State‐dependent risk‐taking by green sea turtles mediates top‐down effects of tiger shark intimidation in a marine ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it