A new approach to prioritizing marine fish and shellfish populations for conservation
Bibliographic record
Abstract
Abstract There has been increasing awareness of the vulnerability of marine organisms to population extirpation and species extinction. While very few documented cases of species extinction exist in the marine environment, it is anticipated that managers will face the dilemma of prioritizing populations of marine fish and shellfish for protection in the near future. Current prioritization procedures have been developed from salmonid models with the intent of applying them to all marine organisms, and in some cases to freshwater and terrestrial taxa. In this review we provide evidence for the relevance of such a process for marine species and further suggest five broad categories of marine organisms that have distinctive traits influencing their genetic structure. The current prioritization models have been adapted to account for each of these species groups. Emphasis is placed on ‘Classical Marine Species’ which represent the opposite end of the continuum from the salmon model, displaying high within‐population genetic variance. From this category, three cod (Gadus morhua) stocks were selected to evaluate a revised scheme developed specifically for ‘Classical Marine Species’ that includes performance measures such as (i) reduction in number of spawning populations; (ii) reduction of Ne : Nc (ratio of effective to census population size); (iii) changes in life‐history traits; (iv) critical density for spawning success; and (v) patchy vs. continuous distribution pattern. When the salmonid scheme was applied, the cod examples were allocated low values, indicating that they were not under threat. However, when the revised scheme was applied, all three cod stocks were allocated high values, indicating that the revised scheme was more reflective of the particular life‐history traits of this category of organisms.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".