ESTIMATING MOOSE ABUNDANCE IN LINEAR SUBARCTIC HABITATS IN LOW SNOW CONDITIONS WITH DISTANCE SAMPLING AND A KERNEL ESTIMATOR

Abstract

Moose (Alces alces) are colonizing previously unoccupied habitat along the tributaries of the lower Kuskokwim River within the Yukon Delta National Wildlife Refuge (YDNWR) of western Alaska. We delineated a new survey area to encompass these narrow (0.7–4.3 km) riparian corridors that are bounded by open tundra and routinely experience winter conditions that limit snow cover and depth necessary for traditional moose surveys. We tested a line-transect distance sampling approach as an alternative to estimate moose abundance in this region. Additionally, we compared standard semi-parametric detection functions available in the program Distance to a nonparametric kernel-based estimator not previously used for moose distance data. A double-observer technique was used to verify that the probability of detection at the minimum sighting distance was 1.0 (standard assumption). Average moose group size was 2.03 and not correlated with distance from the transect line. The top semi-parametric model in the program Distance was a hazard-rate key function with no expansion terms. This model estimated average probability of detection as 0.70 with an estimated abundance of 352 moose (95% CI = 237–540). The CV for the semi-parametric model was 20% and had an estimated bias of 1.4%. The nonparametric kernel-based model had an average probability of detection of 0.73 and an estimated abundance of 340 (95% CI = 238–472) moose. The CV for the kernel method was 18% and the estimated bias was <0.001%. Line-transect distance sampling with a helicopter worked well in the narrow riparian corridors with low snow conditions, and survey costs were similar to traditional surveys with fixed-wing aircraft. The kernel estimator also performed well compared to the standard semi-parametric models used in program Distance. Our technique provides a viable approach for surveying moose in similar areas that have restrictive conditions for standard aerial surveys.