CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Habitat Occupancy of Bobcats (Lynx Rufus) in an Urban Fragmented Landscape a Thesis Subm

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CALIFORNIA STATE UNIVERSITY, NORTHRIDGE

Habitat Occupancy of Bobcats (Lynx rufus) in an Urban Fragmented Landscape

A thesis submitted in partial fulfillment of the requirements

For the degree of Master of Science in Biology

Sean Patrick Dunagan

August 2015

The thesis of Sean Patrick Dunagan is approved:

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Dr. Paul Wilson Date

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Dr. Seth Riley Date

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Dr. Tim Karels, Chair Date

California State University, Northridge

Acknowledgements

I would like to thank my committee members: Tim Karels, Seth Riley, and Paul

Wilson. Tim shared his expertise in ecology and provided insight in the statistical design and analysis of my thesis. Seth provided logistical support needed to complete this project as well as his expertise on urban carnivores and their ecology. Paul offered his knowledge of ecology and statistics and was readily available for advice.

I am grateful for the support provided by the National Parks Service. Specifically,

I would like to thank Joanne Moriarty and Justin Brown for their hard work on urban carnivores. Without their work this project would not have been possible.

Land use permission was provided by Conejo Open Space Conservation Agency and Rancho Simi Recreation and Parks District.

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Table of Contents

Signature Page ii

Acknowledgements iii

List of Tables v

List of Figures vi

Abstract vii

Introduction 9

Methods 14

Results 21

Discussion 25

Literature Cited 29

Appendix 34

List of Tables

Table 1 – Habitat Covariates 34

Table 2 – Pearson’s Correlation Matrix of Habitat Covariates 35

Table 3 – RSF Candidate Models and Model Selection 36

Table 4 – RSF Selection Coefficients 36

Table 5 – Spearman’s Rank Correlation for 5-k Fold Cross Validation 39

Table 6 – Spearman’s Rank Correlation for Individual Bobcats 41

Table 7 – Visual Line Surveys of Cottontail Rabbits 42

Table of Figures

Figure 1 – Study Area 35

Figure 2 – Bobcat Occupancy Models 36

Figure 3 – 5-k Fold Cross Validation 38

Figure 4 – Individual Bobcat Cross Validation 40

Figure 5 – Cottontail Rabbit Fecal Pellet Counts 42

Abstract

Habitat Occupancy of Bobcats (Lynx rufus) in an Urban Fragmented Landscape

Sean Patrick Dunagan

Master of Science in Biology

Urbanization subdivides natural landscapes creating isolated fragments separated by novel urban habitats. Species vary in their sensitivity to the process of urban fragmentation where some species can tolerate living in urban areas by exploiting resource subsidies. Mammalian carnivores have been shown to vary in their sensitivity to urban fragmentation where more tolerant species can exploit anthropogenic resources.

Bobcats (Lynx rufus) represent an intermediate response to urban fragmentation as they are present in fragmented natural areas but do not thrive in urban development. Bobcats are known to enter urban areas and may tolerate urban fragmented landscapes by harvesting prey from urban environments. Using resource selection functions (RSFs), I modeled the habitat occupancy of 7 female bobcats in the urban fragmented landscape of

Thousand Oaks, California. Occupancy models were compared to the distribution and abundance of cottontail rabbits (Sylvilagus spp.) to test if bobcats use urban areas due to an inflated urban rabbit population. Bobcats did go into urban areas, primarily at night; however, rabbit densities in urban areas varied more than rabbit densities in natural habitats. Bobcats occurred more frequently in coastal sage scrub habitats and used habitat

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edges during nocturnal hours. Rabbit densities in natural habitat patches were the most stable with highest densities in natural edge habitats. Bobcats appear to tolerate urban fragmented landscapes by behaviorally adjusting to resource distribution in natural habitat patches, and not by exploiting urban resource subsidizes. As landscapes become more urbanized, the presence of bobcats can be used to evaluate the ecological integrity of natural fragments as bobcat presence in these areas is likely not mitigated by urban resources.

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Introduction

Urban development causes the loss of habitats and leaves remaining natural areas subdivided and isolated as habitat fragments. In contrast to the original natural landscape, these fragments have reduced area, distinct boundaries, and more complex shapes with higher perimeter-to-area ratios, resulting in more edge habitats (Ewers and Didham

2007). This restructuring of the landscape often causes a loss of species diversity

(McDonald et al. 2008); however, some species can tolerate this change and persist in urban fragmented landscapes. Human activities in urban and suburban habitats, such as irrigation of gardens and lawns, can increase primary productivity, potentially buffering against seasonal variation, extending animal breeding seasons, and increasing the abundance of urban exploiters (Shochat et al. 2006). Whether a species can adapt or exploit urban habitats is a result of its sensitivity to these processes of fragmentation

(Henle et al. 2005). This depends on how the animal views the landscape in terms of resource distribution and mortality risks, which is likely to be variable and even contradictory causing responses to be context dependent (Haila 2002; Belisle 2005).

Mammalian carnivores vary in their response to fragmentation with larger species more likely to persist in large patches of natural habitat, or with increasing connectivity among smaller patches (Crooks 2002). Mountain lions (Puma concolor), which require large contiguous patches of unaltered habitat, have been shown to be sensitive to urbanization having a strong negative relationship with proximity to and intensity of urban development (Crooks 2002; Ordenana et al. 2010). Other species, such as raccoons

(Procyon lotor; Prange et al. 2003) and skunks (Mephitis mephitis; Rosatte et al. 2010), tolerate or even thrive in urban habitats taking advantage of urban food resources and

structures (Hadidian et al. 2010). Bobcats (Lynx rufus) and coyotes (Canis latrans) represent intermediate responses to urban fragmentation (Crooks 2002), both being widely distributed in fragmented areas (Ordenana et al. 2010). Coyotes are more tolerant of urban intensity and frequent urban habitats, exploiting urban food subsidies, such as garbage, fruits, and pet food. Bobcats are strict carnivores and do not consume anthropogenic food items, but they do enter urban areas, primarily at night (Fedriani et al.

2001; Riley et al. 2003). Whether bobcats exploit food subsidies from urban environments is not well known but has been cited as an explanation for their presence in urban areas (Riley et al. 2003). Bobcats may exploit prey, such as rabbits, that might inhabit urban habitats to take advantage of lawns, gardens, and urban parks.

Predator habitat selection has been linked to the hunting success associated with different habitat types (Gorini et al. 2012; Hopecraft et al. 2005). For bobcats, variation in habitat use is associated with increased abundance of prey resources (e.g. Litvatis et al.

1986; Knick 1990), which affects females more strongly than males (Benson et al. 2006;

Ferguson et al. 2009). Specifically, female bobcats maintain discrete home ranges that do not overlap with conspecific females to partition resources, whereas males have larger home ranges to encompass multiple female territories to increase mating opportunities

(Bailey 1974; Benson et al. 2006; Ferguson et al. 2009). Non-overlapping home range behaviors for females are adaptive to ensure that information about resource abundance and distribution does not become obsolete from harvesting by neighboring bobcats

(Spencer 2012).

Home range analysis is often used to assess habitat associations by animals; however, home ranges alone often misrepresent habitat relationships, especially when

resources are patchy. Disproportionate use of habitats by animals within home range boundaries can reveal habitat preferences or selection caused by habitat-specific differences in resource acquisition and mortality risk (Mitchell and Powell 2008; Gorini et al. 2012). Bobcats living in urban fragmented areas may receive fitness benefits from nocturnal foraging in urban habitats when perceived mortality risk is lower and if prey is available. However, bobcats do suffer mortality risks from human activities such as vehicle collisions (Riley et al. 2003) and rodenticide exposure (Riley et al. 2007), thus bobcat use of urban areas may represent a high-risk–high-reward situation where the benefits of harvesting from an inflated prey base outweigh the increased risk of perceived mortality.

In the urban fragmented landscape of Thousand Oaks, California, at the border of

Los Angeles and Ventura Counties, bobcat diet is primarily composed of rabbits (the desert cottontail rabbit Sylvilagus audubonii, and the brush rabbit Sylvilagus bachmani), pocket gophers (Thomomys bottae), California ground squirrels (Otospermophilus beecheyi) and rodents (e.g. dusky footed wood rat Neotoma fuscipes, desert wood rat

Neotoma lepida, and various Peromyscus spp.) (Fedriani et al. 2000; Riley et al. 2010)

Rabbits comprise the majority of bobcat diet, occurring three times more in bobcat scats than any other food item (NPS, unpublished data) and the consumption of rabbits by bobcats does not vary seasonally (Riley et al. 2010). Rabbits occur in both natural fragments and urban areas, and are often considered pests by residents (personal observation). It has been conjectured that rabbit densities might be greater or at least buffered against seasonal variability in these urban areas with lawns and gardens. Rabbit population dynamics have only recently been studied in North American urban areas but

not in southern California. For example, Hunt et al. (2014) reported higher population densities of eastern cottontail rabbits (Sylvilagus floridanus) in a Chicago urban park when compared to other studies of eastern cottontails in undeveloped areas of the

Midwestern United States.

In addition to urban resource subsidies, structural aspects of fragmented landscapes, such as edge habitats, may also facilitate cottontail rabbit populations. Pierce et al. (2011) showed that desert cottontail rabbits used areas in a sagebrush community

(Artemisia spp.) that had increased amounts of edge habitat. Similarly, Palmores (2001) showed that European rabbit (Oryctolagus cuniculus) fecal pellets were more abundant along scrubland edge and ash stand habitats, while other habitats were marginally used.

Increased amounts of edge habitat can create open areas where rabbits can be more vigilant while not giving up access to shrub cover.

Here I quantify the relative probability of the occurrence using resource selection functions (Manly et al. 2002; Johnson et al. 2006; Lele et al. 2013) of 7 female bobcats over one year from 2013 to 2014 to test bobcat habitat occupancy in an urban fragmented landscape. Bobcats may exploit rabbits in urban areas if abundances are high or if rabbit populations are buffered against seasonal variation. Additionally, within natural areas, bobcats may respond to increased use of habitat edges by rabbits. Bobcats in urban fragmented areas are less active during the day (Tigas et al. 2002) and less likely to rest in urban development (Riley et al. 2003). Thus I consider separately two sets of resource selection functions: one for dawn+dusk+nighttime hours, when bobcats are relatively more active, and another for daytime hours when bobcats are relatively less active. Under the assumption that lawns increase rabbit densities and bobcats hunt them, I expected

bobcat use of urban habitats to increase at night compared to peak daylight hours. Such a pattern of habitat use would reduce interactions with human activity while still allowing for use of urban resource subsidies.

Methods

Study Area

I studied bobcat habitat occupancy in Thousand Oaks, California (34.1894° N,

118.8750° W, 270 m above sea level), adjacent to Santa Monica Mountains National

Recreation Area. This landscape consists of natural-habitat fragments subdivided by an urban matrix. Natural fragments are composed of coastal sage scrub, dominated by purple sage (Salvia leucophylla), black sage (Salvia mellifera), California sage-brush (Artemisia californica), California buckwheat (Eriogonum fasciculatum), and ashy leaf buckwheat

(Eriogonum cinereum), with coastal live oak (Quercus agrifolia), laurel sumac (Malosma laurina), and coyote bush (Baccharis pilularis), and of grass habitats dominated by annuals such brome grasses (Bromus spp.), wild oats (Avena spp.), black mustard

(Brassica nigra), and shortpod mustard (Hirschfeldia incana). Urbanization is primarily residential housing, but also includes altered open areas such as urban parks and a golf course. Urban areas tend to occur in valleys leaving the undeveloped natural areas as hills. Fuel reduction management is carried out annually around the majority of the urban edge leaving a barren dirt strip 10 to 50 m in width.

I derived habitat covariates (Table 1) for use in habitat occupancy models from satellite photography (USGS, Landsat 7) using ArcGIS 10.1 (ESRI, Redlands California).

Categories of landscape variables were: urbanization, urban edge, and natural habitats.

Natural habitat patches were further stratified into three categories: coastal sage scrub, grassland, and natural edge habitats. I also derived two continuous variables to quantify edge effects: distance to urban edge, and distance to natural edges. Data resolution was set to 30 × 30-m grid cells.

Resource Selection Function

I modeled the relative probability of occurrence of seven female bobcats in habitats occupied by cottontail rabbits within their home-range boundaries (3rd order selection, Johnson 1980) using resource selection functions (RSF). My study was conducted in a use/available framework where bobcat locational fixes were used areas assigned 1 whereas random points were available habitat locations assigned 0. Each RSF took on the form of w(x) = exp(β1 X1+β2 X2+β3 X3+… βi Xi) where βi are selection coefficients for each habitat covariate Xi (Johnson et al. 2006). Habitat covariates were compared using Pearson’s correlation matrix (Table 2), and when a variable was correlated with another variable at r > 0.60 one of them was eliminated to avoid redundancy in explanatory variables.

Bobcat location data from global positioning system (GPS) collars was provided by the National Parks Service’s study of the effect of urbanization on carnivore ecology

(e.g. Fedriani et al. 2001; Riley et al. 2003; 2006; 2007) over one year from 2013 to 2014.

For bobcat capture and collaring protocols, see Riley et al. (2006; 2007). I first defined the extent of habitat available to each individual bobcat as its 100% minimum convex polygon (MCP) home range estimate. Bobcats are relatively less active during midday periods, more active at night, and the most active during crepuscular hours. GPS location data was then partitioned into the two diel activity periods to be used in separate models for the daytime and crepuscular + nighttime period. Furthermore, within urban fragmented areas, bobcats are even less active during daylight hours than bobcats in non- fragmented landscapes possibly to avoid human activity (Tigas et al. 2002). Relatively less active periods, or the day activity model, were defined as locational fixes during

daylight hours after the sun had risen but before sunset, and relatively more active times, night activity model, during crepuscular and night hours where crepuscular times were defined as dawn and dusk hours. GPS locational data included in the night activity model ranged from 1800 to 0700 hours in spring, 2000 to 0600 hours in summer, 1800 to 0700 hours in fall, and 1700 to 0800 hours in winter. Additionally, the data for each activity period was thinned to gain independence among points so only a single location point for each bobcat per period per day was used in each model. A random distribution of points equal to the number of locational fixes was generated using the Geospatial Modeling

Environment (Beyer, H.I. version 0.7.2.1) for each bobcat during each activity period within the boundaries of its MCP home range. These points represent available habitats or the pattern of use that would be expected if the animal used habitats randomly. Data from all seven bobcats were pooled within each period to be used in the two RSFs.

For each activity period, I considered the same 13 candidate models (Table 3).

Combinations of habitat covariates used in candidate models are based on patterns of the distribution of cottontail rabbits obtained in this study and known spatial behaviors of bobcats within fragmented habitat patches (Tigas et al. 2002; Riley et al. 2003). Model selection was based on Akaike Information Criterion corrected for small sample sizes

(AICc) (Burnham and Anderson, 2002). I then calculated the differences in AICc values

(Δi) and used model AICc weights (wi) to distinguish the best model among competing models. All estimated models and model selection were performed using the glm function in R (R Foundation for Statistical Computing, Vienna, Austria) and projected

RSFs were performed in ArcGIS 10.1.

Models were validated using 5 k-fold cross validation. Data from each period was separated into 5 k-fold partitions where four of the five folds trained the model and validation was tested on the remaining 20% of the data. This was done iteratively so that each fold (representing 20% of the data) had been validated by the other 80%. Following the procedures in Boyce et al. (2002), model performance was evaluated by comparing

RSF scores for the partition testing data against categories of RSF score or bins. Then a

Spearman rank correlation performed between the “area-adjusted frequency of cross- validation points within individual bins and the bin rank” for each cross validated model.

Models with good predictive value are those that have a strong positive correlation. The number of bins is arbitrary; however, if there are too many bins, no points will fall in lower bin ranks. I used 10 equal area bins as increasing the number of bins increased the number of lower ranked bins with zero points.

Also, to test if any single bobcat strongly influenced occupancy patterns, I followed the same procedures as a 5 k-fold cross validation but rather than using five equal partitions I tested each individual bobcat with a training model consisting of the other six bobcats. Similarly, a Spearman rank correlation was performed for each testing set to determine its predictive value.

Rabbits

Line transect sampling

I used visual line transect sampling to assess the abundance and distribution of rabbits (Sylvilagus audubonii and Sylvilagus bachmani). Twenty-four transects were stratified to occur equally in urban, natural, and urban edges. Depending on the size of the habitat surveyed, transect length varied between 0.96 km and 3.4 km. Total sampling

distances were 15.5 km in urban, 10.9 km in natural, and 9.3 km in urban edge. Transect length averages were 1.94 km in urban, 1.36 km in natural, and 1.16 km in urban edge habitats. Each transects was surveyed once per season (spring, summer, fall, winter) over a one-year period from March 2013 to February 2014. Transects were surveyed by the same observer during crepuscular hours occurring either 15-20 min before sunrise and continuing for 30-40 min or 30-40 min before sunset and continuing for 15-20 min until it was too dark to see rabbits at distances around 50 m.

The placement of random transects was not logistically possible due to landscape structure, thus transects followed established roads and trails. The nonrandom placement along features of the landscape can cause bias in estimates of abundance if rabbit density is affected by the presence of roads and trails (Burnham 1980; Marques et al. 2013).

Additionally, these roads and trails had a tendency to curve, but Hiby and Krishna (2001) suggest it is the nonrandom placement of roads that is more important than their curving, especially in shrub and forested areas where detection distances are short. I acknowledge the problems associated with nonrandom placement and take caution in interpreting the results from this transect sampling.

For each observed rabbit, I measured its perpendicular distance from the transect line using a digital range finder. To increase the robustness of the density estimation, observational distances were grouped into 10-m intervals, and the data were truncated at

50 m, the greatest distance at which a rabbit was observed. Rabbit densities were estimated using the default settings of the conventional distance sampling option in the software DISTANCE 6.0 (Thomas et al. 2010). The 95% confidence intervals provided were used to assess difference among habitats and season.

Fecal pellet counts

Within natural fragments, I also estimated the relative abundance of rabbits using fecal pellet counts in scrub, edge, and grass habitats. This is a common method for obtaining indices of abundance for lagomorph species (e.g. Krebs et al. 2001; Pierce et al.

2011) and has been shown to produce reliable estimates of abundance in a Mediterranean environment (Palomares, 2001). Pellet plots were not used in urban areas as it is primarily composed of privately owned homes where obtaining land-use permission was not feasible.

Seventy sets of one square-meter circular fecal-pellet plots were established within natural habitat fragments. The size and shape of the fecal pellet plots was chosen from data gathered during a pilot study comparing plot areas and shapes. One-meter circular pellets plots showed the least amount of variation in pellet counts compared with square plots of the same area and circular plots of smaller area. Each set was composed of three pellet plots with a plot first established along a natural edge habitat, and plots perpendicularly placed 50 m into neighboring coastal sage scrub and 50 m into neighboring grassland vegetation types. Plot locations were marked with 0.5-m long steel rebar stakes and were cleared of all fecal pellets. Plots were counted and then cleared four times in three month intervals from spring 2013 through the end of winter 2014. Over the year, five sets of pellets plots were removed due to the excavation of rebar by animals and repeated anthropogenic interference. Therefore, analysis was conducted using the remaining 65 sets of plots that were sampled over the entire duration. An index of the abundance of rabbits per habitat stratum was calculated as the number of pellets/day/m2. I tested for the differences in the deposition of fecal pellets per habitat and season using a

multivariate repeated-measures ANOVA where habitat was the between-subjects factor and season was the repeated measure. Fecal pellet counts were transformed for analysis as loge(x + 1).

The purpose of counting fecal pellets was to determine the variability in resource distribution available to bobcats. It is reasonable to think that bobcats do not discriminate between rabbit species as prey items, thus fecal pellets deposited by different rabbit species were pooled. I also recorded the presence or absence of wood rat pellets

(Neotoma spp.), another prey resource known to be important for local bobcats. Dusky footed wood rats and desert wood rats co-occur within the study site, but wood rat species were not distinguished.

Results

Resource selection functions

A total of 1075 bobcat locational points were used for the night activity model and 918 points for the day activity model.

Pearson’s correlations of the habitat covariates revealed only a single redundancy

(scrub by urban habitat r = -0.656 in Table 2). Since the purpose of this study is to determine how urbanization effects occupancy patterns of bobcats, the scrub variable was removed and models were run using the urban habitat variable. Interpretation of the sign of the β value for the urban habitat variable can, therefore, be considered the inverse of being present in coastal sage scrub. Although the models did not explicitly use the scrub habitat variable, use of scrub is important to bobcats and is considered when interpreting the urban habitat variable in the model.

Model selection for day activity supported the model containing all six habitat covariates (AICc weight wi = 0.89 in Table 3). The second highest ranking model composed of categorical habitat variables had a ΔAICc = 4.29 and a much lower relative probability of being fit as the best model (wi = 0.10); thus the highest ranking model was chosen. The 5 k-fold model cross validation provided a mean Spearman’s rank correlation of rs = 0.952 (p < 0.001) indicating that the model consistently predicted the habitat occupancy patterns of bobcats (Figure 3A, Table 5). In addition, model cross validation procedures of individual bobcats resulted in a mean Spearman’s rank correlation of rs = 0.95 (p < 0.001). This suggests that there is no single bobcat driving the habitat occupancy patterns (Figure 4A, Table 6).

Model selection for night activity indicated a model composed of five of the six habitat covariates with the second highest ranking model having a ΔAICc = 2.46. Model weights favored the highest ranking model with wi = 0.70, compared to the next model with wi = 0.20 (Table 3). The 5 k-fold model cross validation provided a mean

Spearman’s rank correlation of rs = 1.00 (p < 0.001), indicating that the model consistently predicted the habitat occupancy patterns of bobcats during night activity periods (Figure 3B, Table 5). The individual bobcat model cross validation procedures produced a mean Spearman’s rank correlation rs = 0.85 (p = 0.002). It appears that individual bobcats did not drive the occupancy patterns of the model (Figure 4B, Table

6). However, two bobcats produced no significant Spearman’s rank correlations (B255; rs

= 0.28, p = 0.425; B258 rs = -0.10, p = 0.78) during this activity period. These two individuals had an increase in the number of points falling in mid-level resource selection bin ranks (bins 4 and 5). These bobcats occupy the eastern most portion of the study area and may be exposed to a resource or ecological pressure that the five others in this study do not encounter. However, given the strong overall rank correlation, I will not consider these two as separate in interpreting the crepuscular + night model.

Five of the six habitat covariables for the day activity model had 95% confidence intervals that did not include zero (Table 4). The variable “distance to natural edge habitat” did include zero in its 95% CI, suggesting this parameter does not predict occurrence patterns for bobcats in this model. Urban, grass, natural edge, and urban edge variables had negative values for β indicating a low relative probability of occurrence for bobcats outside of scrub habitat. As β is the change in the probability of occurrence for a habitat variable, the negative value for β in the distance variables indicates a decrease in

the probability of occurrence as distance (m) increases, thus negative values of β for the continuous distance variables indicates an increase in the probability of occurrence of bobcats with proximity to the respective edge variable. In the daytime inactive model, the negative value for the distance of urban edge suggests bobcats are more likely to occur closer to urbanization than occurring within the middle of natural habitat patches.

All five habitat covariates used in the night activity model had 95% CI that did not include zero. The change in the confidence interval for the distance to natural edge habitat variable between models indicates an increase in the probability of occurrence of bobcats with proximity to natural edge habitats during the night + crepuscular hours.

Furthermore, the day activity model showed selection against occupying natural edge habitats, whereas this variable is not present in the night model suggesting it is being used relatively equal to its availability during active periods. The other three categorical habitat variables still have negative values for β, showing selection against occupancy, but the values are all closer to zero. The relaxation of these negative values designates an overall change the occurrence of bobcats between activity periods where bobcats are occurring in more habitat types at night.

Overall, both models show an increased probability of occurrence with proximity to urban edges, occupying natural habitats closer to urbanization over core areas within natural habitat patches and avoiding the urban habitat itself (Table 4, Figure 2).

Prey Abundance and Distribution

Line transect sampling

During the study period I counted 305 rabbits. They were abundant in all seasons in all habitats (Table 7). Rabbit densities averaged 58 rabbits/km2 in urban habitats, 49.4

rabbits/km2 in urban edges, and 50.1 rabbits/km2 in natural habitat fragments. Urban habitats had the largest seasonal change in density declining 48% from 72.7 rabbits/km2 in summer to 37.6 rabbits/km2 in fall. Urban edges also showed seasonal variation with rabbit density increasing over spring to summer but declined from fall to winter. Rabbit densities in natural habitats were the most stable with the least seasonal variation in density throughout the year averaging ~50 rabbits/km2.

Fecal pellet counts

In total 72,259 rabbit fecal pellets were counted and cleared. Fecal pellet density varied significantly by habitat (F2,768 = 58.2, p < 0.001) but not by season (F3,768= 0.165, p = 0.92). The interaction between season and habitat was far from significant (F6,768 =

0.223, p = 0.969) (Figure 3). Following the two-way ANOVA, I performed a single factor ANOVA on habitat (F2,777 = 45.017, p < 0.001) with Tukey’s multiple comparisons. All pairwise comparisons of pellet densities in each habitat yielded p

<0.001 with pellet densities ranked as Edge > Grassland > Scrub (Figure 5). Although pellet deposition was lowest in scrub habitats, pellets were still abundant in all seasons.

The presence of wood-rat fecal pellets over a one year period was dependent on habitat type (2 × 3 contingency: χ2 = 78.20, df = 2, p < 0.001) with pellets present in 55 plots in coastal sage scrub, 27 plots in natural edges, and 5 plots in grass habitats.

Number of plots in each habitat type was 65.

Discussion

With the exception of coastal sage scrub, all other habitat types were used more by bobcats during active than inactive hours. Most notably, bobcats used natural edges and habitats closer to natural edges during active times. This suggests natural edges are important areas for hunting as bobcats are likely responding to the greater abundance of rabbits in natural edges than in coastal sage scrub. Although my occupancy models used habitat covariates as surrogates for prey abundance, Keim et al. (2011) argue that if prey is abundant and noncyclical, predators do not have to discriminate between responding to prey or prey habitats. In this study, rabbit fecal pellets were most abundant in natural- edge habitats (Figure 5), and rabbit densities did not vary seasonally in natural habitat patches (Table 7). Additionally, during both activity periods bobcats consistently use habitats near urban edges. Urban edge habitats showed some variation in rabbit densities

(Table 7), but rabbits remained present in these areas, and bobcats may use these areas for foraging.

In urban areas, the probability of bobcat occurrence increased during night + crepuscular periods. Night occupancy models still show a negative selection coefficient for urban habitats. Contrary to initial predictions, rabbit densities were not higher in urban habitat, and varied the most there. This was surprising since I expected rabbits to be food limited in natural habitats during the drought conditions occurring at the time of this study. Bobcats are probably not exploiting urban resource subsidies in a manner similar to coyotes and more urban-tolerant species. Bobcats may still enter urban areas to hunt rabbits; however, it appears less than expected. If bobcats do enter urban areas to hunt, they likely traverse larger areas in search of prey. This trend has been observed by

Riley et al. (2003) where more urbanized bobcats increased their home range sizes when compared to less urban bobcats. The use of urban areas by bobcats may be an attempt to disperse out of habitat patches by testing the permeability of the urban matrix, or perhaps they are being competitively excluded into un-preferred urban areas by other bobcats.

The habitat used the most by bobcats during both activity periods was coastal sage scrub, the area where rabbits were least abundant. Fecal pellet data show that wood rats almost exclusively used sage-scrub and natural-edge habitats. Consistent use of coastal sage scrub by bobcats may be to harvest wood rats as a supplemental prey item that is not available in grass habitats. Additionally, bobcats are ambush predators known to stalk their prey. Occupying dense scrub cover near natural edge habitats can provide an advantage by remaining undetected by a rabbit before a predation attempt. This may also be true for bobcats along the urban edge as occupancy models showed consistent use of these urban edges by bobcats.

Bobcats and coyotes have been described as representing intermediate responses to fragmentation from urbanization where bobcats are more sensitive to increases in urban densities than coyotes (Crooks 2002; Ordenana et al. 2010). A study of coyotes has shown that they exploit urban food subsidies and in urbanized areas have diets composed up to 25% anthropogenic food items (Fedriani et al. 2001). My study suggests that bobcats, unlike coyotes, are likely not harvesting urban food subsidies in a beneficial way. Behavioral and dietary plasticity are important for living in an urban environment

(Lowry et al. 2013) and is a probable mechanism for the opposing responses to urban development between these bobcats and coyotes. However, even though bobcats are

sensitive to urban development, they appear to be less sensitive to the fragmented natural areas.

Having the ability to use natural-edge habitats may allow bobcats to persist in fragmented areas, while not being willing to exploit urban habitats to the extent that was predicted. Generally, predators are thought to increase activity along edge habitats using them as foraging habitats or travel corridors, but research on avian nest predation by mammalian predators suggests responses of those predators are variable and depend on spatial scale, landscape context, geographic region, and predator species (Chalfoun et al.

2002). Ries and Sisk (2010) argue that changes in vulnerability to predation pressure determine edge sensitivity, where animals that are more edge sensitive are more vulnerable to predation along habitat edges. Research has demonstrated that the perception of risk has strong effects on space use. Broekhuis et al. (2013) found that cheetahs (Acinonyx jubatus) avoid immediate mortality risks from competitors by positioning themselves in habitats at greater distances from lions (Panthera leo) and spotted hyenas (Crocuta crocuta) than predicted from random use. Similarly, bobcats are at risk of being killed by coyotes (Fedriani et al. 2000). In open grassy areas, with reduced cover, bobcats may perceive higher risks from predation by coyotes (or people) where they could be more easily detected. Furthermore, as coyotes are less sensitive to urban fragmentation (Ordenana et al. 2002), interactions with bobcats may increase in urban environments. Temporal changes in activity patterns could mitigate risk by using similar habitats at different times. However, peak activity hours for both species in fragmented areas occur during crepuscular + night hours compared to during the day

(Tigas et al. 2002). Behavioral changes in space use would be the most plausible

mechanism for bobcats to reduce their risk of predation where edge and scrub habitats are the least risky habitats.

Social organization can also affect the spatial behaviors and habitat-use patterns of bobcats. Social structure affects the size and distribution of bobcat home ranges

(Benson et al. 2006; Ferguson et al. 2009). Also, within fragmented landscapes, female bobcats generally restrict their use of space to a single fragment (Tigas et al. 2002). The reduced area associated with fragmentation may cause natural landscapes to become easily saturated with individuals, forcing females to partition space in a suboptimal manner by increasing proximity to urban areas. The increased probability of occurrence along the urban edges in both models may reflect such partitioning by females.

Individuals would be more likely to encounter conspecifics, a potentially negative interaction, within the center of natural areas. Additionally, conspecific avoidance may drive female bobcats to move into urban environments to forage in suboptimal habitats.

Bobcats and coyotes have been previously identified as important indicator species for understanding landscape-level effects of urban fragmentation (Crooks 2002;

Ordenana et al. 2010). Coyotes are more plastic in their diet, consuming urban resource subsidies, and thus subsequently less sensitive to urbanization. In contrast, bobcats do not appear to thrive in urban areas by adapting to exploit urban resources, rather they are able to tolerate fragmentation by behaviorally adapting to resource distribution within natural areas. Further, bobcats use natural habitats right up to the edge of urban development and while they do not appear to be greatly exploiting urban resource subsidies, bobcats may still enter urban areas to eat rabbits.

If natural fragments become too small or poorly connected, important foraging habitats available to bobcats will be lost. Conserving large contiguous natural areas would be the best way to accommodate mammalian carnivore diversity; however, urban planners could use bobcat presence as a measure of remaining environmental integrity as their presence is linked to ecological processes occurring within natural patches.

Carnivores are commonly focal species in conservation planning (e.g. Carroll et al. 2001) and should continue to be used similarly in landscapes that become urbanized.

Conclusion

I did not find evidence that bobcats preferred to use urbanization due to an increased abundance of rabbits. Contrary to predictions, rabbit abundances varied the most in urban habitats, suggesting resource subsidies from watered lawns may not stabilize rabbit population dynamics from environmental changes in food resources.

Furthermore, habitat occupancy statistics show that bobcats under-utilize urban areas.

Rather, they use natural edge habitats during active periods presumably because of the high use by rabbits. Use of natural edge habitats by bobcats may increase hunting success by use of scrub habitat as cover for ambush attempts on rabbits, while mitigating their own perceived risk of predation. There is some evidence that urban edges may act similarly to natural edges as they are structurally composed of high amounts of fragmented coastal sage scrub; however, bobcat occurrence along urban edges may also reflect a conspecific avoidance by females to avoid other bobcats. As bobcats remain a focal species in studies of the effects of urban fragmentation on carnivore ecology, future research on social interactions and behavioral risk mitigation would elucidate ecological process important to their persistence in these landscapes.

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Appendix

Table 1. Description of explanatory covariates derived from the geographic information system used in occupancy models from resource selection functions (RSF). Habitat Covariate RSF Variable Type Description Urbanization Urban Categorical Dummy variable coding whether a point occurred in an urban habitat Fuel Reduction Zone Fuel Categorical Dummy variable coding whether a point occurred in a fuel reduction zone Dummy variable coding whether a point occurred in coastal sage shrub Coastal Sage Shrub Shrub Categorical habitat Grassland Grass Categorical Dummy variable coding whether a point occurred in grassland habitat Natural Edge Edge Categorical Dummy variable coding whether a point occurred in natural edge habitat Distance to Urban Edge DST_Urban Continuous Distance to urban edge (m) Distance to Natural Edge DST_Edge Continuous Distance to natural edge (m)

Figure 1. Study area in Thousand Oaks, California. Fragmented natural habitats (white) subdivided by urban development/altered areas (grey)

Table 2. Pearson’s correlation matrix of habitat covariates used in resource selection functions. DST_Urban DST_NatEdge Urban Urb_Edge Nat_Edge Grass Scrub DST_Urban 1.000 DST_NatEdge -0.094 1.000 Urban -0.262 0.381 1.000 Urb_Edge -0.177 -0.024 -0.091 1.000 Nat_Edge 0.081 -0.222 -0.211 -0.061 1.000 Grass -0.021 -0.083 -0.102 -0.030 -0.069 1.000 Scrub 0.236 -0.143 -0.656 -0.190 -0.443 -0.214 1.000

Table 3. Candidate models and model selection used to evaluate resource selection functions for different activity periods (Day and Night) for bobcats where k = number of parameters, AIC = Akiake’s information criterion, AICc =AIC corrected for small sample sizes, Δi = Change in AICc, wi = AICc weights. Selected model bold-faced Day Night

Candidate Models k AIC AICC Δi wi AIC AICC Δi wi Urban + Grass 2 2165.78 2172.88 46.10 0.00 2909.94 2918.08 45.08 0.00 Urb_Edge + Nat_Edge 2 2546.87 2555.22 428.45 0.00 2977.29 2985.62 112.62 0.00 Urban + Grass + Urb_Edge + Nat_Edge 4 2119.45 2131.06 4.29 0.10 2913.30 2926.91 53.91 0.00 DST_NatEdge + Nat_Edge 2 2492.40 2500.57 373.80 0.00 2962.60 2970.89 97.89 0.00 DST_Urban + Urban 2 2210.13 2217.37 90.60 0.00 2888.69 2896.77 23.76 0.00 DST_Urban + DST_NatEdge 2 2495.54 2503.73 376.95 0.00 2937.84 2946.06 73.06 0.00 DST_Urban + DST_NatEdge + Urban 3 2212.08 2221.76 94.99 0.00 2886.87 2897.65 24.65 0.00 DST_Urban + DST_NatEdge + Grass 3 2465.73 2476.52 349.75 0.00 2922.36 2933.27 60.27 0.00 DST_Urban + DST_NatEdge + Urb_Edge + Nat_Edge 4 2487.24 2500.86 374.09 0.00 2936.93 2950.66 77.65 0.00 DST_Urban + DST_NatEdge + Urban + Grass 4 2164.65 2176.50 49.73 0.00 2862.09 2875.47 2.46 0.20 DST_Urban + DST_NatEdge + Urban + Grass + Nat_Edge 5 2130.90 2144.92 18.14 0.00 2864.09 2880.16 7.16 0.02 DST_Urban + DST_NatEdge + Urban + Grass + Urb_Edge 5 2149.43 2163.57 36.80 0.00 2856.97 2873.00 0.00 0.70 DST_Urban + DST_NatEdge + Urban + Grass + Urb_Edge + Nat_Edge 6 2110.56 2126.77 0.00 0.89 2858.71 2877.45 4.44 0.08

Table 4. Selection coefficients (β), standard error (SE), and upper/lower 95% confidence intervals (CI) for habitat covariates used in projected resource selection functions. Lower 95% Upper 95% Variable β SE CI Upper 95% CI β SE Lower 95% CI CI DST_Urban -1.41E-03 4.46E-04 -2.29E-03 -5.41E-04 -3.03E-03 4.25E-04 -3.87E-03 -2.21E-03 DST_NatEdge -5.80E-04 3.13E-04 -1.20E-03 3.36E-05 -5.37E-04 2.65E-04 -1.06E-03 -2.15E-05 Urban -2.685 0.166 -3.018 -2.366 -0.904 0.110 -1.120 -0.689 Grass -2.341 0.343 -3.063 -1.706 -1.152 0.222 -1.597 -0.725 Nat_Edge -0.969 0.152 -1.267 -0.672 Urb_Edge -1.472 0.316 -2.109 -0.861 -0.552 0.205 -0.954 -0.148

Figure 2. Relative probability of occurrence for seven female bobcats in Thousand Oaks, CA. during (A) daytime activity and (B) nighttime activity from 2013-2014. Light areas represent an increase in the probability of use.

3.5

A 3 2.5

1.5

AreaAdjusted Frequency 0.5

1 2 3 4 5 6 7 8 9 10

B 2.5

1.5

AreaAdjusted Frequency 0.5

0 1 2 3 4 5 6 7 8 9 10 Binned RSF Score

Figure 3. Area-adjusted frequency of categories (bins) of RSF scores for bobcat occupancy models in Thousand Oaks, CA for (A) daytime activity and (B) nighttime activity. Data are mean frequency ± SD of Spearman rank correlation of 5-k fold cross validation.

Table 5. Spearman rank correlations of 5-k fold cross validation techniques of each individual cross and model average for both activity models. Model averages are the mean frequency values by bins across all five crosses.

Set Day Night

r r s p s p

1 0.939 <0.001 0.964 <0.001

2 0.927 <0.001 0.927 <0.001

3 0.988 <0.001 0.939 <0.001

4 0.952 <0.001 0.952 <0.001

5 0.939 <0.001 0.976 <0.001

Average 0.952 <0.001 1.000 <0.001

3.5 A

3 2.5 2 1.5 1

0.5 AreaAdjusted Frequency 0 1 2 3 4 5 6 7 8 9 10 Binned RSF Scores

2.5

B 2

1.5

0.5 AreaAdjusted Frequency

0 1 2 3 4 5 6 7 8 9 10 Binned RSF Score

Figure 4. Area-adjusted frequency of categories (bins) of RSF scores for bobcat occupancy models in Thousand Oaks, CA for (A) daytime activity and (B) nighttime activity. Data are mean frequency ± SD of Spearman rank correlation of individual bobcat fold cross validation.

Table 6. Spearman rank correlations of cross validation techniques of each individual bobcat and model average for both activity models. Model averages are the mean frequency values by bins across all seven individual crosses.

Set Day Night

r r s p s p

B255 0.66 0.038 0.28 0.425 B258 0.78 0.008 -0.10 0.78 B292 0.87 0.001 0.95 <0.001 B293 0.96 <0.001 0.65 0.043 B295 0.90 <0.001 0.92 <0.001 B302 0.71 0.022 0.58 0.08 B303 0.99 <0.001 0.75 0.013 Average 0.95 <0.001 0.85 0.002

Table 7. Results of visual line surveys of rabbits per square kilometer with lower and upper 95% confidence intervals (CI). Season Stratum Rabbits/km2 Lower 95% CI Upper 95% CI Urban 67.99 53.84 85.85 Spring Urban Edge 37.78 25.25 56.54 Natural 52.24 36.92 73.92 Urban 72.79 58.02 91.31 Summer Urban Edge 62.13 41.79 92.37 Natural 52.05 36.01 75.24 Urban 37.65 27.85 50.91 Fall Urban Edge 61.39 39.82 94.63 Natural 42.35 28.55 62.82 Urban 53.76 41.66 69.37 Winter Urban Edge 36.50 24.32 54.78 Natural 53.65 37.93 75.90

1.8

1.6

1.4 /day 2 1.2 Edge 1 Grass 0.8 Scrub

# Pellets/m # 0.6

0.4

0.2

0 Spring Summer Fall Winter

Figure 5. Fecal pellet counts represented as a rate of pellet deposition (number of pellets per meter square per day) for cottontail rabbits in scrub, grass, and natural edge habitats. Error bars represent 95% confidence intervals.