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Layer: Biophysical_Units_L4A (ID: 3)

Name: Biophysical_Units_L4A

Display Field: Biophysic

Type: Feature Layer

Geometry Type: esriGeometryPolygon

Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Biophysical units represent distinct physiographic and oceanographic conditions/processes, including bathymetry, related to biotic composition if data are available or evidence in the literature.Rubidge et al (2016) determined biophysical units using species composition cluster analysis and multivariate analysis of environmental data.</SPAN></P><P><SPAN>Under the Pacific Marine Ecological Classification System (PMECS; DFO 2016; Rubidge et al. 2016), biophysical units are areas of distinct physiographic and oceanographic conditions and processes that shape species composition at spatial extents of 1000s of km. Rubidge et al. (2016) used a two-step process to identify biophysical units in British Columbia. First, a cluster analysis based on the similarity of species composition was used to group sites with similar species into distinct biological assemblages. Second, a random forest analysis was used to identify environmental correlates of the biological assemblages identified by the cluster analysis and to predict and assign the biological assemblage present in areas with too few biological data. Indicator species for each assemblage (biophysical unit) were also identified.</SPAN></P><P><SPAN>Three large-scale biophysical units were identified: shelf, slope and banks. Full methods are available in Rubidge et al. 2016.</SPAN></P><P><SPAN>Two nearshore grid cells were removed from the published version of the biophysical units for the analyses carried out in the NSB Marine Protected Area Network planning process.</SPAN></P><P><SPAN>Uncertainty - Although the biophysical units are presented as polygons that suggest hard spatial boundaries and a clear distinction between adjacent units, the boundaries between the units should be considered to be transition zones where the biological and environmental conditions are changing over gradients. Rubidge et al. (2016) show that uncertainty in the random forest model is highest at the boundaries between units, particularly at the southern boundary of Dogfish Bank, around the Other Banks unit, and running along the length of the boundary between the Shelf and Slope units. The model also performs poorly in the Strait of Juan de Fuca, possibly because the influences of complex local currents, and because eddies in the area are not accurately captured in the broad-scale environmental data used to predict the biophysical units.</SPAN></P><P><SPAN>References</SPAN></P><P><SPAN>DFO. 2016. Evaluation of Hierarchical Marine Ecological Classification Systems for Pacific and Maritimes Regions. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2016/003.</SPAN></P><P><SPAN>Rubidge, E., Gale, K.S.P., Curtis, J.M.R., McClelland, E., Feyrer, L., Bodtker, K., and Robb, C. 2016. Methodology of the Pacific Marine Ecological Classification System and its application to the Northern and Southern Shelf Bioregions. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/035: xi + 124 p.</SPAN></P></DIV></DIV></DIV>

Service Item Id: d274fdd3065c4af98b3d0f099cfbf9d3

Copyright Text: Rubidge, E., Gale, K.S.P., Curtis, J.M.R., McClelland, E., Feyrer, L., Bodtker, K., and Robb, C. 2016. Methodology of the Pacific Marine Ecological Classification System and its application to the Northern and Southern Shelf Bioregions. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/035: xi + 124 p. DFO Science, Pacific Region

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