Functional responses in habitat selection are density dependent in a large herbivore
Bibliographic record
Abstract
Habitat selection is complex due to density dependence and functional responses, defined as variation in relative habitat use depending on availability. In this study we unite these concepts by empirically testing for density‐dependent functional responses in habitat selection using a large herbivore, elk Cervus canadensis manitobensis , as a model species. Theory on density‐dependent habitat selection predicts specialised behaviour when densities are low with a gradual switch to generalist behaviour (more even selection of habitat) as competition intensifies. This suggests that functional responses in habitat selection should be positive when competition is low, but that density may have a negative effect on the functional response due to constraining effects of competition on habitat use and availability. We tested this prediction using data from Global Positioning System (GPS) collared elk (n = 33) and empirical data on spatiotemporal variation in local density during winter in Riding Mountain National Park, Manitoba, Canada (2002–2011). As local density increased, winter home range size decreased and the proportion of mixed forest (providing shelter and forage to elk) used and available within the home range also decreased. Our resource selection function (RSF) revealed clear density‐dependent effects in selection, being strongest (or weakest) for high quality (or low quality) habitat types at lowest observed density leading to more even selection as densities increased. The functional response in mixed forest selection was negatively affected by local density. Increasing availability of mixed forest in the home range was associated with higher selection at low density (positive functional response); no effect of availability on selection at moderate density (no functional response); and lower selection as availability increased at high density (negative functional response). Our study demonstrates that this process is largely driven by the negative effect of density on home range size as it constrains use and availability of habitat.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".