Meta-analysis reveals the tempo of evolutionary parallelism of local adaptation between native and introduced ranges of invasive plant species
Bibliographic record
Abstract
Abstract Invasive species are valuable systems for evaluating evolutionary predictability, as populations in native and introduced ranges evolve separately, yet often encounter similar environmental challenges that select for parallel patterns of local adaptation across each range. However, it remains unclear how pervasive and strong such parallelism is and how rapidly it evolves. To address these questions, we first extended cline theory to predict evolutionary patterns of trait cline parallelism between ranges. We then carried out a meta-analysis of clinal divergence in native and introduced populations of the same plant species and evaluated the tempo of trait cline parallelism between ranges. We found that clines in introduced ranges were somewhat shallower than native range clines, and directions of clinal divergence were, on average, partially aligned between ranges, with extensive variation in parallelism among traits and species, including counter-clines between native and introduced ranges. The degree of evolutionary parallelism of clinal divergence strongly increased, and the prevalence of counter-clines decreased, with the time since introduction, where greater parallelism in older introductions was primarily caused by increased alignment in the direction of clinal divergence between ranges rather than changes in their relative magnitudes of divergence. We argue that these observations are consistent with a two-phased process of cline evolution across introduced ranges. In the first phase, misaligned clines between introduced and native ranges can arise during range expansions, often owing to demographic and genetic constraints that initially hinder the evolution of local adaptation within the new range. During the second phase, the evolution of local adaptation eventually resolves early instances of maladaptation across introduced ranges, leading to strong trait cline parallelism between native and introduced ranges.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".