科研之友
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摘要
Ecological studies of communities have become increasingly focused on the role of genetics. These studies often conclude that genetics and evolution play an important role in community structure and function. For instance, studies have shown that the structure of insect communities associated with a host plant is heritable and therefore can potentially evolve. However, when studying communities of interacting species two problems are faced: (1) the traits that determine the outcomes of these interactions are often unknown, and (2) communities are normally highly multidimensional (n-dimensional for n species). In order to surmount these problems, we adapt a commonly used approach for studying the evolution of multivariate quantitative traits to the study of biological communities. Specifically, we propose utilizing a community-based genetic covariance matrix (G-matrix) and an associated vector of community selection gradients for predicting changes in community composition, where the %26quot;traits%26quot; under study are the abundances, or other properties, of various interacting species. This approach capitalizes on the relative ease with which data on the abundance of individuals interacting with individuals of a focal species (e.g., abundances of various herbivorous insects on a plant) can be collected and on the utility of the quantitative genetic approach for predicting multidimensional evolution. In order to evaluate the utility and accuracy of the G-matrix approach for predicting the evolution of communities, we develop and analyze numerical simulations of evolving communities. Results of these simulations show that an approach based on community G-matrices and selection gradients provides a rich understanding of how underlying genetics shape community structure and, in many cases, accurately predicts how community structure changes over time.
- 出版日期2014-5
