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Predicting evolution with generalized models of divergent selection: a case study with poeciliid fish
1Biological Station and Department of Zoology, University of Oklahoma, Norman, OK 73019, USA. langerhans@ncsu.edu
Integrative and Comparative Biology
|May 12, 2011
Summary
Evolutionary predictability is explored using generalized models of divergent selection (GMDS). These models accurately predicted repeatable phenotypic divergence in Gambusia fishes driven by predation, suggesting a general framework for evolutionary prediction.
Area of Science:
- Evolutionary biology
- Ecology
- Genetics
Background:
- The predictability of evolution under natural selection has been a long-standing question.
- Natural selection has deterministic components, suggesting evolution may be predictable.
Purpose of the Study:
- To evaluate the utility of generalized models of divergent selection (GMDS) for predicting evolutionary trajectories.
- To provide a unifying framework for understanding the causes and consequences of evolutionary change.
Main Methods:
- Described and tested a GMDS focusing on traits like body shape, gonopodium size, and swimming performance in Gambusia fishes.
- Analyzed phenotypic and genetic evolution in response to varying predation pressures.
Main Results:
- GMDS accurately predicted evolutionary divergence in Gambusia, with predation intensity identified as a key driver.
- Predation-driven divergence in body shape was observed to be repeatable across disparate fish species and habitats.
- The models highlighted predation as a significant factor in ecological speciation.
Conclusions:
- GMDS offers a promising approach to assessing evolutionary predictability.
- The models successfully identified environmental factors, like predation, that drive replicated patterns of evolution and diversification.
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