Density-dependent selection during range expansion affects expansion load in life-history traits
Bibliographic record
Abstract
Abstract Models of range expansion have independently explored fitness consequences of life-history trait evolution and increased rates of genetic drift—or “allele surfing”—during spatial spread, but no previous model has examined the interactions between these two processes. Here, we explore an ecologically complex range expansion scenario that combines density-dependent selection with allele surfing, using spatially explicit simulations to asses the genetic and fitness consequences of density-dependent selection on the evolution of life-history traits. We demonstrate that density-dependent selection on the range edge acts differently depending on the life-history trait and can either diminish or enhance allele surfing. Specifically, we show that selection on the range edge is always weaker at sites affecting competitive ability ( K -selected traits) than intrinsic growth rate (( r -selected traits). We then link differences in the frequency of deleterious mutations to differences in the efficacy of selection and rate of mutation accumulation across distinct life-history traits. Finally, we demonstrate the fitness consequences of accumulated deleterious mutations for different life-history traits are related to the population density in which they are expressed. Our work highlights the complex relationship between ecology and expressed genetic load, which will be important to consider when interpreting both experimental and field studies of range expansion.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".