We conducted a study to assess the efficacy of using a parentage-based tagging survival model (PBT N-mixture model) to evaluate two sources of mortality for juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon. The model was originally developed to evaluate reservoir mortality because of predation from piscivorous fish. However, recent studies have also found that juvenile Chinook salmon experience high infection rates from parasitic copepods (Salmincola californiensis), which are known to negatively affect performance and survival. Our study was conducted to determine if the PBT N-mixture model could separately estimate mortality because of predation from non-native fish and mortality resulting from copepod infection. This assessment was conducted in two parts: (1) data collected in Lookout Point Reservoir during 2018 were re-analyzed; and (2) a simulation was conducted to evaluate a multi-year study that included inter-annual variation in copepod infection rate and two subsampling strategies (10 fish per month, 30 fish per month) to characterize monthly copepod infection rate. Results from each of these efforts suggest that the survival model is unlikely to provide reliable survival estimates for the two mortality sources that we evaluated. The re-analysis of 2018 data showed that “predation only” and “copepod only” models estimated a negative coefficient for the respective covariate, but the model that included both covariates provided coefficient estimates that differed from the other models and were highly uncertain. Similarly, the simulation results showed that most models failed to correctly estimate the magnitude and direction of mortality due to predation and copepods. These results suggest that additional data will be required if a model is desired that can separately estimate mortality effects due to both predation and copepods in the future. The existing data are limited by factors including low detection probabilities from previous field studies, existing uncertainties about copepod effects on mortality in a natural setting and expected limitations in the number of years that a field study could realistically be expected to receive funding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221096","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hance, D.J., Kock, T.J., Perry, R.W., and Pope, A.C., 2022, Assessing the efficacy of using a parentage-based tagging survival model to evaluate two sources of mortality for juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon: U.S. Geological Survey Open-File Report 2022–1096, 14 p., https://doi.org/10.3133/ofr20221096.","productDescription":"v, 14 p.","onlineOnly":"Y","ipdsId":"IP-141621","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408997,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1096/ofr20221096.XML"},{"id":408996,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1096/images"},{"id":408995,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221096/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1096"},{"id":408994,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1096/ofr20221096.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1096"},{"id":408993,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1096/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.84577497588536,\n 43.952222617898286\n ],\n [\n -122.84577497588536,\n 43.78093867902544\n ],\n [\n -122.51343855010415,\n 43.78093867902544\n ],\n [\n -122.51343855010415,\n 43.952222617898286\n ],\n [\n -122.84577497588536,\n 43.952222617898286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
A collaborative acoustic telemetry study was conducted to describe behavior and movement patterns of juvenile green sturgeon (Acipenser medirostris) in the lower Sacramento River, California during 2016–19. For the study, juvenile green sturgeon were collected, tagged, and released in the Sacramento River between river kilometer (rkm) 467 and rkm 419 near Red Bluff, California. Telemetry monitoring sites were located between rkm 464 and rkm 1 to detect tagged fish that moved downstream. In this report, we describe movement patterns of juvenile green sturgeon in the lower Sacramento River between rkm 167 and rkm 52. In total, 98 juvenile green sturgeon were tagged and released during the study and 46 of these fish moved downstream and were detected in the lower Sacramento River. Downstream movement appeared to be associated with periods of increasing river flow, and the greatest percentage of tagged fish were detected moving downstream during the first period of increased streamflow each autumn. The number of tagged fish that were detected decreased in lower reaches of the study area, but it’s not clear if this was because fish experienced mortality while moving downstream, stopped moving downstream to rear in study reaches, or their transmitters stopped working due to battery life limitations. We did find that several fish were detected moving upstream between telemetry monitoring sites in the lower reaches of the study area. This study provides new insights into movement patterns and behavior of juvenile green sturgeon in the lower Sacramento River, but additional research will be required to better understand factors such as survival and how fish respond to estuarine conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221091","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hansen, A.C., Chase, R.D., Kock, T.J., Perry, R.W., Gruber, J.J., and Poytress, W.R., 2022, Juvenile green sturgeon (Acipenser medirostris) movement during autumn and winter in the lower Sacramento River, California, 2016–20: U.S. Geological Survey Open-File Report 2022–1091, 17 p., https://doi.org/10.3133/ofr20221091.","productDescription":"vii, 17 p.","onlineOnly":"Y","ipdsId":"IP-139543","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408893,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1091/ofr20221091.XML"},{"id":408892,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1091/images"},{"id":408890,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1091/ofr20221091.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1091"},{"id":408889,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1091/coverthb.jpg"},{"id":408891,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221091/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1091"}],"country":"United States","state":"California","otherGeospatial":"Lower Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.22303530664792,\n 37.892351455555556\n ],\n [\n -121.08045718164777,\n 37.892351455555556\n ],\n [\n -121.08045718164777,\n 38.64643627131957\n ],\n [\n -122.22303530664792,\n 38.64643627131957\n ],\n [\n -122.22303530664792,\n 37.892351455555556\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 60th annual report of the River Master of the Delaware River. It covers the 2013 River Master report year, the period from December 1, 2012 to November 30, 2013.
During the report year, precipitation in the upper Delaware River Basin was 44.50 inches or 100 percent of the long-term average. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs remained high until October 2013 when it decreased below 80 percent combined capacity. The lowest combined storage of the report year was 70.2 percent of combined capacity on November 26, 2013. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the Montague flow objective for the Delaware River at the Montague, New Jersey streamgage on 71 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year. An agreement was signed on July 16, 2013 to temporarily increase releases to provide thermal protection below Cannonsville Reservoir.
The quality of water in the Delaware River estuary between streamgages at Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at several locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221068","isbn":"978-1-4113-4486-0","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2022, Report of the River Master of the Delaware River for the period December 1, 2012–November 30, 2013: U.S. Geological Survey Open-File Report 2022–1068, 99 p., https://doi.org/10.3133/ofr20221068.","productDescription":"xii, 99 p.","numberOfPages":"99","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123834","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":501821,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113786.htm","linkFileType":{"id":5,"text":"html"}},{"id":408577,"rank":4,"type":{"id":31,"text":"Publication 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Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed throughout the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at the lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data to be acquired during the 2022 Northern Hemisphere fire and growing season. Additional information about the Landsat 7 orbit lowering is here:
https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Water, bed sediment, and invertebrate tissue were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the Clark Fork Basin. The sampling program was completed by the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, to characterize aquatic resources in the Clark Fork Basin and monitor trace elements associated with historical mining and smelting activities. Sampling sites were on the Clark Fork River and a subset of its tributaries. Water samples were collected periodically at 22 sites from October 2019 through September 2020. Bed-sediment and tissue samples were collected once at 12 sites during July 2020.
Water-quality data included concentrations of major ions, dissolved organic carbon, nitrogen (nitrate plus nitrite), trace elements, and suspended sediment. Daily values of turbidity were determined at four sites. Bed-sediment data included trace-element concentrations in the fine-grained (less than 0.063 millimeter) fraction. Biological data included trace-element concentrations in whole-body tissue of selected aquatic benthic invertebrates. Statistical summaries of water-quality, bed-sediment, and invertebrate tissue trace-element data for sites in the Clark Fork Basin were provided for the period of record: March 1985–September 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Clark, G.D., Hornberger, M.I., Hepler, E.J., and Heinert, T.L., 2022, Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019–September 2020: U.S. Geological Survey Open-File Report 2022–1090, 17 p., https://doi.org/10.3133/ofr20221090.","productDescription":"Report: vii, 17 p.; Data Release; Dataset","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-138065","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":501834,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113781.htm","linkFileType":{"id":5,"text":"html"}},{"id":408546,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221090/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408387,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":408386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93BP9P8","text":"USGS data release","linkHelpText":"Results of water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019– September 2020"},{"id":408385,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1090/images"},{"id":408384,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.XML"},{"id":408383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1090"},{"id":408381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1090/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.67714251796224,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 47.58172589143089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
In this report, we apply the stream salmonid simulator (S3) to coho salmon (Oncorhynchus kisutch) in the Klamath River Basin by extending the original model to account for life history and disease dynamics specific to coho salmon. This version of S3 includes tracking of three separate life-history strategies representing the different time periods and ages at which fish leave natal tributaries such as the Scott and Shasta Rivers (age-0 spring, age-0 fall, or age-1 smolt). Once fish leave their natal tributaries and enter the Klamath River, the deterministic life-stage-structured population model simulates daily growth, movement, and survival. We extend the model to include non-natal tributary dynamics, where spring age-0 fish entry to non-natal tributaries is simulated based on environmental conditions in the main-stem Klamath River. Fish that use non-natal tributaries then reenter the Klamath River during the winter or spring as smolts and actively migrate downstream. We also consider the life history strategy where fish rear in natal tributaries and enter the Klamath River as age-1 smolts. In addition to simulating different life history pathways that coho salmon may take, we model disease dynamics, incorporating new information on Ceratonova shasta related infection and mortality. We incorporate competitive interactions between juvenile coho and Chinook salmon (Oncorhynchus tshawytscha) by simulating density-dependent movement dynamics in response to Chinook salmon abundance.
Model simulations suggest that total abundance and survival to the ocean differed between life-history strategies. In general, spring age-0 fish that leave their natal tributaries in their first spring had lower survival compared with fish that remained in natal tributaries and out-migrated later. Spring age-0 fish also had higher disease related mortality, owing to their residence in the main-stem Klamath River overlapping with periods of elevated C. shasta spore concentrations. Age-0 fish leaving their natal tributaries in the fall had near-zero disease related mortality. Most non-natal tributary use occurred at upstream tributary locations and was variable between the brood years depending on passage timing and environmental conditions. The inclusion of Chinook salmon in simulations resulted in decreased abundance and survival of Coho salmon reaching the ocean. In addition, we developed an R package to facilitate use of and continued development of S3 as a tool to guide management of juvenile salmonid populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221071","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Dodrill, M.J., Perry, R.W., Som, N.A., Manhard, C.V., and Alexander, J.D., 2022, Extending the Stream Salmonid Simulator to accommodate the life history of coho salmon (Oncorhynchus kisutch) in the Klamath River Basin, Northern California: U.S. Geological Survey Open-File Report 2022–1071, 70 p., https://doi.org/10.3133/ofr20221071.","productDescription":"viii, 70 p.","onlineOnly":"Y","ipdsId":"IP-129401","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408507,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.XML"},{"id":408506,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1071/images"},{"id":408505,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1071"},{"id":408503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1071/coverthb.jpg"},{"id":408504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1071"}],"country":"United States","state":"California","otherGeospatial":"Klamath River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.16748046874999,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 41.071069130806414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from calendar year 2019, and additionally uses streamflow statistics from November and December 2018. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2019, total groundwater withdrawals were estimated to be 3,070 acre-feet (acre-ft), industrial withdrawals were 670 acre-ft, and municipal withdrawals were estimated to be 2,400 acre-ft. Total withdrawals during 2019 were about 58 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline after 2005 and cessation of mining operations in 2019.
Water levels measured in 2019 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 10 of 16 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 16 wells ranged from +8.2 feet (ft) to −40.0 ft, and the median change was −1.7 ft. Water levels also showed decline in 16 of 18 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −38.8 ft, with changes across all 18 wells ranging from +12.9 ft to −185.0 ft.
Spring flow was measured at four springs in 2019. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2019), Dinnebito Wash near Sand Springs 09401110 (1993 to 2019), Polacca Wash near Second Mesa 09400568 (1994 to 2019), and Pasture Canyon Springs 09401265 (2004 to 2019). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was indicated at Moenkopi Wash and Dinnebito Wash.
In 2019, water samples collected from four springs and three wells in the Black Mesa area were analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Concentrations of dissolved solids, chloride, and sulfate increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids and chloride at Burro Spring have varied for the period of record, but there is no statistical trend in the data; however, concentrations of sulfate from Burro Spring now show a trend towards lower concentrations. No statistical trend tests were performed for the three wells sampled in 2019 since less historical water-quality data were available for comparison.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221086","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2022, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2018–2019: U.S. Geological Survey Open-File Report 2022–1086, 47 p., https://doi.org/10.3133/ofr20221086.","productDescription":"vii, 47 p.","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-119897","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":501833,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113767.htm","linkFileType":{"id":5,"text":"html"}},{"id":408436,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211124","text":"Open-File Report 2021-1124","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2016–2018"},{"id":408427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1086/covrthb.jpg"},{"id":408428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1086/ofr20221086.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1086"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.06054687499999,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 34.94899072578227\n ],\n [\n -109.390869140625,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 36.96744946416934\n ],\n [\n -112.06054687499999,\n 34.94899072578227\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Bathymetric and topographic data were collected from May 2013 to February 2016 along the 15.84-mile reach of the Colorado River spanning from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona. Channel bathymetry was mapped using multibeam and singlebeam echo sounders; subaerial topography was mapped using a combination of ground-based total stations and aerial photogrammetry. These data were combined to produce a digital elevation model (DEM), spatially variable estimates of DEM uncertainty, and bed-substrate distribution maps. This project is part of a larger effort to monitor the status and trends of sand storage along the Colorado River in Glen Canyon National Recreation Area and Grand Canyon National Park. This report documents the study methodologies (survey methods and post-processing procedures, DEM production and uncertainty assessment, and bed-substrate classification) and presents the resulting datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221057","collaboration":"Prepared in cooperation with Northern Arizona University and Marda Science LLC","usgsCitation":"Kaplinski, M., Hazel, J.E., Jr., Grams, P.E., Gushue, T., Buscombe, D.D., and Kohl, K., 2022, Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona: U.S. Geological Survey Open-File Report 2022-1057, 20 p., https://doi.org/10.3133/ofr20221057.","productDescription":"Report: v, 20 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-120853","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":408061,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1057/ofr20221057.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 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The reservoir, located under a semi-tropical climate, is the principal water supply for the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there was an adequate supply of freshwater in the reservoir in 2022, water-quality concerns are present over taste-and-odor and saltwater-intrusion issues. From 2013 to 2015, the U.S. Geological Survey (USGS), in cooperation with the Charleston Water System, engaged in a multi-year study of the hydrology and hydrodynamics of Bushy Park Reservoir to better understand factors affecting water-quality conditions in the reservoir. As part of this study, Charleston Water System worked with Tetra Tech, Inc., a consulting and engineering firm, to develop a Bushy Park Reservoir hydrodynamic and water-quality modeling framework, built upon earlier efforts by both Tetra Tech and the U.S. Army Corps of Engineers. At the completion of the new modeling framework, the USGS was requested to evaluate the calibrated hydrodynamic and water-quality model.
The Bushy Park Reservoir Environmental Fluid Dynamics Code (EFDC) model was calibrated for the time period from January 1, 2012, to December 31, 2015. The general modeling approach for the newly revised modeling framework, as briefly detailed in this report, was developed with EFDC. The EFDC is a grid-based modeling package that can simulate three-dimensional flow, transport, and water quality in surface-water systems. This report evaluated the capacity of Tetra Tech’s Bushy Park EFDC model to simulate water discharge, water circulation, surface elevations, temperature, salinity, and other water-quality parameters.
The USGS model review focused specifically on the following criteria: (1) determine if the model, with additional effort, could be developed into an adequate planning tool for Bushy Park Reservoir; (2) assess the capacity of the model to specifically address water-quality issues in the reservoir related to taste-and-odor and saltwater intrusions; and, (3) evaluate three preliminary water-management scenarios related to reduced water withdrawals in the reservoir and the effect on saltwater intrusion.
Overall, the model was able to simulate discharge, flow velocity, and water-surface elevations with generally good agreement between the simulated and measured values. Specifically, the model was able to demonstrate good agreement for discharge at two USGS continuous discharge locations (USGS station 02172002; USGS station 02172040), with Wilmott index of agreements of 0.86 and 0.75, respectively. A total of seven USGS streamgages, located on the West Branch of the Cooper River, Durham Canal, and the Cooper River, were available for water-surface elevations, with index of agreements ranging from 0.74 to 0.99. However, model-simulated water-surface elevation ranges were appreciably high (compared to measured ranges) for two locations near Pinopolis Dam, farthest upstream on the West Branch of the Cooper River. This result may indicate that too much simulated tidal energy propagated through the model domain.
For water temperature, 16 calibration stations were available for at least part of the 4-year simulation. The index of agreement range for temperature comparisons was from 0.95 to 1.00, indicating excellent agreement between the measured and simulated results. One of the primary future applications for the Bushy Park Reservoir EFDC model is to determine the extent of saltwater intrusions. A wide range in the salinity prediction quality was simulated with the model. The prediction quality ranged from an index of agreement of 0.15 at Cooper River approximately 2.75 miles southeast of the Tee, South Carolina, to 0.92 at West Branch Cooper River near Moncks Corner, South Carolina. Although the model did not accurately simulate some of the larger salinity deviations resulting during individual hydrologic events, the seasonal salinity trends were adequately simulated with the model during the study period (2012–15). Therefore, it may be difficult to simulate extreme hydrologic events, such as during large storms, where high salinity water is exchanged with Bushy Park Reservoir. There was agreement in model simulation with the measured data either on the quantitative index of agreement values or qualitative agreement in the seasonal salinity data trends.
For water quality, the index of agreement values were generally low for total nitrogen, ammonia, nitrate, total Kjeldahl nitrogen, total phosphorus, and orthophosphate. Although general trends were adequately simulated at specific stations, particularly for Bushy Park Reservoir, the model-simulated fit was low across all the constituents described above with index of agreements usually below 0.50. A limitation for simulating nutrient concentrations across the model domain was the lack of characterization for the constituents directly entering Bushy Park Reservoir, or the lack of data directly attributed to the boundary condition (for example, the Cooper River). The other two calibrated water-quality constituents (besides the nutrients mentioned above) were dissolved oxygen and chlorophyll a. Dissolved oxygen varied from index of agreement values from 0.58 to 0.94 for 11 stations, generally indicating agreement with the available measured data. Chlorophyll a, calibrated for seven stations, had a wider range from 0.11 to 0.74 for the index of agreement.
With the current modeling framework, taste-and-odor events, related to cyanobacterial blooms, cannot be directly simulated. However, indirect estimates of cyanobacteria concentrations may be obtained by using the chlorophyll a model outputs, which represent total phytoplankton biomass, and the phytoplankton biovolume data by group (diatoms, green algae, cyanobacteria and others) collected from 2012 to 2015. For the Bushy Park Reservoir modeling framework to be used directly for taste-and-odor issues, cyanobacteria must be simulated and calibrated based on observations of cyanobacteria biomass concentrations. In addition to the cyanobacteria sampling conducted within the reservoir between 2012 and 2015, the new model calibration would also require new algae biomass data-collection efforts to characterize the external sources of cyanobacteria entering the Bushy Park Reservoir from tributaries, as well as the internal cycling, production, and decay of cyanobacteria in the hydrologic system.
Further improvements to the EFDC model would include expanding the collection of boundary condition datasets, such as water-quality monitoring to determine improved nutrient loads into the model domain. Along with improved water-quality monitoring for the major boundary conditions, continuous discharge, for both Foster Creek and the Back River, would further constrain the flow balance and the loads into Bushy Park Reservoir. In addition to better boundary-condition characterization, it is important to better characterize possible shortcomings specifically to the model domain, such as the grid resolution, bathymetry, and numerical hydrodynamic errors. Further consideration of the model may involve a sensitivity analysis to determine if errors in the simulation outputs, such as discharge, water-surface elevations, and salinity, were more likely caused by poor boundary condition characterization or, specifically, the model setup.
Three model scenarios were run with the revised Bushy Park Reservoir model: (1) reduced withdrawals from one of the large intake-discharge locations for Bushy Park Reservoir, the Williams Station; (2) elevated (above background levels) ocean water level causing saltwater intrusion from the ocean through Durham Canal into Bushy Park Reservoir; and (3) overtopping of the Back River Dam at the southernmost end of Bushy Park Reservoir. For the reduced withdrawals scenarios, the largest shift in flow resulted near the Williams Station intake, with the next largest flow change at the southern end of Bushy Park Reservoir, and a net increase in flow out of the Bushy Park Reservoir to the Cooper River by way of the Durham Canal. The effect resulting from scenario 3 on water quality and salinity was small, with larger increases for dissolved oxygen than other constituents at several monitoring stations. For the two scenarios related to saltwater intrusion (including dam overtopping), the changes in salinity generally were found to dissipate in the following 2 weeks and generally back to baseline salinity conditions within 3 months. This result did vary depending on the severity of the storm or length of the dam overtopping event.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221079","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Smith, E.A., Akasapu-Smith, M., Petkewich, M.D., and Conrads, P.A., 2022, Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15: U.S. Geological Survey Open-File Report 2022–1079, 35 p., https://doi.org/10.3133/ofr20221079.","productDescription":"Report: ix, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087955","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501828,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113612.htm","linkFileType":{"id":5,"text":"html"}},{"id":407629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1079/coverthb.jpg"},{"id":407630,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1079"},{"id":407632,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.XML"},{"id":407633,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1079/images/"},{"id":407634,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","linkHelpText":"Water quality data for Bushy Park Reservoir, South Carolina 2013–2015"},{"id":407631,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221079/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1079"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.39794921875,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 32.676372772089806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
Landslide hazards are present in all 50 States and most U.S. territories, and they affect lives, property, infrastructure, and the environment. Landslides are the downslope movement of earth materials under the force of gravity. They can occur without any obvious trigger. Widespread or severe landslide events are often driven by such hazards as hurricanes, earthquakes, volcanic eruptions, heavy rain events, flooding, and wildfires. Landslides can also cause their own cascading consequences, such as the spread of hazardous materials or the creation of devastating local tsunamis.
This strategy document describes goals and strategic actions of a comprehensive strategy to meet key challenges to reducing the Nation’s risk from landslide hazards equitably and effectively. The document follows the direction of the National Landslide Preparedness Act (Public Law 116–323) by presenting a strategy for addressing landslide hazards, including risk reduction and response. The act directs the Department of the Interior to establish a program that will work with State, Tribal, and local governments as well as with academia, the private sector, community-based groups, and nonprofit organizations to identify landslide hazards and risk and improve communication, coordination, and emergency preparedness, with the objective of reducing landslide losses. As the only Federal program dedicated to landslide hazard science, the U.S. Geological Survey’s Landslide Hazards Program will lead and coordinate many of the efforts described in this strategy document. Landslide hazard risk reduction must be undertaken collectively and collaboratively across the Federal Government. This strategy document will provide a framework for the creation of an interagency management plan that describes the programs, projects, workforce, and budgets required to carry out the national strategy.
The strategy outlined in this document presents a vision of how to equitably produce, communicate, and apply landslide data and science to support a broad range of land management, infrastructure, planning, and emergency response decisions. These decisions are made by a variety of actors, including private and nonprofit landholders; State, Tribal, territorial, city, and county planners; emergency managers; engineers; infrastructure managers; Federal agencies and their partners; and community leaders and individuals. Supporting those decisions and reducing the Nation’s vulnerability to landslides requires overcoming three main challenges: (1) gaps in basic information needed to describe and understand landslide occurrence and societal risk, (2) difficulty in accurately mapping and forecasting landslide hazards, and (3) communication and coordination among the many jurisdictions and sectors that have responsibility for and interest in reducing landslide losses. To address those challenges, this strategy document puts forward a series of strategic actions to achieve four goals:
These strategic actions focus on expanding the knowledge of societal risk posed by landslides as well as better understanding of where, when, and why they occur. They focus on applying that knowledge to support landslide risk reduction efforts and decisions, including the establishment of new advisory, coordination, and working groups focused on landslide hazard and risk. They take into account that supporting landslide loss reduction decisions also requires new guidance, tools, and training codeveloped with the entities, organizations, and individuals faced with making those decisions. Finally, they address actions needed to support and expand landslide warning information and improve the technical response to landslide emergencies.
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Natural Hazards Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
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Collection of accurate suspended-sediment data using depth-integrating samplers requires that they operate isokinetically, that is, that they sample at the local stream velocity unaffected by the presence of the suspended-sediment sampler. Sub-isokinetic suspended-sediment sampling causes grain-size dependent positive biases in the suspended-sediment concentration measured by the suspended-sediment sampler. Collapsible bag suspended-sediment samplers like the US D-96 and the lighter US D-96-A1 depth-integrating samplers have shown a tendency to sample sub-isokinetically under low stream velocities (below ~3.5 feet per second), colder water temperatures, and longer sampling durations. Previous work concluded that the time-dependent decrease in the intake efficiency of the US D-96-type sampler could be partially overcome by increasing the venting of water from the sampler cavity by shortening the sampler tray. The standard-length sampler tray partially blocks the rear vent hole; shortening the sampler tray effectively increases the area of the sampler-cavity rear vent hole. This previous work showed that removing the partial blockage of the rear vent hole caused by the sampler tray resulted in both an increase in intake efficiency and a decrease in the positive bias in measured suspended-sand concentration.
Herein, a series of tests were conducted on the Colorado River in Arizona using different modifications to a US D-96-A1 sampler to see if physical enlargement of the rear vent hole would produce further improvements in intake efficiency. Results from these tests show that physical enlargement of the rear vent hole, beyond that already effectively achieved by shortening the sampler tray, did not result in any further improvement in intake efficiency. However, these tests also indicated that physically increasing the area of the rear vent hole did not affect the suspended-sediment data collected by the US D-96-A1 sampler. Furthermore, comparisons of suspended-sediment data collected using the US D-96-A1 sampler and the isokinetic US P-61-A1 point-integrating sampler show that the suspended-sediment data collected by the US D-96-type sampler can be accurate in certain circumstances despite the tendency of this sampler to sample sub-isokinetically over the entire depth of a sampling vertical. We surmise that this result could arise from the US D-96-A1 sampler collecting sample isokinetically when the water-sediment mixture enters the nozzle, but that the water-sediment mixture only enters the nozzle intermittently while the sampler transits a sampling vertical.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221077","usgsCitation":"Sabol, T.A., Topping, D.J., Griffiths, R.E., and Dramais, G., 2022, Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements: U.S. Geological Survey Open-File Report 2022-1077, 14 p., https://doi.org/10.3133/ofr20221077.","productDescription":"v, 14 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-127691","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501827,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113586.htm","linkFileType":{"id":5,"text":"html"}},{"id":407352,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1077/ofr20221077.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1077"},{"id":407351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1077/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.58950805664062,\n 36.830722025409784\n ],\n [\n -111.54144287109375,\n 36.830722025409784\n ],\n [\n -111.54144287109375,\n 36.88236678807325\n ],\n [\n -111.58950805664062,\n 36.88236678807325\n ],\n [\n -111.58950805664062,\n 36.830722025409784\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.38714599609375,\n 35.755149755962755\n ],\n [\n -113.33358764648438,\n 35.755149755962755\n ],\n [\n -113.33358764648438,\n 35.777435736805614\n ],\n [\n -113.38714599609375,\n 35.777435736805614\n ],\n [\n -113.38714599609375,\n 35.755149755962755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Passive membrane samplers—semipermeable membrane devices and polar organic chemical integrative samplers—were deployed for 22 continuous days at 7 sites along the West Maui, Hawaiʻi, coastline in February and March 2017 to assess organic contaminants at shallow coral reef ecosystems from diverse upstream inputs. The distribution of organic compounds observed at these coastal sites showed considerable variability; high concentrations of microbially sourced organic compounds observed at all sites, with pentadecane as the predominant normal alkane, showed the relative importance of marine and microbial organic matter to the coastal carbon pool. Pharmaceuticals and personal care products, as well as flame retardants, were also detected at all sites. Of the seven sites sampled, the Kahekili Beach Park site had the highest number of unique contaminants and the Honokōwai Stream site had the highest concentrations of compounds. Two individual compounds, a flame retardant and a fragrance, were ubiquitous across the studied West Maui reefs, including at the least-developed site. A direct correlation to upstream land-use practices or legacy agricultural inputs was not readily observed since polychlorinated biphenyls, pesticides, herbicides, or insecticides were not detected. Results provide a snapshot of relative contaminant abundances as well as inputs to select nearshore environments along the West Maui coastline captured during the 2017 wet season, which was drier than expected. These data can be useful for understanding the range of stressors potentially affecting nearshore ecosystems, such as groundwater inputs and watershed runoff.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221065","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the State of Hawai‘i Department of Health","usgsCitation":"Campbell, P.L., Prouty, N.G., and Storlazzi, C.D., 2022, Pharmaceuticals and personal care products in passive samplers at seven coastal sites off West Maui, Hawaiʻi: U.S. Geological Survey Open-File Report 2022–1065, 14 p., https://doi.org/10.3133/ofr20221065.","productDescription":"Report: vii, 12 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-132890","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501819,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113585.htm","linkFileType":{"id":5,"text":"html"}},{"id":435674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFE0OP","text":"USGS data release","linkHelpText":"Pharmaceuticals and personal care products measured in passive samplers at seven coastal sites off West Maui during February and March 2017"},{"id":407356,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1065/ofr20221065.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1065"},{"id":407355,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1065/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.708984375,\n 20.78693059257028\n ],\n [\n -156.5826416015625,\n 20.78693059257028\n ],\n [\n -156.5826416015625,\n 21.04349121680354\n ],\n [\n -156.708984375,\n 21.04349121680354\n ],\n [\n -156.708984375,\n 20.78693059257028\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Coastal communities in Alaska are undergoing rapid environmental change from increasing temperatures and baseline data are needed to monitor potential impacts. We conducted the first surveys of the abundance and distribution of eelgrass (Zostera marina) and seaweeds in the western part of Izembek National Wildlife Refuge at the end of the Alaska Peninsula. Six embayments and two offshore islands were surveyed in August–September of 2012. Biotic (percent cover of eelgrass/seaweeds, presence/absences of five sessile invertebrates), and abiotic (water temperature, salinity, and depth) data were recorded at 257 survey points (range =9–74 points per site) across all sites. Twenty-two genera/species of seaweeds were identified at the six embayments. New seaweed species for the offshore islands of Sanak and Caton were added to an existing seaweed collection accessioned at the University of British Columbia Herbarium. We also collected samples of eelgrass to be accessioned at U.S. Geological Survey, Alaska Science Center-Molecular Ecology Laboratory, for future genetic analyses. Fifty-three species of birds and 13 species of mammals were observed and recorded during the survey period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211034","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., Dau, N.C., Lind, O., Payne, K.J., and Lindstrom, S.C., 2022, Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012: U.S. Geological Survey Open-File Report 2021–1034, 14 p., https://doi.org/10.3133/ofr20211034.","productDescription":"Report: iv, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-118597","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405872,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K1ZOMY","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data from eelgrass (Zostera marina), seaweeds and selected invertebrates at six embayments and two islands at the end of the Alaska Peninsula"},{"id":405873,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405874,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"},{"id":405870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1034/coverthb.jpg"},{"id":405871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1034/ofr20211034.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1034"},{"id":405875,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405876,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405877,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.10253906249997,\n 53.98193516209167\n ],\n [\n -161.0595703125,\n 53.98193516209167\n ],\n [\n -161.0595703125,\n 56.19448087726972\n ],\n [\n -165.10253906249997,\n 56.19448087726972\n ],\n [\n -165.10253906249997,\n 53.98193516209167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Eelgrass (<em>Zostera marina</em>) is a highly productive seagrass that plays an essential role in the health of the estuarine and coastal ecosystems; however, information about its abundance and distribution is insufficient in the Bering Sea along the Yukon Delta National Wildlife Refuge. We inventoried the spatial extent and abundance of eelgrass and seaweed in Duchikthluk and Shoal bays on Nunivak Island in July 2010. Using Landsat Thematic Mapper imagery, we estimated the spatial extent of eelgrass to be 1,232 hectares in Duchikthluk Bay and 40 hectares in Shoal Bay. The overall accuracy of the assessments was high (86–87 percent) based on ground truthing using field reference points. We used point-sampling methodology to assess eelgrass abundance relative to the presence of associated seaweeds and selected macro-invertebrates within each of bays. Eelgrass was found at water depths ranging from 0.1 to 2.9 meters across both bays, but the greatest density (>75 percent cover) occurred primarily in moderate to deep water (0.7–1.4 meters) in Duchikthluk Bay and deeper water (>2 meters) in Shoal Bay. The mean aboveground biomass was 39.4±4.0 grams per meter squared in Duchikthluk Bay. The eelgrass biomass was greater (67.6±11.0 grams per meter squared) in Shoal Bay, but this estimate was based on a small sample size (n=3). Seaweeds, representing six species, occurred in low abundance across both bays and were primarily associated with eelgrass. Gastropods were the most common macro-invertebrate, occurring at 45 percent of field points in Duchikthluk Bay.
Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
We conducted a point-sampling survey to determine eelgrass (Zostera marina) and seaweed abundance in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska, in July 2008–10. Eelgrass was known to be abundant in protected embayments of the southeastern Bering Sea and near the Togiak National Wildlife Refuge, but prior to this study, no systematic ground surveys had been conducted in these areas. We determined mean aboveground biomass of eelgrass to be highly variable among years observed, ranging from 32–72 grams dry weight per square meter (g/m2) during successive years in Nanvak Bay and among the studied embayments in 2010: 47±4 g/m2 in Nanvak Bay, 69±7 g/m2 in Chagvan Bay, and 74±15 g/m2 in Goodnews Bay. Seaweed density, abundance, and frequency scores were also highly variable among years and among embayments and were lower for seaweeds than for eelgrass in Nanvak and Chagvan bays, but not in Goodnews Bay. For all bays, mussels (Mytilus spp.) and gastropods were the most common macro-invertebrates detected during surveys, whereas sea stars, crabs, and sponges were not observed in the embayments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201114","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H, Hogrefe, K.R, Swaim, M.A., Donnelly, T.F., and Fairchild, L.L., 2022, Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10: U.S. Geological Survey Open-File Report 2020–1114, 14 p., https://doi.org/10.3133/ofr20201114.","productDescription":"Report: v, 14 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-117779","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405817,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) and seaweed distribution and abundance in bays adjacent to the Togiak National Wildlife Refuge, Alaska"},{"id":405818,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":405822,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405815,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1114/coverthb.jpg"},{"id":405816,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1114/ofr20201114.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1114"},{"id":405819,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405820,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"},{"id":405821,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405823,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.25,\n 58.5\n ],\n [\n -160,\n 58.5\n ],\n [\n -160,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Surveyr
4210 University Driver
Anchorage, Alaska 99508
Declines in the distribution and abundance of seagrasses worldwide have prompted a need for baseline distribution maps of eelgrass (Zostera marina) in Alaska. We used high-resolution digital-color aerial photography and multi-spectral satellite imagery to map the distribution and spatial extent of eelgrass at 21 sites in coastal waters adjacent to Togiak National Wildlife Refuge (TNWR) in northwestern Bristol Bay and southern Kuskokwim Bay. The total spatial extent of eelgrass meadows was estimated to be 6,489 hectare (ha) almost equally divided between Bristol Bay (3,001 ha) and Kuskokwim Bay (3,488 ha). The four largest eelgrass beds occurred in Chagvan Bay (1,933 ha), the north side of Hagemeister Island (1,168 ha), Goodnews Bay (874 ha), and Nanvak Bay (599 ha). This report provides key baseline data useful for establishing a monitoring plan to assess trends in eelgrass along the coast of TNWR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., and Swaim, M.A., 2022, Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska: U.S. Geological Survey Open-File Report 2020–1080, 21 p., https://doi.org/10.3133/ofr20201080.","productDescription":"Report: v, 21 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-114072","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384513,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) abundance adjacent to the Togiak National Wildlife Refuge, Alaska"},{"id":384512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1080/ofr20201080.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1080"},{"id":384511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1080/coverthb1.jpg"},{"id":405745,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405747,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405748,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405746,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384514,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":405749,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.25,\n 58.5\n ],\n [\n -159.75,\n 58.5\n ],\n [\n -159.75,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Eelgrass (Zostera marina) meadows are expansive along the lower Alaska Peninsula, supporting a rich diversity of marine life, yet little is known about their status and trends in the region. We tested techniques to inventory and monitor trends in the spatial extent and abundance of eelgrass in lagoons of the Izembek National Wildlife Refuge. We determined if Landsat imagery could be used to assess eelgrass spatial extent in shallow (less than 4 meter water depth) coastal waters of the refuge. We determined that this seagrass could be differentiated using Landsat imagery from other cover types (that is, channels and unvegetated tidal flats) with a high degree of accuracy (greater than 80 percent) in Izembek and Kinzarof Lagoons. Eelgrass meadows represented the largest cover type in Izembek (about 16,000 hectares) and Kinzarof (about 900 hectares) Lagoons, comprising between 45 and 50 percent of the spatial extent of these lagoons, respectively. When compared to estimates of spatial extent of eelgrass from previous studies, our results suggest little change in the spatial extent of eelgrass in Izembek Lagoon during the 28-year period 1978 through 2006. Preliminary mapping of eelgrass in other embayments indicated that this seagrass was also expansive in Big Lagoon (about 900 hectares; or 34 percent of the lagoon area) and Hook Bay (about 900 hectares; or 36 percent of the bay area) but not in Cold Bay (about 100 hectares; less than 5 percent of the bay area). We conducted an embayment-wide point sampling technique to assess aboveground biomass and distribution of eelgrass and seaweeds and presence of six macro-invertebrates during a 4-year period (2007–10). We determined that, when present, mean aboveground biomass of eelgrass was greater in Kinzarof Lagoon (182.5 plus or minus 12.1 grams dry weight per square meter) than in Izembek Lagoon (152.1 plus or minus 7.1 grams dry weight per square meter) in 2008–10, possibly reflecting the warmer sea temperatures and higher salinities found on the Gulf of Alaska side of the Alaska Peninsula. Seaweeds were more abundant in Kinzarof Lagoon than in Izembek Lagoon, surpassing aboveground biomass of eelgrass in both lagoons in 2008. Gastropods (4 percent of all points) and Caprella shrimp (25 percent) were the most common of the six macro-invertebrates surveyed in Izembek Lagoon, and Telmessus crab was the most common macro-invertebrate in Kinzarof Lagoon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201035","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly,T.F., Fairchild, L.L., Sowl, K.M., and Lindstrom, S.C., 2022, Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10: U.S. Geological Survey Open-File Report 2020–1035, 30 p., https://doi.org/10.3133/ofr20201035.","productDescription":"Report: vi, 30 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-112900","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZUDIOH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling for eelgrass (Zostera marina) and seaweeds in embayments adjacent to the Izembek National Wildlife Refuge, Alaska"},{"id":384515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":435682,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XNSWES","text":"USGS data release","linkHelpText":"Sampling Data for Eelgrass (Zostera marina) in Norma Bay, Izembek Lagoon, Alaska, 1987"},{"id":405752,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405751,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":379325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1035/ofr20201035.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1035"},{"id":405754,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"},{"id":405753,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1035/coverthb.jpg"},{"id":405750,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.37081909179688,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.00755132274014\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time.
First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term.
A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221081","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the U.S. Fish and Wildlife Service","usgsCitation":"Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.","productDescription":"Report: viii, 38 p.; Data Release; 3 Figures: 7.99 × 6.10 inches or smaller","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138940","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":407103,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig10.pdf","text":"Figure 10, full size","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Conifer 2020"},{"id":407104,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig11.pdf","text":"Figure 11, full size","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Human Modification 2020"},{"id":407025,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig09.pdf","text":"Figure 9, full size","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Invasive Annual Grass 2020"},{"id":407023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081.pdf","text":"Report","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":407022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1081/coverthb.jpg"},{"id":407024,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y5CDV","text":"USGS data release","linkHelpText":"Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 33\n ],\n [\n -101.25,\n 33\n ],\n [\n -101.25,\n 49\n ],\n [\n -121.6845703125,\n 49\n ],\n [\n -121.6845703125,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Sagebrush Ecosystem Specialist
Land Management Research Program
Ecosystems Mission Area
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526
Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland and wetland ecosystems to compensate for unavoidable environmental damage from anthropogenic disturbances such as energy development and road construction. Energy-extraction and -generation facilities continue to proliferate across the natural landscapes of the United States, yet mitigation tools to ameliorate the negative behavioral effects on wildlife from these types of facilities are rarely implemented. Scientists from the U.S. Geological Survey conducted a 10-year before-after-control-impact (commonly referred to as BACI) study that evaluated the displacement effects of wind facilities on breeding grassland birds. The study determined behavioral avoidance for 7 of 9 species. This research is notable because of its design, geographical scope, and duration, which allowed for the determination of immediate, short-term effects; delayed or sustained effects; and discrete distances at which effects occurred. In addition, the U.S. Fish and Wildlife Service and Ducks Unlimited conducted a 3-year concurrent-year paired-reference study to determine behavioral avoidance for five species of dabbling ducks. By quantifying displacement rate from these two studies, U.S. Geological Survey and U.S. Fish and Wildlife Service scientists developed the Avian-Impact Offset Method (AIOM) to quantify and compensate for loss in value of breeding habitat. The AIOM converts the biological value (that is, number of bird pairs) lost by way of avoidance and estimates the site-specific number of hectares of grasslands and number of wetlands needed to compensate for displaced pairs of grassland birds and waterfowl. By converting biological value to traditional units of measure in which land is described and purchased or sold, the AIOM lends itself readily to the delivery of offsetting measures such as easement protections and restoration projects. The AIOM tool is applicable to wind, solar, oil, gas, and transportation infrastructure.
This tutorial was designed to increase awareness of the AIOM and to promote its proper application. The tutorial is divided into four sections, each of which explains a discrete topic concerning aspects of behavioral displacement. The first section provides geographical and biological context, and the second section describes the field and statistical methods and results. The third section provides step-by-step instructions for applying the AIOM to several scenarios involving grassland birds or waterfowl at wind or oil facilities. The fourth section describes decision-support tools created to implement the AIOM. The appendices provide the actual field protocols constituting the methods for the research, provide detailed results by species and wind facility for that research, and provide detailed instructions for downloading and applying the decision-support tools.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221049","collaboration":"Prepared in collaboration with the U.S. Fish and Wildlife Service","usgsCitation":"Shaffer, J.A., Loesch, C.R., and Buhl, D.A., 2022, Understanding the Avian-Impact Offset Method—A tutorial: U.S. Geological Survey Open-File Report 2022–1049, 227 p., https://doi.org/10.3133/ofr20221049.","productDescription":"Report: v, 227 p.; 2 Data Releases","numberOfPages":"238","onlineOnly":"Y","ipdsId":"IP-134338","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":406394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1049/coverthb.jpg"},{"id":406396,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J6QUF6","text":"USGS data release","linkHelpText":"North American Breeding Bird Survey dataset 1966–2019"},{"id":406395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1049/ofr20221049.pdf","text":"Report","size":"95.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1049"},{"id":406397,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43SDG","text":"USGS data release","linkHelpText":"Effects of wind-energy facilities on breeding grassland bird distributions"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 52.10650519075632\n ],\n [\n -113.73046875,\n 52.16045455774706\n ],\n [\n -114.78515624999999,\n 51.17934297928927\n ],\n [\n -112.8515625,\n 48.922499263758255\n ],\n [\n -111.796875,\n 47.45780853075031\n ],\n [\n -107.40234375,\n 47.040182144806664\n ],\n [\n -103.798828125,\n 47.81315451752768\n ],\n [\n -102.39257812499999,\n 47.338822694822\n ],\n [\n -100.72265625,\n 45.82879925192134\n ],\n [\n -100.37109375,\n 44.5278427984555\n ],\n [\n -99.49218749999999,\n 43.32517767999296\n ],\n [\n -96.064453125,\n 41.96765920367816\n ],\n [\n -94.39453125,\n 42.87596410238256\n ],\n [\n -95.09765625,\n 45.644768217751924\n ],\n [\n -95.97656249999999,\n 49.03786794532644\n ],\n [\n -98.7890625,\n 50.45750402042058\n ],\n [\n -112.236328125,\n 52.10650519075632\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
This report summarizes data-collection activities associated with the U.S. Geological Survey Humboldt Bay Water-Quality and Salt Marsh Monitoring Project. This work was undertaken to gain a comprehensive understanding of water-quality conditions, salt marsh accretion processes, marsh-edge erosion, and soil-carbon storage in Humboldt Bay, California. Multiparameter sondes recorded water temperature, specific conductance, and turbidity at a 15-minute timestep at two U.S. Geological Survey water-quality stations: (1) Mad River Slough near Arcata, California (U.S. Geological Survey station 405219124085601) and (2) Hookton Slough near Loleta, California (U.S. Geological Survey station 404038124131801). At each station, discrete water samples were collected to develop surrogate regression models that were used to compute a continuous time series of suspended-sediment concentration from continuously measured turbidity. Data loggers recorded water depth at a 6-minute timestep in the primary tidal channels (Mad River Slough and Hookton Slough) in two adjacent marshes (Mad River marsh and Hookton marsh). The marsh monitoring network included five study marshes. Three marshes (Mad River, Manila, and Jacoby) are in the northern embayment of Humboldt Bay and two marshes (White and Hookton) are in the southern embayment. Surface deposition and elevation change were measured using deep rod surface elevation tables and feldspar marker horizons. Sediment characteristics and soil-carbon storage were measured using a total of 10 shallow cores, distributed across 5 study marshes, collected using an Eijkelkamp peat sampler. Rates of marsh edge erosion (2010–19) were quantified in four marshes (Mad River, Manila, Jacoby, and White) by estimating changes in the areal extent of the vegetated marsh plain using repeat aerial imagery and light detection and ranging (LiDAR)-derived elevation data. During the monitoring period (2016–19), the mean suspended-sediment concentration computed for Hookton Slough (50±20 milligrams per liter [mg/L]) was higher than Mad River Slough (18±7 mg/L). Uncertainty in mean suspended-sediment concentration values is reported using a 90-percent confidence interval. Across the five study marshes, elevation change (+1.8±0.6 millimeters per year [mm/yr]) and surface deposition (+2.5±0.5 mm/yr) were lower than published values of local sea-level rise (4.9±0.8 mm/yr), and mean carbon density was 0.029±0.005 grams of carbon per cubic centimeter. From 2010 to 2019, marsh edge erosion and soil carbon loss were greatest in low-elevation marshes with the marsh edge characterized by a gentle transition from mudflat to vegetated marsh (herein, ramped edge morphology) and larger wind-wave exposure. Jacoby Creek marsh experienced the greatest edge erosion. In total, marsh edge erosion was responsible for 62.3 metric tons of estuarine soil carbon storage loss across four study marshes. Salt marshes are an important component of coastal carbon, which is frequently referred to as “blue carbon.” The monitoring data presented in this report provide fundamental information needed to manage blue carbon stocks, assess marsh vulnerability, inform sea-level rise adaptation planning, and build coastal resiliency to climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221076","collaboration":"Prepared in cooperation with the California State Coastal Conservancy, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service—Humboldt Bay National Wildlife Refuge","usgsCitation":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, A summary of water-quality and salt marsh monitoring, Humboldt Bay, California: U.S. Geological Survey Open-File Report 2022–1076, 30 p., https://doi.org/10.3133/ofr20221076.","productDescription":"Report: viii, 30 p.; 3 Data Releases","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133425","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":501826,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113519.htm","linkFileType":{"id":5,"text":"html"}},{"id":406874,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221076/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406865,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TVX0Z8","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA","description":"Curtis, J.A., 2021b, Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9TVX0Z8."},{"id":406863,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1076/images"},{"id":406864,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RJTAIL","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA","description":"Curtis, J.A., 2021a, Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9RJTAIL."},{"id":406866,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLAL7B","text":"Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage","description":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage: U.S. Geological Survey data release, https://doi.org/10.5066/P9QLAL7B."},{"id":406860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1076/covrthb.jpg"},{"id":406861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":406862,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.xml"}],"country":"United States","state":"California","otherGeospatial":"Humboldt Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.35699462890624,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.55972134684838\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Preliminary models for gate operations at the new outlet control structure for Reelfoot Lake were developed by the U.S. Geological Survey, using calibrated ratings of the lift gates, to support the U.S. Fish and Wildlife Service in managing lake level. In 2018, the old structure at the outlet of Reelfoot Lake was buried and lake level control was transferred to a new structure. The transition from lake-level management of the old control structure to the new control structure was documented using historical lake level and discharge measurements and records of stop-log management from March 7, 2013, to August 12, 2018. Discharge into Running Reelfoot Bayou was determined using a standard stage-discharge rating curve. Discharge measured using an acoustic Doppler current profiler was used to calibrate gate-discharge equations for free and submerged orifice flow at the new structure.
Two lake operation models, one for the summer season and another for the winter season, are provided for the new structure based on data from this period. The summer operation model is based on operation of the gates once the lake level exceeds an elevation of 282.7 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Free flow begins when lake level reaches 282.3 ft above NAVD 88 and becomes transitional once the lake level exceeds 282.8 ft above NAVD 88. Submerged flow begins once the lake level reaches 283 ft above NAVD 88 and the tail-water depth is above critical flow depth. The winter operation model is based on operation of the gates once the lake level exceeds 283.2 ft above NAVD 88. Submerged flow begins when the lake rises to an elevation of 283.5 ft above NAVD 88 and the tail-water depth is above critical flow depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221073","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency","usgsCitation":"Heal, E.N., Diehl, T.H., and Garrett, J.W., 2022, Preliminary models relating lake level gate operation and discharge at Reelfoot Lake in Tennessee and Kentucky: U.S. Geological Survey Open-File Report 2022–1073, 27 p., https://doi.org/10.3133/ofr20221073.","productDescription":"Report: vii, 27 p.; Data Release; Database","onlineOnly":"Y","ipdsId":"IP-103756","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":406684,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GY1UF4","text":"USGS data release","linkHelpText":"Preliminary model data for lake level gate operation and discharge at Reelfoot Lake—Tennessee and Kentucky"},{"id":501823,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113520.htm","linkFileType":{"id":5,"text":"html"}},{"id":406683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.pdf","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1073"},{"id":406682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1073/coverthb.jpg"},{"id":406685,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":406844,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1073/images"},{"id":406845,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.xml"},{"id":409059,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221073/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1073"}],"country":"United States","state":"Kentucky, Tennessee","otherGeospatial":"Reelfoot Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.46441650390625,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.30350540784278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Since 2008, large-scale restoration programs have been implemented along the Willamette River, Oregon, to address historical losses of floodplain habitats caused by dam construction, bank protection, large wood removal, land conversion, and other anthropogenic influences. The Willamette Focused Investment Partnership (WFIP) restoration initiative brings together more than 16 organizations to improve floodplain habitats on more than 35,000 hectares upstream from Willamette Falls with the overarching goal to expand and enhance native fish habitats through the following restoration activities implemented along the floodplains and off-channel areas of the Willamette River: (A) modify floodplain topography and human-made barriers to inundation; (B) enhance gravel pits; (C) remove revetments; (D) construct off-channel features; (E) increase and enhance floodplain forest vegetation; and (F) treat aquatic invasive plant species (AIS). The WFIP Effectiveness Monitoring Program was initiated to inform future refinement of Willamette River restoration program goals and activities and has three goals: (1) evaluate the effectiveness of different restoration activities at increasing and enhancing native fish habitat, (2) improve overall understanding of the physical and ecological responses associated with different restoration activities undertaken by the WFIP, and (3) relate site-scale responses to restoration with broader patterns of fish communities, hydrogeomorphology, stream temperature, and vegetation across the Willamette River floodplain, so that the relative importance of restoration activities on habitat availability for native fish can be assessed.
A monitoring framework was developed to evaluate effectiveness of floodplain restoration activities at increasing and enhancing habitat for native fish in the Willamette River corridor, northwestern Oregon. This framework describes monitoring indicators, metrics, and approaches for evaluating responses in native fish communities and physical habitat conditions to restoration activities and determining effectiveness of restoration activities at improving habitats for native fish. The monitoring indicators and approaches are grouped into five restoration monitoring categories that are useful for characterizing ecological and physical habitat responses to restoration activities: fish, hydrogeomorphology, floodplain forest vegetation, birds, and AIS. This monitoring framework provides a common science foundation to support collaborative decisions on future interdisciplinary effectiveness monitoring activities for Willamette River restoration programs. To evaluate restoration effectiveness, data must be evaluated according to metrics and thresholds that permit direct comparison between habitat conditions at the restoration site and restoration program goals; this framework provides examples of metrics and thresholds for evaluating data, recognizing that the precise evaluation criteria for a particular site or program will need to be tailored to meet program questions and available resources. Refining restoration goals and activities as part of an adaptively managed process requires addressing critical uncertainties between restoration goals, restoration activities, and outcomes for habitats used by native fish. Although the monitoring activities of this framework will generate important datasets useful for evaluating restoration effectiveness, additional research, syntheses, and reporting is ultimately necessary to provide a common science foundation to support adaptively managed restoration programs. This report is intended as a resource for restoration program managers, practitioners, scientists, and contractors as they develop detailed annual monitoring plans for data collection and identify the monitoring indicators, metrics, and approaches that are appropriate for evaluating effectiveness of different restoration activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221037","collaboration":"Prepared in cooperation with Benton Soil and Water Conservation District and Oregon Watershed Enhancement Board","usgsCitation":"Keith, M.K., Wallick, J.R., Flitcroft, R.L., Kock, T.J., Brown, L.A., Miller, R., Hagar, J.C., Guillozet, K., and Jones, K.L., 2022, Monitoring framework to evaluate effectiveness of aquatic and floodplain habitat restoration activities for native fish along the Willamette River, northwestern Oregon: U.S. Geological Survey Open-File Report 2022–1037, 116 p., https://doi.org/10.3133/ofr20221037.","productDescription":"Report: xi,116 p.; Data Reease","onlineOnly":"Y","ipdsId":"IP-117547","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":501771,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113503.htm","linkFileType":{"id":5,"text":"html"}},{"id":405744,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N55MYW","text":"USGS data release","description":"USGS data release","linkHelpText":"Native and non-native fish species in the Willamette River Basin, Oregon"},{"id":405742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1037/coverthb.jpg"},{"id":405743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1037/ofr20221037.pdf","text":"Report","size":"77.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1037"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.42041015624999,\n 43.96119063892024\n ],\n [\n -122.36572265625,\n 43.96119063892024\n ],\n [\n -122.36572265625,\n 45.537136680398596\n ],\n [\n -123.42041015624999,\n 45.537136680398596\n ],\n [\n -123.42041015624999,\n 43.96119063892024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 1 (January–March), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221072","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022: U.S. Geological Survey Open-File Report 2022–1072, 39 p., https://doi.org/10.3133/ofr20221072.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-140787","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405985,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221072/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405885,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":405884,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1072/images"},{"id":405883,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.XML"},{"id":405880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1072/coverthb.jpg"},{"id":405882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.pdf","text":"Report","size":"4.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1072"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Deepwater Horizon oil spill caused extensive injury to natural resources in the Gulf of Mexico, and Gavia immer (common loon) were negatively affected from the spill. The Open Ocean Trustee Implementation Group funded the project Restoration of Common Loons in Minnesota to restore common loons lost to the spill. In 2020–21, priority lakes in an eight-county region in north-central Minnesota were identified to focus project activities. In 2021, surveys on these lakes were started to monitor common loon territory occupancy, nest success, and chick survival. We surveyed 62 lakes and identified 110 common loon territories that will be included in the project. At least 1 nest attempt was observed in 78 of 110 territories, and a second nest attempt was observed in 23 territories. A third nest attempt was observed in one territory. Successful nesting was observed in 32 of 110 territories. We present no formal data analysis and plan to analyze the data after the collection of all field data in subsequent years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221074","usgsCitation":"Beatty, W.S., Fara, L.J., Houdek, S.C., Kenow, K.P., and Gray, B.R., 2022, Restoration of Gavia immer (common loon) in Minnesota—2021 annual report: U.S. Geological Survey Open-File Report 2022–1074, 7 p., https://doi.org/10.3133/ofr20221074.","productDescription":"vi, 7 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-139232","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435709,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LA536E","text":"USGS data release","linkHelpText":"Summary of Detection Data for Breeding Common Loons in North-central Minnesota (2021-2022) (ver. 1.1, August 2024)"},{"id":405938,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405361,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.XML"},{"id":405360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1074"},{"id":405359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1074/coverthb.jpg"},{"id":405362,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1074/images"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.78955078125,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 46.27103747280261\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Surveys and monitoring for the coastal Cactus Wren (Campylorhynchus brunneicapillus) were completed in San Diego County between March 2015 and July 2019. A total of 383 plots were surveyed across 3 genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto). From 2015 to 2019, 317 plots were surveyed 8 times (twice per year in 2015, 2017–19). Additional plots were added in later years as wrens were discovered in new locations. We found differences in the proportion of plots occupied in the genetic clusters, with a lower proportion of plots occupied in the Otay cluster than in the Lake Jennings and Sweetwater/Encanto clusters in all years. Plot occupancy increased each year in the Otay and Sweetwater/Encanto clusters but not in the Lake Jennings cluster. The number of Cactus Wren territories increased from 2015 through 2018, and then decreased in 2019 in all three genetic clusters.
We monitored nesting activities for two populations of Cactus Wrens in southern San Diego County. The Otay population consisted of two sites within the Otay genetic cluster, and the San Diego population consisted of two sites within the Sweetwater/Encanto and Lake Jennings genetic clusters. Nest monitoring occurred at 10–13 territories per year in the Otay population and 14–18 territories in the San Diego population from 2015 through 2019. All territories were occupied by pairs except two territories in 2015, five in 2016, and two in 2019. Between 46 and 74 Cactus Wren nests were monitored each year, which totaled 295 monitored nests from 2015 to 2019. To evaluate the direct influence of precipitation on breeding success, bio-year precipitation (“precipitation”) was calculated from July 1 of the prior year through June 30 of the breeding season year. Overall apparent nest success was positively influenced by precipitation with the lowest apparent nest success of 50 percent in 2015 and the highest apparent nest success of 72 percent in 2017, corresponding to the second lowest and the highest precipitation years, respectively. Apparent nest success also was higher in the Otay population than in the San Diego population. The number of brood nests initiated per pair and the number of renesting attempts per pair also were higher in years with more precipitation. Other metrics of Cactus Wren nesting success and productivity were positively influenced by the amount of precipitation, including clutch size and egg hatching success. The percent of hatchlings that fledged was greater in the Otay population than in the San Diego population but was not influenced by precipitation. The number of fledglings per pair was higher in years with more precipitation and was greater in the Otay population than in the San Diego population. Predation was the predominant cause of nest failure in both populations.
Analysis of Cactus Wren daily nest survival rate indicated that there was a population, and possibly a precipitation effect on nest survival, with the daily survival rate for the Otay population significantly higher than for the San Diego population and weak increase in the daily survival rate with more precipitation.
A total of 629 Cactus Wrens were banded during the course of the study, 360 in the San Diego population and 269 in the Otay population. Between 2015 and 2019, we resighted 301 color-banded adult birds that ranged between 1 and 8 years old. One additional color-banded bird was resighted in San Pasqual Valley (as part of a separate study); this bird originated in the San Diego population and was excluded from our analyses.
Annual survival was higher for adult Cactus Wrens (ranging from 60 to 70 percent) than for first-year wrens (ranging from 20 to 28 percent) and varied by year. Annual survival was also weakly but positively correlated with precipitation. Annual survival was higher for first year and adult Cactus Wrens following years with increased precipitation. We found no evidence that survival differed by population.
Banding also allowed us to examine whether there were differences in movement of adult and first-year Cactus Wrens by year or by population. We found that average dispersal distance for first-year Cactus Wrens was 1.9 kilometers in the Otay population and 1.6 kilometers in the San Diego population and did not differ by population or year. Dispersal between populations was not common. We detected five instances of movement of first-year wrens between the San Diego and Otay populations. All movements into and out of the San Diego population were from or into territories in the Sweetwater area. We detected no movement between the Lake Jennings site and either of the Sweetwater or Otay sites; however, we did detect one wren that dispersed from Lake Jennings to the San Pasqual Valley population in 2019, which was a distance of 26.4 kilometers. Adult Cactus Wrens were site-faithful, with 87 percent of adults remaining on the same territory between breeding seasons. Precipitation may be a weak driver of movement for adult Cactus Wrens, with adults more likely to remain on the same territory following years of increased precipitation. There was no difference in adult movement between populations.
Arthropods were collected in pitfall traps and by vacuum in 23 Cactus Wren territories during 3 sampling periods in 2016 (early nesting, peak nesting, and late nesting). Arthropods of 19 orders and at least 128 families were collected. Analysis of 43 Cactus Wren fecal samples identified 10 arthropod orders that were present in more than 10 percent of fecal samples. The most abundant arthropod order collected was Hymenoptera; however, Cactus Wrens consumed arthropods in the order Hymenoptera significantly less than their availability, suggesting that this order was avoided. No other orders were significantly selected or avoided; however, selection indices of arthropod families identified that two families of arthropods (Isopoda Porcellionidae [woodlice] and Hymenoptera Formicidae [ants]) were avoided. After excluding the taxa that were avoided or not represented in fecal samples, 95 percent of Cactus Wren prey items were collected in pitfall traps and 5 percent were collected by vacuum. The most abundant prey orders captured were Diptera, Coleoptera, Hemiptera, Hymenoptera, and Aranea.
Analysis of the abundance of Cactus Wren prey items by vegetation type and sampling period indicated that vegetation type by itself was not a significant predictor of arthropod abundance but interacted with sampling period. Seasonal availability of arthropods was highest in the peak nesting period, followed by early and late nesting periods for California sagebrush (Artemisia californica), lemonadeberry (Rhus integrifolia), non-native grass, and bare ground, whereas availability increased from early to late nesting periods for blue elderberry (Sambucus mexicana spp. caerulea), cactus (Opuntia spp. and Cylindropuntia spp.), California buckwheat (Eriogonum fasciculatum), native bunch grasses, and black mustard (Brassica nigra). During the early nesting period, arthropods were most abundant in native bunch grasses and least abundant in lemonadeberry. During the peak nesting period, arthropods were most abundant in native bunch grasses and in areas of bare ground and were least abundant in cactus and blue elderberry. During late nesting, arthropods were most abundant in blue elderberry and non-native grass and least abundant in lemonadeberry and mustard.
Each year from 2015 to 2019, vegetation data were collected at the same 23 territories where arthropods were sampled: 9 territories in the Otay population and 14 territories in the San Diego population. Cactus, California buckwheat, and non-native grasses were detected within at least 60 percent of sampling points in the Otay population. Cactus, California sagebrush, California buckwheat, non-native grass, and black mustard each were detected within an average of 40 percent of sampling points in the San Diego population. No native bunch grass or lemonadeberry were recorded at the Lake Jennings site within the San Diego population. The cover of shrub species was relatively stable throughout the 5 years. Cover of herbaceous species and bare ground had greater annual variation than shrub species.
We found that vegetation cover varied widely among territories, with territory accounting for 69 percent of the variation in vegetation cover. Redundancy analysis allowed us to identify the vegetation types that accounted for the most variation. We used the top scores from the redundancy analysis to identify six vegetation types to be used in generalized linear mixed models analyzing the relationships between vegetation type, precipitation, and Cactus Wren breeding productivity. Three vegetation variables influenced the number of fledglings produced per pair. California sagebrush had a positive effect on the number of fledglings per pair whereas non-native grass and black mustard had a negative effect.
Breeding productivity, survival, and movements of adult and first-year Cactus Wrens indicated that the Otay population behaved similarly to, if not out-performed, the San Diego population during the span of our project, suggesting that the driving forces behind low numbers of Cactus Wrens in the Otay population before 2015 were no longer in effect. The Cactus Wren populations in Otay and San Diego reached a peak in 2018, which followed a year of high productivity and survivorship, both of which were correlated with high precipitation. This peak in population size was consistent with reproductive timing and productivity in other bird populations in semi-arid ecosystems that were linked to precipitation and arthropod abundance. We did not find a strong link among arthropod abundance, vegetation composition, and Cactus Wren breeding productivity, likely in part because arthropod abundance varied by vegetation type and sampling period, suggesting that different vegetation types provided important sources of prey at different periods of the breeding season. Arthropod abundance also may not represent arthropod availability when vegetation structure discourages the ground foraging behavior of species such as Cactus Wrens. Cover of non-native grass negatively influenced breeding productivity, although arthropods were abundant in non-native grass. Other factors that could have influenced differential breeding productivity between the Otay and San Diego populations were habitat restoration, control of annual herbaceous vegetation, human disturbance, lingering effects of wildfire, and nest predation. Overall, precipitation appeared to be a driver of Cactus Wren breeding productivity and possibly survival, potentially obscuring proximate effects of arthropod or vegetation composition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221044","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., Houston, A., and Kus, B.E., 2022, Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19: U.S. Geological Survey Open-File Report 2022-1044, 44 p., https://doi.org/10.3133/ofr20221044.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-136839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435711,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143ZTB2","text":"USGS data release","linkHelpText":"Cactus Wren Invertebrate Diet Derived from Sequencing of Nestling Fecal Samples in San Diego County, California"},{"id":405951,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1044"},{"id":405841,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1044/images"},{"id":405840,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.xml"},{"id":405839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.pdf","text":"Report","size":"4 MB"},{"id":405838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1044/covrthb.jpg"},{"id":405842,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Diego County: U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3065185546875,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.52365781569917\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819