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Juvenile Salmonid Emigration Monitoring in the Lower American River, California.
Overview Operation of rotary screw traps on the Lower American River is part of a collaborative effort by the U.S. Fish and Wildlife Service, Pacific States Marine Fisheries Commission, and the California Department of Fish and Wildlife. The primary objectives of the study are to collect data that can be used to estimate the passage of juvenile fall-run Chinook Salmon Oncorhynchus tshawytscha and to quantify the raw catch of steelhead Oncorhynchus mykiss as well as winter, spring, and late fall runs of Chinook Salmon. Secondary objectives of the trapping operations focus on collecting biological data on juvenile salmonids and gathering environmental data that will be used to develop models that correlate environmental parameters with salmonid size, temporal presence, abundance, and production. The data package contains seven datasets including: raw catch, trap operation, environmental, and trap efficiency data. Raw Catch – Chinook Dataset This dataset covers ALL Chinook Salmon captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked fall-, spring-, and winter-run Chinook Salmon 2) marked (adipose clipped OR "fin clip") hatchery origin Chinook Salmon 3) recaptured marked fall-run (BBY OR "Pigment / Dye", Photonic Dye, and VIE OR "Elastomer") Chinook Salmon utilized in trap efficiency trials. Raw Catch – Steelhead Dataset This dataset covers ALL steelhead captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked (natural origin) steelhead 2) marked (adipose clipped OR "fin clip") hatchery origin steelhead 3) recaptured marked steelhead utilized in trap efficiency trials. Raw Catch – ByCatch Dataset This dataset provides biological data on ALL catch (EXCLUDING Chinook Salmon or steelhead) captured by the rotary screw traps. All catch in this table is of natural origin. Trap Operations Dataset This dataset provides trap operation data for each trap visit. Specifically, it includes data on the visit
USFWS Red Bluff Diversion Dam Rotary Screw Trap Juvenile Fish Monitoring Database
The United States Fish and Wildlife Service (USFWS) has conducted direct monitoring of juvenile Chinook Salmon Oncorhynchus tshawytscha passage at the Red Bluff Diversion Dam (RBDD), river kilometer (RKM) 391 on the Sacramento River, in Northern California since 1994 (Johnson and Martin 1997). Martin et al. (2001) developed quantitative methodologies for indexing juvenile Chinook passage using rotary-screw traps (RST) to assess the impacts of the United States Bureau of Reclamation’s (USBR) RBDD Research Pumping Plant. Absolute abundance (passage and production) estimates were needed to determine the level of impact from the entrainment of salmonids and other fish community populations through RBDD’s experimental ‘fish friendly’ Archimedes and internal helical pumps (Borthwick and Corwin 2001). The original project objectives were met by 2000 and funding of the project was discontinued. From 2001 to 2008, funding was secured through a CALFED Bay-Delta Program grant for annual monitoring operations to determine the effects of restoration activities in the upper Sacramento River aimed primarily at winter Chinook Salmon*. The USBR, the primary proponent of the Central Valley Project (CVP), has funded this project since 2010 due to regulatory requirements contained within the National Marine Fisheries Service’s (NMFS) Biological Opinion for the Long-term Operations of the CVP and State Water Project (NMFS 2009 and 2019). The project began sampling in 1994 with (4) 2.4-m diameter RST’s which sampled through March of 2020. From March 25, 2020 through June 25, 2020, in order to protect employee health and safety during the Coronavirus global pandemic (COVID-19), sampling ceased. Just prior to resuming sampling operations in July of 2020, (4) 1.5-m diameter and one 2.4-m diameter RSTs were re-installed across the transect at the RBDD site. This new five-trap configuration provides a solution to sampling a location that has become shallower since the RBDD gates were permanen
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.
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on the Sacramento River near Tisdale Weir
The Tisdale RST sampling site is operated by the California Department of Fish and Wildlife (CDFW) to obtain information on the temporal distribution, relative abundance, and composition of race and species of juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (O. mykiss) emigrating from the upper Sacramento River and tributaries to the Sacramento-San Joaquin Delta (Delta). The project collects data near the Tisdale Weir on the Sacramento River, using two paired rotary screw traps (RSTs) outfitted with two 8-ft diameter cones. The RST monitoring site at Tisdale Weir was established meet a requirement of the 2011 amendment to the reasonable and prudent alternative (RPA) of the 2009 biological and conference opinion (BO) on the long-term operations of the Central Valley Project (CVP) and State Water Project (SWP). The amendment required the Bureau of Reclamation (USBR) and the California Department of Water Resources (DWR) to fund a new juvenile salmonid monitoring site on the Sacramento River between Red Bluff Diversion Dam (RBDD) and Knights Landing. The purpose of the new site was to provide early warning of fish movement and determine survival of listed fish species leaving spawning habitat in the upper Sacramento River. CDFW issued ITP 2081-2019-066-00 to DWR on March 31, 2020, for the long-term operation of the SWP in the Delta. Condition 7.5.2 of the ITP requires the development and establishment of a spring-run Chinook salmon juvenile production estimate (JPE) to increase understanding regarding the impacts water operations have on the spring-run Chinook salmon population in the Sacramento River watershed and inform the development of minimization measures to reduce take of spring-run Chinook salmon at Delta fish salvage facilities. Data from the Tisdale RST will be used along with other datasets from juvenile salmonid monitoring programs in the Sacramento River Watershed to inform the development of JPE modeling approaches. Salmonid data c
Monitoring juvenile Chinook salmon outmigration using rotary screw traps on the Lower Feather River
CDFW issued Incidental Take Permit No. 2081-2019-006-00 (ITP) to the California Department of Water Resources (DWR) on March 31, 2020, for the long-term operation of the State Water Project (SWP) in the Sacramento San Joaquin Delta (Delta). Condition 7.5.2 of the ITP requires the development and establishment of a spring-run Chinook salmon (Oncorhynchus tshawytscha) juvenile production estimate (JPE) to increase understanding of the impacts that water operations have on the spring-run Chinook salmon population in the Sacramento River watershed and to inform the development of minimization measures to reduce take of spring-run Chinook salmon at Delta fish salvage facilities. As a part of the JPE effort, CDFW began operating a new rotary screw trap (RST) monitoring station on the lower Feather River near River Mile 17, approximately 1 mile downstream of Star Bend Park and Boat Ramp near Olivehurst, in January 2022. This RST location represents the lowest point in the Feather River Watershed where juvenile salmon are sampled with an RST prior to entering the Sacramento River and includes salmon emigrating from the Yuba River. The expanded juvenile monitoring effort will help resource agencies and water managers identify numbers of salmon emigrating from the Feather River Watershed and contributing to the spring-run Chinook salmon population entering the Delta. Monitoring is conducted annually from October through June utilizing a pair of eight-foot rotary screw traps (RST). Data collected at the Lower Feather River RST site provides information on the temporal distribution, relative abundance, and race composition of juvenile Chinook salmon; and temporal distribution and relative abundance of steelhead trout (O. mykiss) emigrating from the Feather River and tributaries, including the Yuba River, to the Delta. Salmonid data collected from the Lower Feather River RST, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of ju
GRIME AI Water Segmentation Model for the USGS Monitoring Site Discovery Farms Waterway AO1 Near Antigo, WI, 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 location Discovery Farms Waterway AO1 Near Antigo, WI (2023-2024). All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_AO1_STAFF 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 during this process.
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 Chippewa River at Grand Ave at Eau Claire, WI, 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 Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_Chippewa_River_at_Grand_Ave_at_Eau_Claire 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 gen
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
GRIME AI Water Segmentation Model for the USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI, 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 at East River at County Trunk HWY ZZ near Greenleaf, WI. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_East_River_at_HWY_ZZ_near_Greenleaf 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 duri
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024
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 at Kearney Outdoor Learning Area, NE, 2024-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Kearney_Outdoor_Learning_Area 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 during this pr
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024
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 at Missouri River at Hermann, MO, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MO_Missouri_River_at_Hermann 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 during this process. Th
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Pecos River near Acme, NM, 2022-2024
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) USGS Monitoring Site at Pecos River near Acme, NM, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Pecos_River_near_Acme 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 during this process. This project was
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 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 at Rio Grande below Elephant Butte Dam, NM, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Rio_Grande_below_Elephant_Butte_Dam 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
Juvenile Salmonid Emigration Monitoring in the Stanislaus River at Caswell Memorial State Park, California, 2017-2025
Overview Operation of rotary screw traps on the lower Stanislaus River at Caswell Memorial State Park is part of the U.S Fish and Wildlife Service’s Anadromous Fish Restoration Program and Comprehensive Assessment and Monitoring Program under the National Marine Fisheries Service Reasonable and Prudent Alternatives actions and Central Valley Project Improvement Act. The primary objectives of the study are to collect data that can be used to estimate the passage of juvenile fall-run Chinook Salmon Oncorhynchus tshawytscha and to quantify the raw catch of steelhead Oncorhynchus mykiss. Secondary objectives of the trapping operations focus on collecting biological data on juvenile salmonids and gathering environmental data that will be used to develop models that correlate environmental parameters with salmonid size, temporal presence, abundance, and production. The data package contains seven datasets including: raw catch, trap operation, environmental, and trap efficiency data. Raw Catch – Chinook Dataset This dataset covers ALL Chinook Salmon captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked fall- and spring-run Chinook Salmon 2) recaptured marked fall-run (BBY OR "Pigment / Dye", Photonic Dye, Fin Clip, and VIE OR "Elastomer") Chinook Salmon utilized in trap efficiency trials. Raw Catch – Steelhead Dataset This dataset covers ALL steelhead captured by the rotary screw traps. All steelhead captured are unmarked and presumed to be natural origin steelhead. Raw Catch – ByCatch Dataset This dataset provides biological data on ALL catch (EXCLUDING Chinook Salmon or steelhead) captured by the rotary screw traps. All catch in this table is of natural origin. Trap Operations Dataset This dataset provides trap operation data for each trap visit. Specifically, it includes data on the visit type, trap functioning status, start and end sampling dates and times, total revolutions and instantaneous revolution speeds, livewell intake st
Air temperature at core phenology sites and additional bird monitoring sites in the Andrews Experimental Forest, 2009 to present
The H.J Andrews phenology study air temperature network includes 16 core phenology sites, 40 core bird sites and 128 auxiliary bird sites. This study examines air temperatures at multiple sites within the Andrews Experimental Forest. Air temperatures were recorded 1.5 m above ground at 184 sites distributed on an 800-m incomplete grid throughout much of the Andrews Forest. Data were collected using automated sensors starting in June of 2009 at 56 sites and in June 2011 128 additional sensors were added. These data document the complex spatial and temporal patterns of air temperature variation within the Andrews Forest, which is governed by multiple processes including inversions, regional air mixing, cold air drainage and pooling, and the effects of vegetation on temperature extremes. The data entities provided indicate various methods of data quality checking over time.
Point-count bird censusing: long-term monitoring of bird abundance and diversity in central Arizona-Phoenix, ongoing since 2000
### project overview Over the past half-century, the greater Phoenix metropolitan area (GPMA) has been one of the fastest growing regions in the US, experiencing rapid urban expansion in addition to urban intensification. This backdrop provides an ideal setting to monitor biodiversity changes in response to urbanization, and the CAP LTER has been using a standardized point-count protocol to monitor the bird community in the GPMA and surrounding Sonoran desert region since 2000. The bird survey locations in this CAP LTER core monitoring program include six general site groupings: 1. ESCA. Forty bird survey locations were selected from a subset of the CAP LTER's Ecological Survey of Central Arizona (ESCA; formerly named Survey200) long-term monitoring sites. ESCA sites were located using a tessellation-stratified dual-density sampling design, and, as such, span a diversity of habitats including urban, suburban, rural, commercial areas, parks, agricultural fields, and native Sonoran desert. Earlier versions of this data package included data from the ESCA project that was intended to complement the bird data. However, while positioned in close proximity, the bird survey locations do not necessarily overlap with the 30m x 30m plot that constitutes an ESCA sampling location, and leveraging data from these two monitoring programs should be addressed carefully. ESCA data have corresponding survey location names, and those data are available through the CAP LTER and LTER network data portals. At the conclusion of the 2016 spring survey, fifteen of the ESCA-correlated sites were discontinued as the core monitoring program refocused its efforts on desert parks and PASS neighborhoods. Among the deleted locations were all agricultural and commercial sites, as well as sites where access had become restrictive. 2. North Desert Village (NDV). Additional bird survey locations were positioned in treatment areas of the North Desert Village (NDV). This was a site of intense study on t
Long-term monitoring of stormwater runoff and water quality in urbanized watersheds of the greater Phoenix metropolitan area, ongoing since 2008
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Stormwater sampling is conducted at numerous locations. The longest running sampling location is near the outflow of the IBW ~0.6 km above its confluence with the Salt River. The sampling location coincides with a permanent USGS gauging sta
Long-term monitoring of ground-dwelling arthropods in the McDowell Sonoran Preserve, Scottsdale, Arizona (2012-2025)
*Project overview* Protected lands, such as the McDowell Sonoran Preserve (hereafter referred to as the Preserve) in Scottsdale, Arizona, provide critical refuge for native biota and natural, ecological processes within and near urban environments. At the same time, a key feature that makes urban, open-space preserves so valuable − their proximity to urban areas − places strain on the ecological integrity of these systems through visitation, habitat fragmentation, and the introduction of exotic species among others. Effective management of these systems requires detailed knowledge of the biota within the protected area, and monitoring of ecological indicators through time. Arthropods are well suited to monitoring ecological health. This diverse group of organisms typically reflects overall biological diversity of a system, and includes several trophic levels; their short generation times mean they will likely respond quickly to change; and they are relatively easy to sample. As part of a broad effort by the McDowell Sonoran Conservance Field Institute, an organization that oversees science and research in Preserve, to establish a baseline inventory of biota in the Preserve, investigators with the Central Arizona−Phoenix Long-Term Ecological Research (CAP LTER) program at Arizona State University (ASU) in collaboration with Field Institute Citizen Scientists are monitoring ground-dwelling arthropods at select locations that reflect a diversity of habitat within the Preserve. Investigators employ a sampling design that is intended to provide insight regarding influence of the urban-wildland interface on the arthropod community within the protected area. The simple but effective technique of pitfall trapping is used to sample ground-dwelling arthropods at select locations spanning a wide range of habitat with the Preserve. Additional collections of vegetation-dwelling arthropods have been conducted at the sampling locations at periodic intervals. *Project design and sa
Annual monitoring of high marsh plots dominated by Juncus and Borrichia at the GCE LTER from 2013 - 2025
Annual monitoring of high marsh plots dominated by Juncus and Borrichia. Plots were established at GCE sites 6 and 10 in 2013, and are monitored annually. The goal is to determine how annual variation in climate and other abiotic factors affects the vegetation composition.
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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)
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.