The relationship between species diversity (i.e., the number of species) of an ecological community and its productivity (e.g., the amount of biomass produced in a year) has been studied intensively in ecology, both from a theoretical point of view, and through some of the largest experiments ever conducted in the field. Understanding how biodiversity and productivity are related is essential for applications, such as the development of biofuels, as well as to predict the effects of species loss. The project aims to leverage the large amount of experimental data available to develop new statistical, mathematical and computational tools that can be used to predict the productivity of untested ecological communities, and to determine the contribution of shared evolutionary history to the functioning of ecosystems. Importantly, the same mathematical machinery developed for the study of ecological communities can be used to answer distant questions, such as the effect of combinations of antibiotics on the growth of bacteria. The work is complemented by the publication of software packages, lecture notes and teaching material, to facilitate community adoption and extension of the newly developed tools.
The project aims to develop a statistical framework to fit data stemming from Biodiversity-Ecosystem Functioning experiments that is consistent with the Generalized Lotka-Volterra model, one of the best-studied models for population dynamics. To keep the number of free parameters sufficiently small, interactions between any two species are modeled as a function of their shared evolutionary history. In turn, this makes it possible to test the effects of phylogeny on productivity, by including the whole structure of the evolutionary tree connecting all members of an ecological community, rather than summary statistics as in current practice. By moving beyond the linear regression approaches traditionally used to model these data, the project will bridge the gap between theory of population dynamics and experimental approaches. To further connect population dynamics with phylogenetic information, the project will develop models with explicit accounting for phylogeny, allowing research to investigate the effects of shared evolutionary history on the coexistence and stability of ecological communities.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
