Collaborative Research: A Unified Theoretical Approach to Community Coevolution

了解物种如何在复杂的生态群落中共同进化是进化生物学中最大的技术挑战之一。共同进化的数学模型，即使是少量的生物现实，通常也会违背对共同进化的透明、普遍的分析。该项目将开发创新的数学工具，用于分析复杂群落的共同进化，包括进化和种群动态。当选择的强度相对较弱或较强时，两种主要的分析方法将利用不同的数学近似。这些新方法的范围和准确性将通过计算机模拟来验证。新的数学工具还将用于解决群落共同进化中的两个重要问题。首先，潜在宿主的数量是否会影响通才寄生虫和专才寄生虫之间的转变？第二，共同进化是否导致了共生群落和对抗群落中不同的生态网络结构？在每种情况下，理论预测都将在经验合作者的实验室中进行测试。正在进行的噬菌体宿主使用研究将评估寄生虫中专才向通才转变的预测；经过充分研究的植物-昆虫相互作用的数据将检验群落网络结构共同进化的预测。拟议的工作将有助于分析方法继续融入生物科学。研究结果将通过出版物、科学报告和项目期间开发的软件包广泛传播。培训将引进至少三名博士后研究人员，四名研究生和五名本科生尖端的分析和计算技术。这项研究合作还加强了地理位置接近华盛顿州立大学和爱达荷大学的生物学和数学系之间的现有互动。此外，该项目支持独特的国际合作，并加强华盛顿、爱达荷州和不列颠哥伦比亚省各机构数学生物学家之间新兴的区域联系。

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.