Habitat use by mammals varies along an exurban development gradient in northern Colorado

Biological Conservation 176 (2014) 172–182

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Habitat use by mammals varies along an exurban development gradient in northern Colorado Erica H. Goad a,⇑, Liba Pejchar a, Sarah E. Reed a,c, Richard L. Knight b a

Graduate Degree Program in Ecology, Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO 80523, United States Graduate Degree Program in Ecology, Department of Human Dimensions of Natural Resources, Colorado State University, Fort Collins, CO 80523, United States c North America Program, Wildlife Conservation Society, Bozeman, MT 59715, United States b

a r t i c l e

i n f o

Article history: Received 20 February 2014 Received in revised form 6 May 2014 Accepted 7 May 2014

Keywords: Development gradient Exurban development Landscape permeability Multiple-season occupancy Remotely-triggered camera Mammals

a b s t r a c t Exurban development, defined as residential development outside of cities and towns, occupies nearly five times more land in the United States than urban and suburban development combined. Understanding the effects of exurban development on biodiversity thus has important and wide-ranging implications for the planning, construction and stewardship of sustainable communities and surrounding rural lands. To assess the impact of exurban development on mammalian habitat use, wildlife cameras were placed along a unique gradient of landscape permeability through varying densities and configurations of housing development in a rapidly growing rural region of Colorado. Multiple-season species occupancy and relative activity levels were measured in summer and winter seasons. The impacts of exurban housing on mammals were species specific and varied along the development gradient. Bobcats (Lynx rufus), elk (Cervus canadensis), and coyotes (Canis latrans), showed decreased activity and occupancy levels in more developed areas, whereas red foxes (Vulpes vulpes) occurred more frequently in these areas. Other species did not show significant responses to exurban development. In addition, black bears (Ursus americanus) used embedded greenbelts more frequently in high-density exurban subdivisions. Using our development gradient provides ecologically relevant insight into how mammals respond to residential development, and our results demonstrate that some species respond positively to residential development, but that others decline or disappear as the development gradient intensifies. Incorporating well-designed and naturally vegetated open spaces into development projects and minimizing human disturbance could mitigate impacts to mid-large sized mammals in regions undergoing exurban expansion. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Exurban development, or development outside of cities and towns, is increasing rapidly across the United States, yet has ecological impacts that are poorly understood (Hansen et al., 2005). These former ranch, farm, and forest lands are typically subdivided for small-acreage residential development within a matrix dominated by native vegetation (Brown et al., 2005; Knight, 1999). Eighty percent of housing development in the 1990s was in rural areas, and over half of these homes were on lots greater than four hectares (Heimlich and Anderson, 2001). As of 2004, exurban development covered five times more land than all suburban and ⇑ Corresponding author. Present address: Estes Valley Land Trust, 1191 Woodstock Drive Suite 5, P.O. Box 663, Estes Park, CO 80517, United States. Tel.: +1 3032495760. E-mail address: [email protected] (E.H. Goad). http://dx.doi.org/10.1016/j.biocon.2014.05.016 0006-3207/Ó 2014 Elsevier Ltd. All rights reserved.

urban development combined (Theobald, 2004). Thus, exurban development has the potential to result in a higher per-capita footprint on landscapes relative to other forms of development. Exurban development and associated infrastructure can lead to habitat fragmentation, homogenization of animal and plant communities, and increased human-wildlife conflict (McKinney, 2006). Habitat fragmentation from dispersed housing development can alter animal movement patterns and behavior, cause ‘‘pileup’’ or overlap of home ranges, and reduce animal fitness by intensifying inter- and intra-specific interactions (Riley, 2006). Fragmentation can also constrain dispersal and the ability of some species to persist in these landscapes by limiting both functional and structural connectivity (Saunders et al., 1991; Crooks and Sanjayan, 2006). In addition, exurban development may also disproportionately impact protected lands and could decrease their conservation value (Knight et al., 1995; Leinwand et al., 2010; Radeloff et al., 2010). Increasingly, conservationists argue that the cumulative impacts

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of exurban development on biodiversity and ecological processes exceed those of forestry or ranching (Hansen et al., 2005; Marzluff and Ewing, 2001). A large number of studies have demonstrated changes in large mammal communities across the urban–rural gradient, with a strong focus on contrasting urban and suburban development to natural areas (Bateman and Fleming, 2012; Gehrt et al., 2009; Riley et al., 2003; Theobald et al., 2005). The impacts of development along the urban–rural gradient on the abundance and diversity of small mammals (Riem et al., 2012), birds (Blair, 2004; SuarezRubio et al., 2011), and butterflies (Bergerot et al., 2011) have also been examined. Until recently, however, few studies have addressed the conservation implications of the widespread conversion of the rural end of the gradient (e.g. natural and agricultural lands) to low-density exurban development (Hilty and Merenlender, 2003). In particular, previous investigators have compared mammalian presence and species richness among various types of land use and development (Lenth et al., 2006; Maestas et al., 2003; Pita et al., 2009; Riem et al., 2012), but none to date investigated species occurrence along a landscape permeability gradient (Theobald et al., 2012), nor have they evaluated patterns of habitat use exclusively within exurban areas. Because exurban development is characterized by varying levels of development intensity interspersed with relatively larger patches of remnant native vegetation (Hansen et al., 2005), mammals may utilize this landscape differently than urban or suburban environments. Specifically, there is very little information on whether development density restricts the movement of animals through mosaics of open space and exurban development, and to what degree the cumulative influence of housing effects are species-dependent. Understanding how mammalian habitat use in exurban environments differs from urban and suburban landscapes may have implications for land use planning in regions experiencing rapid exurban growth. The use of gradients to analyze urbanizing landscapes is welldocumented (Atwood et al., 2004; Marzluff and Ewing, 2001; McDonnell and Hahs, 2008; McDonnell and Pickett, 1990). A development gradient is a broad measure of urbanization that captures complex conditions (McDonnell and Hahs, 2008) and can be used as a proxy for land use intensity in a rural landscape (Hansen et al., 2005). Considering habitat use and landscape permeability along a continuous gradient is useful and captures more landscape complexity in areas where patch classification and delineation is difficult such as human-altered environments (Manley et al., 2009; Theobald et al., 2012). Here, we assessed mammalian habitat use in and around rural housing developments in northern Larimer County, Colorado. We used remotely-triggered wildlife cameras to detect medium- and large-bodied mammals and assessed their habitat use and relative activity along a development gradient based on landscape permeability through varying densities and configurations of housing (Theobald et al., 2012). Remotely-triggered cameras, combined with occupancy analysis, can be used to estimate site use with known site-specific habitat variables (MacKenzie et al., 2003). Because ‘‘exurban’’ is defined geographically for this study, we incorporated housing developments with a range of lot sizes (some of which are smaller than is typical for exurban developments) to test the relative impacts of varying housing density and configuration on mammals. We also compared the relative activity of various species along the development gradient to further assess how frequently species are using these areas. Relative activity provides information on how often a species is using a site and potential spatial displacement due to disturbance (George and Crooks, 2006). Because some housing developments are spatially designed to contain green spaces, we also assessed mammal habitat use in the most highly-developed region of the study area to determine the importance of these greenbelts. Greenbelts, or blocks of undeveloped land

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embedded within high density subdivisions, may provide movement corridors and preferred habitat (Atwood et al., 2004) for some species, and may contribute to landscape connectivity. The primary objective of this study was to use camera trap data and occupancy modeling to understand the variables that affect both mammalian habitat use (w) and detectability (p) in exurban regions. Based on previous studies of mammal species in urban and suburban areas, we predicted that typically synanthropic species such as red foxes (V. vulpes) (Cove et al., 2012; Gosselink et al., 2007) and striped skunks (Mephitis mephitis) (Bateman and Fleming, 2012) would show higher levels of habitat use in more developed areas. Additionally, species that have generalist, adaptable diets and social systems may benefit from housing development due to subsidized food resources and predator refuge (Farias et al., 2012; Hebblewhite et al., 2005; Gosselink et al., 2007; Pita et al., 2009; Riley, 2006); therefore, we expected species such as mule deer (Odocoileus hemionus), elk (Cervus canadensis), mountain cottontails (Sylvilagus nuttallii), Abert’s squirrels (Sciurus aberti), and American black bears (Ursus americanus) to use the higher end of our development gradient more frequently. We also predicted lower habitat use at this end of the gradient by human-sensitive species, such as bobcats (Lynx rufus), mountain lions (Puma concolor), and moose (Alces alces) (Boyer et al., 1999; Crooks, 2002; Gehrt et al., 2009; Ordeñana et al., 2010). Because coyote response to urbanization is variable (e.g. Atwood et al., 2004), we predicted that exurban development could either negatively or positively impact coyote habitat use. In addition, we hypothesized that relative activity patterns of each species would exhibit the same relationship to housing development as habitat use, since animals that capitalize on the resources available in higher density regions are likely to use them more frequently (Cove et al., 2012). 2. Material and methods 2.1. Study area Our study took place in the North Fork of the Cache la Poudre River watershed in Larimer County, Colorado (lat. 40°500 N, long. 105°150 W), approximately 40 km northwest of Fort Collins (Maestas et al., 2003). Land uses in the North Fork watershed are diverse, consisting of ranches, national forests and state lands, conservation easements, and exurban residential developments. The population of unincorporated Larimer County grew from 26,000 in 1970 to 71,164 in 2010 (US Census Bureau 2010). Nearly 1400 houses are located in the nearly 660 km2 study area, and mean lot sizes for subdivisions range from approximately 0.5–50 ha. The study area is bounded to the south by the Cache la Poudre River, to the north by the Wyoming border, and to the east and west by elevation (Fig. 1a). Sampling was restricted to elevations ranging between 1700 m and 2400 m to minimize variation in vegetation communities and soil types. The vegetation community is dominated by Rocky Mountain Ponderosa Pine Woodland and Rocky Mountain Lower Montane-Foothill Shrubland (Fry et al., 2011). Dominant grasses include blue gramma (Boutela gracilis), needle-and-thread (Hesperostipa comata), western wheatgrass (Pascopyrum smithii), and cheat grass (Bromus tectorum). Common shrubs include mountain mahogany (Cerocarpus montanus), bitterbrush (Purshia tridentata), and wax currant (Ribes cereum). The soils in this area are similar mixtures of Rocky Loam, Stony Loam, and Loamy Foothill Range sites (Moreland, 1980). 2.2. Sampling design To select study sites, houses (excluding outbuildings) were digitized using ArcGIS 10.0 (ESRI, 2011) and National Agricultural

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Fig. 1. (a) Study area in Northern Colorado. The southern boundary is the Cache la Poudre River; the northern boundary is the state border with Wyoming. Eastern and western boundaries represent elevation boundaries at 1700 m and 2400 m, respectively. Dots show the distribution of individual housing units. (b) Study area with development gradient scaled to the coyote home range size (dark to light shading reflects high to low housing density) and study sites (dots) where wildlife cameras were located. (c) Zoomed-in view of panel (b) to the high-density subdivision and study site locations (dots) to reflect that study sites are stratified among higher gradient values (all sites are at least 500 m apart).

Imagery Program satellite imagery at a scale of 1:4000 (Fig. 1a). A development gradient representing landscape permeability through varying densities and configurations of housing was created following the methods of Theobald et al. (2012). A fixed kernel density estimator in ArcGIS was used to represent landscape resistance in the vicinity of housing. The kernel density bandwidth was scaled to published values of average home range size for each species (Appendix A), and the kernel density layer was used as a resistance layer to generate at least cost distance map for each species. Four cost distance surfaces with starting locations along the cardinal boundaries of the study area were averaged to produce the final development gradient used for sampling (Fig. 1b). Least cost distance surfaces, based on percolation theory, provide an ecologically relevant surface that captures the important differences between areas close to one or a few dispersed houses, areas located at the edge of a higher-density subdivision, and areas that are embedded within a subdivision, distinctions that may influence habitat use (Theobald, 2006). The resulting maps represent the mean cumulative cost, in terms of exposure to housing, of travel from any point to the boundaries of the study area. The least cost distance maps were rescaled into intervals of equal area with values ranging from 1 to 100. The development gradients we generated are quantitatively relative to our study area; yet the same process could be applied to any landscape, for any species (see Appendix B for a schematic of geoprocessing steps). The processes used in this study are distinct from those of Theobald et al. (2012) in two ways. First, the start locations for this study were based on study area boundaries rather than random locations, which better represents permeability for an animal moving across the study area. Second, the betweenness centrality was not calculated or used for sampling in this study because the objective was to measure relative permeability at every point rather than to identify the most permeable routes on the landscape. The development gradient layer scaled to the coyote (Canis latrans) home range size was used to select study sites, as it approximates the median home range size of the suite of mammal species that occur in the study area, and because coyotes serve as useful indicators of functional connectivity in highly fragmented

areas (Crooks, 2002). Since housing is highly clustered in this landscape, areas that were classified as a value of 0 in the development gradient were removed from sampling prior to the selection of study sites to ensure an even representation of the development gradient as a continuous variable. Thus, the gradient encompasses a range from very low to high-intensity exurban development. Remote undeveloped areas were excluded because the objective of the study was to understand where along the development gradient the effects become evident, rather than specifically comparing undeveloped to developed areas. Random points were placed via a stratified random sampling of rescaled gradient values (Fig. 1b and c). Fifty-four final sites were selected based on the number of successful requests for permission to access private properties and an effort to represent the development gradient values as evenly as possible (Fig. 1b and c). All points were located a minimum of 500 m apart (range: 500–3819 m). Although many of the sites are located near subdivisions (Fig. 1), they encompass the full distribution of housing density values represented in the landscape (mean: 14.42 houses per km2, range: 0.32–50.93 houses per km2), as measured within 1 km of the sampling points. 2.3. Field methods Data collection occurred during the summer (May–August 2012) and winter (December 2012–March 2013). At each study site, an unbaited remotely-triggered camera was placed within 150 m of the original point in an area most likely to detect species, such as wildlife trails, drainages, passes, and ridgelines. Twentyseven remotely-triggered cameras (Cuddeback Capture, Primos TruthCam 35, or Wildgame Innovations Micro Red 4) outfitted with security boxes were placed at half of the study sites and remained in their locations for six weeks (42 trap nights). All cameras were visited every 1–2 weeks for routine maintenance (e.g. to replace memory cards and batteries). The cameras were then moved to a new set of 27 sites for another six weeks (42 trap nights), for a total of 54 sites over the sampling period. In the winter season, the cameras were placed in the same locations and operated for the same amount of time, to maintain the assumption of closure of seasonal

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Table 1 Description of variables included in our analysis of the impact of a gradient of exurban development on mammalian habitat use. Covariate

Abbreviation

Description

Development gradient Human activity

gr

Road density Private ownership High Park Fire impact Local percent canopy cover Forest and woodlands cover Shrub and grasslands cover Elevation

road own fire

Cumulative influence of housing development based on housing density and landscape permeability. Scaled to each species’ average home range size, sliced to a value on scale of 1–100 Average percent time anthropogenic noise is audible, calculated from three sound surveys at each camera location for each season (summer and winter) Density (m/m2) of all roads around camera location, sourced from US Census Bureau Tiger Roads 2012 data set Proportion of area under private ownership within a fixed buffer with a radius scaled to each species’ average home range size Proportion of area burned in the High Park Fire in the summer of 2012 within a fixed buffer with a radius scaled to each species’ average home range size Ocular estimate of canopy cover within a 25 m radius of camera location, collected in the field (summer and winter)

pas, paw

pcs, pcw forest shrub elev

Proportion of area with NVC land cover class Forest and Woodland within a fixed buffer with a radius scaled to each species’ average home range size, obtained from USGS National GAP Analysis Proportion of area with NVC land cover class Shrub and Grassland within a fixed buffer with a radius scaled to each species’ average home range size, obtained from USGS National GAP Analysis Elevation of camera location, in meters, collected in the field

occupancy status (MacKenzie et al., 2003). Due to the High Park Fire during the summer of 2012, five camera sites were taken down three weeks prematurely to avoid equipment loss as the fire tracked north into the study area. Four of the five sites were utilized in the winter season and one site, which completely burned, was excluded from final analysis. Human activity in the vicinity of the study sites was documented using sound disturbance sampling during the two seasons. A handheld PDA, programmed by the National Park Service’s Sounds and Night Skies division, was used to collect sound information via attended audibility (Fristrup et al., 2008; Lynch et al., 2011). At each study site, three sound surveys were completed on days when the cameras were visited. Each survey lasted for 15 min, during which the presence and duration of natural and anthropogenic sounds were continuously noted. The average percent time non-natural noise was audible for each site was considered as a proxy for human activity and included as a covariate in analysis. Hereafter, this covariate will be referred to as ‘‘human activity.’’ The acoustic metric was used instead of human detections on remotely-triggered cameras as a measure of ‘‘human activity’’ because it more adequately encompasses the full suite of potential anthropogenic disturbance to wildlife in exurban areas (e.g. the sounds of cars, lawn mowers, barking dogs). This metric also captures the seasonality of human activity more effectively than the presence of houses alone, and provides a more robust dataset than the limited number of photographs of people captured on our cameras. Because human activity is heavily concentrated in daylight hours, we restricted sound surveys to daytime. 2.4. Statistical analysis Species detections (and non-detections) at each site were documented for summer and winter seasons and incorporated into a multiple-season occupancy modeling framework where each season is a primary sampling period, and each week is a secondary sampling occasion (MacKenzie et al., 2003; MacKenzie et al., 2006). This framework links the probability that an animal occupies an area with the probability that it is detected (Reed, 2011), and is an effective way to account for the possibility that a species may not be detected even if it is present. The probabilistic, stratified random sampling technique utilized in this study adheres to the assumption of spatial independence in occupancy modeling. In addition, the inclusion of spatially correlated habitat covariates in the analysis ensures that the estimates of occupancy and standard errors should be unbiased (MacKenzie et al., 2006). The perceived risks and benefits of occupying an area may depend on surrounding landscape characteristics (Fahrig, 2007). In addition to the development gradient and human activity metric, we included the following covariates in our analysis to evaluate

this influence (Table 1): road density, proportion of privatelyowned land, area burned by the High Park Fire, Forest/Woodland cover, and Shrub/Grassland cover, derived from the USGS National Gap Analysis dataset (US Geological Survey, 2011). All landscape variables were measured as proportional values within a fixed radius scaled to each species’ home range size around the camera locations. At a local scale, the ocular estimate of percent canopy cover in a 25 m radius around each camera site was collected in the field and also tested. Although the presence of water may be a limiting factor for wildlife in xeric habitats, accurate data including the spatial and temporal distribution of water in stock tanks, ephemeral streams, and other natural and non-natural sources in our study area were not available. All analyses were completed at a cell size of 30 m, since this was the highest resolution consistently available for most spatial data in the study area. Correlations among predictor variables were informally assessed prior to analysis using Spearman’s correlation coefficient to develop the candidate model set (Appendix C). Covariates that had a correlation coefficient value greater than 0.7 were considered collinear (Whittington et al., 2005), and one of the collinear covariates was eliminated in model sets to avoid model overfitting (Burnham and Anderson, 2002). In this analysis, the only two variables that were highly correlated were road density and the development gradient (e.g., r = 0.769 for coyote, Appendix C). Since our questions pertain mainly to the development gradient, we retained this covariate and removed road density from further analyses. Program PRESENCE (Hines, 2006) was used to model site occupancy across multiple seasons. Maximum likelihood estimation and model averaging were used to estimate occupancy probabilities and the potential effects of habitat and anthropogenic covariates for each species (MacKenzie et al., 2006). An ad-hoc hierarchical model building procedure was used (Doherty et al., 2012), where detection probability (p) was compared by season, held constant, or varied by all covariates while all other parameters were held constant. The best detection structure was then used to assess whether colonization and extinction rates (c and e) varied by elevation, were constant, or were fixed to zero. The resulting best structure was then used to assess occupancy probabilities (w) based on site-specific covariates. Models were run separately for each species which were detected at least once, although some species could only support simple, univariate models due to limited numbers of detections. If no models converged for a species, the species were not included in final analyses. Models were then ranked, compared and evaluated using Akaike’s Information Criterion (AIC) with adjustments for overdispersion and small sample size, QAICc (Burnham and Anderson, 2002). Model fit was assessed by comparing the observed Pearson v2 statistic for the most general model for each species with v2

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statistics obtained from 10,000 simulated bootstrap datasets to obtain a modified overdispersion parameter suitable for occupancy models (MacKenzie and Bailey, 2004). The number of camera sites served as the effective size of each model set. Regression coefficients (b estimates) for covariates on w retained in the top models were examined for the direction of each species’ relationship to housing influence and other habitat covariates, and model-averaged estimates of w1 for individual sites were used for graphing the relationship between habitat use and the development gradient (Fig. 2). An index of relative activity (RA), an indicator of frequency of habitat use along the development gradient, was also calculated for each study site by dividing the number of images of a species detected in a photograph by the number of nights the camera operated at that station for all sample sessions (Cutler and Swann, 1999; George and Crooks, 2006). Because individuals were not identified in the photographs, absolute density was not measured at each sampling point, but the relative activity index can provide a useful measure of relative habitat use by a species at each sampling point. If multiple individuals were captured within a single photograph, each individual (image) was counted separately. Relative activity was calculated for each site for each species, as well as across sites for all species. Greenbelts may be used by mammals as movement corridors through developed areas. In our study area, greenbelts include embedded blocks of undeveloped land owned by the US Forest Service or the Glacier View Meadows Association within the Glacier View Meadows subdivision, a subdivision at the upper end of the development gradient. To determine if species preferentially use habitat in greenbelts in high density subdivisions, mean RA levels for each species detected at least twice in greenbelt (n = 12) and non-greenbelt sites (n = 12) within the high-density subdivision in the study area were compared using a one-way ANOVA and the Wilcoxon Signed Rank test. The non-parametric test was used because the distribution of RA values did not conform to assumptions of normality.

3. Results 3.1. Habitat use Our remotely-triggered camera stations detected mule deer, elk, moose, coyotes, red foxes, American black bears, mountain lions, bobcats, striped skunks, Abert’s squirrels, red squirrels (Tamiasciurus hudsonicus), mountain cottontails, yellow-bellied marmots (Marmota flaviventris), domestic dogs (Canis familiaris), domestic cats (Felis catus), cows (Bos primigenius), horses (Equus ferus caballus) and humans (Homo sapiens) over the summer and winter seasons (see Appendix D for sample photographs). There were 2147 total detections in the summer season (2138 trap nights) and 1034 detections in the winter season (2209 trap nights) and the number of detections varied greatly by species (Table 2). Mountain lions, red squirrels, marmots, domestic cats, horses, and cows were not detected frequently enough or at enough sites to allow for valid occupancy analysis using Program PRESENCE as models did not converge. Mountain lions were included in relative activity metrics. Coyote, bobcat, Abert’s squirrel, moose, elk, and striped skunk data best supported model sets that assumed vital rates (colonization and extinction) were fixed to 0, which extends the closure assumption of occupancy to the entire sample year. The development gradient covariate on w was retained in top model sets (DQAIC < 2) for all species included in occupancy analysis except mule deer and domestic dogs (Table 3). Elk, coyotes, and red foxes were the only species whose top-ranked model

retained the gradient. Model results for all species indicated some level of uncertainty, where no model received > 90% of the QAICc weight. The habitat use of red foxes was strongly and positively correlated with development. (bFox = 3.17, Fig. 2). Striped skunks (bSkunk = 1.51), black bears (bBear = 1.75), Abert’s squirrels (bSquirrel = 1.96), cottontails (bCottontail = 2.16), also showed higher probabilities of habitat use at higher development gradient levels, although some uncertainty surrounded the estimates and strength of the effect of the development gradient on habitat use. Coyotes (bCoyotes=2.51, Fig. 2) and elk responded negatively to high exurban housing influence (bElk = 4.11), as did bobcats (bBobcats=0.73) and moose (bMoose = 0.66), although the relationship for the latter two species was not strong. Because the fire burned in the southern portion of the study area, only a few sites were affected by the fire. Models that included the fire covariate were only supported by three species which had enough detections at the sites affected by the fire (mule deer, coyote, and cottontail). The High Park Fire covariate was retained in top models for the cottontail, although the relationship is not strong (bCottontail–Fire = 6.82, Table 3). Vegetation cover type emerged as important for bobcats (bBobcats–Forest=4.80), black bears (bBear–Shrub = 2.53), moose (bMoose–Forest = 4.96), and cottontails (bCottontail–Forest=2.14), which all had top models that included the forest or shrub cover covariate. Moose habitat use was positively related to forest cover, but this relationship was negative for all other species. Elevation was also included in top models for Abert’s squirrels (bSquirrel–Elevation = 1.99), red foxes (bFox–Elevation = 1.50), and mule deer (bMuleDeer-Elevation = 2.34). Finally, Abert’s squirrels, cottontails, bobcats, coyotes, and mule deer all retained seasonally-varying covariates (season, human activity, or percent cover) in top models, indicating differences in habitat use and detection between seasons for these species (Table 3). 3.2. Relative activity Mule deer were detected the most often, and had the highest mean RA across sites (Table 2). Both domestic dogs and coyotes were detected at 45.3% of sites, but only 24 images of coyotes were captured, whereas 89 images of domestic dogs were collected, leading to their strong difference in relative activity. Although the gradient is a weak explanatory variable in occupancy analysis for mule deer, their RA showed that they used sites at higher values of the development gradient more often than sites at lower values sites. Red foxes also used habitat more frequently in areas with higher values along the gradient. Notably, elk were not detected in any of the sites with relatively higher values along the development gradient in this study; thus, their activity was focused in areas with less development. Bobcats also used habitat less frequently at sites higher along the development gradient. Greenbelt sites were used more often by black bears than nongreenbelt sites. Black bears demonstrated significantly higher RA on greenbelt sites versus non-greenbelt sites (F = 7.46, p = 0.0341, Fig. 3). Cottontails also exhibited higher activity on greenbelts, but the difference was marginally significant (F = 3.69, p = 0.0507). Results for other species were not significant (Appendix E). 4. Discussion Our study demonstrates that even in relatively low-density exurban areas, residential housing can affect habitat use for some mammalian species. The direction and strength of this impact varied along the development gradient, and our occupancy analysis revealed other important factors that drive habitat use in this exurban region. We discuss findings for each group of species

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Fig. 2. Model-averaged estimates of site occupancy (w1) for mammals in Northern Colorado for which the development gradient was included in top model sets, graphed along the development gradient. Standard error bars are the unconditional standard error provided by model averaging.

frequently observed in our study area in light of our predictions and past research. 4.1. Carnivores Research on the impact of habitat fragmentation and isolation on bobcat densities is mixed (Riley et al., 2003; Ruell et al., 2009), and has occurred largely in urban or agricultural settings,

where the boundary between developed and undeveloped areas is more distinct. Our results show some evidence that in low-density exurban environments, bobcats are also sensitive to human activity. We found that bobcats exhibited a weak negative response to higher housing influence and their detectability was negatively correlated to higher human activity levels. This corroborates previous studies which show some larger carnivores, such as mountain lions, coyotes, and bobcats, are more sensitive than

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Table 2 Species captured by the wildlife cameras, the percent of sites (n = 53) at which each species was observed, the total number of images of each species, and the weighted mean relative activity rates averaged over both seasons. Species

Percent of sites

Number of images

Weighted mean relative activity (SE)

Mule Deer Domestic Dogs Coyotes Humans Cottontails Red Foxes Black Bears Bobcats Elk Abert’s Squirrels Striped Skunks Mountain Lions Horses Moose

100.0 45.3 45.3 41.5 30.2 22.6 15.1 15.1 13.2 11.3 11.3 11.3 9.4 5.7

2059 89 24 351 110 55 24 11 14 40 13 8 137 31

0.474 0.020 0.006 0.080 0.025 0.013 0.011 0.003 0.003 0.009 0.003 0.002 0.031 0.007

other mammals to human presence and activity due to large space requirements, low population densities, and low birth rates (Crooks, 2002; Noss et al., 1996). Telemetry studies of bobcats in urbanizing regions showed that they occupy home ranges predominately characterized by natural habitat, and that females are highly sensitive to fragmentation (Riley et al., 2003; Riley, 2006). A meta-analysis of the impacts of urban proximity and intensity on mammalian carnivores in California concluded that bobcats react negatively to increases in both of these variables (Ordeñana et al., 2010). George and Crooks (2006) also noted that bobcats were detected less frequently by cameras along trails with higher human activity, indicating that bobcats exhibit avoidance of high human use areas. Because bobcats are solitary and strictly carnivorous, this species may be similarly sensitive to increased development in both urban and exurban environments. Coyotes also exhibited a negative response to both exurban housing and human activity. Although coyotes are considered to be highly adaptable to humans and are known to colonize urban areas (Atwood et al., 2004; Bateman and Fleming, 2012; Gehrt et al., 2009), coyotes may preferentially use natural and less-developed habitat if available (Gehrt et al., 2009; Randa and Yunger, 2006; Riley et al., 2003). However, individual coyote responses to development are highly variable and may depend on ecological context (Grinder and Krausman, 2001). Thus, we find that the avoidance behavior often exhibited by coyotes in urban areas likely extends to exurban regions as well. Our research suggests that red foxes strongly display higher probabilities of habitat use with increasing cumulative housing influence. Elevation also appeared in all top model sets; however, elevation may be confounded with the development gradient (Appendix C). Our results are consistent with previous studies, which show that red foxes are habitat generalists and may be attracted to more developed areas due to anthropogenic food subsidies (Contesse et al., 2004; Odell and Knight, 2001) and refuge from competitors and predators (Bateman and Fleming, 2012; Cove et al., 2012; Gosselink et al., 2007). The contrasting patterns of habitat use between foxes and coyotes in this study’s exurban environment are consistent with similar patterns observed in other landscapes (Farias et al., 2012; Gosselink et al., 2007; Randa and Yunger, 2006). Urban areas are refugia from canid predation for foxes, and it appears that relatively low-density development in exurban areas function similarly. This pattern could also be driven by other factors, including interspecific competition, disease outbreaks, and ‘‘mesopredator release’’ (Crooks and Soule, 1999; Farias et al., 2012; Gosselink et al., 2007), although Cove et al. (2012) did not find strong evidence that the absence of coyotes ‘‘released’’ smaller predators in a fragmented landscape.

(0.011) (0.001) (0.008) (0.005) (0.002) (0.081) (0.0005) (0.016) (0.00004) (0.008) (0.0002) (0.0001) (0.003) (0.0009)

Although the null model received the most weight for American black bears in this study, the gradient was retained in the top model set and reflected a weak positive response to exurban development. Black bears may be attracted to development because of garbage and other food resources (Baruch-Mordo et al., 2011), and urban areas can act as sinks for black bear populations (Bateman and Fleming, 2012; Long et al., 2011). Although beyond the scope of this study, measuring human-subsidized resources and subsequent fitness effects in exurban areas is a priority for testing one of the most likely mechanisms behind mammalian habitat use in this system. Greenbelts are used more frequently by black bears, which exhibit significantly higher rates of relative activity on greenbelts versus non-greenbelt sites within a high-density subdivision (Fig. 3, Appendix E). In Glacier View Meadows, many yards are not fenced, so the boundaries between a backyard and the adjacent greenbelt are not always clear. Thus, our estimate of greenbelt importance is likely conservative. Greenbelts may be increasingly important in developments where yards are fenced more often, since greenbelts could ‘funnel’ wildlife through habitat embedded in areas of higher housing density. Our findings suggest that further research on the role of greenbelts in facilitating wildlife movement through exurban landscapes is warranted. Several carnivores (e.g. bobcats and coyotes) had higher detection probabilities in the winter (Table 3), when human activity levels were lower, indicating that these species may respond differently to diverse types of disturbance, whether the disturbance is the visual stimulus of a house or the sounds that indicate varying levels of human activity. The QAIC weight of models including the ownership covariate was always smaller than models including the development gradient. Thus, whether a site is largely surrounded by private or public land is likely less important than other characteristics (e.g. land use or degree of disturbance) for predicting wildlife habitat use. The top model sets for bobcats, black bears, and moose also included the forest covariate. These results confirm that maintaining natural forested patches within mosaics of human disturbance, including exurban development, may enhance habitat use by carnivores and other mammals (Bateman and Fleming, 2012; Pita et al., 2009). 4.2. Ungulates There is some indication that moose are negatively correlated with housing; however, this relationship is weak because moose were infrequently detected. Elk patterns of habitat use decreased in high-density exurban areas, which was not consistent with our predictions but does support past research in an exurbanizing region of Colorado where elk showed a strong aversion to exurban

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Table 3 QAIC results for all mammal species detected by remotely triggered wildlife cameras along an exurban development gradient (DQAICc < 2). w = probability of habitat use, p = detection probability. Model

DQAICc

w

K

2 l

b1 (SE)

b2 (SE)

Abert’s squirrels w(elev),p(t.) w(elev + gr),p(t.) w(elev + shrub),p(t.)

0 1.88 1.9

0.2715 0.106 0.105

4 5 5

109.17 108.53 108.55

1.99 (1.12) 1.81 (1.16) 2.63 (1.44)

1.96 (2.39) 1.50 (1.90)

0 0.1 0.38 0.68 0.82 0.96 1.38 1.54 1.54 1.91

0.1027 0.0977 0.0849 0.0731 0.0682 0.0636 0.0515 0.0476 0.0476 0.0395

2 3 3 3 3 4 3 3 3 4

111.26 109.11 109.39 109.69 109.83 107.63 110.39 110.55 110.55 108.58

w(.),p(pa) w(forest),p(pa) w(forest),p(t.) w(gr),p(pa) w(.),p(t.) w(shrub),p(pa)

0 0.34 0.61 0.97 1.94 1.99

0.1385 0.1169 0.1021 0.0853 0.0525 0.0512

4 5 4 5 3 5

92.25 90.06 92.88 90.72 96.69 91.78

Cottontails w(forest)c(elev)e(elev)p(t.) w(forest + gr)c(elev)e(elev)p(t.) w(forest)c(.)e(.)p(t.) w(fire)c(elev)e(elev)p(t.) w(.)c(elev)e(elev)p(t.) w(fire)c(.)e(.)p(t.) w(.)c(.)e(.)p(..) w(shrub)c(elev)e(elev)p(t.) w(.)c(.)e(.)p(t.) w(shrub)c(.)e(.)p(t.) w(elev)c(elev)e(elev)p(t.) w(elev)c(.)e(.)p(t.) w(fire + gr)c(elev)e(elev)p(t.) w(gr)c(elev)e(elev)p(t.)

0 0.29 0.31 0.47 0.72 0.76 0.95 0.96 1.02 1.25 1.45 1.74 1.83 1.84

0.0893 0.0773 0.0765 0.0706 0.0623 0.0611 0.0556 0.0553 0.0536 0.0478 0.0433 0.0374 0.0358 0.0356

8 9 6 8 7 6 4 8 5 6 8 6 9 8

239.43 237.36 244.63 239.99 242.71 245.17 250.22 240.59 247.89 245.76 241.18 246.35 239.22 241.65

2.14 (1.28) 2.71 (1.50) 2.13 (1.27) 6.82 (7.12)

Coyotes w(gr),p(pa) w(elev),p(.) w(elev),p(pa)

0 0.75 1.33

0.2662 0.183 0.1369

5 3 5

166.86 172.4 168.19

2.51 (1.37) 2.36 (1.43) 2.62 (1.40)

0 1.8

0.2733 0.1111

6 7

302.18 300.74

1.18 (1.01)

w(gr)p(..)

0

0.5185

3

82.92

4.11 (7.25)

Moose w(.),p(..) w(.),p(t.) w(forest),p(..) w(forest),p(t.) w(elev),p(..) w(elev),p(t.) w(gr),p(t.)

0 0.11 0.47 0.73 0.92 1.11 1.58

0.1523 0.1442 0.1204 0.1057 0.0962 0.0874 0.0691

2 3 3 4 3 4 4

50.52 48.38 48.74 46.66 49.19 47.04 47.51

4.96 (3.93) 4.78 (3.75) 1.03 (1.10) 1.03 (1.09) 0.66 (0.78)

w(elev),c(.),e(.),p(t.)

0

0.5639

6

734.95

2.34 (0.79)

Red foxes w(gr + elev),c(elev),e(elev),p(.) w(gr),c(elev),e(elev),p(.) w(gr + own),c(elev),e(elev),p(.)

0 0.16 1.77

0.3172 0.2928 0.1309

8 7 8

164.04 167.02 165.83

3.17 (1.41) 1.84 (0.61) 1.90 (0.64)

0 1 1.5 1.78 1.88

0.1738 0.1054 0.0821 0.0714 0.0679

2 4 3 3 3

96.56 92.02 95.62 95.97 96.1

0

0.3179

8

327.58

American black bears

w(.),p(.) w(shrub),p(.) w(gr),p(.) w(own),p(.) w(.),p(forest) w(gr),p(pa) w(.),p(shrub) w(forest),p(.) w(elev),p(.) w(pa),p(pa)

2.53 (1.89) 1.75 (1.96) 1.75 (1.47) 2.19 (1.45) 1.86 (2.31) 0.38 (0.46) 2.25 (1.55)

Bobcats 4.80 (4.04) 5.33 (3.62) 0.73 (0.69) 1.45 (2.21)

2.16 (1.56)

6.81 (7.08) 1.58 (1.11) 1.58 (1.11) 0.41 (0.32) 0.41 (0.32) 6.69 (7.53) 1.41 (1.35)

1.24 (1.38)

Domestic Dogs

w(.),c(.),e(.),p(pa) w(shrub),c(.),e(.),p(pa) Elk

Mule deer

1.50 (0.98) 2.04 (1.92)

Striped skunks

w(.)p(.) w(.)p(pa) w(.)p(t.) w(gr)p(.) w(.)p(forest)

1.51 (1.91)

Humans

w(pa),c(pa),e(.),p(pc)

0.57 (0.33) (continued on next page)

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Table 3 (continued) Model

DQAICc

w

K

2 l

b1 (SE)

w(gr),c(.),e(.),p(pc) w(.),c(.),e(.),p(pc) w(pa + gr),c(pa),e(.),p(pc)

0.99 1.07 1.81

0.1938 0.1862 0.1286

7 6 9

332.01 335.22 326.29

0.54 (0.32) 0.37 (0.36)

b2 (SE)

0.39 (0.35)

Notes: Definitions of column heads and covariates: DQAICc = QAICc distance from top-ranked model; w = Akaike weight; K = number of estimable parameters; 2 l = 2  log likelihood; b1 and 2 = Untransformed coefficients of covariates for covariates on w, listed in order. t = time (differentiates summer and winter seasons), gr = development gradient scaled to each species, pa = percent time anthropogenic noise is audible (human activity), elev = elevation, shrub = proportion of fixed buffer of shrub cover, forest = proportion of fixed buffer of forest cover, own = proportion of fixed buffer of private ownership, pc = percent canopy cover within a 25 m radius around camera site.

and Gehrt, 2004). Model weights also indicated a strong influence of vegetation type or elevation on habitat use for several small mammals. Forest cover was retained in the top model for cottontails (negative influence), which are often associated with the edges of forested plant communities (Fuller and DeStefano, 2003). Elevation appeared in top models for Abert’s squirrels, which are dependent on Ponderosa pine (Pinus ponderosa), the coniferous tree that dominates the higher elevations of the study area (Keith, 1965). 4.4. Development gradient implications

Fig. 3. Relative activity of mammalian species on greenbelt (n = 12) and nongreenbelt (n = 12) sites within an exurban development. Species that are shown were detected at least twice in each type of site.  indicates significant results from Wilcoxon Signed Rank test comparison (p < 0.05 when a = 0.05, Appendix E),  indicates significance at p < 0.1.

parcels that were < 4 ha (Wait and McNally, 2004). Although elk can habituate to high density human development for predator refuge (Hebblewhite et al., 2005; Schultz and Bailey, 1978), it is likely that the elk in our study region have not reached this point. Maintaining functional elk winter range in the face of exurban development will be critical to keeping elk populations stable in this region, and long-term studies are needed to fully capture the cumulative impacts of encroaching development on ungulates (Polfus, 2011). Mule deer used areas at the higher end of the development gradient with greater frequency, although the species was detected everywhere. Previous research on an urbanizing valley in Montana found deer abundance to be highest at intermediate levels of development (Vogel, 1989), and although we did not test for non-linear models due to small sample sizes, our highest density sites likely correspond to intermediate disturbance levels in other studies that encompass a full rural–urban gradient (Fig. 2). It has been suggested that exurban developments will create ‘‘private reserves,’’ where deer and other species may concentrate to escape exposure to hunting and predation (Hansen et al., 2005). The top model for mule deer included detection probabilities that were lower in the winter (Table 3), which supports evidence that mule deer on the Front Range in Colorado will rest more often when snow levels are higher (Kufeld et al., 1988), reducing the probability of detection by wildlife cameras. 4.3. Small mammals Striped skunks did not show a strong response to exurban development, although there is considerable uncertainty surrounding the occupancy estimates due to low detections. However, weak positive responses to both the gradient and human activity appeared in the top model set for striped skunks, which indicate that striped skunks were occupying habitat at the higher end of the development gradient. Our results are consistent with past research of mesopredator communities which found that opossums and striped skunks do not avoid developed areas (Prange

This study demonstrates a unique application of landscape permeability modeling techniques developed by Theobald et al. (2012) to the design and analysis of a field study. In contrast to prior studies of species habitat use in exurban landscapes (e.g. Odell and Knight, 2001; Ordeñana et al., 2010; Maestas et al., 2003), which measure housing influence by land use type, distance from a home, or housing density within a fixed radius, our approach is able to account for the broader configuration of housing and permeability of the landscape. Using this gradient more effectively illustrates the cumulative influence of housing on wildlife, as the risks or benefits of using habitat at the core of highdensity subdivisions could be markedly different than the edges. Visualizing landscape permeability and testing the subsequent patterns of wildlife habitat use along exurban gradients not only allows for a greater understanding of anthropogenic land use impacts, but also provides a platform for prioritizing conservation initiatives at the regional scale (Theobald et al., 2012). This methodology can be applied to any species on any landscape to address similar questions elsewhere (Appendix B). In addition, landscape permeability is closely related to structural and functional connectivity, the conservation of which has become an important objective of scientists and practitioners (Crooks and Sanjayan, 2006). For example, in this study, moose, bobcats, and mountain lions were all detected at the high end of the development gradient, but used these areas infrequently. Although we did not have enough detections of mountain lions to model habitat use, we did detect several individuals in a high housing influence area, so this habitat is permeable to and still occasionally used by this species, even if rarely. Given that exurban development is permeable to many species and that these developments are often embedded within protected lands in the larger landscape, designing and maintaining exurban development to allow wildlife connectivity will be increasingly important for sustaining local biodiversity. This is particularly important in areas where strict land protection is not possible and the demand and value of land for housing is high. 4.5. Conclusions Our study demonstrates that the density and distribution of housing in exurban areas can impact habitat use for some species. Where development occurs, we must be cognizant that wildlife exhibit species-specific responses to houses and human activity. Several of the patterns reported here are congruent with previous

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research regarding mammalian response to high-density urbanization, including red foxes, coyotes and bobcats, indicating that some species respond similarly to exurban development and urban development. However, the counter-intuitive or inconclusive results found for some species, such as elk and American black bears, emphasize the need for further research into the drivers behind habitat use changes in exurbanizing landscapes. In the face of the rapid increase in exurban development across the United States, more research is needed before development strategies that incentivize well-planned clustered housing and set aside open space, such as conservation development (Milder, 2007; Pejchar et al., 2007; Wait and McNally, 2004), are considered part of a viable, regional-scale conservation strategy for mammals. Although this study only includes two seasons of data, the use of occupancy and non-invasive techniques to collect data on multiple taxa across a large-scale development gradient can serve as a potential model for future research. Ultimately, if exurban development is to sustain both human and wildlife communities, it will require thoughtful planning across subdivision and open space boundaries. Our study demonstrates that these patterns are evident at a watershed scale. Acknowledgements We thank Colorado State University’s Warner College of Natural Resources, Center for Collaborative Conservation and School of Global Environmental Sustainability; the Harry S. Truman Foundation; and the Denver Audubon Society for funding this research. L. Bailey provided expert statistical advice, and E. Lynch, K. Crooks and J. Lewis generously lent field equipment. Thanks to E. Nigon and A. Miller for assistance with data collection. We are grateful to all participating private landowners, homeowners associations, Colorado Parks and Wildlife, and the US Forest Service for allowing this research to be conducted on their properties. J. Sibold offered constructive comments on earlier drafts of this manuscript. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biocon.2014. 05.016. References Atwood, T.C., Weeks, H.P., Gehring, T.M., 2004. Spatial ecology of coyotes along a suburban-to-rural gradient. J. Wildlife Manage. 68, 1000–1009. Baruch-Mordo, S., Breck, S.W., Wilson, K.R., Broderick, J., 2011. The carrot or the Stick? evaluation of education and enforcement as management tools for human-wildlife conflicts. PLoS ONE 6 (1), e15681. http://dx.doi.org/10.1371/ journal.pone.0015681. Bateman, P.W., Fleming, P.A., 2012. Big city life: carnivores in urban environments: urban carnivores. J. Zool. 287, 1–23. Bergerot, B., Fontaine, B., Julliard, R., Baguette, M., 2011. Landscape variables impact the structure and composition of butterfly assemblages along an urbanization gradient. Landscape Ecol. 26, 83–94. Blair, R.B. 2004. The effects of urban sprawl on birds at multiple levels of biological organization. Ecol. Soc. 9: 2. [online]. Boyer, R.T., Van Ballenberghe, V., Kie, J.C., Maier, J.A.K., 1999. Birth-selection site by Alaskan moose: maternal strategies for coping with a risky environment. J. Mammol. 80, 1070–1083. Brown, D.G., Johnson, K.M., Loveland, T.R., Theobald, D.M., 2005. Rural land-use trends in the conterminous United States, 1950–2000. Ecol. Appl. 15, 1851–1863. Burnham, K.P. and D.R. Anderson. 2002. Model selection and inference: a practical information-theoretic approach. 2nd ed. Spring-Verlag, New York, New York.Cederlund, G. and H. Sand. 1994. Home-range size in relation to age and sex in moose. Journal of Mammalogy 75: 1005-1012. In: Contesse, P., D. Hegglin, S. Gloor, F. Bontadina, and P. Deplazes. 2004. The diet of urban foxes (Vulpes vulpes) and the availability of anthropogenic food in the city of Zurich, Switzerland. Mammalian Biology – Zeitschrift für Säugetierkunde 69:81-95. Contesse, P., Hegglin, D., Gloor, S., Bontadina, F., Deplazes, P., 2004. The diet of urban foxes (Vulpes vulpes) and the availability of anthropogenic food in the city of

181

Zurich. Switzerland, Mammalian Biology - Zeitschrift für Säugetierkunde 69, 81–95. Cove, M.V., Jones, B.M., Bossert, A.J., Clever Jr., D.R., Dunwoody, R.K., White, B.C., Jackson, V.L., 2012. Use of camera traps to examine the mesopredator release hypothesis in a fragmented Midwestern landscape. Am. Midland Naturalist 168, 456–465. Crooks, K.R., 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502. Crooks, K.R., Sanjayan, M.A. (Eds.), 2006. Connectivity conservation: maintaining connections for nature. Cambridge University Press. Crooks, K.R., Soule, M.E., 1999. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566. Cutler, T.L., Swann, D.E., 1999. Using remote photography in wildlife ecology: a review. Wildlife Soc. Bull. 27, 571–581. Doherty, P.F., White, G.C., Burnham, K.P., 2012. Comparison of model building and selection strategies. J. Ornithol. 152, 317–323. ESRI 2011. ArcGIS Desktop: Release 10.0. Redlands, CA: Environmental Systems Research Institute. Fahrig, L., 2007. Non-optimal animal movement in human-altered landscapes. Funct. Ecol. 21, 1003–1015. Farias, V., Fuller, T.K., Sauvajot, R.M., 2012. Activity and distribution of gray foxes (Urocyon cinereoargenteus) in southern California. Southwestern Naturalist 57, 176–181. Fristrup, K., Joyce, D., Lynch, E., 2008. Measuring and monitoring soundscapes in national parks. Parkscience 26, 32–36. Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., Wickham, J., 2011. Completion of the 2006 national land cover database for the conterminous United States. PE&RS 77, 858–864. Fuller, T.K., DeStefano, S., 2003. Relative importance of early-successional forests and shrubland habitats to mammals in the northeastern United States. Forest Ecol. Manage. 185, 75–79. Gehrt, S.D., Anchor, C., White, L.A., 2009. Home range and landscape use of coyotes in a metropolitan landscape: conflict or coexistence? J. Mammal. 90, 1045–1057. George, S.L., Crooks, K.R., 2006. Recreation and large mammal activity in an urban nature reserve. Biol. Conserv. 133, 107–117. Gosselink, T.E., Van Deelen, T.R., Warner, R.E., Mankin, P.C., 2007. Survival and cause-specific mortality of red foxes in agricultural and urban areas of Illinois. J. Wildlife Manage. 71, 1862–1873. Grinder, M.I., Krausman, P.R., 2001. Home range, habitat use, and nocturnal activity of coyotes in an urban environment. J. Wildlife Manage. 65, 887–898. Hansen, A.J., Knight, R.L., Marzluff, J., Powell, S., Brown, K., Hernandez, P., Jones, K., 2005. Effects of exurban development on biodiversity: patterns, mechanisms, research needs. Ecol. Appl. 15, 1893–1905. Hebblewhite, M., White, C.A., Nietvelt, C.G., McKenzie, J.A., Hurd, T.E., Fryxell, J.M., Bayley, S.E., Paquet, P.C., 2005. Human activity mediates a trophic cascade caused by wolves. Ecology 86, 2135–2144. Heimlich, R.E., and W.D. Anderson. 2001. Development at the urban fringe and beyond: impacts on agriculture and rural land. Agricultural economic report 803. Economic Research Service, US. Department of Agriculture, Washington, DC. Hilty, J. and A.M. Merenlender. 2003. Studying biodiversity on private lands. Conservation Biology 17:132–137. In: Keith, J.O. 1965. The Abert squirrel and its dependence on Ponderosa pine. Ecology: 46: 150–163. Hines, J.E. 2006. PRESENCE4- Software to estimate patch occupancy and related parameters. USGS-PWRC. http://www.mbr-pwrc.usgs.gov/software/ presence.shtml. Keith, J.O., 1965. The Abert squirrel and its dependence on Ponderosa pine. Ecology 46, 150–163. Knight, R.L., 1999. Private lands: the neglected geography. Conserv. Biol. 13, 223– 224. Knight, R.L., Wallace, G.N., Riebsame, W.E., 1995. Ranching the view: subdivisions versus agriculture. Conserv. Biol. 9, 459–461. Kufeld, R.C., Bowden, D.C., Schrupp, D.L., 1988. Habitat selection and activity patterns of female mule deer in the Front Range, Colorado. J. Range Manage. 41, 515–522. Leinwand, I.I.F., Theobald, D.M., Mitchell, J., Knight, R.L., 2010. Landscape dynamics at the public-private interface: a case study in Colorado. Landscape Urban Planning 97, 182–193. Lenth, B.A., Knight, R.L., Gilgert, W.C., 2006. Conservation value of clustered housing developments. Conserv. Biol. 20, 1445–1456. Long, R.A., Donovan, T.M., MacKay, P., Zielinski, W.J., Buzas, J.S., 2011. Predicting carnivore occurrence with noninvasive surveys and occupancy modeling. Landscape Ecol. 26, 327–340. Lynch, E., Joyce, D., Fristrup, K., 2011. An assessment of noise audibility and sound levels in US National Parks. Landscape Ecol. 26, 1297–1309. MacKenzie, D.I., Bailey, L.L., 2004. Assessing the fit of site-occupancy models. J. Agr. Biol. Envir. St. 9, 300–318. MacKenzie, D.I., Nichols, J.D., Hines, J.E., Knutson, M.G., Franklin, A.B., 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207. MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.A., Bailey, L.L., Hines, J.E., 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier, Boston, Massachusetts. Maestas, J.D., Knight, R.L., Gilgert, W.C., 2003. Biodiversity across a rural land use gradient. Conserv. Biol. 17, 1425–1434.

182

E.H. Goad et al. / Biological Conservation 176 (2014) 172–182

Manley, P.N., Parks, S.A., Campbell, L.A., Schlesinger, M.D., 2009. Modeling urban land development as a continuum to address fine-grained habitat heterogeneity. Landscape Urban Planning 89, 28–36. Marzluff, J.M., Ewing, K., 2001. Restoration of fragmented landscapes for the conservation of birds: a general framework and specific recommendations for urbanizing landscapes. Restoration Ecol. 9, 280–292. McDonnell, M.J., Hahs, A., 2008. The use of gradient analysis studies in advancing our understanding of the ecology of urbanizing landscapes: current status and future directions. Landscape Ecol. 23, 1143–1155. McDonnell, M.J., Pickett, S.T.A., 1990. Ecosystem structure and function along urban rural gradients – an unexploited opportunity for ecology. Ecology 71, 1232–1237. McKinney, M.L., 2006. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260. Milder, J.C., 2007. A framework for understanding conservation development and its ecological implications. Bioscience 57, 757. Moreland, D.C. 1980. Soil survey of Larimer County Area, Colorado. US Department of Agriculture, Soil Conservation Service and Forest Service, 239–812/48. Government Printing Office, Washington, DC. Noss, R.F., Quigley, H.B., Hornocker, M.G., Merrill, T., Paquet, P.C., 1996. Conservation biology and carnivore conservation in the Rocky Mountains. Conserv. Biol. 10, 949–963. Odell, E.A., Knight, R.L., 2001. Songbird and medium sized mammal communities associated with exurban development in Pitkin County Colorado. Conserv. Biol. 15, 1–8. Ordeñana, M.A., Crooks, K.R., Boydston, E.E., Fisher, R.N., Lyren, L.M., Siudyla, S., Haas, C.D., Harris, S., Hathaway, S.A., Turschak, G.M., Miles, A.K., Van Vuren, D.H., 2010. Effects of urbanization on carnivore species distribution and richness. J. Mammal. 91, 1322–1331. Pejchar, L., Morgan, P., Caldwell, M., Palmer, C., Daily, G.C., 2007. Evaluating the potential for conservation development: biophysical, economic, and institutional perspectives. Conserv. Biol. 21, 69–78. Pita, R., Mira, A., Moreira, F., Morgado, R., Beja, P., 2009. Influence of landscape characteristics on carnivore diversity and abundance in Mediterranean farmland. Agric. Ecosyst. Environ. 132, 57–65. Polfus, J.L., 2011. Literature review and synthesis on the effects of residential development on ungulate winter range in the Rocky Mountain West. Helena, MT, Report prepared for Montana Fish, Wildlife and Parks. Prange, S., Gehrt, S.D., 2004. Changes in mesopredator-community structure in response to urbanization. Canada J. Zool. 82, 1804–1817. Radeloff, V.C., Stewart, S.I., Hawbaker, T.J., Gimmi, U., Pidgeon, A.M., Flather, C.H., Hammer, R.B., Helmers, D.P., 2010. Housing growth in and near United States protected areas limits their conservation value. Proc. Nat. Acad. Sci. 107, 940–945.

Randa, L.A., Yunger, J.A., 2006. Carnivore occurrence along an urban-rural gradient: a landscape-level analysis. J. Mammal. 87, 1154–1164. Reed, S.E., 2011. Non-invasive methods to assess co-occurrence of mammalian carnivores. Southwestern Naturalist 56, 231–240. Riem, J.G., Blair, R.B., Pennington, D.N., Solomon, N.G., 2012. Estimating mammalian species diversity across an urban gradient. Am. Midland Naturalist 168, 315–332. Riley, S.P.D., 2006. Spatial ecology of bobcats and gray foxes in urban and rural zones of a national park. J. Wildlife Manage. 70, 1425–1435. Riley, S.P.D., Sauvajot, R.M., Fuller, T.K., York, E.C., Kamradt, D.A., Bromley, C., Wayne, R.K., 2003. Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. Conserv. Biol. 17, 566–576. Ruell, E.W., Riley, S.P.D., Douglas, M.R., Pollinger, J.P., Crooks, K.R., 2009. Estimating bobcat population sizes and densities in a fragmented urban landscape using noninvasive capture–recapture sampling. J. Mammal. 90, 129–135. Saunders, D.A., Hobbs, R.J., Margules, C.R., 1991. Biological consequences of ecosystem fragmentation – a review. Conserv. Biol. 5, 18–32. Schultz, R.D., Bailey, J.A., 1978. Responses of National Park elk to human activity. J. Wildlife Manage. 42, 91–100. Suarez-Rubio, M., Leimgruber, P., Renner, S.C., 2011. Influence of exurban development on bird species richness and diversity. J. Ornithol. 152, 461–471. Theobald, D.M., 2004. Placing exurban land-use change in a human modification framework. Frontiers Ecol. Environ. 2, 139–144. Theobald, D.M., Spies, T., Kline, J., Maxwell, B., Hobbs, N.T., Dale, V.H., 2005. Ecological support for rural land-use planning. Ecol. Appl. 15, 1906–1914. Theobald, D.M., 2006. Exploring the functional connectivity of landscapes using landscape networks. In: Crooks, K.R., Sanjayan, M.A. (Eds.), Connectivity Conservation: Maintaining Connections for Nature. Cambridge University Press. Theobald, D.M., Reed, S.E., Fields, K., Soule, M., 2012. Connecting natural landscapes using a landscape permeability model to prioritize conservation activities in the United States. Conserv. Lett. 5, 123–133. US Census Bureau. 2010. http://www.census.gov/2010census/data/. US Geological Survey, Gap Analysis Program (GAP). May 2011. National Land Cover, Version 2. Vogel, W.O., 1989. Response of deer to density and distribution of housing in Montana. Wildlife Soc. Bull. 17, 406–413. Wait, S. and H. McNally. 2004. Selection of habitats by wintering elk in a rapidly subdividing area of La Plata County, Colorado. In: W. W. Shaw, L. K. Harris, and L. VanDruff, (Eds.) Pages 200–209 in Proceedings of the fourth international symposium on urban wildlife conservation, Tucson, Arizona, USA. Whittington, J., St. Clair, C.C., Mercer, G., 2005. Spatial responses of wolves to roads and trails in mountain valleys. Ecol. Appl. 15, 543–553.