| Abstract | Designing a representative network of high seas marine protected areas (MPAs) requires an acceptable
scheme to classify the benthic (as well as the pelagic) bioregions of the oceans. Given the lack of
sufficient biological information to accomplish this task, we used a multivariate statistical method with 6
biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved
oxygen and bottom temperature) to objectively classify the ocean floor into 53,713 separate
polygons comprising 11 different categories, that we have termed ‘‘seascapes’’. A cross-check of the
seascape classification was carried out by comparing the seascapes with existing maps of seafloor
geomorphology and seabed sediment type and by GIS analysis of the number of separate polygons,
polygon area and perimeter/area ratio. We conclude that seascapes, derived using a multivariate
statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to
contain different biological associations, in as much as different geomorphological units do the same.
Less than 20% of some seascapes occur in the high seas while other seascapes are largely confined to the
high seas, indicating specific types of environment whose protection and conservation will require
international cooperation. Our study illustrates how the identification of potential sites for high seas
marine protected areas can be accomplished by a simple GIS analysis of seafloor geomorphic and
seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were
generated in which heterogeneity hotspots identify themselves as MPA candidates. The use of computeraided
mapping tools removes subjectivity in the MPA design process and provides greater confidence to
stakeholders that an unbiased result has been achieved.
This analysis is based on six maps of numeric data for the world
ocean, namely: (1) ocean water depth from ETOPO-2 [23] 2-min
(w3.7 km) spatial resolution bathymetry model; (2) seafloor slope
(Fig. 2), derived from the same bathymetry model; (3) net ocean
primary productivity (Fig. 3), which is a 9 km spatial resolution
model derived from Seawifs satellite image analysis; (4) total
sediment thickness (Fig. 4) of the world ocean, 5-min (w9.3 km)
spatial resolution model (from Divins; [24]); (5) ocean bottom
water temperature (Fig. 5) gridded at 1 degree (w100 km) from the
NOAAWorld Ocean Atlas (2005); and (6) ocean bottom water dissolved
oxygen (Fig. 6) also from the NOAAWorld Ocean Atlas [25].
In addition, maps of global seabed geomorphic classes (Fig. 7) and
bottom sediment type (Fig. 8; [46]) provide non-numeric information
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