OOS 54-2
Forecasting the forest and the trees: Consequences of competition and climate for biodiversity change

Wednesday, August 12, 2015: 1:50 PM
336, Baltimore Convention Center
James S. Clark, Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC
Brian Beckage, Plant Biology, The University of Vermont, Burlington, VT
Aaron Berdanier, Nicholas School of the Environment, Duke University, Durham, NC
Michael Dietze, Earth and Environment, Boston University, Boston, MA
Christopher M. Gough, Department of Biology, Virginia Commonwealth University, Richmond, VA
Brady Hardiman, Boston University
Matthew Kwit, Duke University
Jacqueline Mohan, Odum School of Ecology, University of Georgia, Athens, GA
Scott M. Pearson, Department of Biology, Mars Hill University
William J. Platt, Louisiana State University
Amanda Schwantes, Nicholas School of the Environment, Duke University, Durham, NC
Bijan Seyednasrollah, Nicholas School of the Environment, Duke University, Durham, NC
Bradley J. Tomasek, University Program in Ecology, Duke University, Durham, NC
Christopher W. Woodall, Northern Research Station, USDA Forest Service, Saint Paul, MN
Peter H. Wyckoff, Biology Discipline, University of Minnesota, Morris, Morris, MN
Kai Zhu, Department of Global Ecology, Carnegie Institution for Science, Stanford, CA

Models that translate individual tree responses to distribution and abundance of competing populations are needed to understand forest vulnerability to climate change.  These are individual-to-biodiversity connections.  Currently, biodiversity predictions rely on one scale or the other, but do not combine them.  Synthesis is accomplished here by modeling data together, each with their respective scale-dependent connections to the scale needed for prediction—landscape to regional biodiversity.  The process model is equivalently expressed as a partial differential equation or a projection model (matrix or integral projection). The approach we summarize integrates three scales, i) individual growth, reproduction, and survival, ii) size-species structure of stands, and iii) regional forest biomass. Data include 24,347 USDA Forest Inventory and Analysis (FIA) plots and 135 Long-term Forest Demography plots.  Climate, soil moisture, and competitive interactions are predictors.  We infer and predict the four-dimensional size/species/space/time (SSST) structure of forests, where all demographic rates respond to winter temperature, growing season length, moisture deficits, local moisture status, and competition.


There is substantial variation in demographic responses to climate and competition, and relationships vary geographically across the subcontinent.  Variation in growth dynamics with respect to the environment is the strongest predictor of distributions and abundances, stronger even than Species Distribution Models that are fitted directly to species distribution data.  Responses to soil moisture are highly non-linear and not strongly related to responses to climatic moisture deficits over time.  For example, in the Southeast the species that are most sensitive to moisture on dry sites are not the same as those that are most sensitive on moist sites.  Those that respond most to spatial moisture gradients are not the same as those that respond most to regional moisture deficits. Responses to winter temperature vs growing seasons vs moisture deficits do not follow ‘moist-to-wet’ or ‘cold-to-warm’ syndromes that can define general functional types.  There is little evidence of simple tradeoffs in responses. Direct responses to climate constrain the ranges of few tree species, north or south; there is little evidence that range limits are defined by fecundity or survival responses to climate. By contrast, recruitment and the interactions between competition and climate that affect growth and survival are predicted to limit ranges of many species.  Taken together, results suggest a rich interaction involving demographic responses at all size classes to neighbors, landscape variation in moisture, and regional climate change.