Ecologically based definition of seasons clarifies predator–prey interactions
Bibliographic record
Abstract
Species interactions within food webs are driven by multiple constraints, including those imposed by seasonal changes in the environment. Ecologically sound definitions of seasons may therefore be a prerequisite for clarifying predator prey interactions. Most studies define biological seasons based on fixed schedules or on temporal changes in a single movement measurement. We used a novel clustering approach based on homogeneous space‐use patterns of GPS‐collared animals to reveal 7 biological seasons for caribou Rangifer tarandus caribou , and 5 for both moose Alces alces and grey wolves Canis lupus interacting in a boreal ecosystem. Subsequent evaluation of niche overlap showed that, as predicted, wolves had a stronger spatio‐temporal connection with moose, its main prey, than with caribou. Movement constraints and limiting resource distributions similarly affected all species in some instances, but also caused temporal changes in the extent of niche overlap between wolves and its two prey. The risk that caribou faced was not only linked to the niche overlap with wolves, but also to the extent of wolf‐moose niche overlap during the same period. Food‐web properties emerged from the analysis, with temporal changes in relative niche overlap reflecting the strength of trophic interactions during the year. Our study demonstrates how the study of trophic interactions can benefit from comprehensive definitions of biological seasons.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".