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

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...

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Bibliographic Details
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.
Format: Article in Journal/Newspaper
Language:unknown
Published: Frontiers Media SA 2023
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Online Access:http://dx.doi.org/10.3389/fmars.2023.1150603
https://www.frontiersin.org/articles/10.3389/fmars.2023.1150603/full
Description
Summary: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.