Next-generation phylogeography reveals unanticipated population history and climate and human impacts on the endangered floodplain bitterling (Acheilognathus longipinnis)
Bibliographic record
Abstract
BACKGROUND: Floodplains harbor highly biodiverse ecosystems, which have been strongly affected by both past climate change and by recent human activities, resulting in a high prevalence of many endangered species in these habitats. Understanding the history of floodplain species over a wide range of timescales can contribute to effective conservation planning. We reconstructed the population formation history of the Itasenpara bitterling Acheilognathus longipinnis, an endangered floodplain fish species in Japan, over a broad timescale based on phylogenetic analysis, demographic modeling, and historical demographic analysis using mitogenome and whole-genome sequences. A genome sequence was newly assembled as a reference for the resequencing analysis. This bitterling is distributed in three plains separated by high mountain ranges and exhibits ecological characteristics well adapted to floodplain environments. RESULTS: Our analyses revealed an unexpected population branching pattern, gene flow, and timing of the differentiation that occurred within a few hundred thousand years, i.e., long after the mountain uplift that was assumed to be the primary geological cause of the population differentiation. The analyses also showed that all local populations experienced a severe decline during the last glacial and post-glacial periods. CONCLUSIONS: Our results suggest that the floodplain bitterling was able to disperse through unknown routes after mountain uplift and that its populations were strongly influenced by climatic and geographic changes in glacial-interglacial cycles and subsequent human activities, probably related to its floodplain-dependent ecology. The genomic data highlight the unanticipated distribution process of this species and the magnitude of the impact of human activities, with important implications for its conservation.
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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 | 0.001 | 0.001 |
| 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".