USING DISTANCE SAMPLING TO ESTIMATE MOOSE ABUNDANCE IN ISLE ROYALE NATIONAL PARK

Abstract

Efficiently and accurately estimating moose (Alces alces) abundance in geographically isolated ecosystems like Isle Royale National Park (IRNP), Michigan, USA, is important for planning management actions. To estimate the population size of moose in IRNP, we flew a Robinson R44 helicopter to apply distance sampling in February 2022. We surveyed the entirety of IRNP with 149 transects that were 500-m wide and 0.16–13.36 km long depending on island width. We observed 439 moose in 253 groups, and fitted eight competing distance sampling models using two distance key functions (half-normal and hazard-rate), each with four covariate models: a null model, a univariate model for canopy size, a univariate model for group size, and a model with both canopy and group size. We used a Horvitz-Thompson-like estimator with the best model to calculate moose abundance on IRNP with 90 and 95% confidence intervals. The top model included a hazard-rate key function and the group size covariate. The estimated moose population was 1039 (90% CI 835–1293, 95% CI 800–1349). To test how sampling effort affected our estimates of moose abundance, we systematically removed 1/3 or 2/3 of transects from the dataset and repeated the analyses. The estimated abundance was similar (each 95% CI contained all 3 point estimates) when using all, 2/3, or 1/3 of transects, with the most precise estimate derived from the full dataset. Our population estimate was within the historical range of moose population estimates in IRNP and similar to a concurrent Gasaway-type estimate. While our survey covered the entirety of IRNP, we found that reducing effort by 1/3 provided a similar abundance estimate and precision. We concluded that distance sampling is a reasonable and efficient method to estimate moose density in IRNP. Monitoring how moose abundance varies in response to predator restoration efforts and climate change will help inform long-term management and planning in IRNP.