Modeling links between climate-driven three-dimensional habitat compression and fishing effort in the Eastern Tropical Pacific
Bibliographic record
Abstract
The Eastern Tropical Pacific Marine Corridor (CMAR) is a crucial environmental initiative formed by Colombia, Costa Rica, Ecuador, and Panama. This collaboration aims to protect and sustainably manage the marine biodiversity in the Eastern Tropical Pacific Ocean, an area known for its high productivity and biological diversity. A key challenge for CMAR is enhancing surveillance, monitoring, and enforcement within its Marine Protected Areas (MPAs). Climate change adds to this challenge by shifting fish distribution and spatial fishing patterns. Specifically, ocean warming and expanding oxygen minimum zones are expected to compress the vertical habitats of large pelagic fishes and their prey, creating high density patches of fish close to the surface that may attract high levels of fishing effort. Understanding how climate-driven shifts in fish and fishing distributions impact MPAs is essential for developing targeted enforcement strategies that adapt to these changes and support sustainable resource management. Here, we use a three-dimensional global database of oxygen data to elucidate how the oxygen minimum zone in the CMAR has expanded in the last few decades, and how this impacts large pelagic fish distribution in the 3-dimensional space of the ocean, along with its implications for tuna fishing effort. We then examine how fish and fishing effort may shift during marine heatwaves and under climate change (2040-2051 relative to 2004-2016 under SSP 1-2.6 and 5-8.5). Our study reveals that climate change is exerting significant pressure on large pelagic fish populations in the ETP by compressing their vertical habitats due to ocean warming and the expansion of oxygen minimum zones. This compression towards the ocean surface is contributing to an increase in catchability, raising concerns about the sustainability of these fish stocks within and around MPAs. The direct correlation between the shoaling of oxygen minimum zones and heightened fishing efforts indicates an urgent need for adaptive management strategies in fisheries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".