Data‐Driven Modeling of the Distribution of Diazotrophs in the Global Ocean

Type Article
Date 2019-11
Language English
Author(s) Tang Weiyi1, Cassar Nicolas1, 2
Affiliation(s) 1 : Division of Earth and Ocean Sciences, Nicholas School of the EnvironmentDuke University Durham NC USA
2 : Laboratoire des Sciences de l'Environnement Marin, UMR 6539 UBO/CNRS/IRD/IFREMER Institut Universitaire Européen de la Mer Brest, France
Source Geophysical Research Letters (0094-8276) (American Geophysical Union (AGU)), 2019-11 , Vol. 46 , N. 21 , P. 12258-12269
DOI 10.1029/2019GL084376
WOS© Times Cited 34
Keyword(s) diazotrophs, marine nitrogen fixation, meta-analysis, machine learning

Diazotrophs play a critical role in the biogeochemical cycling of nitrogen, carbon, and other elements in the global ocean. Despite their well‐recognized role, the diversity, abundance, and distribution of diazotrophs in the world's ocean remain poorly characterized largely due to limited observations. Here we update the database of diazotroph nifH gene abundances and assess how environmental factors may regulate diazotrophs at the global scale. Our meta‐analysis more than doubles the number of observations in the previous database. Using linear and nonlinear regressions, we find that the abundances of Trichodesmium, UCYN‐A, UCYN‐B, and Richelia relate differently to temperature, light, and nutrients. We further apply a random forest algorithm to estimate the global distributions of these diazotrophic groups, identifying undersampled potential hot spots of diazotrophy in the South Atlantic and southern Indian Ocean, and in coastal waters. The distinct ecophysiologies of diazotrophs highlighted here argue for separate parameterizations of different diazotrophs in model simulations.

Plain Language Summary

Microbial communities drive the cycling of critical elements like carbon and nitrogen in the ocean. By converting N2 into more bioavailable nitrogen, diazotrophs alleviate nitrogen limitation and support primary production. Despite their importance, their distributions are poorly characterized in great part due to limited observations. Here we compile from the literature observations to update the global database of marine diazotrophs. We also assess how the abundance and distribution of different types of diazotrophs at the global scale relate to environmental factors, including temperature, depth, and nutrients. Finally, we use a random forest machine learning method to predict the distribution of different types of diazotrophs in the world's ocean. Our results highlight the need for observations over broader oceanic regimes and a more granular representation of diazotrophy in models.

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