Articles | Volume 13, issue 10
https://doi.org/10.5194/essd-13-4967-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4967-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Collection and analysis of a global marine phytoplankton primary-production dataset
Francesco Mattei
CORRESPONDING AUTHOR
Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
CoNISMa, Piazzale Flaminio, 9, Rome, 00196, Italy
Michele Scardi
Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
CoNISMa, Piazzale Flaminio, 9, Rome, 00196, Italy
Related subject area
Biological oceanography
Deepwater red shrimp fishery in the eastern–central Mediterranean Sea: AIS-observed monthly fishing effort and frequency over 4 years
Global dataset on seagrass meadow structure, biomass and production
The Green Edge cruise: investigating the marginal ice zone processes during late spring and early summer to understand the fate of the Arctic phytoplankton bloom
A global marine particle size distribution dataset obtained with the Underwater Vision Profiler 5
Application of a new net primary production methodology: a daily to annual-scale data set for the North Sea, derived from autonomous underwater gliders and satellite Earth observation
The COSMUS expedition: seafloor images and acoustic bathymetric data from the PS124 expedition to the southern Weddell Sea, Antarctica
How to learn more about hydrological conditions and phytoplankton dynamics and diversity in the eastern English Channel and the southern bight of the North Sea? the SRN data set (1992–2021)
Primary productivity measurements in the Ross Sea, Antarctica: a regional synthesis
Patos Lagoon estuary and adjacent marine coastal biodiversity long-term data
Weight-to-weight conversion factors for benthic macrofauna: recent measurements from the Baltic and the North seas
The Plankton Lifeform Extraction Tool: a digital tool to increase the discoverability and usability of plankton time-series data
The ADRIREEF database: a comprehensive collection of natural/artificial reefs and wrecks in the Adriatic Sea
Diets of the Barents Sea cod (Gadus morhua) from the 1930s to 2018
A global viral oceanography database (gVOD)
PhytoBase: A global synthesis of open-ocean phytoplankton occurrences
A long-term (1965–2015) ecological marine database from the LTER-Italy Northern Adriatic Sea site: plankton and oceanographic observations
An interactive atlas for marine biodiversity conservation in the Coral Triangle
A synthetic satellite dataset of the spatio-temporal distributions of Emiliania huxleyi blooms and their impacts on Arctic and sub-Arctic marine environments (1998–2016)
A 40-year global data set of visible-channel remote-sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue
Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set
Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications
KRILLBASE: a circumpolar database of Antarctic krill and salp numerical densities, 1926–2016
A trait database for marine copepods
Global ocean particulate organic carbon flux merged with satellite parameters
A compilation of global bio-optical in situ data for ocean-colour satellite applications
Data compilation on the biological response to ocean acidification: an update
CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters
Vertical distribution of chlorophyll a concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean
Biogeography of key mesozooplankton species in the North Atlantic and egg production of Calanus finmarchicus
Biogeography of jellyfish in the North Atlantic, by traditional and genomic methods
A metadata template for ocean acidification data
Spatially explicit estimates of stock sizes, structure and biomass of herring and blue whiting, and catch data of bluefin tuna
A new compilation of stomach content data for commercially important pelagic fish species in the northeast Atlantic
Spatially explicit estimates of stock size, structure and biomass of North Atlantic albacore tuna (Thunnus alalunga)
Data compilation of fluxes of sedimenting material from sediment traps in the Atlantic Ocean
Global database of surface ocean particulate organic carbon export fluxes diagnosed from the 234Th technique
Distribution of known macrozooplankton abundance and biomass in the global ocean
Global marine plankton functional type biomass distributions: coccolithophores
The MAREDAT global database of high performance liquid chromatography marine pigment measurements
Distribution of mesozooplankton biomass in the global ocean
Calibration procedures and first dataset of Southern Ocean chlorophyll a profiles collected by elephant seals equipped with a newly developed CTD-fluorescence tags
The global distribution of pteropods and their contribution to carbonate and carbon biomass in the modern ocean
A global diatom database – abundance, biovolume and biomass in the world ocean
Global marine plankton functional type biomass distributions: Phaeocystis spp.
Picoheterotroph (Bacteria and Archaea) biomass distribution in the global ocean
Picophytoplankton biomass distribution in the global ocean
EPOCA/EUR-OCEANS data compilation on the biological and biogeochemical responses to ocean acidification
Jacopo Pulcinella, Enrico Nicola Armelloni, Carmen Ferrà, Giuseppe Scarcella, and Anna Nora Tassetti
Earth Syst. Sci. Data, 15, 809–820, https://doi.org/10.5194/essd-15-809-2023, https://doi.org/10.5194/essd-15-809-2023, 2023
Short summary
Short summary
Deep-sea fishery in the Mediterranean Sea was historically driven by the commercial profitability of deepwater red shrimps. Understanding spatiotemporal dynamics of fishing is key to comprehensively evaluate the status of these resources and prevent stock collapse. The observed monthly fishing effort and frequency dataset released by the automatic identification system (AIS) may help researchers as well as those involved in fishery management and in the update of existing management plans.
Simone Strydom, Roisin McCallum, Anna Lafratta, Chanelle L. Webster, Caitlyn M. O'Dea, Nicole E. Said, Natasha Dunham, Karina Inostroza, Cristian Salinas, Samuel Billinghurst, Charlie M. Phelps, Connor Campbell, Connor Gorham, Rachele Bernasconi, Anna M. Frouws, Axel Werner, Federico Vitelli, Viena Puigcorbé, Alexandra D'Cruz, Kathryn M. McMahon, Jack Robinson, Megan J. Huggett, Sian McNamara, Glenn A. Hyndes, and Oscar Serrano
Earth Syst. Sci. Data, 15, 511–519, https://doi.org/10.5194/essd-15-511-2023, https://doi.org/10.5194/essd-15-511-2023, 2023
Short summary
Short summary
Seagrasses are important underwater plants that provide valuable ecosystem services to humans, including mitigating climate change. Understanding the natural history of seagrass meadows across different types of environments is crucial to conserving seagrasses in the global ocean. This dataset contains data extracted from peer-reviewed publications and highlights which seagrasses have been studied and in which locations and is useful for pointing out which need further investigation.
Flavienne Bruyant, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Lise Artigue, Lucas Barbedo de Freitas, Guislain Bécu, Simon Bélanger, Pascaline Bourgain, Annick Bricaud, Etienne Brouard, Camille Brunet, Tonya Burgers, Danielle Caleb, Katrine Chalut, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Marine Cusa, Fanny Cusset, Laeticia Dadaglio, Marty Davelaar, Gabrièle Deslongchamps, Céline Dimier, Julie Dinasquet, Dany Dumont, Brent Else, Igor Eulaers, Joannie Ferland, Gabrielle Filteau, Marie-Hélène Forget, Jérome Fort, Louis Fortier, Martí Galí, Morgane Gallinari, Svend-Erik Garbus, Nicole Garcia, Catherine Gérikas Ribeiro, Colline Gombault, Priscilla Gourvil, Clémence Goyens, Cindy Grant, Pierre-Luc Grondin, Pascal Guillot, Sandrine Hillion, Rachel Hussherr, Fabien Joux, Hannah Joy-Warren, Gabriel Joyal, David Kieber, Augustin Lafond, José Lagunas, Patrick Lajeunesse, Catherine Lalande, Jade Larivière, Florence Le Gall, Karine Leblanc, Mathieu Leblanc, Justine Legras, Keith Lévesque, Kate-M. Lewis, Edouard Leymarie, Aude Leynaert, Thomas Linkowski, Martine Lizotte, Adriana Lopes dos Santos, Claudie Marec, Dominique Marie, Guillaume Massé, Philippe Massicotte, Atsushi Matsuoka, Lisa A. Miller, Sharif Mirshak, Nathalie Morata, Brivaela Moriceau, Philippe-Israël Morin, Simon Morisset, Anders Mosbech, Alfonso Mucci, Gabrielle Nadaï, Christian Nozais, Ingrid Obernosterer, Thimoté Paire, Christos Panagiotopoulos, Marie Parenteau, Noémie Pelletier, Marc Picheral, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Llúcia Ribot Lacosta, Jean-François Rontani, Blanche Saint-Béat, Julie Sansoulet, Noé Sardet, Catherine Schmechtig, Antoine Sciandra, Richard Sempéré, Caroline Sévigny, Jordan Toullec, Margot Tragin, Jean-Éric Tremblay, Annie-Pier Trottier, Daniel Vaulot, Anda Vladoiu, Lei Xue, Gustavo Yunda-Guarin, and Marcel Babin
Earth Syst. Sci. Data, 14, 4607–4642, https://doi.org/10.5194/essd-14-4607-2022, https://doi.org/10.5194/essd-14-4607-2022, 2022
Short summary
Short summary
This paper presents a dataset acquired during a research cruise held in Baffin Bay in 2016. We observed that the disappearance of sea ice in the Arctic Ocean increases both the length and spatial extent of the phytoplankton growth season. In the future, this will impact the food webs on which the local populations depend for their food supply and fisheries. This dataset will provide insight into quantifying these impacts and help the decision-making process for policymakers.
Rainer Kiko, Marc Picheral, David Antoine, Marcel Babin, Léo Berline, Tristan Biard, Emmanuel Boss, Peter Brandt, Francois Carlotti, Svenja Christiansen, Laurent Coppola, Leandro de la Cruz, Emilie Diamond-Riquier, Xavier Durrieu de Madron, Amanda Elineau, Gabriel Gorsky, Lionel Guidi, Helena Hauss, Jean-Olivier Irisson, Lee Karp-Boss, Johannes Karstensen, Dong-gyun Kim, Rachel M. Lekanoff, Fabien Lombard, Rubens M. Lopes, Claudie Marec, Andrew M. P. McDonnell, Daniela Niemeyer, Margaux Noyon, Stephanie H. O'Daly, Mark D. Ohman, Jessica L. Pretty, Andreas Rogge, Sarah Searson, Masashi Shibata, Yuji Tanaka, Toste Tanhua, Jan Taucher, Emilia Trudnowska, Jessica S. Turner, Anya Waite, and Lars Stemmann
Earth Syst. Sci. Data, 14, 4315–4337, https://doi.org/10.5194/essd-14-4315-2022, https://doi.org/10.5194/essd-14-4315-2022, 2022
Short summary
Short summary
The term
marine particlescomprises detrital aggregates; fecal pellets; bacterioplankton, phytoplankton and zooplankton; and even fish. Here, we present a global dataset that contains 8805 vertical particle size distribution profiles obtained with Underwater Vision Profiler 5 (UVP5) camera systems. These data are valuable to the scientific community, as they can be used to constrain important biogeochemical processes in the ocean, such as the flux of carbon to the deep sea.
Benjamin R. Loveday, Timothy Smyth, Anıl Akpinar, Tom Hull, Mark E. Inall, Jan Kaiser, Bastien Y. Queste, Matt Tobermann, Charlotte A. J. Williams, and Matthew R. Palmer
Earth Syst. Sci. Data, 14, 3997–4016, https://doi.org/10.5194/essd-14-3997-2022, https://doi.org/10.5194/essd-14-3997-2022, 2022
Short summary
Short summary
Using a new approach to combine autonomous underwater glider data and satellite Earth observations, we have generated a 19-month time series of North Sea net primary productivity – the rate at which phytoplankton absorbs carbon dioxide minus that lost through respiration. This time series, which spans 13 gliders, allows for new investigations into small-scale, high-frequency variability in the biogeochemical processes that underpin the carbon cycle and coastal marine ecosystems in shelf seas.
Autun Purser, Laura Hehemann, Lilian Boehringer, Ellen Werner, Santiago E. A. Pineda-Metz, Lucie Vignes, Axel Nordhausen, Moritz Holtappels, and Frank Wenzhoefer
Earth Syst. Sci. Data, 14, 3635–3648, https://doi.org/10.5194/essd-14-3635-2022, https://doi.org/10.5194/essd-14-3635-2022, 2022
Short summary
Short summary
Within this paper we present the seafloor images, maps and acoustic camera data collected by a towed underwater research platform deployed in 20 locations across the eastern Weddell Sea, Antarctica, during the PS124 COSMUS expedition with the research icebreaker RV Polarstern in 2021. The 20 deployments highlight the great variability in seafloor structure and faunal communities present. Of key interest was the discovery of the largest fish nesting colony discovered globally to date.
Alain Lefebvre and David Devreker
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-146, https://doi.org/10.5194/essd-2022-146, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
The SRN dataset includes long-term time series on marine phytoplankton and physicochemical measures in the eastern English Channel and the southern bight of the North Sea. These data sets should be useful to compare contrasted coastal marine ecosystems to further knowledge about the direct and indirect effects of human pressures and environmental changes on ecosystem structure and function, including eutrophication and Harmful Algal Blooms issues.
Walker O. Smith Jr.
Earth Syst. Sci. Data, 14, 2737–2747, https://doi.org/10.5194/essd-14-2737-2022, https://doi.org/10.5194/essd-14-2737-2022, 2022
Short summary
Short summary
The rate of photosynthesis of marine phytoplankton – primary productivity – is typically measured by quantifying the rate of radioisotope incorporation. However, generally such measurements are not collected by one individual through time and so are difficult to compare due to methodological differences. A data set compiled by one investigator over more than 20 years in the Ross Sea demonstrates the importance of the region as a "hot spot" for growth and synthesis.
Valéria M. Lemos, Marianna Lanari, Margareth Copertino, Eduardo R. Secchi, Paulo Cesar O. V. de Abreu, José H. Muelbert, Alexandre M. Garcia, Felipe C. Dumont, Erik Muxagata, João P. Vieira, André Colling, and Clarisse Odebrecht
Earth Syst. Sci. Data, 14, 1015–1041, https://doi.org/10.5194/essd-14-1015-2022, https://doi.org/10.5194/essd-14-1015-2022, 2022
Short summary
Short summary
The Patos Lagoon estuary and adjacent marine coast (PLEA) has been a site of the Brazilian Long-Term Ecological Research (LTER) program since 1998. LTER-PLEA contributes information about the biota composition, distribution and abundance, and estuarine ecological processes. The LTER-PLEA database (8 datasets containing 6972 sampling events and records of 275 species) represents one of the most robust and longest databases of biological diversity in an estuarine coastal system of South America.
Mayya Gogina, Anja Zettler, and Michael L. Zettler
Earth Syst. Sci. Data, 14, 1–4, https://doi.org/10.5194/essd-14-1-2022, https://doi.org/10.5194/essd-14-1-2022, 2022
Short summary
Short summary
For the first time we publish a taxonomically detailed and robust dataset of biomass conversion factors for macro-zoobenthos, often required in many studies. Georeferenced raw data for 497 taxa empower the user to make the best selections for combining them with their own data, and aggregation can help to quantify natural variability and uncertainty and refine current ecological theory. Standardised measurements were done on material collected for over 2 decades in the Baltic and the North seas.
Clare Ostle, Kevin Paxman, Carolyn A. Graves, Mathew Arnold, Luis Felipe Artigas, Angus Atkinson, Anaïs Aubert, Malcolm Baptie, Beth Bear, Jacob Bedford, Michael Best, Eileen Bresnan, Rachel Brittain, Derek Broughton, Alexandre Budria, Kathryn Cook, Michelle Devlin, George Graham, Nick Halliday, Pierre Hélaouët, Marie Johansen, David G. Johns, Dan Lear, Margarita Machairopoulou, April McKinney, Adam Mellor, Alex Milligan, Sophie Pitois, Isabelle Rombouts, Cordula Scherer, Paul Tett, Claire Widdicombe, and Abigail McQuatters-Gollop
Earth Syst. Sci. Data, 13, 5617–5642, https://doi.org/10.5194/essd-13-5617-2021, https://doi.org/10.5194/essd-13-5617-2021, 2021
Short summary
Short summary
Plankton form the base of the marine food web and are sensitive indicators of environmental change. The Plankton Lifeform Extraction Tool brings together disparate plankton datasets into a central database from which it extracts abundance time series of plankton functional groups, called
lifeforms, according to shared biological traits. This tool has been designed to make complex plankton datasets accessible and meaningful for policy, public interest, and scientific discovery.
Annalisa Minelli, Carmen Ferrà, Alessandra Spagnolo, Martina Scanu, Anna Nora Tassetti, Carla Rita Ferrari, Cristina Mazziotti, Silvia Pigozzi, Zrinka Jakl, Tena Šarčević, Miranda Šimac, Claudia Kruschel, Dubravko Pejdo, Enrico Barbone, Michele De Gioia, Diego Borme, Emiliano Gordini, Rocco Auriemma, Ivo Benzon, Đeni Vuković-Stanišić, Sandi Orlić, Vlado Frančić, Damir Zec, Ivana Orlić Kapović, Michela Soldati, Silvia Ulazzi, and Gianna Fabi
Earth Syst. Sci. Data, 13, 1905–1923, https://doi.org/10.5194/essd-13-1905-2021, https://doi.org/10.5194/essd-13-1905-2021, 2021
Short summary
Short summary
This data paper describes a dataset of natural and artificial reefs and wrecks in the Adriatic Sea collected, from a survey, in the frame of the ADRIREEF Interreg project. Information about the identification of the reef and its physical characteristics, surrounding area, and management actions/facilities has been collected in order to create a very detailed dataset, which has been harmonized and published in the SEANOE repository (https://doi.org/10.17882/74880).
Bryony L. Townhill, Rebecca E. Holt, Bjarte Bogstad, Joël M. Durant, John K. Pinnegar, Andrey V. Dolgov, Natalia A. Yaragina, Edda Johannesen, and Geir Ottersen
Earth Syst. Sci. Data, 13, 1361–1370, https://doi.org/10.5194/essd-13-1361-2021, https://doi.org/10.5194/essd-13-1361-2021, 2021
Short summary
Short summary
A dataset on the diet of Atlantic cod in the Barents Sea from the 1930s to 2018 has been compiled to produce one of the largest fish diet datasets available globally. A top predator, cod plays a key role in the food web. The data from Norway, the United Kingdom and Russia include data from 2.5 million fish. Diets have changed considerably from the start of the dataset in the 1930s. This dataset helps us understand how the environment and ecosystems are responding to a changing climate.
Le Xie, Wei Wei, Lanlan Cai, Xiaowei Chen, Yuhong Huang, Nianzhi Jiao, Rui Zhang, and Ya-Wei Luo
Earth Syst. Sci. Data, 13, 1251–1271, https://doi.org/10.5194/essd-13-1251-2021, https://doi.org/10.5194/essd-13-1251-2021, 2021
Short summary
Short summary
Viruses play key roles in marine ecosystems by killing their hosts, maintaining diversity and recycling nutrients. In the global viral oceanography database (gVOD), 10 931 viral abundance data and 727 viral production data, along with host and other oceanographic parameters, were compiled. It identified viral data were undersampled in the southeast Pacific and Indian oceans. The gVOD can be used in marine viral ecology investigation and modeling of marine ecosystems and biogeochemical cycles.
Damiano Righetti, Meike Vogt, Niklaus E. Zimmermann, Michael D. Guiry, and Nicolas Gruber
Earth Syst. Sci. Data, 12, 907–933, https://doi.org/10.5194/essd-12-907-2020, https://doi.org/10.5194/essd-12-907-2020, 2020
Short summary
Short summary
Phytoplankton sustain marine life, as they are the principal primary producers in the global ocean. Despite their ecological importance, their distribution and diversity patterns are poorly known, mostly due to data limitations. We present a global dataset that synthesizes over 1.3 million occurrences of phytoplankton from public archives. It is easily extendable. This dataset can be used to characterize phytoplankton distribution and diversity in current and future oceans.
Francesco Acri, Mauro Bastianini, Fabrizio Bernardi Aubry, Elisa Camatti, Alfredo Boldrin, Caterina Bergami, Daniele Cassin, Amelia De Lazzari, Stefania Finotto, Annalisa Minelli, Alessandro Oggioni, Marco Pansera, Alessandro Sarretta, Giorgio Socal, and Alessandra Pugnetti
Earth Syst. Sci. Data, 12, 215–230, https://doi.org/10.5194/essd-12-215-2020, https://doi.org/10.5194/essd-12-215-2020, 2020
Short summary
Short summary
The present paper describes a database containing observations for 21 parameters of abiotic, phytoplankton, and zooplankton data collected in the northern Adriatic Sea region (Italy) from 1965 to 2015. Due to the long temporal coverage, the majority of parameters changed collection and analysis method over time. These variations are reported in the database and detailed in the paper.
Irawan Asaad, Carolyn J. Lundquist, Mark V. Erdmann, and Mark J. Costello
Earth Syst. Sci. Data, 11, 163–174, https://doi.org/10.5194/essd-11-163-2019, https://doi.org/10.5194/essd-11-163-2019, 2019
Short summary
Short summary
This atlas is a compendium of geospatial online and open-access data describing biodiversity conservation in the Coral Triangle of the Indo-Pacific biogeographic realm. It consists of three sets of interlinked digital maps: (1) biodiversity features; (2) areas of importance for biodiversity conservation; and (3) recommended priorities for Marine Protected Area (MPA) Network Expansion. These maps provide the most comprehensive biodiversity datasets available to date for the region.
Dmitry Kondrik, Eduard Kazakov, and Dmitry Pozdnyakov
Earth Syst. Sci. Data, 11, 119–128, https://doi.org/10.5194/essd-11-119-2019, https://doi.org/10.5194/essd-11-119-2019, 2019
Short summary
Short summary
This paper presents a description of the original database of blooms of the calcifying phytoplankton in sub-Arctic and Arctic seas, their spatio-temporal features and associated environmental influences. This type of phytoplankton is efficient in decreasing the ability of the ocean to intake external carbon dioxide and hence amplifies the greenhouse effect. The published database can be used by a large community of users involved in studies of both aquatic ecology and carbon cycles.
Benjamin Roger Loveday and Timothy Smyth
Earth Syst. Sci. Data, 10, 2043–2054, https://doi.org/10.5194/essd-10-2043-2018, https://doi.org/10.5194/essd-10-2043-2018, 2018
Short summary
Short summary
A 40-year data set of ocean reflectance is derived from an atmospherically corrected climate quality record of top-of-atmosphere signals taken from the satellite-based AVHRR sensor. The data set provides a unique view of visible changes in the global ocean over timescales where climatic effects are demonstrable and spans coverage gaps left by more traditional satellite ocean colour sensors. It is particularly relevant to monitoring bright plankton blooms, such as coccolithophores.
Heather A. Bouman, Trevor Platt, Martina Doblin, Francisco G. Figueiras, Kristinn Gudmundsson, Hafsteinn G. Gudfinnsson, Bangqin Huang, Anna Hickman, Michael Hiscock, Thomas Jackson, Vivian A. Lutz, Frédéric Mélin, Francisco Rey, Pierre Pepin, Valeria Segura, Gavin H. Tilstone, Virginie van Dongen-Vogels, and Shubha Sathyendranath
Earth Syst. Sci. Data, 10, 251–266, https://doi.org/10.5194/essd-10-251-2018, https://doi.org/10.5194/essd-10-251-2018, 2018
Short summary
Short summary
The photosynthetic response of marine phytoplankton to available irradiance is a central part of satellite-based models of ocean productivity. This study brings together data from a variety of oceanographic campaigns to examine how the parameters of photosynthesis–irradiance response curves vary over the global ocean. This global synthesis reveals biogeographic, latitudinal and depth-dependent patterns in the photosynthetic properties of natural phytoplankton assemblages.
Emanuele Organelli, Marie Barbieux, Hervé Claustre, Catherine Schmechtig, Antoine Poteau, Annick Bricaud, Emmanuel Boss, Nathan Briggs, Giorgio Dall'Olmo, Fabrizio D'Ortenzio, Edouard Leymarie, Antoine Mangin, Grigor Obolensky, Christophe Penkerc'h, Louis Prieur, Collin Roesler, Romain Serra, Julia Uitz, and Xiaogang Xing
Earth Syst. Sci. Data, 9, 861–880, https://doi.org/10.5194/essd-9-861-2017, https://doi.org/10.5194/essd-9-861-2017, 2017
Short summary
Short summary
Autonomous robotic platforms such as Biogeochemical-Argo floats allow observation of the ocean, from the surface to the interior, in a new and systematic way. A fleet of 105 of these platforms have collected several biological, biogeochemical, and optical variables in still unexplored regions. The quality-controlled databases presented here will enable scientists to improve knowledge on the functioning of marine ecosystems and investigate the climatic implications.
Angus Atkinson, Simeon L. Hill, Evgeny A. Pakhomov, Volker Siegel, Ricardo Anadon, Sanae Chiba, Kendra L. Daly, Rod Downie, Sophie Fielding, Peter Fretwell, Laura Gerrish, Graham W. Hosie, Mark J. Jessopp, So Kawaguchi, Bjørn A. Krafft, Valerie Loeb, Jun Nishikawa, Helen J. Peat, Christian S. Reiss, Robin M. Ross, Langdon B. Quetin, Katrin Schmidt, Deborah K. Steinberg, Roshni C. Subramaniam, Geraint A. Tarling, and Peter Ward
Earth Syst. Sci. Data, 9, 193–210, https://doi.org/10.5194/essd-9-193-2017, https://doi.org/10.5194/essd-9-193-2017, 2017
Short summary
Short summary
KRILLBASE is a data rescue and compilation project to improve the availability of information on two key Southern Ocean zooplankton: Antarctic krill and salps. We provide a circumpolar database that combines 15 194 scientific net hauls (1926 to 2016) from 10 countries. These data provide a resource for analysing the distribution and abundance of krill and salps throughout the Southern Ocean to support ecological and biogeochemical research as well as fisheries management and conservation.
Philipp Brun, Mark R. Payne, and Thomas Kiørboe
Earth Syst. Sci. Data, 9, 99–113, https://doi.org/10.5194/essd-9-99-2017, https://doi.org/10.5194/essd-9-99-2017, 2017
Short summary
Short summary
We compiled data to understand the organization of marine zooplankton based on their fundamental traits, such as body size or growth rate, rather than based on species names. Zooplankton, and in particular the dominant crustacean copepods, are central to marine food webs and the carbon cycle. The data include 14 traits and thousands of copepod species and may be used for comparisons between species or communities and ultimately to inspire better large-scale models of planktonic ecosystems.
Colleen B. Mouw, Audrey Barnett, Galen A. McKinley, Lucas Gloege, and Darren Pilcher
Earth Syst. Sci. Data, 8, 531–541, https://doi.org/10.5194/essd-8-531-2016, https://doi.org/10.5194/essd-8-531-2016, 2016
Short summary
Short summary
Particulate organic carbon (POC) flux estimated from POC concentration observations from sediment traps and 234Th are compiled across the global ocean. By providing merged coincident satellite imagery products, the dataset can be used to link phytoplankton surface process with POC flux. Due to rapid remineralization within the first 500 m of the water column, shallow observations from 234Th supplement the more extensive sediment trap record.
André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Malcolm Taberner, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Vittorio Brando, Elisabetta Canuti, Francisco Chavez, Hervé Claustre, Richard Crout, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Richard Gould, Stanford Hooker, Mati Kahru, Holger Klein, Susanne Kratzer, Hubert Loisel, David McKee, Brian G. Mitchell, Tiffany Moisan, Frank Muller-Karger, Leonie O'Dowd, Michael Ondrusek, Alex J. Poulton, Michel Repecaud, Timothy Smyth, Heidi M. Sosik, Michael Twardowski, Kenneth Voss, Jeremy Werdell, Marcel Wernand, and Giuseppe Zibordi
Earth Syst. Sci. Data, 8, 235–252, https://doi.org/10.5194/essd-8-235-2016, https://doi.org/10.5194/essd-8-235-2016, 2016
Short summary
Short summary
A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite data records. Here we describe the compilation of global bio-optical in situ data (spanning from 1997 to 2012) used for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The compilation merges and harmonizes several in situ data sources into a simple format that could be used directly for the evaluation of satellite-derived ocean-colour data.
Y. Yang, L. Hansson, and J.-P. Gattuso
Earth Syst. Sci. Data, 8, 79–87, https://doi.org/10.5194/essd-8-79-2016, https://doi.org/10.5194/essd-8-79-2016, 2016
Short summary
Short summary
The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation was initiated in 2008 and is updated on a regular basis. By January 2015, a total of 581 data sets (over 4,000,000 data points) from 539 papers had been archived.
B. Nechad, K. Ruddick, T. Schroeder, K. Oubelkheir, D. Blondeau-Patissier, N. Cherukuru, V. Brando, A. Dekker, L. Clementson, A. C. Banks, S. Maritorena, P. J. Werdell, C. Sá, V. Brotas, I. Caballero de Frutos, Y.-H. Ahn, S. Salama, G. Tilstone, V. Martinez-Vicente, D. Foley, M. McKibben, J. Nahorniak, T. Peterson, A. Siliò-Calzada, R. Röttgers, Z. Lee, M. Peters, and C. Brockmann
Earth Syst. Sci. Data, 7, 319–348, https://doi.org/10.5194/essd-7-319-2015, https://doi.org/10.5194/essd-7-319-2015, 2015
Short summary
Short summary
The CoastColour Round Robin (CCRR) project (European Space Agency) was designed to set up the first database for remote-sensing algorithm testing and accuracy assessment of water quality parameter retrieval in coastal waters, from satellite imagery. This paper analyses the CCRR database, which includes in situ bio-geochemical and optical measurements in various water types, match-up reflectance products from the MEdium Resolution Imaging Spectrometer (MERIS), and radiative transfer simulations.
R. Sauzède, H. Lavigne, H. Claustre, J. Uitz, C. Schmechtig, F. D'Ortenzio, C. Guinet, and S. Pesant
Earth Syst. Sci. Data, 7, 261–273, https://doi.org/10.5194/essd-7-261-2015, https://doi.org/10.5194/essd-7-261-2015, 2015
W. Melle, J. A. Runge, E. Head, S. Plourde, C. Castellani, P. Licandro, J. Pierson, S. H. Jónasdóttir, C. Johnson, C. Broms, H. Debes, T. Falkenhaug, E. Gaard, A. Gislason, M. R. Heath, B. Niehoff, T. G. Nielsen, P. Pepin, E. K. Stenevik, and G. Chust
Earth Syst. Sci. Data, 7, 223–230, https://doi.org/10.5194/essd-7-223-2015, https://doi.org/10.5194/essd-7-223-2015, 2015
P. Licandro, M. Blackett, A. Fischer, A. Hosia, J. Kennedy, R. R. Kirby, K. Raab, R. Stern, and P. Tranter
Earth Syst. Sci. Data, 7, 173–191, https://doi.org/10.5194/essd-7-173-2015, https://doi.org/10.5194/essd-7-173-2015, 2015
L.-Q. Jiang, S. A. O'Connor, K. M. Arzayus, and A. R. Parsons
Earth Syst. Sci. Data, 7, 117–125, https://doi.org/10.5194/essd-7-117-2015, https://doi.org/10.5194/essd-7-117-2015, 2015
Short summary
Short summary
With the rapid expansion of studies on biological responses of organisms to OA, the lack of a common metadata template to document the resulting data poses a significant hindrance to effective OA data management efforts. In this paper, we present a metadata template that can be applied to a broad spectrum of OA studies, including those studying the biological responses of organisms to OA. This paper defines best practices for documenting ocean acidification (OA) data.
G. Huse, B. R. MacKenzie, V. Trenkel, M. Doray, L. Nøttestad, and G. Oskarsson
Earth Syst. Sci. Data, 7, 35–46, https://doi.org/10.5194/essd-7-35-2015, https://doi.org/10.5194/essd-7-35-2015, 2015
J. K. Pinnegar, N. Goñi, V. M. Trenkel, H. Arrizabalaga, W. Melle, J. Keating, and G. Óskarsson
Earth Syst. Sci. Data, 7, 19–28, https://doi.org/10.5194/essd-7-19-2015, https://doi.org/10.5194/essd-7-19-2015, 2015
Short summary
Short summary
This work describes a 148-year compilation of stomach content data for five pelagic fish species (herring, blue whiting, mackerel, albacore and bluefin tuna) sampled over a broad geographic region of the northeast Atlantic. We describe the main results in terms of diet composition and predator–prey relationships. The analyses suggests significant differences in the prey items selected by predators in different parts of the area at different times of year.
P. Lehodey, I. Senina, A.-C. Dragon, and H. Arrizabalaga
Earth Syst. Sci. Data, 6, 317–329, https://doi.org/10.5194/essd-6-317-2014, https://doi.org/10.5194/essd-6-317-2014, 2014
S. Torres Valdés, S. C. Painter, A. P. Martin, R. Sanders, and J. Felden
Earth Syst. Sci. Data, 6, 123–145, https://doi.org/10.5194/essd-6-123-2014, https://doi.org/10.5194/essd-6-123-2014, 2014
F. A. C. Le Moigne, S. A. Henson, R. J. Sanders, and E. Madsen
Earth Syst. Sci. Data, 5, 295–304, https://doi.org/10.5194/essd-5-295-2013, https://doi.org/10.5194/essd-5-295-2013, 2013
R. Moriarty, E. T. Buitenhuis, C. Le Quéré, and M.-P. Gosselin
Earth Syst. Sci. Data, 5, 241–257, https://doi.org/10.5194/essd-5-241-2013, https://doi.org/10.5194/essd-5-241-2013, 2013
C. J. O'Brien, J. A. Peloquin, M. Vogt, M. Heinle, N. Gruber, P. Ajani, H. Andruleit, J. Arístegui, L. Beaufort, M. Estrada, D. Karentz, E. Kopczyńska, R. Lee, A. J. Poulton, T. Pritchard, and C. Widdicombe
Earth Syst. Sci. Data, 5, 259–276, https://doi.org/10.5194/essd-5-259-2013, https://doi.org/10.5194/essd-5-259-2013, 2013
J. Peloquin, C. Swan, N. Gruber, M. Vogt, H. Claustre, J. Ras, J. Uitz, R. Barlow, M. Behrenfeld, R. Bidigare, H. Dierssen, G. Ditullio, E. Fernandez, C. Gallienne, S. Gibb, R. Goericke, L. Harding, E. Head, P. Holligan, S. Hooker, D. Karl, M. Landry, R. Letelier, C. A. Llewellyn, M. Lomas, M. Lucas, A. Mannino, J.-C. Marty, B. G. Mitchell, F. Muller-Karger, N. Nelson, C. O'Brien, B. Prezelin, D. Repeta, W. O. Jr. Smith, D. Smythe-Wright, R. Stumpf, A. Subramaniam, K. Suzuki, C. Trees, M. Vernet, N. Wasmund, and S. Wright
Earth Syst. Sci. Data, 5, 109–123, https://doi.org/10.5194/essd-5-109-2013, https://doi.org/10.5194/essd-5-109-2013, 2013
R. Moriarty and T. D. O'Brien
Earth Syst. Sci. Data, 5, 45–55, https://doi.org/10.5194/essd-5-45-2013, https://doi.org/10.5194/essd-5-45-2013, 2013
C. Guinet, X. Xing, E. Walker, P. Monestiez, S. Marchand, B. Picard, T. Jaud, M. Authier, C. Cotté, A. C. Dragon, E. Diamond, D. Antoine, P. Lovell, S. Blain, F. D'Ortenzio, and H. Claustre
Earth Syst. Sci. Data, 5, 15–29, https://doi.org/10.5194/essd-5-15-2013, https://doi.org/10.5194/essd-5-15-2013, 2013
N. Bednaršek, J. Možina, M. Vogt, C. O'Brien, and G. A. Tarling
Earth Syst. Sci. Data, 4, 167–186, https://doi.org/10.5194/essd-4-167-2012, https://doi.org/10.5194/essd-4-167-2012, 2012
K. Leblanc, J. Arístegui, L. Armand, P. Assmy, B. Beker, A. Bode, E. Breton, V. Cornet, J. Gibson, M.-P. Gosselin, E. Kopczynska, H. Marshall, J. Peloquin, S. Piontkovski, A. J. Poulton, B. Quéguiner, R. Schiebel, R. Shipe, J. Stefels, M. A. van Leeuwe, M. Varela, C. Widdicombe, and M. Yallop
Earth Syst. Sci. Data, 4, 149–165, https://doi.org/10.5194/essd-4-149-2012, https://doi.org/10.5194/essd-4-149-2012, 2012
M. Vogt, C. O'Brien, J. Peloquin, V. Schoemann, E. Breton, M. Estrada, J. Gibson, D. Karentz, M. A. Van Leeuwe, J. Stefels, C. Widdicombe, and L. Peperzak
Earth Syst. Sci. Data, 4, 107–120, https://doi.org/10.5194/essd-4-107-2012, https://doi.org/10.5194/essd-4-107-2012, 2012
E. T. Buitenhuis, W. K. W. Li, M. W. Lomas, D. M. Karl, M. R. Landry, and S. Jacquet
Earth Syst. Sci. Data, 4, 101–106, https://doi.org/10.5194/essd-4-101-2012, https://doi.org/10.5194/essd-4-101-2012, 2012
E. T. Buitenhuis, W. K. W. Li, D. Vaulot, M. W. Lomas, M. R. Landry, F. Partensky, D. M. Karl, O. Ulloa, L. Campbell, S. Jacquet, F. Lantoine, F. Chavez, D. Macias, M. Gosselin, and G. B. McManus
Earth Syst. Sci. Data, 4, 37–46, https://doi.org/10.5194/essd-4-37-2012, https://doi.org/10.5194/essd-4-37-2012, 2012
A.-M. Nisumaa, S. Pesant, R. G. J. Bellerby, B. Delille, J. J. Middelburg, J. C. Orr, U. Riebesell, T. Tyrrell, D. Wolf-Gladrow, and J.-P. Gattuso
Earth Syst. Sci. Data, 2, 167–175, https://doi.org/10.5194/essd-2-167-2010, https://doi.org/10.5194/essd-2-167-2010, 2010
Cited articles
Anderson, T. R., Martin, A. P., Lampitt, R. S., Trueman, C. N., Henson, S.
A., and Mayor, D. J., Quantifying carbon fluxes from primary production to mesopelagic fish using a simple food web model, edited by: Link, J.,
ICES J. Mar. Sci., 76, 690–701, https://doi.org/10.1093/icesjms/fsx234, 2019.
Arrigo, K. R., van Dijken, G. L., and Bushinsky, S.: Primary production in
the Southern Ocean, J. Geophys. Res. Oceans, 113, 1997–2006,
https://doi.org/10.1029/2007JC004551, 2008.
Barange, M., Merino, G., Blanchard, J. L., Scholtens, J., Harle, J.,
Allison, E. H., Allen, J. I., Holt, J., and Jennings, S.: Impacts of climate
change on marine ecosystem production in societies dependent on fisheries,
Nat. Clim. Change, 4, 211–216, https://doi.org/10.1038/nclimate2119, 2014.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from
satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20,
https://doi.org/10.4319/lo.1997.42.1.0001, 1997.
Behrenfeld, M. J., O'Malley, R. T., Siegel, D. A., McClain, C. R.,
Sarmiento, J. L., Feldman, G. C., Milligan, A. J., Falkowski, P. G.,
Letelier, R. M., and Boss, E. S.: Climate-driven trends in contemporary
ocean productivity, Nature, 444, 752–755, https://doi.org/10.1038/nature05317, 2006.
Behrenfeld, M. J., O'Malley, R. T., Boss, E. S., Westberry, T. K., Graff, J.
R., Halsey, K. H., Milligan, A. J., Siegel, D. A., and Brown, M. B.:
Revaluating ocean warming impacts on global phytoplankton, Nat. Clim.
Change, 6, 323–330, https://doi.org/10.1038/nclimate2838, 2016.
Blanchard, J. L., Jennings, S., Holmes, R., Harle, J., Merino, G., Allen, J.
I., Holt, J., Dulvy, N. K., and Barange, M.: Potential consequences of
climate change for primary production and fish production in large marine
ecosystems, Philos. T. Roy. Soc. B., 367, 2979–2989,
https://doi.org/10.1098/rstb.2012.0231, 2012a.
Blanchard, J. L., Jennings, S., Holmes, R., Harle, J., Merino, G., Allen, J.
I., Holt, J., Dulvy, N. K., and Barange, M.: Potential consequences of
climate change for primary production and fish production in large marine
ecosystems, Philos. T. Roy. Soc. B., 367, 2979–2989,
https://doi.org/10.1098/rstb.2012.0231, 2012b.
Blythe, J., Armitage, D., Alonso, G., Campbell, D., Esteves Dias, A. C.,
Epstein, G., Marschke, M., and Nayak, P.: Frontiers in coastal well-being
and ecosystem services research: A systematic review, Ocean Coast. Manag.,
185, 105028, https://doi.org/10.1016/j.ocecoaman.2019.105028, 2020.
Bouman, H. A., Platt, T., Doblin, M., Figueiras, F. G., Gudmundsson, K., Gudfinnsson, H. G., Huang, B., Hickman, A., Hiscock, M., Jackson, T., Lutz, V. A., Mélin, F., Rey, F., Pepin, P., Segura, V., Tilstone, G. H., van Dongen-Vogels, V., and Sathyendranath, S.: Photosynthesis–irradiance parameters of marine phytoplankton: synthesis of a global data set, Earth Syst. Sci. Data, 10, 251–266, https://doi.org/10.5194/essd-10-251-2018, 2018.
Caldeira, K. and Duffy, P. B.: The Role of the Southern Ocean in Uptake and
Storage of Anthropogenic Carbon Dioxide, Science, 287, 620–622,
https://doi.org/10.1126/science.287.5453.620, 2000.
Campbell, J., Antoine, D., Armstrong, R., Arrigo, K., Balch, W., Barber, R.,
Behrenfeld, M., Bidigare, R., Bishop, J., Carr, M.-E., Esaias, W.,
Falkowski, P., Hoepffner, N., Iverson, R., Kiefer, D., Lohrenz, S., Marra,
J., Morel, A., Ryan, J., Vedernikov, V., Waters, K., Yentsch, C., and Yoder,
J.: Comparison of algorithms for estimating ocean primary production from
surface chlorophyll, temperature, and irradiance, Glob. Biogeochem. Cycles,
16, 9-1-9–15, https://doi.org/10.1029/2001GB001444, 2002.
Carr, M.-E., Friedrichs, M. A. M., Schmeltz, M., Noguchi Aita, M., Antoine,
D., Arrigo, K. R., Asanuma, I., Aumont, O., Barber, R., Behrenfeld, M.,
Bidigare, R., Buitenhuis, E. T., Campbell, J., Ciotti, A., Dierssen, H.,
Dowell, M., Dunne, J., Esaias, W., Gentili, B., Gregg, W., Groom, S.,
Hoepffner, N., Ishizaka, J., Kameda, T., Le Quéré, C., Lohrenz, S.,
Marra, J., Mélin, F., Moore, K., Morel, A., Reddy, T. E., Ryan, J.,
Scardi, M., Smyth, T., Turpie, K., Tilstone, G., Waters, K., and Yamanaka,
Y.: A comparison of global estimates of marine primary production from ocean color, Deep Sea Res. Part II Top. Stud. Oceanogr., 53, 741–770,
https://doi.org/10.1016/j.dsr2.2006.01.028, 2006.
Carvalho, M. C., Schulz, K. G., and Eyre, B. D.: Respiration of new and old
carbon in the surface ocean: Implications for estimates of global oceanic
gross primary productivity, Glob. Biogeochem. Cycles, 31, 975–984,
https://doi.org/10.1002/2016GB005583, 2017.
Catucci, E. and Scardi, M.: A Machine Learning approach to the assessment of
the vulnerability of Posidonia oceanica meadows, Ecol. Indic., 108, 105744,
https://doi.org/10.1016/j.ecolind.2019.105744, 2020.
Dickson, M.-L., Orchardo, J., Barber, R., Marra, J., Mccarthy, J., and
Sambrotto, R.: Production and respiration rates in the Arabian Sea during
the 1995 Northeast and Southwest Monsoons, Deep Sea Res. Part II Top. Stud.
Oceanogr., 48, 1199–1230, https://doi.org/10.1016/S0967-0645(00)00136-3,
2001.
Duarte, C. M. and Cebrián, J.: The fate of marine autotrophic
production, Limnol. Oceanogr., 41, 1758–1766,
https://doi.org/10.4319/lo.1996.41.8.1758, 1996.
Falkowski, P. G. and Raven, J. A.: Aquatic Photosynthesis, second edn., STU-Student edition, Princeton University Press, Princeton, New Jersey, USA, ISBN 978-0-6911-5511, 2007.
Falkowski, P. G. and Wilson, C.: Phytoplankton productivity in the North
Pacific ocean since 1900 and implications for absorption of anthropogenic CO2, Nature, 358, 741–743, https://doi.org/10.1038/358741a0, 1992.
Field, C. B., Behrenfeld, M. J., Randerson, J. T., and Falkowski, P.:
Primary Production of the Biosphere: Integrating Terrestrial and Oceanic
Components, Science, 281, 237–240,
https://doi.org/10.1126/science.281.5374.237, 1998.
Fox, J., Behrenfeld, M. J., Haëntjens, N., Chase, A., Kramer, S. J.,
Boss, E., Karp-Boss, L., Fisher, N. L., Penta, W. B., Westberry, T. K., and
Halsey, K. H.: Phytoplankton Growth and Productivity in the Western North
Atlantic: Observations of Regional Variability From the NAAMES Field
Campaigns, Front. Mar. Sci., 7, 24, https://doi.org/10.3389/fmars.2020.00024,
2020.
Franceschini, S., Mattei, F., D'Andrea, L., Di Nardi, A., Fiorentino, F.,
Garofalo, G., Scardi, M., Cataudella, S., and Russo, T.: Rummaging through
the bin: Modelling marine litter distribution using Artificial Neural
Networks, Mar. Pollut. Bull., 149, 110580,
https://doi.org/10.1016/j.marpolbul.2019.110580, 2019.
Friedrichs, M. A. M., Carr, M.-E., Barber, R. T., Scardi, M., Antoine, D.,
Armstrong, R. A., Asanuma, I., Behrenfeld, M. J., Buitenhuis, E. T., Chai,
F., Christian, J. R., Ciotti, A. M., Doney, S. C., Dowell, M., Dunne, J.,
Gentili, B., Gregg, W., Hoepffner, N., Ishizaka, J., Kameda, T., Lima, I.,
Marra, J., Mélin, F., Moore, J. K., Morel, A., O'Malley, R. T.,
O'Reilly, J., Saba, V. S., Schmeltz, M., Smyth, T. J., Tjiputra, J., Waters,
K., Westberry, T. K., and Winguth, A.: Assessing the uncertainties of model
estimates of primary productivity in the tropical Pacific Ocean, J. Mar.
Syst., 76, 113–133, https://doi.org/10.1016/j.jmarsys.2008.05.010, 2009.
GEBCO Compilation Group: GEBCO 2021 Grid, GEBCO Compilation Group [data set], https://doi.org/10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f, 2021.
Gibert, K., Izquierdo, J., Sànchez-Marrè, M., Hamilton, S. H.,
Rodríguez-Roda, I., and Holmes, G.: Which method to use? An assessment
of data mining methods in Environmental Data Science, Environ. Model.
Softw., 110, 3–27, https://doi.org/10.1016/j.envsoft.2018.09.021, 2018.
Giering, S. L. C., Sanders, R., Lampitt, R. S., Anderson, T. R., Tamburini,
C., Boutrif, M., Zubkov, M. V., Marsay, C. M., Henson, S. A., Saw, K., Cook,
K., and Mayor, D. J.: Reconciliation of the carbon budget in the ocean's
twilight zone, Nature, 507, 480–483, https://doi.org/10.1038/nature13123,
2014.
Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V.,
Brockmann, C., Chauhan, P., Choi, J., Chuprin, A., Ciavatta, S., Cipollini,
P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M.,
Krasemann, H., Lavender, S., Pardo-Martinez, S., Mélin, F., Platt, T.,
Santoleri, R., Skakala, J., Schaeffer, B., Smith, M., Steinmetz, F.,
Valente, A., and Wang, M.: Satellite Ocean Colour: Current Status and Future
Perspective, Front. Mar. Sci., 6, 485, https://doi.org/10.3389/fmars.2019.00485,
2019.
Hays, G., Richardson, A., and Robinson, C.: Climate change and marine
plankton, Trends Ecol. Evol., 20, 337–344,
https://doi.org/10.1016/j.tree.2005.03.004, 2005.
Huisman, J. and Weissing, F. J.: Competition for Nutrients and Light in a
Mixed Water Column: A Theoretical Analysis, Am. Nat., 146, 536–564,
https://doi.org/10.1086/285814, 1995.
Huot, Y., Babin, M., Bruyant, F., Grob, C., Twardowski, M. S., and Claustre, H.: Relationship between photosynthetic parameters and different proxies of phytoplankton biomass in the subtropical ocean, Biogeosciences, 4, 853–868, https://doi.org/10.5194/bg-4-853-2007, 2007.
Jackson, R. B., Friedlingstein, P., Andrew, R. M., Canadell, J. G.,
Quéré, C. L., and Peters, G. P.: Persistent fossil fuel growth
threatens the Paris Agreement and planetary health, Environ. Res. Lett., 14,
121001, https://doi.org/10.1088/1748-9326/ab57b3, 2019.
Jäger, C. G., Diehl, S., and Schmidt, G. M.: Influence of water-column
depth and mixing on phytoplankton biomass, community composition, and
nutrients, Limnol. Oceanogr., 53, 2361–2373,
https://doi.org/10.4319/lo.2008.53.6.2361, 2008.
Jenks, G.: The Data Model Concept in Statistical Mapping, Int. J. Cartogr., 7, 186–190, 1967.
Kwak, I.-S. and Park, Y.-S.: Food Chains and Food Webs in Aquatic
Ecosystems, Appl. Sci., 10, 5012, https://doi.org/10.3390/app10145012,
2020a.
Kwak, I.-S. and Park, Y.-S.: Food Chains and Food Webs in Aquatic
Ecosystems, Appl. Sci., 10, 5012, https://doi.org/10.3390/app10145012,
2020b.
Kulk, G., Platt, T., Dingle, J., Jackson, T., Jönsson, B. F., Bouman, H. A., Babin, M., Brewin, R. J. W., Doblin, M., Estrada, M., Figueiras, F. G.,
Furuya, K., González-Benítez, N., Gudfinnsson, H. G., Gudmundsson,
K., Huang, B., Isada, T., Kovač, Ž., Lutz, V. A., Marañón,
E., Raman, M., Richardson, K., Rozema, P. D., Poll, W. H. van de, Segura,
V., Tilstone, G. H., Uitz, J., van Dongen-Vogels, V., Yoshikawa, T., and
Sathyendranath, S.: Primary Production, an Index of Climate Change in the
Ocean: Satellite-Based Estimates over Two Decades, Remote Sens., 12, 826,
https://doi.org/10.3390/rs12050826, 2020.
Lee, Y. J., Matrai, P. A., Friedrichs, M. A. M., Saba, V. S., Antoine, D.,
Ardyna, M., Asanuma, I., Babin, M., Bélanger, S., Benoît-Gagné,
M., Devred, E., Fernández-Méndez, M., Gentili, B., Hirawake, T.,
Kang, S.-H., Kameda, T., Katlein, C., Lee, S. H., Lee, Z., Mélin, F.,
Scardi, M., Smyth, T. J., Tang, S., Turpie, K. R., Waters, K. J., and
Westberry, T. K.: An assessment of phytoplankton primary productivity in the
Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models,
J. Geophys. Res. Oceans, 120, 6508–6541,
https://doi.org/10.1002/2015JC011018, 2015.
Levitus, S. and Boyer, T. P.: World Ocean Atlas 1994, Volume 4, Temperature,
National Environmental Satellite, Data, and Information Service, Washington DC, USA, 130 pp., ISBN 0-16-04509-4, 1994.
Levitus, S., Burgett, R., and Boyer, T. P.: World Ocean Atlas 1994, Volume
3, Salinity, National Environmental Satellite, Data, and Information
Service, Washington, DC, USA, 112 pp., ISBN 0-16-043200-6
Longhurst, A. R. and Glen Harrison, W.: The biological pump: Profiles of
plankton production and consumption in the upper ocean, Prog. Oceanogr., 22,
47–123, https://doi.org/10.1016/0079-6611(89)90010-4, 1989.
Mattei, F. and Scardi, M.: Embedding ecological knowledge into artificial
neural network training: A marine phytoplankton primary production model
case study, Ecol. Model., 421, 108985,
https://doi.org/10.1016/j.ecolmodel.2020.108985, 2020.
Mattei, F. and Scardi, M.: Global marine phytoplankton production dataset,
PANGAEA [data set], https://doi.org/10.1594/PANGAEA.932417, 2021.
Mattei, F., Franceschini, S., and Scardi, M.: A depth-resolved artificial
neural network model of marine phytoplankton primary production, Ecol.
Model., 382, 51–62, https://doi.org/10.1016/j.ecolmodel.2018.05.003, 2018.
Maureaud, A., Gascuel, D., Colléter, M., Palomares, M. L. D., Du
Pontavice, H., Pauly, D., and Cheung, W. W. L.: Global change in the trophic
functioning of marine food webs, PLOS ONE, 12, e0182826,
https://doi.org/10.1371/journal.pone.0182826, 2017.
Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K.,
Fiedler, E., Good, S. A., Mittaz, J., Rayner, N. A., Berry, D., Eastwood,
S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., and Donlon, C.:
Satellite-based time-series of sea-surface temperature since 1981 for
climate applications, Sci. Data, 6, 223,
https://doi.org/10.1038/s41597-019-0236-x, 2019.
Moigne, F. A. C. L., Henson, S. A., Cavan, E., Georges, C., Pabortsava, K.,
Achterberg, E. P., Ceballos-Romero, E., Zubkov, M., and Sanders, R. J.: What
causes the inverse relationship between primary production and export
efficiency in the Southern Ocean?, Geophys. Res. Lett., 43, 4457–4466,
https://doi.org/10.1002/2016GL068480, 2016.
NASA OBPG: MODIS Aqua Global Level 3 Mapped SST, Ver. 2019.0. PO.DAAC, CA, USA, NASA [data set], https://doi.org/10.5067/MODSA-1D4D9, 26 January 2020.
NASA Ocean Biology Processing Group (OBPG) and Stumpf, R. P.: Distance to Nearest Coastline: 0.04-Degree Grid, NASA [data set], https://oceancolor.gsfc.nasa.gov/docs/distfromcoast/, 26 January 2012.
NOAA/OAR/ESRL PSL: NODC_WOA94 data, Boulder, Colorado, USA, NOAA [data set], available at: https://psl.noaa.gov/data/gridded/data.nodc.woa94.html, last access: 26 January 2020, 2021.
Olden, J. D., Lawler, J. J., and Poff, N. L.: Machine Learning Methods
Without Tears: A Primer for Ecologists, Q. Rev. Biol., 83, 171–193,
https://doi.org/10.1086/587826, 2008.
Paerl, H. W., Rudek, J., and Mallin, M. A.: Stimulation of phytoplankton
production in coastal waters by natural rainfall inputs: Nutritional and
trophic implications, Mar. Biol., 107, 247–254,
https://doi.org/10.1007/BF01319823, 1990.
Peters, D. P. C., Havstad, K. M., Cushing, J., Tweedie, C., Fuentes, O., and
Villanueva-Rosales, N.: Harnessing the power of big data: infusing the
scientific method with machine learning to transform ecology, Ecosphere, 5,
67, https://doi.org/10.1890/ES13-00359.1, 2014.
Peters, G. P., Andrew, R. M., Canadell, J. G., Friedlingstein, P., Jackson,
R. B., Korsbakken, J. I., Le Quéré, C., and Peregon, A.: Carbon
dioxide emissions continue to grow amidst slowly emerging climate policies,
Nat. Clim. Change, 10, 3–6, https://doi.org/10.1038/s41558-019-0659-6,
2020.
Platt, T. and Sathyendranath, S.: Oceanic Primary Production: Estimation by
Remote Sensing at Local and Regional Scales, Science, 241, 1613–1620,
https://doi.org/10.1126/science.241.4873.1613, 1988.
Recknagel, F.: Applications of machine learning to ecological modelling,
Ecol. Model., 146, 303–310, https://doi.org/10.1016/S0304-3800(01)00316-7,
2001.
Reuer, M. K., Barnett, B. A., Bender, M. L., Falkowski, P. G., and
Hendricks, M. B.: New estimates of Southern Ocean biological production
rates from ratios and the triple isotope composition of O2, Deep Sea Res. Part Oceanogr. Res. Pap., 54, 951–974,
https://doi.org/10.1016/j.dsr.2007.02.007, 2007.
Richardson, A. J. and Schoeman, D. S.: Climate Impact on Plankton Ecosystems
in the Northeast Atlantic, Science, 305, 1609–1612,
https://doi.org/10.1126/science.1100958, 2004.
Russo, T., Carpentieri, P., D'Andrea, L., De Angelis, P., Fiorentino, F.,
Franceschini, S., Garofalo, G., Labanchi, L., Parisi, A., Scardi, M., and
Cataudella, S.: Trends in Effort and Yield of Trawl Fisheries: A Case Study
From the Mediterranean Sea, Front. Mar. Sci., 6, 153,
https://doi.org/10.3389/fmars.2019.00153, 2019.
Saba, V. S., Friedrichs, M. A. M., Carr, M.-E., Antoine, D., Armstrong, R.
A., Asanuma, I., Aumont, O., Bates, N. R., Behrenfeld, M. J., Bennington,
V., Bopp, L., Bruggeman, J., Buitenhuis, E. T., Church, M. J., Ciotti, A.
M., Doney, S. C., Dowell, M., Dunne, J., Dutkiewicz, S., Gregg, W.,
Hoepffner, N., Hyde, K. J. W., Ishizaka, J., Kameda, T., Karl, D. M., Lima,
I., Lomas, M. W., Marra, J., McKinley, G. A., Mélin, F., Moore, J. K.,
Morel, A., O'Reilly, J., Salihoglu, B., Scardi, M., Smyth, T. J., Tang, S.,
Tjiputra, J., Uitz, J., Vichi, M., Waters, K., Westberry, T. K., and Yool,
A.: Challenges of modeling depth-integrated marine primary productivity over
multiple decades: A case study at BATS and HOT, Glob. Biogeochem. Cycles,
24, https://doi.org/10.1029/2009GB003655, 2010.
Sabine, C. L., Feely, R. A., Gruber, N., Key, R. M., Lee, K., Bullister, J.
L., Wanninkhof, R., Wong, C. S., Wallace, D. W. R., Tilbrook, B., Millero,
F. J., Peng, T.-H., Kozyr, A., Ono, T., and Rios, A. F.: The Oceanic Sink
for Anthropogenic CO2, Science, 305, 367–371,
https://doi.org/10.1126/science.1097403, 2004.
Sammartino, M., Marullo, S., Santoleri, R., and Scardi, M.: Modelling the
Vertical Distribution of Phytoplankton Biomass in the Mediterranean Sea from
Satellite Data: A Neural Network Approach, Remote Sens., 10, 1666,
https://doi.org/10.3390/rs10101666, 2018.
Scardi, M.: Artificial neural networks as empirical models for estimating
phytoplankton production, Mar. Ecol. Prog. Ser., 139, 289–299,
https://doi.org/10.3354/meps139289, 1996.
Scardi, M.: Advances in neural network modeling of phytoplankton primary
production, Ecol. Model., 146, 33–45,
https://doi.org/10.1016/S0304-3800(01)00294-0, 2001.
Shurin, J. B., Gruner, D. S., and Hillebrand, H.: All wet or dried
up? Real differences between aquatic and terrestrial food webs, Proc. R.
Soc. B Biol. Sci., 273, 1–9, https://doi.org/10.1098/rspb.2005.3377, 2006.
Siegel, D. A., Behrenfeld, M. J., Maritorena, S., McClain, C. R., Antoine,
D., Bailey, S. W., Bontempi, P. S., Boss, E. S., Dierssen, H. M., Doney, S.
C., Eplee, R. E., Evans, R. H., Feldman, G. C., Fields, E., Franz, B. A.,
Kuring, N. A., Mengelt, C., Nelson, N. B., Patt, F. S., Robinson, W. D.,
Sarmiento, J. L., Swan, C. M., Werdell, P. J., Westberry, T. K., Wilding, J. G., and Yoder, J. A.: Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission, Remote Sens. Environ., 135, 77–91,
https://doi.org/10.1016/j.rse.2013.03.025, 2013.
Teixeira, I. G., Arbones, B., Froján, M., Nieto-Cid, M.,
Álvarez-Salgado, X. A., Castro, C. G., Fernández, E., Sobrino, C.,
Teira, E., and Figueiras, F. G.: Response of phytoplankton to enhanced
atmospheric and riverine nutrient inputs in a coastal upwelling embayment,
Estuar. Coast. Shelf Sci., 210, 132–141,
https://doi.org/10.1016/j.ecss.2018.06.005, 2018.
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based
primary productivity modeling with vertically resolved photoacclimation,
Glob. Biogeochem. Cycles, 22, GB2024, https://doi.org/10.1029/2007GB003078, 2008.
Westberry, T. K. and Behrenfeld, M. J.: Oceanic Net Primary Production, in:
Biophysical Applications of Satellite Remote Sensing, edited by: Hanes, J.
M., Springer, Berlin, Heidelberg, Germany, 205–230,
https://doi.org/10.1007/978-3-642-25047-7_8, 2014.
Wollast, R.: Evaluation and comparison of the global carbon cycle in the
coastal zone and in the open ocean, in: The Sea, edited by: Brink, K. H. and Robinson, A. R., Wiley, New York, USA, 10, 213–252, 1998.
Short summary
Data paucity hinders the understanding of natural processes such as phytoplankton production. Several studies stressed how the lack of data is the main constraint for modeling phytoplankton production. We created a global and ready-to-use dataset regarding phytoplankton production, collecting and processing data from several sources. We performed a general data analysis from a numerical and an ecological perspective. This dataset will help enhance the understanding of phytoplankton production.
Data paucity hinders the understanding of natural processes such as phytoplankton production....