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10 results for “algae blooms”
Harmfull algae bloom monitoring program dataset; ERDDAP, ERA5 and ONI datasets; and R script for multicriteria analisys in Santa Catarina coastal zone, Brazil.
<p>Project Harmful Algae Bloom (HAB) Monitoring Network in Santa Catarina, Brazil - Database and R script with data analysis. This project was funded by the Foundation for Research Support of the State of Santa Catarina – FAPESC and generated a database combining a HAB monitoring dataset with oceanographic (from ERDDAP) and climatic (from ERA5 and ONI) data which was submitted to multicriteria analysis using R. The HAB monitoring dataset was obtained from Cidasc/SC State Government (http://www.cidasc.sc.gov.br/defesasanitariaanimal/monitoramento-de-algas-nocivas/) and contains results of phytoplankton counts in water samples and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline. Oceanographic data were obtained from the ERDDAP/NOAA website (https://coastwatch.pfeg.noaa.gov/erddap/index.html), including the variables mean chlorophyll concentration (mg.m-3) and mean sea surface temperature (ºC); Climate data were obtained from Copernicus/ERA5 (https://cds.climate.copernicus.eu/) including the variables mean air temperature (ºC), mean pressure (Pasc.), mean cloud cover (%), mean precipitation (kg.m-2), radiation (Einsteins.m-2.day-1), mean U wind (m.s-1), and mean V wind (m.s-1).; Oceanic Niño Index (ONI) data were obtained from the NOAA website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php); The R script involves a pre-processing routine aimed at summarizing and integrating all datasets and the subsequent data analyses carried out to evidence temporal patterns related to different type of algal blooms. Detailed methods will be provided in a scientific article.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
Hypereutrophic lake sensor data during summer algae blooms in Iowa, USA, 2014 - 2018
High frequency measurements of temperature, dissolved oxygen saturation, pH, chlorophyll a and phycocyanin concentration were measured using a multiparameter sonde in the surface waters of four lakes during the ice-free season to monitor for harmful algal blooms. The lakes are located in the state of Iowa, USA. Green Valley Lake as monitored in 2014 and 2015, Blackhawk Lake was monitored in 2015, and Swan Lake and South Twin Lake were monitored in 2018. The high frequency data were aggregated to a daily average value for each parameter in each lake. These data were used to test for statistical early warning indicators of harmful algal blooms.
Data from: Salinity decline promotes growth and harmful blooms of a toxic alga by diverting carbon flow
<p>Global climate change intensifies the water cycle and makes freshest waters become fresher and vice‑versa. But how this change impacts phytoplankton in coastal, particularly harmful algal blooms (HABs), remains poorly understood. Here, we monitored a coastal bay for a decade and found a significant correlation between salinity decline and the increase of <em>Karenia mikimotoi</em> blooms. To examine the physiological linkage between salinity decreases and <em>K. mikimotoi</em> blooms, we compare chemical, physiological and multi-omic profiles of this species in laboratory cultures under high (33) and low (25) salinities. Under low salinity, photosynthetic efficiency and capacity as well as growth rate and cellular protein content were significantly higher than that under high salinity. More strikingly, the omics data show that low salinity activated the glyoxylate shunt to bypass the decarboxylation reaction in the tricarboxylic acid cycle, hence redirecting carbon from CO<sub>2</sub> release to biosynthesis. Furthermore, the enhanced glyoxylate cycle could promote hydrogen peroxide metabolism, consistent with the detected decrease in reactive oxygen species (ROS). These findings suggest that salinity declines can reprogram metabolism to enhance cell proliferation, thus promoting bloom formation in HAB species like <em>K. mikimotoi</em>, which has important ecological implications for future climate-driven salinity declines in the coastal ocean with respect to HAB outbreaks.</p>
Data from: Salinity decline promotes growth and harmful blooms of a toxic alga by diverting carbon flow
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Data from: A hypervariable mitochondrial protein coding sequence associated with geographical origin in a cosmopolitan bloom-forming alga, Heterosigma akashiwo
Geographic distributions of phytoplankton species can be defined by events on both evolutionary time and shorter scales, e.g., recent climate changes. Additionally, modern industrial activity, including the transport of live fish and spat for aquaculture and aquatic microorganisms in ship ballast water, may aid the spread of phytoplankton. Obtaining a reliable marker is key to gaining insight into the phylogeographic history of a species. Here, we report a hypervariable mitochondrial gene in the cosmopolitan bloom-forming alga, Heterosigma akashiwo. We compared the entire mitochondrial genome sequences of seven H. akashiwo strains from Japanese and North American coastal waters and identified a hypervariable segment. The region codes for a hypothetical protein with no defined function, and its variations between Japanese and North American isolates, were prominent, while the sequences were more conserved among Japanese strains and North American isolates. Comparison of the sequence in isolates obtained from different geographical points in the Northern Hemisphere revealed that the sequence variations largely correlated with latitude and longitude (i.e. Pacific/Atlantic oceans). Our results demonstrate the usefulness of the sequence in determining the phylogeographic history of H. akashiwo.
Data from: A hypervariable mitochondrial protein coding sequence associated with geographical origin in a cosmopolitan bloom-forming alga, Heterosigma akashiwo
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