Comparing population patterns for genetic and morphological markers with uneven sample sizes. An example for the butterfly <i>Maniola jurtina</i>
Bibliographic record
Abstract
Summary Integrating genetic and/or phenotypic traits at population level is considered a fundamental approach in the study of evolutionary processes, systematics, biogeography and conservation. But combining the two types of data remain a complex task, mostly due to the high, and sometimes different, sample sizes required for reliable assessments of community traits. Data availability has been increasing in recent years, thanks to online resources, but it is uncommon that different types of markers are available for any given specimen. We provide new R functions aimed at directly correlating traits at population level, even if data sets only overlap partially. The new functions are based on a modified Procrustes algorithm that minimizes differences between bidimensional ordinations of two different markers, based on a subsample of specimens for which both characters are known. To test the new functions, we used a molecular and morphological data set comprising Mediterranean specimens of the butterfly Maniola jurtina . By using this method, we have been able to maximize similarities between genotypic and phenotypic configurations obtained after principal coordinate analysis for the model species and evaluated their degree of correlation at both individual and population level. The new recluster.procrustes function retained the information of the relative importance of different morphological variables in determining the observed ordinations and preserved it in the transformed configurations. This allowed calculating the best combination of morphological variables mirroring genetic relationships among specimens and populations. Finally, it was possible to analyse the modality and variance of the phenotypic characters correlated with the genetic structure among populations. The genetic and phenotypic markers displayed high overall correlation in the study area except in the contact zone, where discrepancies for particular populations were detected. Interestingly, such discrepancies were spatially structured, with southern populations displaying typical western morphotype and eastern haplotypes, while the opposite occurred in the northern populations. The methodology here described can be applied to any number and type of traits for which bidimensional configurations can be obtained, and opens new possibilities for data mining and for meta‐analyses combining existing data sets in biogeography, systematics and ecology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".