A global consistent database of plankton and detritus from in situ imaging by the Underwater Vision Profiler 5

Abstract

Plankton and detritus are essential components of the Earth's oceans influencing biogeochemical cycles and carbon sequestration. Climate change impacts their composition and fluxes, and marine ecosystems as a whole. To improve our understanding of these changes, standardized observation methods and integrated global datasets are needed to enhance the accuracy of ecological and climate models. Here, we present a global dataset for plankton and detritus obtained by two versions (SD and HD) of the Underwater Vision Profiler 5 (UVP5). This release contains the images classified in 33 homogenized categories, as well as the metadata associated with them, reaching 3114 profiles and ca. 8 million objects acquired between 2008–2018 at global scale. The geographical distribution of the dataset is unbalanced, with the Equatorial region (30° S–30° N) being the most represented, followed by the high latitudes in the northern hemisphere and lastly the high latitudes in the Southern Hemisphere. Detritus is the most abundant category in terms of concentration (90 %) and biovolume (95 %), although its classification in different morphotypes is still not well established. Copepoda was the most abundant planktonic taxon, followed by Trichodesmium colonies. The two versions of UVP5 have different imagers, resulting in a different effective size range to analyze plankton and detritus from the images (HD objects > 600 µm, SD objects > 1 mm) and morphological properties (grey levels, etc.); however, both systems capture comparable qualitative patterns in object morphology and relative abundance across size classes, even though the absolute ranges differ. Therefore, recommendations are provided for the appropriate use of this data when conducting studies. A large number of images of plankton and detritus will be collected in the future by the UVP5, and the public availability of this dataset will help it being utilized as a training set for machine learning and being improved by the scientific community. This will reduce uncertainty by identifying previously unclassified objects and expand the classification categories, ultimately enhancing biodiversity quantification. The dataset that constitutes this first release is available on SEANOE at https://doi.org/10.17882/107583 (Nocera et al., 2025).