Predicting the conservation status of Europe’s Data Deficient sharks and rays
Bibliographic record
Abstract
ABSTRACT Shark and ray biodiversity is threatened primarily by overfishing and the globalisation of trade, and Europe has been one of the most documented heavily fished regions for a relatively long time. Yet, we have little idea of the conservation status of the hundreds of Data Deficient shark and ray species. It is important to derive some insight into the status of these species, both to understand global extinction rates and also to ensure that any threatened Data Deficient species are not overlooked in conservation planning. Here, we developed a biological and ecological trait model to predict the categorical conservation status of 26 Northeast Atlantic and 15 Mediterranean Sea Data Deficient sharks and rays. We first developed an explanatory model based on all species evaluated on the International Union for Conservation of Nature (IUCN) Red List of Threatened Species TM , using maximum body size, median depth (as a proxy for fisheries exposure), and reproductive mode, and then predicted the status of all Data Deficient species. Almost half of Northeast Atlantic (46%, n =12 of 26), and two-thirds of Mediterranean (67%, n =10 of 15) Data Deficient species are predicted to be in one of the three IUCN threatened categories. Northeast Atlantic Data Deficient species are predicted to be 1.2 times more threatened than evaluated species (38%, n =36 of 94), whereas threat levels in the Mediterranean Sea are relative for each (66%, n =38 of 58). This case study is intended for extrapolation to the global shark and ray dataset upon completion of the global IUCN Red List assessment. Trait-based, categorical prediction of conservation status is a cost-effective approach towards incorporating Data Deficient species into (i) estimates of lineage-wide extinction rates, (ii) revised protected species lists, and (iii) Red List Indices, thus preventing poorly known species from reaching extinction unnoticed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".