Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 325 2019 NORTHEASTERN NATURALIST 26(2):325–342 Wild Canid Distribution and Co-existence in a Natural–Urban Matrix of the Pioneer Valley of Western Massachusetts Eric G. LeFlore1, Todd K. Fuller1,*, John T. Finn1, Stephen DeStefano2, and John F. Organ3 Abstract - Although development and urbanization are typically believed to have negative impacts on carnivoran species, some species can successfully navigate an urban matrix. Sympatric carnivorans compete for limited resources in urban areas, likely with systemspecific impacts to their distributions and activity patterns. We used automatically triggered wildlife cameras to assess the local distribution and co-existence of Canis latrans (Coyote), Vulpes vulpes (Red Fox), and Urocyon cinereoargenteus (Gray Fox) across the Pioneer Valley, MA, in relation to different levels of human development. We placed cameras at 79 locations in forested, altered, and urban land-use areas from September to November 2012 and accumulated 1670 trap nights. We determined site characteristics and detection rates for 12 other wildlife species for each camera location to develop a generalized linear model for the local distribution of each focal canid species across the study area. We also compared diel activity patterns among Coyotes, Red Foxes, and Gray Foxes, and calculated coefficients of overlap between each pair. The local distribution of Coyotes was positively associated with the detection rates of their prey and not associated with detection rates of sympatric carnivoran species. Red Foxes and Gray Foxes had negative relationships with the detection rate of Coyotes, and none of the 3 canid species showed a positive correlation with increased levels of urbanization. There was a high degree of temporal overlap in diel activity patterns and limited spatial overlap of our focal species, which suggests that any competition avoidance across our study area occurred at the spatial level. Coyotes fill the role of top predator in the Pioneer Valley, and likely have a negative impact on the local distributions of smaller canids, while their own local distributions seem to be driven by prey availability. Introduction Urbanization has fragmented, degraded, and eliminated natural landscapes (Marzluff 2001). This process, in turn, impacts wildlife in urban and adjacent areas (Gehrt 2010), causing changes in animal movements (Villaseñor et al. 2014), behaviors (Riley et al. 2003), density (Chernousova 2001), and distribution (Bonnington et al. 2014). Carnivorans are of interest because they are regularly found living among people and even in urban environments (Gehrt et al. 2010). In general, carnivorans are extremely variable in their behaviors and can be found across the 1Department of Environmental Conservation, University of Massachusetts, Amherst, MA 01003. 2US Geological Survey Massachusetts Cooperative Fish and Wildlife Research Unit, University of Massachusetts, Amherst, MA 01003. 3US Geological Survey, Reston, VA 20192. *Corresponding author - tkfuller@eco.umass.edu. Manuscript Editor: Joseph Johnson Northeastern Naturalist 326 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 globe in vastly different environments, from the arid deserts of Africa to the frozen tundra of Siberia (Fuller et al. 2010). Carnivorans are thought to be especially vulnerable to habitat loss and fragmentation because of their need for large amounts of space, low densities, and conflicts with humans (Crooks 2002, Noss et al. 1996). However, even as development has increased, some carnivorans have expanded their ranges (e.g., Canis latrans (Say) [Coyote]; Parker 1995), increased their numbers (e.g., Lynx rufus (Schreber) [Bobcat]; Roberts and Crimmons 2010), and have higher densities in urban than in rural areas (e.g., Procyon lotor (L.) [Raccoon]; Prange et al. 2003). Carnivorans react differently to pressures of urban environments, but a few generalizations can be made about species that thrive in such regions (Fuller et al. 2010). Urban carnivorans tend to be relatively small to medium in size and usually have higher reproductive capabilities than those found in areas that are less developed. Additionally, urban carnivorans tend to be dietary generalists, surviving on vegetation, live animals, carrion, and human refuse, depending on availability. Finally, urban carnivorans tolerate close proximity to humans. This tolerance is aided by humans who, purposefully or not, provide resources such as food and shelter (e.g., Kanda et al. 2009). Understanding how urbanization, development, and habitat fragmentation affect carnivoran communities is integral to successful conservation in the face of changing landscapes (Niemelä 1999, Prange and Gehrt 2004). Competition among sympatric carnivorans for available resources in urbanized and fragmented ecosystems can be both direct (aggressive interactions) and indirect (exploitative competition for resources) (Di Bitetti et al. 2009, Linnell and Strand 2000). Resource partitioning (spatially or temporally) enables species in competition to occupy the same areas and minimize negative interactions (Carothers and Jaksic 1984, Di Bitetti et al. 2009). Larger carnivorans impact the distribution, population, and activity of smaller carnivorans through competition and predation (Crooks et al. 2010, Palomares and Caro 1999, Sargent et al. 1987). In urban systems, competition among carnivorans likely occurs across the gradient of urbanization (Crooks et al. 2010). In moderately developed areas, limited resources and space constrict carnivoran species and cause higher levels of spatial and temporal overlap, resulting in more competition and agonistic interactions among these species (Crooks et al. 2010). Heavily developed areas where habitat patches are too small and isolated may not support larger predators, allowing mesopredators to thrive, whereas in more natural areas, mesopredators and top predators are able to coexist but mesopredators may use spatiotemporal avoidance to minimize interaction with top predators (Crooks et al. 2010). Carnivoran community interactions in urban environments are likely to differ between systems and species (Crooks 2002, Crooks et al. 2010, Faeth et al. 2005). This study employed automatically triggered wildlife cameras to investigate the local distribution and possible interactions of 3 canid species: Coyotes, Vulpes vulpes (L.) (Red Fox), and Urocyon cinereoargenteus (Schreber) (Gray Fox). Our aim was to understand the habitat features that influenced the ways in which these canids used human-influenced ecosystems while also investigating the impacts each Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 327 canid had on the others. We hypothesized that the local distribution of each focal species would be influenced not only by landscape characteristics (e.g., land-use type, distance to water, roads or urbanization, elevation, etc.) but also by detection rates of prey and sympatric carnivorans (cf. Johnson et al. 1996). We also hypothesized that we would detect Coyotes, Red Foxes, and Gray Foxes most often in areas surrounded by urban development (Cove et al. 2012, Kapfer and Kirk 2012, Ordeñana et al. 2010). We also compared activity patterns to determine the extent to which temporal partitioning influenced intraguild dynamics in our study area because temporal avoidance can facilitate spatial coexistence (Carothers and Jaksic 1984, Di Bitetti et al. 2009). Therefore, we posited that there would be temporal partitioning among Coyote, Red Fox, and Gray Fox. Study Area We conducted this study in the Pioneer Valley of western Massachusetts, a section of the Connecticut River Valley encompassing portions of Hampshire (160,327 people; 114 people/km2) and Franklin (71,631 people; 38 people/km2) counties (US Census Bureau 2018). An increased number of carnivoran sightings in higher human-traffic areas led us to develop this study. The 320-km2 study area was bounded to the south by Mt. Holyoke Range State Park, to the north by Mount Toby State Forest, to the west by the Connecticut River, and to the east by Quabbin Reservoir (Fig. 1). This region contained a mixture of land-cover types with more urban, suburban, and agricultural areas in the developed southwestern half of the study area than the more forested northeastern half. The southwestern portion was 53% forested and the northeast portion was 92.7% forested, providing an opportunity for comparison between more and less developed areas. The forested areas within our study area were characterized as transition hardwoods–Pinus strobus (L.) (White Pine) forest (DeGraaf and Yamasaki 2001). Methods Data collection We placed automatic cameras at 79 sites within the study area (Fig. 1) during September–November 2012. We assigned sites to 1 of 3 different land-use classes: forested (F: areas of natural habitat; e.g., forest stands, wetlands, successional habitat, etc.), altered (A: green spaces which have been modified by humans but are not high human-traffic areas; e.g., pastures, croplands, cemeteries, powerlines, etc.), and urban (U: areas with high levels of human use; e.g., residential areas, commercial areas, transportation land, junkyards, etc.) (Ordeñana et al. 2010, Riley et al. 2003). We used the “merge” tool in Arc GIS (ESRI 2011) to derive the distribution of these consolidated land-use classes from the most current available land-use data layer from the Massachusetts GIS website. We gridded the 320-km2 study area into eighty 4-km2 cells in an attempt to evenly sample and cover the entire study area. In total, we deployed 79 wildlife cameras in 67 of the 80 grid cells representing forested (n = 41), altered (22), and Northeastern Naturalist 328 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 Figure 1. Location of camera traps set from September to November 2012 to assess the local distribution of wild canids across a gradient of human development in the Pioneer Valley in Hampshire and Franklin counties of western Massachusetts. Study area and grid cells determined from coordinates on USGS topographic maps of Belchertown (USGS 1979a), Mount Holyoke (USGS 1979b), Shutesbury (USGS 1990a), and Williamsburg (USGS 1990b). Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 329 urban (16) land-use categories. We did not survey all grid cells, as access to all land parcels was not possible and required approval of the landowner (13 cells were not sampled: 11 predominantly natural and 2 predominantly altered land-use class cells). We set 1 camera in each accessible grid cell within a forest patch in a randomly selected parcel and where the landowner permitted access; the habitat class was determined by the dominant land-use type within a 150-m radius of the camera location. We set cameras near or on what were identified as wildlife paths, determined by finding wildlife spoor or small trails not made by humans. We affixed an infrared and motion-activated Bushnell Trophy Cam (Bushnell Outdoor Products, model numbers: 119436, 119446, 119456) to a tree at about 0.5 m above the ground pointing to a focal area where an animal was likely to pass. We cleared vegetation in the field of view which could trigger the camera, and applied ~15 ml of scent lure (either Badlands Bob [John Graham’s Fur Country Lures, Jordan, MT] or Powder River [O’Gorman Enterprises, Freemont, NE]) in the focal area (~2–3 m from the face of the camera) using vegetation found at the site. We employed scent lures to attract carnivorans moving in the area to the camera’s focal point, but not to artificially attract animals from other land uses. We recognize this practice as a potential bias, though it is not fully known how far these scent lures carry, nor their impact on potential prey species. We did not reapply scent lures to the focal area after the camera was deployed. We deployed cameras for 21 d, though due to camera failures and approval to access camera locations by landowners, actual camera deployment varied from 10 to 25 d (mean = 21.1 d). For each camera station, we recorded the lure used, habitat class (F, A, U), location (UTM coordinates from GPS), date set, and date closed. We recorded independent photo events, noting the camera-station number, species, date, and time. We considered multiple images of the same species occurring within a 30-min interval at 1 location as a single observation and considered photos independent if the interval was longer than 30 min (Yasuda 2004). For each species, we calculated photographic rates by taking the number of independent observations divided by the number of trap nights for that station. For comparison across stations, as well as with published literature, we standardized these rates as number of photographs per 100 trap nights (Cove et al. 2012, Kelly and Holub 2008, O’Brien 2011). GIS and statistical analysis We obtained the aforementioned consolidated land-use layer, as well as the digital elevation-model, Mass DOT roads, and community boundaries (cities and towns) layers from Mass GIS (2005). We downloaded landcover, vegetative structure, and traffic-rate GIS data from the University of Massachusetts Conservation Assessment and Prioritization System (UMASS CAPS 2011). We employed ArcMap 10.0 (ESRI 2011) to identify site-specific characteristics for each camera location (Table 1). We identified elevation and forest-cover type using the “extract value to points” tool; distances to urban edge, forest edge, altered edge, agricultural edge, water, and roads were calculated using the “point distance” tool. We used the “average nearest neighbor” tool to calculate percentage of forest, Northeastern Naturalist 330 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 Table 1. Parameters used in modeling Coyote, Red Fox, and Gray Fox local distributions in the Pioneer Valley of western Massachusetts. Parameter Symbol Description Intercept I Y-Intercept Portion of study area SA Southwest or northeast portion of study area Coyote C Count of independent observations of Coyotes Domestic Dog DD Count of independent observations of Domestic Dogs P/A Domestic Dog paDD Presence/absence of Domestic Dogs Virginia Opossum VO Count of independent observations of Virginia Opossums Domestic Cat DC Count of independent observations of Domestic Cats Humans H Count of independent observations of Humans Wild Turkey WT Count of independent observations of Wild Turkeys White-tailed Deer WTD Count of independent observations of White-tailed Deer Common Raccoon R Count of independent observations of Common Raccoons Gray Squirrel GS Count of independent observations of Eastern Gray Squirrels Cottontail Rabbit CR Count of independent observations of Eastern Cottontails Eastern Chipmunk EC Count of independent observations of Eastern Chipmunks P/A Eastern Chipmunk paEC Presence/absence of Eastern Chipmunks Unknown small mammal USM Count of independent observations of unknown small mammals Gray Fox GF Count of independent observations of Gray Foxes Red Fox RF Count of independent observations of Red Foxes Lure LU Carnivore scent lure used at camera site (Badlands Bob or Powder River) CAPS land value LV Classification of land use value Powerline forest other PFO CAPS land-use value represented as factor with 3 levels: powerline, forest, other CAPS veg structure LS Classification of vegetation from 0 (grassland) to 10 (closed canopy) Distance to forest DF Distance in meters from each camera site to the nearest patch of forested land use Distance to altered DA Distance in meters from each camera site to the nearest patch of altered land use Distance to urban DU Distance in meters from each camera site to the nearest patch of urban land use Distance to water DW Distance in meters from each camera site to the nearest water source Distance to road DR Distance in meters from each camera site to the nearest road Forest avg FA Average percentage of forested land use within 500-m buffer of each camera site Altered avg AA Average percentage of altered land use within 500-m buffer of each camera site Urban avg UA Average percentage of urban land use within 500-m buffer of each camera site Water avg WA Average percentage of water within 500-m buf fer of each camera site Traffic avg TA Average percentage of traffic within 500-m buf fer of each camera site Traffic avg binary TAbi Average percentage of traffic within 500-m buf fer of each camera site in binary form Elevation E Average elevation in meters within 500-m buffer of each camera site Log (elevation) LE Elevation in log form Log (theta) L Log (theta) Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 331 altered, and urban land-use within a 500-m–radius plot centered on the camera location. We calculated station-specific detection rates for captured wildlife species as well as for Canis lupis familiaris (L.) (Domestic Dog), Felis catus (L.) (Domestic Cat), and Homo sapiens (L.) (Human). Although camera traps have mixed success with capturing smaller species, we were able to detect various small mammals, which we grouped into an “unknown small-mammal” category. While these data were likely incomplete, we overlaid our techniques and efforts with data collected for canids and they were thus comparable. We employed R statistical software (versions 2.15.1–3.2.4; R Core Team 2012, 2016) to develop 4 types of generalized linear models (GLMs) for each species (Zuur et al. 2009): Poisson (‘stats’ package; R Core Team 2012), negative binomial (‘MASS’ package; Venables and Ripley 2002), zero-inflated Poisson (‘pscl’ package; Zeileis et al. 2008), and zero-inflated negative binomial (‘pscl’ package; Zeileis et al. 2008). We incorporated an offset for number of trap nights into the models to account for the variation in length of time each camera was deployed (Yan et al. 2009). Starting with a global Poisson model for each species (full suite of parameters in Table 1), we used the drop1 (R Core Team 2012) function to run a stepwise selection process that employs an analysis of deviance test to eliminate parameters not having a major effect on the response variable. We measured the correlation between the independent parameters by variance inflation factors (VIF) and eliminated redundant covariates (Zuur et al. 2013). If the VIF was below 10 for all parameters in the model, it was judged as statistically sound (Montgomery and Peck 1992). If the Poisson model was overdispersed (i.e., variance > mean), we fit a negative binomial regression to better approximate the data distribution (Zuur et al. 2009). Due to the high number of zeros in our response variables, we modeled the zero-inflated versions of both the Poisson and negative binomial regressions, again utilizing a stepwise selection process. After an investigation of covariate coplots (Zuur et al. 2010), we incorporated an interaction effect between portion of study area (northeast vs. southwest) and distance to water into our models because there were more camera locations in the northeast portion that were further away from water than the southwest portion. Once we determined the most appropriate GLM model type for each species, we selected the best models by comparing corrected Akaike information criterion (AICc) values (Burnham and Anderson 2002). During model comparison for each species, we averaged the top models (ΔAICc < 2) using the R package ‘MuMIn’ (Barton 2016). Models within 2 ΔAICc units of the top model are considered to be essentially as good as the best model (Burnham and Anderson 2002, Richards 2005, Symonds and Moussalli 2011). We determined significant parameters through zstatistics and accompanying P-values (which test the null hypothesis that the slope and intercept are equal to 0; P-value threshold of significance was set to 0.05) inherent in this modeling framework (Zuur et al. 2009). We conducted a post-hoc assessment of activity patterns with the R package ‘Overlap’ to evaluate whether our focal species demonstrated temporal avoidance (Meredith and Ridout 2014). We fit kernel-density functions to estimate the activity Northeastern Naturalist 332 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 pattern for each species (Linkie and Ridout 2011, Meredith and Ridout 2014, Ridout and Linkie 2009). For each pair of species, we also used nonparametric estimators to calculate a coefficient of overlap, Δ (Schmid and Schmidt 2006, Weitzman 1970), a measure varying between 0 (no overlap) and 1 (complete overlap). We used Δ1- hat as our coefficient of overlap, because this nonparametric estimator (Schmid and Schmidt 2006) is practical for diel activity-pattern data and is robust for smaller sample sizes (Ridout and Linkie 2009, Meredith and Ridout 2014). Finally, we obtained 95% confidence intervals around each coefficient of overlap by conducting a smoothed bootstrap with 10,000 resamples. In our calculation of confidence intervals, we adjusted the raw percentiles to account for the bootstrap bias as the bootstrap mean differs from the original Δ1-hat value (Meredith and Ridout 2014, Ridout and Linkie 2009). Results Over 1670 trap nights, we detected 15 different species that we subsequently included in statistical analyses (Table 2). We included only species with detection rates of 1 per 100 trap nights in modeling efforts. A full list of species detected is reported in Supplemental Table 1 (see Supplemental File 1, available online at http://www.eaglehill.us/NENAonline/suppl-files/26-2-N1651-Fuller-s1, and, for BioOne subscribers, at https://dx.doi.org/10.1656/N1651.s1). The number of independent observations for these species varied from 19 for Tamias striatus (L.) (Eastern Chipmunk) to 781 for Sciurus carolinensis (Gmelin) (Eastern Gray Squirrel). No species was detected at all 79 camera sites; we detected Eastern Gray Squirrels most often (57 sites, 72%) and Eastern Chipmunks least often (8 sites, 10%). Table 2. Number of independent photos, number of sites where photos were obtained, and overall detection rate (number of photos/100 trap nights; n = 1670 trap nights) of mammal and bird species recorded at 79 sites during September–November 2012 in the Pioneer Valley of western Massachusetts and used in modeling efforts. Scientific name Common name Photos Sites Rate Sciurus carolinensis Eastern Gray Squirrel 781 57 47 Didelphis virginiana Virginia Opossum 265 54 16 Odocoileus virginianus White-tailed Deer 107 44 6 Procyon lotor Common Raccoon 102 38 6 Canis latrans Coyote 74 36 4 Urocyon cinereoargenteus Gray Fox 74 16 4 Canis lupis familiaris Domestic Dog 70 20 4 Sylvilagus floridanus Eastern Cottontail Rabbit 65 20 4 Vulpes vulpes Red Fox 33 16 2 Felis catus Domestic Cat 33 16 2 Homo sapiens Humans 32 16 2 Meleagris gallopavo Wild Turkey 27 14 2 Unknown small mammal 26 9 2 Tamiasciurus hudsonicus American Red Squirrel 22 10 1 Tamias striatus Eastern Chipmunk 19 8 1 Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 333 We detected Coyotes at 36 (46%) camera sites (74 independent observations in 1670 trap nights), with the number of detections at each site ranging from 0 to 10. The local distribution of Coyotes was best described with a negative binomial GLM. We averaged the top 2 models, the result of which indicated that Coyote local distribution and detection rate were positively correlated with the detection rates of Eastern Gray Squirrels, Meleagris gallopavo (L.) (Wild Turkey), Sylvilagus floridanus (J.A. Allen) (Eastern Cottontail Rabbit), and unknown small mammals; and negatively correlated with Eastern Chipmunks and the amount of altered habitat within a 500-m buffer (Tables 3, 4; top Coyote models are listed in Supplemental Table 2 [(see Supplemental File 1, available online at http://www.eaglehill.us/ NENAonline/suppl-files/26-2-N1651-Fuller-s1, and, for BioOne subscribers, at https://dx.doi.org/10.1656/N1651.s1]). There was also a significant interaction effect between portion of study area (northeast vs. southwest) and distance to water; Coyotes were closer to water in the southwest portion of the study area. We detected Red Foxes at 16 of the 79 (20%) camera sites (74 independent observations in 1670 trap nights), with the number of detections at each site varying from 0 to 6. Detection rates for Red Fox were best described by a zero-inflated Poisson model. The top model indicated a positive correlation with the detection rate of Domestic Dogs, distance to urban land use, and the amount of water within a 500-m buffer of the camera site, and a negative correlation with the detection rates of Coyotes, Odocoileus virginianus (Zimmermann) (White-tailed Deer) and the distance to water (Tables 4, 5; top Red Fox models are listed in Supplemental Table 3 (see Supplemental File 1, available online at http://www.eaglehill.us/NENAonline/ suppl-files/26-2-N1651-Fuller-s1, and, for BioOne subscribers, at https://dx.doi. org/10.1656/N1651.s1). There was also a significant interaction effect between portion of study area (northeast vs. southwest) and distance to water, as Red Fox are closer to water in the southwestern portion of the study area. In the binomial Table 3. Summary of variable effects on Coyote local distribution in the Pioneer Valley of Western Massachusetts. Interaction effects are designated with “:” and an asterisk (*) indicates significant variable in the model (P < 0.05). We averaged the top 2 negative binomial models for Coyote. Variable Estimate Standard error z-value P-value (Intercept) -21.30 0.419 49.962 less than 0.001* Altered average -3.929 1.393 2.768 0.005* Distance to forest -0.029 0.018 1.548 0.122 Distance to altered -4.449e-04 2.733e-04 1.598 0.110 Portion of study area -0.082 0.473 0.170 0.865 Distance to water2 5.300e-04 2.268e-04 2.294 0.022* Distance to water2 -1.412e-07 3.800e-08 3.648 less than 0.001* Eastern Gray Squirrel 0.031 0.013 2.364 0.018* Eastern Cottontail Rabbit 0.226 0.092 2.410 0.016* Wild Turkey 0.363 0.155 2.292 0.022* Eastern Chipmunk -1.148 0.373 3.017 0.003* Unknown small mammal 0.452 0.142 3.130 0.002* Distance to water:Portion of study area -7.993e-04 3.884e-04 2.021 0.043* Red Fox 0.188 0.149e-01 1.238 0.216 Northeastern Naturalist 334 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 portion of the model, there was a significant positive correlation with distance to roads (Tables 4 and 5). We detected Gray Foxes at 16 of the 79 (20%) camera sites (33 independent observations in 1670 trap nights), with the number of detections at each Table 4. Summary of variable effects on modeling canid local distribution in the Pioneer Valley of Western Massachusetts. Interaction effects are designated with “:” and an asterisk (*) indicates significant variable in the best model ( P < 0.05). We averaged the top models for Coyote and Gray Fox. Variable Coyote Red Fox Gray Fox Distance to altered (-) (-) Distance to forest (-) Distance to road (+)* Distance to urban (+)* Distance to water (-)* (+)* Distance to water2 (-/+)* Altered average (-)* Water average (+)* Study area (-) (+)* (+)* Eastern Gray Squirrel (+)* (-) Eastern Cottontail Rabbit (+)* Eastern Chipmunk (-)* White-tailed Deer (-)* (+) Wild Turkey (+)* Unknown small mammal (+)* Common Raccoon (-) Coyote (-)* (-) Domestic Dog (+)* Red Fox (+) P/A Chipmunk (+) P/A Domestic Dog (+) Distance to water:Study area (-)* (+)* Table 5. Summary of the top model (zero-inflated Poisson) variable effects on Red Fox local distribution in the Pioneer Valley of Western Massachusetts. Interaction effects are designated with “:” and an asterisk (*) indicates significant variable in the model ( P < 0.05). Variable Estimate Standard error z-value P-value Count portion (Poisson) Intercept -23.990 1.198 -20.029 less than 0.001* Portion of study area 2.462 1.090 2.258 0.024* Distance to water -8.690e-04 7.235e-05 -12.010 less than 0.001* Water average 14.510 3.391 4.278 less than 0.001* Distance to urban 0.002 5.163e-04 3.717 less than 0.001* White-tailed Deer -1.943 0.646 -3.011 0.003* Coyote -0.408 0.204 -2.001 0.045* Domestic Dog 0.335 0.119 2.806 0.005* Distance to water:Portion of study area 0.002 1.929e-04 8.097 less than 0.001* Zero-inflated portion (binomial) Intercept -2.776 1.246 -2.229 0.026* Distance to road 0.008 0.003 2.315 0.021* Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 335 site varying from 0 to 15. Gray Fox local distribution was best described by a zero-inflated negative binomial model (Table 6; top Gray Fox models are listed in Supplemental Table 4 ((see Supplemental File 1, available online at http://www. eaglehill.us/NENAonline/suppl-files/26-2-N1651-Fuller-s1, and, for BioOne subscribers, at https://dx.doi.org/10.1656/N1651.s1). We averaged the top 6 Gray Fox models; the resulting model showed that the detection rate for Gray Fox was positively correlated with distance to water, and there was a significant relationship with portion of study area (northeast vs. southwest) in the negative binomial portion of the model (Table 4). Our analysis of activity patterns suggested a high degree of temporal overlap across focal species (Fig. 2). Coyote and Gray Fox had the highest coefficient of overlap (Δ1-hat = 0.89; LCI = 0.79, UCI = 0.96), Red Fox and Gray Fox had the next highest coefficient of overlap (Δ1-hat = 0.77; LCI = 0.63, UCI = 0.89), and, while still high, Coyote and Red Fox had the lowest measured coefficient of overlap (Δ1- hat = 0.75; LCI = 0.61, UCI = 0.87). All 3 species had the highest levels of activity during the nocturnal and crepuscular diel periods. Although activity patterns suggest high degrees of temporal activity, we detected all 3 species at the same camera site on only 2 occasions (3% of all sites). Including the 2 sites where all species were detected, we detected both Coyote and Gray Fox at 10 sites (13% of all sites), Coyote and Red Fox were both detected at 4 sites (5%), and Red Fox and Gray Fox were both detected at 3 sites (4%). Discussion Our finding that local distribution of Coyotes seems to be driven mostly by prey availability, and that they likely have a negative impact on the local distributions (but not the activity patterns) of Red Foxes and Gray Foxes, underscores the notion Table 6. Summary of variable effects on Gray Fox local distribution in the Pioneer Valley of Western Massachusetts. An asterisk (*) indicates significant variable in the model (P < 0.05). We averaged the top 6 zero-inflated negative binomial models for Gray Fox and th ey are presented below. Variable Estimate Standard error z-value P-value Count portion (negative binomial) Intercept -22.130 1.180 18.758 less than 0.001* Portion of study area 2.367 1.064 2.224 0.026* Distance to water 3.316e-04 1.495e-04 2.218 0.027* Distance to altered -0.003 0.002 1.745 0.081 Common Raccoon -0.172 0.134 1.288 0.198 Coyote -0.432 0.259 1.673 0.094 Zero-inflated portion (binomial) Intercept -14.410 27.480 0.524 0.600 White-tailed Deer 23.610 61.220 0.386 0.700 Common Raccoon -14.410 35.550 0.405 0.685 Eastern Gray Squirrel -6.743 17.080 0.395 0.693 P/A Eastern Chipmunk 141.400 296.500 0.477 0.634 P/A Domestic Dog 17.130 45.010 0.380 0.704 Northeastern Naturalist 336 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 Figure 2. Kernel-density functions for activity pattern comparison between canids. Shaded area is the amount of overlap. Northeastern Naturalist Vol. 26, No. 2 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 337 that habitat is defined by a variety of interacting factors, both spatial and temporal. Not surprisingly, Coyote numbers have been shown to be associated positively with prey abundance (Patterson and Messier 2001), but site-specific habitat predictors such as prey and competitor species’ relative abundances are typically left out of distribution assessments for medium–large carnivorans (e.g., Dodge and Kashian 2013, Gese et al. 2012, Ordeñana et al. 2010). In camera analyses of wildlife distributions, inclusion of data about other species possibly affecting the focal species distributions is important to consider (Bashir et al. 2014, Gompper et al. 2016, Mondal et al. 2013, Ngoprasert et al. 2012), while realizing that some prey species (e.g., smaller mammals used by foxes; Johnson et al. 1996) may be poorly represented or accounted for due to sampling methods. In the Pioneer Valley, local distributions of Gray Fox and Red Fox are limited by landscape habitat parameters, as well as by the presence of Coyotes, but do not appear to be influenced by the photographic rates of prey species as we measured them. That foxes occurred within the same spatial context as Coyotes, but their local distributions and detection rates were negatively correlated with Coyote abundance, is consistent with previous studies showing Coyotes adversely impacting fox distributions and access to resources (Farias et al. 2005, Fedriani et al. 2000, Harrison et al. 1989, Henke and Bryant 1999, Levi and Wilmers 2012, Voigt and Earle 1983). Competition avoidance has an important spatial component and can facilitate canid co-existence (e.g., Gosselink et al. 2003, Mueller et al. 2018, Voigt and Earle 1983). With regard to urbanization, some studies reported that all 3 of the canid species we studied had positive relationships with increasing development (Cove et al. 2012, Kapfer and Kirk 2012), but others indicated that Coyotes preferred non-urban habitats (Gese et al. 2012), Coyotes and Red Foxes had higher detection rates in areas with lower human abundances (Randa and Yunger 2006), and Gray Foxes established core areas of their ranges in a national recreation area rather than in the surrounding urbanized area (Riley 2006). Given our results and the published literature, we suggest that these species’ relationships to urbanization are likely site-specific (Crooks 2002, Crooks et al. 2010, Faeth et al. 2005), and vary with the composition of the matrix of land-use types in an area. To better understand carnivoran distributions across the changing landscape, one must include a full suite of habitat variables in analyses (e.g., prey species local distributions, competitor local distributions, vegetative structure, anthropogenic landscape features). Carnivoran communities respond differently to development and these responses are likely to be system-specific, with carnivorans in some locations having positive relationships with increased development while others have negative relationships (Crooks 2002, Crooks et al. 2010, Faeth et al. 2005). As human population effects on the landscape in New England continue to increase, our results can be applied in land-use planning to maintain a diverse carnivoran community. Northeastern Naturalist 338 E.G. LeFlore, T.K. Fuller, J.T. Finn, S. DeStefano, and J.F. Organ 2019 Vol. 26, No. 2 Acknowledgments We thank members of the 2012 NRC 564 Wildlife Habitat Management course for their assistance in collecting data, Cowls Building Supply and all the private landowners for allowing access to many parcels of land for camera placement and data collection, and the Amherst Conservation Commission for access to conduct research on Amherst town property. Project support was given through the USGS Massachusetts Cooperative Fish and Wildlife Research Unit, University of Massachusetts Amherst, Northeast Alliance for Graduate Education and the Professoriate, US Fish and Wildlife Service, and the Social Science Research Council. A. Sirén, S.P.D. Riley, and 3 anonymous reviewers provided valuable comments on drafts of the manuscript. This study was performed under the auspices of the University of Massachusetts-Amherst Animal Care and Use Committee protocol #2012-0053. 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