Robles 2011, Stebbins & McGinnis 2012, NatureServe 2017 (Crotalus mitchellii pyrrhus) Two-Striped 69 R 15.6 2.5 81.5 LC SS No 16149862.93 Rathbun et al. 1993, Ernst & Ernst Garter Snake C 2003 (T. elegans for sexual maturation), Frost et al. 2007, IUCN (Thamnophis 2012, Stebbins & McGinnis 2012, hammondii) NatureServe 2017 San Diego 106 R 4 1.0 6.75 LC SS No 11463042.01 Parker 1972, Stebbins 2003, Banded Gecko C Hammerson et al. 2007, Stebbins & McGinnis 2012, NatureServe 2017 (Coleonyx variegatus abbotti) Western Fence 8163 R 16 1.5 7.3 LC No No 174950359.60 Tanner & Hopkins 1972, Lizard Hollingsworth & Hammerson 2007, IUCN 2012, Stebbins & McGinnis (Sceloporus 2012, NatureServe 2017 occidentalis) Western Harvest 4466 M 38.3 0.5 6.71 LC No No 837777114.00 IUCN 2012, Cassola 2016, Mouse NatureServe 2017, Tremor 2017 (Reithrodontomys megalotis) 255

Coast Patched- 86 R 8 2.5 84.0 LC SS No 8970325.95 Stebbins 2003, Hammerson 2007, Nosed Snake C Stebbins & McGinnis 2012, Thomson et al. 2016, NatureServe (Salvadora 2017 hexalepis virgultea) Western Skink 3107 R 4 2.5 7.0 LC SS No 133951674.60 Hammerson & Hollingsworth 2007, C IUCN 2012 modified by J. (Plestiodon Richmond, Stebbins & McGinnis skiltonianus) 2012, NatureServe 2017 Western 190 A 200 1.5 5.05 NT SS No 13240800.18 Creusere and Whitford 1976, Santos- Spadefoot C Barrera et al. 2004, Morey 2005, SDNHM 2014, NatureServe 2017 (Spea hammondii) Western 225 R 4 1.5 29.5 LC No No 160632928.70 Hammerson et al. 2007, Stebbins and Threadsnake McGinnis 2012, SDNHM 2014, NatureServe 2017 (Rena humilis) Western Toad 1007 A 5200 5.0 8.9 NT No No 629871642.40 Muths and Nanjappa 2005, SDNHM (Anaxyrus boreas) 2014, IUCN SSC Amphibian Specialist Group 2015, NatureServe 2017 Yellow-bellied 128 R 7 3.0 120. LC No No 198919253.70 Stebbins 2003 using red and Baja Racer 5 racer, Hammerson et al. 2013, (Coluber NatureServe 2017 constrictor mormon)

256

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268

Appendix 3. Beta coefficients for all models. Standard deviation (SD) and 2.5 and 97.5th quartile values provided.

Parameter Mean SD Q2.5 Q97.5

Model: Taxonomic Group

Patch Level

Amphibia 0.09 0.60 -1.11 1.28

Mammalia 2.63 0.64 1.41 3.93

Reptilia 0.78 0.31 0.18 1.39

Amphibia*Patch Size 1.22 0.27 0.69 1.76

Mammalia*Patch Size 0.83 0.28 0.29 1.38

Reptilia*Patch Size 0.63 0.14 0.35 0.89

Site Level

Amphibia -0.07 0.53 -1.11 1.08

Mammalia 1.93 0.54 0.86 2.98

Reptilia 0.27 0.28 -0.27 0.80

Amphibia*Patch Size -0.49 0.21 -0.90 -0.06

Mammalia*Patch Size -0.10 0.21 -0.51 0.30 269

Parameter Mean SD Q2.5 Q97.5

Reptilia*Patch Size -0.19 0.12 -0.43 0.03

Model: Body Size

Patch Level

Body Size -0.25 0.29 -0.82 0.31

Body Size*Patch Size 3.20e-3 0.13 -0.25 0.26

Amphibia -0.10 0.64 -1.36 1.13

Mammalia 2.45 0.71 1.09 3.89

Reptilia 0.84 0.31 0.22 1.45

Amphibia*Patch Size 1.23 0.30 0.65 1.84

Mammalia*Patch Size 0.84 0.29 0.28 1.41

Reptilia*Patch Size 0.62 0.15 0.32 0.91

Site Level

Body Size 0.01 0.22 -0.40 0.44

Body Size*Patch Size 0.03 0.10 -0.17 0.24

Amphibia -0.10 0.49 -1.12 0.83 270

Parameter Mean SD Q2.5 Q97.5

Mammalia 1.91 0.54 0.85 3.04

Reptilia 0.38 0.25 -0.10 0.88

Amphibia*Patch Size -0.46 0.24 -0.94 -2.68e-3

Mammalia*Patch Size -0.08 0.21 -0.50 0.34

Reptilia*Patch Size -0.19 0.12 -0.44 0.03

Model: Fecundity

Patch Level

Fecundity 0.04 0.41 -0.74 0.87

Fecundity*Patch Size 0.33 0.16 0.02 0.66

Amphibia 0.03 0.88 -1.65 1.80

Mammalia 2.57 0.68 1.22 3.94

Reptilia 0.80 0.35 0.10 1.46

Amphibia*Patch Size 0.74 0.36 0.02 1.45

Mammalia*Patch Size 0.80 0.26 0.28 1.32

Reptilia*Patch Size 0.75 0.14 0.46 1.03 271

Parameter Mean SD Q2.5 Q97.5

Site Level

Fecundity 0.24 0.29 -0.31 0.79

Fecundity*Patch Size 0.18 0.13 -0.06 0.45

Amphibia -0.61 0.62 -1.81 0.75

Mammalia 1.85 0.50 0.68 2.74

Reptilia 0.36 0.26 -0.15 0.85

Amphibia*Patch Size -0.75 0.31 -1.38 -0.19

Mammalia*Patch Size -0.12 0.20 -0.55 0.26

Reptilia*Patch Size -0.12 0.12 -0.35 0.12

Model: Range Size

Patch Level

Range Size 0.57 0.27 0.04 1.13

Range Size*Patch Size 0.09 0.13 -0.15 0.34

Amphibia 0.23 0.60 -0.96 1.39

Mammalia 1.99 0.71 0.57 3.40 272

Parameter Mean SD Q2.5 Q97.5

Reptilia 0.89 0.31 0.30 1.51

Amphibia*Patch Size 1.24 0.28 0.71 1.80

Mammalia*Patch Size 0.73 0.32 0.11 1.37

Reptilia*Patch Size 0.62 0.14 0.34 0.89

Site Level

Range Size 0.58 0.20 0.20 1.00

Range Size*Patch Size 0.03 0.10 -0.18 0.22

Amphibia -0.05 0.45 -1.02 0.77

Mammalia 1.36 0.50 0.35 2.32

Reptilia 0.35 0.22 -0.07 0.77

Amphibia*Patch Size -0.47 0.22 -0.90 -0.04

Mammalia*Patch Size -0.13 0.24 -0.62 0.34

Reptilia*Patch Size -0.18 0.11 -0.40 0.05

Model: Sexual Maturity

Patch Level 273

Parameter Mean SD Q2.5 Q97.5

Sexual Maturity -0.33 0.30 -0.92 0.25

Sexual Maturity*Patch Size 4.54e-3 0.14 -0.27 0.27

Amphibia 0.33 0.62 -0.87 1.60

Mammalia 2.15 0.78 0.62 3.69

Reptilia 0.84 0.32 0.19 1.49

Amphibia*Patch Size 1.22 0.29 0.65 1.81

Mammalia*Patch Size 0.85 0.35 0.16 1.54

Reptilia*Patch Size 0.61 0.14 0.32 0.89

Site Level

Sexual Maturity -0.26 0.23 -0.70 0.24

Sexual Maturity*Patch Size 0.14 0.11 -0.09 0.35

Amphibia -3.97e-3 0.50 -0.98 0.97

Mammalia 1.55 0.59 0.43 2.85

Reptilia 0.34 0.25 -0.16 0.79

Amphibia*Patch Size -0.54 0.23 -1.01 -0.09 274

Parameter Mean SD Q2.5 Q97.5

Mammalia*Patch Size 0.11 0.28 -0.45 0.66

Reptilia*Patch Size -0.18 0.11 -0.40 0.04

Model: Maximum Summer Temperature (Hot vs. Core)

Patch Level

Heat - Amphibia 0.72 0.64 -0.33 2.22

Heat - Mammalia 2.00 1.30 0.05 5.15

Heat - Reptilia 0.63 0.29 0.08 1.22

Heat*Patch Size - Amphibia 1.56 0.94 0.16 3.86

Heat*Patch Size - Mammalia 2.98 1.52 0.59 6.50

Heat*Patch Size - Reptilia 0.65 0.35 -0.05 1.32

Amphibia -0.04 0.62 -1.26 1.15

Mammalia 2.55 0.68 1.26 3.92

Reptilia 0.66 0.33 0.02 1.30

Amphibia*Patch Size 1.03 0.28 0.49 1.59

Mammalia*Patch Size 0.74 0.28 0.20 1.30 275

Parameter Mean SD Q2.5 Q97.5

Reptilia*Patch Size 0.56 0.14 0.26 0.84

Site Level

Heat - Amphibia 0.56 0.27 0.05 1.10

Heat - Mammalia 0.61 0.29 0.08 1.24

Heat - Reptilia 0.38 0.14 0.12 0.64

Heat*Patch Size - Amphibia -0.88 0.23 -1.33 -0.45

Heat*Patch Size - Mammalia 0.46 0.27 -0.09 0.98

Heat*Patch Size - Reptilia 0.11 0.12 -0.12 0.33

Amphibia -0.27 0.49 -1.31 0.61

Mammalia 1.73 0.50 0.75 2.71

Reptilia 0.19 0.26 -0.35 0.69

Amphibia*Patch Size -0.25 0.22 -0.71 0.17

Mammalia*Patch Size -0.24 0.22 -0.68 0.18

Reptilia*Patch Size -0.24 0.12 -0.49 -2.72e-3

Model: Total Winter Precipitation (Dry vs. Core) 276

Parameter Mean SD Q2.5 Q97.5

Patch Level

Dry - Amphibia 0.33 0.77 -0.90 2.10

Dry - Mammalia 0.79 0.78 -0.42 2.61

Dry - Reptilia -0.37 0.25 -0.85 0.15

Dry*Patch Size - Amphibia 0.36 0.73 -0.91 1.95

Dry*Patch Size - Mammalia -0.66 0.88 -2.28 1.27

Dry*Patch Size - Reptilia -0.23 0.31 -0.80 0.40

Amphibia 0.08 0.67 -1.19 1.39

Mammalia 2.40 0.62 1.12 3.62

Reptilia 0.86 0.31 0.22 1.52

Amphibia*Patch Size 1.22 0.27 0.69 1.75

Mammalia*Patch Size 0.84 0.26 0.31 1.37

Reptilia*Patch Size 0.61 0.13 0.33 0.87

Site Level

Dry - Amphibia -1.28 0.24 -1.75 -0.81 277

Parameter Mean SD Q2.5 Q97.5

Dry - Mammalia 0.71 0.29 0.19 1.34

Dry - Reptilia 0.29 0.14 0.02 0.57

Dry*Patch Size - Amphibia 0.45 0.23 -1.51e-4 0.91

Dry*Patch Size - Mammalia 0.98 0.34 0.33 1.65

Dry*Patch Size - Reptilia 0.02 0.16 -0.31 0.34

Amphibia 0.10 0.46 -0.79 1.01

Mammalia 1.84 0.48 0.86 2.74

Reptilia 0.28 0.25 -0.23 0.72

Amphibia*Patch Size -0.61 0.21 -1.03 -0.21

Mammalia*Patch Size -0.13 0.22 -0.54 0.32

Reptilia*Patch Size -0.14 0.12 -0.39 0.10

Model: Conservation Status (Listed vs. None)

Patch Level

Listed - Amphibia -2.52 1.25 -5.09 -0.15

Listed - Mammalia -1.07 1.75 -4.48 2.49 278

Parameter Mean SD Q2.5 Q97.5

Listed - Reptilia -0.55 0.67 -1.83 0.80

Listed*Patch Size - 0.57 0.63 -0.72 1.76

Amphibia

Listed*Patch Size - 0.70 0.82 -0.77 2.46

Mammalia

Listed*Patch Size – 0.06 0.29 -0.52 0.65

Reptilia

Amphibia 1.41 0.97 -0.53 3.31

Mammalia 2.78 0.71 1.34 4.17

Reptilia 0.98 0.39 0.19 1.74

Amphibia*Patch Size 0.99 0.50 0.05 2.07

Mammalia*Patch Size 0.72 0.31 0.11 1.35

Reptilia*Patch Size 0.60 0.18 0.25 0.95

Site Level

Listed - Amphibia -0.15 0.96 -2.09 1.88 279

Parameter Mean SD Q2.5 Q97.5

Listed - Mammalia -0.99 1.31 -3.64 1.48

Listed - Reptilia -0.53 0.48 -1.51 0.32

Listed*Patch Size - 0.14 0.46 -0.81 1.01

Amphibia

Listed*Patch Size - 0.19 0.54 -0.88 1.27

Mammalia

Listed*Patch Size - Reptilia 0.15 0.23 -0.32 0.57

Amphibia 2.27e-03 0.68 -1.32 1.34

Mammalia 2.13 0.60 0.92 3.28

Reptilia 0.50 0.31 -0.06 1.16

Amphibia*Patch Size -0.60 0.33 -1.29 0.04

Mammalia*Patch Size -0.12 0.24 -0.59 0.35

Reptilia*Patch Size -0.24 0.15 -0.54 0.05

280

Appendix 4. Species commonness and sensitivity at the patch (A and C) and site (B and D) levels from the model including species range size (ha). We transformed commonness values from the logit to probability scale to show the occupancy of species at the average patch and site within a patch. No correlation existed between fecundity and species commonness at patches (A) or sites (B).

More fecund species (e.g., anurans and rodents) are more sensitive to patch size at the patch (C) but not the site (D) levels. Species that were sensitive to patch size (as judged by whether the credible interval overlapped 0) are highlighted in grey. Species of conservation concern denoted as EN = endangered, NT = near threatened, or California species of special concern (SSC, asterisk). 281

282

Appendix 5. Species commonness and sensitivity at the patch (A and C) and site (B and D) levels from the model including species range size (ha). We transformed commonness values from the logit to probability scale to show the occupancy of species at the average patch and site within a patch. Species with larger range sizes were more common at patches (A) and sites (B). Species with larger range sizes were not more sensitive to patch size at the patch (C) or the site (D) levels. Species that were sensitive to patch size

(as judged by whether the credible interval overlapped 0) are highlighted in grey. Species of conservation concern denoted as EN = endangered, NT = near threatened, or California species of special concern (SSC, asterisk). 283

VITA

Staci M. Amburgey

Staci was born in Colorado Springs, Colorado and double majored in biology and zoology for her Bachelor of Science degree at Colorado State University in Fort Collins in 2010.

During her undergraduate degree, she was mentored by Dr. Donald Mykles and Dr. Chris Funk.

She remained at Colorado State University in Dr. Chris Funk’s lab for her Master of Science degree in zoology, which she received in 2013. Her work focused on understanding the potential effects of climate-altered hydroperiod on pond breeding amphibians. After working as a student contractor for the U.S. Geological Survey at various times during her undergraduate and graduate degrees, she served as a data technician on a USGS Powell Center working group led by Drs. Erin Muths, David Miller, and Evan Grant after graduation. This transitioned into a PhD position with Dr. David Miller at Penn State University, studying demographic and occupancy modeling approaches to understand species range limits and distributions.