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Surficial Sediments of the Eastern Bering Sea Continental Shelf: EBSSED-2 Database Documentation
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Description:Sediment grain size is a fundamental property of benthic marine habitats. Its frequency distribution affects basic physical characteristics of the seafloor such as porosity, permeability, and compaction, as well as plant and animal distributions. Previous studies have reported spatial variation of sediment texture on the eastern Bering Sea (EBS) continental shelf. Many of these studies are limited to specific locations, while others characterize larger areas of the continental shelf by averaging relatively sparse data. The original studies vary in the analytical and descriptive methods used to characterize the sediment samples. The Eastern Bering Sea sediment (EBSSED) database documented here combines the original point data (n = 13,874) in a consistent manner to provide a comprehensive resource for a great variety of research on seafloor habitats of the EBS shelf. The database represents sediment variation over the study area with uncompromised (i.e., original) spatial detail. Two main types of textural data are included: 1) standardized statistics characterizing the grain size distribution of samples with % composition (e.g., gravel, sand, mud) and size-distribution parameters (e.g., mean size) and 2) sample descriptions from more subjective visual/tactile observations establishing size-grade constitutions. Two descriptive fields are added to characterize sample grain size distribution by a single, standardized variable based on the original data. These fields classify samples according to gravel-sand-mud composition using low and high-resolution schemes. The low-resolution scheme (7 classes) is designed to allow unambiguous classifications of nearly all samples (n = 13,742) including those with subjective visual/tactile descriptions. It represents the maximum number of samples according to a single common variable, providing the most spatially detailed data for the study area. The highiii resolution scheme classifies (1,458) samples with detailed granulometric data into 15 textural classes, providing greater detail regarding textural variation.
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