Landscape factors affecting relative abundance of gray foxes Urocyon cinereoargenteus at large scales in Illinois, USA

Abstract

Evaluation of wildlife‐habitat relationships at the landscape level provides insight into how habitat connectivity, fragmentation and land‐use changes may affect wildlife populations. Although previous studies have demonstrated that habitat composition and configuration at large scales may affect the presence, survival and movement of carnivore species, no such analyses have been conducted for the gray fox Urocyon cinereoargenteus. We used a generalized correlative mapping approach to investigate the relationship of gray fox relative abundance to landscape variables at the county scale in Illinois, USA. Relative abundance of gray foxes was high in 37 of 102 (36%) counties. Four models were competitive based on ΔAICc scores, and these models indicated that standard deviation of the perimeter‐area ratio of agricultural patches, interspersion and juxtaposition index of grassland patches, coefficent of variation of the proximity index of forest patches and relative patch richness of the landscape affected gray fox relative abundance. The variables occurring in our competing models indicate that the relative abundance of the gray fox is higher in counties containing a high level of fragmentation of preferred habitat types (i.e. forests and grasslands) and lower dispersion of less preferred habitat types (i.e. agriculture). Our results reflect gray fox habitat use at smaller scales, and at the landscape scale, gray fox abundance is influenced by how cover‐type patches are configured.

Is your ad hoc model selection strategy affecting your multimodel inference?

“…In the simple occupancy bobcat example, multi-covariate models of w received greatest support, but support was similar among multicovariate models and there was not consistent support for any covariate or combination for occupancy (Table 2). However, in the gray fox example there was a strong signal of two correlated covariates with gray fox occupancy increasing with forest cover (w i = 0.53) and decreasing with agriculture (w i = 0.43), as would be expected for the species (Cooper et al 2012). In this case, the top model was identified in all strategies and loss of total Akaike model weights recovered only resulted when the second-best supported covariate (correlated with the most supported covariate) was removed with strict within-stage model selection criteria.…”

“…(Burnham and Anderson 2002). However, in the gray fox example there was a strong signal of two correlated covariates with gray fox occupancy increasing with forest cover (w i = 0.53) and decreasing with agriculture (w i = 0.43), as would be expected for the species (Cooper et al 2012). However, fewer covariate combinations can be construed as reasonable formal hypotheses (see Lesmeister et al 2015 for examples of thoughtfully constructed candidate sets of covariate combinations for each species).…”

Feral wild boar distribution and perceptions of risk on the central Canadian prairies

“…We employed several variable reduction techniques commonly used in large‐scale occupancy modeling (Cooper et al ). We first eliminated the number of commercial crop species that were present in <40% of the rural municipalities (rare crop species: 12 crop species removed).…”

An evaluation of hunter surveys to monitor relative abundance of bobcats

“…Using O B instead of the number of bobcats observed was expected to reduce bias due to chance encounters of bobcat family groups and from repeated observations of the same individual during 1 outing (Mahard ). Although hunters reported the number of hours they spent during each outing, we felt that the number of outings was a better parameter for effort (contrary to Kindberg et al , Cooper et al , and Linde et al ). This was based on the notion that bobcats have large home ranges relative to the area we expected most hunters to cover in 1 outing and many short outings in different locations would likely produce more bobcat detections than a small number of longer outings.…”

“…Agreement among indices is often interpreted as an indication that they are appropriately describing the status of a species (e.g., Rolandsen et al ). Despite cautions against using indices (MacKenzie et al , O'Brien ), they are frequently used by agency biologists (Clark and Andrews , Gese , Roberts and Crimmins , Cooper et al ) and researchers (Conn et al , Evangelista et al , Kindberg et al , Bengsen et al , Letnic et al ) to gauge status or responses by populations to management actions or environmental change. Thus, overcoming some of the limitations of indices is warranted (Sollmann et al ).…”