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598 results for “Phosphorus”
Harmonized Soil Organic Carbon and Phosphorus Data for the Contiguous United States
Soil organic carbon (SOC) and soil phosphorus can strongly influence adjacent water quality by introducing nutrients into aquatic ecosystems and also altering the light environment of those ecosystems. However, national-scale data are uncommon, and even when available, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of soil data with co-located water quality data, we present aggregated SOC and soil phosphorus data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Physical soil characteristics, microbial community composition, extracellular enzymatic activity, biologically based phosphorus (BBP) pools, and available phosphorus from two soil depths, four microhabitats, and four landforms at the Jornada Experimental Range, 2021.
This dataset contains physical soil characteristics, PLFA based microbial community composition, extracellular enzymatic activity, nitrate and ammonium activity, and phosphorus availability in various phosphorus pools (Biologically Based Phosphorus, potassium sulfate, Olsen-P). Soils were collected from two depths (0-2cm, 2-30 cm), four microhabitats (grass, shrub, biocrust, interspace), and four landforms (alluvial flat, alluvial fan remnant, erosional scarplet, fan piedmont – see coordinates) within the Jornada Experimental Range in July 2021 to answer questions about how these variables change across these spatial scales in drylands. This project was a collaboration between researchers at New Mexico State University and The University of Texas at El Paso as part of the Drylands Critical Zone Thematic Cluster within the Critical Zone Network. This dataset is complete.
Summer water chemistry; sediment phosphorus fluxes and sorption capacity; sedimentation and sediment resuspension dynamics; water column thermal structure; and zooplankton, macroinvertebrate, and macrophyte communities in eight shallow lakes in northwest Iowa, USA (2018-2020)
The primary aim of this data product is to characterize change in water chemistry, sediment-water interactions, and biological communities in shallow, eutrophic lakes undergoing a fishery biomanipulation. We studied eight glacial lakes located in northwest Iowa, USA, from 2018 to 2020 during the summer season (May to September). A subset of these lakes (n = 4; Center, Five Island, North Twin, and Silver Lakes) were part of a fishery biomanipulation in which the Iowa Department of Natural Resources (IDNR) incentivized commercial harvest of common carp (Cyprinus carpio) and bigmouth buffalo (Ictiobus cyprinellus). Harvests occurred in Center and Five Island Lakes during 2018-2019 and in North Twin and Silver Lakes during 2019-2020. Between 73 and 373 kg fish biomass per ha were removed each year. The other study lakes (n = 4; Blue, South Twin, Storm, and Swan Lakes) remained unmanipulated during the study period. Over the course of the biomanipulation, we quantified a suite of physical, chemical, and biological parameters across the study lakes. High frequency aquatic sensors were used to measure water column thermal structure, dissolved oxygen concentrations, and algal pigments. Manual water chemistry sampling further quantified suspended solids, total phosphorus and nitrogen, soluble reactive phosphorus, nitrate, and water clarity. We measured flux rates of phosphorus between bottom sediments and the overlying water using ex situ sediment core incubations under both oxic and anoxic conditions. We further quantified sediment phosphorus sorption capacity using equilibrium phosphorus concentration assays. Tiered sediment traps were used to measure sedimentation rates as well as sediment resuspension in bottom waters. We also measured change in zooplankton, macroinvertebrate, and macrophyte community composition and abundance. These data will be used to better understand the mechanisms of internal phosphorus loading in shallow lakes and the ecosystem effects of fisherie
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra nitrogen and phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under nitrogen and phosphorus fertilization conditions.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra phosphorus fertilization simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under phosphorus fertilization conditions.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus in wet sedge tundra, 1994, Arctic LTER, Toolik Lake, Alaska.
Plant biomass, leaf area, carbon, nitrogen, and phosphorus were measured in three wet sedge tundra experimental sites. Treatments at each site included factorial NxP and at the Toolik sites greenhouse and shade house. Treatments started in 1985 (Sag site) and in 1988 (Toolik sites).
Lake Mendota Phosphorus Entrainment at North Temperate Lakes LTER 2005
This dataset contains total (TP) and soluble reactive phosphorus (SRP) data collected in Lake Mendota during the summer of 2005 between 6/28/2005 and 10/14/2005 as well as high-resolution temperature data for that same time period . The phosphorus data were taken at five different locations where buoys were deployed. The buoys were deployed with HOBO temperature data loggers attached at 2 - 4 m intervals. Similarly the phosphorus samples were collected at 2 - 4 m intervals throughout the water column. The position of the five buoys changed a few times during the summer in an effort to monitor circulation patterns due to different wind directions and speeds. Manuscript using this dataset: Kamarainen, A.M., H. Yuan, C. Wu, S.R. Carpenter. 2009. One-dimensional and three-dimensional approaches converge on similar estimates of phosphorus entrainment in Lake Mendota. Limnology and Oceanography Methods 7:553-567 Sampling frequency: Water temperature: generally 1 min; some at 5 min. TP and SRP: approximately at 2 weeks intervals Number of sites: 12
LAGOS - Lake nitrogen, phosphorus, stoichiometry, and geospatial data for a 17-state region of the U.S.
This dataset includes information about total nitrogen (TN) concentrations, total phosphorus (TP) concentrations, TN:TP stoichiometry, and 12 driver variables that might predict nutrient concentrations and ratios. All observed values came from LAGOSLIMNO v. 1.054.1 and LAGOSGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes greater than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, we compiled chemistry data from lakes with concurrent observations of TN and TP from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOSLIMNO v. 1.054.1 (2002-2011). We report the median TN, TP and molar TN:TP values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics that might be important controls on lake nutrients, including: land use (agricultural, pasture, row crop, urban, forest), nitrogen deposition, temperature, precipitation, hydrology (baseflow), maximum depth, and the ratio of lake area to watershed area, which is used to approximate residence time. These data were used to identify drivers of lake nutrient stoichiometry at sub-continental and regional scales (Collins et al, submitted). This research was supported by the NSF Macrosystems Biology program (awards EF-1065786 and EF-1065818) and by the NSF Postdoctoral Research Fellowship in Biology (DBI-1401954).
Cascade Project at North Temperate Lakes LTER Phosphorus, Chlorophyll, DOC, Color, and pH for Twenty UNDERC Lakes 1995 - 2003
Data on total phosphorous, chlorophyll a, dissolved organic carbon, water color, and pH for a set of lakes located at the University of Notre Dame Environmental Research Center (UNDERC). Surface water samples were collected monthly from May through August either from shore with a telescoping pole or from a boat. Twenty lakes were sampled from 1995-2000. Fifteen of these lakes were sampled from 2001-2003.
nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in french watercourses
<p><strong>nuts-STeauRY dataset: hydrochemical and catchment characteristics dataset for large sample studies of Carbon, Nitrogen, Phosphorus and Silicon in French watercourses</strong></p> <p>Antoine Casquin, Marie Silvestre, Vincent Thieu</p> <p>10.5281/zenodo.10830852</p> <p>v0.1, 18<sup>th</sup> March 2024</p> <p><strong>Brief overview of data: </strong></p> <p>· Carbon and nutrients data for 5470 continental French catchments</p> <p>· Modelled discharge for 5128 of catchments out of 5470</p> <p>· Geopackages with catchment delineations and outlets</p> <p>· DEM conditioned to delimit additional catchments</p> <p>· Land-use and climatic data for 5470 continental French catchments</p> <p><strong>Citation of this work<br></strong></p> <p>A data paper is currently being submitted with details of methods and results. Once published, it will be the preferential source to cite. The data paper will be link to the new version of the dataset that will be updated on doi.org/10.5281/zenodo.10830852. If you use this dataset in your research or report, you must cite it.</p> <p><strong>Motivations</strong></p> <p>Data was collected and curated for the nuts-STeauRY project (<a href="http://nuts-steaury.cnrs.fr">http://nuts-steaury.cnrs.fr</a>), which deployed a national generic land to sea modelling chain.</p> <p>Data was primarily used (see related works):</p> <ol> <li>To calibrate concentrations of dissolved organic carbon and dissolve silica in headwaters</li> <li>To validate spatially and temporally the modelling chain (DOC, NO3-, NH4+, TP, SRP, DSi)</li> </ol> <p>Hydrochemical large sample datasets have numerous other uses: trends computations elucidate transfer mechanisms, machine learning, retrospective studies etc.</p> <p>The objective here is to provide a large sample curated dataset of carbon and nutrients concentrations along with modelled discharges, catchment characteristics and delimitations for the continental France. Such large sample dataset aims at easing the large sample studies over France and/or Europe. Although part of the data gathered here is obtainable via public sources, the catchments delineations, their characteristics and modelled hydrology were note not publicly available yet. Moreover, a unification of units and detection and removal of outliers was performed on carbon and nutrients data.</p> <p><strong>Data sources & processing</strong></p> <p>Sampling points where snapped on the CCM database v2.1 (<a href="http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221">http://data.europa.eu/89h/fe1878e8-7541-4c66-8453-afdae7469221</a>)(Vogt et al., 2007) and catchments were delineated using a 100m resolution Digital Elevation Model (DEM) conditioned by the hydrographic network and elementary catchments’ delineations of the CCM data v2.1. <strong>More than 6000 catchments were delineated and screened manually</strong> to check consistency: 5470 were retained<strong>.</strong></p> <p>Nutrient data was collected mainly through the Naiades portal (<a href="https://naiades.eaufrance.fr/">https://naiades.eaufrance.fr/</a>), a database collecting water quality data produced by different water related actors across France. Nutrient data was also collected directly with regional water agencies (<a href="https://www.eau-seine-normandie.fr/">https://www.eau-seine-normandie.fr/</a>, <a href="https://eau-grandsudouest.fr/">https://eau-grandsudouest.fr/</a>, <a href="https://www.eaurmc.fr/">https://www.eaurmc.fr/</a>, <a href="https://www.eau-artois-picardie.fr/">https://www.eau-artois-picardie.fr/</a>, <a href="https://www.eau-rhin-meuse.fr/">https://www.eau-rhin-meuse.fr/</a> and <a href="https://agence.eau-loire-bretagne.fr/home.html">https://agence.eau-loire-bretagne.fr/home.html</a>), and pre-processed using a database management system relying on PostgreSQL with PostGIS extension (Thieu & Silvestre, 2015). A three-pass strategy was used to curate raw carbon and nutrients data: 1. Removal of “obvious outliers”, 2. Detection of baseline change and correction if possible (or removal of data) 3. Removal of outliers using a quantile based approach by element and temporal series.</p> <p>Hydrological time series are interpolation trough hydrograph transfer (de Lavenne et al., 2023) of 1664 time series of discharge completed with GR4J model (Pelletier & Andréassian, 2020; Pelletier 2021).</p> <p>Land cover data was extracted from Corine Land Cover dataset for years 2000, 2006, 2012, and 2018 (EEA, 2020). Raw CLC typology contains 44 classes. Results of percent cover per year per class were computed for each catchment. An aggregated typology of 8 classes is also proposed.</p> <p>Climatological data was extracted from daily reconstruction at 5 arcmin for temperatures and 1 arcmin for precipitation over Europe (Thiemig et al., 2022). Mean by catchment for min&max daily temperature and precipitation were computed for each catchment for the 1990-2019 period.</p> <p><strong>Nuts-STeauRY dataset</strong></p> <p><strong>Carbon and nutrients time series</strong></p> <p>Time series of carbon and nutrients within the 1962-2019 period on 5470 stations: Dissolved Organic Carbon (DOC), Total Organic Carbon (TOC) Nitrates (NO3-), Nitrites (NO2-), Ammonia (NH4+), Soluble Reactive Phosphorus (SRP), Total Phosphorus (TP) and Dissolved Silica (DSi).</p> <p><code>|var | n_unique_station| n_total_meas| mean_duration_y| mean_frequency_y|</code></p> <p><code>|:---|----------------:|------------:|---------------:|----------------:|</code></p> <p><code>|DOC | 4 992| 658 147| 14.3| 9.0|</code></p> <p><code>|DSi | 3 299| 333 866| 12.9| 8.3|</code></p> <p><code>|NH4 | 5 318| 907 343| 19.3| 8.7|</code></p> <p><code>|NO2 | 5 264| 891 886| 19.2| 8.6|</code></p> <p><code>|NO3 | 5 465| 939 279| 19.0| 9.0|</code></p> <p><code>|SRP | 5 361| 910 107| 19.1| 8.7|</code></p> <p><code>|TOC | 935| 111 993| 13.6| 9.6|</code></p> <p><code>|TP | 5 199| 802 841| 17.1| 8.8|</code></p> <p>Note that some SRP and DSi measurements were declared as realized on raw water. A thorough analysis of time series show no evidence of difference on baselines. For more accuracy, it is advised to filter out those analyses using the “fraction” attribute of each measurement.</p> <p><strong>Discharge modelled daily time series</strong></p> <p>Modelled naturalized discharge through hydrograph transfer and interpolated measured discharges when available for the 1980-2019 period.</p> <p>A daily discharge was computed for 5128 catchments. For small catchments (< 1000 km<sup>2</sup>, n = 4530), hydrograph transfer was used, while for big catchments, a direct interpolation of measured/completed discharges was performed. The direct interpolation was only possible for 598 catchments > 1000 km<sup>2</sup>. The criteria retained for a direct interpolation is 0.8*area_discharge_station < area_quality < 1.2*area_discharge_station when discharge and quality stations were nested.</p> <p>Hydrological time series uncertainties varies a lot depending on: quality of data source, distance from pseudo-gauged outlets, land cover of the catchments, natural spatial and temporal variability of discharge, size of the catchment (de Lavenne et al., 2016). We advise a cautious use of those modelled discharges as uncertainties could not be computed.</p> <p><strong>Catchments, outlets and conditioned DEM</strong></p> <p>5470 catchments and outlets are delivered as geopackages (EPSG: 3035).</p> <p>The DEM, conditioned by CCM 2.1 is also delivered as a GeoTIFF (EPSG: 3035) as way to delimit new catchment for the area that are consistent with the dataset.</p> <p><strong>Catchments characteristics and climate</strong></p> <p>Refer to Data sources & processing and File descriptions.</p> <p><strong> </strong></p> <p><strong>File and attributes descriptions: </strong></p> <p>The key “sta_code” is present across all files. For time varying records, “date” can be a secondary key. </p> <p><strong>Description of CNPSi.csv data attributes</strong></p> <p>Each line is a couple measurement/parameter/station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· fraction: "water_filtrated" or "water_raw"</p> <p>· date: date of sampling</p> <p>· hour: hour of sampling</p> <p>· value: analytical result (concentration)</p> <p>· provider: provider of the data</p> <p>· producer: producer of the data</p> <p>· from_db: "Naiades2022" (https://naiades.eaufrance.fr/france-entiere#/ dump from 2022) or "DoNuts" (Thieu, V., Silvestre, M., 2015. DoNuts: un système d’information sur les observations environnementales. Présentation Séminaire UMR Métis)</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· unit: unit of concentration</p> <p>· element: "C" "N" "P" or "Si"</p> <p>· year: year of observation</p> <p>· month: month of observation</p> <p>· day: day of observation</p> <p>· julian_day: julian day observation (1-366)</p> <p>· decade: decade of observation (one of "1961-1970", "1971-1980", "1981-1990", "1991-2000", "2001-2010", "2011-2020")</p> <p> </p> <p> </p> <p><strong>Description of CNPSi_stats.csv data attributes</strong></p> <p>Each line is a couple parameter / station</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· var: Abbreviation of parameter name</p> <p>· n_meas: number of observations for a given parameter / station</p> <p>· start_year: year of first observation for a given parameter / station</p> <p>· end_year: year of last observation for a given parameter / station</p> <p>· duration_y_tot: total duration of observation in years for a given parameter / station</p> <p>· duration_y_tot: duration of observation in years for a given parameter / station for years with at least 1 meas</p> <p>· mean_nmeas_per_y_tot: mean number of observations per year considering total duration</p> <p>· mean_nmeas_per_y_meas: mean number of observations per year considering years with measurements</p> <p>· is_fully_continuous: TRUE if at least one measurement per year for a given parameter / station</p> <p>· start_cont_seq: year in which starts the longest continuous sequence for a given parameter / station</p> <p>· end_cont_seq: year in which ends the longest continuous sequence for a given parameter / station</p> <p>· duration_y_cont_seq: duration in years for the longest continuous sequence for a given parameter / station</p> <p>· nmeas_cont_seq: number of measurements for the longest continuous sequence for a given parameter / station</p> <p>· mean_nmeas_per_y_cont_seq: mean number of observations per year for the longest continuous sequence for a given parameter / station</p> <p>· mean: mean value (concentration) for a given parameter / station</p> <p>· median: median value (concentration) for a given parameter / station</p> <p>· sd: standard deviation (concentration) for a given parameter / station</p> <p>· cv: coeficient of variation (concentration) for a given parameter / station</p> <p>· c05,c25,c50,c75,c95: centiles 5, 25, 50, 75 & 95 for a given parameter / station</p> <p><strong>Description of catchments.gpkg and outlets.gpkg data attributes</strong></p> <p>Each line is a catchment or an outlet (sampling point)</p> <p>File is a .gpkg (EPSG = 3035)</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· sta_name: Name of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· watercourse: Name of the water course (from spatial join on IGN BD Topo)</p> <p>· mun_name: Name of the municipality of the outlet (from spatial join on IGN BD Admin Express)</p> <p>· ccm_wso_id: Seaoutlet id from CCM v2.1 database</p> <p>· ccm_wso1_id: Elementary catchment id from CCM v2.1 database</p> <p>· ccm_strahler: Strahler order of the catchment from CCM v2.1 database</p> <p>· area_km2: Computed area in km2 of the catchment</p> <p><strong>Description of daily discharges data attributes</strong></p> <p>Each line corresponds to a daily modelled discharge at a quality station from 1980 to 2019</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· date: Date in format yyyy-mm-dd</p> <p>· flow_mm: Discharge expressed in mm.d-1</p> <p>· flow_m3s: Discharge expressed in m3.s-1</p> <p><strong>Description of climate data attributes</strong></p> <p>Each line in the pr_tmin_tmax_1990-2019_lt_mean.csv corresponds to a mean value within a catchment for the 1990-2019 period.</p> <p>· sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>· period: 1990-2019</p> <p>· source: EMO-1 (pr) & EMO-5 (tmin, tmax)</p> <p>· pr: mean yearly precipitation (mm)</p> <p>· tmin: mean daily minimal temperature (°C)</p> <p>· tmin: mean daily maximal temperature (°C)</p> <p><strong>Description of land cover data attributes</strong></p> <p>Each line in the clc_8class.csv and clc_44class.csv corresponds to Corine Land Cover (CLC) class for a year (1990, 2000, 2006, 2012, or 2018) and a catchment. Raw CLC typology describes 44 classes that were aggregated to 8 classes (see clc_44class_to_8class.csv).</p> <p>· clc_44class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o clc_name: Description of land cover class in CLC product</p> <p>o clc_code: Code for land cover class in CLC product</p> <p>o percent_cover: Percent cover by CLC class in the catchment (0-100)</p> <p>· clc_8class.csv</p> <p>o sta_code: Code of the station in the Sandre referentiel (public french "dataverse" for water data)</p> <p>o year: Year as stated in CLC product</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o code_clc_8class: Code for land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p>o percent_cover: Percent cover by aggregated CLC class in the catchment (0-100)</p> <p>· clc_44class_to_8class.csv</p> <p>o code_clc: Code for land cover class in CLC product (44 classes)</p> <p>o code_clc_8class: Code for land cover class in aggregated CLC product (8classes)</p> <p>o label_clc_8class: Description of land cover class in CLC product aggregated in 8 classes (see clc_44class_to_8class.csv)</p> <p> </p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This publication has been prepared using European Union's Copernicus Land Monitoring Service information; <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p>The authors thank Vasken Andréassian for communicating the discharge data and discharge station data and Alban de Lavenne for its help in using the transfr package, both for INRAE UR HYCAR.</p> <p> </p> <p><strong>References</strong></p> <p>de Lavenne, A., Skøien, J. O., Cudennec, C., Curie, F., & Moatar, F. (2016). Transferring measured discharge time series: Large-scale comparison of Top-kriging to geomorphology-based inverse modeling: transferring measured discharge time series. Water Resources Research, 52(7), 5555–5576. https://doi.org/10.1002/2016WR018716</p> <p>de Lavenne, A., Loree, T., Squividant, H., & Cudennec, C. (2023). The transfR toolbox for transferring observed streamflow series to ungauged basins based on their hydrogeomorphology. Environmental Modelling & Software, 159, 105562. <a href="https://doi.org/10.1016/j.envsoft.2022.105562">https://doi.org/10.1016/j.envsoft.2022.105562</a></p> <p>EEA. (2020). Corine Land Cover édition 2018. CLC 2018. <a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-corine</a></p> <p>Pelletier, A., & Andréassian, V. (2020). Hydrograph separation: An impartial parametrisation for an imperfect method. Hydrology and Earth System Sciences, 24(3), 1171–1187. <a href="https://doi.org/10.5194/hess-24-1171-2020">https://doi.org/10.5194/hess-24-1171-2020</a></p> <p>Pelletier, A. (2021). Complétion d'hydrogrammes avec le modèle GR4J - Note méthodologique. INRAE, UR HYCAR.</p> <p>Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022</p> <p>Thieu, V., Silvestre, M., 2015. DoNuts : un système d'information sur les observations environnementales. Présentation Séminaire UMR Métis</p> <p>Vogt, J., A. de Jager, E. Rimaviciute, W. Mehl, S. Foisneau, K. Bódis, J. Dusart, M.L. Paracchini, P. Haastrup, & C. Bamps. (2007). A pan-European river and catchment database. (European Commission. Joint Research Centre. Institute for Environment and Sustainability.). Publications Office. https://data.europa.eu/doi/10.2788/35907</p>
Within-plant coexistence of viruses across nitrogen and phosphorus supply rates
Most species can be coinfected by multiple pathogens that may interact through shared resources (i.e., resource competition) or the host immune system (i.e., apparent competition). Community theory developed for free-living organisms suggests that coinfecting pathogens can persist if they satisfy the mutual invasion criterion of coexistence, establishing infections in hosts that are already infected. Furthermore, the likelihood of coexistence may depend on host nutrition which can affect shared resources and host immunity. Here we apply the novel approach of combining a dynamical model and experimental mutual invasibility trials to explore the effects of host nutrient supply on the coexistence of two viral plant pathogens. We focus on among-pathogen interactions mediated by shared resources. First, we used a model to generate hypotheses about how nitrogen (N) and phosphorus (P) supply rates affect the ability of two plant viruses to invade established infections of the other virus. Then, we experimentally manipulated the N and P supplied to oats (Avena sativa) in a growth chamber experiment and tested mutual invasion of two RNA viral pathogens, BYDV-PAV and CYDV-RPV. Nutrient supplies ranged from rates that barely kept hosts alive up to high, but sub-toxic, rates. Model simulations suggested that the viruses were more likely to invade established infections either when they could replicate at lower N and P concentrations or when plant N and P concentrations increased due to a combination of nutrient supply rates and resident virus nutrient use. In the experiment, each virus successfully invaded hosts infected by the other and had consistent growth rates across N and P supply rates. Our results suggest that BYDV-PAV and CYDV-RPV can coexist across a wide range environmental nutrient supply, which is consistent with the high levels of co-occurrence of these two viruses in field populations.
Geochemical Characterizations for Identifying Fugitive Dust Deposition and Enrichment of Surface and Subsurface Subalpine Soils from Phosphorus Mining, Eastern Ashley National Forest, Utah, 2022-2023.
Phosphorus is a non-renewable resource essential for all life. Anthropogenic alterations to the phosphorus cycle have led to widespread phosphorus pollution, and the unsustainable management of P has led to the threat of global depletion of phosphorus resources. Thus, accounting for the natural and anthropogenic flow paths of phosphorus is essential for its conservation and pollution reduction. One such source of human alteration to the phosphorus-cycle is phosphate rock mining. Mining, however, has many adverse environmental effects, including widespread fugitive dust emissions. Dust collection in the Ashley National Forest of northeastern Utah, proximate to a surface phosphorus mine, has shown phosphorus concentrations in dust more than four times that of other regional samples. Elevated phosphorus in dust near active surface mining suggests that mining emissions may alter the natural phosphorus loading of the soils in the National Forest through dust deposition; however, no research has been done to identify the abundance and range of mine-attributable phosphorus enrichment in the soils surrounding phosphate mining activities. The combined geospatial and geochemical approach of this study shows that surface soil phosphorus concentrations were found to be enriched above naturally occurring levels up to 6.5 km from mining activity (enrichment factor > 1.5), with the most significant enrichment occurring within the first 3 km (enrichment factor > 2). On average, surface phosphorus concentrations were significantly enriched by 25% within 6.5 km of phosphorus mining activity. Observed phosphorus enrichment was positively correlated with the presence of fluorapatite in the soil, which is the primary phosphorus-mineral extracted from the nearby mine. Further, bioavailable phosphorus concentrations were also higher for the soils that were enriched in phosphorus. This study shows that fugitive emissions associated with the surface mining of phosphate rock are a significan
Effects of factorial nitrogen, phosphorus, and potassium with micronutrient addition and Host Community on Fungal Endophyte Diversity at Cedar Creek Ecosystem Reserve, Minnesota, USA, 2014
The microbes contained within free-living organisms can alter host growth, reproduction, and interactions with the environment. In turn, processes occurring at larger scales determine the local biotic and abiotic environment of each host that may affect the diversity and composition of the microbiome community. Here, we examine variation in the diversity and composition of the foliar fungal microbiome in the grass host, Andropogon gerardii, across a factorial nitrogen, phosphorus, and potassium addition experiment in Minnesota, USA. We found limited evidence of direct effects of nutrients on endophyte diversity. Instead, the effects of nutrients on endophyte diversity appeared to be mediated by accumulation of plant litter and plant diversity loss. Specifically, nitrogen addition is associated with a 40% decrease in plant diversity and an 11% decrease in endophyte richness. Although nitrogen, phosphorus, and potassium addition increased aboveground live biomass and decreased relative Andropogon cover, endophyte diversity did not covary with live plant biomass or Andropogon cover. Our results suggest that fungal endophyte diversity within this focal host is determined in part by the diversity of the surrounding plant community and its potential impact on immigrant propagules and dispersal dynamics. Our results suggest that elemental nutrients reduce endophyte diversity indirectly via impacts on the local plant community, not direct response to nutrient addition.
Soil and root-associated fungal response to nitrogen and phosphorus addition from grasslands worldwide: 2011-2012.
Ecosystems across the globe receive elevated inputs of nutrients, but the consequences of this for soil fungal guilds that mediate key ecosystem functions remain unclear. We found that nitrogen and phosphorus addition to 25 grasslands distributed across four continents promoted the relative abundance of fungal pathogens, suppressed mutualists, but did not affect saprotrophs. Structural equation models suggested that responses were often indirect and primarily mediated by nutrient-induced shifts in plant communities. Nutrient addition also reduced co-occurrences within and among fungal guilds, which could have important consequences for belowground interactions. Focusing only on plots that received no nutrient addition, soil properties influenced pathogen abundance globally, whereas plant community characteristics influenced mutualists, and climate influenced saprotrophs. These guild-level responses enhance our ability to predict soil functional responses to anthropogenic eutrophication and the associated longer-term responses of plant communities to this important global change factor.
Plant and root biomass, nitrogen, carbon, and phosphorus concentrations in a mesic acidic tussock tundra experimental site established in 1981(MAT81) and harvested in 2015, Arctic LTER, Toolik Lake, Alaska.
Plant and root biomass, nitrogen, carbon, and phosphorus were measured in 2015 in the Arctic LTER tussock tundra experimental site (MAT81). This site was established in 1981 and has been harvested in previous years (see Shaver and Chapin Ecological Monographs, 61(1), 1991, pp.1-31, https://doi.org/10.2307/1942997). Data tables include the biomass for each harvested quadrat and block summaries for percent carbon, nitrogen, and phosphorus for control and fertilized plots from the original 4-block design. New control plots, established in 2015, are in a separate data table and include biomass, percent carbon, nitrogen, and phosphorus for each quadrat.
Point-frame measurments from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains point-frame measurements from a nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016 at a severely burned site and an unburned site. Pin-vegetation contact was recorded using a 0.75 m2 frame with 41 evenly spaced pin-drop points. Data was collected once during the height of the growing season in 2016 (when fertilization began) 2017, 2018 and 2019. This data was used to measure the impact of fertilization and fire on community composition.
Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
Steady state carbon, nitrogen, phosphorus, and water budgets for twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. The carbon, nitrogen, phosphorus, and water budgets presented here are used to calibrate the MEL model prior to running the climate change simulations. Citations and calculations for the data presented here are described in the individual site html files included in this dataset.
Block summaries of biomass, carbon, nitrogen, and phosphorus allocation among tissue types, species, and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment harvests: 2000 and 2015, Toolik Lake Field Station, Alaska.
A complete accounting of biomass, C, N, and P allocation both among tissue types (leaves, stems, rhizomes, roots) and among species and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment’s untreated control plots and plots that were fertilized annually, harvested after 20 and 35 years, near Toolik Lake Field Station, Alaska. Data are gram per meter squared summarized by block.
Sawgrass Above and Below Ground Total Phosphorus from the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, September 2002 - ongoing
Once a year during the dry season (Dec-May) three live sawgrass plants are collected from each site. These plants are divided into their live above and live below ground parts. Then both the above and below ground parts are analyzed for total nitrogen (TN), total carbon (TC), and total phosphorus (TP). The TP data are included in this data package. For Shark River Slough sawgrass TN and TC data, please see package knb-lter-fce.1070 in the FCE LTER website's data catalog or in the EDI repository.
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Allen Brain Atlas
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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
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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.
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