Nutritional state reveals complex consequences of risk in a wild predator–prey community
Bibliographic record
Abstract
Animal populations are regulated by the combined effects of top-down, bottom-up and abiotic processes. Ecologists have struggled to isolate these mechanisms because their effects on prey behaviour, nutrition, security and fitness are often interrelated. We monitored how forage, non-consumptive effects (NCEs), consumptive predation and climatic conditions influenced the demography and nutritional state of a wild prey population during predator recolonization. Combined measures of nutrition, survival and population growth reveal that predators imposed strong effects on the prey population through interacting non-consumptive and consumptive effects, and forage mechanisms. Predation was directly responsible for adult survival, while declining recruitment was attributed to predation risk-sensitive foraging, manifested in poor female nutrition and juvenile recruitment. Substituting nutritional state into the recruitment model through a shared term reveals that predation risk-sensitive foraging was nearly twice as influential as summer forage conditions. Our findings provide a novel, mechanistic insight into the complex means by which predators and forage conditions affect prey populations, and point to a need for more ecological studies that integrate behaviour, nutrition and demography. This line of inquiry can provide further insight into how NCEs interactively contribute to the dynamics of terrestrial prey populations; particularly, how predation risk-sensitive foraging has the potential to stabilize predator-prey coexistence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".