BALANCING SELECTION, RANDOM GENETIC DRIFT, AND GENETIC VARIATION AT THE MAJOR HISTOCOMPATIBILITY COMPLEX IN TWO WILD POPULATIONS OF GUPPIES (POECILIA RETICULATA)
Bibliographic record
Abstract
Our understanding of the evolution of genes of the major histocompatibility complex (MHC) is rapidly increasing, but there are still enigmatic questions remaining, particularly regarding the maintenance of high levels of MHC polymorphisms in small, isolated populations. Here, we analyze the genetic variation at eight microsatellite loci and sequence variation at exon 2 of the MHC class IIB (DAB) genes in two wild populations of the Trinidadian guppy, Poecilia reticulata. We compare the genetic variation of a small (Ne, 100) and relatively isolated upland population to that of its much larger (Ne approximately 2400) downstream counterpart. As predicted, microsatellite diversity in the upland population is significantly lower and highly differentiated from the population further downstream. Surprisingly, however, these guppy populations are not differentiated by MHC genetic variation and show very similar levels of allelic richness. Computer simulations indicate that the observed level of genetic variation can be maintained with overdominant selection acting at three DAB loci. The selection coefficients differ dramatically between the upland (s > or = 0.2) and lowland (s < or = 0.01) populations. Parasitological analysis on wild-caught fish shows that parasite load is significantly higher on upland than on lowland fish, which suggests that large differences in selection intensity may indeed exist between populations. Based on the infection intensity, a substantial proportion of the upland fish would have suffered direct or indirect fitness consequences as a result of their high parasite loads. Selection by parasites plays a particularly important role in the evolution of guppies in the upland habitat, which has resulted in high levels of MHC diversity being maintained in this population despite considerable genetic drift.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".