Extending utility of hierarchical models to multi‐scale habitat selection
Bibliographic record
Abstract
Abstract Aim Characterizing animal habitat selection is central to ecology and conservation, and understanding selection across multiple spatial scales is a particular priority for research. However, the ability of selection models to capture multi‐scale response has been limited by the dual analytical hurdles of cross‐scale collinearity and overlapping landscapes. The aim of this study was to overcome these limitations using a novel, spatially hierarchical approach. Location North America's northern Great Plains (U.S.A. and Canada). Methods We developed a novel adaptation of the occupancy modelling framework that integrates animal response conditionally across scales. We then compared outcomes to those from a traditional multi‐scale model. We illustrated our approach using the breeding distribution of two North American grassland songbirds of conservation concern, Sprague's Pipit Anthus spragueii and Chestnut‐collared Longspur Calcarius ornatus . Results Our model successfully captured bird response to local habitat within a broader landscape context, even when habitat associations occurred in opposite directions across scale. Probabilities of occupancy were more strongly affected by local conditions when landscape context was favourable than when it was unfavourable. The traditional multi‐scale approach extended problems of scale into spatial predictions by over‐estimating occurrence where conditions were locally favourable but regionally unsuitable. Main conclusions The spatially hierarchical approach provides an integrated model of habitat selection across scales by allowing broader landscape context to shape local response to conditions. For grassland songbirds, our application enabled targeting that could enhance the expected benefits of conservation when compared to the traditional approach.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".