Among- and within-patch components of genetic diversity respond at different rates to habitat fragmentation: an empirical demonstration
Bibliographic record
Abstract
Habitat fragmentation is a ubiquitous by-product of human activities that can alter the genetic structure of natural populations, with potentially deleterious effects on population persistence and evolutionary potential. When habitat fragmentation results in the subdivision of a population, random genetic drift then leads to the erosion of genetic diversity from within the resulting subpopulation, random genetic drift then leads to the erosion of genetic diversity from within the resulting subpopulations and greater genetic divergence among them. Theoretical and simulation analyses predict that these two main genetic effects of fragmentation, greater differentiation among resulting subpopulation and reduced genetic diversity within them, will proceed at very different rates. Despite important implications for the interpretation of genetics data from fragmented populations, empirical evidence for this phenomenon has been lacking. In this analysis, we carry out an empirical study in population of an alpine meadow-dwelling butterfly, which have become fragmented increasing forest cover over five decades. We show that genetic differentiation among subpopulations (G(ST)) is most highly correlated with contemporary forest cover, while genetics diversity within subpopulation (expected heterozygosity) is better correlated with the spatial pattern of forest cover 40 years in the past. Thus, where habitat fragmentation has occurred in recent decades, genetic differentiation among subpopulation can be near equilibrium while contemporary measures of within subpopulation diversity may substantially overestimate the equilibrium values that will eventually be attained.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".