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130 results for “Water density”
Lake Mendota, Wisconsin, USA, Zebra Mussel Veliger Water Column Density 2016-2019
We sampled veliger (larval stage) zebra mussels (Dreissena polymorpha) from 2016-2019. Zebra mussels are invasive in Lake Mendota and were first detected in November 2015. Samples were taken at three different sites on Lake Mendota from June to August in 2016, and from June to November in 2018-2019, using a 0.5 m diameter, 64 micrometer mesh size plankton net for an 8 m depth tow. This dataset complements adult zebra mussel, zoobenthos, and phytobenthos data collected during the same time period, for which data is also archived with EDI.
Water, Soil, Floc, Plant Total Phosphorus, Total Carbon, and Bulk Density data (FCE) from Everglades Protection Area (EPA) from 2004 to 2016
These data are a compillation of data from multiple sources including South Florida Water Management District (SFWMD) DBhydro web database, United States Environment Protection Agency Regional, Environmental Monitoring and Assessment (REMAP), Everglades Soil Mapping (ESM), and Florida Coastal Everglades Long Term Ecological Research (FCE-LTER). The matrix of these data were compiled for soil, surface water, floc, and plants where the nutrients are counted for total phosphorus, total carbon, and bulk density. When downloading the data from DBhydro, only regularly collected samples (SAMP) were included these data. As per DBhydro metadata, the regular samples were collected monthly by grab method throughout the year from 2004 to 2016 for SFWMD monitoring stations across the EPA. All flagged and field quality controlled values were excluded to avoid the duplication of data. In order to maintain the quality assurance/ quality control (QA/QC) the method detection limit for water TP was fixed at 2 µg/L by the SFWMD. This data set were used to assess the decadal trend of TP concentration in surface water and soil in EPA. Available data from 2004 to 2014 was collected for soils and from 2004 to 2016 for water to understand a decade of trends. Both Geographic Information System (GIS) and statistical data analysis were applied to determine changes in water quality and soil chemistry. These data are the basis for Shishir Sarker's Master's thesis.
Kawe Gidaa-naanaagadawendaamin Manoomin Tribal-University Research Collaborative, University of Minnesota, Manoomin / Psiη (Wild Rice) Density Survey for Northern Minnesota and Wisconsin Waters
Wild Rice (Ojibwemowin: Manoomin; Dakodiapi: Psiŋ; Latin: Zizania palustris) abundance, harvest, and water level data, across the upper Great Lakes region collected by tribal organizations.
Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection
<p>Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included:</p> <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> <p>Important notes: Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a></strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated.</p> <p>These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Rosetta/VIRTIS-H water column densities, rotational temperatures, and apparent ortho-to-para ratios
<p>Table B.1. in ASCII format from Cheng et. al. "The water Ortho-to-Para ration in the coma of comet 67P/Churyumov-Gerasimenko, A&A 2022</p>
Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Fig. 1 in Transport of jundiá Rhamdia quelen juveniles at different loading densities: water quality and blood parameters
Fig. 1. Plasma cortisol and blood glucose of jundiá juveniles transported at different loading densities. Different letters over the bars indicate significant differences (P <0.05) among treatments within each experimental time. Asterisks show significant differences (P <0.001) when compared to the initial value.
Depth profiles of water column dissolved methane, density and biomass of zooplankton (specifically, Chaoborus spp.), and measurements of methane extracted from Chaoborus spp. during two sampling campaigns in 2016
Depth profiles of water column dissolved methane (CH4), density and biomass of zooplankton (specifically, Chaoborus spp.), and measurements of methane extracted from Chaoborus spp. were collected from Beaverdam Reservoir, Vinton, VA, USA during two, 24-hr intensive sampling campaigns in fall 2016 (3-4 August and 16-17 September). The sampling campaign was designed to test the hypothesis that Chaoborus spp. can transport CH4 from the hypolimnion to the epilimnion of lakes and reservoirs as they migrate to the surface waters from the benthos at night in search of zooplankton prey, and results of the project are published in Carey et al. 2018. During both sampling events, depth profiles of water column methane and Chaoborus spp. density were collected at noon, dusk, midnight, 2 a.m., dawn, and noon the following day at a 1 m resolution. In addition, during the September sampling event depth profiles were collected 1 hour prior to and after dusk. Methane gas was extracted from live Chaoborus individuals at every depth and time point immediately after collection via gentle centrifugation in distilled water following McGinnis et al. 2017. REFERENCES: Carey, C.C., R.P. McClure, J.P. Doubek, M.E. Lofton, N.K. Ward, and D. Scott. 2018. Chaoborus spp. transport CH4 from the sediments to the surface waters of a eutrophic reservoir, but their contribution to water column CH4 concentrations and diffusive efflux is minor. Environmental Science & Technology. 52:1165-1173. DOI 10.1021/acs.est.7b04384 McGinnis, D. F., Flury, S., Tang, K. W., Grossart, H.-P. 2017. Porewater methane transport within the gas vesicles of diurnally migrating Chaoborus spp.: an energetic advantage. Scientific Reports. 7(44478) DOI 10.1038/srep44478
Data from: Selection for seed size: the unexpected effects of water availability and density
1. Seed size is a functional trait with important fitness consequences that potentially extend throughout the life cycle of plants. Dithyrea californica experiences selection for larger seeds in post‐germination stages but it is still uncertain how environmental factors mediate the strength and the direction of natural selection on seed size. 2. D. californica represents a unique opportunity to investigate selection on seed size in natural conditions due to a persistent seed ring that stays attached to the root throughout the plant's life. This makes it possible to unearth plants at any stage and measure the size of the seed from which they originated. 3. We conducted a factorial experiment manipulating water availability and intraspecific competition using plants that naturally germinated in the wild. 4. Selection on seed size via survivorship was nil because all individuals survived to reproduce. The strength and the direction of selection on seed size via fecundity depended on water availability and conspecific density. 5. Contrary to our predictions, increasing conspecific density relaxed directional selection favoring larger seeds, but only in the wettest conditions and an increase in water availability strengthened it, but only at low density. A possible explanation of these counter‐intuitive results relies on the observed absence of survival selection and increased plant growth rates under high water and low density. 6. Larger seeds require more resources to construct and when this cost is taken into account; there is no overall fitness increase with seed size. This nicely follows the life history theory predictions for optimal seed size. At the evolutionary equilibrium, if seeds could be larger, per seed fitness would still increase, which is what we observed. But cost corrected fitness should be flat. Maternal fitness equals per seed fitness times seed number, so any increase to per seed fitness of making a bigger seed is balanced by the resulting cost to seed number. Our results indicate flat cost corrected fitness of seed size as theory predicts.
Leg length and bristle density both necessary for water surface locomotion are genetically correlated in water striders
<p class="MsoNormal"><span>Access to hitherto unexploited ecological opportunities is associated with phenotypic evolution and often results in significant lineage diversification. Yet, our understanding of the mechanisms underlying such adaptive traits remains limited. Water striders have been able to exploit the water-air interface, primarily facilitated by changes in the density of hydrophobic bristles and a significant increase in leg length. These two traits are functionally correlated and are both necessary for generating efficient locomotion on the water surface. Whether bristle density and leg length have any cellular or developmental genetic mechanisms in common is unknown. Here, we combine comparative genomics and transcriptomics with functional RNAi assays to examine the developmental genetic and cellular mechanisms underlying the patterning of the bristles and the legs in <em>Gerris buenoi</em> and <em>Mesovelia mulsanti,</em> two species of water striders. We found that two gene duplication events in the genes <em>beadex </em>and <em>taxi </em>led to a functional expansion of the paralogs to affect bristle density and leg length. We also identified genes for which no function in bristle development has been previously described in other insects. Interestingly, most of these genes play a dual role in regulating bristle development and leg length. In addition, these genes play a role in regulating cell division. This result suggests that cell division may be a common mechanism through which these genes can simultaneously regulate leg length and bristle density. We propose that pleiotropy, by which gene function affects the development of multiple traits, may play a prominent role in facilitating access to unexploited ecological opportunities and species diversification.</span></p> <p> </p>
Data Set "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"
<p>This data set accompanies the publication "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- PyADF input scripts for running the eb-MBE and db-MBE calculations.</p> <p>- raw results data from the eb-MBE and db-MBE calculations</p> <p>- Jupyter notebooks for generating the plots and tables</p>
Population density does not affect seasonal regulation of reproductive physiology in male water voles
<p>Most small rodent species display cyclic fluctuations in their population density. The mechanisms behind these cyclical variations are not yet clearly understood. Density-dependent effects on reproductive function could affect these population variations. The fossorial water vole ecotype, Arvicola terrestris, undergoes a multi-year cycle dynamic with outbreak peaks. Here, we monitored different water vole populations over three years, in spring and autumn, to evaluate whether population density be related to male reproductive physiology. Our results show an effect of season and inter-annual factors in testes mass, plasmatic testosterone level, and androgen-dependent seminal vesicles mass. By contrast, population density does not affect any of these parameters, thus suggesting a lack of modulation of population dynamics of population density.</p>
Liquid water content, ice water content and density measured via the SnowMelt Instrument at Auberge station
<pre><span><span>Liquid water content, ice water content and density measured via the SnowMelt Instrument nearby the Auberge station during 3 consecutive winters, from <span>2021-11-05</span> to <span>2024-03-21, for the bottom snow layer (0-7cm). Data are aggregated at the hourly time step. The instrument was unmounted during no-snow periods. The liquid water content of the bottom snow layer can be used as a proxy for snow melt at the measurement point.</span></span></span></pre>
Data for: Ectoparasite population dynamics affected by host body size but not host density or water temperature in a 32-year long time series
<p>Host density, host body size, and ambient temperature have all been positively associated with increases in parasite infection. However, the relative importance of these factors in shaping long-term parasite population dynamics in wild host populations is unknown due to the absence of long-term studies. Here, we examine long-term drivers of gill lice (Copepoda) infections in Arctic charr (Salmonidae) over 32 years. We predicted that host density and body size and water temperature would all positively affect parasite population size and population growth rate. Our results show that fish size was the main driver of gill lice infections in Arctic charr. In addition, Arctic charr became infected at smaller sizes and with more parasites in years of higher brown trout population size. Negative intraguild interactions between brown trout and Arctic charr appear to drive smaller Arctic charr to seek refuge in deeper areas of the lake, thus increasing infection risk. There was no effect of host density on the force of infection, and the relationship between Arctic charr density and parasite mean abundance was negative, possibly due to an encounter-dilution effect. The population densities of host and parasite fluctuated independently of one another. Water temperature had negligible effects on the temporal dynamics of the gill lice population. Understanding long-term drivers of parasite population dynamics is key for research and management. In fish farms, artificially high densities of hosts lead to vast increases in the transmission of parasitic copepods. However, in wild fish populations fluctuating at natural densities, the surface area available for copepodid attachment might be more important than the density of available hosts.</p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
<p>Damming a stream inserts a lentic system (an impoundment or reservoir) into a lotic system, changing downstream hydrological, biogeochemical, and ecological processes. One such ecological effect of damming is to create a resource subsidy of easily captured and consumed zooplankton, which are preyed upon by filter-feeders and visual predators. The data included here were used to predict the density of lentic zooplankton subsidizing downstream habitats with water quality parameters as an alternative to microscopy. We also used this data to detect three different water quality regimes (high conductivity, high-CDOM, and a remainder) that are associated with differences in the density of zooplankton. This dataset is contained in two parts, both of which are focused on zooplankton density in the effluent of a series of tributary-impoundment reservoirs: 1) zooplankton density for a single summer season with water quality parameters and 2) zooplankton density for a series of three summers without water quality parameters.</p>
Structure and dynamics of liquid water from ab initio simulations: Adding Minnesota density functionals to Jacob's ladder
<p>Supporting data and analysis script for the work</p><p><i>Structure and dynamics of liquid water from ab initio simulations: Adding Minnesota density functionals to Jacob's ladder</i></p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
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Data from: Selection for seed size: the unexpected effects of water availability and density
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Data for: Ectoparasite population dynamics affected by host body size but not host density or water temperature in a 32-year long time series
Open the record for dataset details and reuse information.
Leg length and bristle density both necessary for water surface locomotion are genetically correlated in water striders
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