Setting Conservation Priorities in a Widespread Species: Phylogeographic and Physiological Variation in the Lake Chub, Couesius plumbeus (Pisces: Cyprinidae)
Bibliographic record
Abstract
Defining units of conservation below the species level is a widely accepted conservation priority, but is especially challenging for widespread taxa that have experienced diverse geographic histories and exist across heterogenous environments. The lake chub (Pisces: Couesius plumbeus) is a widespread freshwater fish in North America and occurs from the southcentral USA to northwestern Alaska and Canada. We used mtDNA sequence analysis to test for divergent lineages predicted to occur as a result of survival of lake chub in distinct glacial refugia. Lake chub consisted of two major mtDNA lineages separated by 3.8% sequence divergence which are probably late to pre-Pleistocene in origin. We combined these data with those consistent with thermal adaptation in fish living in thermal springs versus those living in a lake with wide seasonal temperature variation, and with data on distribution of lake chub in major watershed units. We assessed these data against objective criteria developed to identify conservation units under Canadian endangered species legislation. Our analysis identified twelve major units of conservation within C. plumbeus that could be assessed under Canada’s Species-at-Risk Act. Our study illustrates how different character traits manifested at very different spatial scales can be used to define conservation units within widely-distributed taxa.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".