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60 results for “Marine Productivity”
Marine heatwaves and cold spells events based on ESA-CCI SSTs (experimental product)
<p>This repository contains an extension of the catalogues of marine heatwaves (MHWs) and cold spells (MCSs) prepared by the National Research Council - Institute of Marine Sciences (CNR-ISMAR, Italy) within the ESA-funded CAREHeat project. The catalogues are based on the ESA-CCI sea surface temperature (SST) dataset (available from https://doi.org/10.24381/cds.cf608234) for the period 1982-2022, on a regular 1°x1° longitude-latitude grid.</p> <p>Events are identified for each pixel following the methodology of Hobday et al. (2016) after preprocessing. Event categories are provided as daily maps and metrics are given by event. Results are <strong>experimental</strong> since the post-processing procedure effectively removes interannual variability from the SST record, so please use having consulted the documentation and not for operational purposes. </p> <p><br>Please cite the reference paper "Serva, F., et al.: Detection of Satellite Sea Surface Temperature Extremes: Low Frequency Variability and Climate Change, JGR:Oceans, 10.1029/2025JC022886, 2025" when using the dataset in your work.</p>
Global estimates of marine gross primary production based on machine‐learning upscaling of field observations
<p>4 variables (excluding dimension variables):</p> <p>double GPP_LD_MLD_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from the<br> dataset determined by the light-dark bottle incubation using Random Forest<br> algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_LD_ZEU_RF[Lon,Lat,Month] </p> <p> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the light-dark bottle incubation using Random<br> Forest algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_Triple_MLD_RF[Lon,Lat,Month] <br> units: mmol mmol O2 m-2 d-1<br> fillvalue: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm<br> coordinates: [Longitude, Latitude Month]<br> <br> double GPP_Triple_ZEU_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm</p> <p>3 dimensions:</p> <p> Lon Size:181<br> units: degree_north<br> long_name: Longitude</p> <p> Lat Size:91<br> units: degree_east<br> long_name: Latitude</p> <p> Month Size:13<br> units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean<br> long_name: Month</p> <p><br> Author: Yibin Huang & Nicolas Cassar<br> Correspond: nicolas.cassar@duke.edu<br> <br> Request_for_citation: If you use these data in publications or presentations, please cite: Huang,<br> Y., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of<br> marine gross primary production based on machine‐learning upscaling of<br> field observations. Global Biogeochemical Cycles, 35, e2020GB006718.<br> https://doi.org/10.1029/2020GB006718<br> <br> Creation date: Dec/6th/2021</p>
RFR-CCS: A monthly surface pCO2 product for the California Current Large Marine Ecosystem
<p><strong>Description</strong></p> <p>RFR-CCS is a monthly data product of surface pCO2 for the California Current Large Marine Ecosystem. It was built using pCO2 observations from the Surface Ocean CO2 Atlas, fit against a variety of driver variables (sources of which are listed below) using a Random Forest Regression, and provided on a 0.25 degree resolution grid from January 1998 to December 2020.</p> <p><strong>Citation</strong></p> <p>If using this product, please also cite the original manuscript:</p> <p>Sharp, J.D., Fassbender, A.J., Carter, B.C., Lavin, P.D., and Sutton, A.J. <a href="https://doi.org/10.5194/essd-14-2081-2022">A monthly surface pCO2 product for the California Current Large Marine Ecosystem</a>. Earth System Science Data, 14, 2081–2108, 2022. doi: 10.5194/essd-14-2081-2022.</p> <p><strong>Driver Variables (<a href="https://figshare.com/articles/dataset/RFR-CCS/15152013/2">figshare</a>)</strong></p> <p>1. Sea Surface Temperature: <a href="https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html">OISSTv2</a></p> <p>2. Sea Surface Salinity: <a href="https://www.ecco-group.org/products.htm">ECCO2</a></p> <p>3. Chlorophyll-a: <a href="http://sites.science.oregonstate.edu/ocean.productivity/1080.by.2160.monthly.inputData.php">Oregon State (via NASA)</a></p> <p>4. Wind Speed: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5">ERA5</a></p> <p>5. Atmospheric pCO2: <a href="https://gml.noaa.gov/ccgg/mbl/">NOAA MBL</a></p> <p>6. Mixed Layer Depth: <a href="https://www.hycom.org/hycom/">HYCOM</a></p> <p>7. Distance from Shore: Calculated from latitude and longitude (<a href="https://www.chadagreene.com/CDT/CDT_Contents.html">dist2coast, Climate Data Toolbox</a>)</p> <p>8. Year</p> <p>9. Month of Year</p>
Data from: A sedimentary eDNA record of the Atacama Trench reveals biodiversity changes in the most productive marine ecosystem
<p>The hadopelagic environment remains highly understudied due to the inherent difficulties in sampling at these depths. The use of sediment eDNA can overcome some of these restrictions as settled and preserved DNA represent an archive of the biological communities. We use sediment eDNA to assess changes in the community within one of the world's most productive open ocean ecosystems: the Atacama Trench. The ecosystems around the Atacama Trench have been intensively fished and are affected by climate oscillations, but the understanding of potential impacts on the marine community is limited. We sampled five sites using sediment cores at water depths from 2400 to ~8000m. The chronologies of the sedimentary record were determined using 210Pbex. Environmental DNA was extracted from core slices and metabarcoding was used to identify the eukaryote community using two separate primer pairs for different sections of the 18S rDNA gene (V9 and V7) effectively targeting pelagic taxa. The reconstructed communities were similar among markers and mainly composed of chordates and members of the Chromista kingdom. Alpha-diversity was estimated for all sites in intervals of 15 years (from 1842 to 2018), showing a severe drop in biodiversity from 1970 to 1985 that aligns with one of the strongest known El Niño events. We argue that the harsh adverse ENSO events potentially combined with extensive fishing efforts during this period of time resulted in a distinct reduction of marine biodiversity. Fish and cnidarian read abundance was examined separately to determine if fishing had a direct impact, but no direct relation was found. These results demonstrate that sediment eDNA can be a valuable emerging tool providing insight in historical perspectives on ecosystem developments. This study constitutes one of the first steps toward an improved understanding of the importance of environmental and anthropogenic drivers in affecting open and deep ocean communities.</p>
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton. in Spesovicornea Pacltovae Gen. Nov. Et Sp. Nov., A New Elateroid Sporomorph From The Bohemian Cenomanian (Czech Republic)
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton.
Evolution of venom production in marine predatory snails
<p>This repository contains the following datasets:</p> <ul> <li><strong>annotations.zip</strong>: includes two files per species. The "species_code_annot.tsv" file (e.g., CI_annot.tsv) includes the blast hits to Uniprot/SwissProt (_sp), gastropod genomes (_gastr), Cdd (_cdd), Pfam (_pfam), ToxProt (_tox), and Conoserver (_cono); the output of SignalP and of ConoPrec. The "species_code_topGO.tsv" file (e.g., CI_topGO.tsv) corresponds the GO annotations in a format compatible with TopGO.</li> <li><strong>assemblies.zip</strong>: includes the nucleotide coding sequences as "species_code_cds.fasta" (e.g., CI_cds.fasta) and the predicted amino acid sequences as "species_code.faa" (e.g., CI.faa) in fasta format.</li> <li><strong>expression_matrices.zip</strong>: includes the final, quality-filtered expression matrices in TPM for each species separately as "species_code_tpm.tsv" (e.g., CI_tpm.tsv). The multi-species expression matrices based on the random-selection method ("multispecies_tpm_random.tsv") and mean method ("multispecies_tpm_mean.tsv") are provided.</li> <li><strong>tissue_specific_gene_sets.zip</strong>: includes the list of tissue-specific genes for each species as "species_code_fc2_tb.tsv" (e.g., CI_fc2_tb.tsv). The column "tissue1" corresponds to the tissue with the highest TPM value ("tpm1"), therefore the tissue to which that gene is specific, while "tissue2" correspond to the tissue with the second-highest TPM values ("tpm2"). "FC" is the fold-change.</li> <li><strong>orthologer_output</strong>: includes the orthogroup assigment as outputted by the software OrthoLoger. Specifically, "path2proteome_orthogroups.txt" corresponds to the orthogroup assignment for all genes assigned to an orthogroup, while "path2proteome_stats.txt" list some statistic parameters (e.g., size of the orthogroups etc.).</li> <li><strong>CAGEE_output</strong>: includes the output from the software CAGEE as reported in the manual (https://github.com/hahnlab/CAGEE/blob/main/docs/manual/cagee_manual.md#Installation). The results for both, a gene expression matrix based on the random-selection method and the mean-based method are reported in separate folders. Within each folder are reported the results for the gland, salivary glands, and oesophagus separately. Additionally, the ultrametric species tree and the sigma tree, both in in Newick format, are provided.</li> </ul> <p> </p>
Daily Anomalies and High Productivity Zone Mask for Northern Peruvian Coastal Marine Ecosystem during the 2017 Coastal El Niño (December 2016 - May 2017)
<p>This dataset is part of the manuscript entitled "Chlorophyll Response and High Productivity Zone Contraction in Northern Perú During the 2017 Coastal El Niño."</p> <p>The dataset is designed to assess the atmospheric and oceanographic drivers of productivity changes during the 2017 Coastal El Niño. It allows detailed analysis of the interactions between physical processes (e.g., wind-driven upwelling rates, heat flux changes) and biological responses (e.g., chlorophyll concentration variations) in a region highly susceptible to ENSO-related variability. This comprehensive dataset provides valuable insight into the physical-biological coupling and the impacts of rapid climate events on marine ecosystems. The dataset, covering the period from December 1, 2016, to May 31, 2017, includes:</p> <p>1. Chlorophyll-a Anomalies (chla): Represents deviations in surface chlorophyll concentrations, a proxy for phytoplankton biomass, highlighting variations in primary productivity during the event.</p> <p>2. Sea Surface Temperature Anomalies (sst): Captures changes in sea surface temperatures relative to the climatological mean, providing insight into the warming pattern typical of marine heatwaves associated with the Coastal El Niño.</p> <p>3. Sea Level Anomaly (sla): Indicates changes in sea surface height, which reflects thermal expansion of water masses and potential contributions from coastal trapped waves propagating along the Peruvian coast.</p> <p>4. Wind Component Anomalies (u,v): Daily anomalies for both zonal (east-west) and meridional (north-south) wind components, which are critical for understanding changes in wind patterns including upwelling and Ekman transport processes.</p> <p>5. Ekman Pumping Anomalies (w): Represents variations in vertical water movement forced by wind stress curl, highlighting the suppression or enhancement of upwelling during the event.</p> <p>6. Latent Heat Flux Anomalies (lathf): Indicates deviations in heat loss from the ocean surface due to evaporation, affecting surface temperature regulation.</p> <p>7. Shortwave Radiation Anomalies (swrad): Shows changes in solar radiation (and also a proxy for PAR) reaching the ocean surface, influencing upper ocean heat content and the light availability for phytoplankton.</p> <p>8. High Productivity Zone (mask): A binary mask with daily values of 1 indicating areas meeting the HPZ criterion and 0 otherwise, allowing for spatial tracking of the HPZ's extent during the period of study.</p> <p> </p>
Data and code for "Hotspots and drivers of compound marine heatwave and low net primary production extremes"
<p>This repository provides the code and data for the study "Hotspots and drivers of compound marine heatwave and low net primary production extremes". All processed data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch. </p>
Resource allocation to a structural biomaterial: induced production of byssal threads decreases growth of a marine mussel
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Data from: A sedimentary eDNA record of the Atacama Trench reveals biodiversity changes in the most productive marine ecosystem
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Seagrass ecosystems reduce disease risk and economic loss in marine farming production
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Body size and early marine conditions drive changes in Chinook salmon productivity across northern latitude ecosystems
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Productivity-Susceptibility Analysis for Top 250 Marine Aquarium Fish Supplemental Information
<p>Data storage for supplemental information, containting the full dataset, a summarized dataset, and comparing vulnerability scores between FishBase and our Productivity-Susceptibility Analysis. </p>
Production of Marine Shrimp Integrated with Tilapia at High Densities and in a Biofloc System: Choosing the Best Spatial Configuration
<p>Dataset with experimental results.</p>
Datasets for: Primary production and habitat stability organize marine communities
<p><strong>Aim: </strong>The emergence of pattern in the natural world can carry important messages about underlying processes. For example, collections of broadly similar terrestrial ecosystems have historically been categorized as biomes – groupings of systems which sort along energetic and structural process axes. In marine systems however, a similar classification of biomes has not emerged. The aim here is to develop an effective classification scheme for marine biomes and communities.</p> <p><strong>Approach: </strong>Candidate predictor variables that could explain pattern and process in differentiating marine communities, such as light, nutrients, depth, etc., were collected from the existing literature with a systematic review. The candidate predictors were then evaluated in an inductive process, allowing community level observations to demonstrate patterns across marine biomes. Marine biomes and communities were identified <i>a priori</i>,<i> </i>and emergent patterns were evaluated quantitatively, via Principal Component ordination based on the vectors of explanatory predictors, and qualitatively via mapping.</p> <p><strong>Conclusions: </strong>Gross primary production and substrate mobility not only effectively sort marine biomes, but also work on finer scales discriminating communities within biomes. As a result, these predictors were more effective in classifying marine communities than other scales, such as available light and nutrients. The richness of this classification is also demonstrated in revealing other patterns, such as the distribution of human impacts. The effectiveness of this mapping provides support for, but is not a test of, the hypothesis that primary production and substrate mobility are important underlying processes that interact to structure marine ecosystems.<i> </i></p>
Supplementary materials for: Use and detection of a vitamin B1 degradation product yields new views of the marine B1 cycle and plankton metabolite exchange
<p>Vitamin B1 (thiamin) is a vital nutrient for most cells in nature including marine plankton. Early and recent experiments show that B1 degradation products instead of B1 can support the growth of marine bacterioplankton and phytoplankton. However, the use and occurrence of some degradation products remain uninvestigated – namely N-formyl-4-amino-5-aminomethyl-2-methylpyrimidine (FAMP) – which has been a focus of plant oxidative stress research. We investigated the relevance of FAMP in the ocean. Experiments and global ocean meta-omic data indicate that eukaryotic phytoplankton, including picoeukaryotes and harmful algal bloom species, use FAMP while bacterioplankton appear more likely to use deformylated FAMP, 4-amino-5-aminomethyl-2-methylpyrimidine (AmMP). Measurements of FAMP in seawater and biomass revealed that it occurs at picomolar concentrations in the surface ocean, heterotrophic bacterial cultures produce FAMP in the dark – indicating non-photodegradation of B1 by cells, and B1-requiring (auxotrophic) picoeukaryotic phytoplankton produce intracellular FAMP. Our results require an expansion of thinking about vitamin degradation in the sea, but also the marine B1 cycle – where it is now crucial to consider a new B1-related compound pool (FAMP) – as well as generation (dark degradation – likely via oxidation), turnover (plankton uptake), and exchange of the compound within networks of plankton.</p>
Supplementary materials for: Use and detection of a vitamin B1 degradation product yields new views of the marine B1 cycle and plankton metabolite exchange
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Data from: Anthropogenic nutrient inputs affect productivity-biodiversity relationships in marine tintinnid assemblages
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Pseudocobalamin production and use in marine Synechococcus cultures and communities
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Datasets for: Primary production and habitat stability organize marine communities
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Allen Brain Atlas
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OpenNeuro
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