NSF Award
2307196
Drylands (arid and semi-arid ecosystems) cover nearly half the world’s land surface and are socioeconomically critical, globally supporting a third of the human population and more than half the livestock. Drylands also play a dominant role in global cycles of nutrients and carbon. Decomposition of dead plant material such as leaves and branches is a key biological process that affects the availability of nutrients to plants and the cycling of carbon between the biosphere and the atmosphere. Scientific understanding of decomposition in drylands is limited relative to wetter ecosystems, and appears to be affected by mechanisms uniquely important to these systems, such as solar radiation and short periods of moisture availability. In addition, drylands are characterized by extreme variation in environmental conditions through space and time, but knowledge is currently insufficient to characterize this variability sufficiently to develop predictive decomposition models. This project will reveal a quantitative understanding of dryland decomposition from small to large spatial scales, ultimately building a model to predict decomposition across scales. It will do so by leveraging data and resources of the National Ecological Observatory Network (NEON). This will substantially advance predictive capability for cycling of nutrients and carbon over the vast drylands of North America. This project will support several educational initiatives, including a course module where art and science majors collaborate to develop skills for visual communication of scientific ideas. A successful educational outreach platform, the Interactive Model of Leaf Decomposition, will be expanded to encompass drylands. Drylands support billions of people and represent large unknowns in forecasts of future carbon cycling and climate. This work will advance understanding of ecological processes in drylands, which is critical for informed land management decisions in the face of environmental change.<br/><br/>A central challenge to developing an improved predictive understanding of dryland ecosystem function is that decomposition is often measured in locations not representative of where decaying organic material resides. Extreme spatial heterogeneity in drylands exacerbates the scaling challenges of quantifying such a microbial-controlled, macrosystem process. Coarse-scale averaging of environmental controls may fail to capture critical small-scale patterns and processes regulating decomposition. Available decomposition models typically do not capture cross-scale drivers and environmental heterogeneity. To address this knowledge gap, this project will develop a quantitative understanding of dryland decomposition that scales from the microsite to the North American dryland region, by joining field, remote sensing, and a hierarchical continuum of models in a spatially-nested approach that leverages the power of NEON. The project will develop a process understanding of the environmental controls over decomposition across microsites using field and controlled environment studies to formulate a microbial explicit model of decomposition. The project will capture the spatial variation of decaying organic material distribution, environmental conditions, and decomposition at dryland NEON sites. These data will validate a microbial explicit model and inform a reduced complexity model operating at larger spatial scales. Regional scaling of decaying organic material pools will be based on hierarchically-nested spatial scales of remotely-sensed imagery to characterize microsite distributions from four NEON focal sites to the North American dryland region. This explicit hierarchically-next hierarchical-nested model will be able to propagate the fine scale distribution of drivers to coarse scale emergent behavior via a process level understanding of the system. This integrated, system-orientated research that will significantly improve understanding and prediction of litter decomposition at spatial scales ranging from the microsite to the North American drylands region. The project will also provide cross-disciplinary career development opportunities for a diverse group of undergraduate, graduate, and postdoctoral scientists.<br/><br/>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.
