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

PLoS One. 2019 Dec 31;14(12):e0218904. doi: 10.1371/journal.pone.0218904. eCollection 2019.

Abstract

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 front-end 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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Artificial Intelligence
  • Biodiversity
  • Classification / methods*
  • Data Curation / methods
  • Data Curation / standards
  • Databases, Factual
  • Ecology
  • Ecosystem
  • Image Processing, Computer-Assisted / methods
  • Image Processing, Computer-Assisted / standards*
  • Marine Biology / classification
  • Marine Biology / standards*

Grants and funding

KH, NP, RR, NF was supported by the Natural Environment Research Council funded DeepLinks project NE/K011855/1, https://nerc.ukri.org/. The workshop was funded by the Deep Sea Biology Society’s Lounsbery Workshop Award https://dsbsoc.org/. ALA and CLM are supported by Grant Number SFI/15/IA/3100 to ALA from Science Foundation Ireland http://www.sfi.ie/ and the Marine Institute https://www.marine.ie/Home/home under the Investigators Programme co-funded under the European Regional Development Fund 2014-2020, https://ec.europa.eu/regional_policy/en/funding/erdf/. AB-H was supported by the Oceanic Observatory of Madeira project (M1420-01-0145-FEDER-000001-Observatório Oceânico da Madeira- OOM) co-financed by the Madeira Regional Operational Programme (Madeira 14-20) under the Portugal 2020 strategy through the European Regional Development Fund https://ec.europa.eu/regional_policy/en/funding/erdf/, and the Portuguese Foundation for Science and Technology (FCT, Portugal) https://www.fct.pt/, through the strategic project UID/MAR/04292/2013 granted to MARE. JV is supported by Oil and Gas UK https://oilandgasuk.co.uk/ and the ATLAS project funded by the European Commission’s H2020 Scheme https://ec.europa.eu/programmes/horizon2020/en through Grant Agreement 678760. HAR was supported by the CeNCOOS Partnership: Ocean Information for Decision Makers (award number NA16NOS0120021) https://www.cencoos.org/about/program/funding. DOBJ was supported by the UK Natural Environment Research Council National Capability funding: “Climate Linked Atlantic Section Science” (CLASS), grant number NE/R015953/1 https://nerc.ukri.org/. DW was supported by NOAA Deep Sea Coral Research and Technology Program https://deepseacoraldata.noaa.gov/. LW and PS were supported by the Garfield Weston Foundation https://garfieldweston.org/. TM was supported by Program Investigador FCT (IF/01194/2013), IFCT Exploratory Project (IF/01194/2013/CP1199/CT0002) from the Fundação para a Ciência e Tecnologia (POPH and QREN) https://www.fct.pt/, PO2020 MapGes (Acores-01-0145-FEDER-000056) http://www.azores.gov.pt/Portal/en/principal/, and H2020 ATLAS (grant agreement no. 678760) https://ec.europa.eu/programmes/horizon2020/en. RV was funded by the Fundação para a Ciência e a Tecnologia (FCT/SFRH/BD/84030/2012) https://www.fct.pt/, with additional support provided by Cefas through the Science Futures programme https://www.cefas.co.uk/. JRX research is funded by the H2020 EU Framework Programme for Research and Innovation through the SponGES project (grant agreement No. 679849) https://ec.europa.eu/programmes/horizon2020/en and partially supported by the Strategic Funding UID/Multi/04423/2019 through national funds provided by the Foundation for Science and Technology (FCT) https://www.fct.pt/ and the European Regional Development Fund (ERDF) https://ec.europa.eu/regional_policy/en/funding/erdf/, in the framework of the programme PT2020. We would like to make it clear that one of the co-authors is employed by a commercial company (Gardline Limited). This company provided support in the form of salary, travel and subsistence for Michael Thompson to attend the workshop held to discuss end user needs and database structure, as well as to contribute to the preparation of the manuscript. We would also like to make clear that this commercial affiliation does not alter our adherence to all PLOS ONE policies on sharing data and materials.