Published December 31, 2019 | Version v1
Journal article Open

A framework for the development of a global standardised marine taxon reference image database (SMarTaR-ID) to support image-based analyses

Description

Video and image data are regularly used in the field of benthic ecology to document biodiversity.
However, their use is subject to a number of challenges, principally the identification
of taxa within the images without associated physical specimens. The challenge of applying
traditional taxonomic keys to the identification of fauna from images has led to the development
of personal, group, or institution level reference image catalogues of operational taxonomic
units (OTUs) or morphospecies. Lack of standardisation among these reference
catalogues has led to problems with observer bias and the inability to combine datasets
across studies. In addition, lack of a common reference standard is stifling efforts in the
application of artificial intelligence to taxon identification. Using the North Atlantic deep sea
as a case study, we propose a database structure to facilitate standardisation of morphospecies
image catalogues between research groups and support future use in multiple frontend
applications. We also propose a framework for coordination of international efforts to
develop reference guides for the identification of marine species from images. The proposed
structure maps to the Darwin Core standard to allow integration with existing databases. We
suggest a management framework where high-level taxonomic groups are curated by a
regional team, consisting of both end users and taxonomic experts. We identify a mechanism
by which overall quality of data within a common reference guide could be raised over
the next decade. Finally, we discuss the role of a common reference standard in advancing
marine ecology and supporting sustainable use of this ecosystem.

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Additional details

Funding

European Commission
ATLAS - A Trans-AtLantic Assessment and deep-water ecosystem-based Spatial management plan for Europe 678760