scholarly journals Towards omics-based predictions of planktonic functional composition from environmental data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Emile Faure ◽  
Sakina-Dorothée Ayata ◽  
Lucie Bittner
Keyword(s):  
Environmental Context ◽  
Environmental Data ◽  
Data Driven ◽  
Global Ocean ◽  
Functional Composition ◽  
Marine Microbes ◽  
Trophic Networks ◽  
Climate Regulation ◽  
Marine Metagenome ◽  
Planktonic Communities

AbstractMarine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalyze 885 marine metagenome-assembled genomes through a network-based approach and detect 233,756 protein functional clusters, from which 15% are functionally unannotated. We investigate all clusters’ distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters’ abundance. The abundances of 14,585 clusters are predictable from the environmental context, including 1347 functionally unannotated clusters. We analyze the biogeography of these 14,585 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context.

scholarly journals Exploiting Inter- and Intra-Base Crossing with Multi-Mappings: Application to Environmental Data

2018 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Hicham Hajj-Hassan ◽  
Anne Laurent ◽  
Arnaud Martin
Keyword(s):  
Spatial Location ◽  
Point Of View ◽  
Environmental Data ◽  
Sensor Data ◽  
Global Ocean ◽  
Global Changes ◽  
Geospatial Information ◽  
Multiple Points ◽  
Points Of View ◽  
Single Data

Environmental data are currently gaining more and more interest as they are required to understand global changes. In this context, sensor data are collected and stored in dedicated databases. Frameworks have been developed for this purpose and rely on standards, as for instance the Sensor Observation Service (SOS) provided by the Open GeoSpatial Consortium (OGC), where all measurements are bound to a so-called Feature of Interest (FoI). These databases are used to validate and test scientific hypotheses often formulated as correlations and causality between variables, as for instance the study of the correlations between environmental factors and chlorophyll levels in the global ocean. However, the hypotheses of the correlations to be tested are often difficult to formulate as the number of variables that the user can navigate through can be huge. Moreover, it is often the case that the data are stored in such a manner that they prevent scientists from crossing them in order to retrieve relevant correlations. Indeed, the FoI can be a spatial location (e.g., city), but can also be any other object (e.g., animal species). The same data can thus be represented in several manners, depending on the point of view. The FoI varies from one representation to the other one, while the data remain unchanged. In this article, we propose a novel methodology including a crucial step to define multiple mappings from the data sources to these models that can then be crossed, thus offering multiple possibilities that could be hidden from the end-user if using the initial and single data model. These possibilities are provided through a catalog embedding the multiple points of view and allowing the user to navigate through these points of view through innovative OLAP-like operations. It should be noted that the main contribution of this work lies in the use of multiple points of view, as many other works have been proposed for manipulating, aggregating visualizing and navigating through geospatial information. Our proposal has been tested on data from an existing environmental observatory from Lebanon. It allows scientists to realize how biased the representations of their data are and how crucial it is to consider multiple points of view to study the links between the phenomena.

Assessment and Correction of Solar Radiation Measurements with Simple Neural Networks

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1160
Author(s):  
Jason Kelley
Keyword(s):  
Neural Networks ◽  
Solar Radiation ◽  
Low Cost ◽  
Environmental Data ◽  
Data Driven ◽  
Technical Requirements ◽  
Standard Procedures ◽  
Comparable Accuracy ◽  
Radiation Sensors ◽  
Radiation Measurements

Solar radiation received at the Earth’s surface provides the energy driving all micro-meteorological phenomena. Local solar radiation measurements are used to estimate energy mediated processes such as evapotranspiration (ET); this information is important in managing natural resources. However, the technical requirements to reliably measure solar radiation limits more extensive adoption of data-driven management. High-quality radiation sensors are expensive, delicate, and require skill to maintain. In contrast, low-cost sensors are widely available, but may lack long-term reliability and intra-sensor repeatability. As weather stations measure solar radiation and other parameters simultaneously, machine learning can be used to integrate various types of environmental data, identify periods of erroneous measurements, and estimate corrected values. We demonstrate two case studies in which we use neural networks (NN) to augment direct radiation measurements with data from co-located sensors, and generate radiation estimates with comparable accuracy to the data typically available from agro-meteorology networks. NN models that incorporated radiometer data reproduced measured radiation with an R2 of 0.9–0.98, and RMSE less than 100 Wm−2, while models using only weather parameters obtained R2 less than 0.75 and RMSE greater than 140 Wm−2. These cases show that a simple NN implementation can complement standard procedures for estimating solar radiation, create opportunities to measure radiation at low-cost, and foster adoption of data-driven management.

scholarly journals Vertical distribution of chlorophyll <I>a</I> concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean

2015 ◽  
Vol 7 (2) ◽  
pp. 261-273 ◽  
Author(s):  
R. Sauzède ◽  
H. Lavigne ◽  
H. Claustre ◽  
J. Uitz ◽  
C. Schmechtig ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Community Composition ◽  
Phytoplankton Biomass ◽  
Phytoplankton Community ◽  
Environmental Data ◽  
Global Ocean ◽  
Control Procedure ◽  
Chlorophyll A Concentration ◽  
Phytoplankton Community Composition

Abstract. In vivo chlorophyll a fluorescence is a proxy of chlorophyll a concentration, and is one of the most frequently measured biogeochemical properties in the ocean. Thousands of profiles are available from historical databases and the integration of fluorescence sensors to autonomous platforms has led to a significant increase of chlorophyll fluorescence profile acquisition. To our knowledge, this important source of environmental data has not yet been included in global analyses. A total of 268 127 chlorophyll fluorescence profiles from several databases as well as published and unpublished individual sources were compiled. Following a robust quality control procedure detailed in the present paper, about 49 000 chlorophyll fluorescence profiles were converted into phytoplankton biomass (i.e., chlorophyll a concentration) and size-based community composition (i.e., microphytoplankton, nanophytoplankton and picophytoplankton), using a method specifically developed to harmonize fluorescence profiles from diverse sources. The data span over 5 decades from 1958 to 2015, including observations from all major oceanic basins and all seasons, and depths ranging from the surface to a median maximum sampling depth of around 700 m. Global maps of chlorophyll a concentration and phytoplankton community composition are presented here for the first time. Monthly climatologies were computed for three of Longhurst's ecological provinces in order to exemplify the potential use of the data product. Original data sets (raw fluorescence profiles) as well as calibrated profiles of phytoplankton biomass and community composition are available on open access at PANGAEA, Data Publisher for Earth and Environmental Science. Raw fluorescence profiles: http://doi.pangaea.de/10.1594/PANGAEA.844212 and Phytoplankton biomass and community composition: http://doi.pangaea.de/10.1594/PANGAEA.844485

scholarly journals Phosphonate production by marine microbes: exploring new sources and potential function

2020 ◽  
Author(s):  
Marianne Acker ◽  
Shane L. Hogle ◽  
Paul M. Berube ◽  
Thomas Hackl ◽  
Ramunas Stepanauskas ◽  
...  
Keyword(s):  
Biological Function ◽  
Organic Phosphorus ◽  
Niche Partitioning ◽  
Low Frequency ◽  
Phosphorus Cycling ◽  
Global Ocean ◽  
Chemical Analyses ◽  
Marine Microbes ◽  
Surface Ocean ◽  
Phosphorus Source

AbstractPhosphonates, organic compounds with a C-P bond, constitute 20-25% of phosphorus in high molecular weight dissolved organic matter and are a significant phosphorus source for marine microbes. However, little is known about phosphonate sources, biological function, or biogeochemical cycling. Here, we determine the biogeographic distribution and prevalence of phosphonate biosynthesis potential using thousands of genomes and metagenomes from the upper 250 meters of the global ocean. Potential phosphonate producers are taxonomically diverse, occur in widely distributed and abundant marine lineages (including SAR11 and Prochlorococcus) and their abundance increases with depth. Within those lineages, phosphonate biosynthesis and catabolism pathways are mutually exclusive, indicating functional niche partitioning of organic phosphorus cycling in the marine microbiome. Surprisingly, one strain of Prochlorococcus (SB) can allocate more than 40% of its cellular P-quota towards phosphonate production. Chemical analyses and genomic evidence suggest that phosphonates in this strain are incorporated into surface layer glycoproteins that may act to reduce mortality from grazing or viral infection. Although phosphonate production is a low-frequency trait in Prochlorococcus populations (~ 5% of genomes), experimentally derived production rates suggest that Prochlorococcus could produce a significant fraction of the total phosphonate in the oligotrophic surface ocean. These results underscore the global biogeochemical impact of even relatively rare functional traits in abundant groups like Prochlorococcus and SAR11.

scholarly journals Data‐Driven Modeling of the Distribution of Diazotrophs in the Global Ocean

2019 ◽  
Vol 46 (21) ◽  
pp. 12258-12269 ◽  
Author(s):  
Weiyi Tang ◽  
Nicolas Cassar
Keyword(s):  
Data Driven ◽  
Global Ocean ◽  
Data Driven Modeling

Infrastructuring “data-driven” environmental governance in Louisiana’s coastal restoration plan

2020 ◽  
pp. 251484862090972
Author(s):  
Eric Nost
Keyword(s):  
Decision Making ◽  
Mississippi River ◽  
Environmental Governance ◽  
Planning Process ◽  
Environmental Data ◽  
Data Driven ◽  
Data Infrastructure ◽  
Coastal Restoration ◽  
Restoration Planning ◽  
Political Effect

Conservationists around the world advocate for “data-driven” environmental governance, expecting data infrastructures to make all relevant and actionable information readily available. But how exactly is data to be infrastructured and to what political effect? I show how putting together and maintaining environmental data for decision-making is not a straightforward technical task, but a practice shaped by and shaping politico-economic context. Drawing from the US state of Louisiana’s coastal restoration planning process, I detail two ways ecosystem modelers manage fiscal and institutional “frictions” to “infrastructuring” data as a resource for decision-making. First, these experts work with the data they have. They leverage, tweak, and maintain existing datasets and tools, spending time and money to gather additional data only to the extent it fits existing goals. The assumption is that these goals will continue to be important, but building coastal data infrastructure around current research needs, plans, and austerity arguably limits what can be said in and done with the future. Second, modelers acquire the data they made to need. Coastal communities have protested the state’s primary restoration tool: diversions of sediment from the Mississippi River. Planners reacted by relaxing institutional constraints and modelers brought together new data to highlight possible winners and losers from ecological restoration. Fishers and other coastal residents leveraged greater dissent in the planning process. Political ecologists show that technocentric environmental governance tends to foreclose dissent from hegemonic socioecological futures. I argue we can clarify the conditions in which this tends to happen by following how experts manage data frictions. As some conservationists and planners double down on driving with data in a “post-truth” world, I find that data’s politicizing effects stem from what is asked of it, not whether it is “big” or “drives.”

Current Approaches, Challenges, and Perspectives on Spatial OLAP for Agri-Environmental Analysis

2016 ◽  
Vol 7 (4) ◽  
pp. 32-49 ◽  
Author(s):  
Sandro Bimonte
Keyword(s):  
Urban Development ◽  
Environmental Analysis ◽  
Scientific Community ◽  
State Of The Art ◽  
Environmental Context ◽  
Research Community ◽  
The State ◽  
Environmental Data ◽  
Multidimensional Model ◽  
Open Issues

Spatial OLAP (SOLAP) systems are powerful GeoBusiness Intelligence tools for analysing massive volumes of geo-referenced datasets. Therefore, these technologies are receiving considerable attention in the research community and in the database industry as well. Applications of these technologies are current in several domains such as ad marketing, healthcare, and urban development, to name a few. Contrary to other application domains, in the context of agri-environmental data and analysis, SOLAP systems have been underexploited. Therefore, in this paper, the author makes an exhaustive survey of most of the published studies in the domain of the SOLAP analysis of agri-environmental data with an emphasis on the reasons why only few recent works investigate the use of SOLAP systems in the agri-environmental context. In particular, the author focuses on the complexity of the spatio-multidimensional model and its implementation. Moreover, based on surveying the state of the art in this domain, this paper identifies some general guidelines that must be considered by the scientific community to design and implement efficient SOLAP approaches to the analysis of geo-referenced agri-environmental datasets. Finally, open issues about warehousing and OLAPing agri-environmental data are also shown in the paper.

Effects of Ocean Acidification on the Diversity and Activity of Heterotrophic Marine Microorganisms

2011 ◽  
Author(s):  
Markus G. Weinbauer ◽  
Xavier Mari
Keyword(s):  
Ocean Acidification ◽  
Relevant Literature ◽  
Global Ocean ◽  
Future Research ◽  
Marine Microorganisms ◽  
Marine Microbes ◽  
Ph Change ◽  
Cellular Life ◽  
Heterotrophic Microorganisms ◽  
Microbial Plankton

Microbe-mediated processes are crucial for biogeochemical cycles and the functioning of marine ecosystems (Azam and Malfatti 2007 ). If these processes are affected by ocean acidification, major consequences can be expected for the functioning of the global ocean and the systems that it influences, such as the atmosphere. In contrast to phytoplankton, which have been relatively well studied (see Chapter 6), there is comparatively little information on the effect of ocean acidification on heterotrophic microorganisms. Two reviews on the potential effects of ocean acidification on microbial plankton have recently been published (Liu et al. 2010 ; Joint et al. 2011) . In a recent perspective paper, Joint et al. (2011) concluded that marine microbes possess the flexibility to accommodate pH change and that major changes in marine biogeochemical processes that are driven by microorganisms are unlikely. Narrative reviews, which look at some of the relevant literature, are potentially biased and could lead to misleading conclusions (Gates 2002). Metaanalysis was developed to overcome most biases of narrative reviews. It statistically combines the results (effect size) of several studies that address a shared research hypothesis. Liu et al. (2010) used a metaanalytic approach to comprehensively review the current understanding of the effect of ocean acidification on microbes (including phytoplankton) and microbial processes, and to highlight the gaps that need to be addressed in future research. In the following, a brief digest on oceanic microbes and their role is provided for readers unfamiliar with this topic. Then the research that has been performed to assess the effects of ocean acidification on the diversity and activity of heterotrophic marine microorganisms is reviewed. Finally, scenarios are developed and potential implications are discussed. Microorganisms are defined as organisms that are microscopic, i.e. too small to be seen by the naked human eye, and mostly comprise single-celled organisms. Viruses are sometimes also included in this definition but it is hotly debated whether viruses are alive or not (Raoult and Forterre 2008). The current phylogeny considers three domains of cellular life, the Bacteria, the Archaea and the Eukarya.

Community and Environmental Data-Driven Monitoring of Waste Management

2021 ◽  
Author(s):  
Ferenc Péter Pach ◽  
László Morzsa ◽  
Gergely Erdős ◽  
Imre Magyar ◽  
Zoltán Bihari
Keyword(s):  
Waste Management ◽  
Environmental Data ◽  
Data Driven

scholarly journals Special Sensor Microwave Imager (SSM/I) Intersensor Calibration Using a Simultaneous Conical Overpass Technique

2011 ◽  
Vol 50 (1) ◽  
pp. 77-95 ◽  
Author(s):  
Song Yang ◽  
Fuzhong Weng ◽  
Banghua Yan ◽  
Ninghai Sun ◽  
Mitch Goldberg
Keyword(s):  
Precipitable Water ◽  
Calibration Procedure ◽  
Environmental Data ◽  
Sensor Data ◽  
Global Ocean ◽  
Monthly Precipitation ◽  
Tropical Ocean ◽  
Climate Trend ◽  
Surface Precipitation ◽  
Microwave Imager

Abstract A new intersensor calibration scheme is developed for the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) to correct its scan-angle-dependent bias, the radar calibration beacon interference on the F-15 satellite, and other intersensor biases. The intersensor bias is characterized by the simultaneous overpass measurements with the F-13 SSM/I as a reference. This sensor data record (SDR) intersensor calibration procedure is routinely running at the National Oceanic and Atmospheric Administration and is now used for reprocessing all SSM/I environmental data records (EDR), including total precipitable water (TPW) and surface precipitation. Results show that this scheme improves the consistency of the monthly SDR’s time series from different SSM/I sensors. Relative to the matched rain products from the Tropical Rainfall Measuring Mission, the bias of SSM/I monthly precipitation is reduced by 12% after intersensor calibration. TPW biases between sensors are reduced by 75% over the global ocean and 20% over the tropical ocean, respectively. The intersensor calibration reduces biases by 20.6%, 15.7%, and 6.5% for oceanic, land, and global precipitation, respectively. The TPW climate trend is 1.59% decade−1 (or 0.34 mm decade−1) for the global ocean and 1.39% decade−1 (or 0.63 mm decade−1) for the tropical ocean, indicating related trends decrease of 38% and 54%, respectively, from the uncalibrated SDRs. Results demonstrate the large impacts of this calibration on the TPW climate trend.

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