Monitoring elasmobranch assemblages in a data-poor country from the Eastern Tropical Pacific using baited remote underwater video stations
Bibliographic record
Abstract
Understanding how threatened species are distributed in space and time can have direct applications to conservation planning. However, implementing standardized methods to monitor populations of wide-ranging species is often expensive and challenging. In this study, we used baited remote underwater video stations (BRUVS) to quantify elasmobranch abundance and distribution patterns across a gradient of protection in the Pacific waters of Costa Rica. Our BRUVS survey detected 29 species, which represents 54% of the entire elasmobranch diversity reported to date in shallow waters (< 60 m) of the Pacific of Costa Rica. Our data demonstrated that elasmobranchs benefit from no-take MPAs, yet large predators are relatively uncommon or absent from open-fishing sites. We showed that BRUVS are capable of providing fast and reliable estimates of the distribution and abundance of data-poor elasmobranch species over large spatial and temporal scales, and in doing so, they can provide critical information for detecting population-level changes in response to multiple threats such as overfishing, habitat degradation and climate change. Moreover, given that 66% of the species detected are threatened, a well-designed BRUVS survey may provide crucial population data for assessing the conservation status of elasmobranchs. These efforts led to the establishment of a national monitoring program focused on elasmobranchs and key marine megafauna that could guide monitoring efforts at a regional scale.
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How this classification was reachedexpand
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".