Insights into Population Ecology and Sexual Selection in Snakes Through the Application of DNA-Based Genetic Markers
Bibliographic record
Abstract
Hypervariable genetic markers have revolutionized studies of kinship, behavioral ecology, and population biology in vertebrate groups such as birds, but their use in snakes remains limited. To illustrate the value of such markers in snakes, we review studies that have used microsatellite DNA loci to analyze local population differentiation and parentage in snakes. Four ecologically distinct species of snakes all show evidence for differentiation at small spatial scales (2-15 km), but with substantial differences among species. This result highlights how genetic analysis can reveal hidden aspects of the natural history of difficult-to-observe taxa, and it raises important questions about the ecological factors that may contribute to restricted gene flow. A 3-year study of genetic parentage in marked populations of the northern water snake showed that (1) participation in mating aggregations was a poor predictor of genetic-based measures of reproductive success; (2) multiple paternity was high, yet there was no detectable fitness advantage to multiple mating by females; and (3) the opportunity for selection was far higher in males than in females due to a larger variance in male reproductive success, and yet this resulted in no detectable selection on morphological variation in males. Thus genetic markers have provided accurate measures of individual reproductive success in this species, an important step toward resolving the adaptive significance of key features including multiple paternity and reversed sexual size dimorphism. Overall these studies illustrate how genetic analyses of snakes provide previously unobtainable information of long-standing interest to behavioral ecologists.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".