Using GIS-based and remotely sensed data for early winter moose (Alces alces gigas) survey stratification

Abstract

Stratification of moose survey areas is a key step to reduce population estimation variance. In the Yukon and Alaska, use of fixed-area grids for early winter moose counts combined with the increasing availability of GIS and remotely sensed data provide the opportunity to develop standardized and repeatable habitat-based stratifications. I used univariate comparisons, stepwise regression and AIC modeling to describe moose distribution as a function of landscape level variables for an area in west central Yukon during 1998 and 1999. Results quantified early winter habitat use of upland shrub habitats and support previous observations for early winter moose habitat use in Alaska, Minnesota and Montana. Number of patches, in association with areas of alpine and shrubs, were found to be highly influential for survey blocks where moose are expected to be present and in high numbers. Overall, model performance based on relative abundance of moose was less predictive than for blocks where moose were present or absent. Spatial resolution of GIS and remotely sensed data used in this study (25 m grid cells) provided sufficient spatial detail to generate correlations between moose presence and habitat for a first level stratification.