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5,784 results for “Density”
Nekton species counts and density from flume net collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The flume nets were deployed with the purpose of capturing salt marsh nekton. Nekton species were identified to the lowest taxonomic level using species keys. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Aboveground plant biomass and density in control and fertilized plots in a Spartina alterniflora-dominated marsh, Rowley River, Plum Island Ecosystem LTER, MA (1999-2025).
Aboveground plant biomass and density is determined non-destructively during the growing season in permanent control and fertilized plots in a Spartina alterniflora-dominated salt marsh at Laws Point on the Rowley River within the Plum Island Ecosystems (PIE) LTER site.
SBC LTER: Reef: Benthic Composition Experiment: fish, algal, and invertebrate density
These data describe the average size and abundance of fishes, understory algae, and benthic invertebrates across 5 sampling sites (Arroyo Quemado, Naples, Isla Vista, Mohawk, Carpentaria) along the Santa Barbara Coast and 2 sites at Santa Cruz Island (San Pedro Point, and Cavern Point). Sampling began September 2021 and is conducted seasonally every 3 months. Data are collected within two circular plots at each sampling site. Plot 1 represents the control plot with no giant kelp removal and plot 2 represents the kelp clearing plot where all giant kelp are removed. Additionally, understory algae are removed seasonally from half of the rock plates for both plots (Plot 1 rock plates #1-6 and Plot 2 rock plates #13-18).
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Invertebrate and algal density
These data describe the abundance of common reef associated species of macro invertebrates and macroalgae within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The number of individuals of approximately 50 taxa were recorded by divers along 40 m transects within each plot. Small species of macroalgae and macinvertebrates were counted within six permanent 1 m2 quadrats positioned uniformly along the 40 m transect, while larger species were counted within four contiguous 20 m2 sub-sections of each 40 m x 2 m transect. Also included at the quadrat scale are estimates of an average size-related measurement of each species, which was developed specifically for each species for the purpose of estimating its biomass. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.
SBC LTER: Reef: Kelp Forest Community Dynamics: Invertebrate and algal density
These data describe the density and size of a select group of about 50 species of benthic invertebrates and understory algae in fixed plots (either 1m x 1m or 20m x 1m) along permanent transects. These data are part of SBCLTER’s kelp forest monitoring program to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset are at nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. Sampling began in 2000, and these data are updated annually. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. See Methods for more information.
Data package supporting manuscript "Widespread Heterogeneity in Density-Dependent Mortality of Nearshore Fishes"
This repository contains the complete data synthesis and analysis pipeline for a global meta-analysis on density-dependent mortality in reef fishes. We estimated mortality parameters (α and β) from >30 ecological studies and explored how ecological traits, experimental methods, and phylogenetic history explain variation in density dependence. It comprises eight data tables in csv format, three .tre files for phylogenetic trees (see method document for data sources), and the zipped code folder (including 12 R scripts) to ensure transparent, end-to-end reproducibility of data processing, analysis, and visualization. This package supports the manuscript “Widespread Heterogeneity in Density-Dependent Mortality of Nearshore Fishes” by Stier & Osenberg (Ecology Letters).
SBC LTER: Reef: Long-term experiment: Kelp removal: Invertebrate and algal density
These data describe the abundance of common reef associated species of macro invertebrates and macroalgae within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The number of individuals of approximately 50 taxa were recorded by divers along 40 m transects within each plot. Small species of macroalgae and macinvertebrates were counted within six permanent 1 m2 quadrats positioned uniformly along the 40 m transect, while larger species were counted within four contiguous 20 m2 sub-sections of each 40 m x 2 m transect. Also included at the quadrat scale are estimates of an average size-related measurement of each species, which was developed specifically for each species for the purpose of estimating its biomass. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
Point-Quarter Distance and Dimension Measurements to Calculate Shrub Density and Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico
In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeated measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected, sorted, and weighed to develop regressions for biomass. Purpose: Data was collected initially to determine density and dimensions of creosote bush in the Five points area on core rodent webs and on burned and unburned plots following the 2003 prescribed burn. It was decided to expand the project by continuing measurements through time to quantify the change in shrub size and with simultaneous harvest of shrubs to measure NPP.
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
3D density models of the Los Humeros and Acoculco geothermal fields, Mexico.
<p>The GEMex project addresses different challenges in the development of Enhanced Geothermal Systems (EGS) and Superhot Geothermal Systems (SHGS) in the Trans-Mexican Volcanic Belt. Although they are located in similar tectonic settings, the geothermal conditions in Acoculco and Los Humeros differ and they can be categorized as an EGS and a SHGS system, respectively. The Los Humeros field is currently under conventional exploitation. North of the current production area, temperatures higher than 380°C are expected. The Acoculco site presents temperatures >300°C at a depth of 2 km, but a reservoir has not been identified. The main goal of this work is to visualize and characterize the reservoir conditions using gravity data. To accomplish this, we processed data from a total of 344 gravity stations at Los Humeros and 84 stations at Acoculco. The datasets contain the 3D density model of the Los Humeros and Acoculco geothermal fields as density contrasts values in g/cm³. The background density is 2.67 g/cm³.</p>
iSDAsoil: soil fine-earth bulk density for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil fine-earth bulk density in 10×kg/m3 predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_db_od_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density mean value,</li> <li>sol_db_od_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: db_od R-square: 0.819 Fitted values sd: 0.269 RMSE: 0.126 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.06778 -0.06450 0.00215 0.06585 0.90016 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.05538 0.04860 -1.140 0.25451 regr.ranger 0.86305 0.01577 54.733 < 2e-16 *** regr.xgboost 0.15383 0.01651 9.315 < 2e-16 *** regr.cubist 0.02039 0.01113 1.832 0.06695 . regr.nnet 0.03465 0.03710 0.934 0.35036 regr.cvglmnet -0.03021 0.01032 -2.927 0.00343 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1263 on 13565 degrees of freedom Multiple R-squared: 0.8194, Adjusted R-squared: 0.8193 F-statistic: 1.231e+04 on 5 and 13565 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to kg/m-cubic use:</p> <pre><code>kg/m3 = y * 10</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Density functional theory calculations of coherent bcc Fe-Cu interfacial energy densities
<p>File contains the data required to calculate interfacial energy densities of {100}, {110}, {111}, {210}, {211} and {221} orientated coherence bcc Fe-Cu interfaces.</p> <p>Data produced for the study detailed in: Cu nanoprecipitate morphologies and interfacial energy densities in bcc Fe from density functional theory (DFT) A.M. Garrett and C.P. Race.</p> <p>Submitted to Computational Materials Science.</p> <p>.txt files contain the total energies calculated for relaxed interface-containing and bulk simulation cells at a range of interfacial spacings. This data can be used to calculate the size independent interfacial energy densities for a range of Fe-Cu interface orientations using standard fitting approaches. Columns of the tables in the .txt files are no. atoms, interface-containing simulation cell length, interfacial area, total energy of the relaxed interface-containing simulation cell, total energy of the reference bulk Fe and total energy of the reference bulk Cu. Lengths are in Angstrom and energies are in eV.</p>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed
<p><strong>General Description</strong></p><p>The <i>monthly aggregated Methane Volume Mixing Ratio </i>dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.</p><p>For more info about the s5p Methane product see: <a href="">https://maps.s5p-pal.com/ch4/</a>.</p><p>The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.</p><ul><li><strong>Monthly time-series:</strong></li></ul><p>Methane monthly average value December 2021 – November 2023. Derived using the <a href="https://eumap.readthedocs.io/en/latest/">eumap</a> and <a href="https://github.com/openlandmap/scikit-map">scikitmap</a> package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).</p><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> December 2021 – November 2023</li><li><strong>Type of data:</strong> Methane Volume Mixing Ratio (Unit: ppbv)</li><li><strong>How the data was collected or derived:</strong> Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> and <a href="https://eumap.readthedocs.io/en/latest/">eumap </a>Python package.</li><li><strong>Statistical methods used:</strong> percentiles 10, 50, and 90.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset is not completed gap-filled. Certain areas have no data in the whole time series</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -61.9966697, 180.0000072, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/60 d.d. = 0.016666667 (2km)</li><li><strong>Image size:</strong> 21,600 x 8,962</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact or inconsistency, 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><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li><strong>generic variable name:</strong> ch4.vmr = methane density methane volume mixing ratio</li><li><strong>variable procedure combination:</strong> m.seacov = monthly aggregated and gap filled by seasonal convolution</li><li><strong>Position in the probability distribution / variable type:</strong> p10/p50/p90 = 10th/50th/90th percentile</li><li><strong>Spatial support:</strong> 2km</li><li><strong>Depth reference:</strong> a = above surface</li><li><strong>Time reference begin time:</strong> 20211201 = 2021-12-01</li><li><strong>Time reference end time:</strong> 20231131 = 2023-11-31</li><li><strong>Bounding box:</strong> go = global (without Antarctica)</li><li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li><li><strong>Version code:</strong> v20230628 = 2023-12-08 (creation date)</li></ol>
Density Layers of selected Points of Interest from Open Street Map
<p>This dataset contains a raster layer of 100*100m resolution showing the density of selected amenities from Open Street Map. The amenities are selected as points of interest, where electric vehicles owners are likely to stop for a moment and recharge their vehicles. This dataset covers Europe and was obtained with the Overpass API. This dataset can be used to identify possible charging location for electric vehicles or any other purpose requiring to quantify the number of amenities in an area.</p> <p> </p> <p>This dataset shows densities of a selection of Points of Interest in Europe from Open Street Map [1].</p> <p>Included countries are:</p> <p> </p> <p><em><strong>country_codes</strong> = ['AT', 'BE', 'BG', 'HR', 'CY', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR', 'HU', 'IE', 'IT','LV', 'LT', 'LU', 'MT', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI', 'ES', 'SE', 'AL', 'AD', 'AM', 'BY', 'BA', 'FO', 'GE', 'GI', 'IS', 'IM', 'XK', 'LI', 'MK', 'MD', 'MC', 'ME', 'NO', 'SM', 'RS', 'CH', 'TR', 'UA', 'GB', 'VA']</em></p> <p> </p> <p>The requests of Points of Interest have been performed with the Overpass API [2] (free of charge).</p> <p> </p> <p>The codes included in each density are listed below :</p> <p> </p> <p><strong><em>'highway'</em></strong><em> = ['"highway"="motorway"', '"highway"="rest_area"'];</em></p> <p><strong><em>'parkings'</em></strong><em> = ['"parking"="surface"', '"parking"="multi-storey"', '"parking"="street_side"', '"parking"="underground"' , '"park_ride"' ];</em></p> <p><strong><em>'school'</em></strong><em> = ['"amenity"="college"', '"building"="college"', '"building"="university"', '"amenity"="university"', '"amenity"="school"' , '"amenity"="school"', '"amenity"="kindergarten"', '"amenity"="library"'];</em></p> <p><strong><em>'health'</em></strong><em>= ['"amenity"="clinic"', '"amenity"="dentist"', '"amenity"="school"' , '"amenity"="doctors"', '"amenity"="hospital"', '"amenity"="pharmacy"','"amenity"="veterinary"']; </em></p> <p><strong><em>'cafe'</em></strong><em>= ['"amenity"="cafe"','"amenity"="ice_cream"', '"amenity"="internet_cafe"']; </em></p> <p><strong><em>'supermarket'</em></strong><em> = ['"shop"="supermarket"', '"shop"="mall"', '"shop"= "department_store"', '"shop"= "convenience"'];</em></p> <p><strong><em>'restaurant'</em></strong><em>= ['"amenity"="restaurant"'];</em></p> <p><strong><em>'fastfood'</em></strong><em> = ['"amenity"="fast_food"'];</em></p> <p><strong><em>'sport'</em></strong><em>= ['"sport"']; </em></p> <p><strong><em>'hotel'</em></strong><em> = ['"tourism"="hotel"', '"building"="hotel"', '"tourism"="guest_house"','"tourism"="apartment"','"tourism"="hostel"','"tourism"="motel"','"tourism"="camp_site"']; </em></p> <p><strong><em>'pubs'</em></strong><em> = ['"amenity"="bar"','"amenity"="pub"', '"amenity"="biergarten"'];</em></p> <p><em>'theatre'= ['"amenity"="theatre"', '"amenity"="cinema"', '"amenity"="music_venue"', '"leisure"="stadium"' ]; </em></p> <p><strong><em>'night'</em></strong><em> = ['"amenity"="nightclub"', '"amenity"="casino"','"amenity"="gambling"','"amenity"="stripclub"']; </em></p> <p><strong><em>'socio'</em></strong><em>= ['"amenity"="arts_centre"', '"amenity"="community_centre"', '"amenity"="social_centre"', '"amenity"="music_school"', '"amenity"="language_school"']; </em></p> <p><strong><em>'shop'</em></strong><em> = ['"shop"'];</em></p> <p><strong><em>'tourism'</em></strong><em> = ['"amenity"="exhibition_centre"', '"tourism"="attraction"','"tourism"="viewpoint"','"tourism"="aquarium "','"leisure"="beach_resort "','"tourism"="gallery"','"tourism"="museum"','"tourism"="theme_park"','"tourism"="zoo"','"tourism"="artwork"'];</em></p> <p> </p> <p>The pixel values are the sum of the number of POIs of each type located in the pixel.</p> <p> </p> <p><em>Limitations of the dataset</em></p> <p>- The dataset provides densities of only a selection of points of interests, regardless of its type. The complete list of amenity codes can be found on the OSM Wiki [3].</p> <p>- Ways are only considered through their centre points.</p> <p> </p> <p>[1] “Open Street Map.” <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a> (accessed Sep. 05, 2023).</p> <p>[2] “Overpass API.” <a href="https://wiki.openstreetmap.org/wiki/Overpass_API">https://wiki.openstreetmap.org/wiki/Overpass_API</a> (accessed Sep. 05, 2023).</p> <p>[3] “Open Street Map Wiki.” <a href="https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance">https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance</a> (accessed Sep. 05, 2023).</p>
A fading radius valley towards M-dwarfs, a persistent density valley across stellar types -- data
Open the record for dataset details and reuse information.
Experimental data for "Measurement Report: Influence of particle density on secondary ice production by graupel and ice pellet collisions"
<p>This dataset includes measurement data on secondary ice production due to bare graupel - bare graupel, and ice pellet - ice pellet collisions carried out in the Mainz Cold Room (M-CR) of the Johannes Gutenberg University of Mainz. </p>
Inter-Chemical Correlation results for the study: HHEARx2017-1977 (Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density.)
Title: Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density. <br>Species: Homo sapiens <br>Number of samples: 1116 <br>Number of named analytes: 41 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=46 <br>
Database from: Developing a lateral topographic density model for Brazil.
<p>This dataset is part of the article entitled "DEVELOPING A LATERAL TOPOGRAPHIC DENSITY MODEL FOR BRAZIL".</p> <p>This dataset includes the topographic Lateral Topographic Density model for Brazil (LTDBrasil) and standard deviations (sdLTDBrasil), in Kg/m³, with 30 arc-seconds grid spacing.</p> <p>The files are in *tif and *.tfw format.</p> <p>Reference: Medeiros D.F., Marotta G.S., Yokoyama E., Franz I.B., Fuck R.A. 2021. Developing a lateral topographic density model for Brazil. Journal of South American Earth Sciences, v. 110, p. 103425. https://doi.org/10.1016/j.jsames.2021.103425</p>
Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset
<p>Materials and Methods</p> <p>Fieldwork</p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m<sup>2</sup> quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m<sup>2</sup> of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab.</p> <p>All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).</p> <p> </p> <p>Extraction and high-throughput sequencing of spider gut DNA</p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key <sup>1</sup>. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens <sup>2</sup>. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination.</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR <sup>3</sup> amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR <sup>3</sup> via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).</p> <p> </p> <p>Statistical analysis</p> <p>All analyses were conducted in R v4.0.0 <sup>6</sup>. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data <sup>7,8</sup>. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package <sup>9</sup> with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships.</p> <p>Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package <sup>10</sup> with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette <sup>11</sup>. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R.</p> <p>All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m<sup>2</sup> area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package <sup>12</sup>. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package <sup>13</sup>. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’.</p> <p><em>In situ</em> spider prey choice was analysed using network-based null models in the ‘econullnetr’ package <sup>14</sup> with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.</p> <p> </p> <p>References</p> <p>1. Roberts, M. J. <em>The Spiders of Great Britain and Ireland (Compact Edition)</em>. (Harley Books, 1993).</p> <p>2. Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods Ecol. Evol.</em> <strong>8</strong>, 126–134 (2017).</p> <p>3. Cuff, J. P. <em>et al.</em> Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecol. Entomol.</em> <strong>46</strong>, 249–261 (2021).</p> <p>4. Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. <em>Environmental DNA</em>. (Oxford University Press, 2018).</p> <p>5. Drake, L. E. <em>et al.</em> An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods Ecol. Evol.</em> <strong>in press</strong>, (2021).</p> <p>6. R Core Team. R: A language and environment for statistical computing. (2020).</p> <p>7. Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? <em>Mol. Ecol. Resour.</em> <strong>13</strong>, 620–633 (2013).</p> <p>8. Deagle, B. E. <em>et al.</em> Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? <em>Mol. Ecol.</em> <strong>28</strong>, 391–406 (2019).</p> <p>9. Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. <em>Methods Ecol. Evol.</em> <strong>3</strong>, 471–474 (2012).</p> <p>10. Oksanen, J. <em>et al.</em> vegan: Community Ecology Package. (2016).</p> <p>11. Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014).</p> <p>12. Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. <em>R News</em> <strong>2</strong>, 7–10 (2002).</p> <p>13. Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020).</p> <p>14. Vaughan, I. P. <em>et al.</em> econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. <em>Methods Ecol. Evol.</em> <strong>9</strong>, 728–733 (2018).</p>
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