Landscape conditions and elevation interact to influence the distribution and density of state-endangered Humboldt martens
| Authors: | Erika L. Anderson, Marie E. Martin, Micaela S. Gunther, Kristine L. Pilgrim, Scott A. Demers, Sean M. Matthews |
| Year: | 2025 |
| Type: | Scientific Journal |
| Station: | Rocky Mountain Research Station |
| DOI: | https://doi.org/10.1016/j.gecco.2025.e03980 |
| Source: | Global Ecology and Conservation |
Abstract
Estimating population demographic parameters and habitat relationships is central to recovery planning for imperiled species. These parameters and relationships are often unavailable for rare and elusive species but are necessary to inform their conservation and management. The Humboldt marten (Martes caurina humboldtensis) is a subspecies of the Pacific marten that was historically distributed in coastal forests in California and Oregon, was extirpated from more than 95 % of its historical distribution, and is listed as endangered in California and federally listed as threatened. To better understand their distribution, abundance, and habitat relationships in northwestern California, we non-invasively surveyed for Humboldt martens and fit an integrated spatial capture-recapture model to estimate their abundance and density, and a post-hoc generalized linear model to evaluate the influence of landscape and forest conditions within different elevation bands on marten density. We estimated a posterior median density (95 % credible interval) of 0.276 (CRI 0.183–0.364) martens per 1 km2 and a posterior median abundance of 111 (CRI 73–146) martens within our 399 km2 study area. Our results suggest that marten density was heterogeneous and influenced by topographic position, ladder fuel density, and diameter diversity index and these effects varied by elevation across a 399 km2 portion of the 1580 km2 North Coastal California Extant Population Area. Our estimates of marten density and abundance establish a baseline for this endangered population and will inform future monitoring to estimate apparent survival and recruitment, additional relationships to environmental conditions and change, and demographic trends over time.