Vegetation influences wolf fine-scale habitat selection and movement rate in a logged coastal rainforest
Bibliographic record
Abstract
Vegetation and its modification by humans can shape wildlife habitat selection and movement. A better understanding of how wolves select and move through natural and human modified vegetative cover can be used to implement forest management that considers impacts on wolves and their prey. We analyzed fine-scale wolf habitat selection and movement in a coastal temperate rainforest (Prince of Wales Island, Alaska, USA) in relation to: (1) young (≤ 30 years) and old (> 30 years) logged areas, (2) continuous measures of vegetative cover (as estimated via LiDAR), and (3) distance to roads, using integrated step-selection analysis (iSSA). Wolves selected areas with less forest canopy and understory cover at the population level, although they switched to selecting understory when within logged forest stands. The continuous canopy and understory measures vary at a fine spatial scale and thus appear to better explain fine-scale wolf selection and movement than categorical landcover classes representing the age of logged stands. Wolf selection of young (≤ 30 years) and old (> 30 years) successional logged areas, and areas near roads, was mixed across individuals. All individual wolves avoided canopy cover, but varied in their selection of logged stands, understory, and roads. Similarly, there was variability in movement rate response across individual wolves, although at the population level wolves moved faster through old (> 30 years) logged areas and through areas with less understory vegetation. Open vegetation including that present recently after logging is selected by wolves, and facilitates wolf movement, but this effect may be ephemeral as vegetation undergoes succession.
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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".