SG: Synthetic analysis of the importance of species richness to ecosystem services in natural systems

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Nature provides ecosystem services that benefit humans: crops rely on pollinators, marshes protect coasts from storm surges, and forests remove carbon dioxide from the atmosphere. The world is rapidly losing biodiversity, and understanding how biodiversity loss will affect ecosystem services is important for human well-being. Most attempts to do this have used experiments in the field and laboratory. Natural communities, in which ecosystem services are provided, rarely look like experiments. Scientists acknowledge this, but it has been hard to study the effect of biodiversity loss on ecosystem services in the real world, in large part because of limitations in the analysis of complex natural communities. In this project, the researchers will use a new method that overcomes previous limitations to analyze some of the world's largest data sets, with the goal of understanding what aspects of natural communities are needed for ecosystem services. For example, is it enough to have many individuals of a few species, or do we need many different species to provide ecosystem services? This research will provide society benefits through a better understanding of how biodiversity influences ecosystem function, which could have relevance for science policy. Additionally, data and coding from the project will be made available to the public. Knowledge of what drives ecosystem services at large scales is rudimentary, and the widespread belief that species richness is necessary for ecosystem services in natural systems is more of an assumption than an established fact. This project will use a novel and powerful analytical approach, based on the Price equation from evolutionary biology, to determine the importance of biodiversity to multiple ecosystem services. Specifically, the Price equation partitions differences in ecosystem services into three additive terms: richness, composition, and context dependence (which includes abundance variation as well as environmental factors). The PIs will compile the largest and most complete publicly-available data sets, and use them to conduct a synthetic analysis. The research will determine: (1) the relative importance of richness, composition, and context dependence in producing the mean levels and temporal variance of ecosystem services, (2) the role of spatial and temporal scale in ecosystem functions, and (3) the extent to which data from the worlds' largest biodiversity-function experiments align with results from ecosystems.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.