The status of marine biodiversity in the Eastern Central Atlantic (West and Central Africa)
Bibliographic record
Abstract
Abstract The status of marine biodiversity in the Eastern Central Atlantic (ECA), especially of coastal and pelagic fishes, is of concern owing to a number of threats including overharvesting, habitat loss, pollution, and climate change combined with inadequate policy responses, legislation, and enforcement. This study provides the first comprehensive documentation of the presence, status, and level of extinction risk, based on IUCN Red List assessment methodology, for more than 1800 marine species, including all taxonomically described marine vertebrates (marine mammals, sea turtles, seabirds, fishes); complete clades of selected marine invertebrates (sea cucumbers, cone snails, cephalopods, lobsters, reef‐building corals); and marine plants (mangroves, seagrasses). Approximately 8% of all marine species assessed in the ECA are in threatened categories, while 4% are listed as Near Threatened, 73% are Least Concern, and 15% are Data Deficient. Fisheries and overharvesting are the biggest threats to living marine resources in the ECA, with 87% of threatened species across all taxonomic groups affected by both large‐ and small‐scale targeted fisheries, excessive capture as by‐catch, or unsustainable harvest. The results of this study will transform the current state of knowledge and increase capacity for regional stakeholders to identify and enact marine conservation and research priorities, as a number of species are identified as having high conservation and/or research priorities in the region. Through the process of marine species data collection and risk assessments conducted over the past 5 years, several key conservation actions and research needs are identified to enable more effective conservation of marine biodiversity in the ECA, including increased governance, multilateral collaboration, taxonomic training, and improved reporting of fisheries catch and effort.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".