BOBCATS IN NEW HAMPSHIRE: UNDERSTANDING THE RELATIONSHIPS BETWEEN HABITAT SUITABILITY, CONNECTIVITY AND ABUNDANCE IN A CHANGING LANDSCAPE

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1 BOBCATS IN NEW HAMPSHIRE: UNDERSTANDING THE RELATIONSHIPS BETWEEN HABITAT SUITABILITY, CONNECTIVITY AND ABUNDANCE IN A CHANGING LANDSCAPE By GREGORY CABELL REED B.S., Montana State University 2009 Thesis Submitted to the University of New Hampshire in Partial Fulfillment of the Requirements for the Degree of Master of Science In Natural Resources: Wildlife Ecology December 2013

2 This thesis has been examined and approved. Thesis Director, Dr. John A. Litvaitis, Professor of Wildlife Ecology, University of New Hampshire Dr. Rebecca J. Rowe, Assistant Professor, University of New Hampshire Catherine Callahan, GIS Specialist, New Hampshire Fish and Game Department Date

3 ACKNOWLEDGMENTS Funding for this project was provided through NH Wildlife Restoration program grant W-97-R-1 in cooperation with the U.S. Fish and Wildlife Service, Wildlife and Sport Fish Restoration Program. iii

4 TABLE OF CONTENTS ACKNOWLEDGMENTS... iii LIST OF TABLES... vi LIST OF FIGURES... viii ABSTRACT... xi CHAPTER 1 INTRODUCTION...1 Objectives...5 CHAPTER 2 MODELING BOBCAT HABITAT SUITABILITY ACROSS NEW HAMPSHIRE: A HIERARCHICAL APPROACH...6 Methods...10 Study Areas...10 Factors Affecting Statewide Distribution...11 Habitat Features that Influence Selection within Home Range...14 Modeling Habitat Selection...15 Environmental Variables...15 Habitat Suitability Modeling...16 Model Extrapolation and Validation...18 Results...20 Factors Affecting Statewide Distribution...20 Habitat Features that Influence Selection within Home Range...23 Habitat Suitability Maps...25 Discussion...28 Incidental Sightings to Explore Distribution...28 Fine-Scale Habitat Selection...31 Implications of Multi-Scale Habitat Selection...33 CHAPTER 3 MODELING CONNECTIVITY FOR CARNIVORES IN A DEVELOPED LANDSCAPE: WHAT IS THE BEST APPROACH?...36 Modeling Connectivity...37 Evaluating Connectivity Models...39 Focal Species in Connectivity Models...41 Objectives...42 Methods...42 iv

5 Study Areas...42 Resistance Layers...43 Expert-Opinion...45 Resource Selection Probability Functions...47 Connectivity Modeling and Assessment...47 Circuit Theory...47 Brownian Bridge Movement Models...49 Extrapolation of Connectivity Models...51 Bobcats as Surrogates...52 Results...54 Resistance Layers...54 Connectivity Modeling and Assessment...56 Circuit Theory...56 Brownian Bridge Movement Models...57 Extrapolation of Connectivity Models...57 Bobcats as Surrogates...61 Discussion...64 Assessment of Connectivity Methods...64 Statewide Connectivity Models...68 Surrogate Species...70 Conclusions...71 CHAPTER 4 POPULATION ESTIMATE FOR BOBCATS IN NEW HAMPSHIRE BASED ON HOME-RANGE SIZE AND COMPOSITION...74 Methods...77 Home-range Estimation...77 Population Estimation...77 Results...79 Home-range Estimation...79 Population Estimate...81 Discussion...83 Potential Population Estimate...84 Implications of Home-range Size...86 Comparison to New England States...88 LITERATURE CITED...92 APPENDICIES APPENDIX A APPENDIX B APPENDIX C APPENDIX D APPENDIX E v

6 LIST OF TABLES TABLE PAGE 2-1 Habitat variables, justification for inclusion, data source, and resolution for GIS layers used to model bobcat habitat selection at second and third order Models for Second Order-Home Range used three variables, average monthly (Nov-March) maximum snow depth (Snow_Max), average monthly (Nov-March) mean snow depth (Snow_Mean), and elevation (Elevation). Sightings reported between 2007 and 2013 were used to model the relationship between probability of use and the habitat variables Parameter estimates from the best third-order habitat suitability model. Models were made utilizing GPS-location data from 18 collared bobcats in New Hampshire from Rank correlations (Spearman) and expected vs. observed regression for the best RSPF model. The model was validated with k-fold cross-validation utilizing 5 folds Variables used to model probability of detection and occupancy at camera sites along Highway 101 from January-April Temporal variables measured differences in deployment order and duration, and were used to model differences in the probability of detection. Probability of occupancy was modeled using habitat and modeling variables. Habitat variables were measured in the field during camera deployment. Modeling variables utilized connectivity scores, with low scores indicating poor connectivity and high scores indicating good connectivity Spearmen correlations between BBMM and circuit theory models for all bobcats and separate individuals, and all roads and major roads. Significant correlations are highlighted in bold, and indicate agreement between BBMM models and circuit theory models Gray fox occupancy model set. Model selection was based on AIC weight (AIC w ). The number of parameters (K), Akaike s Information Criterion adjusted for small sample sizes (AIC c ), and the difference in AIC c score ( AIC c ) are also reported. Ψ = the probability of occupancy, p = detection probability Fisher occupancy model set. Model selection was based on AIC weight (AIC w ). The number of parameters (K), Akaike s Information Criterion adjusted for small sample sizes (AIC c ), and the difference in AIC c score ( AIC c ) are also reported. Ψ = the probability of occupancy, p = detection probability...63 vi

7 TABLE PAGE 3-5 Coyote occupancy model set. Model selection was based on AIC weight (AIC w ). The number of parameters (K), Akaike s Information Criterion adjusted for small sample sizes (AIC c ), and the difference in AIC c score ( AIC c ) are also reported. Ψ = the probability of occupancy, p = detection probability Raccoon occupancy model set. Model selection was based on AIC weight (AIC w ). The number of parameters (K), Akaike s Information Criterion adjusted for small sample sizes (AIC c ), and the difference in AIC c score ( AIC c ) are also reported. Ψ = the probability of occupancy, p = detection probability Sex, age, weight, study area, number of usable locations, home range (95% utilization distribution) and core area (50% utilization distribution) of bobcats in the study. Bobcats from the southwest study area were captured and collared in , and bobcats in the southeast were captured and collared in Population estimates of bobcats in New Hampshire generated using home range requirements and habitat suitability. Total population estimate is the sum of resident adults and kittens. A survival rate from May/June to October/November of 0.85 (Knick 1990) was assumed for adults, and a survival rate of 0.36 (Rolley 1985) for kittens The current status of bobcat populations in the six New England states. Table was adapted from Broman (2012) and Roberts and Crimmins (2010). Information was obtained from state wildlife agencies. Whether there is currently a harvest, monitoring techniques, estimates of potential abundance, suitable habitat area (km 2 ), and overall population status are included. Monitoring methods included harvest analysis (HA), incidental harvest (IH), monitored individuals (MI), public sightings (PS), and vehicle collisions...90 vii

8 LIST OF FIGURES FIGURE PAGE 2-1 Geographic range of bobcats (from Hansen 2007; top). Study areas where bobcats were monitored in southwest ( ) and southeast New Hampshire ( ; bottom left) along with names of geographic regions in New Hampshire. Road density in the state generated with a 1-km 2 moving window (bottom middle). Mean monthly snow depth (SNODAS, Callahan) from November-March (bottom right) Probability of habitat use given the average monthly mean snow depth between November-March Bobcat habitat suitability in New Hampshire modeled at second-order selection using incidental observations collected from and mean monthly snow depth measured from Parameters estimated for each of the habitat variables used to fit the best model of third-order habitat selection. Estimates are bounded by two standard deviations. Dummy variables were used for all land cover and aspect variables, and therefore comparison between these categorical variables and the numerical variables (road density, forest edge, slope, ruggedness) should not be used to determine the relative effect of each variable Graph of bin number vs. area-adjusted frequency of withheld locations from k- fold cross-validation Bobcat habitat suitability in New Hampshire modeled at third-order selection using GPS-telemetry data collected from from 18 collared bobcats Bobcat habitat-suitability in New Hampshire based on two spatial scales. Incidental observations were used to model second-order selection. GPStelemetry locations from 18 collared bobcats were used to model third-order habitat selection. These analyses were then combined to generate the scaleintegrated map of habitat selection. Major freeways are shown, and the White Mountain region of northern New Hampshire is labeled for reference GPS locations from marked bobcats in the southwest and southeastern portions of New Hampshire. Bobcats were monitored from Major highways and names of regions are shown for reference...44 viii

9 FIGURE PAGE 3-2 Resistance scores for riparian areas are a function of distance from stream (top left). Resistance scores for slopes are resistance a function of percent slope (top right). Resistance scores for roads are a function of traffic volume, and separated into three classes-low, medium, and high (lower left). Land cover variables were assigned resistance scores from 1-10 with 1 being lowest resistance and 10 being the highest (lower right) Figures were recreated from NH Audubon and NHFG (2010) Comparison of circuit theory and least cost pathways. (A) Cost surface layer used for modeling. The white patches in the lower left and upper right corners are the source patch patches. Resistance is shown using a gray scale. Areas with low resistance are light grey, areas of high resistance are dark grey, and black areas are barriers to movement. (B) Least cost modeled corridor is shown in white. (C) Output from circuit theory. Areas of blue are have a lower probability of utilization, while areas of red have a higher chance. Areas in yellow show extremely high utilization points and thus identify pinch-points of movement. These areas are constrictions in the landscape where organisms are forced to travel, highlighting the area of highest conservation value. A real world example of such points may be an undeveloped underpass on a major freeway system (from McRae et al. 2008) Modeling scheme used to test connectivity predictions for bobcats. RSF generated using all location except from Bobcat #47, with his locations overlaid. This served as the conductivity layer (top left). Sampling protocol adopted from Anderson et al. (2012) to map connectivity in bobcat home range with minimal source/ground bias (top right). Output from circuit theory using top as source/bottom as ground, bottom as source/top as ground, left as source/right as ground, and right as source/left as ground (middle panels L to R). Cumulative circuit theory map with actual path of bobcat #47 (bottom left). Actual path overlaid with 100 random paths, rotated and shifted (bottom right) Resistance models used for mapping bobcat connectivity. The resource selection probability function (RSPF) model was generated using GPS-telemetry data from collared bobcats (right). The expert opinion model was generated independently by agency biologists (NHFG and NH Audubon 2010) Plot depicting the difference between actual and random path mean scores for resource selection probability function and expert opinion connectivity models. The connectivity score for the actual path was standardized to 0 for each individual bobcat. The difference between the mean connectivity score of random paths and actual paths was then plotted. If connectivity was successfully predicted, the mean score of the random paths will be less (below zero) than the actual path...56 ix

10 FIGURE PAGE 3-7 Connectivity models for New Hampshire generated using RSPF habitat suitability models and Program Circuitscape (left). Areas with highest conductance or movement are in yellow and lowest are in dark blue. Connectivity scores for major roads in New Hampshire (right) Connectivity models for New Hampshire generated using expert-opinion resistance models and Program Circuitscape (left). Areas with highest conductance or movement are in yellow, and lowest are in dark blue. Connectivity scores for major roads in New Hampshire (right) Habitat suitability map generated from incidental observations and marked bobcats. Pixels in the map have been re-scaled to the size of the average marked females home range (23.8 km 2 ) and habitat suitability scores (range 0-1) of the aggregated cells were averaged (left). Home-ranged size cells were classified as either occupied ( 0.5) or unoccupied (<0.5) based on average habitat suitability scores. Occupied cells were summed to obtain an estimate of resident female bobcat territories...82 x

11 ABSTRACT BOBCATS IN NEW HAMPSHIRE: UNDERSTANDING THE RELATIONSHIPS BETWEEN HABITAT SUITABILITY, CONNECTIVITY AND ABUNDANCE IN A CHANGING LANDSCAPE By Gregory C. Reed University of New Hampshire, September 2013 I examined bobcat habitat suitability in New Hampshire at the landscape and local-scale using incidental sightings and telemetry locations, respectively. By modeling habitat selection at two scales I was able to make inferences about bobcat distribution, while still getting detailed information about home range and habitat requirements. Bobcats appeared to be limited by greater snow depths at large scales, and human development at finer scales. Because bobcats may be limited by development, mainly roads, I modeled connectivity using empirical and expert-opinion based methods. Empirical methods performed better than expert-opinion methods when assessed at the home-range scale. When models were extrapolated statewide, major differences were apparent and this could be assessed using genetic methods. Habitat suitability and home range requirements were used to estimate a potential resident carrying capacity and associated reproduction for bobcats at the birth pulse (n=2237) and six months later (n=1386). xi

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13 CHAPTER I INTRODUCTION Habitat fragmentation and loss are worldwide concerns (e. g., Noss 1987, Theobald et al. 2011) because isolation of populations of plants and animals makes them susceptible to stochastic extinctions (e. g., Andren 1994, Keller and Waller 2002). These effects may be most severe among large carnivores that have extensive home ranges, exist at low densities, and have low reproductive rates (Crooks 2002, Singleton et al. 2002). Increasing habitat connectivity, so that individuals may move between separated patches of suitable habitat, may mitigate these effects (Taylor et al. 1993, Tischendorf and Fahrig 2000). There are two leading solutions to the problem of conserving connectivity: set aside large tracts of suitable land for conservation, or maintain corridors connecting protected land or highly suitable habitat. Most current efforts to increase or maintain connectivity focus on corridor design. Corridors have become a widely cited approach for assuring connectivity in humanaltered landscapes (e. g., Beier and Noss 1998, Hilty and Merenlender 2004, Gilbert- Norton et al. 2010). Essentially, a corridor is a linear strip of land that is surrounded by lower quality habitat (the matrix) to facilitate movement between suitable patches of habitat to another, thus enhancing population viability (Beier and Noss 1998). However, 1

14 despite the widespread endorsement of corridors as a conservation tool, limited evidence exists of their success (e.g., Haddad et al. 2003). Much of the criticism of corridors has focused on three arguments: i.) ecological disadvantages of corridors outweigh the benefits; ii.) the amount of money required to design, implement, and protect corridors; and iii.) lack of empirical evidence of their success (Simberloff and Cox 1987, Hobbs 1992, Simberloff et al. 1992, Bennett 1999). Among the disadvantages of corridors, Simberloff and Cox (1987) suggested that corridors can act as transmission zones for diseases, provide pathways for invasive species, and subject animals to increased risks of edge effects. Given these costs it has been argued that without demonstrated success of corridors, monies could be better spent on enhancing existing habitats. Increasingly, benefits of corridors have been observed, including: areas for dispersal (Beier 1995), assuring gene flow (Keller and Waller 2002), re-colonization of uninhabited habitat patches (Hanski and Gilpin 1991), paths for migration (Berger 2004), movement paths for climate change induced displacement (Heller and Zavaleta 2009), and avoidance of direct mortality from roads (Clevenger and Waltho 2000). Empirical evidence on the value of corridors has been shown within experimental mesocosms and field studies (Haddad and Baum 1999, Haddad and Tewksbury 2005, Levey et al. 2005, Damschen et al. 2006). Additionally, Gilbert-Norton et al. (2010) used a meta-analysis of 78 studies, and determined that corridors increased movement by 50%. Despite the focus on corridors, this approach to increasing connectivity may not be a panacea for all organisms, especially those that do not fit within a core patch/ matrix 2

15 framework. Many species exist across landscapes because of generalist tendencies, with habitat preferences and selection being displayed across a gradient. These species are still susceptible to barriers and isolation, and thus connectivity must be considered, but modeling corridors through the landscape may not be ideal. Focal species, particularly large carnivores, are often used to plan for connectivity (Beier 1993, Noss and Daly 2006, Servheen et al. 2001, Singelton et al. 2002), but caution is warranted given diverse habitat requirements and movement capabilities of animals that should benefit from corridors (Beier et al. 2008, Chetkiewicz and Boyce 2009). However, given the time, money, and data requirements needed to design empirically defensible connectivity schemes, we may not be able to abandon the use of focal species (Haddad and Tewksbury 2006, Cushman et al. 2010a). Bobcats may be an appropriate animal to evaluate the focal species approach because they are a wide-ranging carnivore (Beier 1993, Cushman et al. 2006, Servheen et al. 2001, Singleton et al. 2002) and rely on specific habitat features (Litvaitis 1986, Broman 2012), yet are considered a generalist species (Anderson and Lovallo 2003) that are adapted to many different biomes across their geographic range. In New England, they select early-successional forests, scrub-shrub, and wetlands, and avoid agricultural lands, developments, and high road densities (Litvaitis 2001, Litvaitis et al. 2006, Litvaitis and Tash 2008, Donovan et al. 2011, Broman 2012). Despite their sensitivity to development and roads (Crooks 2002, Riley 2006), bobcats can adapt to living in fragmented environments (Tigas et al. 2002). Therefore, they should show some behavioral response to roads and development resulting in obvious selection of pathways that can be used to map connectivity. 3

16 Before understanding and conserving connectivity for a species it is imperative to understand its distribution and habitat preferences. This information can be attained via sightings, identifying sign (tracks, scat, etc.), telemetry or GPS collars, and trapping. While this information can provide the researcher or manager with knowledge of where an organism has been; to understand which resources it is selecting for, a comparison must be made between what is being used and what is being unused or avoided. Currently, this is most often done with resource selection (probability) functions (RSF or RSPF; Manly et al. 2002, Boyce et al. 2002, Johnson et al. 2004, Johnson et al. 2006), which use the logistic model to compare sites of used or unused, or used or available to determine which resources an animal utilizes compared to what is available on the landscape. This resource use informs how the landscape affects the animals processes and movements, and to do what degree we can facilitate conservation of those resources. Bobcats in New Hampshire have experienced dramatic fluctuations in abundance since European settlement (Litvaitis et al. 2006) due to anthropogenic land alterations (Litvaitis 1993, Litvaitis 2001). Harvest records, paired with knowledge of land use, show a dramatic increase in bobcat numbers through the mid-20 th century followed by an abrupt decline in the 1970s (Litvaitis et al. 2006), which led to a closure on harvest in Since that time, bobcat abundance seems to be increasing based on incidental captures and sightings (Litvaitis et al. 2006, Roberts and Crimmins 2010, Broman 2012), yet more formal and detailed information on current habitat selection and potential connectivity is lacking. 4

17 OBJECTIVES The goals of this research are: (1) to create a multi-scale habitat-suitability model for bobcats in New Hampshire using incidental sightings and telemetry locations, (2) compare predictive connectivity models designed with circuit theory using two types of resistance layers; expert opinion and RSFs, (3) validate corridor modeling techniques and use of focal species with field data, and (4) estimate the potential carrying capacity of the state given bobcat habitat and home range requirements 5

18 CHAPTER II MODELING BOBCAT HABITAT SUITABILITY ACROSS NEW HAMPSHIRE: A HIERARCHICAL APPROACH Where an organism is found in space and time is a function of its life-history strategies (Southwood 1977), and by studying an animal s habitat selection we can gain insight into the ecological processes that influence its life. These habitat choices are made hierarchically (Johnson 1980, Weins 1987, Orains and Wittenberg 1991). Johnson (1980) described four orders of habitat selection, all of which must allow an animal to meet its most basic life-history requirements. First-order selection confers the animal s physical or geographic range; second-order selection is the choosing of a home range within that geographic range; third-order is the selection of various habitat components within the given home range; finally, fourth-order is the procurement of food or shelter from that habitat component (Johnson 1980). Because of the intrinsic linkages across spatial scales; it is important to study multiple orders of selection when studying the habitat suitability of an animal (Bergin 1992, Luck 2002, Johnson et al. 2004, DeCesare et al. 2012). Bobcats (Lynx rufus) are the most widespread and abundant felid in North America due to their ability to adapt to a wide variety of habitats (Anderson and Lovallo 2003). They inhabit every state in the continental United States except Delaware, and have a range that spans from central Mexico to Florida in the south and from British Columbia to Nova Scotia in the north (Sunquist and Sunquist 2002). In southern regions, 6

19 they are limited by competitive interactions with other felid species (Sanchez-Cordero 2008), and to the north, by limited adaptations to low temperatures (Gustafson 1984, Mautz and Pekins 1989) and deeper snow (McCord 1974, Hamilton 1982). This avoidance of greater snow depths has generally separated them from lynx (Lynx canadensis; Buskirk 2000); however, there are instances of sympatric populations and hybridization has been reported (Schwartz et al. 2004, Homyack et al. 2008). Additionally, bobcats are limited by large-scale agriculture in the Midwest and intense urban development in portions of the eastern United States, at the landscape level. Absent these limiting factors, they select home ranges across a broad array of habitats in areas that contain broken and rugged topography, with a thick and dense understory. Additionally, unfragmented or natural habitat is needed to support core areas (Riley 2006). Within home ranges, habitat selection is largely influenced by high prey abundance (Litvaitis et al. 1986) and breeding opportunities (Lovallo and Anderson 2003). Females are more often associated with better quality habitats; specifically areas that support high prey densities, rocky and cliffy areas for escape cover and denning, and avoidance of roads (Bailey 1981, Hamilton 1982, Lovallo 1999). These areas generally have a dense understory that allows for stalking and hunting of prey. Males seek to maximize breeding opportunities, establishing home ranges that overlap 2-3 female home ranges (Lovallo and Anderson 2003). Within their home range, males may also choose areas where they can hunt successfully, such as near deer yards in winter (Fox 1990) or adjacent to agricultural areas in spring (Broman 2012). 7

20 New Hampshire is completely contained within the current geographic range of bobcats (Fig. 2-1). The southern boundary of lynx distribution also falls within New Hampshire, and the two distributions overlap in the northern portion of the state. Historically, the northern portion of the state was dominated by lynx, whereas bobcats were most common in the southwestern portion of the state (Seton 1925). After European colonization, land was cleared for agriculture and timber harvests allowing bobcats to expand their range northward into lynx habitat (Seton 1925, Litvaitis 1993). As farms were abandoned, there was an abundance of early-successional forests, and the prey densities associated with them, resulting in a large increase in the bobcat population (Litvaitis 2006). Bobcats were abundant across the state, with particularly large harvests in the north. However, forests are maturing into later successional classes (Morin and Woodall 2012), potentially changing habitat suitability for bobcats and lynx in the northern portion of the state. In addition to changes in land-use practices, New Hampshire is experiencing rapid growth, especially in the southeastern portion of the state and along major transportation corridors (Sunquist and Hewes 2010). Crooks (2002) found that bobcats are moderately sensitive to habitat fragmentation. Similarly, Riley (2006) noted that bobcats situated their home ranges in predominantly natural areas opposed to urban areas. Finally, Lee et al. (2012) found that highways and urban areas could genetically isolate populations of bobcats. Our ability to understand bobcat habitat selection in New Hampshire is further complicated by three things. First, the state of New Hampshire has dramatically different climates because of the range of elevations; from sea-level to the alpine tundra. This 8

21 range in elevation results in drastic differences in snow pack across the state (Fig. 2-1). Second, population density and the associated development, also vary across the state. In the south, the Merrimack Valley and Seacoast region have major urban areas, whereas Coos County, in the north, is predominantly rural and sparsely populated. Finally, we need an appropriate way to integrate models of habitat suitability that occur at different scales. Currently, most methods used to analyze wildlife habitat focus on broad scale distributions (i.e. species distribution modeling) or fine scale telemetry studies. Recently, however, efforts have been made to integrate analyses of selection at multiple scales into one cohesive map of habitat suitability (Johnson et al. 2004, DeCesare et al. 2012). Integrating maps of habitat suitability across scales requires appropriate data to fit those scales, a potential constraint when resources are limited. While GPS technology and analytical techniques are advancing, relevant information on habitat use and distribution can still be obtained from less technical approaches and can then be used to inform our knowledge of habitat selection at a broader scale. Use of incidental observations, hunter surveys, and citizen surveys to define habitat selection have been increasing among studies of carnivores (e.g., Cooper et al. 2012, Linde et al. 2010), and have been a vital part of tracking population and distribution of birds for many decades (e.g., breeding bird surveys). Although application of occurrence data presents challenges (e.g., inaccurate reports leading to errors) they can still be useful for relatively common species if a priori guidelines are followed (McKelvey et al. 2008). In New Hampshire, bobcats have experienced dramatic population declines (Litvaitis et al. 2006) precipitated by major changes in land use practices (Litvaitis 1993, 9

22 Litvaitis 2001). However, recent observations suggest bobcats are rebounding in abundance and expanding into previously sparsely populated areas (Litvaitis et al. 2006, Broman 2012), warranting an investigation into habitat use across scales. Objectives for this chapter were: i.) identify how environmental factors limit the distribution of bobcats in New Hampshire, ii.) identify local features that influence habitat selection within home ranges, and iii.) combine habitat suitability maps to generate one hierarchical model of habitat selection for the state that can be used in abundance estimates and connectivity modeling. Study Areas METHODS To understand bobcat habitat suitability in New Hampshire habitat analyses were conducted at two scales. Incidental sightings were solicited statewide for landscape scale analysis, while fine scale habitat selection was assessed using bobcats outfitted with GPS collars in two portions of the state (Fig. 2-1, see Chapter III for detailed description). New Hampshire has experienced many changes in land use throughout its history. After European settlement, the state was dominated by agriculture, with only 47% of the state forested in 1880 (Litvaitis 1993). Farm abandonment in the 18 th and 19 th century resulted in forest regeneration, with an abundance of early-successional forests in the mid-20 th century. By % of New Hampshire was forested (Sunquist and Hewes 2010), however those forests matured into later-successional, closed-canopy forests, resulting in dramatic effects on wildlife species (Litvaitis 1993, Litviatis 2001). While 10

23 forest stands continue to mature (Morin and Woodall 2012), overall forest cover has decreased to 82%. This decline is continuing and forest cover is expected to be 78.5% by 2030 due to rapid human development and increases in population (Sunquist and Hewes 2010). Development is most pronounced in the southeastern portion of the state and along transportation corridors (Sunquist and Hewes 2010) where road density is dramatically higher than in the northern portion of the state (Fig. 2-1). In addition to these anthropogenic effects on habitat, the state spans a range of climatic conditions resulting in different levels of environmental suitability for bobcats. New Hampshire is positioned at the northern extent of bobcat s geographic range (Fig. 2-1). Elevation ranges from sea level, along the coast in the southeastern portion of that state, up to 1917 m. on the summit of Mt. Washington. These differences result in a large gradient of snow levels throughout the state, with areas in the White Mountains and northern portion of the state receiving significantly more snow than coastal areas, as well as the Connecticut and Merrimack River valleys (Fig. 2-1). Factors Affecting Statewide Distribution To investigate environmental factors influencing bobcat distribution throughout the state (second-order selection), I relied on bobcat locations that were opportunistically collected statewide since 2007 via reports to New Hampshire Fish and Games employees, and a project website ( Broman (2012) used a subset of this data to model habitat selection at the third-order, but found the data to be biased when compared to telemetry based methods and thus 11

24 inappropriate for use at this scale. Therefore, these locations were used to model landscape level effects on bobcat habitat selection at second-order selection. Fig Geographic range of bobcats (from Hansen 2007; top). Study areas where bobcats were monitored in southwest ( ) and southeast New Hampshire ( ; bottom left) along with names of geographic regions in New Hampshire. Road density in the state generated with a 1-km 2 moving window (bottom middle). Mean monthly snow depth (SNODAS; Callahan 2013) from November-March (bottom right). 12

25 The accuracy of incidental observation is a cause for concern when attempting to model habitat use or geographic range of a species (e.g. McKelvey et al. 2008, Broman 2012). Species can either be misidentified or located imprecisely, compromising their use in habitat analysis. To address this, reported bobcat sightings were verified for accuracy either by a submitted picture or a detailed and accurate description. Reported sightings were then scored on a scale of 1-3. Sightings that received a score of 1 contained either an address verifiable on Google Earth or geographic coordinates. Sightings received a score of 2 if the description was less precise, but an estimate of location could still be made. Sightings with a score of 3 contained a very general location (i.e., the town sighted or location based on major landmarks). Only locations with a score of 1 or 2 and from January January 2013 were utilized for analysis. Additionally, location data solicited from the public are often spatially biased to developed areas (e.g. Snall et al. 2011). To address this, each sighting was buffered by the radius of the maximum home-range size of marked bobcats (9.6 km, see Results- Chapter IV). The maximum home-range was used to insure that all possible areas suitable for home ranges were sampled. Next, 10,000 random points were then generated throughout the state. Points contained within home range buffers were considered used, and ones that were not within the range were considered unused. This sampling scheme eliminates the clusters of incidental observations and ensures an even distribution of points across the study area. Resulting locations were used in Type I design, as described by Manly et al. (2002), in which used and available resources are sampled for the entire study area and individual animals are not identified. Given the coarse-scale habitat analysis, the screening of reports, the commonality of bobcats, and the sampling scheme 13

26 utilized, I believed using this approach is useful to describe statewide distribution of bobcats (second-order habitat selection). Habitat Features that Influence Selection within Home Ranges Bobcats were trapped and collared during the winters of and in the southwestern and southeastern portions of the state, respectively (Fig. 2-1). Bobcats were fitted with either Siritrak drop-off collars (Internal Release, 220g, Siritrack Limited, Havelock North, New Zealand) or Lotek Wildcell collars (Wildlcell, 270g, Lotek Wireless, Newmarket, Ontario). All study animals were handled in accordance with the University of New Hampshire Institutional Animal Care and Use Committee (Protocol #081201, Appendix A). Siritrack collars were retrieved after collar release, or the bobcat was re-captured if the release mechanism failed. Lotek collars sent locations via short message services (SMS) to a ground station located at the University of New Hampshire. Location error, habitat-induced GPS fix bias, and issues of independence are important considerations when using GPS technology (Frair et al. 2004, Lewis et al. 2007, Hebblewhite et al. 2007, Frair et al. 2010). Mallet (2013) measured location error for both types of collars in Maine and found that location error averaged 17.8 m during the leaf-on season, and 12.9 m during the leaf-off season. Both are within the highest resolution (30m) used for habitat layers. Additionally, locations were screened to ensure accurate data while still utilizing the greatest amount of data possible, following Lewis et al. (2007). All locations generated from 3D fixes were kept, and locations generated from 2D fixes were kept if the dilution of precision was less than or equal to 5.0 (Lewis et al 14

27 2007). Mallet (2013) found overall fix success was 87% and 95% for Lotek collars and 80% and 100% for Sirtrack track collars during leaf-on and leaf-off seasons, respectively. Hebblewhite et al. (2007) recommend weighting locations when GPS-bias is >10%. Given the different study areas and types of collars, GPS locations were weighted after model building to assess potentials effects of habitat-induced GPS fix bias. Linear logistic model coefficients for forest (closed conifer = 1.83; deciduous = 1.1; mixed forest = 0.27) and slope (percent slope = 0.03; percent slope * conifer = 0.046; percent slope * deciduous = 0.056; percent slope * mixed=0.014) obtained from Frair et al. (2004) were used to weight GPS locations. Temporal independence should also be considered. However, correlation in GPS studies has been found up to 30 days (Cushman 2010) and withholding this amount of data would have resulted in destructive sampling (Swihart and Slade 1985, Broman et al. 2012), so I elected to utilize all locations despite autocorrelation. Resulting locations served as used points in model generation. An equal number of available points were randomly generated within individual home ranges. These points were used to compare habitat selection at the third-order selection, where individuals are defined, following Design III by Manley et al. (2002). Modeling Habitat Selection Environmental Variables. Three GIS layers were used to model second-order habitat selection (Table 2-1). Due to the potential bias associated with using sightings data, only large scale topographic and climatic layers were used. Two snow depth layers were compiled from the NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center s (NOHRSC) Snow Data Assimilation System (SNODAS; NOHRSC 2004). These layers consisted of the average of the maximum 15

28 monthly snow depth and the average of the mean monthly snow depth from November through March in the years Elevation was also included as a potential variable because bobcats show preference for lower elevations (Lovallo and Anderson 1996) and less productive forests may limit bobcats at higher ecosystems. Given the large number of potential explanatory variables available to describe habitat selection, exploratory data analysis was used to test an initial suite of different approaches (Appendix B); including the use of the National Land Cover Database 2006 (Fry et al. 2011), an Ecological Land Unit dataset (The Nature Conservancy 2008), and an unfragmented habitat layer (NHFG) combined with variables measuring proximity to important habitat features. From these analyses ten variables were selected for model development of third-order habitat selection (Table 2-1), including; National Land Cover Database 2006 (Fry et al. 2011), distance to forest edge, distance to stream, elevation, slope, aspect, a vector ruggedness measurement (VRM, Sappington et al. 2007), distance to road (km), road density (km/km 2 ), and traffic density (unit/km 2 ). Fitting of Habitat Suitability Model. Resource selection probability functions (RSPF), as defined by Manley et al. (2002), were used to model habitats at both orders. Following Manly et al. (2002), a RSPF was fitted using generalized linear models and the R (R Core Team 2012) package ResourceSelection for second and third order, respectively (Lele et al. 2013). Typically, the RSPF is approximated with the proportional RSF by exponentiating the parameters fitted with the logistic function (Johnson et al. 2006). Lele (2009; see also Lele and Keim 2006) demonstrated that the stable estimations of parameters in an RSPF can be obtained by using weighted distributions. The 16

29 advantages of this approach are a potentially better fit model, compared to the exponential RSF, and output values represent the true probability of selection. Table 2-1. Habitat variables, justification for inclusion, data source, and resolution for GIS layers used to model bobcat habitat selection at second and third order. Habitat Variable (units) Justification Source Resolution Second Order Selection Snow - max (mm) Avoidance of deep snow depth SNODAS 1 1km Snow - mean (mm) Avoidance of deep snow depth SNODAS 1 1km Elevation (m) Lower productivity at high elev. DEM 2 30m Third Order Selection Land Cover* Represents habitat NLCD m Elevation (m) Avoidance of deep snow depth DEM 2 30m Aspect (flat, north, south) Sun exposure influences snow DEM 2 30m Slope (degrees) Terrain for dens, escape cover DEM 2 30m VRM** Terrain for dens, escape cover DEM 2 30m Distance to edge (km) Prey densities, travel corridors NLCD m Distance to stream (km) Prey densities, travel corridors GRANIT 4 30m Distance to road (km) Avoidance of roads GRANIT 4 30m Road density (km/km 2 ) Avoidance of roads GRANIT 4 30m Traffic Density (unit/km 2 ) Avoidance of roads GRANIT 4 30m *Collapsed into development, deciduous forests, coniferous forest, mixed-woods, shrub/scrub, agriculture, wetlands, and open water **Vector Ruggedness measurement (Sappington et al. 2007) 1 Snow Data Assimilation System 2 USGS Digital Elevation Model National Land Cover Dataset 4 New Hampshire Geographically Referenced Analysis and Information Transfer System Second-order habitat models were fit using the three explanatory variables (snowmean, snowmax, and elevation) into single variable models because of collinearity. Models were then re-fit using quadratic terms in case the relationship between selection and the variable was non-linear. The best model was selected using Akaike s Information 17

30 Criterion (Akaike 1973) adjusted for small sample sizes (AICc, Burnham and Anderson 2002). Recommendations by Hosmer and Lemeshow (2000) were used to fit third-order habitat suitability models with logistic regression. First, to avoid collinearity, all continuous variables were compared using Pearson s Correlation coefficient. If variables were highly correlated (r>0.7), and biologically redundant they were not included in the same model. Next, univariate models were fit for all variables and only statistically important (P>0.25) variables were kept (Hosmer and Lemeshow 2000). This conservative approach to significance was used to insure that all biologically relevant variables were included. I then fit a multivariate logistic model using a manual forward-stepping procedure (Squires et al. 2012, Hosmer and Lemeshow 2000) starting will a null model that only contained land cover variables. Each variable was added to the null model sequentially according to its univariate strength, measured by Wald statistics. To determine if a variable was retained, likelihood-ratio tests between sequential models were utilized (P<0.05) because each subsequent model was nested in the previous one. Variables excluded after univariate tests were then added to the model to determine if they improved model fit. During model fitting procedures, attention was paid to any large changes or reversals of sign in the coefficient estimates to insure a stable model was built. Model Extrapolation and Validation. The top second-order model was generated using a used-versus-unused design, so standard logistic regression validation techniques were used, including; confusion matrices, Kappa statistic, and Receive Operating Characteristic (ROC) curves, as recommended by Boyce et al. (2002). 18

31 The top third-order model was tested using k-fold cross-validation following Boyce et al. (2002), with refinements by Johnson et al. (2006). This entailed partitioning the data into five folds and using four folds as training sets and the fifth fold as a validation set. The training sets were used to re-fit the model, and then the estimated parameters were spatially applied in ArcMap The resulting raster grid was divided into 10 ordinal bins using quantile breakpoints. The number of validation points in each bin was divided by the area of the bin to get area-adjusted frequencies. The Spearman correlation between this area-adjusted score and bin number was then computed (Boyce et al. 2002). Next the proportion of observed validation points in each bin was compared to the expected proportions for each bin using linear regression. Expected proportions for each bin were computed by multiplying the midpoint RSPF score of each bin by the area of the bin and then dividing by the sum of these products for each bin. The two final models were then mapped in ArcMap 10.0 using Raster Calculator. All habitat variables present in the RSPF were represented by raster layers in ArcMap The model equation was then used to extrapolate the RSPF for New Hampshire at both levels of selection. Continuous variables were capped at the minimum and maximum values measured, so that models were not extrapolated outside the observed data (DeCesare et al. 2012). 19

32 RESULTS Factors affecting statewide distribution of bobcats Bobcat habitat suitability was modeled at second-order selection using incidental observations (Fig. 2-3). A total of 729 sightings were reported between December 2007 and January 2013 to the project website or NHFG. After screening, 665 were deemed sufficiently accurate to assign GPS coordinates. These locations were buffered by the largest home-range radius of male bobcats (9.6 km). The model with average monthly (Nov-March) mean snow depth with a quadratic term was considered the best model as judged by AICc (Table 2-2). Probability of bobcat use declined with increased mean monthly snow depth (Fig. 2-2). The model predicted well for used sites (sensitivity = 0.96 ± 0.002), but poorly for unused sites (specificity = 0.50 ± 0.013). A receiver operating characteristic (ROC) curve showed Area Under the Curve (AUC) score of 0.90 suggests a good predictive model. However, the model received a moderate kappa statistic of 0.51 (± 0.013) because of the high false positive. 20

33 Table 2-2. Models for Second Order-Home Range used three variables, average monthly (Nov-March) maximum snow depth (Snow_Max), average monthly (Nov-March) mean snow depth (Snow_Mean), and elevation (Elevation). Sightings reported between 2007 and 2013 were used to model the relationship between probability of use and the habitat variables. Model K AIC c AIC c AIC w log(l) Snow_Mean Snow_Mean Snow_Max Snow_Max Elevation Elevation Fig Probability of habitat use given the average monthly mean snow depth between November-March

34 Fig Bobcat habitat suitability in New Hampshire modeled at second-order selection using incidental observations collected from and mean monthly snow depth measured from

35 Habitat Features that Influence Selection within Home Ranges Bobcat habitat suitability was modeled at third-order selection using GPStelemetry locations (Fig. 2-6). Variables used included NLCD 2006 (Fry et al. 2011; layers collapsed to open water, light development, heavy development, barren, evergreen forests, deciduous forests, mixed forests, shrub/scrub, agriculture, and wetlands), distance to forest edge (excluding open water/forest edge), elevation, slope, aspect, and a vector ruggedness measurement (VRM, Sappington et al. 2007). Bobcats selected for forests, shrub/scrub, and wetlands, and selected against developed areas, agricultural areas, and open water (Table 2-3, Fig. 2-4). They showed avoidance of high road density, and selected areas closer to forest edge. They preferred more rugged and steeper sloped areas, with southern facing slopes. Finally, there was selection for areas closer to streams, but no significant relationship to elevation at this order of selection. The third-order model validated well. K-fold cross validations with 5 folds were all highly correlated (Table 2-4, Fig. 2-5). A mean spearman correlation of r s = (σ = 0.014) indicated that bin number and area-adjusted frequency of used points were highly correlated. All slopes (b 1 ) were significantly different from 0, indicating the model performed better than neutral model where use would be equal to availability. All slopes (b 1 ) were not significantly different from 1, indicating the model was proportional to use. Additionally, none of the intercepts (b 0 ) were significantly different from zero (Table 2-3) which is expected for a model that was proportional to use. Model fit for expected vs. observed regression was moderately strong (range = ) indicating a good fitting model. 23

36 Table 2-3. Parameter estimates from the best third-order habitat suitability model. Models were made utilizing GPS-location data from 18 collared bobcats in New Hampshire from Habitat Variable β estimate SE P value (Intercept) agricultural deciduous developed < evergreen open water shrub/scrub wetlands aspect-flat aspect-south distance to forest edge < VRM slope road density < elevation distance to stream Fig Parameters estimated for each of the habitat variables used to fit the best model of third-order habitat selection. Estimates are bounded by two standard deviations. Dummy variables were used for all land cover and aspect variables, and therefore comparison between these categorical variables and the numerical variables (road density, forest edge, slope, ruggedness) should not be used to determine the relative effect of each variable. 24

37 Table 2-4. Rank correlations (Spearman) and expected vs. observed regression for the best RSPF model. The model was validated with k-fold cross-validation utilizing 5 folds. Rank Correlation Expected vs. Observed Regression Fold r s p b 0 b 1 R < < < < < Fi g Graph of bin number vs. area-adjusted frequency of withheld locations from k-fold cross-validation. Habitat Suitability Maps. The resulting models of second and third order selection were combined by multiplying them together using Raster Calculator in ArcMap 10 (Fig. 2-7; Johnson et al. 2004), and includes the two relevant scales of selection for this area. It can be seen that there is a strong selection for more southern areas of the state or lower elevations, and an avoidance of areas of deeper snow in the White Mountains and northern portion of the state. Additionally, highly developed areas are avoided, especially along the I-93 corridor and throughout the Merrimack Valley. Outside of these areas, it seems that most of the state is moderate to good habitat for bobcats. 25

38 Fig Bobcat habitat suitability in New Hampshire modeled at third-order selection using GPS-telemetry data collected from from 18 collared bobcats. 26

39 Fig Bobcat habitat-suitability in New Hampshire based on two spatial scales. Incidental observations were used to model second-order selection. GPS-telemetry locations from 18 collared bobcats were used to model third-order habitat selection. These analyses were then combined to generate the scale-integrated map of habitat selection. Major freeways are shown, and the White Mountain region of northern New Hampshire is labeled for reference. 27

40 DISCUSSION An animal s habitat requirements that are both geographically relevant and biologically detailed can be generated by analyzing habitat selection at multiple scales. Application of these methods to the analysis of bobcat habitat suitability in New Hampshire improved results when compared to single scale analyses. For instance, measuring bobcat response to snow depth is difficult to do with fine-scale analysis because there is little heterogeneity within a bobcat home range. However, snow depth and overall winter severity can change suitability drastically over a relatively small area (Fox 1990). Extrapolation of the telemetry-based model resulted in the northern portion of the state looking like the best habitat (Fig 2-6), however, when snow was taken into account this area became one the poorest regions in the state (Fig 2-7). Likewise, the habitat map based on incidental sightings depicted the entire southern portion of New Hampshire to be suitable habitat (Fig 2-3). However, when the telemetry-based model was added, the highly developed areas in the Merrimack Valley and Seacoast were seen to be low suitability. Using only one method of mapping bobcat habitat suitability would have resulted in incorrect conclusions about suitability in the state. By using incidental sightings, coupled with telemetry data, a map that more realistically reflected bobcat habitat suitability was made, which could then be used to assess connectivity (Chapter III) and statewide abundance (Chapter IV). Incidental Sightings to Assess Distribution. Incidental sightings offer an inexpensive method to track broad-scale habitat use (Palma et al. 1999) and allowed us to extrapolate fine-scale data statewide. This was especially important because bobcats in New Hampshire are near the northern extent of their geographic range (Anderson and 28

41 Lovallo 2003). Since European settlement, their geographic range has expanded northward (Seton 1925), potentially limiting lynx populations (Parker et al. 1983, Hoving et al. 2005). Recently, though, breeding lynx have been documented within the state (NHFG 2011) in areas where the two species ranges overlap. Additionally, large changes in habitat use are possible across relatively small distances within a state. Fox (1990), studying bobcats in New York, found that home ranges in the Catskills (µ=36.0 km 2 and µ=31.0 km 2, for males and females respectively) were much smaller compared to those in the Adirondacks (µ= km 2 and µ=86.4 km 2 ). He believed this was largely due to differences in winter severity. New Hampshire has a similar change in climate across the state. Due to this, any fine-scale habitat suitability data had to be examined in the context of the larger climatic features of the state. Consistent with previous studies (McCord 1974, Koehler and Hornocker 1989) second-order habitat maps indicated that bobcats avoided areas of deeper snow pack (Figs. 2-2). However, historic harvest records in the northern portion of the state saw some of the highest number of bounties (Litvaitis et al. 2006). Due to high snow depths, this area is considered the poorest habitat when analyzed at second-order selection (Fig. 2-3). The dissimilarities could be due to a number of reasons. First, we know the abundance of early-successional habitat has decreased (Litvaitis 2003), and that forests have matured (Morin and Woodall 2012), potentially leading to lower prey densities. Because bobcat abundance is related to their prey (Litvaitis 1986) we should expect a population decline. Throughout the early to mid- 1900s, land alterations may have increased prey populations significantly enough to 29

42 support bobcats, despite the greater snowfall. Once those prey populations declined, bobcat abundance likely did as well. The alternate explanation is that our sightings data is not sufficient to track changes in bobcat habitat use in the northern portion of the state. We know the human population density is much lower there, providing fewer opportunities for sightings. Additionally, decreased interest in our study from the northern portion of the state may have led to less reported sightings. However, data from hunter surveys and road-killed bobcats also show few occurrences (Tate, unpublished data). And while both of these metrics have similar biases as sightings data, taken together they could highlight an overall trend in the state. Furthermore, while the sightings may be biased, they still highlight that snow depth can limit bobcats, and thus habitat is of poorer quality compared to other parts of the state. And while bobcats certainly exist in the area, they will be more susceptible to starvation during intense winters, so specific monitoring and management of their population may be warranted. Outside of the northern portion of the state and the White Mountains, the state seemed to be relatively high-quality habitat in all areas. Only one variable was used in modeling, however, so few conclusions about bobcat habitat use could be made from this type of data if it was used independently. Bobcat home ranges are also limited by development (Riley et al. 2003, Riley et al. 2006), yet an analysis of how development limits bobcats was not possible because the incidental sightings data was associated with roads and people s houses (Broman 2012). Furthermore, Broman (2012) showed that if fine-scale analysis is done using these data incorrect conclusions about bobcat habitat could be made. The bias that accompanies much of the occurrence data we have is a 30

43 major drawback (Philips et al. 2009, Broman 2012). However, careful use of occurrence data to map broad-scale distributions can be useful, as seen here. Fine-Scale Habitat Selection. The third-order habitat selection model gave a more detailed view of habitat use by bobcats, and is comparable to other habitat studies in the area (Donovan et al. 2011). Similar to Broman (2012) the greatest selection was for wetlands (Fig. 2-4). Wetlands may function as superior hunting areas for bobcats because they offer good cover for stalking, as well as higher prey densities along their edges. Additionally, they may act as a refuge from human development, due to their protected status. Bobcats also showed selection for scrub/shrub habitat. Similar to wetlands, these areas offer good hunting opportunities because of the combination of good cover and high prey densities. Deciduous and evergreen forests were shown to be selected for, but not significantly. The majority of bobcat locations were in forest habitats (>72%), and bobcats selected for areas closer to forest edges. Edge habitats presumably offer higher prey abundance whereas forests can provide good cover, therefore while there was not strong selection for forests, they are still crucial to bobcat habitat suitability. Bobcats selected against development and high road densities, which was expected from past studies (Crooks 2002, Riley et al. 2003, Riley 2006). However, incidental observations indicate that bobcats occasionally use habitat features adjacent to or in developed areas. Whereas bears and coyotes have adapted to anthropogenic changes on the landscape (e.g. Grinder and Krausman 2001, Beckamnn and Berger 2003), bobcats have always been considered to have a shy and secretive nature (e.g., Pollack 1951) and avoided humans. Through reported sightings and accompanying pictures it seems some individual bobcats have adapted to human settlements. They have been documented 31

44 exploiting bird feeders to prey on birds and squirrels in the winter ( Additional studies on the differences in tolerance for development, and related life history metrics could help us better understand how bobcats will adapt to increased development in the future. Topography played a role in determining third-order habitat selection. The model demonstrates that bobcats are selecting areas that have steeper slopes and greater overall ruggedness, corroborating bobcat use of rough, broken habitat, interspersed with ledges (McCord 1974, Broman 2012). South-facing slopes are being selected for, while flat areas are being selected against compared to northern aspects, further providing evidence that bobcats use areas with higher sun exposure and decreased snow depth (Koehler and Hornocker 1989, McCord 1974). Finally, both elevation and distance to stream were included in the final model, but explained little variability, even though in previous studies these have been shown to be influential (Kolowski and Woolf 2002). Comparisons between the third-order habitat selection map (Fig. 2-6) and historical records of bobcat harvests (see Litvaitis et al. 2006) highlight many agreements, but also some potential problems when the third-order map is extrapolated beyond the study areas and surrounding region. First, the northern portion of the state is shown to be the best habitat by the third order map. It is an area largely devoid of roads, one of the key limiting factors for bobcats, and has an abundance of wetlands, making it good habitat throughout most of the year. However, based on current sightings reports, there do not seem to be an abundance of bobcats in the area. Furthermore, the secondorder habitat (Fig. 2-3) showed that bobcats may be limited by snow depth in this area. During severe winters, bobcats are probably limited in their mobility (McCord 1974) and 32

45 thus ability to catch prey, making the area unsuitable at this time of the year. This disagreement is one instance in which the third-order habitat maps fails when extrapolated outside of its context. Second, areas in the southeastern portion of the state, where both road and population density are highest (Sunquist and Hewes 2010), are intermixed areas of very high and low suitability according to the third-order habitat map. Bobcats were mostly absent from the southeast based on historical records, however they have been reported there lately. Given the relative abundance of wetlands and early successional habitat, this area may provide good sources of prey for bobcats. However, while the area may be suitable in many spots, it could also function as a population sink, given the high road density (Nielsen and Woolf 2002) that often leads to higher mortalities (Litvaitis and Tash 2008). Extrapolating the third-order habitat map to this area may not fully capture the degree to which increased road densities effect bobcats in this area. Throughout the rest of southern New Hampshire historic records and current assessments largely agree on bobcat habitat suitability. Harvest rates show that the majority of bobcats were found in in the southwestern portion of the state west of the Merrimack River, as well as the western border of the state along the Connecticut River with sporadic higher densities around Lake Winnipesauke and to the northeast of Concord. The third-order habitat map largely recognizes these as highly suitable habitat, and numerous sightings have also come from these areas. Implications of Multi-Scaled Habitat Selection. It is imperative to consider the issue of scale when modeling habitats (Boyce 2006), and extra care must be used when 33

46 extrapolating beyond study areas (Johnson et al. 2004, DeCesare et al. 2012). For this particular study, the third-order habitat suitability map is justified in the southern portion of the state, where the two study areas took place. However, extrapolation to the rest of the state was not justified without taking into account selection at larger scales. New Hampshire varies considerably in both elevation and climate, important drivers of bobcat ecology that cannot be considered at the third-order level of selection. Conversely, the second-order habitat model developed with sightings made sense across the state in regards to snow depth, but lacked the fine scale detail needed for decision making. By combining the second and third-order models to make a scale-integrated resource selection probability function better informed habitat selection and subsequent management decisions at an appropriate scale can be made (Johnson et al. 2004, DeCesare et al. 2012). The use of incidental observations allowed insights gained using GPS technology to be applied at a more regional scale, which is especially important for bobcats at the edge of their range because changes in climate will directly affect them. In the past 50 years more precipitation in New England is falling as rain instead of snow due to increasing temperatures (Huntington et al. 2004). These trends are expected to continue (Huntington et al. 2009). Because bobcats are limited by snow, this may result in them range expansion northward into lynx territory. Increased range overlap between bobcats and lynx could lead to more instances of hybridization (Schwartz et al. 2004, Homyack et al. 2008) or increased interspecific competition (Buskirk et al 2000). Incidental sightings may help track these changes in distribution. However, understanding the mechanisms 34

47 that drive the movements, as well competitive interactions, will require more fine-scale data. Finally, New Hampshire s human population is growing, and therefore the amount of suitable bobcat habitat will likely decline, while previous areas of connectivity may be severed. Measures must be taken to ensure that areas of high quality habitat, such as wetlands and scrub/shrub, are maintained. Furthermore, areas that contain good habitat features, but also high road suitability must be monitored closely for changes in the population. Incidental sightings will aid in describing where bobcats are, but data describing movement and survival will be more useful, especially in these potential sink habitats. Additionally, an abundance of occurrences does not ensure a healthy population, so other measures of bobcat status should be used. These areas may be most susceptible to large changes in population numbers because of increased risk associated with developed areas. In light of this, areas of high suitability that also have relatively less developments, such as the southwestern region of the state, may contain the most stable source populations. 35

48 CHAPTER III MODELING CONNECTIVITY FOR CARNIVORES IN A DEVELOPED LANDSCAPE: WHAT IS THE BEST APPROACH? Connectivity conservation plays a major role in the conservation and management of many species today (Crooks and Sanjayan 2006). It has been incorporated in numerous state wildlife action plans (e.g., New Hampshire Wildlife Action Plan 2010, California Essential Habitat Connectivity Project: A Strategy for Conserving a Connected California, Washington Wildlife Habitat Connectivity Working Group), become the driving force behind nongovernmental organizations (e.g., Yukon to Yellowstone Conservation Initiative, Quabbin-to-Cardigan Partnership), and plays an integral role in management plans for endangered or imperiled species (e.g., Noss 1987, Ferreras 2001). At its most basic level, connectivity conservation serves as a focused type of land conservation, most often with a special emphasis on conserving land fragmented by development and roads (Theobald et al. 2010). Conserving land for connectivity, whether through easements or outright protections, or altering highway transportation thruways, is an expensive proposition in the best of circumstances. Therefore, we must be sure that our efforts to improve connectivity follow proven and tested methods. Recent advancements in the ability to assess connectivity via genetic methods has substantially advanced our understanding of landscape integrity (Cushman et al. 2009, Schwartz et al. 2009, Wasserman et al. 2012); however genetic methods are not always 36

49 available or applicable to the temporal or spatial scale with which connectivity must be considered (Cushman et al. 2010). Because of this, analysis of telemetry-based data with geographic information systems may be a better asset when exploring the current status of fragmentation (Cushman et al. 2010, Squires et al. 2013). Furthermore, if more finescale movements by individuals are protected, dispersal could be better facilitated, and it is reasonable to assume that gene flow will remain high as well. Unfortunately, both genetic and GPS methods are expensive, and not always applicable. Often, it is necessary to make connectivity plans for multiple species, and do it inexpensively, and expert-opinion or literature review efforts provide the most feasible opportunity (Beier et al. 2008). Additionally, focal species are often implemented in further attempts to cut costs by reducing workloads. While these economic realities are unavoidable, we should make an effort to assess the validity of these methods. Modeling Connectivity Connectivity is the ability of organisms to move between separated patches of suitable habitat based on the structure of the landscape (Taylor et al 1993), and corridors have become a widely cited approach for assuring connectivity in human-altered landscapes (e. g., Beier and Noss 1998, Hilty et al. 2006, Gilbert-Norton et al. 2010). Proposed benefits of corridors include: areas for dispersal (Beier 1995), assuring gene flow (Keller and Waller 2002), re-colonization of uninhabited habitat patches (Hanski and Gilpin 1991), paths for migration (Berger 2006), movement paths for climate change induced displacement (Heller and Zavaleta 2009), and avoidance of direct mortality along roadways (Clevenger and Waltho 2000). 37

50 Despite the apparent benefits of corridors, they may not be applicable for all organisms and their associated landscapes. Many connectivity projects seek to connect large tracts of public land through an intervening matrix of poor or less suitable habitat. Unfortunately, in many regions these large areas of undeveloped or protected land do not exist, and ways to increase connectivity may not be so straightforward. In New Hampshire, for example, bobcats (Lynx rufus) inhabit a variety of land covers with varying intensities of development interspersed, with relatively small blocks of protected land. Therefore, identifying one or two major connections between protected lands may not be a relevant conservation strategy in such landscapes. Instead, it may be more appropriate to identify where connectivity exists. Such an approach could consider multiple spatial scales. For example, at the home range level, bobcats are in danger of vehicular collisions during daily forays. While at the landscape scale, considerations to assure successful dispersal and gene flow among populations are needed. An integration of these factors, with attention to differing spatial concerns, is necessary to mitigate fragmentation for bobcats and wide ranging species. Developing connectivity conservation strategies requires a balance between assumptions, data availability, on-the-ground feasibility, and appropriate modeling techniques. Noss and Daly (2006) identified three approaches toward corridor design: intuitive, empirical, and modeling. An intuitive approach uses existing knowledge, expert opinion, or even best guesses to locate important areas of connectivity. Intuitively designed corridors are routinely implemented because they can be designed quickly, are often considered cost effective, and can be applied to numerous animals for which habitat preferences are known. Conversely, empirical approaches use data to inform decisions on 38

51 connectivity. As a result, an empirical design can be more easily defended and may be considered more accurate. Empirically designed connectivity plans often rely on a focal species, an organism with specific habitat preferences chosen to represent the movement needs of a broad group of species (Beier et al. 2008). They need information on habitat associations, which can be based on telemetry, camera trapping, sightings, or sign surveys (Noss and Daly 2006). Finally, modeling involves the inclusion of either intuitive or empirical approaches to inform the parameters used in a model. Models are usually based on movement (Colchero et al. 2011), genetics (Cushman et al. 2009), permeability analysis (Singleton et al. 2002), least-cost pathways (Squires et al. 2013), sourcedestination points (Cushman et al. 2009, Schwartz et al. 2009), resistant kernels (Compton et al. 2007), or circuit theory (Anderson et al. 2012). All connectivity models attempt to represent an organism s potential movements across the landscape given a variety of environmental factors and then identify the areas that minimize those movement costs. Given the abundance of options to choose from when planning for connectivity conservation it is important to highlight the potential strengths and weakness of each tool. Evaluating Connectivity Models Models of connectivity have been assessed in the field (Hilty and Merenlender 2004, Driezen et al. 2007, Leoniak et al. 2012) and using GPS-location data (Pullinger and Johnson 2010), although more widespread validation is needed. Given the increased use of GPS-telemetry data in connectivity modeling, more opportunities to compare and validate different models of resistance and connectivity are available. Although, dispersal events are rare and difficult to observe, identifying movements within home ranges may 39

52 help identify potential large-scale areas of connectivity. In fragmented areas, animals may avoid roads, development, or other landscape features. This may provide a smallscale version of how dispersing animals move through a modified landscape. Using location data, movement paths can be inferred by connecting subsequent locations (Cushman et al. 2010) or by using Brownian bridge movement models (BBMM; Horne et al. 2007, Sawyer et al. 2009, Lewis et al. 2011, Sawyer et al 2013). These approaches move beyond simple point-location data, and acknowledge that we are concerned with animal movement. Additionally, they provide answers to connectivity issues occurring in present time that may not be observed with genetic data. Connecting subsequent locations provides the easiest way to assess how well our connectivity models work, but the limitations are numerous, including; dependency on acceptable GPS fix rates, the need for small amounts of time between fixes, and inherent inaccuracies of assuming straightline movement between locations. However, Cushman et al. (2010) demonstrated the usefulness when parameterizing elephant movement models in relation to human development. Building upon connections of subsequent locations, BBMMs use location data to model potential movement paths as utilization distributions, which takes into account the error in connecting paths. They have been used to map migratory corridors (Horne et al. 2007, Sawyer et al. 2009), identify barriers to movement (Sawyer et al. 2013), and locate road crossings (Lewis et al. 2011). Similar to these methods, I believe they can aid in validating connectivity models by comparing utilization distribution (UD) scores with modeled connectivity scores from Circuitscape. Applying these types of validation techniques helps address some of the uncertainty present in connectivity modeling. 40

53 Focal Species in Connectivity Modeling Focal species, particularly large carnivores, are often used to plan for connectivity conservation (Beier 1993, Noss and Daly 2006, Servheen et al. 2001, Singelton et al. 2002). However, given the diverse habitat requirements and movement capabilities of animals, care must be taken when evaluating the use of focal species (Beier et al 2008, Chetkiewicz and Boyce 2009). Given the time, expense, and data requirements needed to design empirically defensible corridors, we may not be able to abandon the use of focal species (Haddad and Tewksbury 2006, Cushman et al. 2010). Bobcats may be an appropriate animal to evaluate the focal species approach because they are a wide-ranging carnivore (Beier 1993, Cushman et al. 2008, Servheen et al. 2001, Singleton et al. 2002) and rely on specific habitat features (Litvaitis et al. 1986, Broman 2012). In New England, they select early-successional forests, scrub-shrub, and wetland habitat types, and avoid agriculture, development, and high road densities (Litvaitis 2001, Litvaitis et al. 2006, Litvaitis and Tash 2008, Donovan et al. 2011, Broman 2012). Despite their sensitivity to development and roads (Crooks 2002, Riley 2006), bobcats can adapt to living in fragmented environments (Tigas et al. 2002). Therefore, they should show some behavioral response to roads and development resulting in clear areas of suitable and unsuitable habitats that can be used to assess connectivity for similar species. Objectives The objectives for this study were: i.) compare methods used to design connectivity conservation plans, ii.) validate connectivity modeling for bobcats at the 41

54 home range scale using telemetry-based data and Brownian bridge movement models, iii.) extrapolate validated connectivity models statewide and identify potential bottlenecks of movement, and iv.) assess the applicability of using bobcats as surrogate mesocarnivores in connectivity planning. METHODS Study Areas New Hampshire is the second most forested state in the nation at 82% forest cover, but has fallen from a high of 87% in 1960, and is expected to fall to 78.5% by 2030 due to rapid human development and increases in population (Sunquist and Hewes 2010). This is most pronounced in the southeastern portion of the state and along transportation corridors (Sunquist and Hewes 2010). Major transportation corridors included Interstate Highways 89, 93, and 95, and State Highways 3, 16, and 101. All operate as divided expressways. Heaviest development is concentrated in these regions, especially the seacoast and Merrimack Valley. This alteration from a predominantly rural to an increasingly fragmented state provides an excellent opportunity to observe the effects of fragmentation on habitat suitability and connectivity. Connectivity models were made using bobcats that were outfitted with GPS collars (n=18) in two portions of the state, the southwest and southeastern portion (Fig. 3-1). The first study area was selected because of a high historic occurrence of bobcats. The second study area was chosen to determine how differing levels of road and population density affected bobcats, in addition to there being an abundance of recent sightings in 42

55 the area. Road densities are 1.14 km/km 2 and 1.21 km/km 2 for the southwest and southeastern studies areas, respectively. The southwestern study area had a higher mean elevation (308.3 m), compared to SA2 (186.0 m.). Both study areas contain a mix of forest types including eastern hemlock (Tsuga canadensis), white pine (Pinus strobus), American beech (Fagus grandifola), yellow birch (Betula allegheniensis), paper birch (Betula papyrifera), northern red oak (Quercus rubra), red maple (Acer rubrum), sugar maple (Acer saccharum), and white oak (Quercus alba). Wetlands, agriculture areas, and varying levels of development are also present in both areas. Resistance Layers Two types of resistance layers were compared: an expert-opinion layer developed from an independently generated model (by members of New Hampshire Fish and Game and New Hampshire Audubon) and the inverse of a habitat suitability map generated from GPS-telemetry data from bobcats modeled with Resource Selection Probability Functions (Chapter II). Expert-opinion layers are often used in connectivity planning (e.g., Singleton et al. 2004, LaRue and Nielsen 2008) because of their low cost and wide applicability. Recently, resource selection functions (RSF), have been used to generate a cost layer for a species, providing an empirically-based layer to model corridors (Chetkiewicz and Boyce 2009, Pullinger and Johnson 2010, Squires et al. 2013). When applied to connectivity modeling, the inverse of the habitat suitability map generated from the RSF is used as the cost layer (Chetkiewicz et al. 2006). This method assumes traversing less suitable habitat is avoided, and that animals have some prior knowledge or at least the ability to infer the relative cost of moving through a landscape and choose the path that maximizes chance of survival (Driezen et al. 2007). Unlike expert opinion 43

56 Fig GPS locations from marked bobcats in the southwest and southeastern portions of New Hampshire. Bobcats were monitored from Major highways and names of regions are shown for reference. 44

57 models, RSFs that utilize GPS-telemetry data can be expensive, time intensive, and may be only applicable to the organism for which data are available. Despite these limitations, it is believed that they are a better representation of an animal s movement through its habitat and are a superior method when resources are allowable. Expert Opinion. Resistance layers based on expert-opinions and literature review were independently developed by biologists at New Hampshire Fish and Game (NHFG) and New Hampshire Audubon (NHA). For a complete description of methods, see NH Audubon and NHFG (2010). Briefly, the expert opinion-based resistance layer used five habitat variables to model resistance (Fig. 3-2), with each receiving a relative influence (or weight) score: land cover = 40%, distance to roads = 40%, distance to riparian = 10%, and slope = 10%. Resistance layers were parameterized with logistic functions: riparian (Equation 1), slope (Equation 2), and distance to road (Equations 3, 4, 5). Resistance by roads was determined by average annual daily traffic (AADT) and divided into three class; low (>100 AADT; equation 3), medium ( AADT; equation 4), and high (3000+; equation 5). Each cover type (NLCD 2001, with modifications from NLCD 2006) was assigned a resistance score (Fig. 3-2). Additionally, a ridgeline modifier was added that subtracted two points from all ridgeline resistance scores. 45

58 Equation #1: Equation #2: Equation #3: Equation #4: Equation #5: Fig Resistance scores for riparian areas are a function of distance from stream (top left). Resistance scores for slopes are resistance a function of percent slope (top right). Resistance scores for roads are a function of traffic volume, and separated into three classes-low, medium, and high (lower left). Land cover variables were assigned resistance scores from 1-10 with 1 being lowest resistance and 10 being the highest (lower right) Figures were recreated from NH Audubon and NHFG (2010). 46

59 Resource Selection Probability Functions. Resource selection probability functions were fit at the second and third-order of habitat selection using incidental observations and GPS-telemetry data, respectively (see Chapter II). These models were refit to test predictability using location data from each individual bobcat. This entailed withholding data from one bobcat, fitting the model using the data from the remaining seventeen bobcats, and then using the data from the withheld bobcat to validate the resistance model. By using the withheld bobcats location data to validate each connectivity model, I had a quasi-independent method of testing connectivity models. Individual RSPF models were fit using the package ResourceSelection (Lele et al. 2013) in the statistical program R (R Core Team 2012). Each individual model then served as a conductance layer in connectivity modeling using the program Circuitscape (McRae and Shah 2009), with the assumption that areas of higher suitability would have greater conductance than areas of lower suitability. Functionally, the conductance layer is the inverse of a resistance layer. Connectivity Modeling and Assessment Circuit Theory. Circuit theory, through the program Circuitscape (McRae and Shah 2009), uses algorithms that were originally developed for electrical circuit theory, but have the potential to be applied to a variety of connectivity conservation issues (McRae et al. 2008, Fig. 3-3). Circuitscape incorporates multiple random walk pathways into one model, so instead of modeling a single path, as in least-cost path modeling, Circuitscape displays the relative cost of moving through the entire landscape. McRae and Beier (2007) applied this to the gene flow of a plant and an animal population and found that it performed better than isolation by distance and least-cost-path methods. 47

60 This could be beneficial in identifying potential pinch-points of movement through high-cost area because random walkers will select these points at a higher probability. This approach may be more applicable to organisms that inhabit a variety of habitats with few large blocks of protected land. F ig Comparison of circuit theory and least cost pathways. (A) Cost surface layer used for modeling. The white patches in the lower left and upper right corners are the source patch patches. Resistance is shown using a gray scale. Areas with low resistance are light grey, areas of high resistance are dark grey, and black areas are barriers to movement. (B) Least cost modeled corridor is shown in white. (C) Output from circuit theory. Areas of blue are have a lower probability of utilization, while areas of red have a higher chance. Areas in yellow show extremely high utilization points and thus identify pinch-points of movement. These areas are constrictions in the landscape where organisms are forced to travel, highlighting the area of highest conservation value. A real world example of such points may be an undeveloped underpass on a major freeway system (from McRae et al. 2008). Using program Circuitscape (McRae and Shah 2009), connectivity was modeled for both resistance layers. To ensure that source (start points) and ground (end points) did not bias the connectivity maps, methods used by Anderson et al. (2012) were adopted with some modification for bobcats (Fig. 3-4). A minimum-convex polygon (MCP) was generated around all bobcat locations. The centroid of this was determined in GIS and the length from the centroid of the MCP to the furthest point on the MCP was used as a radius to generate a circle around the locations. A second circle was added at twice the 48

61 radius of the initial circle. Squares were then generated around both circles. The inner square served as the unbiased analysis area. The outer square defined the full analysis area. Program CircuitScape was run four times through this area: once using the top side of the square as a source and the bottom as the ground, once using the bottom side as a source and the top as the ground, and similarly from left to right and right to left. The average of these four outputs was used to make the cumulative map. Using this method resulted in an analysis area unbiased by source or ground points (Anderson et al. 2012). Bobcat locations were sequentially connected to produce a movement path, referred to as the actual path. Segments that were not the result of two consecutive GPS fixes were removed. The actual path was then randomly rotated and shifted 100 times to generate 100 random paths using the Geospatial Modeling Environment program (GME; Beyer 2008). Mean conductivity scores were then computed for each path using GME (Beyer 2008). A one sample t-test was used to test if the actual paths were significantly different from the mean conductivity score of the random paths, indicating bobcats were using areas predicted by the connectivity model. Brownian Bridge Movement Models. Brownian bridge movement models were generated for each individual bobcat using the package BBMM (Nielsen et al. 2013) in the statistical program R. Location error was set to 20 m based on collar tests done by Mallet (2013). Only consecutive locations were used to estimate BBMMs. Utilization distribution scores were then summed to get cumulative distribution functions for each individual, and only areas with scores within 95% utilization distribution were used in the analysis. 49

62 Fig Modeling scheme used to test connectivity predictions for bobcats. RSPF generated using all location except from Bobcat #47, with his locations overlaid. This served as the conductivity layer (top left). Sampling protocol adopted from Anderson et al. (2012) to map connectivity in bobcat home range with minimal source/ground bias (top right). Output from circuit theory using top as source/bottom as ground, bottom as source/top as ground, left as source/right as ground, and right as source/left as ground (middle panels L to R). Cumulative circuit theory map with actual path of bobcat #47 (bottom left). Actual path overlaid with 100 random paths, rotated and shifted (bottom right). 50

63 A roads layer was overlaid onto individual BBMMs and circuit theory models (both expert opinion and RSPF). The roads layer was divided into sampling points, spaced every 100 m. Values for BBMM utilization distributions, expert-opinion based circuit theory, and RSPF based circuit theory models were obtained for each point in GIS. Spearmann pairwise correlations were then derived between BBMM scores and the two connectivity models at the road points. Significant negative correlations between BBMM UD scores and circuit theory scores were used to evaluate agreement between the two modeling procedures. As the BBMM UD score decreases, the level of estimated use for that area increases, therefore a negative correlation between the BBMM UD and connectivity scores indicates that areas of high use and high connectivity are correlated. Extrapolation of Connectivity Models After model validation and methods comparison took place at the home range level, the models were extrapolated to the rest of New Hampshire. The RSPF resistance layer utilizing both second and third-order selection (see Chapter II) was used. The expert-opinion model was designed to be applicable statewide and no alterations were necessary. Due to differences in spatial extent of some layers (i.e., being constrained to either New Hampshire or the continental U.S.), the RSPF layer and expert-opinion layer were buffered by random pixels with scores between 0 and 1 (Koen et al. 2010). This assured that the sources and ground points did not bias the connectivity model (Koen et al. 2010). Program Circuitscape was run to and from each cardinal direction (as described in previous section). Connectivity scores were then extracted to major roads in GIS and divided into degrees of connectivity. Segments of roads that resulted in bottleneck that could be limiting connectivity were identified. 51

64 Bobcats as a Surrogate-Species The use of bobcats as a surrogate species to model connectivity was tested by using remote cameras and the extrapolated connectivity models. During January to April 2013, 29 sites were sampled with remote trail cameras 1-5 times with a mean sampling time of 11.5 days (range 2-18 days) along New Hampshire Route 101. Camera sampling time varied due to camera malfunctions and site visit logistics. The sampling area spanned from Epping, NH to Auburn, NH where Route 101 is a four-line divided expressway with a traffic volume of ~40,000 vehicles per day (NH DOT Traffic Volume). This size road represents a potential barrier to movement for bobcats and other forest carnivores (Riley et al. 2006), and therefore serves as suitable location to test connectivity models. Sites were systematically distributed at approximately 1 km intervals and were mapped in ArcMap 10.0 before being located in the field. Each site served as a potential crossing point, and it was believed that areas with higher connectivity scores would better explain carnivore presence. Sites were accessed by auxiliary roads that bisected Route 101. Only sites located in forested areas were used to limit the types of habitats sampled, and because they provided structure to mount cameras and baits. Sites were located between m from the highway, depending on highway shoulder width and available vegetation. Four habitat measurements were taken for each site (Table 3-1) to assess whether animals were responding to site specific habitat features versus the landscape level features measured by the connectivity models. These variables were whether the road was visible from the site, if the site was adjacent to a stream, whether there was an open or closed-canopy forest, and the distance to the road. 52

65 Camera sites were baited with both coyote urine and catnip oil, and a compact disc was hung nearby to act as a visual attractant. Coyote urine was sprayed at the base of a tree 3-5 m from and within view of the camera. Catnip oil was contained in 15 ml. test tubes and zip-tied to a sapling or branch 3-5 m from and within view of the camera. Compact discs were tied to adjacent branches, out of view of the camera to limit incidental camera activation. All animals that triggered the camera were noted, but data analysis focused on carnivore species; including, bobcats, coyotes (Canis latrans), gray foxes (Urocyon cinereoargenteus), red foxes (Vulpes vulpes) fishers (Martes pennanti), raccoons (Procyon lotor), and opossums (Didelphi virginiana). Consecutive days within a session were collapsed to represent one event (i.e. Long et al. 2011, Tempa et al. 2011); therefore only one detection was counted for each species in each sampling occasion. Probability of occupancy and detection was modeled in program Presence (Hines 2006). Singleseason models were run for each species for which there was sufficient data (coyotes, gray foxes, fishers, and raccoons). Model fitting followed a stepwise procedure and was judged using AIC. First, species specific differences in probability of detection across sites and sampling periods were tested against the null model with probability of occupancy held constant. Next, the best fitting model for probability of detection was then re-fit with all possible variables that affect occupancy, including connectivity scores. Models only included one variable to model the probability of detection and one variable to model probability of occupancy. Predictive capability of circuit theory models was determined by whether they were included in top models. 53

66 Table 3-1. Variables used to model probability of detection and occupancy at camera sites along Highway 101 from January-April Temporal variables measured differences in deployment order and duration, and were used to model differences in the probability of detection. Probability of occupancy was modeled using habitat and modeling variables. Habitat variables were measured in the field during camera deployment. Modeling variables utilized connectivity scores, with low scores indicating poor connectivity and high scores indicating good connectivity. Category Variable Justification Units Range Temporal # of prior sessions Knowledge of trap Count 1-4 Camera days Sampling time Count 2 to 18 Session Temporal variation Count 1-5 Habitat Road visible Avoidance of roads Category yes or no Adjacent to stream Potential corridors Category yes or no Canopy Provides cover Category open or closed Distance to road Tolerance of roads Meters Modeling Circuit theory EO Measure of connectivity Unitless 0-5 Circuit theory - RSPF Measure of connectivity Unitless 0-5 Resistance Layers RESULTS The resistance layer developed by NHFG and NH Audubon (2010) was used as the expert opinion-based approach (Fig. 3-5). The empirically-based resistance layer (Fig. 3-5) was modeled using resource selection probability functions described in Chapter II. This layer showed habitat selection by bobcats, and resistance was assumed to be negatively correlated with selection. 54

67 Fig Resistance models used for mapping bobcat connectivity. The resource selection probability function (RSPF) model was generated using GPS-telemetry data from collared bobcats (right). The expert opinion model was generated independently by agency biologists (NHFG and NH Audubon 2010). 55

68 Connectivity Modeling and Assessment Circuit Theory. Connectivity modeling at the home-range level using Circuitscape was completed for all 18 collared bobcats using both the individual RSPFs layers for each bobcat, and the expert opinion layer. Scores of actual and random paths were extrapolated using GME. Circuit theory modeling using RSPFs resulted in 16 out of 18 actual paths scoring higher than random paths (p<0.05; Fig. 3-6; Appendix C), indicating actual bobcat paths had greater connectivity compared to random paths. The expert-opinion model resulted in 12 of 18 (p<0.05; Fig. 3-6; Appendix C) actual bobcat paths scoring higher than random paths. Fig Differences between actual and random path mean scores for resource selection probability function and expert opinion connectivity models. The connectivity score for the actual path was standardized to 0 for each individual bobcat. The difference between the mean connectivity score of random paths and actual paths was then plotted. If connectivity was successfully predicted, the mean score of the random paths will be less (below zero) than the actual path. 56

69 Brownian Bridge Movement Models. Brownian bridge movement models were generated using location data from each individual bobcat with the R package BBMM. Maximum amount of time between points was 5 hours and 7 hours for Lotek and Siritrack collars, respectively. Location error was set to 20 m (Mallet 2013). BBMM models and Circuitscape models were overlaid in GIS, and scores for each were extracted from 10,292 points along roads within 95% UD of each bobcat. Spearman correlations (Table 3-2) for RSPF models utilizing all locations revealed a significant negative correlation for all roads (r s = -0.12, p < ) and major roads (3330 points, r s = -0.09, p < ). Spearman correlations (Table 3-2) for expert opinion models utilizing all locations did not have a significant negative correlation for all roads (r s = 0.02, p < ) and major roads (r s = 0.04, p < ). Individual RSPF models were significantly negatively correlated for 12 of 18 bobcats when utilizing points on all roads, and 9 of 17 bobcats for points that were only on major roads. Individual expert-opinion models were significantly negatively correlated for 5 of 18 bobcats when utilizing points on all roads, and 4 of 17 bobcats for points that were only on major roads. One bobcat (41) had no major roads within its home range, so it was not included in this analysis. Extrapolation of Connectivity Models. Circuitscape models were extrapolated to the entire state using both resistance layers (Figs. 3-7 & 3-8). Models showed similar low flow through most of the Merrimack Valley, the southeastern portion/coastal portion of the state, and other highly developed areas. The RSPF model showed relatively high flow throughout the state, with the exception of the White Mountains and the northern portion of the state. Potential bottlenecks of movement were between Concord and Manchester, between Lake 57

70 Winnipesaukee and the White Mountains, and along the southeastern corner of the White Mountains. The expert opinion model showed highest flow through the northern part of the state, the White Mountains, and along the western edge. Circuitscape models extracted to the roads (Figs. 3-7 & 3-8) highlighted the lack of connectivity in heavily developed areas, and along higher traffic volume roads, with the latter seen in expert opinion model in particular. Table 3-2. Spearmen correlations between BBMM and circuit theory models for all bobcats and separate individuals, and all roads and major roads. Significant correlations are highlighted in bold, and indicate agreement between BBMM models and circuit theory models. RSPF Expert Opinion ID Roads-All p-val Roads-Major p-val Roads-All p-val Roads-Major p-val All < < < < < < < < < < < N/A N/A N/A N/A < < < < <

71 Fig Connectivity models for New Hampshire generated using RSPF habitat suitability models and Program Circuitscape (left). Areas with highest conductance or movement are in yellow and lowest are in dark blue. Connectivity scores for major roads in New Hampshire (right). 59

72 Fig Connectivity models for New Hampshire generated using expert-opinion resistance models and Program Circuitscape (left). Areas with highest conductance or movement are in yellow, and lowest are in dark blue. Connectivity scores for major roads in New Hampshire (right). 60