Climate‐induced changes to the ancestral population size of two Patagonian galaxiids: the influence of glacial cycling
Bibliographic record
Abstract
Patagonia is one of the few areas in the Southern Hemisphere to have been directly influenced by Quaternary glaciers. In this study, we evaluate the influence that Quaternary glacial ice had on the genetic diversity of two congeneric fish species, the diadromous Galaxias maculatus and the nondiadromous Galaxias platei, using multilocus estimates of effective population size through time. Mid-Quaternary glaciations had far-reaching consequences for both species. Galaxias maculatus and G. platei each experienced severe genetic bottlenecks during the period when Patagonia ice sheet advance reached its maximum positions c. 1.1-0.6 Ma. Concordant drops in effective size during this time suggest that range sizes were under similar constraints. It is therefore unlikely that coastal (brackish/marine) environments served as a significant refuge for G. maculatus during glacial periods. An earlier onset of population declines for G. platei suggests that this species was vulnerable to modest glacial advances. Declines in effective sizes were continuous for both species and lasted into the late-Pleistocene. However, G. maculatus exhibited a strong population recovery during the late-Quaternary (c. 400,000 bp). Unusually long and warm interglacials associated with the late-Quaternary may have helped to facilitate a strong population rebound in this primarily coastal species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".