Relating predation mortality to broad‐scale habitat selection
Bibliographic record
Abstract
Summary Few studies have related metrics of fitness to broad‐scale, multivariate patterns of resource selection. Our objective was to relate long‐term predation‐mortality patterns for adult woodland caribou [Rangifer tarandus caribou(Banfield)] from Alberta, Canada (1991–2002), with patterns of multivariate habitat selection. We first compared probabilities of radio‐tracked caribou dying (n = 55) from predation in habitats within the home range, controlling for habitat availability, with that expected from habitat selection probabilities for the same animals during life. We then compared survival rates of caribou (n = 141) possessing dissimilar patterns of habitat selection using a Cox proportional hazards regression model. Patterns in habitat‐specific predation mortality differed significantly from expected given probabilities of habitat selection during life (P < 0·0001). Cox regression indicated that mortality rates of caribou due to predation were affected significantly by and can be predicted from patterns of selection (P = 0·02). Our results strongly suggest that uplands (primarily mixed deciduous and coniferous forest) present caribou with higher than expected levels of predation risk, and that caribou can avoid predation by maximizing selection of peatlands (open, conifer‐dominated bogs and fens). Approaches presented in this study may be useful for ecologists interested in assessing the influence of mortality factors on broad‐scale, multivariate resource selection. Linking metrics of fitness to multivariate resource selection will enable us to ask questions of evolutionary ecology once restricted to only the finest ecological scales.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".