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
By
Sean Patrick Dunagan
August 2015
The thesis of Sean Patrick Dunagan is approved:
_________________________________________
______________
Dr. Paul Wilson
Date
_________________________________________
______________
Dr. Seth Riley
Date
_________________________________________
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Dr. Tim Karels, Chair
Date
California State University, Northridge
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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
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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
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Table 7 – Visual Line Surveys of Cottontail Rabbits
42
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Table of Figures
Figure 1 – Study Area
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Figure 2 – Bobcat Occupancy Models
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Figure 3 – 5-k Fold Cross Validation
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Figure 4 – Individual Bobcat Cross Validation
40
Figure 5 – Cottontail Rabbit Fecal Pellet Counts
42
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Abstract
Habitat Occupancy of Bobcats (Lynx rufus) in an Urban Fragmented Landscape
By
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
vii
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
9
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
10
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
11
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
12
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.
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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.
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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 nonfragmented 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
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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.
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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 crossvalidation 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
17
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.
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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
19
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.
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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).
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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
22
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
23
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.
24
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 naturaledge 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
25
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
26
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
27
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.
28
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.
29
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33
Appendix
Table 1. Description of explanatory covariates derived from the geographic information system used in occupancy models from resource selection
functions (RSF).
Habitat Covariate
Urbanization
Fuel Reduction Zone
RSF Variable
Urban
Fuel
Type
Categorical
Categorical
Coastal Sage Shrub
Grassland
Natural Edge
Distance to Urban Edge
Distance to Natural Edge
Shrub
Grass
Edge
DST_Urban
DST_Edge
Categorical
Categorical
Categorical
Continuous
Continuous
Description
Dummy variable coding whether a point occurred in an urban habitat
Dummy variable coding whether a point occurred in a fuel reduction zone
Dummy variable coding whether a point occurred in coastal sage shrub
habitat
Dummy variable coding whether a point occurred in grassland habitat
Dummy variable coding whether a point occurred in natural edge habitat
Distance to urban edge (m)
Distance to natural edge (m)
34
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
DST_Urban
DST_NatEdge
Urban
Urb_Edge
Nat_Edge
Grass
Scrub
1.000
-0.094
-0.262
-0.177
0.081
-0.021
0.236
1.000
0.381
-0.024
-0.222
-0.083
-0.143
1.000
-0.091
-0.211
-0.102
-0.656
35
Urb_Edge Nat_Edge Grass
1.000
-0.061
-0.030
-0.190
1.000
-0.069
-0.443
1.000
-0.214
Scrub
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
Candidate Models
Urban + Grass
Urb_Edge + Nat_Edge
Urban + Grass + Urb_Edge + Nat_Edge
DST_NatEdge + Nat_Edge
DST_Urban + Urban
DST_Urban + DST_NatEdge
DST_Urban + DST_NatEdge + Urban
DST_Urban + DST_NatEdge + Grass
DST_Urban + DST_NatEdge + Urb_Edge + Nat_Edge
DST_Urban + DST_NatEdge + Urban + Grass
DST_Urban + DST_NatEdge + Urban + Grass + Nat_Edge
DST_Urban + DST_NatEdge + Urban + Grass + Urb_Edge
DST_Urban + DST_NatEdge + Urban + Grass + Urb_Edge + Nat_Edge
k
2
2
4
2
2
2
3
3
4
4
5
5
6
AIC
2165.78
2546.87
2119.45
2492.40
2210.13
2495.54
2212.08
2465.73
2487.24
2164.65
2130.90
2149.43
2110.56
Day
AICC
2172.88
2555.22
2131.06
2500.57
2217.37
2503.73
2221.76
2476.52
2500.86
2176.50
2144.92
2163.57
2126.77
Δi
46.10
428.45
4.29
373.80
90.60
376.95
94.99
349.75
374.09
49.73
18.14
36.80
0.00
wi
0.00
0.00
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.89
AIC
2909.94
2977.29
2913.30
2962.60
2888.69
2937.84
2886.87
2922.36
2936.93
2862.09
2864.09
2856.97
2858.71
Night
AICC
2918.08
2985.62
2926.91
2970.89
2896.77
2946.06
2897.65
2933.27
2950.66
2875.47
2880.16
2873.00
2877.45
Δi
45.08
112.62
53.91
97.89
23.76
73.06
24.65
60.27
77.65
2.46
7.16
0.00
4.44
wi
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.02
0.70
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.
Variable
β
SE
Lower 95%
CI
Upper 95% CI
β
SE
Upper 95%
Lower 95% CI CI
DST_Urban
DST_NatEdge
Urban
Grass
Nat_Edge
Urb_Edge
-1.41E-03
-5.80E-04
-2.685
-2.341
-0.969
-1.472
4.46E-04
3.13E-04
0.166
0.343
0.152
0.316
-2.29E-03
-1.20E-03
-3.018
-3.063
-1.267
-2.109
-5.41E-04
3.36E-05
-2.366
-1.706
-0.672
-0.861
-3.03E-03
-5.37E-04
-0.904
-1.152
4.25E-04
2.65E-04
0.110
0.222
-3.87E-03
-1.06E-03
-1.120
-1.597
-2.21E-03
-2.15E-05
-0.689
-0.725
-0.552
0.205
-0.954
-0.148
36
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.
37
Area Adjusted Frequency
3.5
A
3
2.5
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
Area Adjusted Frequency
3
B
2.5
2
1.5
1
0.5
0
1
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.
38
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
rs
p
rs
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
39
p
Area Adjusted Frequency
4
A
3.5
3
2.5
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
9
10
7
8
9
10
Binned RSF Scores
Area Adjusted Frequency
2.5
B
2
1.5
1
0.5
0
1
2
3
4
5
6
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.
40
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
rs
p
rs
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
41
Table 7. Results of visual line surveys of rabbits per square kilometer with lower and upper 95%
confidence intervals (CI).
Season
Stratum
Spring
Summer
Fall
Winter
Rabbits/km2
Lower 95% CI
Upper 95% CI
Urban
67.99
53.84
85.85
Urban Edge
37.78
25.25
56.54
Natural
52.24
36.92
73.92
Urban
72.79
58.02
91.31
Urban Edge
62.13
41.79
92.37
Natural
52.05
36.01
75.24
Urban
37.65
27.85
50.91
Urban Edge
61.39
39.82
94.63
Natural
42.35
28.55
62.82
Urban
53.76
41.66
69.37
Urban Edge
36.50
24.32
54.78
Natural
53.65
37.93
75.90
2
1.8
# Pellets/m2/day
1.6
1.4
1.2
Edge
1
Grass
Scrub
0.8
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.
42