Individual Differences in Habitat Selection Mediate Landscape Level Predictions of a Functional Response
Bibliographic record
Abstract
Abstract Context Conserving and managing habitat for animals requires robust models to predict their space use. The functional response in habitat selection posits that animals adjust their habitat selection according to availability. Objectives Habitat availability can change over short time periods and small spatial distances, and thus failing to account for changes in habitat availability while modelling may not produce reliable predictions in the near–term or future. However, because individuals may respond to habitat availability differently, the functional response is also limited for predicting habitat selection by individuals. Methods Using a functional response in elk (Cervus canadensis) selection for mixed forest in response to road proximity, we compared habitat selection predictions made by population-level resource selection functions (RSFs) with random effects to incorporate individual differences in selection, to generalized functional response (GFR) RSFs. Results We found that since not all individuals followed the road-dependent functional response, the random effects model both predicted the distributions of individuals more accurately (R2 = 0.62 vs. R2 = 0.51) and produced coefficient estimates that matched their selection for mixed forest and distance from roads better than the GFR model (RMSE = 0.25 vs. RMSE = 0.29 and 0.37 vs. 0.46). Conclusions Individual habitat selection often varies within populations, and revealing those differences shows how individuals help populations respond to environmental change. We suggest that evaluating individual differences using multiple predictive approaches is necessary to forecast long–term habitat selection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".