Density‐dependent mortality is mediated by foraging activity for prey fish in whole‐lake experiments
Bibliographic record
Abstract
Whereas the effects of density-dependent growth and survival on population dynamics are well-known, mechanisms that give rise to density dependence in animal populations are not well understood. We tested the hypothesis that the trade-off between growth and mortality rates is mediated by foraging activity and habitat use. Thus, if depletion of food by prey is density-dependent, and leads to greater foraging activity and risky habitat use, then visibility and encounter rates with predators must also increase. We tested this hypothesis by experimentally manipulating the density of young rainbow trout (Oncorhynchus mykiss) at risk of cannibalism, in a replicated single-factor experiment using eight small lakes, during an entire growing season. We found no evidence for density-dependent depletion of daphnid food in the nearshore refuge where most age-0 trout resided. Nonetheless, the proportion of time spent moving by individual age-0 trout, the proportion of individuals continuously active, and use of deeper habitats was greater in high density populations than in low density populations. Differences in food abundance among lakes had no effect on measures of activity or habitat use. Mortality of age-0 trout over the growing season was higher in high density populations, and in lakes with lower daphnid food abundance. Therefore, population-level mortality of age-0 trout is linked to greater activity and use of risky habitats by individuals at high densities. We suspect that food resources were depleted at small spatial and temporal scales not detected by our plankton sampling in the high density treatment, because food-dependent activity and habitat use by age-0 trout occurs in our lakes when food abundance is experimentally manipulated (Biro, Post & Parkinson, in press).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".