Density‐dependent functional responses in habitat selection by two hosts of the raccoon rabies virus variant
Bibliographic record
Abstract
Spatio‐temporal variations in conspecific density and resource availability are two of the main factors responsible for plasticity in habitat selection. Despite the need for habitat selection models that can accurately predict animal distribution given the plasticity in the selection process, no study has assessed the synergistic effects of these factors on habitat selection. We investigated density‐dependent functional responses by raccoons (Procyon lotor) and striped skunks (Mephitis mephitis), two of the main hosts of the rabies virus in North America. We monitored 54 raccoons and 12 striped skunks with Global‐Positioning‐System collars in a landscape dominated by corn fields and forest patches. We built resource selection functions to evaluate if the selection of corn fields varied with conspecific density and corn field availability within 100% minimum convex polygons. Raccoons altered their selection of corn fields depending on both conspecific density and corn‐forest edge density or corn field proportion. In areas of low corn‐forest edge densities and a low corn field proportion, raccoons showed stronger selection for corn fields when few conspecifics were present. At high conspecific densities, the selection of corn fields was stronger in areas with high corn‐forest edge densities and a low corn field proportion. For striped skunks, we did not detect any synergistic effect of density‐dependence and functional responses. Unlike raccoons, striped skunks displayed a selection that was strongest for agricultural corridors. We show that functional responses in habitat selection can be density‐dependent. In a context of infectious disease dynamics, modeling density‐dependence in functional responses increases the ability to predict spatio‐temporal variations in the distribution of reservoir species and thus, to delineate areas at high animal densities where the risk of disease outbreaks is relatively high. For example, the omission of density‐dependence in functional responses underestimated the relative probability of raccoon occurrence in corn fields, while overestimating the relative probability of occurrence in anthropogenic areas and wetlands. Our study underscores the relevance of considering the complexity of habitat selection by all hosts of a zoonosis. Cost‐effective control and prevention programs used to limit disease spread can benefit from accounting for density‐dependent functional responses of a multi‐host disease system.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".