Influence of Reproductive Status on Occupancy of Salvage‐Logged Boreal Forest by Moose (<scp><i>Alces americanus</i></scp>)
Bibliographic record
Abstract
ABSTRACT Wildlife‐habitat relationships reflect the behavioral choices made by species in response to perceived risks and rewards. Ungulates must often choose between habitats that provide forage and those offering concealment from predators, yet natural and anthropogenic disturbances create risky landscapes where tradeoffs may be difficult to navigate. Ungulate responses to forest disturbance may vary by sex and reproductive state, given that reproductive females with calves often prioritize predator avoidance. We investigated state‐dependent habitat use by reproductive and solitary moose (Alces americanus) in response to salvage logging after a widespread infestation by spruce beetle (Dendroctonus rufipennis) in the boreal forest of Yukon, Canada. We used camera traps and multistate occupancy models to examine moose occurrence in unsalvaged and salvage‐logged forests at different regenerative stages (0–10 years and 11–25 years postlogging) and levels of tree retention after logging. We compared results to single‐state occupancy models that did not account for reproductive status. As predicted, single‐state models showed high use of stands with low canopy cover and maximum tree removal (i.e., clear‐cuts). This suggested that moose capitalized on shrubby forage available in logged stands, regardless of regenerative stage. However, this result was overly simplistic. Multistate occupancy models revealed that forest age was the most important factor for female moose with calves, in contrast to solitary moose. Females with calves tended to avoid newly logged areas and preferred regenerating and unsalvaged forests with hiding cover, although estimates of effect size had low precision. Climate change is contributing to the rising frequency and severity of bark beetle outbreaks, and post‐infestation salvage logging has been implicated in the decline of moose populations in western Canada. Our results support the need to maintain diverse, mixed‐age forest landscapes to meet the food and cover requirements of moose in different demographic classes.
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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".