Modeling forage mediated aggregation in a gregarious ruminant
Bibliographic record
Abstract
Two explanations exist for the evolutionary origin of grouping in primary consumers: reduction of individual predation risk and resource‐mediated aggregation. While several studies have assessed relationships between aggregation and predation risk, few studies have examined the circumstances under which resource‐mediated aggregation can lead to stable group formation. Using a model, we examined if forage preference alone can generate stable aggregation, and what were the circumstances of its emergence and stability. The model was a spatially explicit grazing model using empirically derived parameters to simulate large ruminant foraging in a meadow. Simulation results indicated that aggregation can spontaneously arise if grazers exhibit preference for forage of higher nutritional quality, usually associated with intermediate stages of forage growth. In this case, foragers could establish and maintain ‘islands’ of high quality forage as a result of revisiting continuous paths of previously grazed patches. However, aggregation was an intermittent phenomenon and occurred only within a narrow range of parameters. If grazer density was low compared to the amount of forage, the grazers’ foraging paths intersected too rarely to form contiguous islands of high forage quality; if their density was too high, the entire available area was uniformly utilized and foraging movements resembled unbounded random walks. We conclude that it is difficult to conceive of the evolution of grouping without the involvement of predators, since the relationship between grazer and forage abundance is ultimately co‐regulated by predator abundance, and because in modern grazers, predator avoidance and foraging behavior seem to be functionally inseparable. Future research should consider the reinforcing effects of predator avoidance as well as foraging behavior on consumer aggregation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".