Data from: Functional responses in animal movement explain spatial heterogeneity in animal-habitat relationships
Bibliographic record
Abstract
1. Understanding why heterogeneity exists in animal-habitat spatial relationships is critical for identifying the drivers of animal distributions. Functional responses in habitat selection – whereby animals adjust their habitat selection depending on habitat availability – are useful for describing animal-habitat spatial heterogeneity. However, they could be yielded by different movement tactics, involving contrasting inter-specific interactions. 2. Identifying functional responses in animal movement, rather than in emergent spatial patterns like habitat selection, could disentangle the effects of different movement behaviours on spatial heterogeneity in animal-habitat relationships. This would clarify how functional responses in habitat selection emerge and provide a general tool for understanding the mechanistic drivers of animal distributions. 3. We tested this approach using data from GPS-collared woodland caribou (Rangifer tarandus), a prey species under top-down control. We tested how caribou selected and moved with respect to a key resource (lichen-conifer stands) as a function of the availability of surrounding refuge land-cover (closed-conifer stands), using step selection functions. 4. Caribou selected resource patches more strongly in areas richer in refuge land-cover – a functional response in habitat selection. However, adjustments in multiple movement behaviours could have generated this pattern: stronger directed movement towards resources patches and/or longer residency within resource patches, in areas richer in refuges. Different contributions of these behaviours would produce contrasting forager spatial dynamics. 5. We identified functional responses in both movement behaviours: caribou were more likely to move towards resource patches in areas richer in refuge land-cover, and to remain in these patches during movement steps. This tactic enables caribou to spend longer foraging in safer areas where they could rapidly seek refuge in dense cover when predators are detected. 6. Our study shows that functional responses in movement can expose the context-dependent movement decisions that generate heterogeneity in animal-habitat spatial relationships. We used these functional responses to characterise anti-predator movement tactics employed by a large herbivore, but they could be applied in many different scenarios. The movement rules from functional responses in movement are well-suited to integration in spatial explicit individual-based models for forecasting animal distributions in landscapes undergoing environmental change.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.026 |
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".