Sea turtle nesting activity along Eglin Air Force Base on Cape San Blas and Santa Rosa Island, Florida from 1994 to 1997.

Along EAFB on Cape San BIas, the only sea turtle species nest observed has been the loggerhead turtle. The first green turtle nest documented along the Florida panhandle coast was observed on EAFB property, however (D. Atencio, EAFB, pers. comm). Santa Rosa Island, located approximately 150 miles west of Cape San BIas supports a small but consistent, group of nesting green turtles (Fig. 2). Although erosion is not as severe along Santa Rosa Island as it is on Cape San BIas, and vehicular traffic is not permitted, sea turtles nesting on this barrier island must survive severe tropical storms, predation, and artificial lighting to be successful. Because this area supports a rare group of nesting green turtles and is disturbed by intense artificial lighting from Air Force missions and adjacent resort towns, continued monitoring is necessary. The sea turtle species that nest along this barrier island, and the human activities that disturb those sea turtles present unique circumstances for management ofthis area. Protection ofthe significant nesting populations of sea turtles on EAFB properties on Cape San BIas and Santa Rosa Island requires yearly monitoring of the nesting activity and the natural and human disturbances influencing the nesting females. The objectives ofthis study were to monitor sea turtle nesting along EAFB on Cape San BIas to determine number of nests and hatching success, assess disturbances, and determine proper management to ensure successful nesting and hatching.(56 page document

The Cape San Blas Ecological Study

Eglin AFB on Cape San Blas consists of approximately 250 acres located about 180 miles east of the main Eglin reservation. This area lies on the S1. Joseph peninsula, part of a dynamic barrier island chain that extends across the northern Gulf of Mexico. Due to the natural forces that formed Cape San Blas and those that maintain this area, St. Joseph Peninsula has experienced severe land form change over time (see GIS land form change maps). These changes allow for fluctuations in habitat types along Cape San Blas (see GIS land cover change maps)that influence the floral and faunal species using this area. The dynamic environment along Cape San Blasincludes flatwoods, interdunal swale, rosemary scrub, and beachfront. These habitats support a wide array of species, including several threatened and endangered species such as the loggerhead sea turtle (Caretta caretta), PipingPlover (Charadnus melodus), Least Tern (Sterna antillarum), and Bald Eagle (Haliaeetus leucocephalus). Proper management of these species and their habitats require knowledge of their abundance and distribution, and the effects disturbances have on their survival. In addition to threatened and endangered flora and fauna, Cape San Blas also supports tourists and recreationists. Although Gulf County is sparsely populated, with approximately 13,000 inhabitants throughout 578 square miles, summer tourism and heavy recreational use of beaches for fishing, crabbing, and shelling place continued and increasing pressure on the natural resources of these areas (Rupert 1991). Gulf County is also one of the few remaining counties in Florida that permits vehicular traffic on its beaches, including Cape San Blas. In addition to recreational use of these habitats;EAFB also uses the area for military missions. Air Force property on Cape San Blas is primarily used for radar tracking of flying missions over the Gulf of Mexico, although in recent years it has been used for missile launchings and other various military activities. To allow continued military and public use of Air Force property while also protecting the unique flora and fauna of the area,EAFB proposed a characterization of the resources found along Cape San Blas. A complete inventory of the physical features of the area included investigating topography, soil chemistry, hydrology, archeology, and the dynamics of land mass and land cover change over time. Various thematic layers within a geographic information system (GIS) were used to spatially portray georeferenced data. Large scale changes over time were assessed using stereo aerial photography. Vegetation transects, soil samples, elevation transects, an archeological survey, freshwater wells, and a tidal monitor were used to investigate the remaining features. (247 page document

Application of a Coupled Vegetation Competition and Groundwater Simulation Model to Study Effects of Sea Level Rise and Storm Surges on Coastal Vegetation

Global climate change poses challenges to areas such as low-lying coastal zones, where sea level rise (SLR) and storm-surge overwash events can have long-term effects on vegetation and on soil and groundwater salinities, posing risks of habitat loss critical to native species. An early warning system is urgently needed to predict and prepare for the consequences of these climate-related impacts on both the short-term dynamics of salinity in the soil and groundwater and the long-term effects on vegetation. For this purpose, the U.S. Geological Survey’s spatially explicit model of vegetation community dynamics along coastal salinity gradients (MANHAM) is integrated into the USGS groundwater model (SUTRA) to create a coupled hydrology–salinity–vegetation model, MANTRA. In MANTRA, the uptake of water by plants is modeled as a fluid mass sink term. Groundwater salinity, water saturation and vegetation biomass determine the water available for plant transpiration. Formulations and assumptions used in the coupled model are presented. MANTRA is calibrated with salinity data and vegetation pattern for a coastal area of Florida Everglades vulnerable to storm surges. A possible regime shift at that site is investigated by simulating the vegetation responses to climate variability and disturbances, including SLR and storm surges based on empirical information

AERIAL DIGITAL IMAGERY By LEONARD G. PEARLSTINE

xv CHAPTERS

Developing a Fine-Resolution Digital Elevation Model to Support Hydrological Modeling and Ecological Studies in the Northern Everglades

Accurate high-resolution terrain data are essential for hydrological modeling in lowlands. This study integrates elevation survey data and vegetation data at the point and 50 m scales to develop a fine-resolution digital elevation model (DEM) for the northern Everglades of Florida. The terrain was divided into two vertical strata (lowland and highland) based on a 50 m scale vegetation map. The DEM in high-lands was interpolated with all the survey points and later adjusted using an association between vegetation and hydroperiod (the number of days per year that land is flooded). The DEM in lowlands was interpolated with elevation surveys tagged as lowland types. The two DEMs were then combined, forming a new DEM with a 7.7 cm mean absolute validation error— a significant (2.3 cm) improvement over the previous DEM

Joint species distribution models of Everglades wading birds to inform restoration planning.

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0-1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community

Análisis de la composición de humedales a escala fina utilizando imágenes de alta resolución y características de textura

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features)

Do bioclimate variables improve performance of climate envelope models?

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models

Clasificación de comunidades de humedales espacialmente heterogéneas utilizando algoritmos de aprendizaje automático y características espectrales y texturales

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge from remotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using high spatial resolution imagery and machine learning image classification algorithms for mapping heterogeneous wetland plant communities. This study addresses this void by analyzing whether machine learning classifiers such as decision trees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedge communities using high resolution aerial imagery and image texture data in the Everglades National Park, Florida. In addition to spectral bands, the normalized difference vegetation index, and first- and second-order texture features derived from the near-infrared band were analyzed. Classifier accuracies were assessed using confusion tables and the calculated kappa coefficients of the resulting maps. The results indicated that an ANN (multilayer perceptron based on back propagation) algorithm produced a statistically significantly higher accuracy (82.04 %) than the DT (QUEST) algorithm (80.48 %) or the maximum likelihood (80.56 %) classifier (?<0.05). Findings show that using multiple window sizes provided the best results. First-order texture features also provided computational advantages and results that were not significantly different from those using second-order texture features