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The concept of alternate stable states is important in ecological theory and models, but the application and implementation of these models have the potential to make significant future advances in the field of patterned landscapes. The bistable ridge and slough landscape is a central feature of Everglades restoration and provides an important opportunity to test stable state theory with multistate transition models. We used these models to estimate environmental parameters associated with state changes (water depths, edaphic factors, etc.) and develop a quantitative method to measure resilience and stability. The multistate model indicates that long‐term, local hydrology (15‐yr mean maximums and 15‐yr mean amplitude) and edaphic factors control the local scale shifts between ridge and slough states. We show that multistate models can provide hydrologic envelopes for managers, produce a tool to help assess future water management scenarios, and address issues of sustainability, resilience, and restoration for any bistable system. [ABSTRACT FROM AUTHOR]

Historical vegetation data are important to ecological studies, as many structuring processes operate at long time scales, from decades to centuries. Capturing the pattern of variability within a system (enough to declare a significant change from past to present) relies on correct assumptions about the temporal scale of the processes involved. Sufficient long-term data are often lacking, and current techniques have their weaknesses. To address this concern, we constructed multistate and artificial neural network models (ANN) to provide fore- and hindcast vegetation communities considered critical foraging habitat for an endangered bird, the Florida Snail Kite (Rostrhamus sociabilis). Multistate models were not able to hindcast due to our data not satisfying a detailed balance requirement for time reversibility in Markovian dynamics. Multistate models were useful for forecasting and providing environmental variables for the ANN. Results from our ANN hindcast closely mirrored the population collapse of the Snail Kite population using only environmental data to inform the model. The parallel between the two gives us confidence in the hindcasting results and their use in future demographic models. [ABSTRACT FROM AUTHOR]