Predator‐induced maternal stress and population demography in snowshoe hares: the more severe the risk, the longer the generational effect
Bibliographic record
Abstract
Abstract The risk of predation can cause strong antipredator behaviors and marked stress‐induced changes in physiology. In mothers, predator‐induced stress can reduce reproductive fitness and alter offspring phenotypes. Acting via these generational, maternal stress effects, predation risk may continue to influence the demography of prey populations even when the predators are no longer present. The 10‐year snowshoe hare cycle is the classic top‐down predator‐driven example in nature and is caused both by direct mortality and by indirect risk effects. During the decline phase, virtually, all hares die because they are killed and simultaneously hares exhibit pronounced stress effects caused by high predation risk. However, the rapidity of the decline phase varies among cycles. When the decline is extremely rapid, we expect that the risk experienced by hares is much greater than when the decline is prolonged. The enigma of these cycles is the low phase following the decline, when there is little or no population growth in spite of the absence of predators and ample food. Previously, we have shown that predator‐induced maternal stress decreases reproduction and compromises the offspring stress axis. Here, we examine how the severity of predation risk during six separate population declines is related to the length of the subsequent low phase. We show that the more severe the decline, as indicated by the greater rate of loss of hares, the longer the subsequent low phase. These results support the hypothesis that the greater the degree of risk, the longer the generational impact on population demography (the longer the low phase of the hare population). Our findings have broad applicability to conservation and management efforts; even when a stressor (predator, human disturbance) is removed or when exposure may be short term (drought, fire or translocation), the signature of the stressor may be evident over several future generations.
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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 | 0.001 |
| 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) | 0.001 | 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".