Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (Ne). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.
Bathymetric data were collected at 12 water-supply lakes in northwestern Missouri by the U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources and in collaboration with various local agencies, as part of a multiyear effort to establish or update the surface area and capacity tables for the surveyed lakes. Ten of the lakes were surveyed from July to September 2019, one of the original 10 was resurveyed in March 2020, and two lakes of high interest near Maryville were surveyed in June 2020. Six of the lakes had been surveyed by the U.S. Geological Survey before, and the recent surveys were compared to the earlier surveys to document the changes in the bathymetric surface and capacity of the lake and to produce a bathymetric change map.
Bathymetric data were collected using a high-resolution multibeam mapping system mounted on a boat. Supplemental depth data were collected in shallow areas with an acoustic Doppler current profiler on a remote-controlled boat. At Hamilton Reservoir, a Global Navigation Satellite System survey receiver was used to collect additional bathymetric data at several points across four transects and around the perimeter of a substantial shallow area filled with aquatic vegetation upstream from a low-clearance bridge on the northern arm.
Data points from the various sources were exported at a gridded data resolution appropriate to each lake. Data outside the multibeam echosounder survey extent and greater than the surveyed water-surface elevation generally were obtained from data collected using aerial light detection and ranging point cloud data, 1/9 arc-second National Elevation Dataset data based on aerial light detection and ranging data, or both. A linear enforcement technique was used to add points to the dataset in areas of sparse data (the upper ends of coves where the water was shallow or aquatic vegetation precluded data acquisition) based on surrounding multibeam and upland data values. The various point datasets were used to produce a three-dimensional triangulated irregular network surface of the lake-bottom elevations for each lake. A surface area and capacity table was produced from the three-dimensional surface showing surface area and capacity at specified lake water-surface elevations. Various quality-assurance tests were conducted to ensure quality data were collected with the multibeam, including beam angle checks and patch tests. Additional quality-assurance tests were conducted on the gridded bathymetric data from the survey, the bathymetric surface created from the gridded data, and the contours created from the bathymetric survey.
If data from a previous bathymetric survey existed at a given lake, a bathymetric change map was generated from the elevation difference between the previous survey and the 2019 bathymetric survey data points. After applying any vertical elevation changes to the previous survey data to ensure a match to the 2019 survey datum, coincident points between the surveys were found, and a bathymetric change map was generated using the coincident point data.
A decrease in capacity was observed at all the lakes for which a previous survey existed. The decrease in capacity at the primary spillway or intake elevation ranged from 0.8 percent at Lake Viking to 21.4 percent at Middle Fork Grand River Reservoir. The mean bathymetric change ranged from 0.33 foot at Willow Brook Lake to 1.18 feet at Middle Fork Grand River Reservoir. The computed sedimentation rate generally ranged from 0.54 to 4.19 acre-feet per year at Maysville Lake and Middle Fork Grand River Reservoir, respectively; however, Lake Viking had the largest sedimentation rate of 14.9 acre-feet per year, despite having the smallest decrease in capacity at the spillway elevation of only 0.8 percent and a mean bathymetric change of only 0.4 foot. Evidence of dredging was observed in the bathymetric surface for Lake Viking. Some changes observed in some bathymetric change maps are hypothesized to result from the difference in data collection equipment and techniques between the previous and present bathymetric surveys. Certain erosional features around the perimeter of certain lakes may be the result of wave action during low-water years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3486","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Huizinga, R.J., Oyler, L.D., and Rivers, B.C., 2022, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in northwestern Missouri, 2019 and 2020: U.S. Geological Survey Scientific Investigations Map 3486, 12 sheets, includes 21-p. pamphlet, https://doi.org/10.3133/sim3486.","productDescription":"Pamphlet: vi, 21 p.; 13 Sheets: 44.00 x 34.00 inches or smaller; Data Release","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-127919","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501902,"rank":31,"type":{"id":36,"text":"NGMDB Index 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Viking","linkFileType":{"id":5,"text":"html"}},{"id":501894,"rank":23,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112854.htm","text":"Bethany near City Lake","linkFileType":{"id":5,"text":"html"}},{"id":501893,"rank":22,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112853.htm","text":"Willow Brook Lake","linkFileType":{"id":5,"text":"html"}},{"id":501892,"rank":21,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112852.htm","text":"King City South Lake","linkFileType":{"id":5,"text":"html"}},{"id":398203,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet11.1.pdf","text":"Sheet 11.1","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Mozingo Lake near Maryville, Missouri, 2020"},{"id":398201,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet10.pdf","text":"Sheet 10","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Maysville Reservoir near Maysville, Missouri, 2019"},{"id":398196,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet06.pdf","text":"Sheet 6","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Middle Fork Grand River Reservoir near Stanberry, Missouri, 2019"},{"id":398195,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet05.pdf","text":"Sheet 5","size":"1.05 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Old Bethany City Lake near Bethany, Missouri, 2019"},{"id":398194,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet04.pdf","text":"Sheet 4","size":"1.71 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Bethany New City Lake near Bethany, Missouri, 2020"},{"id":398193,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet03.pdf","text":"Sheet 3","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Willow Brook Lake near Maysville, Missouri, 2019"},{"id":398200,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet09.pdf","text":"Sheet 9","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for King City Reservoir system near King City, Missouri, 2019"},{"id":398199,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet08.pdf","text":"Sheet 8","size":"1.86 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Harrison County Lake near Bethany, Missouri, 2019"},{"id":398198,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet07.pdf","text":"Sheet 7","size":"2.36 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Lake Viking near Gallatin, Missouri, 2019"},{"id":501896,"rank":25,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112856.htm","text":"Middle Fork Grand River Reservoir","linkFileType":{"id":5,"text":"html"}},{"id":398192,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet02.pdf","text":"Sheet 2","size":"1.50 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for King City South Lake near King City, Missouri, 2019"},{"id":398214,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet01.pdf","text":"Sheet 1","size":"1.38 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Hamilton Reservoir near Hamilton, Missouri, 2019"},{"id":501895,"rank":24,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112855.htm","text":"Old Bethany City lake","linkFileType":{"id":5,"text":"html"}},{"id":501891,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112851.htm","text":"Hamilton Reservoir","linkFileType":{"id":5,"text":"html"}},{"id":398831,"rank":19,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sim3486/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":398206,"rank":18,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet12.pdf","text":"Sheet 12","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Maryville Reservoir near Maryville, Missouri, 2020"},{"id":398204,"rank":17,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet11.2.pdf","text":"Sheet 11.2","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Mozingo Lake near Maryville, Missouri, 2020"},{"id":398189,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3486/images"},{"id":398188,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3486/sim3486.XML"},{"id":398186,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3486/coverthb.jpg"},{"id":398190,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92M53NJ","text":"USGS data release","linkHelpText":"Bathymetric and supporting data for various water supply lakes in northwestern Missouri, 2019 and 2020 (ver. 1.1, September 2021)"},{"id":398187,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3486/sim3486.pdf","text":"Pamphlet","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3486"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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U.S. Geological Survey
1400 Independence Rd
Rolla, MO 65401
As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground‐motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain‐derived ground‐motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground‐motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground‐motion metrics from DAS data. To obtain high‐quality ground‐motion observations, EEW‐compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.
Groundwater development has increased substantially in southeastern Oregon’s Harney Basin since 2010, mainly for the purpose of large-scale irrigation. Concurrently, some areas of the basin experienced groundwater-level declines of more than 100 feet, and some shallow wells have gone dry. The Oregon Water Resources Department has limited new groundwater development in the basin until an improved understanding of the groundwater-flow system is available. This report describes the results of a hydrologic investigation undertaken to provide that understanding. The investigation encompasses the groundwater hydrology of the entire 5,240-square-mile Harney Basin.
Most of the precipitation in the Harney Basin falls in the higher-elevation areas of the Blue Mountains and Steens Mountain. Although considerable groundwater recharge occurs in these upland areas, most (83 percent) re-emerges as streams and springs in the uplands. Groundwater recharge in the lowlands is provided through infiltration of surface water flowing onto the lowlands from rivers and streams leaving the uplands and as groundwater flow from the surrounding upland rocks. Water-balance calculations indicate that the rate of groundwater recharge to the Harney Basin lowlands (where most groundwater is withdrawn) averages 173,000 acre-feet per year (acre-ft/yr).
Groundwater in the Harney Basin lowlands mainly discharges through evapotranspiration from groundwater-irrigated (supplied from wells) crops or from natural vegetation drawing groundwater from the shallow water table and capillary fringe. Groundwater discharge in the lowlands is estimated to be about 283,000 acre-ft/yr, which exceeds the estimated groundwater recharge to the lowlands by about 110,000 acre-ft/yr. This imbalance results in removal of groundwater from storage in the aquifer system and is evidenced by the large declines observed in groundwater levels in the areas of greatest groundwater pumpage.
To a large degree, the location and depth of pumpage dictate the timing and distribution of the effects of groundwater use in the Harney Basin. Pumpage is commonly greatest in the areas where higher-permeability geologic units allow for higher well yields. However, many of these higher-permeability units are bounded by lower-permeability units that cannot supply groundwater at a sufficient rate to replenish the areas of greatest pumpage, resulting in groundwater-level declines. Three Harney Basin areas with a combined area exceeding 140 square miles have experienced groundwater-level declines exceeding 40 feet compared to pre-development conditions: near the Weaver Spring/Dog Mountain area, in the northeastern floodplains along Highway 20, and near Crane. Areas of more modest groundwater-level decline (about 10 feet) were identified in the Virginia Valley area and the Silver Creek floodplain north of Riley. Smaller localized areas of groundwater-level depression have also formed around individual wells or groups of wells throughout the Harney Basin lowlands.
Most groundwater being pumped from the Harney Basin lowlands, including all three areas experiencing large groundwater-level declines, was recharged more than 12,000 years ago, near the end of the last glacial period when the climate in the basin was cooler and wetter than today. Geochemical evidence indicates that modern recharge generally circulates to a depth no greater than 100 feet below the floodplains of major rivers and streams in the lowlands. Away from the major river and stream corridors, pre-modern water commonly is found at the water table. Recharge to groundwater and recovery of groundwater levels in the most heavily pumped areas in the Harney Basin lowlands are restricted by the limited spatial extent and depth of modern recharge in the Harney Basin lowlands and the relatively fine-grained deposits underlying most of the lowland areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215103","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Gingerich, S.B., Johnson, H.M., Boschmann, D.E., Grondin, G.H., and Garcia, C.A., 2022, Groundwater resources of the Harney Basin, southeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2021–5103, 118 p., https://doi.org/10.3133/sir20215103.","productDescription":"Report: xii, 118 p.; 3 Plates: 30.00 x 42.00 inches or smaller; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119872","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502118,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112850.htm","linkFileType":{"id":5,"text":"html"}},{"id":397922,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5103/"},{"id":397921,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5103/images"},{"id":397920,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate03.pdf","text":"Plate 3","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 3"},{"id":398172,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J0FE5M","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Location information, discharge, and water-quality data for selected wells, springs, and streams in the Harney Basin, Oregon"},{"id":398171,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZJTZUV","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Contour data set of the potentiometric surfaces of shallow and deep groundwater-level altitudes in Harney Basin, Oregon, February–March 2018"},{"id":397917,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103.pdf","text":"Report","size":"28.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103"},{"id":397918,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate01.pdf","text":"Plate 1","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 1"},{"id":397916,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5103/coverthb.jpg"},{"id":397919,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5103/sir20215103_plate02.pdf","text":"Plate 2","size":"27.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5103 Plate 2"}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Groundwater-level declines and limited quantitative knowledge of the groundwater-flow system in the Harney Basin prompted a cooperative study between the U.S. Geological Survey and the Oregon Water Resources Department to evaluate the groundwater-flow system and budget. This report provides a hydrologic budget of the Harney Basin groundwater system that includes separate groundwater budgets for upland and lowland areas to avoid double counting water that recharges in the uplands, discharges to streams and springs in the uplands, flows downstream to the lowlands, and recharges the lowland groundwater system. Lowlands generally represent the conterminous valleys within the center of the basin, including floodplains of the major streams and uplands represent all other areas in the basin.
The upland groundwater budget is minimally affected by groundwater development and generally represents the budget of the natural system. In upland areas during 1982–2016, mean-annual recharge totaled 288,000 acre-feet (acre-ft) and mean-annual discharge totaled 239,000 acre-ft, resulting in a net recharge of 49,000 acre-ft. Upland groundwater recharge occurs as infiltration of precipitation and snowmelt and was estimated using the USGS Soil-Water-Balance model calibrated to estimates of runoff, evapotranspiration (ET), base flow, and snow-water equivalent. Groundwater discharge to streams is the predominant discharge mechanism in upland areas and was estimated as 225,000 acre-feet per year (acre-ft/yr) during 1982–2016 using hydrograph separation and summer low-flow estimates in streamgaged watersheds and a linear relation between estimated streamflow and base flow in ungaged watersheds. The remaining upland discharge occurs through springs (14,000 acre-ft/yr) that either emerge downgradient of locations where groundwater discharge to streams was estimated or are routed to irrigated areas. Spring discharge was estimated as a compilation of current and historical measurements. The net upland recharge, which is 17 percent of total upland recharge, ultimately recharges lowland areas as groundwater flow from uplands to lowlands.
The lowland groundwater budget for the Harney Basin represents a combination of natural conditions and human activity as more than 99 percent of groundwater development has occurred either inside or within 2 miles of the lowland boundary. In lowland areas during 1982–2016, mean annual groundwater recharge totaled 173,000 acre-ft and groundwater discharge totaled 283,000 acre-ft, indicating discharge exceeded recharge by more than 60 percent.
Excluding groundwater pumping, the lowland groundwater budget is more in balance with a mean annual recharge of 165,000 acre-ft and a mean annual discharge of 131,000 acre-ft during 1982–2016. The 23-percent difference between non-pumping recharge and discharge mostly represents the cumulative uncertainty in the estimates of the various groundwater budget components but also likely includes a small reduction in natural groundwater discharge captured by pumping. Lowland groundwater is predominantly recharged by infiltration of surface water (116,000 acre-ft/yr) through streams, floodwater, and irrigation, with a lesser amount as groundwater inflow from uplands and minimal recharge beneath Malheur and Harney Lakes. Recharge from streams and floodwater (natural and irrigation) was estimated using a balance of measured and estimated surface-water inflow to and outflow from lowland areas including streamflow, springflow, and ET where a portion of surface-water inflow to lowland areas is comprised of upland discharge to streams and springs. Groundwater ET (119,000 acre-ft/yr) is the predominant natural discharge mechanism in lowland areas and was estimated as the mean from two remote-sensing based approaches incorporating groundwater ET measurements from other similar basins and 23 years (1987–2015) of Landsat imagery. Discharge of lowland groundwater into Malheur and Harney Lakes is about 700 acre-ft/yr and is represented in groundwater ET estimates. The remaining natural groundwater discharge from lowland areas issues from Sodhouse Spring (8,900 acre-ft/yr) and as groundwater flow to the Malheur River Basin through Virginia Valley (3,100 acre-ft/yr). The relatively large amount of groundwater discharged to springs in Warm Springs Valley (25,000 acre-ft/yr) is accounted for in groundwater ET estimates. Natural groundwater discharge in lowland areas of the Harney Basin has remained relatively constant during the last 80 years based on comparisons with estimates north of Malheur Lake and west of Harney Lake published in the 1930s.
Annual net amount of groundwater pumped (pumpage) from the Harney Basin during 2017–18 averaged 144,000 acre-ft. The net value is the difference between pumpage (about 152,000 acre-ft/yr) and reinfiltration of groundwater pumped for irrigation and non-irrigation purposes (about 8,000 acre-ft/yr). Net pumpage was estimated in concurrent studies that compiled groundwater-use data and coupled reported groundwater pumpage data from wells with remote-sensing-based ET estimates from groundwater-irrigated fields. Total pumpage for irrigation has increased from about 54,000 acre-ft/yr during 1991–92 to 145,000 acre-ft/yr during 2017–18. Presently, pumpage is greatest in the lowland region north of Malheur Lake (81,000 acre-ft/yr), with lesser amounts to the north and northwest of Harney Lake (41,000 acre-ft/yr) and to the south and east of Malheur Lake (22,000 acre-ft/yr).
During this study, mean annual lowland groundwater discharge (including pumpage) exceeded mean annual recharge, indicating that the lowland hydrologic budget is out of balance. Net groundwater pumpage during 2017–18 is similar to groundwater discharge from all other sources in the lowlands and is four times the imbalance between non-pumping lowland recharge and discharge (34,000 acre-ft/yr). Declining groundwater levels at depth across many parts of the Harney Basin lowlands indicate that pumpage is depleting aquifer storage and is likely capturing a small amount of natural groundwater discharge to springs and ET in some lowland areas. If pumping continues, aquifer storage depletion will continue until the capture rate of natural discharge to springs and ET is equal to the pumping rate. If groundwater development occurs in upland areas and reduces either the streamflow or groundwater inflow to lowland areas, the deficit in the lowland water budget will increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215128","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Garcia, C.A., Corson-Dosch, N.T., Beamer, J.P., Gingerich, S.B., Grondin, G.H., Overstreet, B.T., Haynes, J.V., and Hoskinson, M.D., 2021, Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon (ver. 1.1, November 2022): U.S. Geological Survey Scientific Investigations Report 2021–5128, 144 p., https://doi.org/10.3133/sir20215128.","productDescription":"Report: xiii, 144 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119839","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112849.htm","linkFileType":{"id":5,"text":"html"}},{"id":398083,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QABFML","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data–Hydrologic budget of the Harney Basin groundwater system, Oregon"},{"id":398082,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NH4D8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil- Water-Balance (SWB) model archive used to simulate mean annual upland recharge from infiltration of precipitation and snowmelt in Harney Basin, Oregon, 1982–2016"},{"id":409214,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5128/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5128 Version History"},{"id":398080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5128/coverthb2.jpg"},{"id":398081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5128/sir20215128.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5128"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 2022; Version 1.1: November 2022","contact":"Director, Oregon Water Science Center
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2130 SW 5th Avenue
Portland, Oregon 97201
Groundwater is an important source of water for New Mexico. An estimated 48 percent of the total water used comes from groundwater sources, and groundwater levels generally are declining over large areas of New Mexico. Groundwater levels are affected by local and regional recharge or discharge processes. Groundwater hydrographs show the history of groundwater-level changes at a well. A single hydrograph is not necessarily representative of the larger regional area; however, individual hydrographs from several wells can be combined into a composite hydrograph to show average groundwater changes for a regional area. The U.S. Geological Survey, in cooperation with the New Mexico Office of the State Engineer, has been measuring groundwater levels in a network of wells since about 1925. Although groundwater levels in the statewide well network have been measured at various frequencies, most wells have been measured in 5-year cycles since about 1980. The composite hydrographs in this report were developed to show groundwater-level changes for selected principal aquifers in New Mexico. Composite hydrographs were developed using wells in the Colorado Plateaus aquifers, the High Plains aquifer, the Pecos River Basin alluvial aquifer, the Rio Grande aquifer system, and the Roswell Basin aquifer system. Statewide, groundwater levels generally have declined or remained steady over the time period in aquifers analyzed for this study. The largest water-level declines occurred in the Colorado Plateaus and High Plains aquifers and in the Rio Grande aquifer system (north-central New Mexico), where median water-level declines ranged from 17 to 40 feet and mean water-level declines ranged from 3.8 to 32 feet. Groundwater-level declines (or rises) were generally smaller in other areas of New Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221008","collaboration":"Prepared in cooperation with the New Mexico Office of the State Engineer","usgsCitation":"Myers, N.C., 2022, Composite regional groundwater hydrographs for selected principal aquifers in New Mexico, 1980–2019: U.S. Geological Survey Open-File Report 2022–1008, 51 p., https://doi.org/10.3133/ofr20221008.","productDescription":"Report: vii, 51 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-128607","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501755,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112848.htm","linkFileType":{"id":5,"text":"html"}},{"id":397979,"rank":4,"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":397978,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MWE68L","text":"USGS data release","linkHelpText":"Refined principal aquifer boundaries for New Mexico, United States"},{"id":397977,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1008/ofr20221008.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1008"},{"id":397976,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1008/coverthb.jpg"}],"country":"United States","state":"New 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Mexico\",\"nation\":\"USA \"}}]}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Water-level fluctuations are critical in maintaining diversity of plant communities in Great Lakes wetlands. Sedge-grass meadows are especially sensitive to such fluctuations. We conducted vegetation sampling in a sedge-grass dominated Lake Michigan drowned river mouth wetland in 1995, 2002, and 2010 following high lake levels in 1986 and 1997. We also conducted photointerpretation studies in 16 years dating back to 1965 to include responses to high lake levels in 1952 and 1974. Topographic data were collected to assess their influence on areal extent of sedge-grass meadow. Dominant species in short emergent and submersed/floating plant communities changed with water availability from 1995 to extreme low lake levels in 2002 and 2010. Sedge-grass meadow was dominated by Calamagrostis canadensis and Carex stricta in all years sampled, but Importance Values differed among years partly due to sampling in newly exposed areas. Photointerpretation studies showed a significant relation between percent of wetland in sedge-grass meadow and summer lake level, as well as the number of years since an extreme high lake level. From the topographic map created, we calculated the cumulative area above each 0.2-m contour to determine the percent of wetland dewatered in select years following extreme high lake levels. When compared with percent sedge-grass meadow in those years, relative changes in both predicted land surface and sedge-grass meadow demonstrated that accuracy of lake level as a predictor of area of sedge-grass meadow is dependent on topography. Our results regarding relations of plant-community response to hydrology are applicable to other Great Lakes wetlands.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01534-w","usgsCitation":"Wilcox, D., Bateman, J.A., Kowalski, K., Meeker, J., and Dunn, N., 2022, Extent of sedge-grass meadow in a Lake Michigan drowned river mouth wetland dictated by topography and lake level: Wetlands, v. 42, 34, 15 p., https://doi.org/10.1007/s13157-022-01534-w.","productDescription":"34, 15 p.","ipdsId":"IP-133710","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448142,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s13157-022-01534-w","text":"External Repository"},{"id":435882,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91W73ON","text":"USGS data release","linkHelpText":"Wetland vegetation and elevation of Arcadia Marsh, Michigan (1995-2010)"},{"id":403071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Arcadia Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.24258995056152,\n 44.478258188004965\n ],\n [\n -86.21297836303711,\n 44.478258188004965\n ],\n [\n -86.21297836303711,\n 44.498280755008004\n ],\n [\n -86.24258995056152,\n 44.498280755008004\n ],\n [\n -86.24258995056152,\n 44.478258188004965\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilcox, Douglas A.","contributorId":244846,"corporation":false,"usgs":false,"family":"Wilcox","given":"Douglas A.","affiliations":[{"id":48999,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, Brockport, NY","active":true,"usgs":false}],"preferred":false,"id":845731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bateman, John A","contributorId":292626,"corporation":false,"usgs":false,"family":"Bateman","given":"John","email":"","middleInitial":"A","affiliations":[{"id":62949,"text":"Finger Lakes Community College","active":true,"usgs":false}],"preferred":false,"id":845732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":845733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meeker, James E","contributorId":292627,"corporation":false,"usgs":false,"family":"Meeker","given":"James E","affiliations":[{"id":18886,"text":"Northland College","active":true,"usgs":false}],"preferred":false,"id":845734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunn, Nicole 0000-0002-8234-3845","orcid":"https://orcid.org/0000-0002-8234-3845","contributorId":292759,"corporation":false,"usgs":false,"family":"Dunn","given":"Nicole","email":"","affiliations":[{"id":62993,"text":"University of Wisconsin-Whitewater","active":true,"usgs":false}],"preferred":false,"id":845735,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262184,"text":"70262184 - 2022 - Community-powered urban stream restoration: A vision for sustainable and resilient urban ecosystems","interactions":[],"lastModifiedDate":"2025-01-15T15:58:00.260552","indexId":"70262184","displayToPublicDate":"2022-04-11T09:44:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Community-powered urban stream restoration: A vision for sustainable and resilient urban ecosystems","docAbstract":"Urban streams can provide amenities to people living in cities, but those benefits are reduced when streams become degraded, potentially even causing harm (disease, toxic compounds, etc.). Governments and institutions invest resources to improve the values and services provided by urban streams; however, the conception, development, and implementation of such projects may not include meaningful involvement of community members and other stakeholders. Consequently, project objectives may be misaligned with community desires and needs, and projects may fail to achieve their goals. In February 2020, the 5th Symposium on Urbanization and Stream Ecology, an interdisciplinary meeting held every 3 to 5 y, met in Austin, Texas, USA, to explore new approaches to urban stream projects, including ways to maximize the full range of potential benefits by better integrating community members into project identification and decision making. The symposium included in-depth discussion about 4 nearby field case studies, participation of multidisciplinary urban stream experts from 5 continents, and input from the Austin community. Institutional barriers to community inclusion were identified and analyzed using real-world examples, both from the case studies and from the literature, which clarified disparities in power, equity, and values. Outcomes of the symposium have been aggregated into a vision that challenges the present institutional approach to urban stream management and a set of strategies to systematically address these barriers to improve restoration solutions. Integrating community members and other stakeholders throughout the urban restoration process, and a transparent decision-making process to resolve divergent objectives, can help identify appropriate goals for realizing both the ecological and social benefits of stream restoration.
","language":"English","publisher":"University of Chicago Press Journals","doi":"10.1086/721150","usgsCitation":"Scoggins, M., Booth, D., Fletcher, T., Fork, M., Gonzalez, A., Hale, R., Hawley, R., Roy, A.H., Bilger, E., Bond, N., Burns, M., Hopkins, K.G., Alessi, M.A., Marti, E., McKay, S.K., Neale, M., Paul, M.J., Rios-Touma, B., Russell, K.L., Smith, R., Wagner, S., and Wenger, S.J., 2022, Community-powered urban stream restoration: A vision for sustainable and resilient urban ecosystems: Freshwater Science, v. 41, no. 3, p. 404-419, https://doi.org/10.1086/721150.","productDescription":"16 p.","startPage":"404","endPage":"419","ipdsId":"IP-133263","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/721150","text":"Publisher Index 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Kyle","contributorId":169086,"corporation":false,"usgs":false,"family":"McKay","given":"S.","email":"","middleInitial":"Kyle","affiliations":[],"preferred":false,"id":923598,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Neale, Martin W.","contributorId":348571,"corporation":false,"usgs":false,"family":"Neale","given":"Martin W.","affiliations":[],"preferred":false,"id":923599,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Paul, Michael J.","contributorId":244526,"corporation":false,"usgs":false,"family":"Paul","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":16286,"text":"Tetra Tech","active":true,"usgs":false}],"preferred":false,"id":923600,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Rios-Touma, Blanca","contributorId":348572,"corporation":false,"usgs":false,"family":"Rios-Touma","given":"Blanca","affiliations":[],"preferred":false,"id":923601,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Russell, Kathryn L 0000-0002-9613-4665","orcid":"https://orcid.org/0000-0002-9613-4665","contributorId":292735,"corporation":false,"usgs":false,"family":"Russell","given":"Kathryn","email":"","middleInitial":"L","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":923602,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Smith, Robert F.","contributorId":264899,"corporation":false,"usgs":false,"family":"Smith","given":"Robert F.","affiliations":[{"id":54577,"text":"Lycoming College Clean Water Institute","active":true,"usgs":false}],"preferred":false,"id":923603,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wagner, Staryn","contributorId":348573,"corporation":false,"usgs":false,"family":"Wagner","given":"Staryn","affiliations":[],"preferred":false,"id":923604,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wenger, Seth J.","contributorId":64786,"corporation":false,"usgs":true,"family":"Wenger","given":"Seth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":923605,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70231327,"text":"70231327 - 2022 - Increased mercury and reduced insect diversity in linked stream-riparian food webs downstream of a historical mercury mine","interactions":[],"lastModifiedDate":"2022-07-08T13:26:36.04797","indexId":"70231327","displayToPublicDate":"2022-04-11T09:03:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Increased mercury and reduced insect diversity in linked stream-riparian food webs downstream of a historical mercury mine","docAbstract":"Historical mining left a legacy of abandoned mines and waste rock in remote headwaters of major river systems in the western United States. Understanding the influence of these legacy mines on culturally and ecological important downstream ecosystems is not always straight-forward because of elevated natural levels of mineralization in mining-impacted watersheds. To test the ecological effects of historic mining in the headwaters of the upper Salmon River watershed (USA), we measured multiple community and chemical endpoints in downstream linked aquatic-terrestrial food webs. Mining inputs impacted downstream food webs through increased mercury accumulation and decreased insect biodiversity. Total mercury (THg) in seston, aquatic insect larvae, adult aquatic insects, riparian spiders, and fish at sites up to 7.6 km downstream of mining was in much higher concentrations (1.3 to 11.3-fold) and isotopically distinct compared with sites immediately upstream of mining inputs. Methylmercury (MeHg) concentrations in bull trout and riparian spiders were sufficiently high (732 – 918 and 347 – 1,140 ng MeHg g-1dw) to affect humans, birds, and piscivorous fish. Furthermore, the alpha-diversity of benthic insects was locally depressed by 12-20% within 4.3 to 5.7 km downstream of from the mine. However, because total insect biomass was not affected by mine inputs, the mass of mercury in benthic insects at a site (i.e., ng Hg m-2) was extremely elevated downstream (10 – 1,778-fold) compared with directly upstream of mining inputs. Downstream adult aquatic insect-mediated fluxes of total mercury were also high (~16 ng THg m-2d-1). Abandoned mines can have ecologically important effects on downstream communities, including reduced biodiversity and increased mercury flux to higher order consumers, including fish, birds, and humans.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5342","usgsCitation":"Kraus, J.M., Holloway, J.M., Pribil, M., Mcgee, B.N., Stricker, C.A., Rutherford, D., and Todd, A., 2022, Increased mercury and reduced insect diversity in linked stream-riparian food webs downstream of a historical mercury mine: Environmental Toxicology and Chemistry, v. 41, no. 2, p. 1696-1710, https://doi.org/10.1002/etc.5342.","productDescription":"15 p.","startPage":"1696","endPage":"1710","ipdsId":"IP-136263","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":448145,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5342","text":"Publisher Index Page"},{"id":435883,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P43XBX","text":"USGS data release","linkHelpText":"Mercury concentrations, isotopic composition, biomass, and taxonomy of stream and riparian organisms in the vicinity of Yellow Pine, Idaho, 2015-2016."},{"id":400281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Cinnabar mine site, upper Salmon River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.3667,\n 44.85\n ],\n [\n -115.2333,\n 44.85\n ],\n [\n -115.2333,\n 44.9833\n ],\n [\n -115.3667,\n 44.9833\n ],\n [\n -115.3667,\n 44.85\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":842308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holloway, JoAnn M. 0000-0003-3603-7668","orcid":"https://orcid.org/0000-0003-3603-7668","contributorId":201855,"corporation":false,"usgs":true,"family":"Holloway","given":"JoAnn","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":842309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pribil, Michael J. 0000-0003-4859-8673 mpribil@usgs.gov","orcid":"https://orcid.org/0000-0003-4859-8673","contributorId":141158,"corporation":false,"usgs":true,"family":"Pribil","given":"Michael","email":"mpribil@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":842310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mcgee, Ben N. 0000-0001-8798-0037 bmcgee@usgs.gov","orcid":"https://orcid.org/0000-0001-8798-0037","contributorId":167273,"corporation":false,"usgs":true,"family":"Mcgee","given":"Ben","email":"bmcgee@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":842311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":842312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rutherford, Danny 0000-0003-1013-8006","orcid":"https://orcid.org/0000-0003-1013-8006","contributorId":201857,"corporation":false,"usgs":true,"family":"Rutherford","given":"Danny","email":"","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":842313,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Todd, Andrew S.","contributorId":212872,"corporation":false,"usgs":false,"family":"Todd","given":"Andrew S.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":842314,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249628,"text":"70249628 - 2022 - Soft pressure sensor for underwater sea lamprey detection","interactions":[],"lastModifiedDate":"2023-10-20T12:19:32.935059","indexId":"70249628","displayToPublicDate":"2022-04-11T07:16:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9956,"text":"IEEE Sensors Journal","active":true,"publicationSubtype":{"id":10}},"title":"Soft pressure sensor for underwater sea lamprey detection","docAbstract":"In this paper, an economical and effective soft pressure sensor for underwater sea lamprey detection is proposed, which consists of an array of piezoresistive elements between two layers of perpendicular copper tape electrodes, forming a passive resistor network. With multiplexers, the apparent resistance corresponding to each pixel of the sensing matrix can be measured directly, where the pixel is identified with the row and the column of the respective electrodes. However, this measured two-point resistance is not equal to the actual cell resistance for that pixel due to the crosstalk effect in the resistor network. Since the cell resistance reflects directly the pressure applied on each pixel, the relationship between the cell resistance and the measured two-point resistance is analyzed for a passive matrix of any size. More importantly, several regularized least-squares algorithms are proposed to reconstruct the cell resistance profile from the two-point resistance measurements, with enhanced robustness of the reconstruction in the presence of measurement noises and modeling errors. The proposed pressure sensor is applied to detect the suction attachment of sea lampreys, a devastating invasive species in the Great Lakes region. Experimental results demonstrate that the pressure sensor can successfully capture the rim profile of the lamprey’s sucking mouth. Moreover, the performance and computational complexity of the reconstruction algorithms with different regularization functions are compared.
","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/JSEN.2022.3166693","usgsCitation":"Shi, H., Gonzalez-Afanador, I., Holbrook, C., Sepulveda, N., and Tan, X., 2022, Soft pressure sensor for underwater sea lamprey detection: IEEE Sensors Journal, v. 22, no. 10, p. 9932-9944, https://doi.org/10.1109/JSEN.2022.3166693.","productDescription":"13 p.","startPage":"9932","endPage":"9944","ipdsId":"IP-137437","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":422010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hongyang 0000-0003-4135-3673","orcid":"https://orcid.org/0000-0003-4135-3673","contributorId":214760,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":886497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gonzalez-Afanador, Ian","contributorId":270225,"corporation":false,"usgs":false,"family":"Gonzalez-Afanador","given":"Ian","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":886498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sepulveda, Nelson","contributorId":264255,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":886500,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":886501,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230409,"text":"70230409 - 2022 - Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse","interactions":[],"lastModifiedDate":"2022-04-12T12:07:43.263687","indexId":"70230409","displayToPublicDate":"2022-04-11T07:06:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse","docAbstract":"Identifying, protecting, and restoring habitats for declining wildlife populations is foundational to conservation and recovery planning for any species at risk of decline. Resource selection analysis is a key tool to assess habitat and prescribe management actions. Yet, it can be challenging to map suitable resource conditions across a wide range of ecological contexts and use the resulting models to identify effective and universal habitat improvement actions. We developed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses that form the basis of location-specific management prescriptions. To demonstrate this approach, we developed a set of habitat selection models for Gunnison sage-grouse (Centrocercus minimus), a threatened species under the U.S. Endangered Species Act. Conservation, species recovery, and habitat management efforts are needed in six isolated satellite populations (San Miguel, Crawford, Piñon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) where environmental conditions differ, and the already small number of birds are declining. We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. All models included key habitat variables often altered through management actions to assess their differential influences across models. We found important similarities and differences among satellites, indicating that, although some rules of thumb are generally well-grounded, the consideration of population-specific environmental differences could increase the efficiency of local habitat improvement actions. Sage-grouse also had diverse responses to resource conditions at different scales, indicating that regional spatial (e.g., landscape) and local patch scale can differentially influence expected habitat improvements associated with where such management actions are implemented. Although context variables such as topography cannot be manipulated, sage-grouse associations revealed information that could guide the siting of improvement actions. This approach to balancing management objectives associated with habitat assessment may benefit spatially-structured populations with different environmental contexts and species with complex habitat needs and associations.
System-scale restoration efforts within the Upper Mississippi River National Wildlife and Fish Refuge have included annual monitoring of submersed aquatic vegetation (SAV) since 1998 in four representative reaches spanning ∼ 440 river kilometers. We developed predictive models relating monitoring data (site-scale SAV abundance indices) to diver-harvested SAV biomass, used the models to back-estimate annual standing stock biomass between 1998 and 2018, and compared biomass estimates with previous abundance measures. We modeled two morphologically distinct groups of SAV with differing sampling efficiencies and estimated each separately: the first category included only wild celery Vallisneria americana, which has long, unbranched leaves and dominates lotic environments, while the second category included 17 branched morphology species (e.g., hornwort Ceratophyllum demersum and Canadian water weed Elodea canadensis) and dominates lentic environments. Wild celery accounted for approximately half of total estimated total biomass in the four reaches, combined branched species accounted for half, and invasive species (Eurasian watermilfoil Myriophyllum spicatum and curly-leaf pondweed Potamogeton crispus), a fraction of the branched species, accounted for < 1.5%. Site-scale SAV estimates ranged from 0 to 535 g·m−2 (dry mass). We observed increases in biomass in most areas between 1998 and 2009 and substantial increases (e.g., from < 10 g·m−2 to ∼ 125 g·m−2) in wild celery in extensive impounded areas between 2002 and 2007. Analyses also indicate a transitional period in 2007–2010 during which changes in biomass trajectories were evident in all reaches and included the start of a 9-y, ∼ 70% decrease in wild celery biomass in the southernmost impounded area. Biomass estimates provided new insights and illustrated scales of change that were not previously apparent using traditional metrics. The ability to estimate biomass from Long Term Resource Monitoring data improves conservation efforts through better understanding of changes in habitat and food resources for biota, improved goal setting for restoration projects and improved quantification of SAV-mediated structural effects such as anchoring of sediments and feedbacks with water quality.
Aeolian landforms are widespread in our solar system. Understanding the exact nature and processes of formation of these features are challenging tasks necessitating a strong collaboration between scientists with different skills and scientific backgrounds. This paper describes the special issue for the 5th International Planetary Dunes Workshop, which includes 15 research papers and three commentaries. Among the 18 papers included in this collection, 16 cover Martian aeolian science and two Titan aeolian science. The papers presented focus on bedform morphology and dynamics via remote sensing data, modelling, analogues studies and laboratory experiments. Here we put the main results of the papers in their appropriate scientific context and discuss potential future lines of research.
Santa Barbara County, California, USA.
To analyze a wide array of newly collected chemical, isotopic, dissolved gas, and age dating tracers in conjunction with historical data from groundwater and oil wells to determine if water and/or thermogenic gas from oil-bearing formations have mixed with groundwater in the Orcutt Oil Field and surrounding area.
Three of 15 groundwater samples had compositions indicating potential mixing with water and/or thermogenic gas from oil-bearing formations. Relevant indicators included salinity tracers (TDS, Cl, Br), NH3, DOC, enriched δ13C-DIC, δ2H-CH4, δ13C-CH4, and δ13C-C2H6 values, and trace amounts of C3-C5 gas. The potential sources/pathways for oil-bearing formation water and/or thermogenic gas in groundwater overlying and adjacent to the Orcutt Oil Field include: (1) upward movement from formations developed for oil production due to: (a) natural migration; or (b) anthropogenic activity such as injection and/or movement along wellbores; and (2) oil and gas shows in overlying non-producing oil-bearing formations. Groundwater age tracers, elevated 4He concentrations, and isotopic compositions of noble gases indicated legacy produced water ponds were not a source. This phase of the study relied on samples and data from existing infrastructure. Additional data on potential end-member compositions from new and existing wells and assessments of potential vertical head gradients and pathways between oil and groundwater zones may yield additional insight.
Understanding the relative strengths of intrinsic and extrinsic factors regulating populations is a long-standing focus of ecology and critical to advancing conservation programs for imperiled species. Conservation could benefit from an increased understanding of factors influencing vital rates (somatic growth, recruitment, survival) in small, translocated populations, which is lacking owing to difficulties in long-term monitoring of rare species. Translocations, here defined as the transfer of wild-captured individuals from source populations to new habitats, are widely used for species conservation, but outcomes are often minimally monitored, and translocations that are monitored often fail. To improve our understanding of how translocated populations respond to environmental variation, we developed and tested hypotheses related to intrinsic (density dependent) and extrinsic (introduced rainbow trout Oncorhynchus mykiss, stream flow and temperature regime) causes of vital rate variation in endangered humpback chub (Gila cypha) populations translocated to Colorado River tributaries in the Grand Canyon (GC), USA. Using biannual recapture data from translocated populations over 10 years, we tested hypotheses related to seasonal somatic growth, and recruitment and population growth rates with linear mixed-effects models and temporal symmetry mark–recapture models. We combined data from recaptures and resights of dispersed fish (both physical captures and continuously recorded antenna detections) from throughout GC to test survival hypotheses, while accounting for site fidelity, using joint live-recapture/live-resight models. While recruitment only occurred in one site, which also drove population growth (relative to survival), evidence supported hypotheses related to density dependence in growth, survival, and recruitment, and somatic growth and recruitment were further limited by introduced trout. Mixed-effects models explained between 67% and 86% of the variation in somatic growth, which showed increased growth rates with greater flood-pulse frequency during monsoon season. Monthly survival was 0.56–0.99 and 0.80–0.99 in the two populations, with lower survival during periods of higher intraspecific abundance and low flood frequency. Our results suggest translocations can contribute toward the recovery of large-river fishes, but continued suppression of invasive fishes to enhance recruitment may be required to ensure population resilience. Furthermore, we demonstrate the importance of flooding to population demographics in food-depauperate, dynamic, invaded systems.
Food web analyses offer useful insights into understanding how species interactions, trophic relationships, and energy flow underpin important demographic parameters of fish populations such as survival, growth, and reproduction. However, the vast amount of food web literature and the diversity of approaches can be a deterrent to fisheries practitioners engaged in on-the-ground research, monitoring, or restoration. Incorporation of food web perspectives into contemporary fisheries management and conservation is especially rare in riverine systems, where approaches often focus more on the influence of physical habitat and water temperature on fish populations. In this review, we first discuss the importance of food webs in the context of several common fisheries management issues, including assessing carrying capacity, evaluating the effects of habitat change, examining species introductions or extinctions, considering bioaccumulation of toxins, and predicting the effects of climate change and other anthropogenic stressors on riverine fishes. We then examine several relevant perspectives: basic food web description, metabolic models, trophic basis of production, mass-abundance network approaches, ecological stoichiometry, and mathematical modeling. Finally, we highlight several existing and emerging methodologies including diet and prey surveys, eDNA, stable isotopes, fatty acids, and community and network analysis. Although our emphasis and most examples are focused on salmonids in riverine environments, the concepts are easily generalizable to other freshwater fish taxa and ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1590","usgsCitation":"Naman, S.M., White, S.M., Bellmore, J.R., McHugh, P.A., Kaylor, M.J., Baxter, C., Danehy, R.J., Naiman, R., and Puls, A.L., 2022, Food web perspectives and methods for riverine fish conservation: Wiley Interdisciplinary Reviews (WIREs): Water, v. 9, no. 4, e1590, 21 p., https://doi.org/10.1002/wat2.1590.","productDescription":"e1590, 21 p.","ipdsId":"IP-134531","costCenters":[{"id":5079,"text":"Pacific Regional Director's Office","active":true,"usgs":true}],"links":[{"id":448168,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1590","text":"Publisher Index Page"},{"id":413654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Naman, Sean M.","contributorId":302860,"corporation":false,"usgs":false,"family":"Naman","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":865646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Seth M.","contributorId":302862,"corporation":false,"usgs":false,"family":"White","given":"Seth","email":"","middleInitial":"M.","affiliations":[{"id":13314,"text":"Columbia River Inter-Tribal Fish Commission","active":true,"usgs":false}],"preferred":false,"id":865647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bellmore, J. Ryan","contributorId":271034,"corporation":false,"usgs":false,"family":"Bellmore","given":"J.","email":"","middleInitial":"Ryan","affiliations":[{"id":56260,"text":"U.S. Forest Service, Pacific Northwest Research Station, 11175 Auke Lake Way, Juneau, Alaska, 99801","active":true,"usgs":false}],"preferred":false,"id":865648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McHugh, Peter A.","contributorId":302865,"corporation":false,"usgs":false,"family":"McHugh","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":65566,"text":"Eco Logical Research / Utah State University","active":true,"usgs":false}],"preferred":false,"id":865649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaylor, Matthew J.","contributorId":302867,"corporation":false,"usgs":false,"family":"Kaylor","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":865650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baxter, Colden V.","contributorId":272243,"corporation":false,"usgs":false,"family":"Baxter","given":"Colden V.","affiliations":[{"id":56375,"text":"isu","active":true,"usgs":false}],"preferred":false,"id":865651,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Danehy, Robert J.","contributorId":302868,"corporation":false,"usgs":false,"family":"Danehy","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":39532,"text":"Catchment Aquatic Ecology","active":true,"usgs":false}],"preferred":false,"id":865652,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Naiman, Robert J.","contributorId":302869,"corporation":false,"usgs":false,"family":"Naiman","given":"Robert J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":865653,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Puls, Amy L. 0000-0002-2686-4187 apuls@usgs.gov","orcid":"https://orcid.org/0000-0002-2686-4187","contributorId":204734,"corporation":false,"usgs":true,"family":"Puls","given":"Amy","email":"apuls@usgs.gov","middleInitial":"L.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":865654,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230445,"text":"70230445 - 2022 - Decline in biological soil crust N-fixing lichens linked to increasing summertime temperatures","interactions":[],"lastModifiedDate":"2022-04-13T11:39:41.160794","indexId":"70230445","displayToPublicDate":"2022-04-11T06:38:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10548,"text":"Proceedings of the National Academies of Science (PNAS)","active":true,"publicationSubtype":{"id":10}},"title":"Decline in biological soil crust N-fixing lichens linked to increasing summertime temperatures","docAbstract":"Panfish support popular, socioeconomically valuable fisheries across the United States. Whereas Bluegill Lepomis macrochirus and Black Crappie Pomoxis nigromaculatus receive considerable research attention, Redear Sunfish L. microlophus are seldom studied despite their wide distribution, large size, socioeconomic contributions, and invasion potential in parts of their introduced range. We evaluated Redear Sunfish occurrence, density, relative abundance, growth, and size structure in 60 Florida lakes with varied surface area (2–12,412 ha), trophic state (oligotrophic to hypereutrophic), and macrophyte abundance (0.3–100% of lake volume inhabited), a range of environmental conditions over which Redear Sunfish populations have scarcely been investigated. Lake surface area, chlorophyll-a concentration, and macrophyte abundance explained 98% of variation in Redear Sunfish occurrence. Redear Sunfish density increased asymptotically with calcium concentration, whereas relative abundance (electrofishing fish/h) peaked at intermediate surface area (50–100 ha) and chlorophyll a (20 μg/L). Mean length at age 3 declined with increasing macrophyte abundance and was parabolically related to Redear Sunfish density, peaking at approximately 450 fish/ha. The proportional size distribution (PSD) and PSD of preferred-length fish were also negatively related to macrophyte abundance, and PSD declined with increasing Redear Sunfish density. Our results suggest that Redear Sunfish fisheries with abundant individuals of quality size (≥180 mm) require large (>100 ha), fertile (>20 μg/L chlorophyll a) lakes with calcium concentrations >5 mg/L, moderate macrophyte abundance (0–25% of lake volume inhabited), and Redear Sunfish densities between 200 and 700 fish/ha. Our modeling approach can help managers predict Redear Sunfish occurrence, density, relative abundance, growth, and size structure based on a suite of abiotic and biotic variables.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10764","usgsCitation":"Carlson, A.K., and Hoyer, M.V., 2022, Redear Sunfish occurrence, abundance, growth, and size structure as related to abiotic and biotic factors in Florida lakes: North American Journal of Fisheries Management, v. 42, no. 3, p. 775-786, https://doi.org/10.1002/nafm.10764.","productDescription":"12 p.","startPage":"775","endPage":"786","ipdsId":"IP-135635","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.70013237375854,\n 28.198847354165324\n ],\n [\n -80.6003025336234,\n 28.36179742477323\n ],\n [\n -81.65425516588003,\n 30.32529189688597\n ],\n [\n -84.74358003616788,\n 30.651101463947313\n ],\n [\n -85.62048476991477,\n 30.913740760414328\n ],\n [\n -85.5963907279543,\n 30.366938990076108\n ],\n [\n -83.93909706361069,\n 30.20020989470504\n ],\n [\n -82.97367274266776,\n 29.41142780425882\n ],\n [\n -82.5311858571122,\n 28.764572626209215\n ],\n [\n -82.70013237375854,\n 28.198847354165324\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-04-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Andrew Kenneth 0000-0002-6681-0853","orcid":"https://orcid.org/0000-0002-6681-0853","contributorId":340581,"corporation":false,"usgs":true,"family":"Carlson","given":"Andrew","email":"","middleInitial":"Kenneth","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoyer, Mark V.","contributorId":340952,"corporation":false,"usgs":false,"family":"Hoyer","given":"Mark","email":"","middleInitial":"V.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908643,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237059,"text":"70237059 - 2022 - Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations","interactions":[],"lastModifiedDate":"2022-09-28T15:46:20.818247","indexId":"70237059","displayToPublicDate":"2022-04-09T10:41:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations","docAbstract":"Over the past ~30 years, hyperspectral remote sensing of chemical variations in white mica have proven to be useful for ore deposit studies in a range of deposit types. To better understand mineral deposits and to guide spectrometer design, this contribution reviews relevant papers from the fields of remote sensing, spectroscopy, and geology that have utilized spectral changes caused by chemical variation in white micas. This contribution reviews spectral studies conducted at the following types of mineral deposits: base metal sulfide, epithermal, porphyry, sedimentary rock hosted gold deposits, orogenic gold, iron oxide copper gold, and unconformity-related uranium. The structure, chemical composition, and spectral features of white micas, in this contribution defined as muscovite, paragonite, celadonite, phengite, illite, and sericite, are given. Reviewed laboratory spectral studies determined that shifts in the position of the white mica 2200 nm combination feature of 1 nm correspond to a change in Aloct content of approximately ±1.05%. Many of the reviewed spectral studies indicated that a shift in the position of the white mica 2200 nm combination feature of 1 nm was geologically significant.
A sensitivity analysis of spectrometer characteristics; bandpass, sampling interval, and channel position, is conducted using spectra of 19 white micas with deep absorption features to determine minimum characteristics required to accurately measure a shift in the position of the white mica 2200 nm combination feature. It was determined that a sampling interval < 16.3 nm and bandpass <17.5 nm are needed to achieve a root mean square error (RMSE) of 2 nm, whereas a sampling interval < 8.8 nm and bandpass <9.8 nm are needed to achieve a RMSE of 1 nm. For comparison, commonly used imaging spectrometers HyMap, AVIRIS-Classic, SpecTIR®'s AisaFENIX 1K, and HySpextm SWIR 384 have 2.1, 1.2, 0.96, and 0.95 nm RMSE in determining the position of the 2200 nm white mica combination feature, respectively.
An additional sensitivity analysis is conducted to determine the effect of signal to noise ratio (SNR) on the RMSE of the position of the white mica 2200 nm combination feature, using spectra of 18 white micas with deep absorption features. For a spectrometer with sampling interval and bandpass of 1 nm, we estimate that RMSEs of 1 and 1.5 nm are achievable with spectra having a minimum SNR of approximately 246 and 64, respectively. For a spectrometer with sampling interval and bandpass of 5 nm, we estimate that RMSEs of 1 and 1.5 nm are attainable with spectra having a minimum SNR of approximately 431 and 84, respectively. When using a spectrometer with a sampling interval 8.8 nm and a bandpass of 9.8 nm, a RMSE of 1 is only achievable with convolved, noiseless reference spectra. For the 8.8_9.8 nm spectrometer, spectra with SNR of 250 and 100 result in RMSE of 1.1 and 1.3, respectively. Therefore, fine spectral resolution characteristics achieve RMSEs better than 1 nm for high SNR spectra while spectrometers with coarse spectral resolution have larger RMSE, perform well with noisy data, and are useful for white mica studies if RMSE of 1.1 to 1.5 nm is acceptable.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113000","usgsCitation":"Meyer, J.M., Holley, E.A., and Kokaly, R.F., 2022, Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations: Remote Sensing of Environment, v. 275, 113000, 18 p., https://doi.org/10.1016/j.rse.2022.113000.","productDescription":"113000, 18 p.","ipdsId":"IP-133226","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":448175,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113000","text":"Publisher Index Page"},{"id":435886,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92VF8HP","text":"USGS data release","linkHelpText":"HySpex by NEO VNIR-1800 and SWIR-384 imaging spectrometer radiance and reflectance data, with associated ASD FieldSpec® NG calibration data, collected at Cripple Creek Victor mine, Cripple Creek, Colorado, 2017"},{"id":407517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"275","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meyer, John Michael 0000-0003-2810-9414","orcid":"https://orcid.org/0000-0003-2810-9414","contributorId":297062,"corporation":false,"usgs":true,"family":"Meyer","given":"John","email":"","middleInitial":"Michael","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":853194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holley, Elizabeth A. 0000-0003-2504-4555","orcid":"https://orcid.org/0000-0003-2504-4555","contributorId":265154,"corporation":false,"usgs":false,"family":"Holley","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":853195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":853196,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255174,"text":"70255174 - 2022 - Optimizing management of invasions in an uncertain world using dynamic spatial models","interactions":[],"lastModifiedDate":"2024-06-13T15:11:21.379743","indexId":"70255174","displayToPublicDate":"2022-04-09T10:01:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing management of invasions in an uncertain world using dynamic spatial models","docAbstract":"Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2628","usgsCitation":"Pepin, K., Davis, A., Epanchin-Niell, R.S., Gormley, A.M., Moore, J., Smyser, T.J., Shaffer, H., Kendall, W.L., Shea, K., Runge, M.C., and McKee, S., 2022, Optimizing management of invasions in an uncertain world using dynamic spatial models: Ecological Applications, v. 32, no. 6, e2628, 21 p., https://doi.org/10.1002/eap.2628.","productDescription":"e2628, 21 p.","ipdsId":"IP-119939","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Kim M. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":187441,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim M.","affiliations":[],"preferred":false,"id":903662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Amy J.","contributorId":279408,"corporation":false,"usgs":false,"family":"Davis","given":"Amy J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":903663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Epanchin-Niell, Rebecca S.","contributorId":175364,"corporation":false,"usgs":false,"family":"Epanchin-Niell","given":"Rebecca","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":903664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gormley, Andrew M.","contributorId":338892,"corporation":false,"usgs":false,"family":"Gormley","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":81209,"text":"Manaaki Whenua – Landcare Research","active":true,"usgs":false}],"preferred":false,"id":903665,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Joslin L.","contributorId":257914,"corporation":false,"usgs":false,"family":"Moore","given":"Joslin L.","affiliations":[{"id":27278,"text":"Monash University","active":true,"usgs":false}],"preferred":false,"id":903666,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smyser, Timothy J.","contributorId":279407,"corporation":false,"usgs":false,"family":"Smyser","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":903667,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaffer, H. Bradley","contributorId":71051,"corporation":false,"usgs":true,"family":"Shaffer","given":"H. Bradley","affiliations":[],"preferred":false,"id":903668,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903661,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":903669,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903670,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McKee, Sophie","contributorId":279410,"corporation":false,"usgs":false,"family":"McKee","given":"Sophie","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":903671,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70241617,"text":"70241617 - 2022 - Fire-driven vegetation type conversion in Southern California","interactions":[],"lastModifiedDate":"2023-03-24T11:52:34.429916","indexId":"70241617","displayToPublicDate":"2022-04-09T06:47:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Fire-driven vegetation type conversion in Southern California","docAbstract":"One consequence of global change causing widespread concern is the possibility of ecosystem conversions from one type to another. A classic example of this is vegetation type conversion (VTC) from native woody shrublands to invasive annual grasslands in the biodiversity hotspot of Southern California. Although the significance of this problem is well recognized, understanding where, how much, and why this change is occurring remains elusive owing to differences in results from studies conducted using different methods, spatial extents, and scales. Disagreement has arisen particularly over the relative importance of short-interval fires in driving these changes. Chronosequence approaches that use space for time to estimate changes have produced different results than studies of changes at a site over time. Here we calculated the percentage woody and herbaceous cover across Southern California using air photos from ~1950 to 2019. We assessed the extent of woody cover change and the relative importance of fire history, topography, soil moisture, and distance to human infrastructure in explaining change across a hierarchy of spatial extents and regions. We found substantial net decline in woody cover and expansion of herbaceous vegetation across all regions, but the most dramatic changes occurred in the northern interior and southern coastal areas. Variables related to frequent, short-interval fire were consistently top ranked as the explanation for shrub to grassland type conversion, but low soil moisture and topographic complexity were also strong correlates. Despite the consistent importance of fire, there was substantial geographical variation in the relative importance of drivers, and these differences resulted in different mapped predictions of VTC. This geographical variation is important to recognize for management decision-making and, in addition to differences in methodological design, may also partly explain differences in previous study results. The overwhelming importance of short-interval fire has management implications. It suggests that actions should be directed away from imposing fires to preventing fires. Prevention can be controlled through management actions that limit ignitions, fire spread, and the damage sustained in areas that do burn. This study also demonstrates significant potential for changing fire regimes to drive large-scale, abrupt ecological change.
Peer review is the formal means by which the scientific community assesses the originality, reproducibility, validity, and quality of a research study (Bakker and Traniello 2019). As such, peer review assures nonexperts that they can trust a study's findings (Jamieson et al. 2019). Despite the critical importance of peer review, graduate students, postdocs, and other early career researchers (ECRs) have limited resources for learning about this process (but see Nicholas and Gordon 2011 and Nature Communications 2021). A recent survey found that most reviewers have not received formal training on peer review and that reviewers of all career stages (77%), especially ECRs (89%), desire further training (Warne 2016). This reflects a need for guidance regarding when and how to engage in peer review, best practices for conducting a peer review, and how editors weigh peer reviews in their editorial decisions.
In an effort to help new reviewers navigate this process, we (the Raelyn Cole Editorial Fellows) hosted an Association for the Sciences of Limnology and Oceanography (ASLO) webinar on peer review in September of 2021 (recording available: https://www.youtube.com/watch?v=utntl1VGy5g). The webinar had 329 registrants, including 198 students or postdocs, underscoring the desire for peer review resources. The webinar content was largely based on a survey of the associate editors (AEs) of ASLO's three peer-reviewed journals (n = 25 respondents), consisting of five open-ended questions about peer review. Here, we use insights from our survey and webinar to describe how ECRs can join the reviewer pool, provide guidance for writing a useful and time-efficient review, and discuss challenges and opportunities in the evolving landscape of peer review.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lol2.10254","usgsCitation":"Gradoville, M.R., and Deemer, B., 2022, Early career researchers have questions about peer review—we asked the ASLO editors for answers: Limnology and Oceanography Letters, v. 7, no. 3, p. 185-188, https://doi.org/10.1002/lol2.10254.","productDescription":"4 p.","startPage":"185","endPage":"188","ipdsId":"IP-142526","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448183,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10254","text":"Publisher Index Page"},{"id":402546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Gradoville, Mary R.","contributorId":292580,"corporation":false,"usgs":false,"family":"Gradoville","given":"Mary","email":"","middleInitial":"R.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":845243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845244,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}