CAREER: Predicting plant functional trait variation across spatial, temporal and biological scales

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Variability is an inherent property of life on Earth. As a result, understanding the causes and consequences of variability is central for understanding the nature of life itself. As mean temperatures increase around the world, many areas are simultaneously experiencing increasing climatic variability. Yet, there is no theory that predicts the variability of natural systems. This research will develop a model for predicting the variability of plant function across scales of biological organization, from organisms to ecosystems. This research will focus on plant functional traits (morphological, physiological, and phenological characteristics) and environmental variability because functional traits link plant performance to ecosystem processes. Further, gradients of environmental variability are ubiquitous in nature across all spatial and temporal scales, and underlie prominent ecological and evolutionary hypotheses. In addition to developing new theories and generating new data essential for predicting species responses to increasing climatic variability, this project will address a national training need in data literacy, data science, and working with different scientific disciplines. To do so, undergraduate course content and training modules will be designed following open science principles. Course content will leverage open biological and environmental data produced by NSF investments in research and infrastructure, including the National Ecological Observatory Network (NEON), the Long-Term Ecological Research (LTER) network, Integrated Digitized Biocollections (iDigBio), and the Global Biodiversity Information Facility (GBIF). Additionally, this project will support the training and professional development of students from historically underrepresented groups in science, contributing to a diverse, skilled, and innovative STEM workforce. Specifically, this research will quantify emergent properties of functional trait variation by decomposing trait-trait and trait-environment relationships; testing the environmental heterogeneity and climatic variability hypotheses; and linking trait variation at the organismal scale to patterns of species distributions. At a continental scale, this project will leverage data from NEON sites across latitude encompassing the eastern United States and Puerto Rico to quantify plant trait variation across spatial, temporal and biological scales. This project will also characterize scaling between plant functional trait variation, environmental heterogeneity, and climatic variability across temperate and tropical mountains, where abiotic gradients in temperature and precipitation are steep in space and time. Measurements of plant abundance, growth, functional traits, and satellite observations of vegetation indices will form one of the few explicit tests of the environmental heterogeneity and the climatic variability hypotheses across spatiotemporal scales. Ultimately, this research will resolve major disparities in the measurement, quantification, and modeling of functional trait variation.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.