Articles | Volume 15, issue 23
https://doi.org/10.5194/bg-15-7347-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-15-7347-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Dec 2018
Research article |
| 13 Dec 2018
| 13 Dec 2018
Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning
GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstrasse 1–3,
24148 Kiel, Germany
Timm Schoening
GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstrasse 1–3,
24148 Kiel, Germany
Evangelos Alevizos
GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstrasse 1–3,
24148 Kiel, Germany
Jens Greinert
GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstrasse 1–3,
24148 Kiel, Germany
Christian Albrechts University Kiel, Institute of Geosciences,
Ludewig-Meyn-Str. 10–12, 24098 Kiel, Germany
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Kaveh Purkiani, Matthias Haeckel, Sabine Haalboom, Katja Schmidt, Peter Urban, Iason-Zois Gazis, Henko de Stigter, André Paul, Maren Walter, and Annemiek Vink
Ocean Sci., 18, 1163–1181, https://doi.org/10.5194/os-18-1163-2022, https://doi.org/10.5194/os-18-1163-2022, 2022
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Based on altimetry data and in situ hydrographic observations, the impacts of an anticyclone mesoscale eddy (large rotating body of water) on the seawater characteristics were investigated during a research campaign. The particular eddy presents significant anomalies on the seawater properties at 1500 m. The potential role of eddies in the seafloor and its consequential effect on the altered dispersion of mining-related sediment plumes are important to assess future mining operations.
Kaveh Purkiani, Matthias Haeckel, Sabine Haalboom, Katja Schmidt, Peter Urban, Iason-Zois Gazis, Henko de Stigter, André Paul, Maren Walter, and Annemiek Vink
Ocean Sci., 18, 1163–1181, https://doi.org/10.5194/os-18-1163-2022, https://doi.org/10.5194/os-18-1163-2022, 2022
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Based on altimetry data and in situ hydrographic observations, the impacts of an anticyclone mesoscale eddy (large rotating body of water) on the seawater characteristics were investigated during a research campaign. The particular eddy presents significant anomalies on the seawater properties at 1500 m. The potential role of eddies in the seafloor and its consequential effect on the altered dispersion of mining-related sediment plumes are important to assess future mining operations.
Wei Chen, Joanna Staneva, Sebastian Grayek, Johannes Schulz-Stellenfleth, and Jens Greinert
Nat. Hazards Earth Syst. Sci., 22, 1683–1698, https://doi.org/10.5194/nhess-22-1683-2022, https://doi.org/10.5194/nhess-22-1683-2022, 2022
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This study links the occurrence and persistence of density stratification in the southern North Sea to the increased number of extreme marine heat waves. The study further identified the role of the cold spells at the early stage of a year to the intensity of thermal stratification in summer. In a broader context, the research will have fundamental significance for further discussion of the secondary effects of heat wave events, such as in ecosystems, fisheries, and sediment dynamics.
Timm Schoening, Autun Purser, Daniel Langenkämper, Inken Suck, James Taylor, Daphne Cuvelier, Lidia Lins, Erik Simon-Lledó, Yann Marcon, Daniel O. B. Jones, Tim Nattkemper, Kevin Köser, Martin Zurowietz, Jens Greinert, and Jose Gomes-Pereira
Biogeosciences, 17, 3115–3133, https://doi.org/10.5194/bg-17-3115-2020, https://doi.org/10.5194/bg-17-3115-2020, 2020
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Seafloor imaging is widely used in marine science and industry to explore and monitor areas of interest. The selection of the most appropriate imaging gear and deployment strategy depends on the target application. This paper compares imaging platforms like autonomous vehicles or towed camera frames and different deployment strategies of those in assessing the megafauna abundance of polymetallic-nodule fields. The deep-sea mining industry needs that information for robust impact monitoring.
Florian Gausepohl, Anne Hennke, Timm Schoening, Kevin Köser, and Jens Greinert
Biogeosciences, 17, 1463–1493, https://doi.org/10.5194/bg-17-1463-2020, https://doi.org/10.5194/bg-17-1463-2020, 2020
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In the course of former German environmental impact studies associated with manganese-nodule mining, the DISCOL experiment was conducted in 1989 in the Peru Basin. The disturbance tracks created by a plough harrow in the area are still apparent and could be located by high-resolution mapping techniques. The analysis presented in this study reveals the age sequence and the temporal change of the tracks which facilitates more detailed sample interpretations within the area.
Jeffrey C. Drazen, Astrid B. Leitner, Sage Morningstar, Yann Marcon, Jens Greinert, and Autun Purser
Biogeosciences, 16, 3133–3146, https://doi.org/10.5194/bg-16-3133-2019, https://doi.org/10.5194/bg-16-3133-2019, 2019
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We investigated the fish and scavenger community after a deep seafloor disturbance experiment intended to simulate the effects of deep-sea mining. Fish density returned to background levels after several years; however the dominant fish was rarely found in ploughed habitat after 26 years. Given the significantly larger scale of industrial mining, these results could translate to population-level effects. The abyssal fish community at the site was similar to that in the Clarion–Clipperton Zone.
Anne Peukert, Timm Schoening, Evangelos Alevizos, Kevin Köser, Tom Kwasnitschka, and Jens Greinert
Biogeosciences, 15, 2525–2549, https://doi.org/10.5194/bg-15-2525-2018, https://doi.org/10.5194/bg-15-2525-2018, 2018
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Manganese nodules are a deep-sea mineral resource considered for mining. This paper provides insights into measuring the distribution of manganese nodules at meter resolution. Nodule abundance was determined by autonomous robots using cameras and echo sounders. Based on the meter-scale abundance measurements, environmental impacts of simulated deep-sea mining were assessed. The spatial extent of a sediment plume was determined and showed correlation to small variations in seafloor topography.
Evangelos Alevizos, Timm Schoening, Kevin Koeser, Mirjam Snellen, and Jens Greinert
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-60, https://doi.org/10.5194/bg-2018-60, 2018
Revised manuscript has not been submitted
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AUV hydro-acoustic and optical data enhance high resolution quantitative mapping of deep sea hard substrates such Mn-nodules. Machine learning algorithms predict with good accuracy the Mn-nodules abundances over large scale areas utilizing one third of ground truth optical data. Accurate maps of Mn-nodule abundances raise new questions about the role of fine scale geomorphology in nodule formation, provide new insights in deep sea ecological studies, and improve mineral assessment estimations.
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Assessing the impact of CO2-equilibrated ocean alkalinity enhancement on microbial metabolic rates in an oligotrophic system
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Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry
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Testing the influence of light on nitrite cycling in the eastern tropical North Pacific
Loss of nitrogen via anaerobic ammonium oxidation (anammox) in the California Current system during the late Quaternary
Drivers of decadal trends of the ocean carbon sink in the past, present, and future in Earth system models
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Seasonal cycles of biogeochemical fluxes in the Scotia Sea, Southern Ocean: a stable isotope approach
Absence of photophysiological response to iron addition in autumn phytoplankton in the Antarctic sea-ice zone
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Stephanie Delacroix, Tor Jensen Nystuen, August E. Dessen Tobiesen, Andrew L. King, and Erik Höglund
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Precious Mongwe, Matthew Long, Takamitsu Ito, Curtis Deutsch, and Yeray Santana-Falcón
Biogeosciences, 21, 3477–3490, https://doi.org/10.5194/bg-21-3477-2024, https://doi.org/10.5194/bg-21-3477-2024, 2024
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We use a collection of measurements that capture the physiological sensitivity of organisms to temperature and oxygen and a CESM1 large ensemble to investigate how natural climate variations and climate warming will impact the ability of marine heterotrophic marine organisms to support habitats in the future. We find that warming and dissolved oxygen loss over the next several decades will reduce the volume of ocean habitats and will increase organisms' vulnerability to extremes.
Charly A. Moras, Tyler Cyronak, Lennart T. Bach, Renaud Joannes-Boyau, and Kai G. Schulz
Biogeosciences, 21, 3463–3475, https://doi.org/10.5194/bg-21-3463-2024, https://doi.org/10.5194/bg-21-3463-2024, 2024
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We investigate the effects of mineral grain size and seawater salinity on magnesium hydroxide dissolution and calcium carbonate precipitation kinetics for ocean alkalinity enhancement. Salinity did not affect the dissolution, but calcium carbonate formed earlier at lower salinities due to the lower magnesium and dissolved organic carbon concentrations. Smaller grain sizes dissolved faster but calcium carbonate precipitated earlier, suggesting that medium grain sizes are optimal for kinetics.
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How quickly do marine microorganisms respond to salinity stress? Our experiments with the calcifying marine plankton Emiliania huxleyi show that growth and cell morphology responded to salinity stress within as little as 24–48 hours, demonstrating that morphology and calcification are sensitive to salinity over a range of timescales. Our results have implications for understanding the short-term role of E. huxleyi in biogeochemical cycles and in size-based paleoproxies for salinity.
Laura Marín-Samper, Javier Arístegui, Nauzet Hernández-Hernández, Joaquín Ortiz, Stephen D. Archer, Andrea Ludwig, and Ulf Riebesell
Biogeosciences, 21, 2859–2876, https://doi.org/10.5194/bg-21-2859-2024, https://doi.org/10.5194/bg-21-2859-2024, 2024
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Our planet is facing a climate crisis. Scientists are working on innovative solutions that will aid in capturing the hard to abate emissions before it is too late. Exciting research reveals that ocean alkalinity enhancement, a key climate change mitigation strategy, does not harm phytoplankton, the cornerstone of marine ecosystems. Through meticulous study, we may have uncovered a positive relationship: up to a specific limit, enhancing ocean alkalinity boosts photosynthesis by certain species.
France Van Wambeke, Pascal Conan, Mireille Pujo-Pay, Vincent Taillandier, Olivier Crispi, Alexandra Pavlidou, Sandra Nunige, Morgane Didry, Christophe Salmeron, and Elvira Pulido-Villena
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Phosphomonoesterase (PME) and phosphodiesterase (PDE) activities over the epipelagic zone are described in the eastern Mediterranean Sea in winter and autumn. The types of concentration kinetics obtained for PDE (saturation at 50 µM, high Km, high turnover times) compared to those of PME (saturation at 1 µM, low Km, low turnover times) are discussed in regard to the possible inequal distribution of PDE and PME in the size continuum of organic material and accessibility to phosphodiesters.
Allison Hogikyan and Laure Resplandy
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Rising atmospheric CO2 influences ocean carbon chemistry leading to ocean acidification. Global warming introduces spatial patterns in the intensity of ocean acidification. We show that the most prominent spatial patterns are controlled by warming-driven changes in rainfall and evaporation, and not by the direct effect of warming on carbon chemistry and pH. This rainfall/evaporation effect opposes acidification in saltier parts of the ocean and enhances acidification in fresher regions.
Jenny Hieronymus, Magnus Hieronymus, Matthias Gröger, Jörg Schwinger, Raffaele Bernadello, Etienne Tourigny, Valentina Sicardi, Itzel Ruvalcaba Baroni, and Klaus Wyser
Biogeosciences, 21, 2189–2206, https://doi.org/10.5194/bg-21-2189-2024, https://doi.org/10.5194/bg-21-2189-2024, 2024
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The timing of the net primary production annual maxima in the North Atlantic in the period 1750–2100 is investigated using two Earth system models and the high-emissions scenario SSP5-8.5. It is found that, for most of the region, the annual maxima occur progressively earlier, with the most change occurring after the year 2000. Shifts in the seasonality of the primary production may impact the entire ecosystem, which highlights the need for long-term monitoring campaigns in this area.
Nicole M. Travis, Colette L. Kelly, and Karen L. Casciotti
Biogeosciences, 21, 1985–2004, https://doi.org/10.5194/bg-21-1985-2024, https://doi.org/10.5194/bg-21-1985-2024, 2024
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We conducted experimental manipulations of light level on microbial communities from the primary nitrite maximum. Overall, while individual microbial processes have different directions and magnitudes in their response to increasing light, the net community response is a decline in nitrite production with increasing light. We conclude that while increased light may decrease net nitrite production, high-light conditions alone do not exclude nitrification from occurring in the surface ocean.
Zoë Rebecca van Kemenade, Zeynep Erdem, Ellen Christine Hopmans, Jaap Smede Sinninghe Damsté, and Darci Rush
Biogeosciences, 21, 1517–1532, https://doi.org/10.5194/bg-21-1517-2024, https://doi.org/10.5194/bg-21-1517-2024, 2024
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The California Current system (CCS) hosts the eastern subtropical North Pacific oxygen minimum zone (ESTNP OMZ). This study shows anaerobic ammonium oxidizing (anammox) bacteria cause a loss of bioavailable nitrogen (N) in the ESTNP OMZ throughout the late Quaternary. Anammox occurred during both glacial and interglacial periods and was driven by the supply of organic matter and changes in ocean currents. These findings may have important consequences for biogeochemical models of the CCS.
Jens Terhaar
EGUsphere, https://doi.org/10.5194/egusphere-2024-773, https://doi.org/10.5194/egusphere-2024-773, 2024
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Despite the ocean’s importance for the carbon cycle and hence the climate, observing the ocean carbon sink remains challenging. Here, I use an ensemble of 12 Models to understand drivers of decadal trends of the past, present and future ocean carbon sink. I show that 80 % of decadal trends in the multi-model mean ocean carbon sink can be explained by changes in decadal trends of atmospheric CO2. The remaining 20 % are due to internal climate variability and ocean heat uptake.
Cathy Wimart-Rousseau, Tobias Steinhoff, Birgit Klein, Henry Bittig, and Arne Körtzinger
Biogeosciences, 21, 1191–1211, https://doi.org/10.5194/bg-21-1191-2024, https://doi.org/10.5194/bg-21-1191-2024, 2024
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The marine CO2 system can be measured independently and continuously by BGC-Argo floats since numerous pH sensors have been developed to suit these autonomous measurements platforms. By applying the Argo correction routines to float pH data acquired in the subpolar North Atlantic Ocean, we report the uncertainty and lack of objective criteria associated with the choice of the reference method as well the reference depth for the pH correction.
Sabine Mecking and Kyla Drushka
Biogeosciences, 21, 1117–1133, https://doi.org/10.5194/bg-21-1117-2024, https://doi.org/10.5194/bg-21-1117-2024, 2024
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This study investigates whether northeastern North Pacific oxygen changes may be caused by surface density changes in the northwest as water moves along density horizons from the surface into the subsurface ocean. A correlation is found with a lag that about matches the travel time of water from the northwest to the northeast. Salinity is the main driver causing decadal changes in surface density, whereas salinity and temperature contribute about equally to long-term declining density trends.
Winfred Marshal, Jing Xiang Chung, and Mohd Fadzil Bin Mohd Akhir
EGUsphere, https://doi.org/10.5194/egusphere-2024-72, https://doi.org/10.5194/egusphere-2024-72, 2024
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This study stands out for thoroughly examining CMIP6 ESMs ability to simulate biogeochemical variables in the southern South China Sea, an economically important region. It assesses variables like chlorophyll, phytoplankton, nitrate and oxygen on annual and seasonal scales. While global assessments exist, this study addresses a gap by objectively ranking 13 CMIP6 ocean biogeochemistry models' performance at a regional level, focusing on replicating specific observed biogeochemical variables.
Takamitsu Ito, Hernan E. Garcia, Zhankun Wang, Shoshiro Minobe, Matthew C. Long, Just Cebrian, James Reagan, Tim Boyer, Christopher Paver, Courtney Bouchard, Yohei Takano, Seth Bushinsky, Ahron Cervania, and Curtis A. Deutsch
Biogeosciences, 21, 747–759, https://doi.org/10.5194/bg-21-747-2024, https://doi.org/10.5194/bg-21-747-2024, 2024
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This study aims to estimate how much oceanic oxygen has been lost and its uncertainties. One major source of uncertainty comes from the statistical gap-filling methods. Outputs from Earth system models are used to generate synthetic observations where oxygen data are extracted from the model output at the location and time of historical oceanographic cruises. Reconstructed oxygen trend is approximately two-thirds of the true trend.
Robert W. Izett, Katja Fennel, Adam C. Stoer, and David P. Nicholson
Biogeosciences, 21, 13–47, https://doi.org/10.5194/bg-21-13-2024, https://doi.org/10.5194/bg-21-13-2024, 2024
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This paper provides an overview of the capacity to expand the global coverage of marine primary production estimates using autonomous ocean-going instruments, called Biogeochemical-Argo floats. We review existing approaches to quantifying primary production using floats, provide examples of the current implementation of the methods, and offer insights into how they can be better exploited. This paper is timely, given the ongoing expansion of the Biogeochemical-Argo array.
Shunya Koseki, Lander R. Crespo, Jerry Tjiputra, Filippa Fransner, Noel S. Keenlyside, and David Rivas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2947, https://doi.org/10.5194/egusphere-2023-2947, 2023
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We investigated how the physical biases of an Earth system model influence the marine biogeochemical processes in the tropical Atlantic. With four different configurations of the model, we have shown that the versions with better SST reproduction tend to represent the primary production and sea-air CO2 flux in terms of climatology, seasonal cycle, and responses to climate variability.
Qian Liu, Yingjie Liu, and Xiaofeng Li
Biogeosciences, 20, 4857–4874, https://doi.org/10.5194/bg-20-4857-2023, https://doi.org/10.5194/bg-20-4857-2023, 2023
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In the Southern Ocean, abundant eddies behave opposite to our expectations. That is, anticyclonic (cyclonic) eddies are cold (warm). By investigating the variations of physical and biochemical parameters in eddies, we find that abnormal eddies have unique and significant effects on modulating the parameters. This study fills a gap in understanding the effects of abnormal eddies on physical and biochemical parameters in the Southern Ocean.
Caroline Ulses, Claude Estournel, Patrick Marsaleix, Karline Soetaert, Marine Fourrier, Laurent Coppola, Dominique Lefèvre, Franck Touratier, Catherine Goyet, Véronique Guglielmi, Fayçal Kessouri, Pierre Testor, and Xavier Durrieu de Madron
Biogeosciences, 20, 4683–4710, https://doi.org/10.5194/bg-20-4683-2023, https://doi.org/10.5194/bg-20-4683-2023, 2023
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Deep convection plays a key role in the circulation, thermodynamics, and biogeochemical cycles in the Mediterranean Sea, considered to be a hotspot of biodiversity and climate change. In this study, we investigate the seasonal and annual budget of dissolved inorganic carbon in the deep-convection area of the northwestern Mediterranean Sea.
Daniela König and Alessandro Tagliabue
Biogeosciences, 20, 4197–4212, https://doi.org/10.5194/bg-20-4197-2023, https://doi.org/10.5194/bg-20-4197-2023, 2023
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Using model simulations, we show that natural and anthropogenic changes in the global climate leave a distinct fingerprint in the isotopic signatures of iron in the surface ocean. We find that these climate effects on iron isotopes are often caused by the redistribution of iron from different external sources to the ocean, due to changes in ocean currents, and by changes in algal growth, which take up iron. Thus, isotopes may help detect climate-induced changes in iron supply and algal uptake.
Chloé Baumas, Robin Fuchs, Marc Garel, Jean-Christophe Poggiale, Laurent Memery, Frédéric A. C. Le Moigne, and Christian Tamburini
Biogeosciences, 20, 4165–4182, https://doi.org/10.5194/bg-20-4165-2023, https://doi.org/10.5194/bg-20-4165-2023, 2023
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Through the sink of particles in the ocean, carbon (C) is exported and sequestered when reaching 1000 m. Attempts to quantify C exported vs. C consumed by heterotrophs have increased. Yet most of the conducted estimations have led to C demands several times higher than C export. The choice of parameters greatly impacts the results. As theses parameters are overlooked, non-accurate values are often used. In this study we show that C budgets can be well balanced when using appropriate values.
Lyuba Novi, Annalisa Bracco, Takamitsu Ito, and Yohei Takano
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-129, https://doi.org/10.5194/bg-2023-129, 2023
Revised manuscript accepted for BG
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We explored the relationship between oxygen and stratification in the North Pacific Ocean, using a combination of data mining and machine learning. We used isopycnic potential vorticity (IPV) as an indicator to quantify ocean ventilation and we analyzed its predictability, a strong O2-IPV connection and predictability for IPV in the tropical Pacific. This open new routes to monitor ocean O2 through few observational sites co-located with more abundant IPV measurements in the tropical Pacific.
Anna Belcher, Sian F. Henley, Katharine Hendry, Marianne Wootton, Lisa Friberg, Ursula Dallman, Tong Wang, Christopher Coath, and Clara Manno
Biogeosciences, 20, 3573–3591, https://doi.org/10.5194/bg-20-3573-2023, https://doi.org/10.5194/bg-20-3573-2023, 2023
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The oceans play a crucial role in the uptake of atmospheric carbon dioxide, particularly the Southern Ocean. The biological pumping of carbon from the surface to the deep ocean is key to this. Using sediment trap samples from the Scotia Sea, we examine biogeochemical fluxes of carbon, nitrogen, and biogenic silica and their stable isotope compositions. We find phytoplankton community structure and physically mediated processes are important controls on particulate fluxes to the deep ocean.
Asmita Singh, Susanne Fietz, Sandy J. Thomalla, Nicolas Sanchez, Murat V. Ardelan, Sébastien Moreau, Hanna M. Kauko, Agneta Fransson, Melissa Chierici, Saumik Samanta, Thato N. Mtshali, Alakendra N. Roychoudhury, and Thomas J. Ryan-Keogh
Biogeosciences, 20, 3073–3091, https://doi.org/10.5194/bg-20-3073-2023, https://doi.org/10.5194/bg-20-3073-2023, 2023
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Despite the scarcity of iron in the Southern Ocean, seasonal blooms occur due to changes in nutrient and light availability. Surprisingly, during an autumn bloom in the Antarctic sea-ice zone, the results from incubation experiments showed no significant photophysiological response of phytoplankton to iron addition. This suggests that ambient iron concentrations were sufficient, challenging the notion of iron deficiency in the Southern Ocean through extended iron-replete post-bloom conditions.
Benoît Pasquier, Mark Holzer, Matthew A. Chamberlain, Richard J. Matear, Nathaniel L. Bindoff, and François W. Primeau
Biogeosciences, 20, 2985–3009, https://doi.org/10.5194/bg-20-2985-2023, https://doi.org/10.5194/bg-20-2985-2023, 2023
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Modeling the ocean's carbon and oxygen cycles accurately is challenging. Parameter optimization improves the fit to observed tracers but can introduce artifacts in the biological pump. Organic-matter production and subsurface remineralization rates adjust to compensate for circulation biases, changing the pathways and timescales with which nutrients return to the surface. Circulation biases can thus strongly alter the system’s response to ecological change, even when parameters are optimized.
Priyanka Banerjee
Biogeosciences, 20, 2613–2643, https://doi.org/10.5194/bg-20-2613-2023, https://doi.org/10.5194/bg-20-2613-2023, 2023
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This study shows that atmospheric deposition is the most important source of iron to the upper northern Indian Ocean for phytoplankton growth. This is followed by iron from continental-shelf sediment. Phytoplankton increase following iron addition is possible only with high background levels of nitrate. Vertical mixing is the most important physical process supplying iron to the upper ocean in this region throughout the year. The importance of ocean currents in supplying iron varies seasonally.
Iris Kriest, Julia Getzlaff, Angela Landolfi, Volkmar Sauerland, Markus Schartau, and Andreas Oschlies
Biogeosciences, 20, 2645–2669, https://doi.org/10.5194/bg-20-2645-2023, https://doi.org/10.5194/bg-20-2645-2023, 2023
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Global biogeochemical ocean models are often subjectively assessed and tuned against observations. We applied different strategies to calibrate a global model against observations. Although the calibrated models show similar tracer distributions at the surface, they differ in global biogeochemical fluxes, especially in global particle flux. Simulated global volume of oxygen minimum zones varies strongly with calibration strategy and over time, rendering its temporal extrapolation difficult.
John C. Tracey, Andrew R. Babbin, Elizabeth Wallace, Xin Sun, Katherine L. DuRussel, Claudia Frey, Donald E. Martocello III, Tyler Tamasi, Sergey Oleynik, and Bess B. Ward
Biogeosciences, 20, 2499–2523, https://doi.org/10.5194/bg-20-2499-2023, https://doi.org/10.5194/bg-20-2499-2023, 2023
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Nitrogen (N) is essential for life; thus, its availability plays a key role in determining marine productivity. Using incubations of seawater spiked with a rare form of N measurable on a mass spectrometer, we quantified microbial pathways that determine marine N availability. The results show that pathways that recycle N have higher rates than those that result in its loss from biomass and present new evidence for anaerobic nitrite oxidation, a process long thought to be strictly aerobic.
Amanda Gerotto, Hongrui Zhang, Renata Hanae Nagai, Heather M. Stoll, Rubens César Lopes Figueira, Chuanlian Liu, and Iván Hernández-Almeida
Biogeosciences, 20, 1725–1739, https://doi.org/10.5194/bg-20-1725-2023, https://doi.org/10.5194/bg-20-1725-2023, 2023
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Based on the analysis of the response of coccolithophores’ morphological attributes in a laboratory dissolution experiment and surface sediment samples from the South China Sea, we proposed that the thickness shape (ks) factor of fossil coccoliths together with the normalized ks variation, which is the ratio of the standard deviation of ks (σ) over the mean ks (σ/ks), is a robust and novel proxy to reconstruct past changes in deep ocean carbon chemistry.
Katherine E. Turner, Doug M. Smith, Anna Katavouta, and Richard G. Williams
Biogeosciences, 20, 1671–1690, https://doi.org/10.5194/bg-20-1671-2023, https://doi.org/10.5194/bg-20-1671-2023, 2023
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We present a new method for reconstructing ocean carbon using climate models and temperature and salinity observations. To test this method, we reconstruct modelled carbon using synthetic observations consistent with current sampling programmes. Sensitivity tests show skill in reconstructing carbon trends and variability within the upper 2000 m. Our results indicate that this method can be used for a new global estimate for ocean carbon content.
Alexandre Mignot, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, Coralie Perruche, Stefano Salon, Raphaëlle Sauzède, Vincent Taillandier, and Anna Teruzzi
Biogeosciences, 20, 1405–1422, https://doi.org/10.5194/bg-20-1405-2023, https://doi.org/10.5194/bg-20-1405-2023, 2023
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Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and monitor ocean health. Here, we demonstrate the use of the global array of BGC-Argo floats for the assessment of biogeochemical models. We first detail the handling of the BGC-Argo data set for model assessment purposes. We then present 23 assessment metrics to quantify the consistency of BGC model simulations with respect to BGC-Argo data.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
Biogeosciences, 20, 1195–1257, https://doi.org/10.5194/bg-20-1195-2023, https://doi.org/10.5194/bg-20-1195-2023, 2023
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Ocean alkalinity is critical to the uptake of atmospheric carbon and acidification in surface waters. We review the representation of alkalinity and the associated calcium carbonate cycle in Earth system models. While many parameterizations remain present in the latest generation of models, there is a general improvement in the simulated alkalinity distribution. This improvement is related to an increase in the export of biotic calcium carbonate, which closer resembles observations.
Jérôme Pinti, Tim DeVries, Tommy Norin, Camila Serra-Pompei, Roland Proud, David A. Siegel, Thomas Kiørboe, Colleen M. Petrik, Ken H. Andersen, Andrew S. Brierley, and André W. Visser
Biogeosciences, 20, 997–1009, https://doi.org/10.5194/bg-20-997-2023, https://doi.org/10.5194/bg-20-997-2023, 2023
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Large numbers of marine organisms such as zooplankton and fish perform daily vertical migration between the surface (at night) and the depths (in the daytime). This fascinating migration is important for the carbon cycle, as these organisms actively bring carbon to depths where it is stored away from the atmosphere for a long time. Here, we quantify the contributions of different animals to this carbon drawdown and storage and show that fish are important to the biological carbon pump.
Alastair J. M. Lough, Alessandro Tagliabue, Clément Demasy, Joseph A. Resing, Travis Mellett, Neil J. Wyatt, and Maeve C. Lohan
Biogeosciences, 20, 405–420, https://doi.org/10.5194/bg-20-405-2023, https://doi.org/10.5194/bg-20-405-2023, 2023
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Iron is a key nutrient for ocean primary productivity. Hydrothermal vents are a source of iron to the oceans, but the size of this source is poorly understood. This study examines the variability in iron inputs between hydrothermal vents in different geological settings. The vents studied release different amounts of Fe, resulting in plumes with similar dissolved iron concentrations but different particulate concentrations. This will help to refine modelling of iron-limited ocean productivity.
Nicole M. Travis, Colette L. Kelly, Margaret R. Mulholland, and Karen L. Casciotti
Biogeosciences, 20, 325–347, https://doi.org/10.5194/bg-20-325-2023, https://doi.org/10.5194/bg-20-325-2023, 2023
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The primary nitrite maximum is a ubiquitous upper ocean feature where nitrite accumulates, but we still do not understand its formation and the co-occurring microbial processes involved. Using correlative methods and rates measurements, we found strong spatial patterns between environmental conditions and depths of the nitrite maxima, but not the maximum concentrations. Nitrification was the dominant source of nitrite, with occasional high nitrite production from phytoplankton near the coast.
Natacha Le Grix, Jakob Zscheischler, Keith B. Rodgers, Ryohei Yamaguchi, and Thomas L. Frölicher
Biogeosciences, 19, 5807–5835, https://doi.org/10.5194/bg-19-5807-2022, https://doi.org/10.5194/bg-19-5807-2022, 2022
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Compound events threaten marine ecosystems. Here, we investigate the potentially harmful combination of marine heatwaves with low phytoplankton productivity. Using satellite-based observations, we show that these compound events are frequent in the low latitudes. We then investigate the drivers of these compound events using Earth system models. The models share similar drivers in the low latitudes but disagree in the high latitudes due to divergent factors limiting phytoplankton production.
Abigale M. Wyatt, Laure Resplandy, and Adrian Marchetti
Biogeosciences, 19, 5689–5705, https://doi.org/10.5194/bg-19-5689-2022, https://doi.org/10.5194/bg-19-5689-2022, 2022
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Marine heat waves (MHWs) are a frequent event in the northeast Pacific, with a large impact on the region's ecosystems. Large phytoplankton in the North Pacific Transition Zone are greatly affected by decreased nutrients, with less of an impact in the Alaskan Gyre. For small phytoplankton, MHWs increase the spring small phytoplankton population in both regions thanks to reduced light limitation. In both zones, this results in a significant decrease in the ratio of large to small phytoplankton.
Margot C. F. Debyser, Laetitia Pichevin, Robyn E. Tuerena, Paul A. Dodd, Antonia Doncila, and Raja S. Ganeshram
Biogeosciences, 19, 5499–5520, https://doi.org/10.5194/bg-19-5499-2022, https://doi.org/10.5194/bg-19-5499-2022, 2022
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We focus on the exchange of key nutrients for algae production between the Arctic and Atlantic oceans through the Fram Strait. We show that the export of dissolved silicon here is controlled by the availability of nitrate which is influenced by denitrification on Arctic shelves. We suggest that any future changes in the river inputs of silica and changes in denitrification due to climate change will impact the amount of silicon exported, with impacts on Atlantic algal productivity and ecology.
Emily J. Zakem, Barbara Bayer, Wei Qin, Alyson E. Santoro, Yao Zhang, and Naomi M. Levine
Biogeosciences, 19, 5401–5418, https://doi.org/10.5194/bg-19-5401-2022, https://doi.org/10.5194/bg-19-5401-2022, 2022
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We use a microbial ecosystem model to quantitatively explain the mechanisms controlling observed relative abundances and nitrification rates of ammonia- and nitrite-oxidizing microorganisms in the ocean. We also estimate how much global carbon fixation can be associated with chemoautotrophic nitrification. Our results improve our understanding of the controls on nitrification, laying the groundwork for more accurate predictions in global climate models.
Zuozhu Wen, Thomas J. Browning, Rongbo Dai, Wenwei Wu, Weiying Li, Xiaohua Hu, Wenfang Lin, Lifang Wang, Xin Liu, Zhimian Cao, Haizheng Hong, and Dalin Shi
Biogeosciences, 19, 5237–5250, https://doi.org/10.5194/bg-19-5237-2022, https://doi.org/10.5194/bg-19-5237-2022, 2022
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Fe and P are key factors controlling the biogeography and activity of marine N2-fixing microorganisms. We found lower abundance and activity of N2 fixers in the northern South China Sea than around the western boundary of the North Pacific, and N2 fixation rates switched from Fe–P co-limitation to P limitation. We hypothesize the Fe supply rates and Fe utilization strategies of each N2 fixer are important in regulating spatial variability in community structure across the study area.
Claudia Eisenring, Sophy E. Oliver, Samar Khatiwala, and Gregory F. de Souza
Biogeosciences, 19, 5079–5106, https://doi.org/10.5194/bg-19-5079-2022, https://doi.org/10.5194/bg-19-5079-2022, 2022
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Given the sparsity of observational constraints on micronutrients such as zinc (Zn), we assess the sensitivities of a framework for objective parameter optimisation in an oceanic Zn cycling model. Our ensemble of optimisations towards synthetic data with varying kinds of uncertainty shows that deficiencies related to model complexity and the choice of the misfit function generally have a greater impact on the retrieval of model Zn uptake behaviour than does the limitation of data coverage.
Yoshikazu Sasai, Sherwood Lan Smith, Eko Siswanto, Hideharu Sasaki, and Masami Nonaka
Biogeosciences, 19, 4865–4882, https://doi.org/10.5194/bg-19-4865-2022, https://doi.org/10.5194/bg-19-4865-2022, 2022
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We have investigated the adaptive response of phytoplankton growth to changing light, nutrients, and temperature over the North Pacific using two physical-biological models. We compare modeled chlorophyll and primary production from an inflexible control model (InFlexPFT), which assumes fixed carbon (C):nitrogen (N):chlorophyll (Chl) ratios, to a recently developed flexible phytoplankton functional type model (FlexPFT), which incorporates photoacclimation and variable C:N:Chl ratios.
Jens Terhaar, Thomas L. Frölicher, and Fortunat Joos
Biogeosciences, 19, 4431–4457, https://doi.org/10.5194/bg-19-4431-2022, https://doi.org/10.5194/bg-19-4431-2022, 2022
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Estimates of the ocean sink of anthropogenic carbon vary across various approaches. We show that the global ocean carbon sink can be estimated by three parameters, two of which approximate the ocean ventilation in the Southern Ocean and the North Atlantic, and one of which approximates the chemical capacity of the ocean to take up carbon. With observations of these parameters, we estimate that the global ocean carbon sink is 10 % larger than previously assumed, and we cut uncertainties in half.
Natasha René van Horsten, Hélène Planquette, Géraldine Sarthou, Thomas James Ryan-Keogh, Nolwenn Lemaitre, Thato Nicholas Mtshali, Alakendra Roychoudhury, and Eva Bucciarelli
Biogeosciences, 19, 3209–3224, https://doi.org/10.5194/bg-19-3209-2022, https://doi.org/10.5194/bg-19-3209-2022, 2022
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The remineralisation proxy, barite, was measured along 30°E in the southern Indian Ocean during early austral winter. To our knowledge this is the first reported Southern Ocean winter study. Concentrations throughout the water column were comparable to observations during spring to autumn. By linking satellite primary production to this proxy a possible annual timescale is proposed. These findings also suggest possible carbon remineralisation from satellite data on a basin scale.
Zhibo Shao and Ya-Wei Luo
Biogeosciences, 19, 2939–2952, https://doi.org/10.5194/bg-19-2939-2022, https://doi.org/10.5194/bg-19-2939-2022, 2022
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Non-cyanobacterial diazotrophs (NCDs) may be an important player in fixing N2 in the ocean. By conducting meta-analyses, we found that a representative marine NCD phylotype, Gamma A, tends to inhabit ocean environments with high productivity, low iron concentration and high light intensity. It also appears to be more abundant inside cyclonic eddies. Our study suggests a niche differentiation of NCDs from cyanobacterial diazotrophs as the latter prefers low-productivity and high-iron oceans.
Coraline Leseurre, Claire Lo Monaco, Gilles Reverdin, Nicolas Metzl, Jonathan Fin, Claude Mignon, and Léa Benito
Biogeosciences, 19, 2599–2625, https://doi.org/10.5194/bg-19-2599-2022, https://doi.org/10.5194/bg-19-2599-2022, 2022
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Decadal trends of fugacity of CO2 (fCO2), total alkalinity (AT), total carbon (CT) and pH in surface waters are investigated in different domains of the southern Indian Ocean (45°S–57°S) from ongoing and station observations regularly conducted in summer over the period 1998–2019. The fCO2 increase and pH decrease are mainly driven by anthropogenic CO2 estimated just below the summer mixed layer, as well as by a warming south of the polar front or in the fertilized waters near Kerguelen Island.
Priscilla Le Mézo, Jérôme Guiet, Kim Scherrer, Daniele Bianchi, and Eric Galbraith
Biogeosciences, 19, 2537–2555, https://doi.org/10.5194/bg-19-2537-2022, https://doi.org/10.5194/bg-19-2537-2022, 2022
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This study quantifies the role of commercially targeted fish biomass in the cycling of three important nutrients (N, P, and Fe), relative to nutrients otherwise available in water and to nutrients required by primary producers, and the impact of fishing. We use a model of commercially targeted fish biomass constrained by fish catch and stock assessment data to assess the contributions of fish at the global scale, at the time of the global peak catch and prior to industrial fishing.
Rebecca Chmiel, Nathan Lanning, Allison Laubach, Jong-Mi Lee, Jessica Fitzsimmons, Mariko Hatta, William Jenkins, Phoebe Lam, Matthew McIlvin, Alessandro Tagliabue, and Mak Saito
Biogeosciences, 19, 2365–2395, https://doi.org/10.5194/bg-19-2365-2022, https://doi.org/10.5194/bg-19-2365-2022, 2022
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Dissolved cobalt is present in trace amounts in seawater and is a necessary nutrient for marine microbes. On a transect from the Alaskan coast to Tahiti, we measured seawater concentrations of dissolved cobalt. Here, we describe several interesting features of the Pacific cobalt cycle including cobalt sources along the Alaskan coast and Hawaiian vents, deep-ocean particle formation, cobalt activity in low-oxygen regions, and how our samples compare to a global biogeochemical model’s predictions.
Nicolas Metzl, Claire Lo Monaco, Coraline Leseurre, Céline Ridame, Jonathan Fin, Claude Mignon, Marion Gehlen, and Thi Tuyet Trang Chau
Biogeosciences, 19, 1451–1468, https://doi.org/10.5194/bg-19-1451-2022, https://doi.org/10.5194/bg-19-1451-2022, 2022
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During an oceanographic cruise conducted in January 2020 in the south-western Indian Ocean, we observed very low CO2 concentrations associated with a strong phytoplankton bloom that occurred south-east of Madagascar. This biological event led to a strong regional CO2 ocean sink not previously observed.
Cited articles
Alevizos, E., Schoening, T., Koeser, K., Snellen, M., and Greinert, J.:
Quantification of the fine-scale distribution of Mn-nodules: insights from
AUV multi-beam and optical imagery data fusion, Biogeosciences Discuss.,
https://doi.org/10.5194/bg-2018-60, in review, 2018.
Anselin, L.: Local Indicators of Spatial Association – LISA, Geogr. Anal.,
27, 93–115, https://doi.org/10.1111/j.1538-4632.1995.tb00338.x, 1995.
Atmanand, M. A. and Ramadass, G. A.: Concepts of Deep-Sea Mining
Technologies, in: Deep-Sea Mining, edited by: Sharma, R., Resource Springer,
Cham. Online ISBN 978-3-319-52557-0, https://doi.org/10.1007/978-3-319-52557-0_6, 2017.
Bellingham, J.: Autonomous underwater vehicles (AUVs), in: Encyclopedia of
Ocean Sciences, edited by: Steele, H., Academic Press, San Diego, 212–216,
https://doi.org/10.1006/rwos.2001.0303, 2001.
Bingham, D., Drake, T., Hill, A., and Lott, R.: The Application of Autonomous
Underwater Vehicle (AUV) Technology in the Oil Industry – Vision and
Experiences. FIG XXII International Congress Washington, DC USA,
19–26 April, 1–13, 2002.
Breiman, L.: Random forests, Machine Learning, 45, 5–32,
https://doi.org/10.1023/A:101093340, 2001a.
Breiman, L.: Statistical Modeling: The Two Cultures, Stat. Sci., 16,
199–215, https://doi.org/10.1214/ss/1009213726, 2001b.
Caress, D. W. and Chayes, D. N.: MB-System: Mapping the Seafloor, available
at: https://www.mbari.org/products/research-software/mb-system (last
access: 6 December 2018), 2017.
Caress, D. W., Thomas, H., Kirkwood, W. J., McEwen, R., Henthorn, R., Clague,
D. A., Paull, C. K., and Paduan, J.: High-resolution multibeam, sidescan and
subbottom surveys using the MBARI AUV D, in: Marine Habitat Mapping
Technology for Alaska, edited by: Allan, B.,Greene, H. G., and Reynolds, J.
R., Alaska Sea Grant College Program, University of Alaska Fairbanks, 47–69,
https://doi.org/10.4027/mhmta.2008.04, 2008.
Carranza, E. J. M. and Laborte, A. G.: Random Forest Predictive Modelling of
Mineral Prospectivity with Small Number of Prospects and Data with Missing
Values in Abra (Philippines),Comput. Geosci., 74, 60–70,
https://doi.org/10.1016/j.cageo.2014.10.004, 2015a.
Carranza, E. J. M. and Laborte, A. G.: Data-driven predictive mapping of gold
prospectivity, Baguio district, Philippines: Application of Random Forests
algorithm, Ore Geol. Rev., 7, 777–787,
https://doi.org/10.1016/j.oregeorev.2014.08.010, 2015b.
Carranza, E. J. M. and Laborte, A. G.: Data-Driven Predictive Modeling of
Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes
Island (Philippines), Nat. Resour. Res., 25, 35–50,
https://doi.org/10.1007/s11053-015-9268-x, 2016.
Chakraborty, B. and Kodagali, V.: Characterizing Indian Ocean manganese
nodule-bearing seafloor using multi-beam angular backscatter, Geo-Mar. Lett.,
24, 8–13, https://doi.org/10.1007/s00367-003-0153-y, 2004.
Chance, T., Kleiner, A., and Northcutt, J.: The autonomous underwater vehicle
(AUV): A cost-effective alternative to deep-towed technology, Integrated
Coastal Zone Management, 2, 65–69, 2000.
Che Hasan, R., Ierodiakonou, D., and Monk, J.: Evaluation of Four Supervised
Learning Methods for BenthicHabitat Mapping Using Backscatter from Multi-Beam
Sonar, Remote Sens., 4, 3427–3443, https://doi.org/10.3390/rs4113427, 2012.
Che Hasan, R., Ierodiaconou, D., Laurenson, L., and Schimel, A.: Integrating
Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for
Benthic Habitat Mapping, PLoS ONE, 9, e97339,
https://doi.org/10.1371/journal.pone.0097339, 2014.
Chen, L., Wang, S., McDonald-Maier K., and Hu, H.: Towards autonomous
localization and mapping of AUVs: a survey, Int. J. Intell. Syst., 1,
97–120, https://doi.org/10.1108/20496421311330047, 2013.
Chung, J. S.: Deep-Ocean Mining: Technologies for Manganese Nodules and
Crusts, Int. J. Offshore Polar, 6, 244–254, 1996.
Clague, D. A., Dreyer, B. M., Paduan, J. B., Martin, J. F., Caress, D. W.,
Gill, J. B., Kelley D. S., Thomas, H., Portner, R. A., Delaney, J.
R., Guilderson, T. P., and McGann, M. L.: Eruptive and tectonic history of the
Endeavour Segment, Juan de Fuca Ridge, based on AUV mapping data and lava
flow ages, Geochem. Geophy. Geosys., 15, 3364–3391,
https://doi.org/10.1002/2014GC005415, 2014.
Clague, D. A., Caress, D. W., Dreyer, B. M., Lundsten, L., Paduan, J. B.,
Portner, R. A., Spelz-Madero, R., Bowles, J. A., Castillo, P. R.,
Guardado-France, R., Le Saout, M., Martin, J. F., Santa Rosa-del Rio, M. A.,
and Zierenberg, R. A.: Geology of the Alarcon Rise, southern Gulf of
California, Geochem. Geophy. Geosy., 19, 807–837,
https://doi.org/10.1002/2017GC007348, 2018.
Clements, A. J., Strong, J. A., Flanagan, C., and Service, M.: Objective
stratification and sampling-effort allocation of ground-truthing in
benthic-mapping surveys, ICES J. Mar. Sci., 67, 628–637, 2010.
Cochran, W. G.: Sampling Techniques, 3rd edn. Wiley, New York, 1977.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L.,
Wehberg, J., Wichmann, V., and Böhner, J.: A system for Automated
Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007,
https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Coulston, J. W., Blinn, C. E., Thomas, V. A., and Wynne, R. H.: Approximating
prediction uncertainty for random forest regression models, Photogramm. Eng.
Rem. S., 807, 189–197, 2016.
Craig, J. D.: The relationship between bathymetry and ferromanganese deposits
in the north equatorial Pacific, Mar. Geol., 29, 165–186,
https://doi.org/10.1016/0025-3227(79)90107-5, 1979.
Cutler, D. R., Edwards, T. C., Beard Karen, H., Cutler, A., Hess, K. T.,
Gibson, J. C., and Lawler, J. J.: Random forests for classification in
ecology, Ecology, 88, 2783–2792, 2007.
D'Agostino, R. B., Belanger, A., and D' Agostino Jr., R. B.: A Suggestion for
Using Powerful and Informative Tests of Normality, Am. Stat., 44, 316–321,
https://doi.org/10.2307/2684359, 1990.
Danson, E.: The Economies of Scale: Using Autonomous Underwater Vehicles
(AUVs) for Wide-Area Hydrographic Survey and Ocean Data Acquisition, FIG XXII
International Congress Washington, DC, USA, 19–26 April, 2002.
Das, R. P. and Anand, S.: Metallurgical Processing of Polymetallic Ocean
Nodules, in: Deep-Sea Mining, edited by: Sharma, R., Resource Springer,
https://doi.org/10.1007/978-3-319-52557-0_12, 2007.
Dawson, R.: How Significant is a Boxplot Outlier?, J. Stat. Educat., 19,
1–13, https://doi.org/10.1080/10691898.2011.11889610, 2011.
De Moustier, C.: Beyond bathymetry: Mapping acoustic backscattering from the
deep seafloor with Sea Beam, J. Acoust. Soc. Am., 79, 316–331, 1986.
Deschamps, A., Maurice, T., Embley, R. W., and Chadwick, W. W.: Quantitative
study of the deformation at Southern Explorer Ridge using high-resolution
bathymetric data, Earth Planet. Sc. Lett., 259, 1–17,
https://doi.org/10.1016/j.epsl.2007.04.007, 2007.
Diaz-Uriarte, R. and de Andres, A.: Gene selection and classification of
microarray data using random forest, BMC Bioinformatics, 7,
https://doi.org/10.1186/1471-2105-7-3, 2006.
Diesing, M. and Stephens, D.: A multi-model ensemble approach to seabed
mapping, J. Sea Res., 100, 62–69, https://doi.org/10.1016/j.seares.2014.10.013, 2015.
Diesing, M., Green, S. L., Stephens, D., Lark, R. M., Stewart, H. A., and
Dove, D.: Mapping seabed sediments: Comparison of manual, geostatistical,
object-based image analysis and machine learning approaches, Cont. Shelf
Res., 84, 107–119, https://doi.org/10.1016/j.csr.2014.05.004, 2014.
Dikau, R.: Geomorphic landform modelling based on hierarchy theory, in:
Proceedings of the 4th International
Symposium on Spatial Data Handling, edited by: Brassel, K. and Kishimoto, H.,
Department of Geography, University of Zürich, Zürich, Switzerland,
230–239, 1990.
Durden, J. M., Schoening, T., Althaus, F., Friedman, A. Garcia, R., Glover,
A. G., Greinert, J., Jacobsen, Stout, N., Jones, D. O. B., Jordt, A., Kaeli,
J. W., Koser, K., Kuhnz, L. A., Lindsay, D., Morris, K. J., Nattkemper, T.
W., Osterloff, J., Ruhl, H. A., Singh, H., Tran, M., and Bett, B. J.:
Perspectives in visual imaging for marine biology and ecology: from
acquisition to understanding, Oceanogr. Mar. Biol., 54, 1–72,
https://doi.org/10.1201/9781315368597, 2016.
Field, A. P.: Discovering statistics using SPSS: (and sex and drugs and rock
“n” roll), (OKS Print.) Los Angeles (i.e. Thousand Oaks), California, SAGE
Publications, 2009.
Frazer, J. and Fisk, M. B.: Geological factors related to characteristics of
sea-floor manganese nodule deposits, Deep-Sea Res. Pt. A, 28, 1533–1551,
https://doi.org/10.1016/0198-0149(81)90096-0, 1981.
Fu, W. J., Jiang, P. K., Zhou, G. M., and Zhao, K. L.: Using Moran's I and
GIS to study the spatial pattern of forest litter carbon density in a
subtropical region of southeastern China, Biogeosciences, 11, 2401–2409,
https://doi.org/10.5194/bg-11-2401-2014, 2014.
Garzón, M. B., Blazek, R., Neteler, M., Sánchez de Dios, R., Ollero,
H. S., and Furlanello, C.: Predicting habitat suitability with machine
learning models: The potential area of Pinus sylvestris L. in the Iberian
Peninsula, Ecol. Model., 197, 383–393,
https://doi.org/10.1016/j.ecolmodel.2006.03.015, 2006.
Genuer, R., Poggi J., Tuleau-Malot, C., and Villa-Vialaneix, N.: Random
Forests for Big Data, Big Data Research, 9, 28–46,
https://doi.org/10.1016/j.bdr.2017.07.003, 2017.
GEOMAR: Helmholtz-Zentrum für Ozeanforschung, Autonomous Underwater
Vehicle “ABYSS”, Journal of large-scale research facilities, 2, A79, 1–5,
https://doi.org/10.17815/jlsrf-2-149, 2016.
Ghasemi, A. and Zahediasl, S.: Normality Tests for Statistical Analysis: A
Guide for Non-Statisticians, Int. J. Endocrinol. Metab., 10, 486–489,
https://doi.org/10.5812/ijem.3505, 2012.
Gilardi, N. and Bengio, S.: Comparison of four machine learning algorithms
for spatial data analysis, Conf. Signals Syst. Comput., 17, 160–167, 2009.
Glasby, G. P.: Distribution of manganese nodules and lebensspuren in
underwater photographs from the Carlsberg Ridge, Indian Ocean, New Zealand,
J. Geol. Geophys., 16, 1–17, https://doi.org/10.1080/00288306.1973.10425383, 1973.
Glasby, G. P.: Manganese nodules in the South Pacific: A review, New Zealand,
J. Geol. Geophys., 19, 707–736, https://doi.org/10.1080/00288306.1976.10426315, 1976.
Goodchild, M. F.: Spatial autocorrelation. Concepts and Techniques in Modem
Geography, 47, 1–56, 1986.
Grasmueck, M., Eberli, G. P., Viggiano, D. A., Correa, T., Rathwell, G., and
Luo, J.: Autonomous underwater vehicle (AUV) mapping reveals coral mound
distribution, morphology, and oceanography in deep water of the Straits of
Florida, Geophys. Res. Lett., 33, L23616, https://doi.org/10.1029/2006GL027734, 2006.
Greinert, J.: Swath sonar multibeam EM122 bathymetry during SONNE cruise
SO239 with links to raw data files, PANGAEA, https://doi.org/10.1594/PANGAEA.859456,
2016.
Greinert, J., Schoening, T., Köser, K., and Rothenbeck, M.: Seafloor
images and raw context data along AUV tracks during SONNE cruises SO239 and
SO242/1, GEOMAR – Helmholtz Centre for Ocean Research Kiel, PANGAEA,
https://doi.org/10.1594/PANGAEA.882349, 2017.
Haase, K. M., Koschinsky, A., Petersen, S., Devey, C. W., German, C.,
Lackschewitz, K. S., Melchert, B., Seifert, R., Borowski, C., Giere, O., and
Paulick, H.: M64/1, M68/1 and M78/2 Scientific Parties. Diking, young
volcanism and diffuse hydrothermal activity on the southern Mid-Atlantic
Ridge: the Lilliput field at 9∘33′ S, Mar. Geol., 266, 52–64,
https://doi.org/10.1016/j.margeo.2009.07.012, 2009.
Hammer, Ø., Harper, D. A. T., and Ryan, P. D.: PAST: Paleontological
statistics software package for education and data analysis, Palaeontol.
Electron., 4, 9 pp., 2001.
Hari, V. N., Kalyan, B., Chitre, M., and Ganesan, V.: Spatial Modeling of
Deep-Sea Ferromanganese Nodules With Limited Data Using Neural Networks, IEEE
J. Ocean. Engin., 43, 1–18, https://doi.org/10.1109/JOE.2017.2752757, 2017.
Herkül, K., Peterson, A., and Paekivi, S.: Applying multibeam sonar and
mathematical modeling for mapping seabed substrate and biota of offshore
shallows, Estuarine, Coast. Shelf Sci., 192, 57–71, 2017.
Henthorn, R., Caress, D. W., Thomas, H., McEwen, R., Kirkwood, W. J., Paull,
C. K., and Keaten, R.: High-resolution multibeam and subbottom surveys of
submarine canyons, deep-sea fan channels, and gas seeps using the MBARI
mapping AUV, Proceedings of the IEEE OCEANS Conference, 1–6,
https://doi.org/10.1109/OCEANS.2006.307104, 2006.
Hoaglin, D. C., Iglewicz, B., and Tukey, J. W.: Performance of Some Resistant
Rules for Outlier Labeling, J. Am. Stat. Assoc., 81, 991–999, 1986.
Horning, N.: RandomForests: An algorithm for image classification and
generation of continuous fields data sets, International Conference on
Geoinformatics for Spatial Infrastructure Development in Earth and Allied
Sciences (GIS-IDEAS), 9–11 December, Hanoi, Vietnam, 2010.
Huang, Z., Justy, S., Scott, L. N., and Brendan, P. B.: Predictive mapping of
seabed substrata using high-resolution multibeam sonar data: A case study
from a shelf with complex geomorphology, Mar. Geol., 357, 37–52, 2014.
Ismail, K., Huvenne, V. A. I., and Masson, D. G.: Objective automated
classification technique for marine landscape mapping in submarine canyons,
Mar. Geol., 362, 17–32, 2015.
Iwahashi, J. and Pike, R. J.: Automated Classifications of Topography from
DEMs by an Unsupervised Nested- Means Algorithm and a Three-Part Geometric
Signature, Geomorphology, 86, 409–440, 2017.
Jakobsson, M., Gyllencreutz, R., Mayer, L. Dowdeswell, J. A. Canals, M.,
Todd, B. J., Dowdeswell, E. K., Hogan K. A., and Larter, R. D.: Mapping
submarine glacial landforms using acoustic methods, Geol. Soc., London,
Memoirs, 46, 17–40, https://doi.org/10.1144/M46.182, 2016.
Jung, H. S., Ko, Y. T., and Moon, J. W.: Characteristics of Seafloor
Morphology and Ferromanganese Nodule Occurrence in the Korea Deep-sea
Environmental Study (KODES) Area, NE Equatorial Pacific, Mar. Georesour.
Geotec., 19, 167–180, https://doi.org/10.1080/10641190109353811, 2001.
Kavenski, M., Pozdnukhov, A., and Timonin, V.: Machine learning for spatial
environmental data. Theory, applications and software, EPFL Press, 1st Edn.,
https://doi.org/10.1201/9781439808085, 2009.
Kim, H. Y.: Statistical notes for clinical researchers: assessing normal
distribution using skewness and kurtosis, Restorative Dentistry and
Endodontics, 38, 52–54, https://doi.org/10.5395/rde.2013.38.1.52, 2013.
Kleinrock, M. C: Capabilities of some systems used to Survey the
Deep-Sea Floor, Chap. 2, in: Handbook of geophysical exploration at sea, edited by:
Richard, G. A., 2nd Edn., Hard Minerals, CRC Press, p. 37, 1992a.
Kleinrock, M. C., Hey, R. N., and Theberger Jr., A. E.: Practical geological
comparison of some seafloor survey instruments, Geophys. Res. Lett., 19,
1407–1410, https://doi.org/10.1029/92GL01390, 1992b.
Knobloch, A., Kuhn, T., Rühlemann, C., Hertwig, T., Zeissler, K. O., and
Noack, S.: Predictive Mapping of the Nodule Abundance and Mineral Resource
Estimation in the Clarion-Clipperton Zone Using Artificial Neural Networks
and Classical Geostatistical Methods, in: Deep-Sea Mining, edited by: Sharma
R., Resource Springer, https://doi.org/10.1007/978-3-319-52557-0_6, 2017.
Ko, Y., Lee, S., Kim, J., Kim, K. H., and Jung, M. S.: Relationship between
Mn Nodule Abundance and Other Geological Factors in the Northeastern Pacific:
Application of GIS and Probability Method, Ocean Sci. J., 41, 149–161,
https://doi.org/10.1007/BF03022420, 2006.
Kodagali, V.: Influence of Regional and Local Topography on the Distribution
of Polymetallic Nodules in Central Indian Ocean Basin, Geo-Mar. Lett., 8,
173–178, 1988.
Kodagali, V. and Chakraborty, B.: Multibeam Echosounder PseudoSidescan Images
as a tool for Manganese Nodule Exploration, Proceedings of the Third Ocean
Mining Symposium Goa, India, 8–10 November, 97–104, 1999.
Kodagali, V. N. and Sudhakar, M.: Manganese nodule distributionin different
topographic domains of the Central Indian Basin, Mar. Georesour. Geotec., 11,
293–309, https://doi.org/10.1080/10641199309379925, 1993.
Kuhn, T. and Rathke, M.: Report on visual data acquisition in the field and
interpretation for SMnN. Deliverable D1.31 of the EU-Project Blue Mining, BGR
Hannover, 34 pp., 2017.
Kuhn, T., Wiedicke-Hombach, M., Barckhausen, U., Schwarz-Schampera, U.,
Rutkowski, J., and Lehmnn, S.: New Insights of Mn Nodule Exploration from the
German License Area in the Pacific Manganese Nodule Belt, Toward the
Sustainable Development of Marine Minerals: Geological, Technological, and
Economic Aspects, 39th Underwater Mining Institute, 4–9 October, Gelendzhik,
Russia, 2010.
Kuhn, T., Rühlemann, C., and Wiedicke-Hombach, M.: Development of Methods
and Equipment for the Exploration of Manganese Nodules in the German License
Area in the Central Equatorial Pacific, Proceedings of the ninth ISOPE Ocean
Mining Symposium, Maui, Hawaii, USA, 19–24 June, 174–177, 2011.
Kuhn, T., Rühlemann C., and Knobloch, A.: Classification of manganese
nodule estimates: can we reach the “measured resource” level? Resource and
Environmental Assessments for Seafloor Mining Development, 45th Underwater
Mining Conference, Incheon, Korea, 9–13 October 2016.
Kwasnitschka, T., Köser, K., Sticklus, J., Rothenbeck, M., Weiß, T.,
Wenzlaff, Em. , Schoening, T., Triebe, L., Steinführer, An., Devey, C.,
and Greinert, J.: DeepSurveyCam – A Deep Ocean Optical Mapping System,
Sensors, 16, 1–17, https://doi.org/10.3390/s16020164, 2016.
Lary, D. J., Alavi, A. H., Gandomi, A. H., and Walker, A. L.: Machine
learning in geosciences and remote sensing, Geosci. Front., 7, 3–10,
https://doi.org/10.1016/j.gsf.2015.07.003, 2016.
Leempoel, K., Parisod, C., Geiser, C., Dapra, L., Vittoz, P., and Joost, S.:
Very high-resolution digital elevation models: are multi-scale derived
variables ecologically relevant?, Methods Ecol. Evol., 6, 1373–1383,
https://doi.org/10.1111/2041-210X.12427, 2015.
Legendre, P. and Legendre, L.: Numerical Ecology, 2nd Edn., Elsevier,
Amsterdam, p. 853, 1998.
Li, J.: Predicting the spatial distribution of seabed gravel content using
random forest, spatial interpolation methods and their hybrid methods. 20th
International Congress on Modelling and Simulation, Adelaide, Australia, 1–6
December, 394–400, 2013.
Li, J. and Heap, A. D.: A review of comparative studies of spatial
interpolation methods in environmental sciences: Performance and impact
factors, Ecol. Inform., 6, 228–241, https://doi.org/10.1016/j.ecoinf.2010.12.003,
2011.
Li, J. and Heap, A. D.: Spatial interpolation methods applied in the
environmental sciences: A review, Environ. Modell. Softw., 53, 173–189,
https://doi.org/10.1016/j.envsoft.2013.12.008, 2014.
Li, J., Potter, A., Huang, Z., Daniell, J. J., and Heap, A. D.: Predicting
Seabed Mud Content across the Australian Margin: Comparison of Statistical
and Mathematical Techniques Using a Simulation Experiment, Geoscience
Australia, Record 2010/11, 146 pp., 2010.
Li, J., Heap, A. D., Potter, A., and Daniell, J. J.: Application of machine
learning methods to spatial interpolation of environmental variables,
Environ. Modell. Softw., 26, 1647–1659, https://doi.org/10.1016/j.envsoft.2011.07.004,
2011a.
Li, J., Heap, A. D., Potter, A., Huang, Z., and Daniell, J. J.: Can we
improve the spatial predictions of seabed sediments? A case study of spatial
interpolation of mud content across the southwest Australian margin, Cont.
Shelf Res., 31, 365–1376, https://doi.org/10.1016/j.csr.2011.05.015, 2011b.
Li, J., Siwabessy, P. J., Maggie, T., Zhi, H., and Andrew, D. H.: Predicting
Seabed Hardness Using Random Forest in R, Data Mining Applications with R, Elsevier, edited by: Zhao, Y. and Cen, Y., 299–329,
https://doi.org/10.1016/B978-0-12-411511-8.00011-6, 2013.
Li, J., Tran, M., and Siwabessy, J.: Selecting Optimal Random Forest
Predictive Models: A Case Study on Predicting the Spatial Distribution of
Seabed Hardness, PLoS ONE, 11, e0149089, https://doi.org/10.1371/journal.pone.0149089,
2016.
Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, L.,
Radke, L., Howard, F., and Nichol, S.: Application of random forest,
generalised linear model and their hybrid methods with geostatistical
techniques to count data: Predicting sponge species richness, Environ.
Modell. Softw., 97, 112–129, 2017.
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R
News, 2, 18–22, 2002.
Martínez Arbizu, P. and Haeckel, M. (Eds.): RV SONNE Fahrtbericht/Cruise
Report SO239: EcoResponse Assessing the Ecology, Connectivity and Resilience
of Polymetallic Nodule Field Systems, Balboa (Panama) – Manzanillo (Mexico),
11.03.–30.04.2015. GEOMAR Report, N. Ser. 025. GEOMAR Helmholtz-Zentrum
für Ozeanforschung, Kiel, Germany, 204 pp., 2015.
Mascaro, J., Asner, G. P., Knapp, D. E., Kennedy-Bowdoin, T., Martin, R. E.,
Anderson, C., Higgins, M., and Chadwick, D.: A Tale of Two “Forests”:
Random Forest Machine Learning Aids Tropical Forest Carbon Mapping, PLoS ONE,
9, e85993, https://doi.org/10.1371/journal.pone.0085993, 2014.
Mayer, L. A.: Frontiers in seafloor mapping and visualization, Mar. Geophys.
Res., 27, 7–17, https://doi.org/10.1007/s11001-005-0267-x, 2007.
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7,
983–999, 2006.
Meinshausen, N.: quantregForest 0.2–3, R package, 2012.
Mentch, L. and Hooker, G.: Quantifying uncertainty in random forests via
confidence intervals and hypothesis tests, J. Mach. Learn. Res., 17,
841–881, 2016.
Millard, K. and Richardson, M.: On the Importance of Training Data Sample
Selection in Random Forest Image Classification: A Case Study in Peatland
Ecosystem Mapping, Remote Sens., 7, 8489–8515, https://doi.org/10.3390/rs70708489,
2015.
Miller, B. A., Koszinski, S., Wehrhan, M., and Sommer, M.: Impact of
multi-scale predictor selection for modeling soil properties, Geoderma,
239/240, 97–106, https://doi.org/10.1016/j.geoderma.2014.09.018, 2015.
Mitchell, M. W.: Bias of the Random Forest Out-of-Bag (OOB) Error for Certain
Input Parameters, Open Journal of Statistics, 1, 205–211,
https://doi.org/10.4236/ojs.2011.13024, 2011.
Moellering, H. and Tobler, W.: Geographical variances, Geogr. Anal., 4,
34–50, https://doi.org/10.1111/j.1538-4632.1972.tb00455.x, 1972.
Moran, P. A. P.: The interpretation of statistical maps, J. Roy. Stat. Soc.
B, 10, 243–251, 1948.
Moran, P. A. P.: Notes on Continuous Stochastic Phenomena, Biometrika, 37,
17–23, https://doi.org/10.2307/2332142, 1950.
Mucha, J. and Wasilewska-Błaszczyk, M.: Variability and Accuracy of
Polymetallic Nodules Abundance Estimations in the IOM Area – Statistical and
Geostatistical Approach, Proceedings of the Tenth (2013) ISOPE Ocean Mining
and Gas Hydrates Symposium, Szczecin, Poland, 22–26 September, 27–31, 2013.
Mukaka, M. M.: Statistics corner: A guide to appropriate use of correlation
coefficient in medical research, Malawi Med. J., 24, 69–71, 2012.
Obermeyer, Z. and Emanuel, E. J.: Predicting the Future – Big Data, Machine
Learning, and Clinical Medicine, N. Engl. J. Med., 29, 1216–1219,
https://doi.org/10.1056/NEJMp1606181, 2016.
Okazaki, M. and Tsune, A.: Exploration of Polymetallic Nodule Using AUV in
the Central Equatorial Pacific. Proceedings of the Tenth ISOPE Ocean Mining
and Gas Hydrates Symposium, Szczecin, Poland, 22–26 September, 2013.
Okun, O. and Priisalu, H.: Random Forest for Gene Expression Based Cancer
Classification: Overlooked Issues, Lect. Notes Comput. Sc., 4478, 483–490,
https://doi.org/10.1007/978-3-540-72849-8_61, 2007.
Paduan, B. J., Caress, D. W., Clague D. A., Paull, C. K., and Thomas, H.:
High-Resolution Mapping of Mass Wasting, Tectonic, and Volcanic Hazards Using
the MBARI Mapping AUV, Rend. online Soc. Geol. It., 7, 181–186, 2009.
Paul, C. K., Anderson, K., Caress, D. W., Lundsten, E., and Gwiazda, R.:
Fine Scale Morphology of Tubenworm Slump, Chap. 15, Monterey Canyon, in:
Submarine mass movements and their consequences, 7th International Symposium,
edited by: Lamarche, G., Mountjoy, J., Bull, S., Hubble, T., Krastel, S.,
Lane, E., Micallef, A., Moscardelli, L., Mueller, C., Pecher, I., and Woelz,
S.: Advances in Natural and Technological Hazards Research 41, Springer,
https://doi.org/10.1007/978-3-319-20979-1_15, 2016.
Paull, L., Saeedi, S., Seto, M., and Li, H.: AUV Navigation and Localization:
A Review, IEEE J. Ocean. Engin., 39, 131–149,
https://doi.org/10.1109/JOE.2013.2278891, 2014.
Petersen S., Hannington, M., and Krätschell, A.: Technology developments
in the exploration and evaluation of deep-sea mineral resources,
Responsabilité and Environment, 85, 14–18, 2017.
Peukert, A., Schoening, T., Alevizos, E., Köser, K., Kwasnitschka, T.,
and Greinert, J.: Understanding Mn-nodule distribution and evaluation of
related deep-sea mining impacts using AUV-based hydroacoustic and optical
data, Biogeosciences, 15, 2525–2549,
https://doi.org/10.5194/bg-15-2525-2018, 2018a.
Peukert, A., Petersen, S., Greinert, J., and Charlot, F.: Seabed Mining,
edited by: Micallef, A, Krastel, S., and Savini, A., Submarine Geomorphology,
Springer Geology, Springer, Chamber, 481–502,
https://doi.org/10.1007/978-3-319-57852-1, 2018b.
Pierdomenico, M., Guida, V. G., Macelloni, L., Chiocci, F. L., Rona, P. A.,
Scranton, M. I., Asper, V., and Diercks, A.: Sedimentary facies, geomorphic
features and habitat distribution at the Hudson Canyon head from AUV
multibeam data, Deep-Sea Res. Pt. II, 121, 112–125,
https://doi.org/10.1016/j.dsr2.2015.04.016, 2015.
Preston, J.: Automated acoustic seabed classification of multibeam images of
Stanton Banks, Appl. Acoust., 70, 1277–1287, 2009.
R Development Core Team: R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN
3-900051-07-0, 2008.
Rahn, M.: Deposit models. Deliverable D3.11 of the EU-Project Blue Mining,
BGR Hannover, 43–47, 2017.
Riley, S. J., DeGloria, S. D., and Elliot, R.: A terrain ruggedness index
that quantifies topographic heterogeneity, Int. J. Sci., 5, 1–4, 1999.
Roberts, J. J., Best, B. D., Dunn, D. C., Treml, E. A., and Halpin, P. N.:
Marine Geospatial Ecology Tools: An integrated framework for ecological
geoprocessing with ArcGIS, Python, R, MATLAB, and C
Rodriguez-Galiano, V. F., Chica-Olmo, M., and Chica-Rivas, M.: Predictive
modelling of gold potential with the integration of multisource information
based on random forest: a case study on the Rodalquilar area, Southern Spain,
Int. J. Geogr. Informat. Sci., 28, 1336–1354,
https://doi.org/10.1080/13658816.2014.885527, 2014.
Rodriguez-Galiano, V. F., Sanchez-Castillo, M., Chica-Olmo, M., and
Chica-Rivas, M.: Machine learning predictive models for mineral
prospectivity: An evaluation of neural networks, random forest, regression
trees and support vector machines, Ore Geol. Rev., 71, 804–818,
https://doi.org/10.1016/j.oregeorev.2015.01.001, 2015.
Rogers, J.: Seismic, Bathymetric, and Photographic Evidence of widespread
erosion and a manganese-nodule pavement along the continental rise of the
southeast Cape Basin, Mar. Geol., 78, 57–76,
https://doi.org/10.1016/0025-3227(87)90068-5, 1987.
Roman, C. and Mather, R.: Autonomous underwater vehicles as tools for
deep-submergence archaeology, P. I. Mech. Eng. M-J. Eng., 224,
327–340, https://doi.org/10.1243/14750902JEME202, 2010.
Rühlemann, C., Kuhn, T., and Wiedicke, M.: Current Status of Manganese
Nodule Exploration in the German License Area Proceedings of the Ninth ISOPE
Ocean Mining Symposium Maui, Hawaii, USA, 19–24 June, 168–173, 2011.
Rühlemann, C., Kuhn, T., Vink, A., and Wiedicke, M.: Methods of Manganese
Nodule Exploration in the German License Area. Recent Developments in
Atlantic Seabed Minerals Exploration and Other Topics, 42nd Underwater Mining
Institute, 21–29 October, Rio de Janeiro and Porto de Galinhas, Brazil,
2013.
Ruß, G. and Kruse, R.: Regression Models for Spatial Data: An Example
from Precision Agriculture. CDM 2010. Lecture Notes in Computer Science, Vol.
6171, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-14400-4_35, 2010.
Santibanez, S., Lakes, T., and Kloft, M.,: Performance Analysis of Some
Machine Learning Algorithms for Regression Under Varying Spatial
Autocorrelation, The 18th AGILE International Conference on Geographic
Information Science, Lisboa, Portugal, 9–12 June, 2015a.
Santibanez, S. F., Kloft, M., and Lakes T.: “Performance Analysis of Machine
Learning Algorithms for Regression of Spatial Variables. A Case Study in the
Real Estate Industry”, 13th International Conference of GeoComputation,
Dallas, USA, 20–23 May, 2015b.
Schmueli, G.: To Explain or to Predict?, Stat. Sci., 25, 289–310,
https://doi.org/10.1214/10-STS330, 2010.
Schoening, T., Kuhn, T., and Nattkemper, T. W.: Estimation of poly-metallic
nodule coverage in benthic images, in: Proceedings of the 41st Conference of
the Underwater Mining Institute, UMI, 2012a.
Schoening, T., Bergmann, M., Ontrup, J., Taylor, J., Dannheim, J., Gutt, J.,
Purser, A., and Nattkemper, T. W.: Semi-Automated Image Analysis for the
Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory
HAUSGARTEN, PLoS ONE, 7, e38179, https://doi.org/10.1371/journal.pone.0038179, 2012b.
Schoening, T., Kuhn, T., and Nattkemper, T. W.: Seabed classification using a
bag-of prototypes feature representation, in: 2014 ICPR Workshop on Computer
Vision for Analysis of Underwater Imagery (CVAUI), IEEE, 17–24,
https://doi.org/10.1109/CVAUI.2014.9, 2014.
Schoening, T., Thomas, K., Bergmann, M., and Nattkemper, T. W.: DELPHI –
fast and adaptive computational laser point detection and visual footprint
quantification for arbitrary underwater image collections, Front. Mar. Sci.,
2, 1–6, https://doi.org/10.3389/fmars.2015.00020, 2015.
Schoening, T., Kuhn, T., Jones, D. O. B., Simon-Lledo, E., and Nattkemper, T.
W.: Fully automated image segmentation for benthic resource assessment of
poly-metallic nodules, Methods Oceanogr., 15/16, 78–89,
https://doi.org/10.1016/j.mio.2016.04.002, 2016.
Schoening, T., Jones, D. O. B., and Greinert, J.: Compact-Morphology-based
polymetallic Nodule Delineation, Sci. Rep., 7, 13338,
https://doi.org/10.1038/s41598-017-13335-x, 2017a.
Schoening, T.: Source code for the Compact Morphology-based Nodule
Delineation (CoMoNoD) algorithm, PANGAEA, https://doi.org/10.1594/PANGAEA.875070,
Supplement to: Schoening T., Jones D. O. B., Greinert, J.:
Compact-Morphology-based poly-metallic Nodule Delineation, Sci. Rep., 7,
1–12, https://doi.org/10.1038/s41598-017-13335-x, 2017b.
Schoening, T.: Results of nodule detection along AUV tracks during SONNE
cruises SO239 and SO242/1, PANGAEA, https://doi.org/10.1594/PANGAEA.883838, 2017c.
Sharma, R.: Quantitative estimation of seafloor features from photographs and
their application to nodule, Mar. Georesour. Geotec., 11, 311–331,
https://doi.org/10.1080/10641199309379926, 1993.
Sharma, R. and Kodagali, V.: Influence of seabed topography on the
distribution of manganese nodules and associated features in the Central
Indian Basin: A study based on photographic observations, Mar. Geol., 110,
153–162, https://doi.org/10.1016/0025-3227(93)90111-8, 1993.
Sharma, R. Sankar, S. J., Samanta, S., Sardar, A. A., and Gracious, D.: Image
analysis of seafloor photographs for estimation of deep-sea minerals,
Geo-Mar. Lett., 30, 617–626, https://doi.org/10.1007/s00367-010-0205-z, 2010.
Sharma, R., Khade, N. H., and Sankar, S. J.: Assessing the distribution and
abundance of seabed minerals from seafloor photography data in the Central
Indian Ocean Basin, Int. J. Remote Sens., 34, 1691–1706,
https://doi.org/10.1080/01431161.2012.725485, 2013.
Sibenac, M., Podder, T., Kirkwood, W., and Thomas, H.: Autonomous Underwater
Vehicles for Ocean Research: Current Needs and State of the Art Technologies,
Mar. Technol. Soc. J., 38, 63–72, https://doi.org/10.4031/002533204787522848, 2004.
Skornyakova, N. and Murdmaa, I.: Local variations in distribution and
composition of ferromanganese nodules in the Clarion-Clipperton Nodule
Province, Mar. Geol., 103, 381–405, https://doi.org/10.1016/0025-3227(92)90028-G,
1992.
SPC: Deep Sea Minerals: Manganese Nodules, a physical, biological,
environmental, and technical review, edited by: Baker, E. and Beaudoin, Y.,
Vol. 1B, Secretariat of the Pacific Community, 2013.
Stephens, D. and Diesing, M.: A Comparison of Supervised Classification
Methods for the Prediction of Substrate Type Using Multibeam Acoustic and
Legacy Grain-Size Data, PLoS ONE, 9, e93950,
https://doi.org/10.1371/journal.pone.0093950, 2014.
Strobl, C. and Zeileis, A.: Danger: High Power! – Exploring the Statistical
Properties of a Test for Random Forest Variable Importance, Proceedings of
the 18th International Conference on Computational Statistics, Porto,
Portugal, 2008.
Strobl, C. Boulesteix, A. L., Zeileis, A., and Hothorn, T.: Bias in random
forest variable importance measures: Illustrations, sources and a solution,
BMC Bioinformatics, 8, https://doi.org/10.1186/1471-2105-8-25, 2007.
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., and Zeileis, A.:
Conditional variable importance for random forests, BMC Bioinformatics, 9,
307, https://doi.org/10.1186/1471-2105-9-307, 2008.
Strobl, C., Malley, J., and Tutz, G.: An introduction to recursive
partitioning: rationale, application, and characteristics of classification
and regression trees, bagging, and random forests, Psychol. Methods, 14,
323–48, 2009.
Tsune, A. and Okazaki, M.: Some Considerations about Image Analysis of
Seafloor Photographs for Better Estimation of Parameters of Polymetallic
Nodule Distribution, Proceedings of the Twenty-fourth (2014) International
Ocean and Polar Engineering Conference, Busan, Korea, 15–20 June, 72–77,
2014.
Tung, N. T., Huang, J. Z., Khan, I., Li, M. J., and Williams, G.: Extensions
to Quantile Regression Forests for Very High-Dimensional Data, in: Advances
in Knowledge Discovery and Data Mining, edited by: Tseng, V. S., Ho, T. B.,
Zhou, Z. H., Chen, A. L. P., and Kao, H. Y., PAKDD 2014, Lecture Notes in
Computer Science, Vol. 8444, Springer, Cham
https://doi.org/10.1007/978-3-319-06605-9_21, 2014.
UNOET: Delineation of mine sites and potential in different sea areas, Vol.
9, Seabed Minerals Series 4, Graham and Trotman, London, 1987.
van der Ploeg, T., Austin, P. C., and Steyerberg, E. W.: Modern modelling
techniques are data hungry: a simulation study for predicting dichotomous end
points, Medical Research Methodology, 14, 1–13,
https://doi.org/10.1186/1471-2288-14-137, 2014.
Volkmann, S. E.: Concept for Sustainable Economic Evaluation. Deliverable
D1.41 of the EU-Project Blue Mining, BGR Hannover, 43–47, 2017.
Volkmann, S. E. and Lehnen, F.: Production key figures for planning the
mining of manganese nodules, Mar. Georesour. Geotec., 36, 360–375,
https://doi.org/10.1080/1064119X.2017.1319448, 2018.
von Stackelberg, U. and Beiersdorf, H.: The formation of manganese nodules
between the Clarion and Clipperton fracture zones southeast of Hawaii, Mar.
Geol., 98, 411–423, https://doi.org/10.1016/0025-3227(91)90113-I, 1991.
Wager, S., Hastie, T., and Efron, B.: Confidence intervals for random
forests: the jackknife and the infinitesimal jackknife, J. Mach. Learn. Res.,
15, 1625–1651, 2014.
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE)
over the root mean square error (RMSE) in assessing average model
performance, Clim. Res., 30, 79–82, https://doi.org/10.3354/cr030079, 2005.
Wilson, M. F. J., O'Connell, B., Brown, C., Guinan J. C., and Grehan, A. J.:
Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping
on the Continental Slope, Mar. Geodesy, 30, 3–35,
https://doi.org/10.1080/01490410701295962, 2007.
Wynn, R. B., Huvenne, V. A. I., Le Bas, T. P., Murton, B. J., Connelly, D.
P., Bett, B. J., Ruhl, H. A., Morris, K. J., Peakall, J., Parsons, D. R.,
Sunner, S. J., Darber S. E., Dorrell, R. M., and Hunt, J. E.: Autonomous
Underwater Vehicles (AUVs): Their past, present and future contributions to
the advancement of marine geoscience, Mar. Geol., 352, 451–468,
https://doi.org/10.1016/j.margeo.2014.03.012, 2014.
Xu, L., Saatchi, S. S., Yang, Y., Yu, Y., and White, L.: Performance of
nonparametric algorithms for spatial mapping of tropical forest structure,
Carbon Balance Manage, 11, 1–14, https://doi.org/10.1186/s13021-016-0062-9, 2016.
Yazici, B. and Yolacan, S.: A comparison of various tests of normality, J.
Stat. Comput. Simul., 77, 175–183, https://doi.org/10.1080/10629360600678310, 2007.
Zevenbergen, L. W. and Thorne, C. R.: Quantitative analysis of land surface
topography, Earth Surf. Proc. Land., 12, 47–56,
https://doi.org/10.1002/esp.3290120107, 1987.
Short summary
The use of high-resolution hydroacoustic and optic data acquired by an autonomous underwater vehicle can give us detailed sea bottom topography and valuable information regarding manganese nodules' spatial distribution. Moreover, the combined use of these data sets with a random forest machine learning model can extend this spatial prediction beyond the areas with available photos, providing researchers with a new mapping tool for further investigation and links with other data.
The use of high-resolution hydroacoustic and optic data acquired by an autonomous underwater...
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