How genotype-by-environment interactions can maintain variation in mutualisms
Bibliographic record
Abstract
Abstract Coevolution requires reciprocal genotype-by-genotype (GXG) interactions for fitness, which occur when the fitnesses of interacting species depend on the match between their genotypes. However, in mutualisms, when GXG interactions are mutually beneficial, simple models predict that positive feedbacks will erode genetic variation, weakening or eliminating the GXG interactions that fuel ongoing coevolution. This is inconsistent with the ample trait and fitness variation observed within real-world mutualisms. Here, we explore how genotype-by-environment (GXE) interactions, which occur when different genotypes respond differently to different environments, maintain variation in mutualisms. We employ a game theoretic model in which the fitnesses of two partners depend on mutually beneficial GXG and GXE interactions. Variation is maintained via migration-selection balance when GXE interactions are slightly stronger than GXG interactions or when they are much stronger than GXG interactions for just one partner. However, unexpectedly, when GXE interactions are much stronger than GXG interactions for both partners and dispersal is high, genotypically mismatched partners can fix, eroding variation and leading to apparent maladaptation between partners. We parameterize our model using data from three published reciprocal transplant experiments and find that the observed strengths of GXE interactions can maintain or erode variation in mutualisms via these mechanisms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".