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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. 

Hurricanes are recurring high-energy disturbances in coastal regions that change community structure and function of mangrove wetlands. However, most of the studies assessing hurricane impacts on mangroves have focused on negative effects without considering the positive influence of hurricane-induced sediment deposition and associated nutrient fertilization on mangrove productivity and resilience. Here, we quantified how Hurricane Irma influenced soil nutrient pools, vertical accretion, and plant phosphorus (P) uptake after its passage across the Florida Coastal Everglades in September 2017. Vertical accretion from Irma’s deposits was 6.7 to 14.4 times greater than the long-term (100 y) annual accretion rate (0.27 ± 0.04 cm y⁻¹). Storm deposits extended up to 10-km inland from the Gulf of Mexico. Total P (TP) inputs were highest at the mouth of estuaries, with P concentration double that of underlying surface (top 10 cm) soils (0.19 ± 0.02 mg cm⁻³). This P deposition contributed 49 to 98% to the soil nutrient pool. As a result, all mangrove species showed a significant increase in litter foliar TP and soil porewater inorganic P concentrations in early 2018, 3 mo after Irma’s impact, thus underscoring the interspecies differences in nutrient uptake. Mean TP loading rates were five times greater in southwestern (94 ± 13 kg ha⁻¹d⁻¹) mangrove-dominated estuaries compared to the southeastern region, highlighting the positive role of hurricanes as a natural fertilization mechanism influencing forest productivity. P-rich, mineral sediments deposited by hurricanes create legacies that facilitate rapid forest recovery, stimulation of peat soil development, and resilience to sea-level rise.