EAGER-NEON: Detecting Disturbance and Ecosystem Response in Continental Observatory Networks

University Of Montana, Missoula MT

Investigators

Abstract

One of the most vital ecosystem services currently provided to society by terrestrial plants and microorganisms is the removal of approximately one-quarter of the human-generated carbon dioixide (CO2) emitted to the atmosphere. However, the future of this so-called "carbon sink" pattern at the global and national level remains uncertain. Some evidence suggests that recent disturbance possibly due to drought in the Western US has caused terrestrial ecosystems to suddenly become sources of carbon to the atmosphere, rather than carbon sinks. As we enter an era of carbon accountability it is imperative that we are able to more accurately account for ecosystem carbon production and consumption across North America. For this study, computer simulation in conjunction with newly acquired data from NSF's NEON observing network will be synthesized, for the first time, to evaluate the NEON data, and better understand how such large-scale environmental disturbance affects the carbon balance of the continent. The project will also involve training at the postdoctoral and graduate student levels, as well as development of active learning experiences that will broaden the participation of undergraduates from underrepresented groups in science. The ultimate goal of this project is to identify possible disturbance mechanisms that have caused a continental scale shift in ecosystems across the US from being a net carbon sink to a net carbon source. In this project we will use satellite data to identify spatial and temporal fingerprints associated with different ecosystem disturbance mechanisms (e.g. insect infestation, drought, fire) and use these fingerprints to simulate the random and mechanistic processes associated with different disturbance mechanisms in land vegetation models. Newly acquired NEON observation data will be used to benchmark these land vegetation simulations and simulations will be used to optimize the observation network for disturbance detections. This project will advance ecosystem science in two ways. First, new algorithms for the optimization of hierarchical observation networks from global to continental to regional scales will be developed. Second, disturbance dynamics in land surface models will be refined, and disturbance over larger spatial scales will be simulated.