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–...
| Published in: | ICES Journal of Marine Science |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2014
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1093/icesjms/fsu052 http://academic.oup.com/icesjms/article-pdf/71/4/899/29148731/fsu052.pdf http://academic.oup.com/icesjms/article-pdf/71/4/899/23614665/fsu052.pdf |
| 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 Bullimore et al., 2013). 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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