MRA: Resolving the multi-scale drivers of tree mortality from field and remote sensing data on co-located ForestGEO-NEON sites

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Tree mortality is on the rise globally. However, patterns and causes of mortality are poorly understood. Because forests are key locations for terrestrial biodiversity and contribute significantly to terrestrial carbon storage, a better understanding of tree mortality is important. A forest can lose biomass, and thus carbon, quickly through tree death, but typically gain carbon only slowly through growth. Thus, understanding the causes of tree mortality will contribute to a better understanding of carbon cycling. Big disturbances like fires or insect outbreaks can kill many trees at once, but most trees do not die from big disturbances. In contrast, most trees die alone, slowly over years for reasons that are not always obvious. The goal of this work is to determine the rate at which trees are dying annually, and most importantly for what reasons. Using these data tools to better detect future trends in tree mortality and forest dynamics will be built. This information will be conveyed to forest managers, policy makers, and other stakeholders. The data and products from this project will be broadly disseminated and integrated within grade 4-12 education modules. The project will also engage undergraduate researchers in all aspects of the research including introducing them to participants of national and international research networks. Data from two national research networks will be combined to answer questions regarding how, where, and why trees are dying. The researchers will perform annual mortality surveys on tens of thousands of trees at five large-area forest research plots that are part of the Smithsonian Institution’s ForestGEO network (https://forestgeo.si.edu/). All these ForestGEO plots are located on National Ecological Observation Network (NEON) sites (https://www.neonscience.org/) where a suite of ecological data, including air-borne remote sensing, is collected on a regular schedule. Detailed ground-truth data from the ForestGEO plots will be combined with high-resolution hyperspectral and lidar drone-based remote sensing data collected at each site. These data will be used to model causes and factors associated with tree mortality. Using NEON remote sensing data, the model of tree mortality will be upscaled to the NEON domain. Lidar and imagery will be used to identify structural changes in forests that indicate tree death. Models will then be up-scaled further using satellite data collected by NASA to make mortality estimates across the larger landscape. The results of these data collection efforts will advance understanding of tree mortality, the pace of forest change, and the dynamics of terrestrial carbon storage. The project will help advance macrosystems biology by pinpointing how the patterns of tree mortality scale from an individual tree to the entire landscape.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.