CAREER: Global change and the functional ecology of grasses

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Grasses are one of the most ecologically and economically important plant families. They include many staple crops such as wheat, corn, and rice, and are defining features of an entire biome. Thus, the responses of grasses to global change will strongly impact human well-being. At the same time, plants can strongly influence the rates of global change through feedback loops. For this reason, accurately forecasting climate requires understanding these feedbacks. However, these feedbacks are currently one of the largest sources of uncertainty in climate models. Physical and chemical characteristics of species, called functional traits, play a central role in predicting both how plants will respond to and influence global change. Classic ecological theory makes predictions regarding how functional traits should relate to climate and other environmental features. This project will test and extend these predictions and perform strong assessments of the predictability of trait-environment relationships. This work will use a combination of long-term monitoring, regional surveys, and analysis of global databases. The results will improve our understanding of plant responses to global change, illuminate the limits of predictability, and produce valuable data that can be used to refine global climate predictions. The project integrates educational opportunities at multiple levels, including field research experience for advanced undergraduates and data science modules developed with and deployed by local high school teachers.The research includes four component projects that range from intensive local studies to global macroecological analyses. The first will test predictions for the influence of precipitation on functional traits across space and through time, and within and among species, by building a ten-year record of grass traits in sites across the San Francisco Bay Area. In project two, the researchers will sample sites across the Western states that differ in precipitation and rate of atmospheric nitrogen deposition to examine their interacting effects on leaf chemistry. Project three will develop an allometry-based model of resource allocation that can predict grass trait variation along environmental gradients and test these predictions with new data collected by collaborators on five continents. Finally, project four will examine broad-scale within-species variation in grass traits, using machine learning tools and high-resolution range maps for more than 1000 grass species.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.