Aim We present a model to account for self-assembly of the slough-ridge-tree island patterned landscape of the central Everglades in southern Florida via feedbacks among landforms, hydrology, vegetation and biogeochemistry. We test aspects of this model by analysing vegetation composition in relation to local and landscape-level drivers.Location We quantified vegetation composition and environmental characteristics in central Water Conservation Area (WCA) 3A, southern WCA-3A and southern WCA-3B in southern Florida, based on their divergence in water management and flow regimes over the past 50 years. MethodsIn 562 quadrats, we estimated species coverages and quantified maximum, minimum and average water depth, soil depth to bedrock, normalized difference vegetation index (NDVI) and proximity to the nearest tree island. We used non-metric multi-dimensional scaling (NMS) to relate compositional variation to local and landscape-level factors, and evaluated environmental differences among eight a priori vegetation types via  .Results Water depth and hydroperiod decreased from sloughs to ridges to tree islands, but regions also differed significantly in the abundance of several community types and the hydroregimes characterizing them. NMS revealed two significant axes of compositional variation, tied to local gradients of water depth and correlated factors, and to a landscape-scale gradient of proximity to tall tree islands. Sawgrass height and soil thickness increased toward higher ridges, and NDVI was greatest on tree islands. Main conclusionsThis study supports four components of our model: positive feedback of local substrate height on itself, woody plant invasion and subsequent P transport and concentration by top predators nesting on taller tree islands, compositional shifts in sites close to tree islands due to nutrient leakage, and flow-induced feedback against total raised area. Regional divergence in the relationship of community types to current hydroregimes appears to reflect a lag of a few years after shifts in water management; a longer lag would be expected for shifts in landscape patterning. Both local and landscape-level drivers appear to shape vegetation composition and soil thickness in the central Everglades.

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, groundelevation modelling, and water-surface modelling that provides scientists and water managers with current (2000-present), on-line water-level and water-depth information for the freshwater Everglades. Continuous daily spatial interpolations of surface water-level gage data from the EDEN water-surface model are presented on grid with 400-m spacing. The direct model output is continuous daily surface-water level, and other hydrologic data such as water depth and hydroperiod can be derived together with ground digital elevation models.This paper validated the spatially continuous EDEN water-surface model for the Everglades, Florida by using an independent field-measured dataset. Three model applications were also demonstrated: to estimate site-specific ground elevation, to create water-depth time series for tree islands, and to generate contiguous water coverage areas. We found that there were no statistically significant differences between model-predicted and field-observed water-level data in central Everglades (p D 0Ð51). Over 95% of the predicted-water levels matched observed-water levels within the range of š5 cm. Overall, the model is reliable by a root mean square error (RMSE) of 3Ð3 cm.The accurate, high-resolution hydrological data, generated over broad spatial and temporal scales by the EDEN watersurface model, provides a previously missing key to understanding the habitat requirements and linkages among native and invasive populations, including fish, wildlife, wading birds, and plants. The EDEN model is a powerful tool that could be adapted for other ecosystem-scale restoration and management programs worldwide.