HR: 0800h
AN: OS51C-1313    [Abstracts]
TI: Efficient and Statistically Valid Method of Textural Sea Floor Characterization in Benthic Habitat Mapping
AU: Kostylev, V E
EM: vkostylev@nrcan.gc.ca
AF: Geological Survey of Canada (Atlantic), Natural Resources Canada PO Box 1006, Dartmouth, NS B2Y 4A2 Canada
AU: * Orpin, A R
EM: aorpin@nrcan.gc.ca
AF: Geological Survey of Canada (Atlantic), Natural Resources Canada PO Box 1006, Dartmouth, NS B2Y 4A2 Canada
AB: The advent of multibeam bathymetric sonar technology and the thematic development of benthic habitat research have spawned renewed interest in the systematic characterization and mapping of the seafloor. This necessitates the application of reliable and accurate sea floor descriptors in combination with a robust means to statistically assess descriptor associations. Traditionally, geoscientific sea floor mapping was comprised primarily of identifying the spatial extent and relationship of geological units, broadly following chronostratigraphic conventions. Classifying seafloor sediments using geological facies may not be meaningful biologically because they incorporate temporal elements that stem from a geochronological qualifier. Textural properties of geological facies are typically reliant on the application of distribution-dependent statistics, which have been shown to be inappropriate with multimodal marine sediments. While the relationship between grain size and biota appears self-evident, there is a compelling argument that granulometric properites alone are not a determinant of species distribution or community composition. The classification process is problematic because most statistical clustering techniques will, by their very nature, form clusters which may or may not represent meaningful and discernable differences. Moreover, as habitat mapping is aimed at boundary definition, the boundaries between clusters in such cases could be based on very subtle differences, or noise (e.g. sampling bias). An independent measure of the appropriate number of groups in a dataset is required. Therefore, we examine a statistical approach pioneered by Calinski & Harabasz (C-H), which was implemented by a computer routine to work in partnership with information entropy analysis of grain size data. We utilize a 30-year legacy of grain size data collected from the Scotian Shelf, Canadian Atlantic continental margin, and show that considerable improvements in textural zonation are obtained using a combined entropy-C-H technique, and these new zones better match known benthic divisions. The statistical validity of the clustering outcomes is tested at each step of the classification and the effects of data reduction resulting from archiving or data processing is examined.
DE: 4804 Benthic processes/benthos
DE: 4219 Continental shelf processes
DE: 3022 Marine sediments--processes and transport
DE: 3099 General or miscellaneous
DE: 0400 Biogeosciences
SC: Ocean Sciences [OS]
MN: 2004 AGU Fall Meeting