Survival by genotype: patterns at <i>Mc1r</i> are not black and white at the <scp>W</scp>hite <scp>S</scp>ands ecotone
Bibliographic record
Abstract
Measuring links among genotype, phenotype and survival in the wild has long been a focus of studies of adaptation. We conducted a 4-year capture-recapture study to measure survival by genotype and phenotype in the Southwestern Fence Lizard (Sceloporus cowlesi) at the White Sands ecotone (transition area between white sands and dark soil habitats). We report several unanticipated findings. First, in contrast with previous work showing that cryptic blanched coloration in S. cowlesi from the heart of the dunes is associated with mutations in the melanocortin-1 receptor gene (Mc1r), ecotonal S. cowlesi showed minimal association between colour phenotype and Mc1r genotype. Second, the frequency of the derived Mc1r allele in ecotonal S. cowlesi appeared to decrease over time. Third, our capture-recapture data revealed a lower survival rate for S. cowlesi individuals with the derived Mc1r allele. Thus, our results suggest that selection at the ecotone may have favoured the wild-type allele in recent years. Even in a system where a genotype-phenotype association appeared to be black and white, our study suggests that additional factors - including phenotypic plasticity, epistasis, pleiotropy and gene flow - may play important roles at the White Sands ecotone. Our study highlights the importance of linking molecular, genomic and organismal approaches for understanding adaptation in the wild. Furthermore, our findings indicate that dynamics of natural selection can be particularly complex in transitional habitats like ecotones and emphasize the need for future research that examines the patterns of ongoing selection in other ecological 'grey' zones.
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 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 | 0.000 |
| 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".