The Temporal and Spatial Scale of Microevolution: Fine-scale Color Pattern Variation in the Lake Erie Watersnake
Bibliographic record
Abstract
Question: What is the temporal and spatial scale of microevolution? \nHypotheses: The combined effects of natural selection and gene flow result in variation in \nheritable traits on fine spatial and geographic scales. \nOrganism: The Lake Erie watersnake, Nerodia sipedon insularum. \nField site: US and Canadian islands in western Lake Erie. \nMethods: We tested for variation in colour pattern frequency within islands, among islands, \nand over time using data from nearly annual censuses conducted since 1980, museum \nspecimens, and published sources. We compared FST for a presumptive major colour pattern \nlocus to FST for allozyme loci to determine whether spatial variation exceeded that expected by \nchance. We computed effective population size (Ne) based on temporal frequency changes in \npresumptive colour pattern alleles to determine whether temporal variation exceeded that \nexpected by chance (Ne significantly less than ∞). \nConclusions: Morph frequencies did not differ significantly within islands or between \nislands separated by short distances. Morph frequencies did sometimes differ significantly \namong distant islands and among sampling periods from 1980 to the present, but no more than \nexpected by chance. In contrast, a marked change in morph frequency occurred between \nhistoric (prior to 1961) and recent (1980–2003) samples. Possible mechanisms include changes \nin the strength of selection (due to changes in predator assemblages and visual environments) \nand rates of gene flow (due to changes in island watersnake population size).
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".