State‐dependent risk‐taking by green sea turtles mediates top‐down effects of tiger shark intimidation in a marine ecosystem
Bibliographic record
Abstract
1. A predictive framework of community and ecosystem dynamics that applies across systems has remained elusive, in part because non-consumptive predator effects are often ignored. Further, it is unclear how much individual-level detail community models must include. 2. Previous studies of short-lived species suggest that state-dependent decisions add little to our understanding of community dynamics. Body condition-dependent decisions made by long-lived herbivores under risk of predation, however, might have greater community-level effects. This possibility remains largely unexplored, especially in marine environments. 3. In the relatively pristine seagrass community of Shark Bay, Australia, we found that herbivorous green sea turtles (Chelonia mydas Linnaeus, 1758) threatened by tiger sharks (Galeocerdo cuvier Peron and LeSueur, 1822) select microhabitats in a condition-dependent manner. Turtles in poor body condition selected profitable, high-risk microhabitats, while turtles in good body condition, which are more abundant, selected safer, less profitable microhabitats. When predation risk was low, however, turtles in good condition moved into more profitable microhabitats. 4. Condition-dependent use of space by turtles shows that tiger sharks modify the spatio-temporal pattern of turtle grazing and their impacts on ecosystem dynamics (a trait-mediated indirect interaction). Therefore, state-dependent decisions by individuals can have important implications for community dynamics in some situations. 5. Our study suggests that declines in large-bodied sharks may affect ecosystems more substantially than assumed when non-lethal effects of these top predators on mesoconsumers are not considered explicitly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".