Density-dependent habitat selection of plains bison in Grasslands National Park
Bibliographic record
Abstract
Habitat selection models are commonly used to inform species conservation and management decisions; however, such models are context dependent, and results may vary depending on how close a population is to the carrying capacity. Despite acknowledgment that habitat selection is density dependent, relatively few researchers account in their analyses for changes in population density over time. Using a long-term dataset (11 years) for GPS-tracked Plains Bison (Bison bison) at Grasslands National Park, Saskatchewan, Canada (n = 22), I examined seasonal habitat selection during a period of natural population growth following reintroduction and a period of population size manipulations. I used resource selection function (RSF) and latent selection difference function (LSD) analyses to model interactions between selection for vegetation productivity and distance from roads and population density. Bison showed decreased avoidance of roads as density increased and increased avoidance of roads following reductions in population density at most spatio-temporal scales examined. The relationship between selection for vegetation productivity and density was highly seasonally variable: bison selected for abundant forage when density was low and became less selective at high density during and immediately after calving. Consistent with predictions of density-dependent habitat selection, bison were free to select for abundant forage in areas far from human activity when density was low and were required to become less selective as density increased during seasons when the herd is most vulnerable and nutritional requirements are high. My study highlights the importance of considering changes in population density when using habitat selection models to inform decisions on wildlife population management.
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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 | 0.001 | 0.000 |
| 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".