Community Structure and Stability: Linking Theory and Data

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9806953 Ives What determines the stability of ecological communities? This question is not only central to the basic science of ecology, but it is relevant to a wide range of practical issues such as land management and global climate change. Community stability is affected by three components of community structure: diversity, composition and pattern of interactions among species. Historically, ecologists have drawn very different conclusions about how community structure affects stability. In part, this is because theoretical analyses of community stability have not been easy to map onto real data. Therefore, there is no common framework for addressing community stability both in theory and in practice. This research will develop a theoretical framework for analyzing the stability of real communities. The theoretical framework is based on stochastic autoregressive models. Autoregressive models provide a flexible and general tool for investigating theoretically how all three components of community structure affect community stability. Using autoregressive models, it is also possible to investigate multiple properties of community dynamics, thereby exploring conceptually different ways to measuring community stability. Although autoregressive models are useful as theoretical tools, their main advantage is that they can be applied directly to analyze time-series data. This research will analyze three data sets: a compilation of zooplankton experimental data sets from 28 freshwater lakes, experimental data on prairie plant communities, and data from a long-term experimental study of a desert rodent community. The particular focal questions for each of these data sets are different, but they can all be addressed using an autoregressive framework. By combining theory and data, the proposed research will give more insight into the relationship between community structure and stability.