Spatial distribution and activity of viruses in the deep-sea sediments of Sagami Bay, Japan

Abstract. Although the deep-sea floor accounts for more than 70 % of the Earth's surface, there has been little progress in relation to deriving maps of seafloor sediment distribution based on transparent, repeatable and automated methods such as machine learning. A new digital map of the spatial distribution of seafloor lithologies in the deep sea below 500 m water depth is presented to address this shortcoming. The lithology map is accompanied by estimates of the probability of the most probable class, which may be interpreted as a spatially-explicit measure of confidence in the predictions, and probabilities for the occurrence of seven lithology classes (Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud). These map products were derived by the application of the Random Forest machine learning algorithm to a homogenised dataset of seafloor lithology samples and global environmental predictor variables that were selected based on the current understanding of the controls on the spatial distribution of deep-sea sediments. The overall accuracy of the lithology map is 69.5 %, with 95 % confidence limits of 67.9 % and 71.1 %. It is expected that the map products are useful for various purposes including, but not limited to, teaching, management, spatial planning, design of marine protected areas and as input for global spatial predictions of marine species distributions and seafloor sediment properties. The map products are available at https://doi.org/10.1594/PANGAEA.911692 (Diesing, 2020).

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Spatial distribution and abundance of ammonia-oxidizing microorganisms in deep-sea sediments of the Pacific Ocean

Antonie van Leeuwenhoek ◽

10.1007/s10482-015-0485-4 ◽

2015 ◽

Vol 108 (2) ◽

pp. 329-342 ◽

Cited By ~ 9

Author(s):

Zhu-Hua Luo ◽

Wei Xu ◽

Meng Li ◽

Ji-Dong Gu ◽

Tian-Hua Zhong

Keyword(s):

Spatial Distribution ◽

Pacific Ocean ◽

Deep Sea ◽

The Pacific ◽

Deep Sea Sediments ◽

The Pacific Ocean

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Deep-sea sediments of the global ocean

Earth System Science Data ◽

10.5194/essd-12-3367-2020 ◽

2020 ◽

Vol 12 (4) ◽

pp. 3367-3381

Author(s):

Markus Diesing

Keyword(s):

Machine Learning ◽

Spatial Distribution ◽

Deep Sea ◽

Learning Algorithm ◽

Marine Species ◽

Global Ocean ◽

Teaching Management ◽

Sediment Distribution ◽

Deep Sea Sediments ◽

Seafloor Sediment

Abstract. Although the deep-sea floor accounts for approximately 60 % of Earth's surface, there has been little progress in relation to deriving maps of seafloor sediment distribution based on transparent, repeatable, and automated methods such as machine learning. A new digital map of the spatial distribution of seafloor lithologies below 500 m water depth is presented to address this shortcoming. The lithology map is accompanied by estimates of the probability of the most probable class, which may be interpreted as a spatially explicit measure of confidence in the predictions, and probabilities for the occurrence of five lithology classes (calcareous sediment, clay, diatom ooze, lithogenous sediment, and radiolarian ooze). These map products were derived by the application of the random-forest machine-learning algorithm to a homogenised dataset of seafloor lithology samples and global environmental predictor variables that were selected based on the current understanding of the controls on the spatial distribution of deep-sea sediments. It is expected that the map products are useful for various purposes including, but not limited to, teaching, management, spatial planning, design of marine protected areas, and as input for global spatial predictions of marine species distributions and seafloor sediment properties. The map products are available at https://doi.org/10.1594/PANGAEA.911692 (Diesing, 2020).

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Diversity and spatial distribution ofamoA-encoding archaea in the deep-sea sediments of the tropical West Pacific Continental Margin

Journal of Applied Microbiology ◽

10.1111/j.1365-2672.2008.04109.x ◽

2009 ◽

Vol 106 (5) ◽

pp. 1482-1493 ◽

Cited By ~ 48

Author(s):

H. Dang ◽

J. Li ◽

X. Zhang ◽

T. Li ◽

F. Tian ◽

...

Keyword(s):

Spatial Distribution ◽

Continental Margin ◽

Deep Sea ◽

West Pacific ◽

Deep Sea Sediments

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PALYNOLOGICAL RECORDS ON DEEP-SEA SEDIMENTS IN THE SOUTH CHINA SEA AND ITS PALEOENVIRONMENT

Marine Geology & Quaternary Geology ◽

10.3724/sp.j.1140.2009.01025 ◽

2009 ◽

Vol 29 (1) ◽

pp. 25-30 ◽

Cited By ~ 1

Author(s):

Yulan ZHANG

Keyword(s):

South China Sea ◽

South China ◽

Deep Sea ◽

The South China Sea ◽

The South ◽

China Sea ◽

Deep Sea Sediments ◽

Palynological Records

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Temporal patterns in diversity and species composition of deep-sea benthic copepods in bathyal Sagami Bay, central Japan

Marine Biology ◽

10.1007/s00227-003-1273-7 ◽

2004 ◽

Vol 144 (6) ◽

pp. 1097-1110 ◽

Cited By ~ 20

Author(s):

Y. Shirayama ◽

H. Kitazato ◽

M. Shimanaga

Keyword(s):

Species Composition ◽

Deep Sea ◽

Temporal Patterns ◽

Sagami Bay ◽

Central Japan

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Mechanism and Influencing Factors of REY Enrichment in Deep-Sea Sediments

Minerals ◽

10.3390/min11020196 ◽

2021 ◽

Vol 11 (2) ◽

pp. 196

Author(s):

Jiangbo Ren ◽

Yan Liu ◽

Fenlian Wang ◽

Gaowen He ◽

Xiguang Deng ◽

...

Keyword(s):

Deep Sea ◽

Research Progress ◽

Bottom Current ◽

Knowledge Map ◽

P2o5 Content ◽

Deep Sea Sediments ◽

Potential Resource ◽

And Migration ◽

Adsorption Desorption ◽

Sediment Interface

Deep-sea sediments with high contents of rare-earth elements and yttrium (REY) are expected to serve as a potential resource for REY, which have recently been proved to be mainly contributed by phosphate component. Studies have shown that the carriers of REY in deep-sea sediments include aluminosilicate, Fe-Mn oxyhydroxides, and phosphate components. The ∑REY of the phosphate component is 1–2 orders of magnitude higher than those of the other two carriers, expressed as ∑REY = 0.001 × [Al2O3] − 0.002 × [MnO] + 0.056 × [P2O5] − 32. The sediment P2O5 content of 1.5% explains 89.1% of the total variance of the sediment ∑REY content. According to global data, P has a stronger positive correlation with ∑REY compared with Mn, Fe, Al, etc.; 45.5% of samples have a P2O5 content of less than 0.25%, and ∑REY of not higher than 400 ppm. The ∑REY of the phosphate component reaches n × 104 ppm, much higher than that of marine phosphorites and lower than that of REY-phosphate minerals, which are called REY-rich phosphates in this study. The results of microscopic observation and separation by grain size indicate that the REY-rich phosphate component is mainly composed of bioapatite. When ∑REY > 2000 ppm, the average CaO/P2O5 ratio of the samples is 1.55, indicating that the phosphate composition is between carbonate fluoroapatite and hydroxyfluorapatite. According to a knowledge map of sediment elements, the phosphate component is mainly composed of P, Ca, Sr, REY, Sc, U, and Th, and its chemical composition is relatively stable. The phosphate component has a negative Ce anomaly and positive Y anomaly, and a REY pattern similar to that of marine phosphorites and seawater. After the early diagenesis process (biogeochemistry, adsorption, desorption, transformation, and migration), the REY enrichment in the phosphate component is completed near the seawater/sediment interface. In the process of REY enrichment, the precipitation and enrichment of P is critical. According to current research progress, the REY enrichment is the result of comprehensive factors, including low sedimentation rate, high ∑REY of the bottom seawater, a non-carbonate depositional environment, oxidation conditions, and certain bottom current conditions.

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