Everglades (Fla)--Environmental conditions

The hydrological and topographical variation of wetlands can affect the behavior,
population growth, and local densities of aquatic species, which in turn can drive the
behavior and density dynamics of gleaning predators. Prey availability, primarily
determined by prey density and water depth in wetlands, is an important limiting factor
for nesting wading bird populations, top predators in the south Florida Everglades. The
Everglades is able to support large colonies of nesting wading birds because of the
microtopographic variation in the landscape. Some types of prey concentrate in flat,
shallow sloughs or become trapped in isolated pools as they move down from higher
elevation ridges with receding water levels. Manipulations to the hydrology and
landscape of the Everglades has negatively impacted nesting wading bird populations in
the past, and may continue to be detrimental by allowing flat, shallow sloughs to be
intersected by deep canals, a potential refuge for wading bird prey. In addition, the subtle
elevation differences between the ridge and slough landscape may be an important mechanism for increasing slough crayfish (Procambarus fallax) prey availability for the
most abundant and seemingly depth-sensitive Everglades wading bird, the White Ibis
(Eudocimus albus). I implemented a 2-year experimental study in four replicated manmade
wetlands with controlled water recession rates in order to determine the effects of
proximate deep water (akin to canals) on fish prey concentrations in the sloughs, as water
levels receded similarly to a natural Everglades dry season. I also calculated average
daily wading bird densities with game cameras (Reconyx PC800 Hyperfire) using timelapse
imagery over 60 days to determine when and where wading birds responded to
changing prey concentrations. I completed an additional observational study on White
Ibis and slough crayfish prey from the first year of data (2017). Crayfish make up the
majority of the diet for nesting White Ibis, and literature has suggested crayfish are most
abundant at slough depths much deeper than previously proposed foraging depth
limitations for White Ibis. This study specifically compared recent determinations about
crayfish movement dynamics in the ridge and slough system with White Ibis foraging
behavior and depth limits. Results from the first experimental study suggest that canals
might be an attractive refuge for relatively large prey fishes (> 3 cm SL) in sloughs, but it
is uncertain if the fencing blocked all prey fish movement. The second observational
study determined White Ibis foraging activity was primarily driven by a down-gradient
crayfish flux from ridge to slough, with the majority of foraging activity occurring at
much deeper slough depths than previously suggested water depth limitations for White
Ibis. Results from both of these studies support the importance of preserving the ridgeslough
landscape of the Everglades to sustain high prey availability for wading birds.

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).