A wavelet‐based approach to evaluate the roles of structural and functional landscape heterogeneity in animal space use at multiple scales
Bibliographic record
Abstract
The functional relationship between habitat utilization and landscape spatial heterogeneity is fundamental to understanding the spatial nature of animal distribution across scales. Although structural and functional properties of landscape spatial heterogeneity can have different consequences for animal species, few studies have explicitly considered both forms of heterogeneity, partly due to the lack of general methods for direct assessment of scale‐specific associations between variables. We present a wavelet‐based approach to evaluate the roles of structural and functional landscape spatial heterogeneity in animal space use at multiple spatial scales. As a case study, we examined scale‐specific space use patterns of American black bears Ursus americanus in response to structural and functional spatial heterogeneity as well as spatial patterns of vegetation age‐classes in a Canadian boreal forest. We found strong differences in the effects of structural and functional spatial heterogeneity and the scales at which they are associated with the patterns of habitat use by black bears. Functional heterogeneity alone affected space use at 800 and 1600‐m scales, but had significant effects when interacting with structural heterogeneity at 400, 800, and 1600‐m scales. Compared with male bears, female black bears were most sensitive to patterns of forage abundance at intermediate scales, or more specifically, in young and regenerating forests that provide abundant soft mast in boreal forests. Our study highlights the importance of accounting for scale‐dependent properties of (structural and functional) spatial heterogeneity in assessing the ecological effects of landscape components and the effectiveness of the wavelet transform technique in identifying such scale‐specific relationships.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".