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TitleDelineation of significant benthic areas in eastern Canada using kernel density analysis and species distribution models
DownloadDownload (whole publication)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorKenchington, E; Beazley, L; Lirette, C; Murillo, F J; Guijarro, J
SourceProgram and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada; by Todd, B JORCID logo; Brown, C J; Lacharité, M; Gazzola, V; McCormack, E; Geological Survey of Canada, Open File 8295, 2017 p. 64, Open Access logo Open Access
LinksGeoHab 2017
PublisherNatural Resources Canada
Meeting2017 GeoHab: Marine Geological and Biological Habitat Mapping; Dartmouth, NS; CA; May 1-4, 2017
Documentopen file
Mediaon-line; digital
RelatedThis publication is contained in Program and abstracts: 2017 GeoHab Conference, Dartmouth, Nova Scotia, Canada
File formatpdf
ProvinceEastern offshore region; New Brunswick; Newfoundland and Labrador; Northern offshore region; Nova Scotia; Prince Edward Island; Quebec
NTS1; 2; 3; 10; 11; 12; 13; 14; 15; 20; 21; 22; 24; 25
Lat/Long WENS -72.0000 -48.0000 61.0000 40.0000
Subjectsmapping techniques; oceanography; marine environments; coastal studies; conservation; marine organisms; marine ecology; resource management; biological communities; environmental studies; ecosystems; benthos; modelling; Corals; Sponges; Biology; Fisheries; Fisheries management; Fisheries policy; Fisheries resources
ProgramOffshore Geoscience
Released2017 09 26
AbstractThe Canadian Policy for Managing the Impact of Fishing on Sensitive Benthic Areas developed by the Department of Fisheries and Oceans Canada (DFO) in 2009 defines Significant Benthic Areas in DFO's Ecological Risk Assessment Framework as "significant areas of cold-water corals and sponge dominated communities".
Kernel density estimation (KDE) was applied to research vessel trawl survey data to create modelled biomass surfaces for corals and sponges. From these, an aerial expansion method was applied to identify significant concentrations of these taxa across eastern Canada. The borders of the areas so identified were refined using species distribution models that predict species presence-absence and/or biomass, both incorporating environmental data. We present such predictive models produced using a random forest (RF) machine-learning technique. A suite of between 54 and 78 environmental predictor variables from different data sources were used. Occurrence models performed well in general with cross-validated AUC (Area Under the Receiver Operating Characteristic Curve) values over 0.8 in most of the cases. Biomass models provided diverse results depending of the taxa and region studied. The biomass models were compared with Generalized Additive Models (GAM), which produced comparable results to random forest, although the fewer assumptions required for RF made this method more convenient.
These results have been used to identify significant concentrations of corals and sponges in eastern Canada, an essential first step in the identification of Sensitive Benthic Areas to ensure Canadian fisheries are conducted in a manner that supports marine conservation and sustainable resource use within and outside Canada's 200 nautical mile exclusive economic zone.
Summary(Plain Language Summary, not published)
The sixteenth annual GeoHab Conference was held this year (2017) at the Waterfront Campus of the Nova Scotia Community College in Dartmouth, Nova Scotia, Canada.

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