Wolverine Occupancy, Spatial Distribution, and Monitoring Design

Abstract

In the western United States, wolverines (Gulo gulo) typically occupy high‐elevation habitats. Because wolverine populations occur in vast, remote areas across multiple states, biologists have an imperfect understanding of this species' current distribution and population status. The historical extirpation of the wolverine, a subsequent period of recovery, and the lack of a coordinated monitoring program in the western United States to determine their current distribution further complicate understanding of their population status. We sought to define the limits to the current distribution, identify potential gaps in distribution, and provide a baseline dataset for future monitoring and analysis of factors contributing to changes in distribution of wolverines across 4 western states. We used remotely triggered camera stations and hair snares to detect wolverines across randomly selected 15‐km × 15‐km cells in Idaho, Montana, Washington, and Wyoming, USA, during winters 2016 and 2017. We used spatial occupancy models to examine patterns in wolverine distribution. We also examined the influence of proportion of the cell containing predicted wolverine habitat, human‐modified land, and green vegetation, and area of the cluster of contiguous sampling cells. We sampled 183 (28.9%) of 633 cells that comprised a suspected wolverine range in these 4 states and we detected wolverines in 59 (32.2%) of these 183 sampled cells. We estimated that 268 cells (42.3%; 95% CI = 182–347) of the 633 cells were used by wolverines. Proportion of the cell containing modeled wolverine habitat was weakly positively correlated with wolverine occupancy, but no other covariates examined were correlated with wolverine occupancy. Occupancy rates (ψ) were highest in the Northern Continental Divide Ecosystem (ψ range = 0.8–1), intermediate in the Cascades and Central Mountains of Idaho (ψ range = 0.4–0.6), and lower in the Greater Yellowstone Ecosystem (ψ range = 0.1–0.3). We provide baseline data for future surveys of wolverine along with a design and protocol to conduct those surveys. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.

Modeling multi‐scale occupancy for monitoring rare and highly mobile species

“…Our model also suggested that asymptotic occupancy of wolverines in the Cascades is high, but short‐term use of any given area is low, consistent with estimates of moderate occurrence probability from previous studies conducted in the region (Lukacs et al. 2020). The difference in precision between continuous‐time and discrete‐time models is due to the need to discard >75% of detection events to create detection histories for the discrete‐time model (Guillera‐Arroita et al.…”

“…We found that this model improved inference relative to a discrete-time multi-scale occupancy model in our wolverine case study, decreasing standard deviation of estimated use probabilities by upwards of 50% (Table 1), and also provided more conservative use and instantaneous occupancy estimates. Our model also suggested that asymptotic occupancy of wolverines in the Cascades is high, but shortterm use of any given area is low, consistent with estimates of moderate occurrence probability from previous studies conducted in the region (Lukacs et al 2020). The difference in precision between continuous-time and discrete-time models is due to the need to discard >75% of detection events to create detection histories for the discrete-time model (Guillera-Arroita et al 2011).…”

“…The difference in precision between continuous-time and discrete-time models is due to the need to discard >75% of detection events to create detection histories for the discrete-time model (Guillera-Arroita et al 2011). Given that discrete-time occupancy models of highly mobile species such as wolverines may bin detections into detection histories as long as 30 d to estimate detection probability (Lukacs et al 2020), such differences in results may occur often. Inference from discrete-time models could be improved by creating longer detection histories with more tertiary occasions, each of shorter length (e.g., 10 3-d tertiary occasions instead of three 10-d tertiary occasions).…”

“…Given that discrete‐time occupancy models of highly mobile species such as wolverines may bin detections into detection histories as long as 30 d to estimate detection probability (Lukacs et al. 2020), such differences in results may occur often. Inference from discrete‐time models could be improved by creating longer detection histories with more tertiary occasions, each of shorter length (e.g., 10 3‐d tertiary occasions instead of three 10‐d tertiary occasions).…”

“…We used data collected in the Cascades Mountains of Washington State to estimate and interpret the parameters in our model. The data were detections of wolverines at 25 baited camera stations in a study area of about 48,000 km 2 operated from 1 December 2016 to 31 March 2017 as part of a larger, multi-state wolverine survey (Lukacs et al 2020). Each station was deployed before 1 December 2016.…”