Modeling the distribution of Gray Fox ( <i>Urocyon cinereoargenteus</i> ) in Indiana using community surveying tools

Abstract Gray Fox (Urocyon cinereoargenteus) in Indiana and several other Midwest states have been experiencing a potential population decline and range contraction in recent years. Developing a species distribution model for Gray Fox in Indiana is necessary for identifying target areas for management efforts. For species with cryptic behaviors, such as Gray Fox, advancements in modeling, e.g., maximum entropy modeling (MaxEnt) have allowed for presence-only data to be used effectively to develop species distribution models. Physically surveying a large area for the presence of a cryptic species is difficult, but using community-based observation data (e.g., Archer’s Index, harvest data) can allow for creation of a discriminatory species distribution model. Our objective was to use multiple sources of community-based observation data to create a species distribution model of Gray Fox in Indiana using a maximum entropy approach. We developed 12 a priori models containing ecologically relevant covariates (e.g., distance to land-cover type, landscape-patch metrics) and trained (n = 71 observations) and tested (n = 57) each model using community-based observation data that were confirmed by experts. Our best-performing models predicted the highest probabilities of presence of Gray Fox in the northern and southern extents of the state, and relatively low probabilities of presence in the central, agriculture-dominated epicenter of the state. The highest probabilities of presence were associated with greater amounts of forest (mixed and evergreen), urban-forest, and forest-scrub edge. These areas may be the most suitable habitat for Gray Fox due to increased amounts of potential cover and reduced competition from other mesocarnivores. Our results support the importance of maintaining contiguous forest patches or a forest-urban-scrub landscape matrix for Gray Fox populations in Indiana and other midwestern states. Given the dominance of privately owned land in the Midwest, collaboration between managers and landowners is critical for making informed habitat-management decisions for Gray Fox.

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Publication Details

Journal
Journal of Mammalogy
Published
2026-04-17
DOI
https://doi.org/10.1093/jmammal/gyag027
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
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article

Modeling the distribution of Gray Fox ( Urocyon cinereoargenteus ) in Indiana using community surveying tools

Scott M. Bergeson, Tim L. Hiller, Geriann Albers, Dawn M. Reding et al.
Journal of Mammalogy
Species Distribution and Climate Change
article

Modeling the distribution of Gray Fox ( Urocyon cinereoargenteus ) in Indiana using community surveying tools

Scott M. Bergeson, Tim L. Hiller, Geriann Albers, Dawn M. Reding, Jordan M. Marshall, C. Langdon White
article en

Abstract

Abstract Gray Fox (Urocyon cinereoargenteus) in Indiana and several other Midwest states have been experiencing a potential population decline and range contraction in recent years. Developing a species distribution model for Gray Fox in Indiana is necessary for identifying target areas for management efforts. For species with cryptic behaviors, such as Gray Fox, advancements in modeling, e.g., maximum entropy modeling (MaxEnt) have allowed for presence-only data to be used effectively to develop species distribution models. Physically surveying a large area for the presence of a cryptic species is difficult, but using community-based observation data (e.g., Archer’s Index, harvest data) can allow for creation of a discriminatory species distribution model. Our objective was to use multiple sources of community-based observation data to create a species distribution model of Gray Fox in Indiana using a maximum entropy approach. We developed 12 a priori models containing ecologically relevant covariates (e.g., distance to land-cover type, landscape-patch metrics) and trained (n = 71 observations) and tested (n = 57) each model using community-based observation data that were confirmed by experts. Our best-performing models predicted the highest probabilities of presence of Gray Fox in the northern and southern extents of the state, and relatively low probabilities of presence in the central, agriculture-dominated epicenter of the state. The highest probabilities of presence were associated with greater amounts of forest (mixed and evergreen), urban-forest, and forest-scrub edge. These areas may be the most suitable habitat for Gray Fox due to increased amounts of potential cover and reduced competition from other mesocarnivores. Our results support the importance of maintaining contiguous forest patches or a forest-urban-scrub landscape matrix for Gray Fox populations in Indiana and other midwestern states. Given the dominance of privately owned land in the Midwest, collaboration between managers and landowners is critical for making informed habitat-management decisions for Gray Fox.

Journal of Mammalogy
Indiana Department of Natural Resources (US), Indiana University – Purdue University Fort Wayne (US), Luther College (US), Purdue University Fort Wayne (US), Wildlife Management Institute (US)
Reduced inequalities
Openalex Percentile: Top 4%
Species Distribution and Climate Change
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