Complex transboundary movements of marine megafauna in the Western Indian Ocean
Bibliographic record
Abstract
Abstract Transboundary marine species have an increased risk of overexploitation as management regimes and enforcement can vary among states. The complex geopolitical layout of exclusive economic zones (EEZs) in the Western Indian Ocean (WIO) introduces the potential for migratory species to cross multiple boundaries, consequently a lack of scientific data could complicate regional management. In the current study, we highlight both the relative lack of spatial data available in the WIO, and the prevalence of transboundary movements in species that have previously been studied in the region. Five tiger sharks Galeocerdo cuvier were tracked with near real‐time positioning (SPOT) satellite tags to determine individual shark movements relative to EEZs within the WIO. Concurrently, a literature search was performed to identify all satellite telemetry studies conducted to date in the WIO for marine megafaunal species, and the results compared to global satellite telemetry effort. Finally, the satellite tracks of all marine species monitored in the WIO were extracted and digitized to examine the scale of transboundary movements that occur in the region. Tiger sharks exhibited both coastal and oceanic movements, with one individual crossing a total of eight EEZs. Satellite telemetry effort in the WIO has not matched the global increase, with only 4.7% of global studies occurring in the region. Species in the WIO remained within the EEZ in which they were tagged in only three studies, while all other species demonstrated some level of transboundary movement. This study demonstrates the lack of spatial data available for informed regional management in an area where transboundary movements by marine megafauna are highly prevalent. Without more dedicated funding and research, the rich biodiversity of the WIO is at risk of overexploitation from the diverse threats present within the various political regions.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".