An ecological characterization of Salt River Bay National Historical Park and Ecological Preserve, U.S. Virgin Islands

Salt River Bay National Historical Park and Ecological Preserve (hereafter, SARI or the park) was created in 1992 to preserve, protect, and interpret nationally significant natural, historical, and cultural resources (United States Congress 1992). The diverse ecosystem within it includes a large mangrove forest, a submarine canyon, coral reefs, seagrass beds, coastal forests, and many other natural and developed landscape elements. These ecosystem components are, in turn, utilized by a great diversity of flora and fauna. A comprehensive spatial inventory of these ecosystems is required for successful management. To meet this need, the National Oceanic and Atmospheric Administration (NOAA) Biogeography Program, in consultation with the National Park Service (NPS) and the Government of the Virgin Islands Department of Planning and Natural Resources (VIDPNR), conducted an ecological characterization. The characterization consists of three complementary components: a text report, digital habitat maps, and a collection of historical aerial photographs. This ecological characterization provides managers with a suite of tools that, when coupled with the excellent pre-existing body of work on SARI resources, enables improved research and monitoring activities within the park (see Appendix F for a list of data products).

Fish Habitat Utilization Patterns and Evaluation of the Efficacy of Marine Protected Areas in Hawaii: Integration of NOAA Digital Benthic Habitat Mapping and Coral Reef Ecological Studies

Over the past four decades, the state of Hawaii has developed a system of eleven Marine Life Conservation Districts (MLCDs) to conserve and replenish marine resources around the state. Initially established to provide opportunities for public interaction with the marine environment, these MLCDs vary in size, habitat quality, and management regimes, providing an excellent opportunity to test hypotheses concerning marine protected area (MPA) design and function using multiple discreet sampling units. NOAA/NOS/NCCOS/Center for Coastal Monitoring and Assessment’s Biogeography Team developed digital benthic habitat maps for all MLCD and adjacent habitats. These maps were used to evaluate the efficacy of existing MLCDs for biodiversity conservation and fisheries replenishment, using a spatially explicit stratified random sampling design. Coupling the distribution of habitats and species habitat affinities using GIS technology elucidates species habitat utilization patterns at scales that are commensurate with ecosystem processes and is useful in defining essential fish habitat and biologically relevant boundaries for MPAs. Analysis of benthic cover validated the a priori classification of habitat types and provided justification for using these habitat strata to conduct stratified random sampling and analyses of fish habitat utilization patterns. Results showed that the abundance and distribution of species and assemblages exhibited strong correlations with habitat types. Fish assemblages in the colonized and uncolonized hardbottom habitats were found to be most similar among all of the habitat types. Much of the macroalgae habitat sampled was macroalgae growing on hard substrate, and as a result showed similarities with the other hardbottom assemblages. The fish assemblages in the sand habitats were highly variable but distinct from the other habitat types. Management regime also played an important role in the abundance and distribution of fish assemblages. MLCDs had higher values for most fish assemblage characteristics (e.g. biomass, size, diversity) compared with adjacent fished areas and Fisheries Management Areas (FMAs) across all habitat types. In addition, apex predators and other targeted resources species were more abundant and larger in the MLCDs, illustrating the effectiveness of these closures in conserving fish populations. Habitat complexity, quality, size and level of protection from fishing were important determinates of MLCD effectiveness with respect to their associated fish assemblages. (PDF contains 217 pages)

Automated, objective texture segmentation of multibeam echosounder data - Seafloor survey and substrate maps from James Island to Ozette Lake, Washington Outer Coast

Without knowledge of basic seafloor characteristics, the ability to address any number of critical marine and/or coastal management issues is diminished. For example, management and conservation of essential fish habitat (EFH), a requirement mandated by federally guided fishery management plans (FMPs), requires among other things a description of habitats for federally managed species. Although the list of attributes important to habitat are numerous, the ability to efficiently and effectively describe many, and especially at the scales required, does not exist with the tools currently available. However, several characteristics of seafloor morphology are readily obtainable at multiple scales and can serve as useful descriptors of habitat. Recent advancements in acoustic technology, such as multibeam echosounding (MBES), can provide remote indication of surficial sediment properties such as texture, hardness, or roughness, and further permit highly detailed renderings of seafloor morphology. With acoustic-based surveys providing a relatively efficient method for data acquisition, there exists a need for efficient and reproducible automated segmentation routines to process the data. Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and through a contracted seafloor survey, we expanded on the techniques of Cutter et al. (2003) to describe an objective repeatable process that uses parameterized local Fourier histogram (LFH) texture features to automate segmentation of surficial sediments from acoustic imagery using a maximum likelihood decision rule. Sonar signatures and classification performance were evaluated using video imagery obtained from a towed camera sled. Segmented raster images were converted to polygon features and attributed using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) for use in a geographical information system (GIS). (PDF contains 41 pages.)