It is increasingly likely that predictions of decadal climate change and land use change will yield the accurate information needed to anticipate ecosystem adaptation to human-induced change (e.g. climate variability, land use change). It is therefore essential that we develop new theories, modeling tools, and data products that are capable of predicting ecosystem adaptation to these changes, and that can anticipate how possible nonlinear thresholds will affect ecosystem structure, function, and services. A new theoretical concept is proposed to measure and predict ecosystem sensitivity and likely adaptation. This award will provide funds to develop complex-systems theoretical approach based on information flow in observed ecological process networks, using data from NEON and existing observational networks, which will be applied to, (1) predict nonlinear transition thresholds in the multiscalar couplings between local and regional ecosystem processes by observing feedback couplings in observed ecosystem process networks, and (2) directly measure the current sensitivity of regional ecosystems in the USA to incremental changes in specific climate variables. Data from existing observational networks (e.g. FLUXNET, LTER, NEON), and the National Phenology Network (NPN) will be used to predict the sensitivity of local and regional ecosystems across the USA to specific types of forcings. This new theoretical approach is able to directly quantify ecosystem sensitivity to changes in forcings, and how nonlinear feedback patterns can help predict possible ecosystem transition thresholds.
Broader Impacts: The proposed work will create and widely disseminate a set of tools and data products that will allow ecologists across the USA to apply process network theory and information theoretic approaches to analyze ecosystem-scale environmental observatory data. This conceptual approach will be a valuable contribution to the "data analysis toolbox" being used by environmental observatories to explain ecological change and its impact on human societies for which these ecosystems represent vital life support. Graduate students and a postdoctoral scholar will be trained in the creation and application of these advanced quantitative methods in ecology and in the synthetic analysis of multi-scalar ecosystem data. The products of the theory-based models will form a critical analytical bridge between raw data collection and the environmental understanding necessary for informed resource management and policy-making in a future driven by historically unprecedented and nonlinear climate change impacts.
