2020 ESA Annual Meeting (August 3 - 6)

COS 117 Abstract - Testing the predictive value of phylogeny for community productivity

Zachary Miller, Carlos A. Serván, Paula Lemos-Costa, Abby Skwara and Stefano Allesina, Ecology & Evolution, University of Chicago, Chicago, IL
Background/Question/Methods

The relationship between biodiversity and ecosystem functioning is central to ecology. The quest to elucidate this relationship has motivated some of the largest experiments in ecology, and touches all corners of the field, from fundamental theory to conservation applications. In this area, the connection between phylogenetic/functional diversity and productivity has special relevance. A large body of ecological theory suggests that in competitive communities, functional similarity should be predictive of productivity. Where functional diversity is high, niche overlap, and consequently strength of competition, is expected to be lower, leading to more productive communities. Assuming some degree of niche conservatism, phylogeny should be similarly predictive. However, there is an active debate in the literature regarding the connection between phylogenetic/functional diversity and productivity. Efforts to resolve it have been stymied by the need to develop statistical models that are sufficiently powerful and tractable to discern the effects of phylogeny in noisy data, and by the difficulty of translating between molecular phylogenies and traits that are relevant for species interactions. Here, we overcome both of these issues by introducing a new statistical framework for analyzing biodiversity-ecosystem functioning data. Our framework is explicitly derived from models of population dynamics, and allows us to naturally include effects of phylogenetic relatedness. Additionally, we develop a computional approach to fitting these models that is informed by the phylogenetic topology, but is agnostic about the mapping between phylogeny and competitive traits.

Results/Conclusions

Using this new approach, we re-analyze a number of existing biodiversity-ecosystem functioning (BEF) data-sets. We use randomization techniques to compare the "true" community phylogeny with alternative tree structures, and find strong support for predictive value of phylogeny. For example, using data from the Biodiversity II experiment, one of the largest BEF experiments to date, we find support for the predictive value of the "true" phylogeny at p < 0.05 in all but 1 of 16 years for which data are available. Similar results are found for other published BEF data-sets. Our analysis suggests a need to re-visit the relationship between phylogeny and community productivity, using theoretically-motivated models for the effects of competition, and more flexible approaches to incorporating phylogeny.