We propose to demonstrate a Proof of Concept of a transformative 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. 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. We propose to develop a transformational tool to measure and predict ecosystem adaptation. Our novel modeling tool will use eddy covariance flux tower data from existing observational networks (e.g. FLUXNET, LTER, NEON, and National Phenology Network), paired with ecosystem phenology data, to predict the sensitivity of local and regional ecosystems across the USA to specific types of incremental and threshold change. Our Proof of Concept will apply process network theory to the emerging NEON data and intrastructure, increasing both the scientific impact of NEON and its societal relevance.
|Effective start/end date||3/1/13 → 9/30/16|
- NSF: Directorate for Biological Sciences (BIO): $263,695.00