If you’re using XSHR data, you might want to compile the ShoreZone data into tables. These tables include Unit, Bio-Band, Cross-Shore, and Habitat-Class identifiers. Then you can combine the data with other information to build a more complete picture of your site’s environment. Below, we’ll discuss the details of these tables. You can also download them as CSV files.
The XShr table is a synthesis of biological information for each across-shore zone. Biobands, or biota band assemblages, are recorded for each zone. Bands grow in typical across-shore elevations at characteristic wave energies and substrate conditions. The width of each band codes the bioband’s VER (vector energy rate).
The database contains the bioband codes and physical description. It also provides information on protected areas and non-floating large browns. A bioband can be coded based on patchiness, continuous coverage, or width. If it has a ruffled appearance, it is classified as a kelp. Sessile blades, on the other hand, have a dusty appearance.
XShr BioUnit Table contains biological information on each across-shore zone. The BioBand Table contains the assemblages of biota that grow in a characteristic across-shore elevation and substrate conditions. Biobands are coded according to the width. In this way, we can compare the composition of the same zone with different biota. We can also compare the same zone with different XShr BioUnit Tables and compare their properties.
The Cross-Shore Table presents a numerical model for determining sediment transport along a coastline. It uses a nondimensional surf zone to normalize cross-shore position and incorporates two distinct nearshore regions: the shoaling zone and the surf zone. The shape function is based on sediment transport characteristics and is affected by hydrodynamic processes. The nondimensional model has its limitations, particularly when considering complex morphology and the influence of wave action on cross-shore sediment transport.
The physical oceanographic data was processed to calculate current velocities, and alongshore was defined as 20degW of true north. The distribution of biological and physical data was then made into contour plots using Noesys Transform with Kriging gridding and interpolation. These contours were typically reasonably accurate close to shore, but they do not represent the entire offshore environment. Therefore, station spacing increased as the distance offshore increased.
The cross-shore flow of sediment is governed by the velocity moments of the waves. In general, the amount of sediment transported is proportional to the velocity moments. To parameterize this, the shape function is used. The shape function also incorporates effects of cross-shore mean flows, short waves, and infragravity waves, as well as their interactions. Ultimately, the shape function describes sediment transport processes along a shoreline.
The normalized velocity moments reflect three distinct nearshore zones: the shoaling zone, the surf zone, and the wetting and drying region. The difference in cross-shore sediment transport between these regions may have implications for profile development, sandbar generation, and sediment flux. If these variables are considered in combination, the Cross-Shore Table may be a useful tool in estimating sediment transport along a shoreline.
The FAO-LCCS scheme assigns classes based on land-cover maps. Although this scheme is more robust against habitat description complexity, it is not optimal for biodiversity conservation studies, as it does not directly relate to the habitat of a species. In contrast, the XSHR scheme uses a habitat-class table based on the FAO-LCCS scheme. It is important to note, however, that the XSHR-based habitat-class table does not have the same drawbacks.
One advantage of the data-driven approach over the expert-knowledge translation method is that it provides a more flexible way to interpret AOH maps. For example, the data-driven method allows for the definition of the habitat-class association as a continuous variable, which means that the results are more reproducible. Moreover, this method can be applied to any set of habitat-class codes and land-cover classes, regardless of the source data.