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Water column nutrient concentrations and autotrophy in oligotrophic ecosystems are sensitive to eutrophication and other long-term environmental changes and disturbances. Disturbance can be defined as an event or process that changes the structure and response of an ecosystem to other environmental drivers. The role disturbance plays in regulating ecosystem functions is challenging because the effect of the disturbance can vary in magnitude, duration, and extent spatially and temporally. We measured changes in total nitrogen (TN), dissolved inorganic nutrient (DIN), total phosphorus (TP), soluble reactive phosphorus (SRP), total organic carbon (TOC), and chlorophyll-a (Chl-a) concentrations throughout the Everglades, Florida Bay, and the Florida Keys. This region has been subjected to a variety of natural and anthropogenic disturbances including tropical storms, fires, eutrophication, and rapid increases in water levels from sea-level rise and freshwater restoration. We hypothesized that the rate of change in water quality would be greatest in the coastal ecotone where disturbance frequencies and marine P concentrations are highest, and in freshwater marshes closest to hydrologic changes from restoration. We applied trend analyses on multi-decadal data (1996–2019) collected from 461 locations distributed from inland freshwater Everglades (ridge and slough) to outer marine reefs along the Florida Keys, USA. Total Organic Carbon decreased throughout the study area and was the only parameter with a systematic trend throughout the study area. All other parameters had spatially heterogeneous patterns in long-term trends. Results indicate more variability across a large spatial and temporal extent associated with changes in biogeochemical indicators and water quality conditions. Chemical and biological changes in oligotrophic ecosystems are important indicators of environmental change, and our regional ridge-to-reef assessment revealed ecosystem-specific responses to both long-term environmental changes and disturbance legacies. 

Over the last century, direct human modification has been a major driver of coastal wetland degradation, resulting in widespread losses of wetland vegetation and a transition to open water. High-resolution satellite imagery is widely available for monitoring changes in present-day wetlands; however, understanding the rates of wetland vegetation loss over the last century depends on the use of historical panchromatic aerial photographs. In this study, we compared manual image thresholding and an automated machine learning (ML) method in detecting wetland vegetation and open water from historical panchromatic photographs in the Florida Everglades, a subtropical wetland landscape. We compared the same classes delineated in the historical photographs to 2012 multispectral satellite imagery and assessed the accuracy of detecting vegetation loss over a 72 year timescale (1940 to 2012) for a range of minimum mapping units (MMUs). Overall, classification accuracies were >95% across the historical photographs and satellite imagery, regardless of the classification method and MMUs. We detected a 2.3–2.7 ha increase in open water pixels across all change maps (overall accuracies > 95%). Our analysis demonstrated that ML classification methods can be used to delineate wetland vegetation from open water in low-quality, panchromatic aerial photographs and that a combination of images with different resolutions is compatible with change detection. The study also highlights how evaluating a range of MMUs can identify the effect of scale on detection accuracy and change class estimates as well as in determining the most relevant scale of analysis for the process of interest. 

Coastal wetlands are globally important stores of carbon (C). However, accelerated sea-level rise (SLR), increased saltwater intrusion, and modified freshwater discharge can contribute to the collapse of peat marshes, converting coastal peatlands into open water. Applying results from multiple experiments from sawgrass (Cladium jamaicense)-dominated freshwater and brackish water marshes in the Florida Coastal Everglades, we developed a system-level mechanistic peat elevation model (EvPEM). We applied the model to simulate net ecosystem C balance (NECB) and peat elevation in response to elevated salinity under inundation and drought exposure. Using a mass C balance approach, we estimated net gain in C and corresponding export of aquatic fluxes ( ) in the freshwater marsh under ambient conditions (NECB = 1119 ± 229 gC m−2 year−1; FAQ = 317 ± 186 gC m−2 year−1). In contrast, the brackish water marsh exhibited substantial peat loss and aquatic C export with ambient (NECB = −366 ± 15 gC m−2 year−1; FAQ = 311 ± 30 gC m−2 year−1) and elevated salinity (NECB = −594 ± 94 gC m−2 year−1; FAQ = 729 ± 142 gC m−2 year−1) under extended exposed conditions. Further, mass balance suggests a considerable decline in soil C and corresponding elevation loss with elevated salinity and seasonal dry-down. Applying EvPEM, we developed critical marsh net primary productivity (NPP) thresholds as a function of salinity to simulate accumulating, steady-state, and collapsing peat elevations. The optimization showed that ~150–1070 gC m−2 year−1 NPP could support a stable peat elevation (elevation change ≈ SLR), with the corresponding salinity ranging from 1 to 20 ppt under increasing inundation levels. The C budgeting and modeling illustrate the impacts of saltwater intrusion, inundation, and seasonal dry-down and reduce uncertainties in understanding the fate of coastal peat wetlands with SLR and freshwater restoration. The modeling results provide management targets for hydrologic restoration based on the ecological conditions needed to reduce the vulnerability of the Everglades' peat marshes to collapse. The approach can be extended to other coastal peatlands to quantify C loss and improve understanding of the influence of the biological controls on wetland C storage changes for coastal management.