Where beavers (<i>Castor canadensis</i>) build: testing the influence of habitat quality, predation risk, and anthropogenic disturbance on colony occurrence
Bibliographic record
Abstract
Species distributions are shaped by numerous factors that vary in importance across spatiotemporal scale. Understanding drivers of the distribution of North American beavers (Castor canadensis Kuhl, 1820) is paramount given their profound influence on ecological communities. Our objectives were to evaluate the influence of habitat quality, risk of gray wolf (Canis lupus Linnaeus, 1758) predation, and anthropogenic disturbance on the occurrence of beaver colonies in northeast British Columbia (BC), Canada. We used mixed-effects multinomial logistic regression to model the occurrence of active and inactive colonies and t tests to compare landscape covariates associated with active versus inactive colonies. We determined that occurrence of beavers was driven by habitat quality. Occurrence increased in areas with higher vegetation-class richness and greater proportions of open water, nutrient-rich fen, and deciduous swamp. We also observed that active colonies were surrounded by greater amounts of deciduous swamps relative to inactive colonies. We found no evidence that predation risk or industrial activities decreased the occurrence of beavers in northeast BC, although numerical changes in abundance might occur without changes in distribution. This research illuminated drivers of beaver distribution while providing a means to predict the occurrence of a keystone species in the boreal ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".