COS 32-4 - A trait-based perspective on ecosystem responses to environmental variation along elevational and latitudinal gradients using Trait Driver Theory

Tuesday, August 8, 2017: 9:00 AM
C125-126, Oregon Convention Center
Daniel J. Wieczynski1, Sandra M. Duran2, Gregory P. Asner3, Lisa Patrick Bentley4, Brad Boyle5, Vanessa R. Buzzard6, Sandra Díaz7, Brian J. Enquist8, Amanda Henderson9, Catherine Hulshof10, Andrew J. Kerkhoff11, Yadvinder Malhi12, Roberta Martin3, Sean Michaletz13, Norma Salinas14, Alexander Shenkin15, Miles Silman16, Nathan Swenson17 and Van M. Savage18, (1)Ecology & Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, (2)Ecology & Evolutionary Biology, University of Arizona, (3)Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, (4)Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, United Kingdom, (5)Ecology and Evolutionary Biology Department, University of Arizona, Tucson, AZ, (6)University of Arizona, Tucson, AZ, (7)Instituto Multidisciplinario de Biología Vegetal, Universidad Nacional de Córdoba, Córdoba, Argentina, (8)Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, (9)University of Arizona, (10)Biology, University of Puerto Rico, Mayaguez, (11)Biology Department, Kenyon College, Gambier, OH, (12)Environmental Change Institute, University of Oxford, Oxford, United Kingdom, (13)Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, (14)Sección Química, Universidad San Antonio Abad del Cusco, Lima, Peru, (15)University of Oxford, Oxford, United Kingdom, (16)Wake Forest University, Andes Biodiversity and Ecosystem Research Group, Winston-Salem, NC, (17)Department of Biology, University of Maryland, College Park, MD, (18)Department of Biomathematics, UCLA, Los Angeles, CA

Understanding the relationship between biodiversity and the dynamics, structure, and function of communities at broad spatial scales is central to ecology. Trait-based methods are expanding this understanding by mechanistically linking environmental variables to organismal growth through individual phenotypes. One recently developed approach, termed “Trait Driver Theory” (TDT), combines methods borrowed from quantitative genetics with metabolic scaling theory to investigate how environmental variables ‘drive’ shifts in the distribution of traits within communities which, in turn, ultimately influence higher-order community- and ecosystem-level processes. Focusing specifically on the shapes of trait distributions, we are able to predict ecosystem functions based on individual-level traits and generate specific hypotheses about community responses to environmental change. We apply TDT to a large dataset of tree foliar traits containing 424 field sites as well as remotely sensed LiDAR and hyperspectral data, altogether spanning elevational and latitudinal gradients throughout the world and representing a wide range of environmental and geographic variation. We describe the shapes of community-level trait distributions using central moments (mean, variance, skewness, and kurtosis), identify correlations between these moments and a suite of environmental variables, and finally, scale up from individual-level traits to estimate forest net primary productivity (NPP).


Here we report significant trends in the distributions of several foliar traits in response to shifts in environmental conditions along both elevational and latitudinal gradients. For many traits, trait means exhibit convergent trends with latitude and elevation (either positive or negative), likely due to convergent correlations between latitude/elevation and several important environmental variables (e.g., mean annual temperature, vapor pressure deficit, etc.). Trait variance (almost) always declines with both latitude and elevation. Patterns in skewness and kurtosis reveal that trait distributions are asymmetric on average, generally tending toward beta distributions with low values of kurtosis relative to skewness, potentially indicating the maintenance of phenotypic evenness within communities. Local patterns in trait skewness are consistent with expectations about community shifts toward novel environmental optima, providing evidence of whole-community responses to recent environmental warming. Finally, traits that are tightly linked to growth (e.g., specific leaf area and leaf nitrogen and phosphorus composition) exhibit complex relationships with environmental variables, resulting in both positive and negative influences on NPP along latitudinal and elevational gradients. Together, these results expose broad patterns in trait variation across space and highlight the value of using trait-based approaches to mechanistically link environmental variation to community/ecosystem processes.