Evidence that drainage basin morphology and trout standing stock are related through a functional link between geomorphic features and stream habitat quality is presented. Numerous significant univariate correlations were found between geomorphic variables, stream habitat variables, and trout standing stock in both high‐elevation forest and low‐elevation rangeland streams. Canonical correlations between geomorphic variables and stream habitat variables provided insight into the form of the functional link. Multiple‐regression equations predicting trout standing stock were dominated by geomorphic variables. When geomorphic variables alone were incorporated into regression models they predicted trout standing stock as accurately as did stream habitat variables.

JUNGE, C. 0. 1963. A quantitative evaluation of the bias in population estimates based on selective samples. Pages 26-28 in North Atlantic fish marking symposium. International Council of North Atlantic Fisheries Special Publication 4. SAMUEL, M. D., E. O. GARTEN, M. W. SCHLEGEL, AND R. G. CARSON. 1987. Visibility bias during aerial surveys of elk in northcentral Idaho. Journal of Wildlife Management 51:622-630. SEBER, G. A. F. 1982. The estimation of animal abundance. Second edition. MacMillan,

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.