Distribution and occupancy of wolverines on tundra, northwestern Alaska
Bibliographic record
Abstract
ABSTRACT Understanding wildlife distribution and habitat use is needed for effectively balancing resource development, wildlife conservation, and Alaska Native subsistence on the North Slope of Alaska, USA. This region includes the National Petroleum Reserve‐Alaska (NPR‐A), a 96,000‐km2 remote area of largely undeveloped lands that is important for wildlife, including caribou (Rangifer tarandus), wolves (Canis lupus), and wolverines (Gulo gulo). We focused our study on spring distribution and occupancy of wolverines in the NPR‐A because a baseline distribution estimate is required to understand current distribution and track changes over time. We conducted aerial surveys of wolverine tracks in snow during March and April of 2014 and 2015, surveying over 84,400 km2 using 100‐km2 hexagonal sampling units. We used hierarchical Bayesian occupancy modeling to determine wolverine distribution and estimate probability of occupancy within each hexagon, relative to measured covariates with potential to affect either detection or occupancy. Probability of wolverine occupancy increased as well‐drained soils increased, suggesting that wolverines prefer drier areas or habitat features associated with well‐drained soils. In addition, as standard deviation of elevation increased, wolverine occupancy also increased, indicating that wolverines may prefer areas with more rugged and variable terrain. Mean elevation was not retained as a covariate in the best‐fitting model, supporting the importance of terrain ruggedness rather than elevation on wolverine distribution within the NPR‐A. Spatially, areas of highest wolverine occupancy occurred within the southern and northeastern portions of the study area, with lowest occupancy in the northern portion of the study area west of Teshekpuk Lake. Based on the spatial pattern of wolverine probability of occupancy, we proposed 4 potential wolverine management zones with varying priorities for monitoring and managing wolverine populations. © 2018 The Wildlife Society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".