Local adaptation in dispersal in multi‐resource landscapes
Bibliographic record
Abstract
The distribution of resources in space has important consequences for the evolution of dispersal-related traits. Dispersal moderates patterns of gene flow and, consequently, the potential for local adaptation to spatially differentiated resource types. We lack both models and experiments that evaluate how dispersal evolves in landscapes with multiple resources. Here, we investigate the evolution of dispersal in landscapes that contain two resource types that differ in their spatial autocorrelations. Individuals may possess ecological traits that give them a fitness advantage on one or the other resource. We find that resources differing in their spatial autocorrelation select for different optimal dispersal strategies and, further, that some multi-resource landscapes can support the stable coexistence of distinct dispersal strategies. Whether divergence in dispersal strategies between resource specialists occurs depends on the underlying structure of the resources and the degree of linkage between dispersal strategies and ecological specialization. This work indicates that the spatial autocorrelation of resources is an important factor in determining when evolutionary branching is likely to occur, and sheds light on when secondary isolating mechanisms should arise between locally adapted specialists.
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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".