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Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada
Ricard, D.; Branton, R.M.; Clark, D.W.; Hurley, P. (2010). Extracting groundfish survey indices from the Ocean Biogeographic Information System (OBIS): an example from Fisheries and Oceans Canada. ICES J. Mar. Sci./J. Cons. int. Explor. Mer 67(4): 638-645. dx.doi.org/10.1093/icesjms/fsp275
In: ICES Journal of Marine Science. Academic Press: London. ISSN 1054-3139; e-ISSN 1095-9289, more
Peer reviewed article  

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Keyword
    Marine/Coastal
Author keywords
    Ocean Biogeographic Information System; stratified random design;temporal stock dynamics; trawl surveys

Authors  Top 
  • Ricard, D.
  • Branton, R.M.
  • Clark, D.W.
  • Hurley, P.

Abstract
    Scientific trawl surveys have been conducted in different regions of the world and by a variety of countries and agencies since the mid-1900s. Although the data are collected in a scientifically and statistically appropriate context and represent an important source of fishery-independent information for agency-specific stock assessments, their use and dissemination has often been limited to the agencies conducting the surveys. In recent years, Internet data portals such as the Ocean Biogeographic Information System have provided an arena for the wider distribution and use of marine fish data. Despite the increased accessibility of such data, their scientific acceptability has been limited by a lack of reproducibility in data analyses. We present a methodology for the computation of time-series of groundfish stock indices using publicly available trawl survey data derived from the Canadian Department of Fisheries and Oceans Maritimes region. Potential pitfalls associated with the computation of time-series are discussed and proper stratified random estimates of temporal abundance trends are compared with other methods for a selected subset of species. Also, the broader applicability of the methods for datasets collected under similar sampling designs is discussed, along with the reproducibility of the analyses and results.

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