Evaluation of wolf density estimation from radiotelemetry data

Abstract

Density estimation of wolves (Canis lupus) requires a count of individuals and an estimate of area those individuals inhabit. With radiomarked wolves, the count is straightforward but estimation of area is more difficult and often given inadequate attention. The population area, based on the mosaic of pack territories, is influenced by sampling intensity similar to individual home ranges. If sampling intensity is low, population area will be underestimated and wolf density will be inflated. Using data from studies in Denali National Park and Preserve, I investigated these relationships using Monte Carlo simulation to evaluate effects of radiolocation effort and number of marked packs on density estimation. As the number of adjoining pack home ranges increase, fewer relocations are necessary to define a given percentage of population area. I evaluated the utility of nonlinear regression to adjust for biases associated with under sampling and present recommendations for monitoring wolves via radiotelemetry.