Hierarchical habitat selection by woodland caribou: its relationship to limiting factors
Bibliographic record
Abstract
Habitat selection is a hierarchical process that may yield various patterns depending on the scales of investigation. We employed satellite radio‐telemetry to examine patterns of habitat selection by female woodland caribou in central Saskatchewan at both coarse (seasonal range) and fine (daily area) scales. At each scale, we converted spatial data describing compositions of available and used habitat to standardised resource selection indices and examined them with multivariate analyses of variance. Seasonal ranges generally showed preferential inclusion of peatlands and black spruce dominated stands relative to recently disturbed stands and early seral stage forests. In all populations, caribou preferred peatlands and black spruce forests to all other habitat types at the daily area scale, in general, these patterns may reveal the effective avoidance of wolves, the primary factor limiting caribou throughout the boreal forest. In three populations where seasonal ranges showed the selective inclusion of either young jack pine stands or clearcuts along with peatlands and black spruce forests, we found a relative avoidance of the clearcuts and young jack pine stands at the daily area scale. As all caribou populations in the area are thought to be relics of a once more continuous distribution, the seasonal range selection by animals in disturbed areas may better describe historic rather than current habitat selection. We found inter‐annual variation in selection at the coarser spatial scale in one population, and inter‐seasonal variation in selection at the finer spatial scale in three populations, indicating that the relative grains of the spatial and temporal scales coincide. We were better able to explain the seasonal variations in finer scale selection by considering available forage, a factor less likely than predation to limit woodland caribou populations. The data agree with the theory that the spatial and temporal hierarchy of habitat selection reflects the hierarchy of factors potentially limiting individual fitness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".