Evolutionary dynamics of dispersal and local adaptation in multi-resource landscapes
Bibliographic record
Abstract
Dispersal can enable access to resources in new locations. Consequently, traits that govern dispersal probability and dispersal distance may impact an individual's ability to acquire resources. However, spatial variation in the quality or quantity of resources may mediate potential adaptive benefits of novel dispersal traits. Ecological traits (i.e., those that determine how an individual processes resources) will also, by definition, affect how an individual interacts with the resource landscape. In a spatially heterogeneous environment, this creates potential for evolutionary feedbacks between dispersal-related traits and ecological traits. For example, dispersal may introduce individuals to novel resources, at which point there may be selection for local adaptation of ecological traits. Conversely, an individual's ability to utilize different resource types may determine how dispersal impacts fitness. Here, we develop an individual-based model to investigate co-evolution of dispersal and ecological traits in a landscape where multiple resources vary independently across space. We find that: 1) resource specialists can emerge and tend to evolve dispersal strategies suited to the structure of their preferred resource type and 2) generalists, when they emerge, tend to possess intermediate dispersal strategies. Lastly, we note that the effect of dispersal on the evolution of the ecological trait is weaker than vice versa and, as a result, appreciable heterogeneity in the abundance of resources across a landscape will likely obscure a signal of co-evolution.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".