Genetic and morphological data supporting the hypothesis of adaptive radiation in the endemic fish of Lake Matano
Bibliographic record
Abstract
Adaptive radiation resulting from differential selection acting on functional features is believed to be an important source of biodiversity. In this study, morphometric measures and mitochondrial DNA are used to test for adaptive radiation within four fish genera (Glossogobius, Oryzias, Dermogenys and Telmatherina) endemic to an ancient island lake (Lake Matano, Sulawesi, Indonesia), using the framework proposed by Schluter (The Ecology of Adaptive Radiation, Oxford University Press, 2000). We demonstrate common ancestry and rapid divergence in one genus (Telmatherina) based on 560 bp of 16S sequence data. We found higher levels of variation in feeding-related traits (N = 8) for Telmatherina relative to the other genera, while no differences were found for sexual display traits (N = 8) or neutral morphological traits (N = 8). Telmatherina also had the highest number of distinct colouration patterns among the four genera. These data, combined with the very low productivity of the lake, are indicative of selection driving adaptive radiation. The morphometric divergence in the Telmatherina likely results from selection acting on feeding traits in this low productivity lake, leading to trophic specialization among closely related morphotypes. These results provide indirect but compelling data supporting the adaptive radiation of Telmatherina in this system.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".