Constraints and tradeoffs: Toward a predictive, mechanism-based understanding of ecological communities

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Constraints and tradeoffs: Toward a predictive, mechanism-based understanding of ecological communities

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2017-08

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It is easy to forget how far ecology has come in a very short time. Less than two decades ago, it was unclear whether predictive models of species-level dynamics in diverse ecological communities would ever be possible. Today, an abundance of methods can accurately forecast these dynamics, driven by explosive growth in the availability of data and modern analytical tools. However, most of these methods rely on matching patterns from historical dynamics to current trends. Thus, while predictions have become much easier, understanding why behavior occurs – and extrapolating predictions to novel circumstances – remains elusive. Here, we apply theoretical insights from tradeoffs to better understand how species in ecological communities assemble and coexist. Tradeoffs describe physiological and ecological constraints that limit the traits and roles of individual organisms. These constraints therefore contain substantial information about species ecological and evolutionary histories, and how they are likely to interact with one another and their environments. We show that information contained in tradeoffs can be used to identify important mechanisms governing community dynamics, and to constrain viable parameter space in otherwise intractable models. These methods could substantially improve mechanism-based predictions in diverse communities, resulting in better understanding of how these complex systems function, and better extrapolations of predictions under novel circumstances.

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University of Minnesota Ph.D. dissertation.August 2017. Major: Ecology, Evolution and Behavior. Advisor: David Tilman. 1 computer file (PDF); viii, 184 pages.

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Clark, Adam. (2017). Constraints and tradeoffs: Toward a predictive, mechanism-based understanding of ecological communities. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/191380.

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