Using traits-based approaches to understand the dynamics of community composition

Background/Question/Methods

Predicting changes in community composition in a rapidly changing world is a major research challenge in ecology with implications for understanding disease dynamics.  Traits-based approaches have elicited much recent interest, yet individual studies are not advancing a more general, predictive ecology.  We propose that significant progress will be facilitated by adopting a coherent theoretical framework comprised of three elements: an underlying trait distribution, a performance filter defining the fitness of traits in different environments, and a dynamic projection of the performance filter along some environmental gradient.  This framework allows changes in the trait distribution and associated modifications to community composition to be predicted across time or space.  The structure and dynamics of the performance filter specify two key criteria by which we judge appropriate quantitative methods for testing traits-based hypotheses.  Bayesian multilevel models, dynamical systems models and hybrid approaches meet both these criteria and have the potential to meaningfully advance traits-based ecology. 

Results/Conclusions

We use a grassland data set from Konza prairie to illustrate the data types that are needed and how this traits-based approach can be applied.  Greenhouse data on traits, climate data and percent cover were used to estimate changes in species composition over a twenty-five year period using a Bayesian multilevel model.  Changes in species richness are accurately captured over this period using models with one to three traits and three environmental drivers.  This system is not a disease system, but related applications in disease ecology will be discussed.  For example, a similar approach could be used to understand the dynamics of vector communities and implications for changes in the force of infection. Many disease questions occur in a community context where this traits-based approach is particularly relevant.  The environment in this framework can encompass either biotic or abiotic drivers in the system which increases the range of questions that can be addressed.