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Large scale patterns of marine diatom richness: Drivers and trends in a changing ocean
doi: 10.1111/geb.13161
handle: 11588/853246
Large scale patterns of marine diatom richness: Drivers and trends in a changing ocean
AbstractAimPlankton diversity is a pivotal element of marine ecosystem stability and functioning. A major obstacle in the assessment of diversity is the lack of consistency between patterns assessed by molecular and morphological data. This work aims to reconcile the two in a single richness measure, to investigate the environmental drivers affecting this measure, and finally to predict its spatio‐temporal patterns.Location and time periodThis is a global scale study, based on data collected within the 2009–2013 interval during the Tara Oceans expedition.Major taxa studiedThe focus of this study is diatoms. They play an important role in several biogeochemical cycles and within marine food webs, and display high taxonomic and functional richness.MethodsWe integrate measures of diatom richness across the global ocean using molecular and morphological approaches, giving particular attention to ‘the rare biosphere’. We then perform a machine‐learning‐based analysis of these reconciled patterns to extrapolate diatom richness at the global scale and to identify the main environmental processes governing it. Finally, we model the response of diatom richness to climate change.ResultsBy filtering out 0.3% of the rarest operational taxonomic units, molecular‐based richness patterns show the best possible match with the morphological approach. Temperature, phosphate, chlorophyll a and the Lyapunov exponent are the major explainers of these reconciled patterns. Global scale predictions provide a first approximation of the global geography of diatom richness and of the possible impacts of climate change.Main conclusionsOur models suggest that diatom richness is controlled by different processes characteristic of distinct environmental scenarios: lateral mixing in highly dynamic regions, and both nutrient availability and temperature elsewhere. We present herein the effects of these processes on richness and how these same effects differ from other diversity indices because of the main component of richness: the rare biosphere.
Microsoft Academic Graph classification: Scale (ratio) Ecology Marine diatom Oceanography Environmental science Species richness
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Global and Planetary Change, Ecology, diatoms; diversity; machine learning; metabarcoding; microscopy; richness; Tara Oceans, Tara Oceans, richne, diatom, diversity, machine learning, metabarcoding, microscopy, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Ecology, Evolution, Behavior and Systematics
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Global and Planetary Change, Ecology, diatoms; diversity; machine learning; metabarcoding; microscopy; richness; Tara Oceans, Tara Oceans, richne, diatom, diversity, machine learning, metabarcoding, microscopy, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Ecology, Evolution, Behavior and Systematics
Microsoft Academic Graph classification: Scale (ratio) Ecology Marine diatom Oceanography Environmental science Species richness
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