Martes americana, Multi scale, Habitat modeling, Logistic regression
We used bivariate scaling and logistic regression to investigate multiple-scale habitat selection by American marten (Martes americana). Bivariate scaling reveals dramatic differences in the apparent nature and strength of relationships between marten occupancy and a number of habitat variables across a range of spatial scales. These differences include reversals in the direction of an observed association from positive to negative and frequent dramatic changes in the apparent importance of a habitat variable as a predictor of marten occurrence. Logistic regression on the optimally scaled input variables suggests that at the scale of home ranges, marten select landscapes with high average canopy closure and low fragmentation. Within these low fragmented landscapes, marten select foraging habitat at a fine scale within late-seral, middle-elevation mesic forests. In northern Idaho, optimum American marten habitat, therefore, consists of landscapes with low road density, low density of non-forest patches with high canopy closure, and large areas of middle-elevation, late successional mesic forest. Comparison of current landscape conditions to those expected under the historic range of variability indicates that road building and timber harvest in the past century may have substantially reduced the amount of suitable marten habitat in northern Idaho. Our results are generally consistent with previous research in the Rocky Mountains, with additional insights related to the relative importance, functional form, and scale at which each habitat variable has the largest influence on marten occurrence.
Rocky Mountain Research Station Research Paper
Required Publisher's Statement
USDA Forest Service, Rocky Mountain Research Station Research Paper, RMRS-RP-94, May 2012.
Wasserman, Tzeidle N.; Cushman, Samuel A.; and Wallin, David O., "Multi Scale Habitat Relationships of Martes americana in Northern Idaho, U.S.A" (2012). Environmental Sciences Faculty Publications. 20.