Understanding how species coevolve within complex ecological communities is one of the greatest technical challenges in evolutionary biology. Mathematical models of coevolution with even modest amounts of biological reality usually defy transparent, general analyses of coevolution. This project will develop innovative mathematical tools for analyzing coevolution in complex communities, including both evolutionary and population dynamics. The two main analytic approaches will make use of different mathematical approximations available when the strength of selection is relatively weak or strong. The scope and accuracy of these new methods will be verified using computer simulations. The new mathematical tools will also be used to address two important questions in community coevolution. First, does the number of potential hosts influence transitions between generalist and specialist parasites? Second, does coevolution lead to different ecological network structure in mutualistic vs. antagonistic communities? In each case, theoretical predictions will be tested in the laboratories of empirical collaborators. Ongoing studies of host use in bacteriophage will evaluate predictions for specialist-generalist transitions in parasites; data from well-studied plant-insect interactions will test predictions for the coevolution of community network structure.
The proposed work will contribute to the continued integration of analytic methods into the biological sciences. Results will be widely disseminated through publication, scientific presentations, and software packages developed during the project. Training will introduce at least three post-doctoral researchers, four graduate students, and five undergraduates to cutting-edge analytical and computational techniques. The research collaboration also reinforces existing interactions between biology and mathematics departments in geographically proximate Washington State University and the University of Idaho. In addition, the project supports a unique international collaboration, and strengthens emerging regional ties between mathematical biologists at institutions in Washington, Idaho, and British Columbia.
Most species live within complex ecological communities where they are forced to interact with many other species as competitors, prey, predators, or parasites. Predicting how evolution influences the structure, function, and stability of these complex ecological communities is a long standing challenge in ecology. What makes prediction so difficult in these cases is that, at a bare minimum, evolutionary change within each species must be described. Although this can be straightforward for small communities (e.g., three or four species), it becomes a formidable mathematical and computational challenge as the number of species increases. Work on this project developed new mathematical and computational tools that allow evolution within species rich communities to be effectively studied. In addition to developing a general set of mathematical tools that allow evolution to be studied within complex communities, work on this project addressed several specific topics of applied importance. To highlight one example, led by Dr. Emily Jones, a postdoctoral researcher supported by this grant, we investigated a long-standing prediction that biological invasions are more likely to be initiated by species that are distantly related to those currently within the native community. Using some of the mathematical tools developed earlier in this project, we formalized earlier verbal predictions. Our results show that, although evolutionary relationships do have power to predict whether a species will invade a native community, whether distantly or closely related species are more likely to invade depends on the ecological details of species interactions. This work will help to develop more effective tools for assessing the threat posed by potentially invasive species. Another important accomplishment of this project was training post-doctoral researchers, graduate students, and undergraduate students to use mathematical models and computer simulations to better understand and predict biological processes. Among those individuals trained on this grant, the postdoctoral researchers moved onto academic research positions, the graduate student moved on to a job at an information technology company (GFI Informatique), one undergraduate student went on to medical school, and another undergraduate student went on to graduate school.
