A decade of submersible observations revealed temporal trends in elasmobranchs in a remote island of the Eastern Tropical Pacific Ocean
Bibliographic record
Abstract
No-take marine protected areas (MPAs) can mitigate the effects of overfishing, climate change and habitat degradation, which are leading causes of an unprecedented global biodiversity crisis. However, assessing the effectiveness of MPAs, especially in remote oceanic islands, can be logistically challenging and often restricted to relatively shallow and accessible environments. Here, we used a long-term dataset (2010-2019) collected by the DeepSee submersible of the Undersea Hunter Group that operates in Isla del Coco National Park, Costa Rica, to (1) determine the frequency of occurrence of elasmobranch species at two depth intervals (50-100 m; 300-400 m), and (2) investigate temporal trends in the occurrence of common elasmobranch species between 2010 and 2019, as well as potential drivers of the observed changes. Overall, we observed 17 elasmobranch species, 15 of which were recorded on shallow dives (50-100 m) and 11 on deep dives (300-400 m). We found a decreasing trend in the probability of occurrence of Carcharhinus falciformis over time (2010-2019), while other species (e.g. Taeniurops meyeni, Sphyrna lewini, Carcharhinus galapagensis, Triaenodon obesus, and Galeocerdo cuvier) showed an increasing trend. Our study suggests that some species like S. lewini may be shifting their distributions towards deeper waters in response to ocean warming but may also be sensitive to low oxygen levels at greater depths. These findings highlight the need for regional 3D environmental information and long-term deepwater surveys to understand the extent of shark and ray population declines in the ETP and other regions, as most fishery-independent surveys from data-poor countries have been limited to relatively shallow waters.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".