Evolutionary Responses to Conditionality in Species Interactions across Environmental Gradients
Bibliographic record
Abstract
The outcomes of many species interactions are conditional on the environments in which they occur. Often, interactions grade from being more positive under stressful or low-resource conditions to more antagonistic or neutral under benign conditions. Here, we take predictions about two well-supported ecological theories on conditionality-limiting resource models and the stress-gradient hypothesis-and combine them with those from the geographic mosaic theory of coevolution (GMTC) to generate predictions for systematic patterns of adaptation and coadaptation between partners along abiotic gradients. When interactions become more positive in stressful environments, mutations that increase fitness in one partner may also increase fitness in the other; because fitnesses are aligned, selection should favor greater mutualistic adaptation and coadaptation between interacting species in stressful ends of environmental gradients. As a corollary, in benign environments antagonistic coadaptation could result in Red Queen or arms-race dynamics or the reduction of antagonism through character displacement and niche partitioning. Here, we distinguish between generally mutualistic or antagonistic adaptation (i.e., mutations in one partner that have similar effects across multiple populations of the other) and specific adaptations to sympatric partners (local adaptation), which can occur either alone or simultaneously. We then outline the kinds of data required to test these predictions, develop experimental designs and statistical methods, and demonstrate these using simulations based on GMTC models. Our methods can be applied to a range of conditional outcomes and may also be useful in assisted translocation approaches in the face of climate change.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".