Breed Locally, Disperse Globally: Fine-Scale Genetic Structure Despite Landscape-Scale Panmixia in a Fire-Specialist
Bibliographic record
Abstract
An exciting advance in the understanding of metapopulation dynamics has been the investigation of how populations respond to ephemeral patches that go 'extinct' during the lifetime of an individual. Previous research has shown that this scenario leads to genetic homogenization across large spatial scales. However, little is known about fine-scale genetic structuring or how this changes over time in ephemeral patches. We predicted that species that specialize on ephemeral habitats will delay dispersal to exploit natal habitat patches while resources are plentiful and thus display fine-scale structure. To investigate this idea, we evaluated the effect of frequent colonization of ephemeral habitats on the fine-scale genetic structure of a fire specialist, the black-backed woodpecker (Picoides arcticus) and found a pattern of fine-scale genetic structure. We then tested for differences in spatial structure between sexes and detected a pattern consistent with male-biased dispersal. We also detected a temporal increase in relatedness among individuals within newly burned forest patches. Our results indicate that specialist species that outlive their ephemeral patches can accrue significant fine-scale spatial structure that does not necessarily affect spatial structure at larger scales. This highlights the importance of both spatial and temporal scale considerations in both sampling and data interpretation of molecular genetic results.
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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 | 0.000 | 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".