A dataset on trophic modes of aquatic protists

关于水生原生生物营养模式的数据集

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Abstract

BACKGROUND: An important functional trait of organisms is their trophic mode. It determines their position within food webs, as well as their function within an ecosystem. For the better part of the 20(th) century, aquatic protist communities were thought to consist mainly of producers (phytoplankton) and consumers (protozooplankton). Phytoplankton cover their energy requirements through photosynthesis (phototrophy), while protozooplankton graze on prey and organic particles (phagotrophy). However, over the past decades, it was shown that another trophic group (mixoplankton) comprise a notable part of aquatic protist communities. Mixoplankton employ a third trophic mode by combining phototrophy and phagotrophy (mixotrophy). Due to the historical dichotomy, it is not straightforward to gain adequate and correct information on the trophic mode of aquatic protists. Long hours of literature research or expert knowledge are needed to correctly assign trophic modes. Additionally, aquatic protists also have a long history of undergoing taxonomic changes which make it difficult to compare past and present literature. While WoRMS, the World Register of Marine Species, keeps track of the taxonomic changes and assigns each species a unique AphiaID that can be linked to its various historic and present taxonomic hierarchy, there is currently no machine-readable database to query aquatic protists for their trophic modes. NEW INFORMATION: This paper describes a dataset that was submitted to WoRMS and links aquatic protist taxa, with a focus on marine taxa, to their AphiaID and their trophic mode. The bulk of the data used for this dataset stems from (routine) monitoring stations in the North Sea and the Baltic Sea. The data were augmented and checked against state-of-the-art knowledge on mixoplankton taxa by consulting literature and experts. Thus, this dataset provides a first attempt to make the trophic mode of aquatic protists easily accessible in both a human- and machine-readable format.

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