Towards a predictive framework for predator risk effects: the interaction of landscape features and prey escape tactics
Bibliographic record
Abstract
1. Risk effects of predators can profoundly affect community dynamics, but the nature of these effects is context dependent. 2. Although context dependence has hindered the development of a general framework for predicting the nature and extent of risk effects, recent studies suggest that such a framework is attainable if the factors that shape anti-predator behaviour, and its effectiveness, in natural communities are well understood. 3. One of these factors, the interaction of prey escape tactics and landscape features, has been largely overlooked. 4. We tested whether this interaction gives rise to interspecific variation in habitat-use patterns of sympatric large marine vertebrates at risk of tiger shark (Galeocerdo cuvier Peron and LeSueur, 1822) predation. Specifically, we tested the a priori hypothesis that pied cormorants (Phalacrocorax varius Gmelin, 1789) would modify their use of shallow seagrass habitats in a manner opposite to that of previously studied dolphins (Tursiops aduncus Ehrenberg, 1833), dugongs (Dugong dugon Müller, 1776), and green turtles (Chelonia mydas Linnaeus, 1758) because, unlike these species, the effectiveness of cormorant escape behaviour does not vary spatially. 5. As predicted, cormorants used interior and edge portions of banks proportional to the abundance of their potential prey when sharks were absent but shifted to interior portions of banks to minimize encounters with tiger sharks as predation risk increased. Other shark prey, however, shift to edge microhabitats when shark densities increase to take advantage of easier escape despite higher encounter rates with sharks. 6. The interaction of landscape features and escape ability likely is important in diverse communities. 7. When escape probabilities are high in habitats with high predator density, risk effects of predators can reverse the direction of commonly assumed indirect effects of top predators. 8. The interaction between landscape features and prey escape tactics can result in a single predator species having differential effects on their sympatric prey that could cascade through ecosystems and should be incorporated into a general framework for context dependence of risk effects.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".