Data from: Predation risk and resource abundance mediate foraging behaviour and intraspecific resource partitioning among consumers in dominance hierarchies
Bibliographic record
Abstract
Dominance hierarchies and the resulting unequal resource partitioning among individuals are key mechanisms of population regulation. The strength of dominance hierarchies can be influenced by size-dependent trade-offs between foraging and predator avoidance whereby competitively inferior subdominants can access a larger proportion of limiting resources by accepting higher predation risk. Foraging-predation risk trade-offs also depend on resource abundance. Yet, few studies have manipulated predation risk and resource abundance simultaneously; consequently, their joint effect on resource partitioning within dominance hierarchies are not well understood. We addressed this gap by measuring behavioural responses of masu salmon (Oncorhynchus masou ishikawae) to experimental manipulations of predation risk and resource abundance in a natural temperate forest stream. Responses to predation risk depended on body size and social status such that larger fish (often social dominants) exhibited more risk-averse behaviour (e.g., lower foraging and appearance rates) than smaller subdominants after exposure to a simulated predator. The magnitude of this effect was lower when resources were elevated, indicating that dominant fish accepted a higher predation risk to forage on abundant resources. However, the influence of resource abundance did not extend to the population level, where predation risk altered the distribution of foraging attempts (a proxy for energy intake) from being skewed towards large individuals to being skewed towards small individuals after predator exposure. Our results imply that size-dependent foraging-predation risk trade-offs can weaken the strength of dominance hierarchies by allowing competitively inferior subdominants to access resources that would otherwise be monopolized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.138 | 0.034 |
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".