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23 results for “environmental covariates”
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico
<p><strong>Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2009 to 2014 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico.</strong><br> <strong>Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni, Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.</strong></p>
Patch-level rates of nitrogen fixation and denitrification and environmental covariates from seven streams in Idaho and Michigan
We hypothesized that environmental variation at the patch scale (1 - 10’s m) would facilitate the co-occurrence of N2 fixation and denitrification through the formation of hot spots in streams. We measured rates of N2 fixation and denitrification and relative abundances of the genes nifH and nirS in patches determined by channel geomorphic units and substrate type in 4 Idaho and 3 Michigan streams encompassing a gradient of N and P concentrations. This data package includes patch-level measurements of N2 fixation and denitrification rates, relative gene abundances of nifH and nirS, and environmental covariates (nutrient concentrations, water temperature, surface and subsurface dissolved oxygen concentrations, organic matter content) that were used to explore the factors that could predict process rates and relative gene abundances across patches and streams.
Data for Nicola Chinook Ricker stock-recruit model with environmental covariates
<ol> <li>Climate change and human activities are transforming river flows globally, with potentially large consequences for freshwater life. To help inform watershed and flow management, there is a need for empirical studies linking flows and fish productivity.</li> <li>We tested the effects of river conditions and other factors on 22 years of Chinook salmon productivity in a watershed in British Columbia, Canada.</li> <li>Freshwater conditions during adult salmon migration and spawning, as well as during juvenile rearing, explained a large amount of variation in productivity.</li> <li>August river flows while salmon fry reared had the strongest effect on productivity – our model predicted that cohorts that experience 50% below average flow in the August of rearing have 21% lower productivity.</li> <li>These contemporary relationships are set within long-term changes in climate, land use, and hydrology. Over the last century, average August river discharge decreased by 26%, air temperatures warmed, and water withdrawals increased. 17% of the watershed was logged in the last 20 years. </li> <li>Our results suggest that, in order to remain stable, this Chinook salmon population being assessed for legal protection requires substantially higher August flow than previously recommended. Changing flow regimes – driven by watershed impacts and climate change – can threaten imperiled fish populations.</li> </ol>
Combined impacts of environmental and socioeconomic covariates on HFMD risk in China
<p>Supplement to "Combined impacts of environmental and socioeconomic covariates on HFMD risk in China"</p>
Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico (2015-2020)
<p><strong>Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2015 to 2020 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico.</strong><br> <strong>Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni, Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.</strong></p>
Data for Nicola Chinook Ricker stock-recruit model with environmental covariates
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Improving genomic prediction for plant disease using environmental covariates
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H-2020 MOOD Scoping review of Leptospirosis, Influenza A and Chikungunya on the environmental covariates
<p>The dataset contains quantitative data on the environmental covariates associated with influenza A, Chikungunya, and Leptospirosis retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), time frame (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, modeling studies with no data), lacking denominators or reference populations, unavailable full-texts, referring to data older than 2000 or gathered outside Europe, were excluded. The final time frame covered a period from 2000 to 2022.</p> <p>The important environmental covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were submitted to a revision process to label them according to a labeling system agreed upon among a group of experts within the MOOD (grant agreement No 874850; <a href="https://mood-h2020.eu/" target="_blank" rel="noopener noreferrer">https://mood-h2020.eu/</a>) project consortium.</p>
Including environmental covariates clarifies the relationship between endangered Atlantic salmon (Salmo salar) abundance and environmental DNA
<p><span>Collecting environmental DNA (eDNA) as a nonlethal sampling approach has been valuable in detecting the presence/absence of many imperiled taxa; however, its application to indicate species abundance poses many challenges. A deeper understanding of eDNA dynamics in aquatic systems is required to better interpret the substantial variability often associated with eDNA samples. Our sampling design took advantage of natural variation in juvenile Atlantic salmon (</span><span><em>Salmo</em> <em>salar</em></span><span>) distribution and abundance along 9 km of a single river in the Province of New Brunswick (Canada), covering different spatial and temporal scales to address the unknown seasonal impacts of environmental variables on the quantitative relationship between eDNA concentration and species abundance. First, we asked whether accounting for environmental variables strengthened the relationship between eDNA and salmon abundance by sampling eDNA during their spring seaward migration. Second, we asked how environmental variables affected eDNA dynamics during the summer as the parr abundance remained relatively constant. Spring eDNA samples were collected over a 6‐week period (12 times) near a rotary screw trap that captured approximately 18.6% of migrating smolts, whereas summer sampling occurred (i) at three distinct salmon habitats (9 times) and (ii) along the full 9 km (3 times). We modeled eDNA concentration as a product of fish abundance and environmental variables, demonstrating that (1) with inclusion of abundance and environmental covariates, eDNA was highly correlated with spring smolt abundance and (2) the relationships among environmental covariates and eDNA were affected by seasonal variation with relatively constant parr abundance in summer. Our findings underscore that with appropriate study design that accounts for seasonal environmental variation and life history phenology, eDNA salmon population assessments may have the potential to evaluate abundance fluctuations in spring and summer.</span></p>
Including environmental covariates clarifies the relationship between endangered Atlantic salmon (Salmo salar) abundance and environmental DNA
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Wood bison migration metrics and environmental covariates
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Environmentally triggered variability in the genetic variance-covariance of herbivory resistance of an exotic plant Solidago altissima
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Influence of environmental covariates on pollinator community occupancy, detection, and richness across urban gardens in Richmond, Virginia (U.S.A.)
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Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal
<p>Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development, reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor-analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual-based demography from yellow-bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneous, negative responses of winter survival and reproductive-status change to declining environmental quality result in a higher risk of population quasi-extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.</p>
H-2020 MOOD Scoping review of Tularemia on the human, animal, vector and environmental covariates
<div> <p><span><span>The dataset </span><span>contains</span><span> quantitative data on the human, animal</span><span>, </span><span>vector</span><span> and environmental covariates associated with </span><span>Tularemia</span><span> retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), </span><span>time frame</span><span> (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, model</span><span>l</span><span>ing studies with no data), lacking denominators or reference populations, unavailable </span><span>full-texts</span><span>, referring to data older than 2000 or gathered outside Europe, were excluded. The final </span><span>time frame</span><span> covered a period from 2000 to 2022.</span></span><span> </span></p> </div> <div> <p><span><span>The important </span><span>human, animal</span><span>, </span><span>vector</span><span> and environmental</span><span> covariates were extracted with the associated quantitative information, and the related information on the diseases. </span><span>The covariates were </span><span>submitted</span><span> to a revision process </span><span>and </span><span>label</span><span>led</span><span> according to a </span><span>labelling</span><span> system</span><span> agreed upon among a group of experts within the MOOD (grant agreement No 874850; </span></span><a href="https://mood-h2020.eu/" target="_blank" rel="noreferrer noopener"><span><span>https://mood-h2020.eu/</span></span></a><span><span>) project consortium.</span></span><span> </span></p> </div>
H-2020 MOOD Scoping review of Leptospirosis, Influenza A and Chikungunya on the human, animal, vector and environmental covariates
<div> <p><span><span>The dataset </span><span>contains</span><span> quantitative data on the </span><span>human, animal</span><span>, </span><span>vector</span><span> and </span><span>environmental covariates associated with influenza A, Chikungunya, and Leptospirosis retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), </span><span>time frame</span><span> (30 years), geographical location (Europe), and publication type. </span><span>Studies without data or with non-original or duplicated data (reviews, editorials, letters, </span><span>modelling</span><span> studies with no data), lacking denominators or reference populations, unavailable </span><span>full-texts</span><span>, referring to data older than 2000 or gathered outside Europe, were excluded.</span><span> The final </span><span>time frame</span><span> covered a period from 2000 to 2022.</span></span><span> </span></p> </div> <div> <p><span><span>The important </span><span>human, animal</span><span>, </span><span>vector</span><span> and environmental</span><span> covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were </span><span>submitted</span><span> to a revision process </span><span>and </span><span>label</span><span>led</span><span> according to a </span><span>labelling</span> <span>system agreed upon among a group of experts within the MOOD (grant agreement No 874850; </span></span><a href="https://mood-h2020.eu/" target="_blank" rel="noreferrer noopener"><span><span>https://mood-h2020.eu/</span></span></a><span><span>) project consortium.</span></span><span> </span></p> </div>
Invasive tree cover covaries with environmental factors to explain the functional composition of riparian plant communities
<p>Invasive species are a major cause of biodiversity loss worldwide, but their impact on communities and the mechanisms driving those impacts are varied and not well understood. This study employs functional diversity metrics and guilds - suites of species with similar traits - to assess the influence of an invasive tree (<em>Tamarix</em> spp.) on riparian plant communities in the southwestern United States. We asked: 1) What traits define riparian plant guilds in this system? 2) How do the abundances of guilds vary along gradients of <em>Tamarix </em>cover and abiotic conditions? 3) How does the functional diversity of the plant community respond to the gradients of <em>Tamarix </em>cover and abiotic conditions? We found nine distinct guilds primarily defined by reproductive strategy, as well as height, seed weight, specific leaf area, drought and anaerobic tolerance. Guild abundance varied along a covarying gradient of local and regional environmental factors and <em>Tamarix </em>cover. Guilds relying on sexual reproduction, in particular those producing many light seeds over a long period of time were more strongly associated with drier sites and higher <em>Tamarix </em>cover. <em>Tamarix </em>itself appeared to facilitate more shade tolerant species with higher specific leaf areas than would be expected in resource poor environments. Additionally, we found a high degree of specialization (low functional diversity) in the wettest, most flood-prone, lowest <em>Tamarix </em>cover sites as well as in the driest, most stable, highest <em>Tamarix </em>cover sites. These guilds can be used to anticipate plant community response to restoration efforts and in selecting appropriate species for revegetation.</p>
Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.