The LTAR Grazing Land Common Experiment at the Great Basin.

The Long-Term Agroecosystem Research Network (LTAR), through its Common Experiment (CE) framework, contrasts prevailing and alternative agricultural practices for efficacy and sustainability within the indicator domains of environment, productivity, economics, and society. Invasive species, wildfire, and climate change are principal threats to Great Basin agroecosystems. Prescribed grazing may be an effective tool for restoring lands degraded by these disturbances. At the Great Basin (GB) LTAR site headquartered in Boise, ID, our contribution to the CE contrasts a prevailing (PRV), cattle grazing practice of fixed moderate stocking and duration with an alternative (ALT), prescribed grazing practice called high-intensity low-frequency (HILF) grazing where stocking and duration are tailored to suppress invasive annual grass competition with native or desirable plant species and thus promote recovery of rangelands degraded by annual grass invasion and recurrent wildfire. Preliminary results indicate cheatgrass density and fuel height have been reduced in ALT-treated paddocks compared to PRV paddocks. Since its inception in 2014, our GB CE has been a research co-production effort among ranchers, public land managers, and researchers. Future directions for this research will center on expanding the experiment to multiple study areas to better address the scope of the annual grass/wildfire problem. We expect this research will lead to effective and sustainable grazing practices for restoring >41 million hectares of degraded rangelands in the Great Basin and other areas of the western United States.

Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape.

Net ecosystem exchange (NEE) is an essential climate indicator of the direction and magnitude of carbon dioxide (CO2) transfer between land surfaces and the atmosphere. Improved estimates of NEE can serve to better constrain spatiotemporal characteristics of terrestrial carbon fluxes, improve verification of land models, and advance monitoring of Earth's terrestrial ecosystems. Spatiotemporal NEE information developed by combining ground-based flux tower observations and spatiotemporal remote sensing datasets are of potential value in benchmarking land models. We apply a machine learning approach (Random Forest (RF)) to develop spatiotemporally varying NEE estimates using observations from a flux tower and several variables that can potentially be retrieved from satellite data and are related to ecosystem dynamics. Specific variables in model development include a mixture of remotely sensed (fraction of photosynthetically active radiation (fPAR), Leaf Area Index (LAI)) and ground-based data (soil moisture, downward solar radiation, precipitation and mean air temperature) in a complex landscape of the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. Predicted results show good agreement with the observed data for the NEE (r2 = 0.87). We then validate the temporal pattern of the NEE generated by the RF model for two independent years at the two sites not used in the development of the model. The model development process revealed that the most important predictors include LAI, downward solar radiation, and soil moisture. This work provides a demonstration of the potential power of machine learning methods for combining a variety of observational datasets to create spatiotemporally extensive datasets for land model verification and benchmarking.

Dynamic process connectivity explains ecohydrologic responses to rainfall pulses and drought.

Ecohydrologic fluxes within atmosphere, vegetation, and soil systems exhibit a joint variability that arises from forcing and feedback interactions. These interactions cause fluctuations to propagate between variables at many time scales. In an ecosystem, this connectivity dictates responses to climate change, land-cover change, and weather events and must be characterized to understand resilience and sensitivity. We use an information theory-based approach to quantify connectivity in the form of information flow associated with the propagation of fluctuations between variables. We apply this approach to study ecosystems that experience changes in dry-season moisture availability due to rainfall and drought conditions. We use data from two transects with flux towers located along elevation gradients and quantify redundant, synergistic, and unique flow of information between lagged sources and targets to characterize joint asynchronous time dependencies. At the Reynolds Creek Critical Zone Observatory in Idaho, a dry-season rainfall pulse leads to increased connectivity from soil and atmospheric variables to heat and carbon fluxes. At the Southern Sierra Critical Zone Observatory in California, separate sets of dominant drivers characterize two sites at which fluxes exhibit different drought responses. For both cases, our information flow-based connectivity characterizes dominant drivers and joint variability before, during, and after disturbances. This approach to gauge the responsiveness of ecosystem fluxes under multiple sources of variability furthers our understanding of complex ecohydrologic systems.