Opportunities and limitations of large open biodiversity occurrence databases in the context of a Marine Ecosystem Assessment of the Southern Ocean

• Published in: Frontiers in Marine Science
• Main Authors: Bonnet-Lebrun, Anne-Sophie, Sweetlove, Maxime, Griffiths, Huw, J, Sumner, Michael, Provoost, Pieter, Raymond, Ben, Ropert-Coudert, Yan, van de Putte, Anton, P
• Other Authors: Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Royal des Sciences Naturelles de Belgique (IRSNB), British Antarctic Survey (BAS), Natural Environment Research Council (NERC), Australian Antarctic Division (AAD), Australian Government, Department of the Environment and Energy, Institute for Marine and Antarctic Studies Hobart (IMAS), University of Tasmania Hobart, Australia (UTAS), Intergovernmental Oceanographic Commission of UNESCO, Université libre de Bruxelles (ULB)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: OBIS; GBIF; sampling bias; biodiversity data; spatial bias; temporal bias; taxonomic bias; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography; Southern Ocean
• Online Access: https://hal.science/hal-04142764; https://hal.science/hal-04142764/document; https://hal.science/hal-04142764/file/BLFMS10_2023.pdf; https://doi.org/10.3389/fmars.2023.1150603

International audience The Southern Ocean is a productive and biodiverse region, but it is also threatened by anthropogenic pressures. Protecting the Southern Ocean should start with well-informed Marine Ecosystem Assessments of the Southern Ocean (MEASO) being performed, a process that will require biodiversity data. In this context, open geospatial biodiversity databases such as OBIS and GBIF provide good avenues, through aggregated geo-referenced taxon locations. However, like most aggregated databases, these might suffer from sampling biases, which may hinder their usability for a MEASO. Here, we assess the quality and distribution of OBIS and GBIF data in the context of a MEASO. We found strong spatial, temporal and taxonomic biases in these data, with several biases likely emerging from the remoteness and inaccessibility of the Southern Ocean (e.g., lack of data in the dark and ice-covered winter, most data describing charismatic or well-known taxa, and most data along ship routes between research stations and neighboring continents). Our identification of sampling biases helps us provide practical recommendations for future data collection, mobilization, and analyses.