WEMo (Wave Exposure Model): Formulation, Procedures and Validation

This report describes the working of National Centers for Coastal Ocean Service (NCCOS) Wave Exposure Model (WEMo) capable of predicting the exposure of a site in estuarine and closed water to local wind generated waves. WEMo works in two different modes: the Representative Wave Energy (RWE) mode calculates the exposure using physical parameters like wave energy and wave height, while the Relative Exposure Index (REI) empirically calculates exposure as a unitless index. Detailed working of the model in both modes and their procedures are described along with a few sample runs. WEMo model output in RWE mode (wave height and wave energy) is compared against data collected from wave sensors near Harkers Island, North Carolina for validation purposes. Computed results agreed well with the wave sensors data indicating that WEMo can be an effective tool in predicting local wave energy in closed estuarine environments. (PDF contains 31 pages)

Classification of Coastal Communities Reporting Commercial Fish Landings in the U.S. Northeast Region: Developing and Testing a Methodology

The National Marine Fisheries Service is required by law to conduct social impact assessments of communities impacted by fishery management plans. To facilitate this process, we developed a technique for grouping communities based on common sociocultural attributes. Multivariate data reduction techniques (e.g. principal component analyses, cluster analyses) were used to classify Northeast U.S. fishing communities based on census and fisheries data. The comparisons indicate that the clusters represent real groupings that can be verified with the profiles. We then selected communities representative of different values on these multivariate dimensions for in-depth analysis. The derived clusters are then compared based on more detailed data from fishing community profiles. Ground-truthing (e.g. visiting the communities and collecting primary information) a sample of communities from three clusters (two overlapping geographically) indicates that the more remote techniques are sufficient for typing the communities for further in-depth analyses. The in-depth analyses provide additional important information which we contend is representative of all communities within the cluster.

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value.