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6,771 results for “freshwater”
Freshwater ecosystem transitions due to artisanal sand mining in Rwanda, Africa
<p>Artisanal small-scale mining (ASM) of sand, gravel and crushed stones plays an economically important role through its value as a ‘development mineral’ for a growing population in sub-saharan Africa. The extracted material is used in developing and expanding urban areas and infrastructure and provides income for the population involved in the sector. However, the extraction of aggregates has shown to have large and often complex ecological and socio-economical consequences with potential significant health effects on the miners and the environment in which the mining takes place. Furthermore, the extent in which these negative effects do arise from ASM in sub-Saharan still remains anecdotal and are largely unknown. Here we show that ASM in a river channel in central Rwanda causes a systemic shift in freshwater biodiversity by changing species assemblages from being riverine towards communities representing standing waters. Based on 101 point samples, we find that ponds created due to mining activities act as habitats for freshwater insects associated with wetland habitats. Furthermore, these mining ponds did also act as breeding sites for mosquitoes and thereby potentially increase the presence of vector borne diseases such as malaria. These findings show how ASM can generate a landscape level shift in freshwater biodiversity and introduces the apparent paradox that while aggregates are critical building blocks in mitigating malaria transmissions and prevalence through improved housing, the mining practices unwillingly can create new breeding ground for malaria mosquitos, thus increasing the risk of malaria spreading to nearby communities.</p>
Fig. 1 in Microhabitat Preference And Relationships B E T W E E N M E Ta Z O A N Pa R A S I T E S O N T H E G I L L A P Pa R At U S O F T H E E U R O P E A N E E L (A N G U I L L A Anguilla) From Freshwaters Of Latvia
Fig. 1. The gill apparatus and sectors of gill arch.
Fig. 6. Astyanax laticeps, ANSP 21852 in Redescription of Astyanax obscurus (Hensel, 1870) and A. laticeps (Cope, 1894) (Teleostei: Characidae): two valid freshwater species originally described from rivers of Southern Brazil
Fig. 6. Astyanax laticeps, ANSP 21852, holotype, 54.1 mm SL, Rio Grande do Sul, Brazil.
Fig. 1 in Free-living freshwater nematodes in Vlasina Lake (Serbia, Pčina District, Surdulica Municipality)
Fig. 1. Vlasina Lake (Photo: Stefan Stoichev).
Fig. 4 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 4. Mass-specific surface area of the gills of different potamotrygonid embryos.
Figure 5 in Impact of alien fishes on the distribution pattern of indigenous freshwater fishes of Punjab, Pakistan
Figure 5. Loading of PCA showed that correlation among the four sites.
Figure 3 in Impact of alien fishes on the distribution pattern of indigenous freshwater fishes of Punjab, Pakistan
Figure 3.Families of fish species documented during surveys.
Fig. 1 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 1 Th_ origin (N = 92) of Paramecium biaurelia strains us_d in pr_s_nt studi_s
Fig. 3 in Marine and freshwater taxa: some numerical trends
Fig. 3 - The interdependence of marine, amphibiotic and freshwater fauna at different taxa levels.
Fig. 1 in Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippiensis, daudin 1802), and the prediction of intermediate hosts
Fig. 1. Collecting localities of alligators in Georgia and Florida.
Fig. 4 in Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippiensis, daudin 1802), and the prediction of intermediate hosts
Fig. 4. Prey stomach contents categorized to taxonomic class levels.
Fig. 1 in First molecular identification of Vorticella sp. from freshwater shrimps in Tainan, Taiwan
Fig. 1. Map of the Taiwan showing the location of the sampling site.
Fig. 1 in Helminth parasites of alien freshwater fishes in Patagonia (Argentina)
Fig. 1. Map of Argentinean Patagonia; the sampling localities of present study are shown.
Effects of the Invasive Freshwater Mussel Limnoperna fortunei on Sediment Properties and Accumulation Rates
<p>Environmental data and results of an experiment conducted in the vicinity of the Río de la Plata Estuary (Argentina), aimed at assessing the influence of the invasive mussel <em>Limnoperna fortunei</em> on sediment properties and accumulation rates. In the experiment, eighteen 20 L flow-through experimental units with and without mussels were used monitoring where changes in the mass and characteristics of the sediments accumulated throughout a yearly cycle in monthly, biannual and annual intervals.</p>
Data for manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing"
<p><strong>Data repository for manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing"</strong></p> <p>Here we describe the data stored in this archive, which has been used for the manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing", submitted to Geophysical Research Letters. This data archive includes two primary folders, one containing the CESM data and one containing the 2D model data. Within each of those folders are appropriately titled subfolders for the different model simulations and variables, in accordance with the descriptions provided in the manuscript. The following provides a description of the data within each folder: </p> <p><br> ----------------------------------------<br> ------------- CESM DATA --------------<br> ----------------------------------------<br> The CESM data folder has been split into four subfolders, one for each simulation: Control simulation (CESM_controlrun), 0.1 Sv freshwater flux simulation (CESM_0pt1Sv_FWFrun), the longer repeat of the 0.1 Sv freshwater flux simulation (CESM_0pt1Sv_FWFrun_repeat), and the 0.15 Sv freshwater flux simulation (CESM_0pt15Sv_FWFrun). Within each of these can be found folders for each of the 6 variables we have used: 4D temperature fields (TEMP), 4D salinity fields (SALT), 4D potential density fields (PD), 3D MOC fields (MOC), 3D mixed layer depth fields (HMXL), and 2D meridional heat transport fields (N_HEAT). Each CESM data file (one per monthly time step) is saved in netcdf format (.nc4), containing all appropriate dimensional data and descriptive meta data. Note that only the data used in the manuscript has been stored (e.g. only MOC data is provided for the CESM_0pt15Sv_FWFrun, while all fields have been provided for the CESM_0pt1Sv_FWFrun). </p> <p>----------------------------------------<br> ------------ 2D MODEL DATA -----------<br> ----------------------------------------<br> As described in the manuscript, the 2D model has been run using various choices of input parameter and freshwater flux. For each selection of parameter choice a new control run is first required, from which a set of experiments is then initiated. The 2D model data folder therefore contains 2 main subfolders, one containing the control run data for each of set of parameter choices (control_runs),and one containing the freshwater perturbation experiments for each set of parameter choices (fwp_change_exps). Within those, each control and experiment folder is titled according to the choices of vertical viscosity (kvd), Southern Ocean wind stress perturbation (txp), and the freshwater perturbation (fwp); when absent from the title, the default value for that variable is used (i.e. kvd=1e4 m2/s; txp=0.2 N/m2). The naming convention for the Southern Ocean wind stress is minpt07 for 0.13 N/m2 (i.e. minus 0.07) and pt07 for 0.27 N/m2. </p> <p>Model output data has been provided in ascii format for: the 2D Eulerian AMOC fields (MOC_EUL.dat.dat), 2D Quasi-Lagrangian fields (MOC_QLag.dat), 2D Salinity fields (salt2D.dat), 2D temperature fields (temp2d.dat), 2D density fields (rho.dat), the latitude values (ytdeg.dat; ydeg.dat) and depth values (zt.dat, zw.dat), and the timeseries values for a number of variables (timeseries.dat): The timeseries.dat data has 19 columns, of which the first 13 are useful: </p> <p> column: description:<br> 1 time (year)<br> 2 mean basin temperature (deg.C)<br> 3 mean basin salinity (psu)<br> 4 mean basin density (kg/m3) <br> 5 kinetic energy (KE) density<br> 6 potential energy (PE)<br> 7 mean surface heat flux<br> 8 mean surface salinity flux<br> 9 minimum meridional overturning streamfunction (Sv)<br> 10 maximum meridional overturning streamfunction (Sv)<br> 11 minimum advective poleward heat transport (PW)<br> 12 maximum advective poleward heat transport (PW)<br> 13 MOC (Sv) = max(psi) in the north Atlantic</p> <p>The final subfolder in the 2D model directory is titled 'additional_data': The first folder contains the snapshot data (fwp025_snapshots; one subfolder for each snapshot) for the experiment run with 25 cm/s freshwater flux as shown in Fig. S6, in which the data is stored in the same format as described above. The second folder contains the tendency terms as shown in Fig. S7 (fwp025_tendency; one subfolder per tendency term for each of the control and 25 cm/s freshwater flux experiment), in which the data is provided in ascii format for the 2D tendency term and the depth and latitude values. </p>
Arctic Ocean freshwater dynamics: transient response to increasing river runoff and precipitation [dataset]
<p>This dataset contains the underlying data for the manuscript Brown et al., Arctic Ocean freshwater dynamics: transient response to increasing river runoff and precipitation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Descriptors in the filenames, shown below as *, correspond to the various simulations, as follows:</p> <p>Simulations forced with JRA-25 reanalysis data:<br> AR: Unperturbed control simulation<br> B1: Simulation involving a step change in river runoff of -30% <br> C1: Simulation involving a step change in river runoff of +30% <br> B7: Simulation involving a step change in precipitation of -30% <br> C8: Simulation involving a step change in precipitation of +30% </p> <p>Simulations forced with the CORE-II climatology:<br> AR_CORE: Unperturbed control simulation <br> P-30_CORE: Simulation involving a step change in precipitation of -30% <br> P+30_CORE: Simulation involving a step change in precipitation of +30% </p> <p>-----------------------------------<br> The files named as fwvolume_Sref35_*.nc contain the variables:</p> <p>freshwater: horizontally-integrated liquid freshwater volume in m^3<br> freshwater_pos: as before, but including only positive values in the integration<br> h_f: basin-mean freshwater height in m<br> h_f_pos: as before, but including only positive values in the integration</p> <p>The reference salinity used in each case is 35.</p> <p>-----------------------------------</p> <p>The files named as freshwater_h_f_*.nc contain horizontally-gridded, depth-integrated liquid freshwater heights in m. The integration is made to a depth of 276.68m.</p> <p>h_f_34p8: freshwater height, using a reference salinity of 34.8 <br> h_f_34p8_pos: as before, but including only positive values in the integration<br> h_f_35: freshwater height, using a reference salinity of 35 <br> h_f_35_pos: as before, but including only positive values in the integration</p> <p>The grid for these files is grid.nc</p> <p>-----------------------------------</p> <p>The files named as fluxes_ed3_*.nc contain strait-integrated volume fluxes (in m^3 s^-1) into and out of the Arctic domain: </p> <p>f: liquid volume flux<br> fw: freshwater, with variables numbered 1 using a reference salinity of 34.8 and those numbered 2 using a reference salinity of 35<br> ice: sea ice volume flux</p> <p>The first dimension, ngate, of the variables indicates the strait:</p> <p>1) Fram Strait<br> 2) Barents Sea Opening<br> 3) Bering Strait<br> 4) Amundsen Gulf<br> 5) McClure Strait<br> 6) Canadian Arctic Archipelago<br> 7) Nares Strait</p> <p>-----------------------------------</p> <p>Sea ice volumes (in m^3) for the JRA-25 forced simulations are contained in the files named area_int_*.nc with variable name "ivol"</p> <p>-----------------------------------</p> <p>Basin-integrated liquid freshwater volumes for a further series of JRA-forced runoff perturbation experiments are contained within the file FWLvol_AO_Sref35_runoff.mat:</p> <p>R_10: a step increase in runoff of 10%<br> R_60: a step increase in runoff of 60%<br> R_100: a step increase in runoff of 100%</p> <p>The reference salinity is 35 and depth of integration 276.68m</p>
The effect of eutrophication and global change on heterocystous cyanobacteria in freshwater lakes
<p>Eutrophication and global change have been suggested to promote cyanobacterial blooms. <em>Anabaena, Aphanizomenon and Cylindrospermopsis</em> are heterocystous genera of toxin-producing cyanobacteria. It is yet unclear how eutrophication and climate change will impact heterocystous cyanobacteria. This study investigates the effects of total nitrogen (TN), total phosphorus (TP), TN:TP ratio, temperature, pH and trophic state on the relative abundance of three heterocystous cyanobacteria, using data of 999 lakes obtained from the National Lake Assessment 2012 (NLA) of the U.S. Environmental Protection Agency (EPA). It was demonstrated that elevated TN and TP levels, as well as higher pH levels are related to higher abundance of heterocystous cyanobacteria. <em>Cylindrospermopsis</em> is correlated with TN and temperature, <em>Aphanizomenon</em> is related with TP and negatively correlated to TN:TP. All three heterocysts are correlated with pH. Eutrophication has a higher effect on the relative abundance of heterocystous cyanobacteria than the increased temperatures and increased CO<sub>2</sub> concentrations. Decreasing the concentration of nitrogen and especially phosphorus in lakes may limit the relative abundance of heterocystous cyanobacteria in fresh waters.</p>
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
Figure 2 in The ecology of freshwater bivalves in the Lake Sapanca basin, Turkey
Figure 2. Growth curves of Unio crassus and U. pictorum from the Lake Sapanca basin.
Figure 1 in The ecology of freshwater bivalves in the Lake Sapanca basin, Turkey
Figure 1. Sampling sites in the Lake Sapanca basin.
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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.