Spatial autocorrelation of the environment influences the patterns and genetics of local adaptation
Bibliographic record
Abstract
Abstract Environmental heterogeneity can lead to spatially varying selection, which can, in turn, lead to local adaptation. Population genetic models have shown that the pattern of environmental variation in space can strongly influence the evolution of local adaptation. In particular, when environmental variation is highly autocorrelated in space local adaptation will more readily evolve. Despite this long-held prediction, the evolutionary genetic consequences of different patterns of environmental variation have not been thoroughly explored. In this study, simulations are used to model local adaptation to different patterns of environmental variation. The simulations confirm that local adaptation is expected to increase with the degree of spatial autocorrelation in the selective environment, but also show that highly heterogeneous environments are more likely to exhibit high variation in local adaptation, a result not previously described. Spatial autocorrelation in the environment also influences the evolution and genetic architecture of local adaptation, with different combinations of allele frequency and effect size arising under different patterns of environmental variation. These differences influence the ability to characterise the genetic basis of local adaptation in different environments. Finally, I analyse a large-scale provenance trial conducted on lodgepole pine and find suggestive evidence that spatially autocorrelated environmental variation leads to stronger local adaptation in natural populations of lodgepole pine. Overall, this work emphasizes the profound importance that the spatial pattern of selection can have on the evolution of local adaptation and how spatial autocorrelation should be considered when formulating hypotheses in ecological and genetic studies. Lay Summary Many species exhibit local adaptation to environmental variation across their ranges. Theoretical population genetics predicts that the evolution of local adaptation and patterns of genetic variation underlying it will be influenced by the spatial pattern of variation across a species’ range. However, this prediction has not been thoroughly explored for cases of complex heterogeneous landscapes. In this paper, I analyse simulations and empirical data to characterise the effects that the spatial pattern of environmental variation can have on the evolution of local adaptation and the genetics underlying it. From these analyses, I show that the pattern of environmental variation influences the average level of local adaptation, variation in local adaptation as well as the genetics underlying this important phenomenon.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".