Over drainage due to water management practices, abundance of native and rare species, and low-lying topography makes the coastal Everglades especially vulnerable to Sea-Level Rise (SLR). Water depths have shown to have a significant relationship to vegetation community composition and organization while also playing a crucial role in vegetation health throughout the Everglades. Modeling potential habitat change and loss caused by increased water depths due to SLR requires better vertical Root Mean Square Error (RMSE) and resolution Digital Elevation Models (DEMs) and Water Table Elevation Models (WTEMs). In this study, an object-based machine learning approach was developed to correct LiDAR elevation data by integrating LiDAR point data, aerial imagery, Real Time Kinematic (RTK)-Global Positioning Systems (GPS) and total station survey data. Four machine learning modeling techniques were compared with the commonly used bias-corrected technique, including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Artificial Neural Network (ANN). The k-NN and RF models produced the best predictions for the Nine Mile and Flamingo study areas (RMSE = 0.08 m and 0.10 m, respectively). This study also examined four interpolation-based methods along with the RF, SVM and k-NN machine learning techniques for generating WTEMs. The RF models achieved the best results for the dry season (RMSE = 0.06 m) and the wet season (RMSE = 0.07 m) WTEMs. Previous research in Water Depth Model (WDM) generation in the Everglades focused on a conventional-based approach where a DEM is subtracted from a WTEM. This study extends the conventional-based WDM approach to a rigorous-based WDM technique where Monte Carlo simulation is used to propagate probability distributions through the proposed SLR depth model using uncertainties in the RF-based LiDAR DEM and WTEMs, vertical datums and transformations, regional SLR and soil accretion rates. It is concluded that a more rigorous-based WDM technique increases the integrity of derived products used to support and guide coastal restoration managers and planners concerned with habitat change under the challenge of SLR. Future research will be dedicated to the extension of this technique to model both increased water depths and saltwater intrusion due to SLR (saltwater inundation). / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Shallow water availability coupled with anthropogenic degradation of seagrass beds limits wading bird food resources in dynamic coastal ecosystems. Identifying prey species critical to wading bird reproductive success and the environmental drivers of key prey species abundance is important for understanding how environmental stressors influence prey and change the quality of foraging patches. Little Blue Herons (Egretta caerulea) are reportedly generalists eating insects, crustaceans, and fish; however, the proportions of prey items in the diet may shift spatially and temporally from freshwater to marine systems during breeding and non-breeding periods. I investigated prey selection by Little Blue Herons in Florida at the Great White Heron National Wildlife Refuge and the western Florida Bay, during 2016 and 2017 breeding seasons by investigating prey availability at low-tide locations along mudflats compared to stomach regurgitate samples collected from Little Blue Heron chicks 1 to 4 weeks old. Little Blue Herons selected Gulf toadfish (Opsanus beta) and prawns (Farfantepenaeus spp.) from the estuarine environment, but also consumed terrestrial prey (e.g. tree crabs) suggesting Little Blue Heron foraging habitat is not restricted to tidal flats. Additionally, these results support the characterization of Little Blue Herons as a generalist. After identifying important prey species, I modeled the associations of selected prey species with submerged aquatic vegetation density and abiotic variables to better understand habitat preferences and important habitat characteristics that drive prey density. Models support total seagrass density and algal density as having the greatest effect on prey selected by Little Blue Herons. Prawn density has a strong positive association with seagrass density. Gulf toadfish (Opsanus beta) and prawns (Farfantepenaeus spp.) had strong positive association with algae while pipefish (Syngnathidae) had a strong negative association with algae suggesting algae density in seagrass meadows should be considered when assessing the quality of seagrass meadows for Little Blue Heron prey and habitat suitability. My results varied from previous studies where prawns and gulf toadfish were associated with specific seagrass species. Therefore, some Little Blue Heron prey species in south Florida may not be affected by changes in submerged aquatic vegetation community composition if submerged aquatic vegetation densities remain constant. Studies are needed that clarify the complex interactions between prey and specific habitat metrics to validate the strength of landscape scale drivers of wading bird prey densities in dynamic coastal ecosystems and to determine how these communities will respond to anthropogenic environmental change. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Global climate change stressors downscale to specific local vulnerabilities, requiring customized adaptation strategies. Southeast Florida has a high likelihood of sealevel rise impact to due to the low-lying porous limestone geology. High risk is coupled with high exposure due to high-valued coastal properties, productive ecosystems, and dense populations. Coastal populations are particularly at risk due to erosion, inundation and storm surge, but interior populations are also susceptible to rising water tables and extended periods of inundation. All of these impacts are amplified by sea-level rise. Robust sea-level rise adaptation options require significant economic costs. If perceived risk does not adequately line up with actual risk, lack of funds and preparation will prevent implementation of the most effective strategies. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection