Isolation by phenology synergizes isolation by distance across a continuous landscape
Bibliographic record
Abstract
Pollen is generally dispersed over short distances, which promotes population genetic structure across continuous two-dimensional space. Quantitative genetic variance in flowering time structures mating pools in the temporal dimension, at least with respect to the phenology loci. We asked if these two phenomena, isolation by distance (IBD) and isolation by phenology (IBP), synergistically promote genetic structure. We constructed an individual-based model that tracked genotype frequencies at flowering time and neutral loci across a uniform landscape, over multiple generations, under four mating schemes: panmixia, IBD only, IBP only, and IBP × IBD. IBD × IBP divided the population into spatial clusters of early-, mid-, and late-flowering genotypes and strongly increased its quantitative genetic variance. Flowering time did not cluster under IBP, but its genetic variance increased moderately. IBD induced mild spatial structure in a nonassortative reference trait but did not change its variance. Importantly, the spatial correlation of genotypes at neutral loci was twice as strong under IBD × IBP compared with IBD alone. IBD × IBP also drew neutral loci into gametic disequilibrium with flowering time loci, structuring them temporally. Temporal and spatial mating pool structure promotes local differentiation. This trend would facilitate adaptation on small spatial scales.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".