4.6 Article

Comparison of road surveys and circuit theory to predict hotspot locations for implementing road-effect mitigation

Journal

BIODIVERSITY AND CONSERVATION
Volume 26, Issue 14, Pages 3445-3463

Publisher

SPRINGER
DOI: 10.1007/s10531-017-1414-9

Keywords

Connectivity; Circuitscape; Sireima; Wildlife management; Reptiles; Amphibians; Large mammals; Road ecology

Funding

  1. NSERC
  2. Ontario Ministry of Natural Resources and Forestry's Species at Risk Stewardship Fund
  3. Ontario Ministry of Environment and Climate Change's Great Lakes Guardian Community Fund
  4. Ontario Parks
  5. Friends of Presqu'ile Provincial Park

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The mitigation of road-effects on wildlife, especially road mortality and habitat fragmentation, has become increasingly common in the last 20 years. However, exclusion fencing and habitat connectivity structures can be very costly and several questions remain regarding how to best determine locations that will optimize mitigation success. Based on data collected across several years and across multiple landscapes and taxa, we present a comparative analysis of two methods: road surveys and circuit theory, and review their benefits and challenges to better inform decision making. Road surveys were completed in two locations over three years for large mammals and herpetofauna to identify road crossing hotspots. Circuit theory was also applied to these systems to identify crossing hotspots using habitat resistance models. The location, number and width of hotspots were compared between methods. Hotspot distributions were similar between methods for some herpetofauna, but different for Mammals, and road surveys produced a significantly greater number of smaller hotspots compared to circuit theory, implying that road surveys provide better hotspot resolution. As circuit model complexity increased, the number and width of hotspots decreased, diffusing across the landscape. Road surveys were better at predicting optimal crossing structure location at a local scale; however, circuit theory is less costly, and can be useful at large scales. As both methods can offer valuable information, we argue that the combination of these two approaches provides a strong basis for managers and biologists to make informed decisions about costly mitigation measures, optimizing both conservation benefits and limited funding.

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