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13 results for “Freshwater reservoir”
Fig. 1 in Faunistic and ecological investigations of the free-living freshwater nematode fauna of the Koprinka Reservoir (Central Bulgaria)
Fig. 1. Map of Koprinka Reservoir with the location of the sites (stations) (based on a Sentinel 2 images, created by Geopolymorphic Cloud.)
Fig. 1. A in New record of the cold freshwater dinoflagellate Palatinus apiculatus (Dinophyceae) from the Paldang Reservoir, Korea
Fig. 1. A map of the Paldang Reservoir, Korea. A black circle represents the sampling site. Black arrows represent direction of water flow.
Fig. 3 in New record of the cold freshwater dinoflagellate Palatinus apiculatus (Dinophyceae) from the Paldang Reservoir, Korea
Fig. 3. Scanning electron microscrope micrographs of Palatinus apiculatus isolated from the Paldang Reservoir, Korea. Plates of the theca are indicated, following to Kofoidian plate formula. A: ventral view showing sulcal region; B, C: apical view from the ventral side; D-F: dorsal view showing different wide of suture and variation of the cingular plate (v). Thick black bar in D-F represents each given length. Sa: anterior sulcal plate. Sd: right sulcal plate. Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar = 10 μm.
Fig. 2 in New record of the cold freshwater dinoflagellate Palatinus apiculatus (Dinophyceae) from the Paldang Reservoir, Korea
Fig. 2. Vegetative cells (A, C) and temporary cysts (B, D) of the Korean Palatinus apiculatus isolated from the Paldang Reservoir. An arrowhead represents an eyespot. Cell size is proportional to a given scale bar (10 μm).
Fig. 4. A maximum likelihood tree constructed from a 28S in New record of the cold freshwater dinoflagellate Palatinus apiculatus (Dinophyceae) from the Paldang Reservoir, Korea
Fig. 4. A maximum likelihood tree constructed from a 28S rDNA dataset of the Korean Palatinus apiculatus and other freshwater dinoflagellates. A total of 5,000 replicates were run for bootstrap analyses. Members of the genus Palatinus are highlighted in orange. The isolate from this study and its GenBank No. are given in bold font.
Date set for the manuscript: The Role of Sediment Gas Storage in the Methane Dynamics of a Shallow Freshwater Reservoir
<p>The dataset supports the findings of the manuscript entitled: Linking Sediment Gas Storage to the Methane Dynamics in a Shallow Freshwater Reservoir. The manuscript presents results based on in-situ monitoring at the main pre-dam of the Wupper reservoir. The reservoir is located in Germany and the monitoring was conducted from March 2020 to November 2021. The main goal of the monitoring was to measure the spatial variability of methane fluxes and its dynamics in the reservoir. The methane budget of the reservoir was then analysed in combination with estimates of the amount of free gas stored in the sediment matrix, which were derived from acoustic observations. The findings are discussed in the manuscript.</p> <p>Therefore, the dataset provided as an xlsx file includes separate sheets for:</p> <ul> <li>Sediment measurements of potential methane production (PMP), loss on ignition (LOI), dissolved methane (CH<sub>4</sub>) in porewater, carbon and nitrogen content.</li> <li>Gas content in the sediment estimated from maximum acoustic backscatter for 2020 and 2021 for the reservoir spatial grid.</li> <li>Monthly time-series of methane fluxes (oxidation, diffusion, ebullition, and potential flux at the sediment water interface - PSWI) for each monitoring location and for the reservoir (average of all monitoring locations) with the inclusion of degassing at the dam overflow and advective methane transport (Net-export) by inflow and outflow.</li> <li>Time series of daily mean values of ebullition and environmental parameters monitored in the reservoir.</li> </ul> <p>Explanations and units are provided in column labels. NaN refers to missing data.</p> <p>In this version (Version 2), the calculation of the PSWI has been corrected. It is now calculated as the vertically integrated potential methane production over the top 30 cm layer of the sediment.</p>
Date set for the manuscript: Exploring the temporal dynamics of methane ebullition in a subtropical freshwater reservoir
<p>The dataset supports the findings of the manuscript entitled ‘Exploring the Temporal Dynamics of Methane Ebullition in a Subtropical Freshwater Reservoir’. The results of the manuscript are based on continuous in-situ measurements conducted at Passaúna Reservoir, located in the southern part of Brazil (South America). The monitoring was carried out from 2017 to 2020, during which a comprehensive set of environmental variables was obtained from various studies. The primary objective of the manuscript was to comprehend the temporal dynamics of ebullition flux in the reservoir. Therefore, time series data of ebullition and relevant environmental variables were analyzed across different time scales, ranging from minutes to daily resolutions. Statistical and data driven models were tested to predict ebullition at varying time scales, considering the influence of environmental variables. The findings are discussed in the manuscript.</p> <p>Here, xlsx files and Matlab scripts are provided. The xlsx files contain time series data for environmental variables ('Environmental_Variables_TimeSeries') and ebullition flux ('Ebullition_Flux_TimeSeries'). Separate sheets were utilized for different time intervals, namely 5 minutes (dt=5min), 10 minutes (dt=10min), 1 hour (dt=1hr), and 1 day (dt=1d). Explanations and units are provided within the column labels, while 'NaN' denotes missing data.</p> <p>Matlab scripts for all the empirical models that were tested, as outlined in the manuscript's supporting information (Tables S1 and S2), have been made available (detailed in the following table). These scripts were developed using MatLab R2023a.</p> <table> <tbody> <tr> <td> <p>File (.m)</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p><strong>Main scripts</strong></p> </td> </tr> <tr> <td> <p>TableS1_Empirical_Models_Literature</p> </td> <td> <p>Empirical models from the literature tested. (Table S1 in the manuscript)</p> </td> </tr> <tr> <td> <p>TableS2_FirstPart_Reffited_Models</p> </td> <td> <p>Empirical models from the literature refitted. (Table S2 in the manuscript)</p> </td> </tr> <tr> <td> <p>TableS2_SecondPart_New_Models</p> </td> <td> <p>New empirical models implemented. (Table S2 in the manuscript)</p> </td> </tr> <tr> <td> <p>TableS2_SecondPart_New_Models_GAM_Timescales</p> </td> <td> <p>Generalized additive models (GAM) applied to predict ebullition at different timescales. (Table S2 in the manuscript).</p> </td> </tr> <tr> <td> <p>PredictedR2_GAM_models</p> </td> <td> <p>Calculate the predicted R-squared for GAM.</p> </td> </tr> <tr> <td> <p><strong>Functions (need for the main scripts)</strong></p> </td> </tr> <tr> <td> <p>load_TimeSeries</p> </td> <td> <p>Import time series of environmental variables from excel sheets into MatLab.</p> </td> </tr> <tr> <td> <p>load_EbullitionTS</p> </td> <td> <p>Import time series of ebullition from excel sheets into MatLab.</p> </td> </tr> <tr> <td> <p>units_description</p> </td> <td> <p>Description containing units of the variables.</p> </td> </tr> <tr> <td> <p>Bin_xdata</p> </td> <td> <p>Creates data binning of X and Y based on X data.</p> </td> </tr> <tr> <td> <p>RelativeError</p> </td> <td> <p>Calculate the relative error of accumulated flux between measured and simulated.</p> </td> </tr> <tr> <td> <p>ANN5Neurons_Retrained_Deshmukh2014</p> </td> <td> <p>Trained Artificial Neural Network based on the ANN proposed by Deshmukh et al. (2014).</p> </td> </tr> <tr> <td> <p>ANN_Passauna_20Neurons</p> </td> <td> <p>Trained Artificial Neural Network with the addition of more environmental variables.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Financial support</strong></p> <p>The field measurements were financed by the German Federal Ministry of Education and Research (BMBF, Grant 02WGR1431A), in the framework of the research project MuDak-WRM (https://www.mudak-wrm.kit.edu). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. Tobias Bleninger received productivity stipend from the National Council for Scientific and Technological Development (CNPq, grant no. 312211/2020-1, call no. 09/2020). Michael Männich received productivity stipend from the National Council for Scientific and Technological Development (CNPq, grant no. 308744/2021-7, call no. 04/2021). Andreas Lorke received financial support from the German Research Foundation (DFG, grant number LO1150/16-1).</p>
Data from: Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved metazoan primer reveals species-rich and reservoir-specific communities
Freshwater habitats are of high conservation value and provide a wide range of ecosystem services. Effective management requires regular monitoring. However, conventional methods based on direct observation or specimen collection are so invasive, expensive and labour-intensive that frequent monitoring is uncommon. Here, we test whether the evaluation of environmental DNA (eDNA) from water based on a simple protocol can be used for assessing biodiversity. We use universal metazoan primers for characterizing water eDNA across horizontal and vertical spatial dimensions in two reservoirs with known species diversity for two key taxa. eDNA obtained directly from 42 samples × 15 ml water (total = 630 ml) per reservoir yielded DNA signatures for more than 500 metazoan species, of which 105 could be identified to species/genus based on DNA barcodes. We show that eDNA can be used to assign each water sample to its reservoir of origin, and that eDNA outperforms conventional survey methods in single-sample richness comparisons, while revealing evidence for hundreds of unknown species that are undetected by conventional bioassessment methods. eDNA also confirms the presence of a recently discovered invasive snail species and provides evidence for the continued survival of a rare native species of goby not sighted in that habitat since 2007. eDNA thus promises to be a useful addition to the bioassessment toolbox for freshwater systems.
FIGURES 8–15 in Pinnularia dongzhenensis sp. nov., a new freshwater diatom species (Bacillariophyceae) from Dongzhen Reservoir, Fujian Province, China
FIGURES 8–15. Pinnularia dongzhenensis sp. nov., SEM external views of P. dongzhenensis sp. nov.. 8, 9. General view of valve. 10, 11. Central area showing proximal raphe fissures bent in the same direction. 12, 13. Details of alveoli with rows of rounded areolae. 14, 15. Detail of the sickle–shaped raphe fissure at pole, and each alveolus with rows of rounded areolae. Scale bars = 10 μm (Figs 8, 9), 2 μm (Figs 10–15).
FIGURES 1–7 in Pinnularia dongzhenensis sp. nov., a new freshwater diatom species (Bacillariophyceae) from Dongzhen Reservoir, Fujian Province, China
FIGURES 1–7. LM micrographs of the type population of Pinnularia dongzhenensis from Dongzhen Reservoir. Scale bar = 10 μm.
Data from: Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved metazoan primer reveals species-rich and reservoir-specific communities
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A mesocosm experiment testing the combined effects on plankton community of a terrestrial organic matter runoff and a warming in small freshwater reservoirs
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FIGURES 16–23 in Pinnularia dongzhenensis sp. nov., a new freshwater diatom species (Bacillariophyceae) from Dongzhen Reservoir, Fujian Province, China
FIGURES 16–23. Pinnularia dongzhenensis sp. nov., SEM internal views of P. dongzhenensis sp. nov.. 16, 17. Entire valve. 18, 19. Central area; an intermissio is apparently lacking. 20, 21. View showing alveoli pattern. 22, 23. Valve apex showing typical helictoglossa. Scale bars = 10 μm (Figs 13, 17), 2 μm (Figs 18–23).
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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)
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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.