Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions

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. 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. The habitat suitability model outputs and prediction explanations from many spatial scales (4 m to 400 km of river reach) can inform research and restoration planning. Métodos de aprendizaje automático para evaluar la vulnerabilidad y el potencial de restauración de las transiciones de estados de los ecosistemas Resumen Las transiciones de estado de los ecosistemas pueden tener consecuencias ecológicas graves o ser un éxito de restauración. Estas transiciones son comunes en los sistemas acuáticos, especialmente si consideramos los cambios mediados por humanos en el uso del suelo y del agua. Generamos un marco conceptual transferible para permitir las evaluaciones multiescalares de la resiliencia del estado y las alertas tempranas de la transición de estado que sirvan para guiar las restauraciones estratégicas y evitar el colapso del ecosistema. Este marco integró predicciones de aprendizaje automático a los conceptos de estado del ecosistema (p. ej.: clasificación del estado, gradientes de vulnerabilidad y pote