The temporal and spatial scale of microevolution: fine-scale colour pattern variation in the Lake Erie watersnake, Nerodia sipedon insularum
Bibliographic record
Abstract
Question: What is the temporal and spatial scale of microevolution? Hypotheses: The combined effects of natural selection and gene flow result in variation in heritable traits on fine spatial and geographic scales. Organism: The Lake Erie watersnake, Nerodia sipedon insularum. Field site: US and Canadian islands in western Lake Erie. Methods: We tested for variation in colour pattern frequency within islands, among islands, and over time using data from nearly annual censuses conducted since 1980, museum specimens, and published sources. We compared FST for a presumptive major colour pattern locus to FST for allozyme loci to determine whether spatial variation exceeded that expected by chance. We computed effective population size (Ne) based on temporal frequency changes in presumptive colour pattern alleles to determine whether temporal variation exceeded that expected by chance (Ne significantly less than ∞). Conclusions: Morph frequencies did not differ significantly within islands or between islands separated by short distances. Morph frequencies did sometimes differ significantly among distant islands and among sampling periods from 1980 to the present, but no more than expected by chance. In contrast, a marked change in morph frequency occurred between historic (prior to 1961) and recent (1980–2003) samples. Possible mechanisms include changes in the strength of selection (due to changes in predator assemblages and visual environments) and rates of gene flow (due to changes in island watersnake population size).
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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 | 0.002 | 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 | 0.001 | 0.002 |
| 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 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".