Quaternary range and demographic expansion of <i><scp>L</scp>iolaemus darwinii</i> (<scp>S</scp>quamata: <scp>L</scp>iolaemidae) in the <scp>M</scp>onte <scp>D</scp>esert of <scp>C</scp>entral <scp>A</scp>rgentina using <scp>B</scp>ayesian phylogeography and ecological niche modelling
Bibliographic record
Abstract
Until recently, most phylogeographic approaches have been unable to distinguish between demographic and range expansion processes, making it difficult to test for the possibility of range expansion without population growth and vice versa. In this study, we applied a Bayesian phylogeographic approach to reconstruct both demographic and range expansion in the lizard Liolaemus darwinii of the Monte Desert in Central Argentina, during the Late Quaternary. Based on analysis of 14 anonymous nuclear loci and the cytochrome b mitochondrial DNA gene, we detected signals of demographic expansion starting at ~55 ka based on Bayesian Skyline and Skyride Plots. In contrast, Bayesian relaxed models of spatial diffusion suggested that range expansion occurred only between ~95 and 55 ka, and more recently, diffusion rates were very low during demographic expansion. The possibility of population growth without substantial range expansion could account for the shared patterns of demographic expansion during the Last Glacial Maxima (OIS 2 and 4) in fish, small mammals and other lizards of the Monte Desert. We found substantial variation in diffusion rates over time, and very high rates during the range expansion phase, consistent with a rapidly advancing expansion front towards the southeast shown by palaeo-distribution models. Furthermore, the estimated diffusion rates are congruent with observed dispersal rates of lizards in field conditions and therefore provide additional confidence to the temporal scale of inferred phylogeographic patterns. Our study highlights how the integration of phylogeography with palaeo-distribution models can shed light on both demographic and range expansion processes and their potential causes.
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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 | 0.000 |
| 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".