Does tiger shark predation risk influence foraging habitat use by bottlenose dolphins at multiple spatial scales?
Bibliographic record
Abstract
Prey availability and predation risk are important determinants of habitat use, but their importance may vary across spatial scales. In many marine systems, predator and prey distributions covary at large spatial scales, but do no coincide at small spatial scales. We investigated the influences of prey abundance and tiger shark ( Galeocerdo cuvier ) predation risk on Indian Ocean bottlenose dolphin ( Tursiops aduncus ) habitat use across multiple spatial scales, in Shark Bay, Western Australia. Dolphins were distributed between deep and shallow habitats and across microhabitats within patches approximately proportional to prey density when shark abundance was low. When shark abundance was high, foraging dolphins greatly reduced their use of dangerous, but productive, shallow patches relative to safer deep ones. Also, dolphins reduced their use of interior portions of shallow patches relative to their edges, which have higher predator density but lower intrinsic risk (i.e. a higher probability of escape in an encounter situation). These results suggest that predation risk and prey availability influence dolphin habitat use at multiple spatial scales, but intrinsic habitat risk, and not just predator encounter rate, is important in shaping dolphin space use decisions. Therefore, studies of habitat use at multiple spatial scales can benefit from integrating data on prey availability and the subcomponents of predation risk.
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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 | 0.000 |
| 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 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".