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4,712 results for “creeks”
Fall 2021 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2021 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Fall 2022 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2022 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Fall 2023 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2023 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE8_Hydro (Altamaha River near Aligator Creek, Georgia) from 01-Jan-2021 through 31-Dec-2021
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE8_Hydro (Altamaha River near Aligator Creek, Georgia) from 01-Jan-2021 through 31-Dec-2021. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 01-Jan-2021 through 31-Dec-2021
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 01-Jan-2021 through 31-Dec-2021. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE8_Hydro (Altamaha River near Aligator Creek, Georgia) from 01-Jan-2022 through 31-Dec-2022
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE8_Hydro (Altamaha River near Aligator Creek, Georgia) from 01-Jan-2022 through 31-Dec-2022. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 01-Jan-2022 through 31-Dec-2022
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 01-Jan-2022 through 31-Dec-2022. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on Butte Creek
The California Department of Fish and Wildlife (CDFW) conducts juvenile salmonid emigration monitoring on Butte Creek at the Parrot-Phelan Diversion Dam near Chico, California. Monitoring is conducted annually from October through June utilizing an 8-ft diameter rotary screw trap (RST) and a diversion screen trap (DST). Data from this monitoring is used to estimate juvenile spring-run Chinook salmon (Oncorhynchus tshawytscha) abundance and passage, identify alevin emergence timing, document juvenile size at emigration, and document rearing and emigration patterns. This data will also be used to inform the development of a juvenile production estimate (JPE) for spring-run Chinook salmon in the Sacramento River Watershed as required by Condition of Approval 7.5.2 of Incidental Take Permit No. 2081-2019-006-00 (ITP) issued by CDFW to California Department of Water Resources (DWR) for the long-term operation of the State Water Project. Salmonid data collected from the Butte Creek RST, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of juvenile salmon in the Sacramento River Watershed to estimate the number of winter-run and spring-run Chinook salmon that have entered the Sacramento-San Joaquin Delta (Delta). SaMT is a real-time operations monitoring team required by Condition of Approval 8.1.2 of the ITP which meets weekly from October through June, to provide advice for real-time management of SWP operations to DWR, CDFW, and the Water Operation Management Team (WOMT) to minimize take of winter-run and spring-run Chinook salmon in the Delta. Note: data within the current year’s monitoring season are considered provisional.
Species diversity and plant dominance influence grassland stability in response to extreme climatic events and anthropogenic drivers across three LTER sites: Cedar Creek, Konza Prairie, and Kellogg Biological Station, 1982-2023.
The data in this package is associated with the analysis for a manuscript titled "Multiple community properties drive ecosystem resistance and resilience to extreme climate events across mesic grasslands". The files include compiled data on plant biomass production, species abundance, experimental treatments, extreme climate event values, and calculated diversity and stability measures from grassland plots in experiments at CDR, KBS, and KNZ LTER sites.
GRIME AI Water Segmentation Model for the USGS Monitoring Site Beggars Bridge Creek Near Dawley Corners, VA, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site Beggars Bridge Creek Near Dawley Corners, VA, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/VA_Beggars_Cr_nr_Dawley_Corners_RSIE for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated du
GRIME AI Water Segmentation Model for the USGS Monitoring Site East Branch Brandywine Creek below Downingtown, PA, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for USGS Monitoring Site East Branch Brandywine Creek below Downingtown, PA. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/PA_East_Branch_Brandywine_Creek_below_Downingtown for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generate
Drought experiment on aquatic vertebrate populations in McRae Creek, HJ Andrews Experimental Forest, 2022
Three distinct reaches in McRae Creek west tributary (MCTW) within the HJ Andrews Experimental Forest in western Oregon were designated for manipulation and data collection. Manipulations included increasing the temperature (T), reducing streamflow (Q), and a reference (R reach). Population estimates of vertebrates, specifically Coastal Cutthroat Trout and Coastal Giant Salamander, were obtained using three-pass depletion methods in each reach. A Before-After-Control-Impact (BACI) design was implemented, distinguishing between the "Before" and "After" periods. "Before" surveys were conducted from July 18th to 20th, 2022, while "After" surveys occurred from September 8th to 9th, 2022. During the surveys, each species was identified, noting life stage, and relevant measurements were taken. For trout, these included the length from the snout to the tail fork (Length_Fork_Vent), the snout to the tail (Length_Tail), and weight. In the "Before" survey, all trout were tagged with elastomer tags: red for the T reach, yellow for the Q reach, and orange for the R reach. Trout larger than 80 mm also received PIT tags in their abdominal cavities. Salamanders were measured similarly, not elastomer or PIT tags were applied. During the "After" survey, no new elastomer or PIT tags were inserted; only previously tagged fish were recorded. Additionally, stream cross-sections were surveyed every 5 meters to document stream dimensions. Recorded data included the location, reach, sample date, BACI status, and distance downstream from the upstream cross-section (0 meters). Measurements at each cross-section included wetted width, bankfull width, and depths at five evenly spaced points. Furthermore, pools were identified and measured in each reach, noting the maximum pool depth, depth at the outflow, width, and length. Temperature sensors were installed in each reach, recording stream temperature every 15 minutes. Sensor locations were recorded as the distance downstream from the top of e
Aquatic Vertebrate Population Study in Mack Creek, Andrews Experimental Forest, 1987 to present
Populations of Coastal Cutthroat trout (Oncorhynchus clarkii clarkii) in two standard reaches of Mack Creek in the H.J. Andrews Experimental Forest have been monitored since 1987. Monitoring of Coastal Giant Salamanders, Dicamptodon tenebrosus began in 1993. The two standard reaches are in a section of clearcut forest (ca. 1963) and an upstream 500 year old coniferous forest. Sub-reaches are sampled with 2-pass electrofishing, and all captured vertebrates are measured and weighed. Additionally, a set of channel measurements are taken with each sampling. This study constitutes one of the longest continuous records of salmonid populations on record.
Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA
We used a DJI Matrice 210 UAV with a MicaSense Altum to collect a total of 20 images from January 2020 - December 2021 in a the Dean Creek marsh on Sapelo Island, GA. Wrack was classified using a principal component analysis. Wrack patches under 1 m2 were excluded from analyses. Wrack classifications were converted to polygon and point data where each point represents a 5 cm x 5 cm pixel. Those files were then used to analyze wrack characteristics, their relation to environmental drivers, and landscape based patterns. For both polygon and point data, we used the National Elevation Dataset (https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/NED.html) to determine the elevation of each wrack patch. Creeks and shorelines were digitized and used to determine each wrack patches' distance to water. We calculated the frequency of wrack deposition at each point by adding together the number of images where that pixel was classified as wrack over the course of the study. Polygon data were related to tide height from a NOAA tidal station data product (Ft. Pulaski, Station 8670870; https://tidesandcurrents.noaa.gov) and wind speed and wind direction from the Marsh Landing weather station (downloaded data for the SAPMLMET met station from: https://cdmo.baruch.sc.edu/) to evaluate the relationship of wrack to environmental drivers.
Borrichia removal experiment at Dean Creek marsh on Sapelo Island, Georgia between 1995 and 2001
To examine the effect of Borrichia on Batis and Sarcocornia sp., I completely clipped all vegetation from six 0.5 x 0.5 m quadrats in a mixed stand of the three species at the Dean Creek marsh in June 1995. Six additional plots were left unmanipulated as controls. Removal and control plots were fully interspersed. I clipped Borrichia from the removal plots every 6 months but allowed other plant species to re-colonize.
Water samples collected for dissolved inorganic carbon and nutrient analysis during tidal creek lateral exchange measurements approximately every 15 minutes from beginning of flood tide to the following low tide, Rowley, MA, PIE LTER.
Measurement of the lateral exchange of nutrients, sediment, and carbon in tidal creek systems draining predominantly low-elevation marsh dominated by Spartina alterniflora and high-elevation marsh dominated by Spartina patens located in Rowley, MA.
PIE LTER 5-minute marsh water table height at Shad Creek, Rowley, MA from May-November 2020.
Measurements of water table height in the Shad Creek marsh located near the Shad Creek eddy flux tower, Rowley, MA. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Shad Creek stream bank at Shad Creek from May-November 2020.
PIE LTER 5-minute marsh water table height at Shad Creek, Rowley, MA from April-October 2021.
Measurements of water table height in the Shad Creek marsh located near the Shad Creek eddy flux tower, Rowley, MA. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Shad Creek stream bank at Shad Creek Island from April-October 2021.
PIE LTER 10-minute marsh water table height at Shad Creek, Rowley, MA from May-November 2023.
Measurements of water table height in the Shad Creek marsh located near the Shad Creek eddy flux tower, Rowley, MA. Measurements were taken every 10 minutes at each logger along a transect of water level loggers running perpendicular to the Shad Creek stream bank at Shad Creek from May-November 2023.
Time series of carbon dioxide and methane fluxes measured with eddy covariance for Falling Creek Reservoir in southwestern Virginia, USA during 2020-2025
We measured carbon dioxide and methane flux exchange with the atmosphere at the deepest site of Falling Creek Reservoir (Vinton, Virginia, USA) every 30 minutes from 04 April 2020 to 31 December 2025. Falling Creek Reservoir is a drinking water supply reservoir owned and managed by the Western Virginia Water Authority (WVWA) as a primary drinking water source. The dataset consists of micrometeorological and flux data collected using an eddy covariance system (LiCor Biosciences, Lincoln, Nebraska, USA) and analyzed with associated Eddy Pro software (Eddy Pro Version 7.0.6), including carbon dioxide, methane, and water vapor. All analysis scripts are included for data processing and quality assurance/quality control following best practices.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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