What carnivore biologists can learn from bugs, birds, and beavers: a review of spatial theories
Bibliographic record
Abstract
Information on the ecology and evolution of a species can be enhanced by studying spatial ecology. Even though space use has been a focus of carnivore research for decades, the theoretical framework for such studies remains poorly developed. Most spatial ecology theory has instead been developed and tested with invertebrates and expanded into fishery and ornithological research. The goal of this review is to examine spatial theories being tested in other taxonomic groups that could positively influence how carnivore biologists design studies. Details are provided from studies that illustrate methods to quantify space use, and four broad areas of spatial theory are reviewed: conspecific attraction, territory establishment, within-territory space use, and inheritance of space use. Suggestions are given on how carnivore biologists could incorporate each of these components into study designs. Carnivore biologists have opportunities to test spatial theory at small and large scales that could ultimately advance the entire field of spatial ecology. Although this review focuses on improving studies of terrestrial carnivores, our suggestions are relevant for studies of spatial theories across taxa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".