Density-dependent habitat selection and partitioning between two sympatric ungulates
Bibliographic record
Abstract
Theory on density-dependent habitat selection predicts that as population density of a species increases, use of higher quality (primary) habitat by individuals declines while use of lower quality (secondary) habitat rises. Habitat partitioning is often considered the primary mechanism for coexistence between similar species, but how this process evolves with changes in population density remains to be empirically tested for free-ranging ungulates. We used resource-selection functions to quantify density effects on landscape-scale habitat selection of two sympatric species of ungulates [moose (Alces alces) and elk (Cervus canadensis manitobensis)] in Riding Mountain National Park, Manitoba, Canada (2000-2011). The density of elk was actively reduced from 1.2 to 0.4 elk km(-2) through increased hunting effort during the period of study, while moose density decreased without additional human influence from 1.6-0.7 moose km(-2). Patterns of habitat selection during winter by both species changed in accordance to expectations from density-dependent habitat-selection theory. At low intraspecific density, moose and elk did not partition habitat, as both species selected strongly for mixed forest (primary habitat providing both food and cover), but did so in different areas segregated across an elevational gradient. As intraspecific density increased, selection for primary habitat by both species decreased, while selection for secondary, lower quality habitat such as agricultural fields (for elk) and built-up areas (for moose) increased. We show that habitat-selection strategies during winter for moose and elk, and subsequent effects on habitat partitioning, depend heavily on the position in state space (density) of both species.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".