The Influence of Evolution on Population Spread through Patchy Landscapes
Bibliographic record
Abstract
Predicting the spatial advance of biological invasions and range-shifting native species under climate change requires understanding how evolutionary processes influence the velocity of spread. Although landscape heterogeneity and the finite nature of individuals are known to influence the ecological dynamics of spreading populations, their effect on evolutionary dynamics influencing spread is poorly understood. We used adaptive dynamics and simulation approaches to evaluate the direction of selection on demographic and competitive traits in annual plant populations spreading through linear landscapes. In contrast to models in homogeneous environments where low-density fecundity is selectively favored, we found that large gaps between suitable habitat could favor the rise of competitively tolerant individuals at the invasion front, even when this ability to tolerate competitors came at the cost of reduced fecundity when rare. Simulations of the same processes incorporating finite individuals revealed a spatial priority effect; the long-term outcome of evolution strongly depended on which strategy initially got ahead. Finally, we found that although evolutionary change in demographic and competitive traits could increase the spread velocity, this increase could be insignificant in patchy landscapes, where competitive traits were favored in both spreading and nonspreading populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".