Quality assurance in the identification of deep-sea taxa from video and image analysis: response to Henry and Roberts

New high-resolution image data obtained from the Hebrides Terrace Seamount and analysed by ourselves and Henry and Roberts (Henry, L-A., and Roberts, J. M. Recommendations for best practice in deep-sea habitat classification: Bullimore et al. as a case study. ICES Journal of Marine Science, 71: 895–...

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Bibliographic Details
Published in:ICES Journal of Marine Science
Main Authors: Howell, Kerry L., Bullimore, Ross D., Foster, Nicola L.
Format: Text
Language:English
Published: Oxford University Press 2014
Subjects:
Reply
Rockall Bank
Hatton-Rockall Basin
Anton Dohrn Seamount
George Bligh Bank
Hebrides Terrace Seamount
Lophelia pertusa
Northeast Atlantic
Online Access:http://icesjms.oxfordjournals.org/cgi/content/short/71/4/899
https://doi.org/10.1093/icesjms/fsu052
Description
Summary:New high-resolution image data obtained from the Hebrides Terrace Seamount and analysed by ourselves and Henry and Roberts (Henry, L-A., and Roberts, J. M. Recommendations for best practice in deep-sea habitat classification: Bullimore et al. as a case study. ICES Journal of Marine Science, 71: 895–898.), suggested that we may have misidentified Solenosmilia variabilis as either Lophelia pertusa or Madrepora oculata in a previously analysed dataset from the Anton Dohrn Seamount (published in <cross-ref type="bib" refid="FSU052C3">Bullimore et al. , 2013</cross-ref>). Therefore, we undertook a reanalysis of our entire image data holdings from multiple sample sites and identified possible records of S. variabilis from four sites previously sampled: Anton Dohrn Seamount, Rockall Bank, George Bligh Bank and the Hatton-Rockall Basin. The reanalysis of our image data holdings together with historic data from the wider literature suggests that, in the Northeast Atlantic region, S. variabilis is distributed from 888–2803 m (mean ∼1500 m) with reef habitat present only on Anton Dohrn Seamount. In this paper we discuss the use of video and imagery as a survey and monitoring too and make recommendations of best practice in data acquisition and analysis. We highlight the need for the development of training materials for deep-sea field identification in order to achieve reliable, replicable and comparable datasets among observers, and suggest possible quality assurance procedures.

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