Trait‐Based Prediction of Conservation Status of North American Small‐Bodied Minnows (<scp><i>Leuciscidae</i></scp>) and Darters (<scp><i>Percidae</i></scp>)
Bibliographic record
Abstract
ABSTRACT With the rapid decline of aquatic biodiversity, conservation tools such as captive breeding for reintroduction are becoming more common. A major challenge, however, lies in determining which species should be prioritized for such efforts. One effective method is to assess species' life history and ecological traits, which are often associated with extinction risk and can provide critical insights for guiding species prioritization. In this study, we assessed all small‐bodied minnow and darter species in North America (i.e., Canada, the United States and Mexico) to determine if life history and ecological traits could predict their conservation status. We analysed 13 life history and ecological traits in relation to the IUCN conservation status for 220 species of minnow and 183 species of darters. For minnows, traits such as substrate, climatic zone, diet, feeding location, total length and maximum water temperature were associated with a higher risk of being threatened. For darters, the traits associated with an increased risk of being threatened were climatic zone and total length. Taken together, this study identifies key life history and ecological traits that influence the conservation status of small‐bodied fishes and provides actionable insights for prioritizing species for captive breeding programmes. These findings can guide conservation practitioners in developing species‐specific, proactive recovery strategies to prioritize species at risk and enhance conservation efforts before they become threatened in the wild.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".