2020 ESA Annual Meeting (August 3 - 6)

COS 107 Abstract - Using fine-scale variation in ecosystem properties to detect peat collapse in the Florida Coastal Everglades

Lukas Lamb-Wotton1, Tiffany G. Troxler2, Stephen Davis3, Daniel Gann4, Sparkle Malone5, Paulo C. Olivas5, David Rudnick6 and Fred H. Sklar7, (1)Department of Biological Sciences, Florida International University, Miami, FL, (2)Southeast Environmental Research Center, Florida International University, Miami, FL, (3)Science Department, Everglades Foundation, Palmetto Bay, FL, (4)GIS and Remote Sensing Center, Florida International University, Miami, FL, (5)Biological Sciences, Florida International University, Miami, FL, (6)Everglades National Park, (7)Everglades Systems Assessment Section, South Florida Water Management District, West Palm Beach, FL
Background/Question/Methods

One of the most important threats to coastal communities is sea level rise (SLR). Within the Florida Coastal Everglades, SLR is pushing saltwater further inland and contributing to a process known as “peat collapse.” Peat collapse has been observed in coastal Everglades sawgrass peat marshes, resulting in rapid declines of soil surface elevation and coastal carbon storage capacity, and has the potential to hinder inland transgression of mangrove forests. While our mechanistic understanding of peat collapse is strong though field and outdoor lab mesocosms experiments, we lack robust characterizations of peat collapse at the landscape level. Therefore, the objective of this study is to evaluate how fine-scale variation in ecosystem properties: soil surface elevation (SSE), soil depth, water depth, and porewater salinity, are associated with the conversion of vegetated marsh to open-water to produce landscape scale patterns in peat collapse. We used GNSS equipment in a collapsing sawgrass marsh to conduct real-time kinematic surveys to measure SSE along three transects across three vegetation classes: sawgrass, submerged aquatic, and open-water. At each SSE survey point, we also measured soil depth, water depth, and porewater salinity.

Results/Conclusions

Kernel density estimates revealed multi-modality across parameters when grouped by vegetation class while ANOVA and Tukey Post-hoc tests indicate significant pairwise differences for all parameters measured, except for pairwise differences in soil depth. Principle component analysis also revealed tight and strong separation of sawgrass and open water clusters along PC1 (87.1% variance explained), with submerged aquatic bridging the two. These results indicate peat collapse in the FCE leaves a detectable signature on the landscape, leading to abrupt transitions on the landscape, resulting in multiple stable states within brackish sawgrass marshes.