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1,133 results for “wetland”
Year 2018, 15 minute measurements of stage, water temperature in a small headwater stream draining draining a mainly forested catchment (55% forest + 19% wetland), Cart Cr., Newbury, MA.
Year 2018, continuous measurements, every 15 minutes, were made of stage, water temperature in Cart Creek, Newbury, MA, a small headwater stream draining a mainly forested catchment (55% forest + 19% wetland) in the Parker River watershed. Discharge is determined from stage using discharge vs stage regressions.
Year 2016, 15 minute measurements of stage, water temperature in a small headwater stream draining a mainly wetland catchment (49% wetlands/swamp + 36% forest), Bear Meadow Brook, draining Cedar Swamp, Reading, MA.
Year 2016, continuous measurements, every 15 minutes, were made of stage and water temperature in a small headwater stream, Bear Meadow Brook , Cedar Swamp, Reading MA, draining a mainly wetland catchment (49% wetland + 36% wetland). Discharge is determined from stage using discharge vs stage regressions.
Year 2017, 15 minute measurements of stage, water temperature in a small headwater stream draining a mainly wetland catchment (49% wetlands/swamp + 36% forest), Bear Meadow Brook, draining Cedar Swamp, Reading, MA.
Year 2017, continuous measurements, every 15 minutes, were made of stage and water temperature in a small headwater stream, Bear Meadow Brook , Cedar Swamp, Reading MA, draining a mainly wetland catchment (49% wetland + 36% wetland). Discharge is determined from stage using discharge vs stage regressions.
Year 2018, 15 minute measurements of stage, water temperature in a small headwater stream draining a mainly wetland catchment (49% wetlands/swamp + 36% forest), Bear Meadow Brook, draining Cedar Swamp, Reading, MA.
Year 2018, continuous measurements, every 15 minutes, were made of stage and water temperature in a small headwater stream, Bear Meadow Brook , Cedar Swamp, Reading MA, draining a mainly wetland catchment (49% wetland + 36% wetland). Discharge is determined from stage using discharge vs stage regressions.
Year 2019-2021, 15 minute measurements of stage, water temperature in a small headwater stream draining a mainly wetland catchment (49% wetlands/swamp + 36% forest), Bear Meadow Brook, draining Cedar Swamp, Reading, MA.
Year 2019, 2020 and 2021 continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential) in the Ipswich River watershed. Discharge is determined from stage using discharge vs stage regressions.
Synthesis of Sea level rise and carbon accumulation rates in United States tidal wetlands
Coastal wetlands accumulate soil carbon more efficiently than terrestrial systems, but sea level rise potentially threatens the persistence of this prominent carbon sink. Here, we combine a published dataset of 372 soil carbon accumulation rates from across the United States with new analysis of 131 sites in coastal Louisiana. The combined database featured 503 measurements of carbon accumulation, spanning broad gradients in mean annual temperature, tide range, and dominant vegetation.
Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning
<p>This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/. </p> <p>The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.</p> <p> </p>
Figure 3 in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 3. Seasonal variation of total taxa richness (A), mean density (A), and relative contribution of biomass (B) of most abundant groups of aquatic invertebrates at three ponds in a Patagonian wetland (Mallín Crespo) during the study period (May 2008 to April 2009). Livestock stocking period is indicated by the black bar.
Drone-based aerial imagery of rivers, wetlands and agricultural systems in Zambia
<p>Unmanned aerial vehicle (UAV) imagery of rivers, wetlands and agricultural systems across Zambia. Collected during two flying seasons in March and September 2018, with a total of 122 scenes at 48 sites.</p> <p>All imagery is made available on OpenAerialMap (<a href="https://map.openaerialmap.org">https://map.openaerialmap.org</a>), a set of tools for searching, sharing, and using openly licensed satellite and UAV imagery. Detailed description of the dataset provided in a .csv file.</p>
Hull Springs Wetland Dissolved Oxygen and Water Temperature Data from 2021-05-04 to 2021-06-11
<p># General Metadata for Hull Springs Restored Wetland Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> HS_wetland_DO-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning.</p> <p>## File Created</p> <p> * 2021-06-16 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from the sampling station in the restored wetland at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252).</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Temperature (dC) collected with a Onset HOBO U26-001 Dissolved Oxygen Logger<br> * Dissolved Oxygen (mg/L) collected with a Onset HOBO U26-001 Dissolved Oxygen Logger</p> <p>The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time - THIS FIELD DOES NOT REPORT THE CORRECT TIME. THE AM and PM WERE NOT PRESERVED. USE 'timestamp'.<br> * observation - the incremental number of each observation<br> * timestamp - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM)<br> * DO - the concentration of dissolved oxygen in the water (mg/L)<br> * Temp - the temperature of the water (dC)<br> </p>
Fig. 2 in Fish functional groups in a tropical wetland of the Yucatan Peninsula, Mexico
Fig. 2. Cluster analyses of the functional traits data. Dendrograms show groups for food acquisition (A, B, C, and D) and locomotion (E, F, G, and H). Species identities (IDs) correspond to the first letter of the genus and species names.
Image 1 in The Odonata of Binyo Penyilam, a unique tropical wetland area in Bintulu Division, Sarawak, Malaysia
Image 1. Map of Sarawak showing the location of the Sarawak Planted Forest Project and the Binyo Penyilam Conservation Area.
Data for "Water (or the Lack Thereof), Management, and Conservation of an Endangered Desert Wetland Obligate, Lilaeopsis schaffneriana var. recurva"
<p>Raw and RData forms of data for "Water (or the Lack Thereof), Management, and Conservation of an Endangered Desert Wetland Obligate, <em>Lilaeopsis schaffneriana </em>var. <em>recurva". </em>Consists of six Excel files, with names corresponding to the type of data.</p> <ol> <li>field_ecology_data.xlsx </li> <li>experiment_randomization.xlsx </li> <li>experiment_entered_data.xlsx </li> <li>resilience_days_to_critical.xlsx </li> <li>resilience_experiment_data.xlsx </li> <li>resilience_leaf_density_data.xlsx </li> </ol> <p>Four RData files of the loaded Excel data, and one text file to explain the coding of the drought experiment data.</p>
Datasets of wetland identification
<p>Datasets were produced for identifying existing wetlands and also finding feasible locations for wetland reconstruction. Orthophotos originate from Land Information New Zealand (https://data.linz.govt.nz/layer/2346-southland-central-otago-04m-rural-aerial-photos-index-tiles-2013-14/). LiDAR DEM used for analysis originates from Environment Southland.Coordinate system NZTM2000.</p>
Data for: A fundamental tradeoff among resilience, resistance, efficiency, and redundancy in tidal wetlands
<p>We filtered the raw NASA-MODIS (MOD13Q1) Enhanced Vegetation Index (EVI) dataset to only inlcude pixels with high tidal wetland class purity and Quality Assurance (QA) reliability scores. We filtered 782,693 tidal wetland pixels with coverage spanning the entire contiguous United States to only include those with greater than 90% tidal wetland class purity. We then further filtered these pixels to only include those where data dropouts in the EVI or QA layer occured fewer than 10% of the time. In the end, we used 145,871 pixels in our analysis. Tidal wetland GPP was calculated by pixel for the dates 3/5/2000 to 12/2/2020 at 16-day intervals using the algorithms published in: </p> <p>R. A. Feagin, I. Forbrich, T.P. Huff, J.G. Barr, J. Ruiz-plancarte, J.D Fuentes, R.G. Najjar, R. Vargas, A. Vazquez-lule, L. Windham-Myers, K. Kroeger, E.J. Ward, G.W. Moore, M. Leclerc, K.W. Krauss, C.L. Stagg, M. Alber, S.H. Knox, K.V.R. Schafer, T.S., Bianchi, J.A. Hutchings, H.B. Nahrawi, A. Noormets, B. Mitra, A. Jaimes, A.L. Hinson, B. Bergamaschi, J. King, and G. Miao., Tidal wetland gross primary production across the continental United States, 2000–2019. Global Biogeochemical Cycles 34, e2019GB006349 (2020).</p> <p>The file named "SWR_90percentFinal.csv" contains the filtered SWR database used to calculate GPP. File named "temp_90percentFinal_rounded.csv" contains the filtered air temperature database used to calculate GPP. The file named "EVI(gapped_filled)_90percentFinal.csv" contains the final gap-filled EVI time series database used to calculate GPP. File named "QA_90%Final.csv" contains the filtered quality assurance (QA) values. Dates in the EVI database where QA = 3 were determined to be of poor quality, removed from the database, and replaced with "NA". Single NA gaps in the EVI database were gap-filled by taking the mean of the dates flanking the NA gap. File named "myGPP(gap_filled)_FINAL.csv" contains the calculated GPP estimates used throughout the study analysis. </p> <p>File named "rawEVI.csv" contains the raw EVI database prior to filtering and gap-filling. File named "rawSWR.csv" contains the raw SWR database prior to filtering. File named "rawAirTemp.csv" contains the air temperature database prior to filtering. File named "rawQA.csv" contains the QA layer of the MOD13 satellite product prior to filtering. These files contain the raw data for all 782,693 tidal wetland pixel locations. These raw files can also be accessed at daac.ornl.gov. </p>
Fig. 2, A in An Overview Of The Ecological Values Of Soumar Wetland On Waterbirds Diversity
Fig. 2, A — monthly trend of abundance and richness of waterbirds in the Soumar wetland (Setif, Algeria). Abundance = number of individuals and Taxa_S = number of species. B — cumulative effect between abundance and richness using the Gini measure of evenness (G').
Fig. 4 in An Overview Of The Ecological Values Of Soumar Wetland On Waterbirds Diversity
Fig. 4. Diversity profile of waterbirds alpha diversity for each month in the Soumar wetland (Setif, Algeria). α = 0 richness; α = 1 Shannon–Weaver index; α = 2 inverse Simpson index (1/D); and α = a high value approximates the Berger–Parker index.
Net Methane Production Predicted by Patch Characteristics in a Freshwater Wetland
<p>Dataset supporting submitted research paper. "flux_patchdata<i>archive.csv" includes all plot-level data and is organized with sample locations and time in rows and data collected as headers in columns. "microbial</i>all.csv" includes only microbial taxonomic data collected from soils in each treatment. Other files include time series data from water level and temperature sensors deployed at each treatment (n=5). Refer to 'metadata.csv' for units and descriptions of data in each file. Data collected 2021-2022. Approved by all authors. </p>
Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels
<p class="MsoNoSpacing"><span>Wetlands of the Laurentian Great Lakes of North America, i.e., lakes Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (<em>n </em>= 34, 48%) than negative trends (<em>n </em>= 5, 7%). </span><span>These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. <span>Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the </span>decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide <span>high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show </span>increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing water levels <span>by removing or preventing barriers to movement, </span>such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes.</span></p>
Application of LiDAR to assess the habitat selection of an endangered small mammal in an estuarine wetland environment
<p>Light detection and ranging (lidar) has emerged as a valuable tool for examining the fine-scale characteristics of vegetation. However, lidar is rarely used to examine coastal wetland vegetation or the habitat selection of small mammals. Extensive anthropogenic modification has threatened the endemic species in the estuarine wetlands of the California coast, such as the endangered salt marsh harvest mouse (<em>Reithrodontomys raviventris</em>; SMHM). A better understanding of SMHM habitat selection could help managers better protect this species. We assessed the ability of airborne topographic lidar imagery in measuring the vegetation structure of SMHM habitats in a coastal wetland with a narrow range of vegetation heights. We also aimed to better understand the role of vegetation structure in habitat selection at different spatial scales. Habitat selection was modeled from data compiled from 15 small mammal trapping grids collected in the highly urbanized San Francisco Estuary in California, USA. Analyses were conducted at three spatial scales: microhabitat (25 m<sup>2</sup>), mesohabitat (2,025 m<sup>2</sup>), and macrohabitat (10,000 m<sup>2</sup>). A suite of structural covariates was derived from raw lidar data to examine vegetation complexity. We found that adding structural covariates to conventional habitat selection variables significantly improved our models. At the microhabitat scale in managed wetlands, SMHM preferred areas with denser and shorter vegetation, and selected for proximity to levees and taller vegetation in tidal wetlands. At the mesohabitat scale, SMHM were associated with a lower percentage of bare ground and with pickleweed (<em>Salicornia pacifica</em>) presence. All covariates were insignificant at the macrohabitat scale.<em> </em>Our results suggest that SMHM preferentially selected microhabitats with access to tidal refugia and mesohabitats with consistent food sources. Our findings showed that lidar can contribute to improving our understanding of habitat selection of wildlife in coastal wetlands and help to guide future conservation of an endangered species.</p>
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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)
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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.