Abstract
Investigators publish new report on Machine Learning. According to news reporting from La Crosse, Wisconsin, by NewsRx journalists, research stated, “Ecosystem state transitions can be ecologically devastating or be a restoration success. State transitions are common within aquatic systems worldwide, especially considering human-mediated changes to land use and water use.” Financial supporters for this research include Upper Mississippi River Restoration Program, administered by the U.S. Army Corps of Engineer and the U.S. Geological Survey, U.S. Army Corps of Engineers' Upper Mississippi River Restoration Program, Winona, Minnesota. The news correspondents obtained a quote from the research from U.S. Geological Survey (USGS), “We created a transferable conceptual framework to enable multiscale assessments of state resilience and early warnings of state transitions that can inform strategic restorations and avoid ecosystem collapse. The conceptual framework integrated machine learning predictions with ecosystem state concepts (e.g., state classification, gradients of vulnerability, and recovery potential leading to state transitions) and was devised to investigate possible environmental drivers. As an application of the framework, we generated prediction probabilities of submersed aquatic vegetation (SAV) presence at nearly 10,000 sites in the Upper Mississippi River (United States). Then, we used an interpretability method to explain model predictions to gain insights into possible environmental drivers and thresholds or linear responses of SAV presence and absence. Model accuracy was 89% without spatial bias. Average water depth, suspended solids, substrate, and distance to nearest SAV were the best predictors and likely environmental drivers of SAV habitat suitability. These environmental drivers exhibited nonlinear, threshold-type responses for SAV. All the results are also presented in an online dashboard to explore results at many spatial scales.”
NETL
NSTL
Wiley