Identifying priority areas for landscape connectivity for three large carnivores in northwestern Mexico and southwestern United States

LANDSCAPE ECOLOGY(2021)

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Abstract
Context Large carnivores are crucial to ecosystem functioning, as they enhance the biodiversity of the native communities in which they live. However, most large carnivores are threatened with extinction resulting from human persecution, habitat encroachment, and the loss of habitat connectivity. Objective To identify areas that favor landscape connectivity for three large carnivores within and between northwestern Mexico and southwestern United States. Methods We performed a habitat suitability analysis for puma ( Puma concolor ), Mexican wolf ( Canis lupus baileyi ), and black bear ( Ursus americanus ) by combining ecological niche modeling with anthropogenic variables to identify high-quality habitat patches to be connected. We also developed a connectivity analysis to identify smaller suitable habitat patches within connecting corridors to evaluate their contributions to connecting larger populations. Results We found existing large, high-quality areas in Mexico and the United States that could connect through smaller patches. Likewise, we identified pinch-point areas, patches and links with high centrality, indicating that some biological corridors promote connectivity among the most extensive suitable patches. Conclusions It is possible to maintain and even enhance adequate landscape connectivity between major suitable habitat patches for the three large carnivores, within and between their distributional areas in Mexico and the United States. In this regard, decision-makers, academia, and civil society need to strengthen their bonds to reduce the pressure on these carnivores and help authorities improve binational plans and agreements to consolidate conservation actions and landscape connectivity between Mexico and the United States.
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Key words
Biological corridors, Connectivity, Transboundary conservation, Large carnivores, Circuit theory, Least-cost path
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