The ecological importance of intact top-predator populations: a synthesis of 15 years of research in a seagrass ecosystem
Bibliographic record
Abstract
The worldwide decline of large-bodied marine taxa has made it difficult to draw conclusions about the relative importance of top-down control, and the mechanisms through which it might operate, in coastal marine ecosystems. Since 1997, the Shark Bay Ecosystem Research Project has used the relatively pristine seagrass community of Shark Bay, Australia, to investigate the potential for tiger sharks, the apex predator in the ecosystem, to have an impact on their large-bodied prey through non-consumptive (‘risk’) effects. Here, we synthesise nearly 15 years of data to demonstrate that tiger sharks have widespread risk effects on both large-bodied herbivores and mesopredators in Shark Bay and explore the possibility that these impacts may cascade to lower trophic levels. Although much work remains to be done, our studies suggest that losses of top predators in subtropical estuaries may have greater consequences than generally appreciated and that efforts to conserve and restore their populations should be a priority. Furthermore, future management strategies and studies must explicitly consider the potential for predators to influence behaviour of even large-bodied marine taxa.
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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".