Research Document - 2013/064

Marine Protected Area Network Planning in the Scotian Shelf Bioregion: Offshore Data Considerations

King, M., Shackell, N., Greenlaw, M., Allard, K., Moors, H., and Fenton, D.

Abstract

Building on the preliminary Marine Protected Area (MPA) network analysis completed by Horsman et al. (2011), Fisheries and Oceans Canada (DFO) is leading a systematic approach to MPA network planning in the Scotian Shelf Bioregion. The major steps in the planning process are described in the National Framework for Canada’s Network of MPAs (Government of Canada 2011). Guidance from the Convention on Biological Diversity (CBD 2009a; COP 9 Decision IX/20, Annexes I-III) will also be carefully considered. Early steps in the planning process include setting clear conservation objectives, compiling habitat classification and Ecologically and Biologically Significant Areas (EBSAs) data, and analyzing these data to identify a set of areas that would collectively satisfy the conservation objectives of the network. This research document offers a review of the habitat classification and EBSA data layers used in Horsman et al. (2011) and identifies additional data sources to be considered in the next iteration of the MPA network analysis for the offshore component of the bioregion. The Kostylev and Hannah (2007) classifications (Scope for Growth and Natural Disturbance) and the Fader^{Footnote 1} classification (Seabed Feature) are recommended to be used as a basis for evaluating habitat representation in designing the MPA network. A data-driven approach similar to that used by Horsman et al. (2011) is recommended for the identification or refinement of offshore EBSAs. Reliable data under each of the CBD EBSA criteria should be compiled and ultimately incorporated into the next iteration of the network design analysis. It is also recommended that the Scientific Expert Opinion (SEO) and Local Ecological Knowledge (LEK) EBSAs (Doherty and Horsman 2007, Maclean et al. 2009) be re-evaluated against the CBD EBSA criteria to ensure that known significant areas are not missed in the data-driven approach.

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