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Evolutionary Responses to Conditionality in Species Interactions across Environmental Gradients
The American Naturalist
|November 17, 2018
Summary
Species interactions shift from mutualistic in stressful environments to antagonistic in benign ones. This study predicts how environmental conditions drive adaptation and coadaptation between interacting species.
Area of Science:
- Ecology
- Evolutionary Biology
- Theoretical Biology
Background:
- Species interaction outcomes are environmentally conditional, often shifting from mutualistic under stress to antagonistic in benign conditions.
- Ecological theories like resource-limited models and the stress-gradient hypothesis explain conditionality.
- The geographic mosaic theory of coevolution (GMTC) provides a framework for understanding coevolutionary patterns.
Purpose of the Study:
- To integrate theories of conditionality with GMTC to predict adaptation patterns along abiotic gradients.
- To differentiate between general and local adaptation in species interactions.
- To propose methods for testing these predictions and their application in conservation.
Main Methods:
- Combining predictions from resource-limited models, the stress-gradient hypothesis, and GMTC.
- Developing a framework to distinguish general vs. local adaptation.
- Utilizing simulations based on GMTC models to demonstrate methods.
Main Results:
- Predicts increased mutualistic adaptation in stressful environments due to aligned fitness benefits.
- Anticipates antagonistic coevolution or niche partitioning in benign environments.
- Highlights the importance of environmental context in shaping adaptive and coadaptive responses.
Conclusions:
- Environmental gradients systematically influence the direction and type of adaptation and coadaptation between interacting species.
- The proposed framework and methods can test these predictions across various conditional interactions.
- Findings have implications for understanding evolutionary dynamics and informing conservation strategies like assisted translocation.
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