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73 results for “phytoplankton community”
Summer water chemistry, phytoplankton and zooplankton community composition, size structure, and biomass in a shallow, hypereutrophic reservoir in southwestern Iowa, USA (2019).
This data product contains data for Green Valley Lake, a hypereutrophic reservoir in southwest Iowa (USA) from the summer of 2019. We sampled and quantified zooplankton, phytoplankton, and nutrient concentrations (total N, total P, soluble reactive P, nitrate) in the lake weekly with the primary aim of assessing consumer nutrient cycling, specifically zooplankton nutrient cycling, in a hypereutrophic reservoir. Weekly plankton sampling included quantifying zooplankton and phytoplankton biomass, community composition, and size structure. Phytoplankton size was measured as the greatest axial linear distance which would be approached by a zooplankton grazer. Allometric equations from the literature were applied to the zooplankton size measurements to estimate zooplankton community excretion of N and P. We found that the estimated contribution of zooplankton excretion to the dissolved P pool was substantial in the spring. Further, we found evidence that zooplankton affected phytoplankton size distributions through selective grazing of smaller phytoplankton cells likely affecting nutrient uptake and storage by phytoplankton.
IISD Experimental Lakes Area LTER: Phytoplankton Abundance, Biomass, and Community Structure, 1969 – 2024
The IISD Experimental Lakes Area (IISD-ELA) LTER Phytoplankton Abundance, Biomass, and Community Structure data package provides data on phytoplankton from five long-term reference lakes (Lake 114, Lake 224, Lake 239, Lake 373, and Lake 442) in northwestern Ontario, Canada. The data package includes metadata and tabular data. Metadata include a lake information table (lake names, locations, morphometric data) and an HTML information sheet that describes the data package and includes methods, a data dictionary, and references. Tabular data include: phytoplankton biomass summarized at the Division (Cyanobacteria) or Class (chlorophyceae, euglenophyceae, chrysophyceae, bacillariophyceae, cryptophyceae, dinophyceae) level of taxonomic resolution ("Group"); phytoplankton abundance and biomass at the lowest level of taxonomic resolution (usually species level); phytoplankton species codes that act as a primary key linking the previous tables to current scientific names; and the phytoplankton trophic feeding type that partitions taxa into functional feeding groups (autotrophic, heterotrophic, mixotrophic). The data package is ongoing – updated data will be provided as data are collected from these long-term reference lakes in subsequent years. If data are not present for the lake you are interested in (e.g. if the lake was part of an experiment rather than a reference lake) please get in touch with us.
Lake snow removal experiment phytoplankton community data, under ice, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents phytoplankton community samples (pooled epilimnion and hypolimnion samples representative of 7 m water column) both under-ice and during some shoulder-season (open water) dates. Samples were collected into amber bottles and preserved with Lugol's solution before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic identification and quantification.
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
<p>Climate model output for paper describing CESM2-UVphyto.</p>
Image-derived indicators of phytoplankton community responses to Pseudo-nitzschia blooms
<p>Data associated with the manuscript "Image-derived indicators of phytoplankton community responses to <em>Pseudo-nitzschia</em> blooms" submitted to the journal <em>Harmful Algae</em>. There is an additional R script that calculates an interaction metric as described in the paper. </p>
Salinity is diagnostic of maximum potential chlorophyll and phytoplankton community structure in an Eastern Boundary Upwelling System
Coastal upwelling ecosystems associated with strong physical stirring exhibit fine-scale hydrographic and biological patchiness. Though many studies have found broad correlations between hydrographic properties (e.g., temperature and salinity) and phytoplankton biomass, we lack a detailed understanding of the underlying mechanisms and how to diagnose patchy distributions. Here, using observational data from coastal waters in the California Current System, we demonstrate that the maximum observed chlorophyll in a water parcel increases with salinity—a conservative water-mass tracer. This relationship arises from sub-euphotic zone nitrate concentrations, which also increase with salinity. Therefore, we can define maximum potential chlorophyll as a function of salinity and nitrate. We show that variations in salinity explain patterns in phytoplankton community structure and discuss how growth, grazing, and light and micronutrient limitation can generate chlorophyll values below the maximum potential. Our mechanistic explanation provides a novel framework for diagnosing biological patchiness using salinity observations.
Shifts in Phytoplankton Community Structure Across Oceanic Boundaries
<p>The two datasets "EnvironmentalData.csv" and "PSD_TransitionZone.csv" provide information about the environmental conditions and the distribution of phytoplankton populations in the North Pacific Ocean. </p> <p>Specifically, the "EnvironmentalData.csv" dataset contains measurements of various environmental parameters, including salinity, temperature, nutrient concentrations, and light availability. These measurements were used to characterize the physical and chemical conditions of the different regions sampled during the study, including the North Pacific Subtropical Gyre (NPSG) and the surrounding regions. The dataset include:</p> <ul> <li><strong>cruise</strong>: The identifier for the research cruise.</li> <li><strong>lat</strong>: Latitude.</li> <li><strong>lon</strong>: Longitude.</li> <li><strong>date</strong>: Date and time of the observation.</li> <li><strong>salinity</strong>: Salinity of the water (PSU).</li> <li><strong>temp</strong>: Water temperature (ºC).</li> <li><strong>par</strong>: Photosynthetically Active Radiation (µmol photons m-2 s-1).</li> <li><strong>SiO4</strong>: Silicate concentration (µM).</li> <li><strong>NO3_NO2</strong>: Nitrate and nitrite concentration (µM).</li> <li><strong>PO4</strong>: Phosphate concentration (µM).</li> <li><strong>MLD</strong>: Mixed layer depth (m).</li> <li><strong>light</strong>: Daily averaged PAR (µmol photons m-2 s-1).</li> </ul> <p>The "PSD_TransitionZone.csv" dataset contains information about the abundance, size, and biomass of different phytoplankton populations, including Prochlorococcus, Synechococcus, picoeukaryotes, and nanoeukaryotes. These data were collected using <a href="https://seaflow.netlify.app/">SeaFlow - a custom-built flow cytometer</a>, and were used to investigate how the distribution and composition of phytoplankton communities change in relation to environmental gradients. The dataset include:</p> <ul> <li><strong>cruise</strong>: The identifier for the research cruise.</li> <li><strong>date</strong>: Date and time of the observation.</li> <li><strong>pop</strong>: Population type.</li> <li><strong>lat</strong>: Latitude.</li> <li><strong>lon</strong>: Longitude.</li> <li><strong>n_per_uL</strong>: Number of cells per microliter (10^6 cells µL-1).</li> <li><strong>c_per_uL</strong>: Carbon per microliter (pgC µL-1).</li> <li><strong>qc</strong>: Carbon quotas (pgC/cell).</li> <li><strong>diam</strong>: Diameter (µm).</li> </ul> <p>The R script "analysis.R" perfoms the analysis of the environmental and phytoplankton data in the North Pacific Ocean. The script performs the following steps:</p> <ol> <li><strong>Data Preprocessing</strong>: Cleans and prepares the data for analysis, including handling missing values and converting date/time formats.</li> <li><strong>Diel Trend Extraction</strong>: Extracts diel (day-night) trends from the phytoplankton data using a custom function that decomposes time series data into seasonal, trend, and residual components.</li> <li><strong>Data Merging</strong>: Merges the environmental and phytoplankton data into a single dataset for analysis.</li> <li><strong>Growth Rate Calculation</strong>: Calculates the cellular growth rate of different phytoplankton populations based on changes in their carbon quotas during daylight hours.</li> <li><strong>North Pacific Subtropical Gyre (NPSG) Boundary Definition</strong>: Defines the boundaries of the NPSG based on changes in salinity along the cruise tracks.</li> <li><strong>Binning and Summarization</strong>: Bins the data over distance from the NPSG boundaries and calculates mean and standard deviation for various parameters within each bin.</li> <li><strong>Figure Generation</strong>: Generates several figures, including: <ul> <li>A map showing the cruise tracks and the location of the NPSG.</li> <li>Plots showing the change in environmental parameters (salinity, temperature, nutrients) across the NPSG boundaries.</li> <li>Plots showing the change in phytoplankton biomass, abundance, and growth rate across the NPSG boundaries.</li> <li>A correlation plot showing the relationship between phytoplankton growth, biomass, and environmental parameters.</li> </ul> </li> </ol>
Supplementary Datasets for "Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario"
<p>These are the four supplementary datasets used in the analysis and work presented in the publication </p> <p><strong><span>Oceanic enrichment of ammonium and its impacts on phytoplankton community composition under a high-emissions scenario</span></strong></p> <p> </p>
Linked collectors and determiners for: Phytoplankton community composition in the water column of East Greenland fjords, August 2022.
Natural history specimen data linked to collectors and determiners held within, "Phytoplankton community composition in the water column of East Greenland fjords, August 2022". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef">https://bionomia.net/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef">https://gbif.org/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef</a>. Formatted as a Frictionless Data package.
Data and scripts for Predictable Ecological Response to Rising CO2 of a Community of Marine Phytoplankton
<p>Rising atmospheric CO<sub>2</sub> and ocean acidification are fundamentally altering conditions for life of all marine organisms, including phytoplankton. Differences in CO<sub>2</sub> related physiology between major phytoplankton taxa lead to differences in their ability to take up and utilise CO<sub>2</sub>. These differences may cause predictable shifts in the composition of marine phytoplankton communities in response to rising atmospheric CO<sub>2</sub>. We report an experiment in which 7 species of marine phytoplankton, belonging to 4 major taxonomic groups (cyanobacteria, chlorophytes, diatoms and coccolithophores) were grown at both ambient (500 µatm) and future (1000 µatm) CO<sub>2</sub> levels. These phytoplankton were grown as individual species, as cultures of pairs of species and as a community assemblage of all seven species in two culture regimes (high-nitrogen batch cultures and lower-nitrogen semi-continuous cultures, though not under nitrogen limitation). All phytoplankton species tested in this study increased their growth rates under elevated CO<sub>2</sub> independent of the culture regime. We also find that, despite species-specific variation in growth response to high CO<sub>2</sub>, the identity of major taxonomic groups provides a good prediction of changes in population growth and competitive ability under high CO<sub>2</sub>. The CO<sub>2</sub>-induced growth response is a good predictor of CO<sub>2</sub>-induced changes in competition (R<sup>2</sup>>0.93) and community composition (R<sup>2</sup>>0.73). This study suggests that it may be possible to infer how marine phytoplankton communities respond to rising CO<sub>2</sub> levels from the knowledge of the physiology of major taxonomic groups, but that these predictions may require further characterisation of these traits across a diversity of growth conditions. These findings must be validated in the context of limitation by other nutrients. Also, in natural communities of phytoplankton, numerous other factors that may all respond to changes in CO2, including nitrogen fixation, grazing and variation in the limiting resource will likely complicate this prediction.</p>
Linked collectors and determiners for: Phytoplankton community composition in the water column of the West Greenland shelf, July 2021.
Natural history specimen data linked to collectors and determiners held within, "Phytoplankton community composition in the water column of the West Greenland shelf, July 2021". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6e5fa973-a7cc-4867-8606-8bcaa395f7de">https://bionomia.net/dataset/6e5fa973-a7cc-4867-8606-8bcaa395f7de</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6e5fa973-a7cc-4867-8606-8bcaa395f7de">https://gbif.org/dataset/6e5fa973-a7cc-4867-8606-8bcaa395f7de</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Phytoplankton community composition at station Svartnes, Balsfjord, Norway in June 2017 and June 2018 (sampled with a GoFlow bottle).
Natural history specimen data linked to collectors and determiners held within, "Phytoplankton community composition at station Svartnes, Balsfjord, Norway in June 2017 and June 2018 (sampled with a GoFlow bottle)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5e2eccfd-47a8-4029-946a-c1bec35d86b7">https://bionomia.net/dataset/5e2eccfd-47a8-4029-946a-c1bec35d86b7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5e2eccfd-47a8-4029-946a-c1bec35d86b7">https://gbif.org/dataset/5e2eccfd-47a8-4029-946a-c1bec35d86b7</a>. Formatted as a Frictionless Data package.
Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"
<p>This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre. </p> <p>The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the <sup>18</sup>O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m.</p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>Date </p> </td> <td> <p>Date of sampling and start of incubation (UTC -10 hours)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Cruise ID</p> </td> <td> <p>Cruise identification</p> </td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Latitude</p> </td> <td> <p>Latitude</p> </td> <td> <p>degrees N</p> </td> </tr> <tr> <td> <p>Longitude</p> </td> <td> <p>Longitude</p> </td> <td> <p>degrees E</p> </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> <p>Incubation time</p> </td> <td> <p>hours</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td> <p>Flag GOP</p> </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Flag CR</p> </td> <td> <p>Flag identification for community respiration (good=1,questionable=2)</p> </td> <td> <p>#</p> </td> </tr> </tbody> </table> <p> </p> <p>The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m) in dawn to dawn incubations, following Williams et al. (2004). </p> <p>Data description</p> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Description</p> </td> <td> <p>Units</p> </td> </tr> <tr> <td> <p>HOT</p> </td> <td>HOT cruise number</td> <td> <p>#</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td> <p> </p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td>Nominal depth of sampling and incubation </td> <td> <p>meters</p> </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>CR</td> <td> <p>Dark community respiration, average of 8 replicates</p> </td> <td> <p>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></p> </td> </tr> <tr> <td> <p>CR SE</p> </td> <td>Dark community respiration standard error</td> <td> <p>meters</p> </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O<sub>2</sub> m<sup>-3</sup> d<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Interaction matters: Bottom-up driver interdependencies alter the projected response of phytoplankton communities to climate change, links to model results
<p>This dataset provides the output of ten model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2023). In addition to information on the mesh, the dataset contains 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, carbonate system parameters (dissolved inorganic carbon, CO2 partial pressure, total alkalinity), temperature, photosynthetically active radiation, and mixed layer depths.</p> <p>File names refer to the figures in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
Data from: evaluating the use of lake sedimentary DNA in palaeolimnology: a comparison with long-term microscopy-based monitoring of the phytoplankton community
<p>Palaeolimnological records provide valuable information about how phytoplankton respond to long-term drivers of environmental change. Traditional palaeolimnological tools such as microfossils and pigments are restricted to taxa that leave sub-fossil remains, and a method that can be applied to the wider community is required. Sedimentary DNA (sedDNA), extracted from lake sediment cores, shows promise in palaeolimnology, but validation against data from long-term monitoring of lake water is necessary to enable its development as a reliable record of past phytoplankton communities. To address this need, 18S rRNA gene amplicon sequencing was carried out on lake sediments from a core collected from Esthwaite Water (English Lake District) spanning ~105 years. This sedDNA record was compared with concurrent long-term microscopy-based monitoring of phytoplankton in the surface water. Broadly comparable trends were observed between the datasets, with respect to the diversity and relative abundance and occurrence of chlorophytes, dinoflagellates, ochrophytes and bacillariophytes. Up to 20% of genera were successfully captured using both methods, and sedDNA revealed a previously undetected community of phytoplankton. These results suggest that sedDNA can be used as an effective record of past phytoplankton communities, at least over timescales of less than 100 years. However, a substantial proportion of genera identified by microscopy were not detected using sedDNA, highlighting the current limitations of the technique that require further development such as reference database coverage. The taphonomic processes which may affect its reliability, such as the extent and rate of deposition and DNA degradation, also require further research.</p>
Data from: Brucite-inspired ocean alkalinity enhancement alters the biogeochemistry and composition of a phytoplankton community: A Santa Barbara channel case report
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Algal lipid distributions and hydrogen isotope ratios reflect phytoplankton community dynamics in Rotsee (Switzerland)
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Data from: Comparative analysis of environmental DNA metabarcoding and spectro-fluorescence for phytoplankton community assessments
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Data from: evaluating the use of lake sedimentary DNA in palaeolimnology: a comparison with long-term microscopy-based monitoring of the phytoplankton community
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Microscope-based phytoplankton community composition for Green Lake 4, 2000 - 2007.
These data represent phytoplankton community composition in Green Lake 4 observed during bi-weekly sampling events following ice-off during the 2000-2007 field seasons. Samples were collected from the deepest part of the lake using a Van Dorn sampler from depths of 0, 3, and 9 m. Samples from the inlet and outlet are grab samples. Phytoplankton samples were preserved in 1% Lugol’s solution. Cell counts for each taxonomic group were conducted using an inverted microscope with 1000x magnification. Taxonomic resolution was based on the imaging capabilities of the instrument and varies based on the size and distinguishing characteristics of the cells in a given taxonomic group. Please see details in Methods.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.