Interpreting Resource Selection at Different Scales for Woodland Caribou in Winter
Bibliographic record
Abstract
Scientists commonly use resource selection functions (RSFs) to identify areas important to large herbivores. Defining availability of resources is scale dependent and may limit inference on biological mechanisms of selection, particularly if variation in selection of resources is high among individuals within a population. We used logistic regression, the information-theoretic approach, and Global Positioning System (GPS) radiotelemetry data from 10 female woodland caribou (Rangifer tarandus caribou) and 5 wolf (Canis lupus) packs to model resource selection by individual caribou in the winter and late-winter seasons. We evaluated the influence of spatial scale on the relative importance of cost of movement and components of predation risk. We examined attributes of the risk of wolf predation within availability data at 2 spatial scales, and quantified variation in resource selection among individual caribou. Energetic cost of movement was the most important covariate for all caribou at a spatial scale defined by seasonal movement. Increasing distance to areas of high wolf risk was more important at the larger spatial scale of home range. Variation was high in the selection of resources among caribou, although commonalities among individuals enabled pooling data on use and availability into 2 selection strategies. Researchers and managers should conduct multiscale analyses with varied definitions of availability, quantify variation among individuals, and pool data into common selection strategies to identify mechanisms of selection and to map a population's selection for resources on the landscape.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".