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      A Deep Seamount Effect Enhanced the Vertical Connectivity of the Planktonic Community Across 1,000 m Above Summit

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          Abstract

          Seamount effects, which are generally defined as hydrographic disturbances caused by topography and nutrient enrichment and biological aggregations around seamounts, are normally observed in shallow seamounts due to limited sampling efforts in deep seamounts. However, it remains unclear how and to what extent do deep seamounts leave their imprint on planktonic communities. Herein ciliates, a representative protist group, were chosen to explore the effect of deep seamount on planktonic community. By investigating the vertical and horizontal distribution of ciliate communities around the Kocebu Guyot (summit at −1,198 m) and in nonseamount area, we revealed an obvious deep seamount effect, which enhanced the vertical mixing of ciliate communities to an extent of over 1,000 m above the summit. The vertical mixing was manifested by a strong uplift of bottom dwellers from waters deeper than 500 m and a weak uplift from the 300 m layer to the deep chlorophyll maximum (about 150 m) layer. Network analysis showed that the ciliate cooccurrence relationship around the seamount was much more complex than that in nonseamount area. Statistical analysis indicated that seamount significantly weakened the limitation that water depth posed on vertical ciliate distribution. Overall, the ciliate communities presented a much higher‐resolution record of deep seamount effects than physico‐chemical data. Deep seamount could enhance the vertical mixing of waters and cooccurrence complexity of planktonic community to the euphotic layer. Considering the wide existence of deep seamounts, such an effect may have ecological significance and enhance the cycles of matter and energy of global oceans.

          Plain Language Summary

          Seamounts are widely distributed undersea mountains. The specific topography and hydrography of seamounts directly or indirectly enrich the concentrations of particle organic matter and subsequently enhance primary production. This phenomenon is known as a seamount effect and is generally found in shallow (summit depth <200 m) and intermediate seamounts (summit depth 200–400 m). In order to find out whether and to what extent can deep seamount (summit depth >400 m) have a seamount effect on surrounding environments, we explored planktons around a deep seamount with a summit depth of about 1,200 m. We found a distinct deep seamount effect, which could enhance vertical mixing of planktons to an extent of over 1,000 m above the summit. The vertical mixing was composed of a strong uplift of benthos in waters deeper than 500 m and a weak uplift from the 300 m layer to deep chlorophyll maximum (about 150 m) layer. Such a deep seamount effect has never been documented before and may have ecological significance and enhance the cycles of matter and energy of global oceans.

          Key Points

          • A deep seamount effect upon microplankton was revealed

          • Deep seamount enhanced vertical connectivity and cooccurrence complexity of ciliate community

          • Ciliate communities presented a much higher‐resolution record of deep seamount effects than physico‐chemical data

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          Most cited references64

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          UPARSE: highly accurate OTU sequences from microbial amplicon reads.

          Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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            Is Open Access

            FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

            Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                Journal
                Journal of Geophysical Research: Oceans
                JGR Oceans
                American Geophysical Union (AGU)
                2169-9275
                2169-9291
                March 2023
                March 04 2023
                March 2023
                : 128
                : 3
                Affiliations
                [1 ] Laboratory of Marine Organism Taxonomy and Phylogeny Qingdao Key Laboratory of Marine Biodiversity and Conservation Center for Ocean Mega‐Science Institute of Oceanology Chinese Academy of Sciences Qingdao China
                [2 ] Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China
                [3 ] Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Qingdao China
                [4 ] Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic University Hung Hom China
                [5 ] State Key Laboratory of Marine Pollution City University of Hong Kong Kowloon Tong China
                [6 ] University of Chinese Academy of Sciences Beijing China
                Article
                10.1029/2022JC018898
                c59fcb46-741f-40ee-bee6-a1847d1efe0b
                © 2023

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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