The influence of climate on the numerical response of a predator (Canis latrans: coyote) population to its prey (Lepus americanus: snowshoe hare) in the Canadian boreal forest
Bibliographic record
Abstract
Predation is an important ecosystem function and much work has been done across\ntrophic levels to elicit the often complex relationships between predators and their prey.\nThe influence of climate on predator-prey relationships, however, remains poorly\nunderstood, particularly for terrestrial mammalian predators and their mammalian prey.\nThe aim of this study was to evaluate evidence of an effect of climate on the coyote\n(Canis latrans) numerical response to their keystone prey snowshoe hares (Lepus\namericanus) in a Canadian boreal forest ecosystem. A set of a priori hypotheses of\ncoyote numerical response were developed that postulated linear, non-linear, additive,\nand interactive effects of prey and climate. Models separately incorporated four largescale\nclimate indices (the North Atlantic Oscillation, the El Niño-Southern Oscillation,\nthe Pacific/North Atlantic, and the North Pacific Index) and eight local scale climate\nvariables (a range of temperature measures, precipitation, rain, and snow). Model\nselection procedures estimated which climate variables most influenced the coyote\nnumerical response.\nThe North Atlantic Oscillation (NAO) had the strongest effect on coyote numerical\nresponse via its interaction with snowshoe hare density, while other large-scale and\nlocal climate indices had relatively weak or no effects. The coyote numerical response\nwas positively influenced by the negative phase of the NAO and, contrary to\nexpectations, negatively influenced by increased local winter temperatures. It is\nproposed that the coyote numerical response is ultimately determined by the coyote\nfunctional response (hunting ability, efficiency, and success) influenced by favourable\nor otherwise winter conditions determined by the NAO. In a time of climate change and\na prevailing trend for a positive phase of the NAO, the results of this study have\npotential longer-term implications for boreal forest coyote populations, as well as for\nsnowshoe hare populations, other snowshoe hare predators, and hence, boreal forest\ncommunity dynamics.\nIn conclusion, this study provides strong support for the inclusion of climate into\nmodels of the predator numerical response. Further, this study illustrates how a largescale\nclimate index can better help explain an ecological process than local climate\nvariables.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".