DOI: 10.5061/dryad.47d7wm3kc
title: Data for: Effects of landcover on mesocarnivore density along an urban to rural gradient

description: Human development has major implications for wildlife populations.
 Urban-exploiter species can benefit from human subsidized resources,
 whereas urban-avoider species can vanish from wildlife communities in
 highly developed areas. Therefore, understanding how the density of
 different species varies in response to landcover changes associated with
 human development can provide important insight into how wildlife
 communities are likely to change and provide a starting point for
 predicting the consequences of those changes. Here, we estimated the
 population density of five common mesocarnivore species (coyote (Canis
 latrans), bobcat (Lynx rufus), red fox (Vulpes vulpes), raccoon (Procyon
 lotor), and Virginia opossum (Didelphis virginiana)) along an urban to
 rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA
 between November 2021, and March 2022. At each study site, we applied the
 Random Encounter Model (REM) to data from motion-triggered cameras to
 calculate the density of our five focal species. Coyote density ranged
 from 0–3.47 with a mean of 0.4 individuals/km2. Raccoon density ranged
 from 0–93.26 with a mean of 4.2 individuals/ km2. Bobcat density ranged
 from 0–8.87 with a mean of 0.33 individuals/km2. Opossum density ranged
 from 0–27.35 with a mean of 0.76 individuals/km2. Red fox density ranged
 from 0–0.73, with a mean of 0.02 individuals/km2. We used generalized
 linear models to evaluate the density of each species against
 environmental and anthropogenic variables. Coyotes and raccoons occurred
 in the greatest densities in areas with high anthropogenic noise levels,
 suggesting that both species are synanthropic and able to co-exist in
 areas of high human activity. Alternatively, Virginia opossum and red fox
 densities were greatest in open, developed areas (lawns, golf courses,
 cemeteries, and parks) and were absent (red fox) or rare (opossum) in
 natural areas. We found no evidence that bobcat density varied along the
 urban to rural gradient studied, but this lack of evidence may have been
 driven by the small spatial scale of many of our sites in relation to
 space needs of this wide-ranging species. The density estimates we report
 based on game camera data of unmarked animals were consistent with reports
 from the literature for these same species derived from traditional
 methods, providing additional support to the REM as a viable, non-invasive
 method to calculate density of unmarked species.
description: This data was collected through camera traps set between November
 1, 2021, and March 14, 2022. Cameras were set at 12 study sites in the
 Ozark Mountain Ecoregion, Northwest Arkansas, USA. Sites were chosen to
 represent a continuum of human activity and ranged from 2km to 60km from
 downtown Fayetteville, Arkansas. Camera trap images were sorted using
 Timelapse 2.0 software. Detections were sorted into 5-minute
 "episodes", and each episode was treated as a single detection
 to avoid double counting individuals.  To estimate the
 density (D) of our five focal species from game camera detections, we
 applied the Random Encounter Model (REM) equation, where
 <em>y</em> refers to the total detections of each animal per
 camera, and <em>t</em> is the total trap nights in hours
 (measure of trapping effort). <em>V</em> is the day range of
 each species, referring to how far an animal travels in a 24-hour period.
 We used published day range estimates for each species and used the median
 day range value for each species from all reported estimates to
 parameterize our models.  Values for the detection radius
 (<em>r</em>), and detection angle (<em>θ</em>)
 were collected for each camera in the field through walk tests. A walk
 test entailed walking directly towards each camera to calculate detection
 radius and from each side at 5m from the camera to calculate detection
 angle in degrees. Detection was determined by whether or not the detection
 light was triggered on the camera during each walk test. The detection
 angle was later converted to radians for density calculations.
 To assess which landcover variables most influenced the density
 of each focal species, we used an iterative approach to assemble 31
 Generalized Linear Mixed Models (GLMM) with additive effects using r
 programming and the “lme4” and “AICcmodavg” packages for five predictor
 variables: HUD, noise, distance to water, and developed open, including a
 global model (all variables with random effect) and a null model (only
 random effect). We used study site as a random effect in each model. The
 zero inflation in the data was accounted for by using a gamma distribution
 in all models. We then used AICc selection criteria with an a priori
 cutoff of two for the ∆AIC delta value. 
description: R programming and the “lme4” and “AICcmodavg” packages
 Microsoft Excel Timelapse 2.0
 ArcGIS Pro (ArcGIS Pro 2.8.3, 2021; Esri Inc, Redlands,
 CA)