Adaptation to fragmentation: evolutionary dynamics driven by human influences
Bibliographic record
Abstract
Fragmentation-the process by which habitats are transformed into smaller patches isolated from each other-has been identified as a major threat for biodiversity. Fragmentation has well-established demographic and population genetic consequences, eroding genetic diversity and hindering gene flow among patches. However, fragmentation should also select on life history, both predictably through increased isolation, demographic stochasticity and edge effects, and more idiosyncratically via altered biotic interactions. While species have adapted to natural fragmentation, adaptation to anthropogenic fragmentation has received little attention. In this review, we address how and whether organisms might adapt to anthropogenic fragmentation. Drawing on selected case studies and evolutionary ecology models, we show that anthropogenic fragmentation can generate selection on traits at both the patch and landscape scale, and affect the adaptive potential of populations. We suggest that dispersal traits are likely to experience especially strong selection, as dispersal both enables migration among patches and increases the risk of landing in the inhospitable matrix surrounding them. We highlight that suites of associated traits are likely to evolve together. Importantly, we show that adaptation will not necessarily rescue populations from the negative effects of fragmentation, and may even exacerbate them, endangering the entire metapopulation.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".