Evolution of dispersal and mating systems along geographic gradients: implications for shifting ranges
Bibliographic record
Abstract
Summary Dispersal affects species' ability to move or adapt in response to environmental change. Successful long‐distance dispersal also requires reproduction in areas with few mates, thus mating systems, especially the capacity for self‐fertilization, may influence the speed and success of range shifts. Here, we review: the theoretical predictions regarding dispersal and mating‐system evolution at equilibrium, expanding and contracting range limits; the empirical support for these predictions; and how these geographic patterns may influence future range evolution. Equilibrium range limits can arise from environmental gradients in habitat quality, temporal variation or habitat heterogeneity. Dispersal has been predicted to increase or decrease towards range edges, depending on which life‐history traits respond to the ecological gradient(s). In general, spatial habitat isolation selects against dispersal, whereas temporal stochasticity favours dispersal. At expanding range fronts, dispersal should increase due to spatial sorting for dispersive individuals and the benefits of colonizing vacant habitat. Dispersal evolution is likely more constrained during native range shifts than invasions. Models of expansion across environmental gradients and during climate‐tracking range shifts are lacking. Little theory considers evolution at contracting range margins. We suggest that increased dispersal should be selected if there is local adaptation to climate, as dispersers from warmer areas will out‐compete nondispersers no longer adapted to new climatic conditions. Dispersal increases should be more pronounced in regions where local adaptation is stronger. Self fertilization may be favoured at equilibrium, expanding or contracting range margins by providing reproductive assurance. However, this benefit depends on how inbreeding depression is influenced by genetic load, the severity of the abiotic environment, and the competitive milieu in edge populations. Models for the joint evolution of mating and dispersal in plants suggest that although selfing may evolve at range limits, it will not necessarily be associated with high dispersal. Empirical evidence to test these predictions is scarce. Geographic surveys of dispersal traits, mating‐system traits and relevant selective factors are needed, especially studies of: (i) stable range limits that identify underlying environmental gradients; (ii) moving range limits that compare traits across space and time; and (iii) contracting limits that assess variation in local adaptation towards the range edge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".