Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,133
datasets available to search
ShareScore release 0.7.1
Dataset results
1,133 results for “wetland”
Dataset: Multi-level network dataset of social-ecological interdependencies in ten Swiss wetlands based on qualitative interviews and quantitative surveys
<p>The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).</p>
Dataset: High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes
<p>Dataset used in the article "High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes"</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Vocus- proton transfer reaction mass spectrometer (Vocus-PTR) at a subarctic fen during 2021. It also contains PAR (Photosynthetic Active Radiation), temperature and flux quality criteria.</p>
Scripts and datas for "Climate-driven projections of future global wetlands extent"
<p>Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study:</p> <p><em><strong>L. Hardouin, B. Decharme, J. Colin, C. Delire: </strong>Climate-driven projections of future global wetlands extent.</em></p> <p>The calculation and input scripts include:<br><em>1_var_comput </em>: Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl.</p> <p><em>2_TOPMODEL </em>: The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included.</p> <p><em>3_alpha_and_beta </em>: Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version.</p> <p>The outputs datasets contain:</p> <p><em>4_fwtl_model_period </em>: The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work.</p> <p><em>5_not_calibrated_fwtl_model_period </em>: The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.</p> <p> </p> <p>Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.</p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016-2022
<p>This dataset (WetCH<sub>4</sub>) contains methane (CH<sub>4</sub>) emissions using three different wetland maps, their uncertainties, and underlying flux intensities from northern wetlands (>45° N). The dataset is a data-driven upscaling product using observations from northern eddy covariance CH<sub>4</sub> flux sites and random forest machine learning. WetCH<sub>4</sub> provides daily CH<sub>4</sub> fluxes of northern wetlands at 10-km resolution from 2016 to 2022 and can be used to study regional CH<sub>4</sub> budgets and wetland responses to climate change. The data products are provided in netCDF format files (.nc) with more details in the attributes of the files.</p> <p>File list:</p> <p>- fch4_nmol_m2_s_10km_intensity.nc.gz and fch4_nmol_m2_s_10km_uncertainty.nc.gz:</p> <p> The underlying flux intensities and associated uncertainties.</p> <p> </p> <p>- fch4_10km_emi_wad2m.nc.gz and fch4_10km_emi_uncertainty_wad2m.nc.gz:</p> <p> Upscaled CH4 emissions and uncertainties using WAD2M monthly dynamic wetland map.</p> <p> </p> <p>- fch4_10km_emi_giems2.nc.gz and fch4_10km_emi_uncertainty_giems2.nc.gz:</p> <p> Upscaled CH4 emissions and uncertainties using GIEMS2 monthly dynamic wetland map.</p> <p> </p> <p>- fch4_10km_emi_glwd.nc.gz and fch4_10km_emi_uncertainty_glwd.nc.gz:</p> <p> Upscaled CH4 emissions and uncertainties using static GLWD v1 wetland map.</p> <p> </p> <p>Time range: 2016-01-01 - 2022-12-31</p> <p>Time steps: daily, 2557</p> <p>Geographic extent: longitude 180W - 180E, latitude 45 - 90 N</p>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Carbon data for: Evidence for the Multiple Benefits of Wetland Conservation in North America
<p>These data were synthesized as part of a rapid evidence assessment of the scientific literature on a wide range of benefits associated with wetland conservation and restoration. Our synthesis emphasized data from North America and especially the United States, although many of the high priority meta-analyses and reviews we incorporated were global in scope. The data in these files represent a range of metrics related to carbon sequestration, storage, or flux compiled from multiple sources for the purposes of summarizing the range of observed values and how they vary across wetland classes or by restoration status. For more detail on the synthesis methods and each set of metrics, please see the full report: </p> <p>Conlisk E, Chamberlin L, Vernon M, Dybala KE. 2022. Evidence for the Multiple Benefits of Wetland Conservation in North America: Carbon, Biodiversity, and Beyond. Point Blue Conservation Science, Petaluma, CA.</p>
Global wetland loss reconstruction over 1700-2020
<p>This repository contains three datasets resulting from the reconstruction of global wetland loss over 1700-2020. The three datasets are listed here and described in more detail below:</p> <ul> <li><strong>A.</strong> National and subnational statistics of drained or converted areas</li> <li><strong>B.</strong> Regional wetland percentage loss estimates and geospatial polygons</li> <li><strong>C.</strong> Gridded reconstruction</li> </ul> <p>The scripts used to process input data, model and calibrate the wetland loss reconstruction, and produce the figures are publicly available at https://github.com/etiennefluetchouinard/wetland-loss-reconstruction.</p> <p> </p> <p><strong>A. National and subnational statistics of drained or converted areas</strong></p> <p>This tabular database containing national and subnational statistics of wetland area drained and peat mass extracted. The database includes four land use types: cropland, forestry, peat extraction and wetland cultivation. These data are used as input to the mapped wetland loss reconstruction. Column descriptions of drainage_db_v10.csv:</p> <ol> <li><strong>unit:</strong> Scale of administrative unit ("national" or "subnational").</li> <li><strong>type:</strong> Land use type ("Cropland", "Forestry", "Peat Extraction" or "Wetland Cultivation")</li> <li><strong>iso_a3:</strong> 3-letter code of each country.</li> <li><strong>region:</strong> Name of subnational unit. Blank if data is national scale.</li> <li><strong>HASC_1:</strong> Hierarchical Administrative Subdivisions Codes for the subnational units. Blank if data is national scale.</li> <li><strong>year:</strong> Year of data.</li> <li><strong>drained_area_1000ha:</strong> Cumulative area drained by the year specified, in thousands of hectares.</li> <li><strong>drained_weight_1000tonsyr:</strong> Annual peat extraction rate for the year, in thousand tons per year.</li> <li><strong>peatland_only:</strong> Label indicating whether the drained area applies to all wetlands or peatlands specifically ("Peatland only" or blank).</li> <li><strong>Comment:</strong> Additional description from original data source, or unit conversion, or data corrections.</li> <li><strong>Source:</strong> Reference of data source and/or compilers.</li> </ol> <p> </p> <p><strong>B. Regional wetland percentage loss estimates and geospatial polygons</strong></p> <p>A shapefile of 151 polygons projected in WGS84. Columns description for polygon shapefile of the regional wetland loss percentage: regional_loss_poly.shp:</p> <ol> <li><strong>id:</strong> Numerical identifier.</li> <li><strong>name:</strong> Name of administrative unit, region or water feature the polygon area covers.</li> <li><strong>country:</strong> Name of country.</li> <li><strong>continent:</strong> Name of continent.</li> <li><strong>wet_categ:</strong> Broad category of wetlands included in the estimate (“<em>Peatlands</em>”, “<em>Inland natural wetlands</em>”, “<em>Coastal natural wetlands</em>”, “<em>Unspecified natural type(s)</em>” or “<em>All wetlands</em>”).</li> <li><strong>yr_start:</strong> Start year of the period over which wetland loss is estimated.</li> <li><strong>yr_end:</strong> End year of the period over which wetland loss is estimated.</li> <li><strong>area_mkm2:</strong> Surface area of the polygon, in million square kilometers (Mkm<sup>2</sup>).</li> <li><strong>perc_loss:</strong> Numerical value of percentage wetland loss (positive value represent loss of wetland area between start and end year.</li> <li><strong>comment:</strong> Additional description of estimate used or estimation method.</li> <li><strong>source:</strong> Citation of original data source.</li> <li><strong>compiler: </strong>Citation of intermediary data compiler.</li> </ol> <p> </p> <p><strong>C. Gridded reconstruction</strong></p> <p>Gridded outputs are stored in a separate NetCDF file for each of the 12 reconstructions of simulated wetland and present-day wetland maps. An ensemble average was also computed from the 12 reconstructions (only individual reconstructions were discussed in the manuscript). These data consist of global maps generated from the drainage reconstruction methodology for 33 decadal intervals (1700-2020 inclusive) for 9 variables:</p> <p>The filenames of ensemble members are labelled to with the name of the input present-day and simulated wetland maps:</p> <p> “<em>wetland_loss_1700-2020_</em>” + simulated input + “_” + present-day input + “<em>_v10.nc</em>”</p> <p>The 4 simulated wetland map inputs are: LPJwsl, SDGVM, ORCHIDEE, DLEM. The 3 present-day wetland map inputs are: GIEMSv2, GLWD3, WAD2M.</p> <p>Description of the 9 variables in each NCDF file:</p> <ol> <li><strong>wetland_loss:</strong> Cumulative wetland area lost (km<sup>2</sup> per grid cell). This variable is equivalent to the sum of area drained for the seven land uses drained</li> <li><strong>nat_wetland:</strong> Remaining natural wetland area (km<sup>2</sup> per grid cell)</li> <li><strong>cropland:</strong> Cropland area drained (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>forestry:</strong> Forestry area drained (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>peatextr:</strong> Peat harvest area drained (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>wetcultiv:</strong> Wetland cultivation area (km<sup>2</sup> per grid cell) leading to wetland loss</li> <li><strong>ir_rice:</strong> Irrigated rice area leading to wetland loss (km<sup>2</sup> per grid cell)</li> <li><strong>pasture:</strong> Pasture area drained leading to wetland loss (km<sup>2</sup> per grid cell)</li> <li><strong>urban:</strong> Urban area drained leading to wetland loss (km<sup>2</sup> per grid cell)</li> </ol> <p>All layers were capped below the land pixel area grid (from HYDE 3.2, excl. open water).</p> <p><strong>Time:</strong> 33 slices; numerical years spread at decadal intervals, ranging between 1700-2020 (inclusive)</p> <p><strong>Extent:</strong> Longitude: -180° to 180°. Latitude: -56° to 84°.</p> <p> </p> <p>See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite <em>Fluet-Chouinard et al. 2023</em>. Please contact Etienne Fluet-Chouinard at <a href="mailto:etienne.fluet@gmail.com">etienne.fluet@gmail.com</a> with any questions or comments with regards to the best usage of our dataset.</p> <p>Fluet-Chouinard E., Stocker B., Zhang Z., Malhotra A., Melton J.R., Poulter B., Kaplan J., Goldewijk K.K., Siebert S., Minayeva T., Hugelius G., Prigent C., Aires F., Hoyt A., Davidson N., Finlayson C.M., Lehner B., Jackson R.B., McIntyre P.B. <em>Nature</em>. Extensive global wetland loss over the last three centuries</p>
Main sediment profiles and 3D-pdf from archaeological wetland excavation at Immensee-Dorfplatz
<p>These are a 3D model and the longest and most representative sediment profiles through the waterlogged Late Neolithic lakeside site of Immensee-Dorfplatz (SZ). </p>
CONUS NG-IDF 2.0: Wetland
<p>The <strong>NG-IDF: Wetland</strong> datasets cover more than 200,000 sites at approximately 6 km resolution in the years 1951–2013 across the CONUS for the <strong>Wetland</strong> land use land cover (LULC).</p> <p>These [<strong>daily time series</strong> + <strong>annual maximum</strong>] datasets provide information on extreme hydrological events and their associated hydrometeorological drivers at a continental scale.</p> <p>They include 1) daily time series of precipitation (P), throughfall (TF), water available for runoff (W), and snow water equivalent (SWE); 2) annual maximum time series of P, W, TF, snowmelt, rain-on-snow (ROS); and 3) NG-IDF curves and their uncertainties.</p> <p>See the "Wetland_readmefirst.txt" file for details.</p>
Southwest United States Wetland Water Quality and Macroinvertebrate Data 2018-2022
Water quality and macroinvertebrate data were collected between 2018 and 2020 from 14 different wetland and riparian sites spanning across West Texas, New Mexico, and Arizona. Water quality data such as Cl, SO4, and conductivity were collected as well as nutrients such as NO3, PO4, and Total Dissolved Nitrogen. Macroinvertebrate data were collected from all sites during the summer months (June, July, and August).
Summer 2017 porewater and sediment geochemistry data at Second Creek, a sulfate-impacted riparian wetland in northeast Minnesota
Water and sediment chemistry data were collected over the summer and fall of 2017 at Second Creek, a riparian wetland study site near Aurora, MN, to understand sulfur and methane processes. Porewaters were collected with two distinct methods “peepers” (multi-chambered equilibrium dialysis samplers) that allow for high vertical resolution but 2-3 week averaged temporal resolution, and rhizon samplers that enable instantaneous temporal resolution but have lower spatial resolution. Porewaters were analyzed for dissolved cations, anions, sulfide, methane, iron(II)/iron(III), and pH. Sediment cores were analyzed for acid volatile sulfide, and sulfur and iron speciation via X-ray absorption spectroscopy.
Long-term response of wetland plant communities to management intensity, grazing abandonment, and prescribed fire
Isolated, seasonal wetlands within agricultural landscapes are important ecosystems. However, they are currently experiencing direct and indirect effects of agricultural management surrounding them. Because wetlands provide important ecosystem services, it is crucial to determine how these factors affect ecological communities. Here, we studied the long-term effects of land use intensification, cattle grazing, prescribed fires, and their interactions on wetland plant diversity, community dynamics, and functional diversity. To do this, we used vegetation and trait data from a 14-year-old experiment on 40 seasonal wetlands located within semi-natural and intensively managed pastures in Florida. These wetlands were allocated different grazing and prescribed fire treatments (grazed vs. ungrazed; burned vs. unburned). Our results showed that wetlands within intensively managed pastures have lower native plant diversity, floristic quality, evenness, higher non-native species diversity, and exhibited the most resource-acquisitive traits. Wetlands embedded in intensively managed pastures were also characterized by lower species turnover over time. We found that 14 years of cattle exclusion reduced species diversity in both pasture management intensities and had no effect on floristic quality. Fenced wetlands exhibited lower functional diversity and experienced a higher rate of community change both due to an increase in tall, clonal, and palatable grasses. The effects of prescribed fires were often dependent on grazing treatment. For instance, prescribed fires increased functional diversity in fenced wetlands but not in grazed wetlands. Our study suggests that cattle exclusion and prescribed fires are not enough to restore wetlands in intensively managed pastures and further highlights the importance of not converting semi-natural pastures to intensively managed pastures. Our study also suggests that grazing levels applied in semi-natural pastures maintained high plant dive
Survey of North Carolina Coastal Plain Ditches for Wetland Characteristics, 2015
We surveyed 32 drainage ditch reaches in the North Carolina Coastal Plain in summer 2015, including forested, freeway (roadside), and agricultural ditches, for wetland structure. We surveyed vegetation, including herbaceous communities and the tree/shrub layer, soils for water and organic matter content, and some hydrology, including USACE hydrologic indicators. This dataset supports a forthcoming publication.
Dissolved CO2 and CH4 dynamics in Delmarva headwater wetlands, 2020-2022
This data product includes measurements of dissolved CO2 and CH4 in surface water and groundwater, along with hydrological and biogeochemical variables, across 20 headwater wetlands in the Delmarva Peninsula in the Mid-Atlantic region of the United States. Greenhouse gas samples and water chemistry samples were collected every 1-3 months over a period of 2 years. We also monitored water level at each of the wetlands with a pressure transducer.
Bottom-up meets top-down: Leaf litter inputs influence predator-prey interactions in wetlands, 2011.
While the common conceptual role of resource subsidies is one of bottom-up nutrient and energy supply, inputs can also alter the structural complexity of environments. This can further impact resource flow by providing refuge for prey and decreasing predation rates. However, the direct influence of different organic subsidies on predator–prey dynamics is rarely examined. In forested wetlands, leaf litter inputs are a dominant energy and nutrient resource and they can also increase benthic surface cover and decrease water clarity, which may provide refugia for prey and subsequently reduce predation rates. In outdoor mesocosms, we investigated how inputs of leaf litter that alter benthic surface cover and water clarity influence the mortality and growth of gray treefrog tadpoles (Hyla versicolor) in the presence of free-swimming adult newts (Notophthalmus viridiscens), which are visual predators. To manipulate surface cover, we added either oak (Quercus spp.) or red pine (Pinus resinosa) litter and crossed these treatments with three levels of red maple (Acer rubrum) litter leachate to manipulate water clarity. In contrast to our predictions, benthic surface cover had no effect on tadpole survival while darkening the water caused lower survival. In addition, individual tadpole mass was lowest in the high maple leachate treatments, suggesting an interaction between bottom-up effects of leaf litter and topdown effects of predation risk that altered mortality and growth of tadpoles. Our results indicate that realistic changes in forest tree composition, which cause concomitant changes in litter inputs to wetlands, can substantially alter community interactions.
Cothran, R. D., F. Radarian, and R. A. Relyea. 2011. Altering aquatic food webs with a global insecticide: Arthropod-amphibian links in mesocosms that simulate wetland communities. Journal of the North American Benthological Society 30:893-912.
Pesticides play a critical role in maximizing yields of economically important crops and minimizing the human health threats of disease-carrying pests, but they often have collateral effects on nontarget species. We used a mesocosm study to address how the most commonly used insecticide in the USA, malathion, applied at low, ecologically relevant concentrations (20 and 110 mg/L) affects species interactions in aquatic communities. Unlike many community ecotoxicology studies, our study assessed how malathion affects both consumptive and nonconsumptive effects of predators. We also considered how the vertical distribution of predator cues and malathion (caused by potential stratification) affects species interactions. We found no evidence for vertical stratification of malathion, a result suggesting that exposure to the pesticide was uniform throughout the water column. Malathion was lethal to some primary consumers (cladocerans) at both concentrations and to top predators (dragonflies) at the highest concentration (110 mg/L). These lethal effects initiated density-mediated indirect effects in both cases. Malathion also may have decreased dragonfly foraging efficiency, resulting in increased tadpole survival (trait-mediated indirect effect), which decreased the resources used by tadpoles (periphyton). Collectively, our results show that malathion alters species interactions. However, we suggest that the degree to which pesticides affect aquatic communities will depend strongly on the species composition of communities. Therefore, the community-level consequences of pesticide exposure are likely to vary across the ecological landscape.
Data associated with a study on freshwater phenanthrene removal by three emergent wetland plants conducted in a microcosm experiment at the IISD Experimental Lakes Area, ON, Canada, in 2022.
The following package includes data from a study that evaluated the efficacy of three common wetland plants, Typha sp. (cattail), Carex utriculata (sedge a), and C. lasiocarpa (sedge b) in enhancing removal of phenanthrene (1 mg/L) from freshwater in a microcosm experiment conducted at the IISD Experimental Lakes Area, northwestern Ontario, Canada, in 2022. Over 21 days, microcosms were monitored for phenanthrene chemistry, basic water quality, plant growth metrics (height and final biomass), and root biofilm oxygen consumption (respirometry) and adenosine triphosphate (ATP). Data included in this package was first collected and used in the paper by Stanley et al., titled Freshwater Phenanthrene Removal by three Emergent Wetland Plants.
Summer 2016 hydrology and water chemistry data at Second Creek, a sulfate-impacted riparian wetland in northeast Minnesota
Hydrological and water chemistry data were collected at Second Creek, a riparian wetland study site near Aurora, MN, to understand sulfur and methane processes. Data were collected over the summer of 2016. Hydrological data were collected using temperature probes and pressure transducers installed in surface water gauges and shallow piezometers. Water chemistry was analyzed in surface water samples and porewater samples collected with “peepers” (passive diffusive samplers).
UCSB SONGS Mitigation Monitoring: Wetland Survey - Fish Abundance
These data describe annual estimates of the density of fish in main channel and tidal creek habitats collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. Monitoring began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Mugu Lagoon in Ventura County, CA. Beginning in 2024, Los Penasquitos Lagoon in San Diego County replaced Tijuana Estuary. The abundance of fish is determined using beach seine and enclosure trap sampling at six main channel and six tidal creek locations at each wetland. Both seine and enclosure trap sampling consist of several hauls through an enclosed volume of water. Sampling is conducted annually in early fall.
ScienceDex guides
Understand access before you commit
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.