Choosing source populations for conservation reintroductions: lessons from variation in thermal tolerance among populations of the imperilled redside dace<sup>1</sup>
Bibliographic record
Abstract
Reintroduction is an increasingly common conservation tool used to recover populations of imperilled species, but its success depends on the suitability of the introduced animals’ phenotype for their new habitat. For fishes, thermal tolerance may be a key trait in urbanized habitats. We compared thermal tolerance (CTmax) among three lineages (western, central, eastern) of imperilled redside dace (Clinostomus elongatus). CTmax of eastern adults was 3–4 °C lower than that of the other lineages, but adults of each lineage had similar thermal acclimation responses. In contrast, the acclimation response of juveniles differed by ∼80% between the central and western lineages. Using these data, we predicted how each lineage would fare in a hypothetical reintroduction to relatively warm urbanized habitats. Owing to the differences in juvenile acclimation responses, predicted thermal safety margins for the central lineage were double those predicted for the western lineage. Overall, we suggest that CTmax is a useful trait to incorporate into the source population selection process; however, there is an urgent need for the establishment of captive experimental research populations of imperilled species to address remaining uncertainties.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".