Is the relationship between predator and prey abundances related to climate for lynx and snowshoe hares?
Bibliographic record
Abstract
Context Predator dynamics may be related to prey abundance and influenced by environmental effects, such as climate. Predator–prey interactions may be represented by mechanistic models that comprise a deterministic skeleton with stochastic climatic forcing. Aims The aim of this study was to evaluate the effects of climate on predator–prey dynamics. The lynx and snowshoe hare predator–prey system in the Kluane region of the Yukon, Canada, is used as a case study. The specific hypothesis is that climate influences the relationship between lynx and hare abundance. Methods We evaluate 10 linear relationships between predator and prey abundance and effects of climate. We use data on lynx and snowshoe hare abundance over 21 years in the Yukon as the predator–prey system, and three alternative broad-scale climate indices: the winter North Atlantic Oscillation (NAO), the Pacific North American (PNA) index and the North Pacific index (NPI). Key results There was more support, as assessed by Akaike weights (?i = 0.600), evidence ratio (=4.73) and R2 (=0.77) for a model of predator (lynx) and prior prey (hare) abundance with an effect of prior climate (winter NAO) when combined in a multiplicative, rather than in an additive, manner. The results infer that climate changes the amplitude of the lynx cycle with lower predator (lynx) abundance with positive values of winter NAO for a given hare density. Conclusions The study provides evidence that predator–prey dynamics are related to climate in an interactive manner. The ecological mechanism for the interactive effect is not clear, and alternative hypotheses are proposed for future evaluation. Implications The study implies that changes in climate may alter predator–prey relationships.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".