The coastal (terrestrial) and benthic environments along the southeast Florida continental shelf show a unique biophysical succession of marine features from a highly urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a protective marine sanctuary in the southeast (i.e. Florida Keys National Marine Sanctuary). However, the establishment of a standard bio-geomorphological classification scheme for this area of coastal and benthic environments is lacking. The purpose of this study was to test the hypothesis and answer the research question of whether new parameters of integrating geomorphological components with dominant biological covers could be developed and applied across multiple remote sensing platforms for an innovative way to identify, interpret, and classify diverse coastal and benthic environments along the southeast Florida continental shelf. An ordered, manageable hierarchical classification scheme was developed to incorporate the categories of Physiographic Realm, Morphodynamic Zone, Geoform, Landform, Dominant Surface Sediment, and Dominant Biological Cover. Six different remote sensing platforms (i.e. five multi-spectral satellite image sensors and one high-resolution aerial orthoimagery) were acquired, delineated according to the new classification scheme, and compared to determine optimal formats for classifying the study area. Cognitive digital classification at a nominal scale of 1:6000 proved to be more accurate than autoclassification programs and therefore used to differentiate coastal marine environments based on spectral reflectance characteristics, such as color, tone, saturation, pattern, and texture of the seafloor topology. In addition, attribute tables were created in conjugation with interpretations to quantify and compare the spatial relationships between classificatory units. IKONOS-2 satellite imagery was determined to be the optimal platform for applying the hierarchical classification scheme. However, each remote sensing platform had beneficial properties depending on research goals, logistical restrictions, and financial support. This study concluded that a new hierarchical comprehensive classification scheme for identifying coastal marine environments along the southeast Florida continental shelf could be achieved by integrating geomorphological features with biological coverages. This newly developed scheme, which can be applied across multiple remote sensing platforms with GIS software, establishes an innovative classification protocol to be used in future research studies. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

The Last Glacial Maximum (LGM) was a period of massive range contraction for numerous taxa, including the water-dispersed mangrove species, Rhizophora mangle L. Following the LGM, R. mangle expanded poleward via propagule transport by ocean currents. In this study, we use microsatellite loci to characterize the genetic structure of nine R. mangle populations and compare potential expansion pathways that resulted in the colonization of the Florida peninsula and Caribbean islands. Results show comparatively greater genetic connectivity between the Caribbean mainland and Florida, a similar pattern between West Africa and Caribbean islands, and substantial admixture on the island of San Salvador, the Bahamas. We conclude that Florida and Caribbean island R. mangle populations were likely recolonized via different expansion pathways. Estimates of recent migration rates are low and populations are structured into three regions (Caribbean mainland, Caribbean islands, Florida). These findings provide insight for future management and conservation initiatives. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

In estuaries, like the Indian River Lagoon, mesozooplankton have a vital role in the food web by connecting trophic levels. In this study, mesozooplankton abundance and species composition were monitored weekly on the incoming and outgoing tides from September 2006 to May 2009. For the incoming tide, the mean abundance was 2298.2 mesozooplankton/m3 (+/-325.2), and for the outgoing tide the mean abundance was 1180.0 mesozooplankton/m3 (+/-153.1). The mesozooplankton abundance on the incoming tide was significantly greater than on the outgoing tide. The most abundant type of mesozooplankton was the copepod Acartia tonsa, representing 35.0% and 52.1% of the individuals on the incoming and outgoing tides respectively. Mesozooplankton abundance values were compared with environmental data obtained from the South Florida Water Management District. The strongest positive correlation was found between chlorophyll a concentrations and A. tonsa abundance, likely due to phytoplankton being the primary food source for A. tonsa. / by Miranda Hoover Kerr. / Thesis (M.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Seagrasses are important foundation species in coastal ecosystems. Genetic diversity of seagrasses can influence a number of ecological factors including, but not limited to, disturbance resistance and resilience. Seagrasses in the Indian River Lagoon (IRL), Florida are considered to be highly disturbed due to frequent events, like algal blooms, that impair water quality, reducing available light for seagrass growth. Halodule wrightii is a dominant seagrass throughout the IRL, but its genetic diversity has only been quantified in a few Gulf of Mexico and Florida Bay populations and little is known about its potential ecological consequences. I quantified the genetic variation of H. wrightii using microsatellite markers in the southern IRL to determine: (i) how disturbance history influenced genetic diversity, (ii) if morphology of clones was, in part, genetically controlled and related to disturbance history, and (iii) if genotypes showed phenotypic plasticity in response to disturbances. In the IRL, H. wrightii populations exhibited moderate genetic diversity that varied with disturbance history. The disturbance history of a population was classified by the variance in the percent occurrence of H. wrightii over a 16-year period. Genotypic richness and clonal diversity of H. wrightii increased with increasing disturbance histories. Other genetic diversity measures (e.g., allelic richness, observed heterozygosity) did not change with disturbance history. These findings suggest that impacts to seagrass coverage over time can change the genotypic composition of populations. When different genotypes of H. wrightii were grown in a common garden, differences in leaf characteristics among genotypes provided evidence that morphological trait variation is, in part, explained by genetic variance. The disturbance history of genotypes did not directly affect morphological traits. However, significant genotype x site (within disturbance history) interactions found greater variation in shoot density and below ground traits of H. wrightii genotypes from sites of intermediate disturbance history. Traits of H. wrightii were shown to be phenotypically plastic. Significant genotype x environment interactions for shoot density and height demonstrated that genotypes responded differently by increasing, decreasing, and not changing sizes in response to light reduction. Genetic diversity of H. wrightii has strong implications for ecological function in coastal communities. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

This study compared models that used remote sensing to assess salinity in Whitewater Bay. The quantitative techniques in this research allow for a less costly and quicker assessment of salinity values. Field observations and Landsat 5 TM imagery from 2003-2006 were separated into wet and dry seasons and temporally matched. Interpolation models of Inverse Distance Weighting and Kriging were compared to empirical regression models (Ordinary Least Squares and Geographically Weighted Regression - GWR) via their Root Mean Square Error. The results showed that salinity analysis is more accurate in the dry season compared with the wet season. Univariate and multivariate analysis of the Landsat bands revealed the best band combination for salinity analysis in this local area. GWR is the most conducive model for estimating salinity because field observations are not required for future predictions once the local formula is established with available satellite imagery. / by Donna Selch. / Thesis (M.A.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.