Genome‐wide single nucleotide polymorphisms reveal population history and adaptive divergence in wild guppies
Bibliographic record
Abstract
Adaptation of guppies (Poecilia reticulata) to contrasting upland and lowland habitats has been extensively studied with respect to behaviour, morphology and life history traits. Yet population history has not been studied at the whole-genome level. Although single nucleotide polymorphisms (SNPs) are the most abundant form of variation in many genomes and consequently very informative for a genome-wide picture of standing natural variation in populations, genome-wide SNP data are rarely available for wild vertebrates. Here we use genetically mapped SNP markers to comprehensively survey genetic variation within and among naturally occurring guppy populations from a wide geographic range in Trinidad and Venezuela. Results from three different clustering methods, Neighbor-net, principal component analysis (PCA) and Bayesian analysis show that the population substructure agrees with geographic separation and largely with previously hypothesized patterns of historical colonization. Within major drainages (Caroni, Oropouche and Northern), populations are genetically similar, but those in different geographic regions are highly divergent from one another, with some indications of ancient shared polymorphisms. Clear genomic signatures of a previous introduction experiment were seen, and we detected additional potential admixture events. Headwater populations were significantly less heterozygous than downstream populations. Pairwise F(ST) values revealed marked differences in allele frequencies among populations from different regions, and also among populations within the same region. F(ST) outlier methods indicated some regions of the genome as being under directional selection. Overall, this study demonstrates the power of a genome-wide SNP data set to inform for studies on natural variation, adaptation and evolution of wild populations.
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 | 0.000 | 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) | 0.000 | 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".