Trace elements within groundwater that originate from aquifer materials and pose potential public-health hazards if consumed are known as geogenic contaminants. The geogenic contaminants arsenic, chromium, and vanadium can form negatively charged ions with oxygen known as oxyanions. Uranium complexes with bicarbonate and carbonate to form negatively charged ions having aqueous chemistry similar to oxyanions. The concentrations of arsenic, chromium, uranium, and vanadium in groundwater result from the combined effects of (1) geologic abundance within aquifer materials; (2) the fraction of these elements that have weathered from and sorbed to the surfaces of mineral grains and are potentially available to groundwater; and (3) the aqueous chemistry of dissolved oxyanions in groundwater during different redox conditions and pH, both of which are affected by hydrogeology, including the length of time groundwater has been in contact with aquifer materials. Concentrations of arsenic, chromium, uranium, and vanadium were measured in samples of (1) rock, surficial alluvium, and drill cuttings using portable (handheld) X-ray fluorescence (pXRF); (2) operationally defined fractions extractable from these materials; and (3) water from wells sampled between 2000 and 2018 within the 3,500 square mile Mojave River area and Morongo area of the western Mojave Desert, southern California.
Regionally, rock and surficial alluvium in the Mojave River and Morongo areas are high in arsenic, low in chromium and uranium, and near the average bulk continental crust concentration for vanadium. Locally, high chromium concentrations are present in mafic rock within the San Gabriel Mountains; high uranium concentrations are present in felsic rock within the San Bernardino Mountains; and high arsenic, uranium, and vanadium concentrations are present in extrusive (volcanic) felsic rock within uplands surrounding groundwater basins along the Mojave River downstream from Barstow, California. Elemental assemblages identified using principal component analyses (PCA) of pXRF data were used to characterize felsic, mafic, and felsic volcanic source terranes in rock, surficial alluvium, and in geologic material penetrated by selected monitoring wells drilled between 1994 and 2018. Highly felsic alluvium associated with recent deposition from the Mojave River was identified along the 90-mile length of the floodplain aquifer along the river. The thickness of these highly felsic alluvial deposits ranged from 200 feet (ft) near Victorville and near Barstow to a thin veneer about 30 ft thick downstream from Victorville and downstream portions of the floodplain aquifer within the Mojave Valley.
Groundwater in the Mojave River and Morongo areas was generally oxic and alkaline (pH≥7.5). Maximum concentrations of arsenic, hexavalent chromium [Cr(VI)], uranium, and vanadium in water from as many as 498 wells sampled between 2000 and 2018 were 360, 140, 1,470, and 690 micrograms per liter (μg/L), respectively. Water from 22 percent of sampled wells exceeded the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) for arsenic of 10 μg/L, with arsenic concentrations commonly exceeding the MCL in water from wells east of Barstow, deep wells in the Victorville fan, and in suboxic or reduced groundwater within the floodplain aquifer. Water from about 1 percent of sampled wells had Cr(VI) concentrations greater than the California MCL for total chromium of 50 μg/L, whereas 13 percent of sampled wells had Cr(VI) concentrations greater than the former California MCL of 10 μg/L. Hexavalent chromium concentrations were highest in water from wells in the Sheep Creek alluvial fan, eroded from mafic rock in the San Gabriel Mountains, although Cr(VI) concentrations greater than the former California MCL also were present elsewhere in the study area where mafic materials or older groundwater were present. Water from about 9 percent of sampled wells exceeded the EPA MCL for uranium of 30 μg/L, with concentrations exceeding the MCL commonly associated with irrigation return from agricultural land overlying the floodplain aquifer. Water from about 7 percent of sampled wells had vanadium concentrations greater than the California notification level of 50 μg/L; most of these wells were in the Victorville fan within the Mojave River area. In general, arsenic concentrations were higher in suboxic or reduced water; chromium concentrations were higher in oxic, alkaline (pH≥7.5) water; uranium concentrations were higher in circumneutral to slightly alkaline water (pH≤7.4); and vanadium concentrations were higher in highly alkaline (pH≥8.0) water, independent of redox status.
Concentrations within geologic source terranes are not the sole factor controlling the concentrations of geogenic elements in groundwater. Differences in mineral weathering, pH-dependent sorption to surface-exchange sites on mineral grains, and aqueous geochemistry (especially redox status and pH) affect geogenic element concentrations in groundwater. Consequently, the relative abundances of arsenic, Cr(VI), uranium, and vanadium in groundwater differ from their relative abundances in the average bulk continental crust and their regional abundances in rock and surficial alluvium within groundwater basins of the western Mojave Desert. Processes that control the concentrations of arsenic, chromium, uranium, and vanadium in groundwater operate at the mineral-grain and aquifer scale.
At the mineral-grain scale, sequential chemical extraction data show arsenic and uranium are more available to groundwater (under specific geochemical conditions) than chromium or vanadium, which largely are unavailable within unweathered mineral grains. Additionally, chromium and vanadium form few aqueous complexes and bind tightly with iron minerals within surface coatings on mineral grains making them less available to groundwater, whereas complexation with other dissolved ions enhances the solubility of uranium and, to a lesser extent, arsenic. Complexation also increases the valence (less negative charge) and increases the size of dissolved oxyanions and uranium complexes with bicarbonate and carbonate making them less readily sorbed to aquifer materials.
At the aquifer scale, hydrogeology (including isolation of water in aquifers from surface sources of recharge, older groundwater age, and long contact times between groundwater and aquifer materials) combined with geochemical processes (such as silicate weathering) to produce alkaline groundwater. Desorption from sorption sites on the surfaces of mineral grains with increasing pH increases arsenic, chromium, and vanadium concentrations in water from wells and increases Cr(VI) concentrations as long as water remains oxic.
Aqueous geochemistry and concentrations of geogenic contaminants also are affected by anthropogenic activities including (1) discharge of treated municipal wastewater, which may change the redox status of groundwater; (2) return from irrigated agriculture, which may alter the chemistry of groundwater and increase the solubility of trace elements such as uranium; and (3) groundwater pumping and subsequent water-level declines, which may change the source of water yielded by wells. The quality of water imported from northern California and infiltrated from ponds for groundwater recharge may be altered by naturally present trace elements, especially uranium in areas of agricultural land use or chromium within mafic alluvium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235089","collaboration":"Prepared in cooperation with the Mojave Water Agency","programNote":"U.S. Geological Survey Cooperative Water Program","usgsCitation":"Izbicki, J.A., Groover, K.D., and Seymour, W.A., 2023, Arsenic, chromium, uranium, and vanadium in rock, alluvium, and groundwater, western Mojave Desert, southern California: U.S. Geological Survey Scientific Investigations Report 2023–5089, 96 p., https://doi.org/10.3133/sir20235089.","productDescription":"Report: xiii, 96 p., 3 Data Releases; 2 Tables","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101005","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501053,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115509.htm","linkFileType":{"id":5,"text":"html"}},{"id":421873,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5089/sir20235089_table2.1.csv","text":"Table 2.1","size":"3 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Well Identification and National Water Information System Record Numbers for Wells Sampled in the Mojave River and Morongo Groundwater Basins as Part of This Study July 2016 to October 2016 and for Wells Sampled as Part of the Groundwater Ambient Monitoring Assessment Program Priority Basin Project Mojave Basin Domestic-Supply Aquifer Study January to May 2018 western Mojave Desert southern California"},{"id":421877,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C7U6DW","text":"USGS Data Release","description":"Groover, K.D., Goldrath, D.A., Bennett, G.L., Johnson, T.D., and Watson, E.E., 2019, Groundwater-quality data in the Mojave Basin Shallow Aquifer Study Unit, 2018—Results from the California GAMA Priority Basin Project: U.S. Geological Survey data release, https://doi.org/10.5066/P9C7U6DW.","linkHelpText":"Groundwater-quality data in the Mojave Basin Shallow Aquifer Study Unit, 2018—Results from the California GAMA Priority Basin Project"},{"id":421878,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://ca.water.usgs.gov/mojave/mojave-water-quality.html","text":"USGS Data Release","description":"Metzger, L.F., Landon, M.K., House, S.F., and Olsen, L.D., 2015, Mapping selected trace elements and major ions, 2000–2012, Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://ca.water.usgs.gov/mojave/mojave-water-quality.html.","linkHelpText":"Mapping selected trace elements and major ions, 2000–2012, Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, San Bernardino County, California"},{"id":421923,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235089/full"},{"id":421973,"rank":10,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5089/sir_20235089.pdf","text":"Report","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":421869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5089/covrthb.jpg"},{"id":421871,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5089/sir20235089.xml"},{"id":421872,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5089/sir20235089_table1.1.csv","text":"Table 1.1","size":"3 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Boreholes having portable (handheld) X-ray fluoresence (pXRF) data from drill cuttings, Mojave River and Morongo groundwater basins, western Mojave Desert, southern California"},{"id":421874,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5089/images"},{"id":421876,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CU0EH3","text":"USGS Data Release","description":"Groover, K.D., and Izbicki, J.A., 2018, Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9CU0EH3.","linkHelpText":"Field portable X-ray fluorescence and associated quality control data for the western Mojave Desert, San Bernardino County, California"}],"country":"United States","state":"California","otherGeospatial":"Western Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.4,\n 35.2\n ],\n [\n -117.4,\n 34.00\n ],\n [\n -116.0,\n 34\n ],\n [\n -116,\n 35.2\n ],\n [\n -117.4,\n 35.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
In the absence of documented surface rupture during the 1 September 1886 Charleston earthquake, there has been considerable speculation about the location and mechanism of the causative fault. We use an inferred coseismic offset of the South Carolina Railroad and additional numerical constraints to develop an elastic deformation model—a west‐dipping fault following strands of two previously identified faults. The constraints are consistent with a blind rupture with 6.5 ± 0.3 m of dextral slip and 2 ± 0.5 m of reverse slip below 450 m depth. We propose that repeated slip on this fault has raised the Penholoway Marine Terrace >6 m since ∼770 ka. The inferred coseismic slip on the fault in an Mw 7.3 earthquake is consistent with the distribution of damage in 1886.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230022","usgsCitation":"Bilham, R., and Hough, S.E., 2023, The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture: The Seismic Record, v. 3, no. 4, p. 278-288, https://doi.org/10.1785/0320230022.","productDescription":"11 p.","startPage":"278","endPage":"288","ipdsId":"IP-152926","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":441868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230022","text":"Publisher Index Page"},{"id":427514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","city":"Charleston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.24634165513044,\n 33.088760020667124\n ],\n [\n -80.24634165513044,\n 32.62202807250516\n ],\n [\n -79.75159258126686,\n 32.62202807250516\n ],\n [\n -79.75159258126686,\n 33.088760020667124\n ],\n [\n -80.24634165513044,\n 33.088760020667124\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bilham, Roger","contributorId":225117,"corporation":false,"usgs":false,"family":"Bilham","given":"Roger","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":898271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":898272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256086,"text":"70256086 - 2023 - BatTool: Projecting bat populations facing multiple stressors using a demographic model","interactions":[],"lastModifiedDate":"2024-07-19T11:57:51.992521","indexId":"70256086","displayToPublicDate":"2023-10-16T06:55:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"BatTool: Projecting bat populations facing multiple stressors using a demographic model","docAbstract":"Bats provide ecologically and agriculturally important ecosystem services but are currently experiencing population declines caused by multiple environmental stressors, including mortality from white-nose syndrome and wind energy development. Analyses of the current and future health and viability of these species may support conservation management decision making. Demographic modeling provides a quantitative tool for decision makers and conservation managers to make more informed decisions, but widespread adoption of these tools can be limited because of the complexity of the mathematical, statistical, and computational components involved in implementing these models. In this work, we provide an exposition of the BatTool R package, detailing the primary components of the matrix projection model, a publicly accessible graphical user interface (https://rconnect.usgs.gov/battool) facilitating user-defined scenario analyses, and its intended uses and limitations (Wiens et al., US Geol Surv Data Release 2022; Wiens et al., US Geol Surv Softw Release 2022). We present a case study involving wind energy permitting, weighing the effects of potential mortality caused by a hypothetical wind energy facility on the projected abundance of four imperiled bat species in the Midwestern United States.
Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.
Land use practices and climate change have driven substantial soil degradation across global drylands, impacting ecosystem functions and human livelihoods. Biological soil crusts, a common feature of dryland ecosystems, are under extensive exploration for their potential to restore the stability and fertility of degraded soils through the development of inoculants. However, stressful abiotic conditions often result in the failure of inoculation-based restoration in the field and may hinder the long-term success of biocrust restoration efforts. Taking an assisted migration approach, we cultivated biocrust inocula sourced from multiple hot-adapted sites (Mojave and Sonoran Deserts) in an outdoor facility at a cool desert site (Colorado Plateau). In addition to cultivating inoculum from each site, we created an inoculum mixture of biocrust from the Mojave Desert, Sonoran Desert, and Colorado Plateau. We then applied two habitat amelioration treatments to the cultivation site (growth substrate and shading) to enhance soil stability and water availability and reduce UV stress. Using marker gene sequencing, we found that the cultivated mixed inoculum comprised both local- and hot-adapted cyanobacteria at the end of cultivation but had similar cyanobacterial richness as each unmixed inoculum. All cultivated inocula had more cyanobacterial 16S rRNA gene copies and higher cyanobacterial richness when cultivated with a growth substrate and shade. Our work shows that it is possible to field cultivate biocrust inocula sourced from different deserts, but that community composition shifts toward that of the cultivation site unless habitat amelioration is employed. Future assessments of the function of a mixed inoculum in restoration and its resilience in the face of abiotic stressors are needed to determine the relative benefit of assisted migration compared to the challenges and risks of this approach.
","language":"English","publisher":"MDPI","doi":"10.3390/microorganisms11102570","usgsCitation":"Jech, S., Day, N.K., Barger, N., Antoninka, A., Bowker, M., Reed, S., and Tucker, C.L., 2023, Cultivating resilience in dryland soils: An assisted migration approach to biological soil crust restoration: Microorganisms, v. 11, no. 10, 2570, 18 p., https://doi.org/10.3390/microorganisms11102570.","productDescription":"2570, 18 p.","ipdsId":"IP-158175","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491458,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/microorganisms11102570","text":"Publisher Index Page"},{"id":491103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.34802973632918,\n 35.587070771119116\n ],\n [\n -116.01065534573146,\n 33.040451734318665\n ],\n [\n -113.56337647791864,\n 32.106918121662446\n ],\n [\n -109.03550909420508,\n 38.80883012106955\n ],\n [\n -108.98810398779125,\n 39.80828416894897\n ],\n [\n -110.68898438844218,\n 39.2840311835302\n ],\n [\n -113.98561161383276,\n 36.06454545435385\n ],\n [\n -117.34802973632918,\n 35.587070771119116\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Jech, Sierra","contributorId":292726,"corporation":false,"usgs":false,"family":"Jech","given":"Sierra","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":941062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barger, Nichole 0000-0002-8765-7974","orcid":"https://orcid.org/0000-0002-8765-7974","contributorId":245370,"corporation":false,"usgs":false,"family":"Barger","given":"Nichole","email":"","affiliations":[{"id":49167,"text":"University of Colorado Boulder, Department of Ecology and Evolutionary Biology,","active":true,"usgs":false}],"preferred":false,"id":941064,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Antoninka, Anita","contributorId":166769,"corporation":false,"usgs":false,"family":"Antoninka","given":"Anita","affiliations":[{"id":24503,"text":"Northern Arizona University, School of Forestry, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":941065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowker, Matthew A.","contributorId":240683,"corporation":false,"usgs":false,"family":"Bowker","given":"Matthew A.","affiliations":[],"preferred":false,"id":941066,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":941067,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":941068,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70257389,"text":"70257389 - 2023 - Seven dam challenges for migratory fish: Insights from the Penobscot River","interactions":[],"lastModifiedDate":"2024-09-05T16:35:50.264408","indexId":"70257389","displayToPublicDate":"2023-10-15T09:16:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Seven dam challenges for migratory fish: Insights from the Penobscot River","docAbstract":"More than a century of impoundments in the Penobscot River, Maine, USA, has contributed to population declines in migratory fish in the system. A decade of change, research, and monitoring has revealed direct and indirect ways that dams have influenced the river habitat, connectivity for migratory fish, and the food web. The removal of two main-stem dams (in 2012 and 2013) and bolstering of fish passage have been part of coordinated restoration efforts in the watershed. Integral to this undertaking was support for short- and long-term monitoring and research that included physical habitat, fish passage, and broad scale ecological assessments. Herein we discuss the seven interconnected and complex ways that dams have affected the Penobscot River ecosystem, particularly for migratory fish. These include familiar influences ascribed to dams: i) impaired access to habitat, ii) injury and mortality, and iii) delays of migration. Other ecological influences are less studied and more subtle: iv) facilitation of predation, v) community shifts, and vi) demographic shifts. Lastly, dams result in vii) a loss of ecosystem services that would otherwise be intact in an unimpounded system. We draw on both direct examples from the Penobscot River and broader information to characterize how impoundments have transformed this ecosystem for more than a century. Recent dam removals and mitigation efforts have reestablished some of these ecological functions.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1253657","usgsCitation":"Zydlewski, J.D., Coghlan, S., Dillingham, C., Figueroa-Munoz, G., Merriam, C., Smith, S., Smith, R., Stich, D.S., Vogel, S.K., Wilson, K., and Zydlewski, G., 2023, Seven dam challenges for migratory fish: Insights from the Penobscot River: Frontiers in Ecology and Evolution, v. 11, 1253657, 19 p., https://doi.org/10.3389/fevo.2023.1253657.","productDescription":"1253657, 19 p.","ipdsId":"IP-155048","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441877,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1253657","text":"Publisher Index Page"},{"id":432855,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -69.5,\n 46\n ],\n [\n -69.5,\n 44\n ],\n [\n -68,\n 44\n ],\n [\n -68,\n 46\n ],\n [\n -69.5,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":910207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coghlan, Stephen M.","contributorId":272185,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dillingham, Cody","contributorId":342595,"corporation":false,"usgs":false,"family":"Dillingham","given":"Cody","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Figueroa-Munoz, Guillermo","contributorId":342597,"corporation":false,"usgs":false,"family":"Figueroa-Munoz","given":"Guillermo","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merriam, Carolyn","contributorId":342599,"corporation":false,"usgs":false,"family":"Merriam","given":"Carolyn","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910211,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Sean","contributorId":276400,"corporation":false,"usgs":false,"family":"Smith","given":"Sean","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910212,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Rylee","contributorId":342603,"corporation":false,"usgs":false,"family":"Smith","given":"Rylee","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910213,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":910214,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vogel, Sarah K.","contributorId":275755,"corporation":false,"usgs":false,"family":"Vogel","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910215,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wilson, Karen","contributorId":275235,"corporation":false,"usgs":false,"family":"Wilson","given":"Karen","affiliations":[{"id":34930,"text":"University of Southern Maine","active":true,"usgs":false}],"preferred":false,"id":910216,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zydlewski, Gayle B.","contributorId":139211,"corporation":false,"usgs":false,"family":"Zydlewski","given":"Gayle B.","affiliations":[{"id":12606,"text":"University of Maine, Dept of Plant, Soil, & Envir Sciences","active":true,"usgs":false}],"preferred":false,"id":910217,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70249575,"text":"70249575 - 2023 - Evaluation of breeding distribution and chronology of North American scoters","interactions":[],"lastModifiedDate":"2024-01-08T17:21:40.311151","indexId":"70249575","displayToPublicDate":"2023-10-14T07:01:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of breeding distribution and chronology of North American scoters","docAbstract":"North America's scoter species are poorly monitored relative to other waterfowl. Black Melanitta americana, surf M. perspicillata, and white-winged M. deglandi scoter abundance and trend estimates are thus uncertain in many parts of these species' ranges. The most extensive source of waterfowl abundance and distribution data in North America is the Waterfowl breeding population and habitat survey (WBPHS). Although the WBPHS effectively monitors most species, both its timing and geographic coverage may preclude accurate scoter monitoring. Therefore, our goal was to better define when and where scoters breed to help interpret survey results and optimize potential supplemental survey efforts for scoters. We integrated satellite telemetry tracking data from scoters marked at multiple molting, staging, breeding, and wintering areas along the Atlantic and Pacific coasts to quantify continent-wide breeding chronology and distribution. We also examined possible drivers of variation in timing of arrival, length of stay, and departure at nesting locations. We documented a northwest to southeast distribution of estimated breeding sites across Alaska and Canada. On average, scoters arrived at nest sites on 1 June. Surf scoters and Pacific black scoters arrived earliest and departed earliest. Pacific-wintering black and white-winged scoters began breeding earlier than Atlantic-wintering birds. Additionally, birds arrived at nesting locations earlier in years with earlier snowmelt, and later snowmelt reduced lengths of stay for males. Breeding chronology also varied by age group, with adults arriving earlier than subadults. Our study is the first to comprehensively describe spatial variation in timing of breeding of both Atlantic and Pacific populations of all three scoter species across North America. Our results increase our understanding of how current surveys enumerate scoters and will inform possible supplemental efforts to improve continental monitoring of scoter populations.
Management and conservation efforts that support the recovery and protection of large rivers are daunting, reflecting the complexity of the challenge and extent of effort (in terms of policy, economic investment, and spatial extent) needed to afford measurable change. These large systems have generally experienced intensive development and regulation, compromising their capacity to respond to disturbances such as climate change or wildfire. Functionally, large river and basin management require insights gained from social, ecological, geophysical, and hydrological sciences. This multi-disciplinary perspective can unveil the integrated relationship between a river network's biotic community and seasonally variable environmental conditions that are often influenced by human activities. Large rivers and their basins are constantly changing due to anthropogenic influences and as climate modifies patterns of temperature and precipitation. Because of these factors, the state of knowledge must advance to address changing conditions. The Willamette River, in western Oregon, USA, is a prime example of a basin that has experienced significant degradation and investment in rehabilitation in recent decades. Innovative science has facilitated development of fine-scale, spatially extensive datasets and models that can generate targeted conservation and rehabilitation actions that are prioritized across the entire river network. This prioritization allows investment decisions to be driven by site-specific conditions while simultaneously considering potentials for ecological improvement. Here, we review hydrologic, geomorphic, ecologic, and social conditions in the Willamette River basin through time—including pre-settlement, river development, and contemporary periods—and offer a future vision for consideration. Currently, detailed information about fish populations and habitat, hydrologic conditions, geomorphology, water quality, and land use can be leveraged to make informed decisions about protection, rehabilitation, and development. The time is ripe for strategic management and goal development for the entire Willamette River, and these efforts can be informed by comprehensive science realized through established institutions (e.g., public agencies, non-profit watershed groups, Tribes, and universities) focused on conservation and management. The approaches to science and social-network creation that were pioneered in the Willamette River basin offer insights into the development of comprehensive conservation-based planning that could be implemented in other large river systems globally.
Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
In the Pacific Northwest (USA), Pacific salmon Oncorhynchus spp. populations have been declining significantly for decades, prompting stakeholders to respond with a variety of conservation and restoration measures. One such measure being considered in the Skagit River basin (Washington, USA) is the introduction of steelhead Oncorhynchus mykiss (anadromous Rainbow Trout) above the impassable Gorge, Diablo, and Ross dams to bolster their populations. Because freshwater growth is key to survival at subsequent life stages, we evaluated current trends in size and growth of Rainbow Trout among key tributaries to Gorge, Diablo, and Ross reservoirs using empirical data collection and bioenergetics modeling.
For nine candidate streams, a bioenergetics model was used to assess how temperature and prey consumption affected growth performance of Rainbow Trout between annuli 1 and 2, and 2 and 3. Thermal scenarios were created to evaluate how fish growth responded to temperature variability while total annual consumption was constrained within empirical growth estimates. We then compared these results to back-calculated size thresholds established by size-at-age observed in wild steelhead adults that returned to the Skagit River below the dams.
Of the streams proposed for introductions, there was one instance (McMillan Creek) in the nominal simulations where growth met or exceeded the size at annulus 2 or 3 of a returning adult steelhead (24.9 g at annulus 2 and 50.3 g at annulus 3). Modeled growth under different thermal scenarios showed that colder temperatures (0.1–10.7°C, Canyon Creek) produced higher growth than under the nominal or warm scenarios (2.0–15.3°C, Canyon Creek), as well as one additional tributary where size at annulus 2 or 3 (±2 SE) was comparable to the threshold established by adult steelhead below the dams (Big Beaver Creek, annulus 3).
These results suggest Rainbow Trout growth is most limited by prey availability in the examined upper Skagit tributaries.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10944","usgsCitation":"Jensen, B.L., Johnson, R.C., Duda, J.J., Ostberg, C.O., Code, T.J., Mclean, J.H., Stenberg, K.D., Larsen, K., Hoy, M.S., and Beauchamp, D., 2023, Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams: North American Journal of Fisheries Management, v. 43, no. 5, p. 1427-1446, https://doi.org/10.1002/nafm.10944.","productDescription":"20 p.","startPage":"1427","endPage":"1446","ipdsId":"IP-147915","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":422838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.76,\n 49\n ],\n [\n -121.335,\n 49\n ],\n [\n -121.335,\n 48.5\n ],\n [\n -120.76,\n 48.5\n ],\n [\n -120.76,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jensen, Benjamin Lorenz 0000-0003-1199-973X","orcid":"https://orcid.org/0000-0003-1199-973X","contributorId":306036,"corporation":false,"usgs":true,"family":"Jensen","given":"Benjamin","email":"","middleInitial":"Lorenz","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ostberg, Carl O. 0000-0003-1479-8458","orcid":"https://orcid.org/0000-0003-1479-8458","contributorId":220731,"corporation":false,"usgs":true,"family":"Ostberg","given":"Carl","middleInitial":"O.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Code, Tessa Julianne 0000-0003-1481-020X","orcid":"https://orcid.org/0000-0003-1481-020X","contributorId":331687,"corporation":false,"usgs":true,"family":"Code","given":"Tessa","email":"","middleInitial":"Julianne","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888477,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mclean, Jonathan H 0000-0001-5940-3689","orcid":"https://orcid.org/0000-0001-5940-3689","contributorId":331688,"corporation":false,"usgs":true,"family":"Mclean","given":"Jonathan","email":"","middleInitial":"H","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888478,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stenberg, Karl D. 0000-0001-9802-2707 kstenberg@usgs.gov","orcid":"https://orcid.org/0000-0001-9802-2707","contributorId":3747,"corporation":false,"usgs":true,"family":"Stenberg","given":"Karl","email":"kstenberg@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888479,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Larsen, Kimberly 0000-0001-7978-2452","orcid":"https://orcid.org/0000-0001-7978-2452","contributorId":202172,"corporation":false,"usgs":true,"family":"Larsen","given":"Kimberly","email":"","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888480,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hoy, Marshal S. 0000-0003-2828-9697","orcid":"https://orcid.org/0000-0003-2828-9697","contributorId":220730,"corporation":false,"usgs":true,"family":"Hoy","given":"Marshal","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888481,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":888482,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70251250,"text":"70251250 - 2023 - Browsing the literature","interactions":[],"lastModifiedDate":"2024-01-31T15:53:08.970125","indexId":"70251250","displayToPublicDate":"2023-10-13T09:51:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Browsing the literature","docAbstract":"No abstract available.
","language":"English","publisher":"Society for Range Management","doi":"10.1016/j.rala.2023.09.001","usgsCitation":"Germino, M., 2023, Browsing the literature: Rangelands, v. 45, no. 5, p. 109-112, https://doi.org/10.1016/j.rala.2023.09.001.","productDescription":"4 p.","startPage":"109","endPage":"112","ipdsId":"IP-157317","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":425148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":893649,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70254202,"text":"70254202 - 2023 - Unraveling mechnisms underlying effects of wetting–drying cycles on soil respiration in a dryland","interactions":[],"lastModifiedDate":"2024-05-13T11:42:23.587808","indexId":"70254202","displayToPublicDate":"2023-10-13T06:37:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Unraveling mechnisms underlying effects of wetting–drying cycles on soil respiration in a dryland","docAbstract":"Rewetting of dry soils usually stimulates soil carbon (C) emission, a phenomenon known as the Birch effect. Soil C cycling in drylands, which store approximately one third of terrestrial soil organic C (SOC), is strongly affected by wetting–drying cycles. However, the physical, chemical, and biological mechanisms that link rewetting cycles with dryland soil C cycling have not been comprehensively studied, nor do we understand how these mechanisms interact with each other. Here, we conducted a dryland soil incubation experiment manipulating four factors related to global change (soil moisture content, soil moisture variability, C availability, and prior warming) in a factorial design. The experiment was divided into two periods: a rewetting period consisting of six 14-d wetting–drying cycles; and a recovery period lasting 28 days during which soil moisture content was held stable, allowing for examination of the legacy effects of the wet-dry cycles. Rewetting cycles decreased soil aggregate stability under some conditions, but their effects on soil microbial biomass and fungal communities, soil enzyme activities, soil priming, and soil dissolved C were not significant. We found lower average soil respiration under the wetting–drying treatment than the stable soil moisture treatment, and Birch effects were observed, but only under some conditions. This was probably because moisture variability exacerbated soil microbial metabolic stress, which showed itself as oxygen limitation during the initial precipitation pulse and as water limitation during soil drying. Notably, respiration rates remained low even after moisture fluctuations ceased, suggesting a legacy effect of rewetting cycles on dryland microbial communities. Overall, rewetting inhibited aggregate formation (physical mechanism), and suppressed soil respiration by inducing soil microbial metabolic stress (biological mechanism), ultimately leading to lower soil C loss under rewetting. Our findings indicate that Birch effects are mediated by the magnitude of moisture variability, the availability of C, and the degree of physiological stress microbes experience.
This publication is a preliminary map and geodatabase of the coseismic surface rupture and other coseismic features generated from the August 9, 2020, Mw 5.1 earthquake near Sparta, North Carolina. Geologic mapping facilitated by analysis of post-earthquake quality level 0 to 1 lidar, document the coseismic surface rupture, named the Little River fault, and other coseismic features. The Little River fault is traced for approximately 4 kilometers and cuts the regional Paleozoic fabric (mean foliation, 063°/57°), and the dominant strike of joint sets are 0°–10°, 130°–150°, and 320°–340°. Individual fault strands occur in an en echelon pattern within an approximately 10-meter-wide zone. Trenches across the Little River fault document a thrust fault oriented 110°/45° with at least 10 centimeters (cm) of displacement. The Little River fault is marked by a flexure or scarp with a height of 5–30 cm and a local maximum height of 50 cm. Southwest-side-up displacement is consistent along the fault and indicates thrust kinematics. The strike of the Little River fault changes from 110° to 130° near Duncan Farm where it crosses Chestnut Grove Church Road (NC Rt. 1426). Although the surface expression of the fault terminates and (or) is imperceptible at both ends, deformation is still clear in residual surface maps showing the change between pre- and post-earthquake lidar elevations. Other coseismic features documented are rockfalls, ground cracks, fissures, lateral spreading on a sandbar, and mass-wasting scarps; several possible faults that were identified from lidar analyses strike E-W and oblique to the Little River fault.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231074","usgsCitation":"Merschat, A.J., and Carter, M.W., 2023, Preliminary map of the surface rupture from the August 9, 2020, Mw 5.1 earthquake near Sparta, North Carolina—The Little River fault and other possible coseismic features: U.S. Geological Survey Open-File Report 2023–1074, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20231074.","productDescription":"Sheet: 47.89 x 19.47 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-144102","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":421654,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S5PGIH","text":"USGS data release","linkHelpText":"Database for the preliminary map of the surface rupture from the August 9, 2020, Mw 5.1 earthquake near Sparta, North Carolina—The Little River fault and other possible coseismic features"},{"id":421652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1074/coverthb.jpg"},{"id":421653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1074/ofr20231074.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1074"},{"id":499788,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115507.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"North Carolina","city":"Sparta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.18690346012897,\n 36.54364449287644\n ],\n [\n -81.18690346012897,\n 36.472458202284926\n ],\n [\n -81.09177792830289,\n 36.472458202284926\n ],\n [\n -81.09177792830289,\n 36.54364449287644\n ],\n [\n -81.18690346012897,\n 36.54364449287644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.
Barrow’s goldeneyes across western North America have been shown to have a high degree of subpopulation independence using several data types. However, evidence for structured populations based on mitochondrial DNA, band recoveries, and tracking of adults is discordant with evidence from autosomal DNA. We used satellite tracking data from both juveniles and adults marked on natal and breeding grounds, respectively, in British Columbia, Canada to evaluate the hypothesis that male-biased juvenile dispersal maintains genetic panmixia of Pacific Barrow’s goldeneyes otherwise structured by migratory movements and high winter and breeding site fidelity of adults. We found that juvenile males traveled to overwintering sites located within the range of the overwintering sites of juvenile females, adult males, and adult females. Juvenile males migrated at the same time, travelled the same distance when moving between natal and overwintering sites, and had the same winter dispersion as juvenile females. Although juveniles did not travel with attendant females, all juveniles overwintered within the wintering range of adults. We tracked some juveniles into the following spring/summer and even second winter. Prospecting juveniles of both sexes travelled from their wintering grounds to potential breeding sites in the proximity of Riske Creek and within the bounds of the breeding locations used by adults. Juveniles tracked for more than a year also showed relatively high winter site fidelity. Because Barrow’s goldeneyes pair on wintering grounds, our tracking data are not consistent with the hypothesis that male-biased juvenile dispersal explains the genetic structure in the mitochondrial DNA and panmixia in the autosomal DNA of Barrow’s goldeneye. We suggest that uncommon or episodic dispersal of males might be enough to homogenize autosomal DNA but is unlikely to influence demographic population structure relevant to contemporary population management.
Limitations and utility of three measures of water use characteristics were evaluated: water use efficiency (WUE), intrinsic WUE and marginal water cost of carbon gain (aE/aA) estimated, respectively, as ratios of assimilation (A) to transpiration (E), of A to stomatal conductance (gs) and of sensitivities of E and A with variation in gs. Only the measure aE/aA estimates water use strategy in a way that integrates carbon gain relative to water use under varying environmental conditions across scales from leaves to communities. This insight provides updated and simplified ways of estimating aE/aA and adds depth to understanding ways that plants balance water expenditure against carbon gain, uniquely providing a mechanistic means of predicting water use characteristics under changing environmental scenarios.
","language":"English","publisher":"Wiley","doi":"10.1111/nph.19308","usgsCitation":"Liang, J., Krauss, K., Finnigan, J., Stuart-Williams, H., Farquhar, G.D., and Ball, M.C., 2023, Linking water use efficiency with water use strategy from leaves to communities: New Phytologist, v. 240, no. 5, p. 1735-1742, https://doi.org/10.1111/nph.19308.","productDescription":"8 p.","startPage":"1735","endPage":"1742","ipdsId":"IP-141421","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441894,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nph.19308","text":"Publisher Index Page"},{"id":422183,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"240","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Jie","contributorId":331223,"corporation":false,"usgs":false,"family":"Liang","given":"Jie","email":"","affiliations":[{"id":79164,"text":"Research School of Biology, Australian National University","active":true,"usgs":false}],"preferred":false,"id":887004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":887005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finnigan, John","contributorId":331224,"corporation":false,"usgs":false,"family":"Finnigan","given":"John","email":"","affiliations":[{"id":79164,"text":"Research School of Biology, Australian National University","active":true,"usgs":false}],"preferred":false,"id":887006,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stuart-Williams, Hilary","contributorId":331225,"corporation":false,"usgs":false,"family":"Stuart-Williams","given":"Hilary","affiliations":[{"id":79164,"text":"Research School of Biology, Australian National University","active":true,"usgs":false}],"preferred":false,"id":887007,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Farquhar, Graham D.","contributorId":331226,"corporation":false,"usgs":false,"family":"Farquhar","given":"Graham","email":"","middleInitial":"D.","affiliations":[{"id":79164,"text":"Research School of Biology, Australian National University","active":true,"usgs":false}],"preferred":false,"id":887008,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ball, Marilyn C.","contributorId":298613,"corporation":false,"usgs":false,"family":"Ball","given":"Marilyn","email":"","middleInitial":"C.","affiliations":[{"id":38167,"text":"The Australian National University, Australia","active":true,"usgs":false}],"preferred":false,"id":887009,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254456,"text":"70254456 - 2023 - Mortality thresholds of juvenile trees to drought and heatwaves: Implications for forest regeneration across a landscape gradient","interactions":[],"lastModifiedDate":"2024-05-28T11:43:30.086705","indexId":"70254456","displayToPublicDate":"2023-10-12T06:41:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"Mortality thresholds of juvenile trees to drought and heatwaves: Implications for forest regeneration across a landscape gradient","docAbstract":"Tree loss is increasing rapidly due to drought- and heat-related mortality and intensifying fire activity. Consequently, the fate of many forests depends on the ability of juvenile trees to withstand heightened climate and disturbance anomalies. Extreme climatic events, such as droughts and heatwaves, are increasing in frequency and severity, and trees in mountainous regions must contend with these landscape-level climate episodes. Recent research focuses on how mortality of individual tree species may be driven by drought and heatwaves, but how juvenile mortality under these conditions would vary among species spanning an elevational gradient—given concurrent variation in climate, ecohydrology, and physiology–remains unclear. We address this knowledge gap by implementing a growth chamber study, imposing extreme drought with and without a compounding heatwave, for juveniles of five species that span a forested life zones in the Southwestern United States. Overall, the length of a progressive drought required to trigger mortality differed by up to 20 weeks among species. Inclusion of a heatwave hastened mean time to mortality for all species by about 1 week. Lower-elevation species that grow in warmer ambient conditions died earlier (Pinus ponderosa in 10 weeks, Pinus edulis in 14 weeks) than did higher-elevation species from cooler ambient conditions (Picea engelmannii and Pseudotsuga menziesii in 19 weeks, and Pinus flexilis in 30 weeks). When exposed to a heatwave in conjunction with drought, mortality advanced significantly only for species from cooler ambient conditions (Pinus flexilis: 2.7 weeks earlier; Pseudotsuga menziesii: 2.0 weeks earlier). Cooler ambient temperatures may have buffered against moisture loss during drought, resulting in longer survival of higher-elevation species despite expected drought tolerance of lower-elevation species due to tree physiology. Our study suggests that droughts will play a leading role in juvenile tree mortality and will most directly impact species at warmer climate thresholds, with heatwaves in tandem with drought potentially exacerbating mortality especially of high elevation species. These responses are relevant for assessing the potential success of both natural and managed reforestation, as differential juvenile survival following episodic extreme events will determine future landscape-scale vegetation trajectories under changing climate.
This study presents a simplified method and empirical relationships for determining organic matter thermal maturity using a portable Raman system equipped with a 785 nm laser, for analysis of crushed, whole-rock samples. Suites of rocks represented by shale and coal samples with various mineralogical composition, thermal maturity, and total organic carbon (TOC) were used to test the method and build correlations between Raman band separation (RBS) values and traditional thermal maturity indicators, organic matter reflectance (Ro), and programmed temperature pyrolysis (Tmax) values. A set of disparate shale samples, where both vitrinite and solid bitumen reflectance values were reported, have Ro values that range from 0.40 to 4.62%. Above 3.35% Ro, the corresponding RBS values plateau at ∼290 cm−1, thus correlations were evaluated with a linear regression (R2 = 0.96) between 0.40 and 3.35% Ro. Shale samples with Ro < 2% and Tmax < 551 were also used to correlate Tmax and RBS, yielding a linear correlation with an R2 of 0.94. For the coal data set, Ro values range from 1.21 to 4.08% and correlated RBS values plateau at ∼250 cm−1 above Ro = 3.0%, suggesting its correlative application below this maturity level. Several sample preparation methods were tested on cuttings material and standard deviation values for RBS were minimized by washing, drying, and hand crushing the material to pass through a 40-mesh sieve, although less preparation can still yield reliable results. The high degrees of correlation between whole-rock RBS data and two thermal maturity indicators demonstrate the utility of this approach for generating source rock thermal maturity data from minimally processed, whole-rock samples which could easily be applied in field or laboratory settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104374","usgsCitation":"Stokes, M., Jubb, A., Hackley, P.C., Birdwell, J.E., Barnhart, E.P., Scott, C., Shelton, J., Sanders, M.M., and Hatcherian, J.J., 2023, Evaluation of portable Raman spectroscopic analysis for source-rock thermal maturity assessments on bulk crushed rock: International Journal of Coal Geology, v. 279, 104374, https://doi.org/10.1016/j.coal.2023.104374.","productDescription":"104374","ipdsId":"IP-157149","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":467086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2023.104374","text":"Publisher Index Page"},{"id":425713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stokes, Martha 0000-0002-2838-8380","orcid":"https://orcid.org/0000-0002-2838-8380","contributorId":269608,"corporation":false,"usgs":true,"family":"Stokes","given":"Martha","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":894935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":894938,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894939,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Clint 0000-0003-2778-2711 clintonscott@usgs.gov","orcid":"https://orcid.org/0000-0003-2778-2711","contributorId":5332,"corporation":false,"usgs":true,"family":"Scott","given":"Clint","email":"clintonscott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894940,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894941,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sanders, Margaret M. 0000-0003-3505-874X","orcid":"https://orcid.org/0000-0003-3505-874X","contributorId":248709,"corporation":false,"usgs":true,"family":"Sanders","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":894942,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894943,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249500,"text":"ofr20231002 - 2023 - The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective","interactions":[],"lastModifiedDate":"2026-02-10T21:24:41.808706","indexId":"ofr20231002","displayToPublicDate":"2023-10-11T11:03:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1002","displayTitle":"The Enigmatic Rattlesnake Knoll, Spring Valley, East-Central Nevada—A Geophysical Perspective","title":"The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective","docAbstract":"Rattlesnake Knoll is a small, 30-meter-high mound of igneous breccia in the center of Spring Valley, east-central Nevada. In the past, researchers have disagreed as to whether the unusual-looking outcrop is intrusive or volcanic. The breccia possesses a normal magnetic polarity, but this is not apparent in aeromagnetic survey data. These data instead show that the knoll lies within a small aeromagnetic low that partially overlaps the extent of a small gravity high. The small gravity anomaly associated with the knoll, combined with an initial, limited ground magnetic survey taken at the knoll, indicates that the knoll rocks extend northward in the subsurface. A second, more extensive ground magnetic traverse was also done north of the knoll. Taking into consideration these new survey data and preexisting data, a two and one-half dimensional modeling program based on Webring (1985) was used to produce a geophysical model that accounts for gravity and magnetic properties, satisfies available geologic information, and conforms to current estimates of basin thickness. This model and the field observations support the interpretation that the knoll consists of gently west-dipping beds of Tertiary volcanic flow breccia, mudflow breccia, and conglomerate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231002","usgsCitation":"Mankinen, E.A., Rowley, P.D., and McKee, E.H., 2023, The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective: U.S. Geological Survey Open-File Report 2023–1002, 13 p., https://doi.org/10.3133/ofr20231002.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-133281","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":435149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WL97XY","text":"USGS data release","linkHelpText":"Ground magnetic data, Spring Valley, White Pine County, Nevada"},{"id":421859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1002/covrthb_.jpg"},{"id":421860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1002/ofr20231002.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499729,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115506.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","otherGeospatial":"Spring Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.36,\n 39.06\n ],\n [\n -114.36,\n 39.00\n ],\n [\n -114.24,\n 39.00\n ],\n [\n -114.24,\n 39.06\n ],\n [\n -114.36,\n 39.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
The segment of the Upper Missouri River between Fort Peck Dam and the headwaters of Lake Sakakawea is home to a population of the endangered Scaphirhynchus albus (pallid sturgeon). Lack of population growth (recruitment failure) has been attributed to inadequate dispersal distance of larvae between spawning locations and the headwaters of Lake Sakakawea, where conventional wisdom holds that anoxic water-quality conditions are lethal to larval sturgeon. River-management objectives to recover pallid sturgeon in this segment therefore focus on increasing available drift distance, decreasing drift rate, increasing larval development rate, or a combination of these objectives. Understanding of channel morphological conditions along this about 400-kilometer segment may provide insight into upstream spawning habitat potential (where sturgeon likely spawn) and into geomorphic factors that may contribute to flow complexity, hence drift rate. This report documents a continuous geomorphic classification of the Fort Peck segment of the Upper Missouri River using remotely sensed datasets to provide contextual information about spatial variations potentially affecting pallid sturgeon reproductive ecology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235109","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Missouri River Recovery Program","usgsCitation":"Jacobson, R.B., Elliott, C.M., and Bulliner, E., 2023, Geomorphic classification framework for assessing reproductive ecology of Scaphirhynchus albus (pallid sturgeon), Fort Peck segment, Upper Missouri River, Montana and North Dakota: U.S. Geological Survey Scientific Investigations Report 2023–5109, 15 p., https://doi.org/10.3133/sir20235109.","productDescription":"Report: vi, 15 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155746","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":421828,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92HVKT3","text":"USGS data release","linkHelpText":"Geomorphic variables for classification of the Upper Missouri River, Montana and North Dakota"},{"id":421829,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235109/full","linkFileType":{"id":5,"text":"html"}},{"id":421827,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5109/images/"},{"id":421824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5109/coverthb.jpg"},{"id":421825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5109/sir20235109.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5109"},{"id":421826,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5109/sir20235109.XML","linkFileType":{"id":8,"text":"xml"}}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Fort Peck segment, Upper Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.31605153925373,\n 48.4037537878354\n ],\n [\n -106.52859788507773,\n 48.4037537878354\n ],\n [\n -106.60549394122786,\n 47.047516337061694\n ],\n [\n -103.30750753301488,\n 47.08228719733623\n ],\n [\n -103.31605153925373,\n 48.4037537878354\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Anadromous fish returning to the lower American River are restricted to 36 kilometers of free-flowing river between Nimbus Dam and American River’s confluence with the Sacramento River, California. Salmon in the American River provide an important freshwater recreational fishery. However, annual salmon production in the American River in recent years has been low relative to the mid-1990s (Surface Water Resources, Inc., 2001). To investigate the low production of fall-run Chinook salmon (Oncorhynchus tshawytscha), the Bureau of Reclamation requested that the U.S. Geological Survey apply the Stream Salmonid Simulator (S3) model to the population of fall-run Chinook salmon on the American River.
The American River was chosen among seven candidate Sacramento Basin rivers for S3 application. The American River was selected because of its management and public interest, recently low anadromous fish production, and rich time series of key demographic data needed for S3 application. Data that were not available, however, were empirical estimates on juvenile salmon habitat suitability in the American River. Therefore, a large component of applying S3 to the American River was devoted to the estimation of juvenile salmon habitat suitability and capacity. This entailed snorkeling the lower American River for 3 weeks in March 2021 during the early out-migration period for juvenile Chinook salmon. These efforts were fruitful and showed that the typically small fish (<55 millimeters) in the American River preferred much shallower depths than predicted by habitat suitability criteria derived from the literature for this population. Having empirical estimates on juvenile salmon in the American River provided a solid foundation from which to simulate the population using the S3 model.
The S3 model is a spatially explicit population model that runs on a daily time step to simulate redd superimposition, egg maturation, fry emergence and the subsequent growth, survival, and emigration of juvenile Chinook salmon from the river. The key features of this model relevant to this report include (1) a temperature-dependent bioenergetics model driving daily growth rates; (2) density-dependent dynamics that are influenced by the effect of flow on suitable habitat area; and (3) within-year habitat, river flow, and water temperature effects specific to spawning, egg incubation, and fry, parr, and smolt life stages. We used estimates of spawning escapement and geo-referenced redd locations to quantify the spatial and temporal distribution of female spawners for brood years 2014–19. These estimates of female spawners initiate the simulation of each year’s juvenile salmon emergence and emigration over a spatial domain extending from Nimbus Dam to the river’s confluence with the Sacramento River.
Using weekly estimates of juvenile salmon abundance and size (fork length) that passed the Watt Avenue fish trap (river kilometer 14.7), we calibrated the S3 model by estimating three key demographic parameters for each year, y: (1) Sy, the average daily survival probability, (2) M0y, the intercept for density-dependence in movement, representing the average daily probability of remaining in a habitat at zero abundance, and (3) Cy, the average daily proportion of maximum consumption. These parameters were obtained by minimizing the Mallow’s distance (Lupu and others, 2017) between distributions of weekly abundances and sizes of fish at the traps and weekly simulated abundances and sizes (by S3). Investigation of model fit showed excellent agreement between simulated annual abundances and the abundance of fish passing the fish trap. However, when we compared weekly abundances at the fish trap, S3 under-predicted peaks and over-predicted troughs in the time series of weekly abundances at the fish trap. Thus, some unknown within-year effects have yet to be identified and incorporated in the S3 model. Identifying these important effects and incorporating them in the S3 model would help explain the lack of fit between estimated and simulated weekly abundances.
We estimated parameters for 6 years that included a wide range of female spawner abundances (3,057–10,753) and water year types (Critical–Wet). We contrast our estimated parameters to the corresponding number of female spawners and the water year type for the Sacramento Valley. By happenstance, years having higher annual spawner abundances concurred with Critical to Dry water year types. Estimates of survival trended lower with higher spawner abundances and Critical to Dry conditions. In contrast, the extremely wet water year of 2017 had the lowest M0y, suggesting less density-dependence in fish movement, and the lowest Cy, suggesting lower average consumption in this year. When this high-flow year was excluded, a trend towards higher probabilities of fish remaining in a habitat at low abundance and lower proportions of maximum consumption was apparent from Critical to Wet conditions, but only 5 years of data were included. Except for 2017, daily proportions of maximum consumption were relatively high (Cy > 0.83), suggesting that fish were feeding at reasonably high proportions relative to the expected maximum consumption as defined by the “Wisconsin” bioenergetics model (Stewart and Ibarra, 1991).
Survival estimates from fry emergence to outmigration at the Sacramento River confluence were generally low when integrated over time. The highest daily survival probability was Sy = 0.93 in 2019, or 50 percent total mortality after 10 days. In contrast, our lowest daily survival probability was Sy = 0.74 in 2015, or 95 percent total mortality after 10 days. Consequently, even our highest estimated daily survival probability might be considered low. This is especially true given that Sy was estimated over a relatively short distance (<14.7 kilometers) from emergence to the Watt Avenue fish trap. Several factors, including our assumed and relatively high daily egg survival rate of 0.9975, could influence juvenile survival estimates. For example, an egg survival rate of 0.9975 results in 3-percent total mortality after 10 days. Egg mortality estimates used in S3 calibration were approximated from egg survivorship studies in the Yakima River, Washington (Johnson and others, 2012), and remains one of the greater uncertainties in S3 when estimating survival across life stages. By including bona fide estimates of egg survival in S3 simulations, the validity of the S3’s current daily egg survival rate could be assessed specifically for the American River. Tagging studies also could provide S3 with direct estimates of juvenile survival and movement; survival during egg incubation then could be estimated indirectly via model fitting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231060","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., Hatton, T.W., Smith, C.D., and Hannon, J.M., 2023, Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California: U.S. Geological Survey Open-File Report 2023–1060, 35 p., https://doi.org/10.3133/ofr20231060.","productDescription":"ix, 35 p.","onlineOnly":"Y","ipdsId":"IP-141661","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":421858,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.XML"},{"id":421857,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1060/images"},{"id":421856,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1060"},{"id":421855,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1060"},{"id":421854,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1060/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"American River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5064051133351,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.727216763718815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Invasive freshwater mussels, such as the zebra (Dreissena polymorpha), quagga (Dreissena rostriformis bugensis), and golden (Limnoperna fortunei) mussel have spread outside their native ranges throughout many regions of the North American, South American, and European continents in recent decades, damaging infrastructure and the environment. This review describes ongoing efforts by multiple groups to develop genetic biocontrol methods for invasive mussels. First, we provide an overview of genetic biocontrol strategies that have been applied in other invasive or pest species. Next, we summarize physical and chemical methods that are currently in use for invasive mussel control. We then describe the multidisciplinary approaches our groups are employing to develop genetic biocontrol tools for invasive mussels. Finally, we discuss the challenges and limitations of applying genetic biocontrol tools to invasive mussels. Collectively, we aim to openly share information and combine expertise to develop practical tools to enable the management of invasive freshwater mussels.
Efforts to incorporate bioavailability adjustments into regulatory water quality criteria in the United States have included four major procedures: hardness-based single-linear regression equations, water-effect ratios (WERs), biotic ligand models (BLMs), and multiple-linear regression models (MLRs) that use dissolved organic carbon, hardness, and pH. The performance of each with copper (Cu) is evaluated, emphasizing the relative performance of hardness-based versus MLR-based criteria equations. The WER approach was shown to be inherently highly biased. The hardness-based model is in widest use, and the MLR approach is the US Environmental Protection Agency's (USEPA's) present recommended approach for developing aquatic life criteria for metals. The performance of criteria versions was evaluated with numerous toxicity datasets that were independent of those used to develop the MLR models, including olfactory and behavioral toxicity, and field and ecosystem studies. Within the range of water conditions used to develop the Cu MLR criteria equations, the MLR performed well in terms of predicting toxicity and protecting sensitive species and ecosystems. In soft waters, the MLR outperformed both the BLM and hardness models. In atypical waters with pH <5.5 or >9, neither the MLR nor BLM predictions were reliable, suggesting that site-specific testing would be needed to determine reliable Cu criteria for such settings. The hardness-based criteria performed poorly with all toxicity datasets, showing no or weak ability to predict observed toxicity. In natural waters, MLR and BLM criteria versions were strongly correlated. In contrast, the hardness-criteria version was often out of phase with the MLR and, depending on waterbody and season, could be either strongly overprotective or underprotective. The MLR-based USEPA-style chronic criterion appears to be more generally protective of ecosystems than other models.
LANDFIRE Help Desk
Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Due to Idaho’s inland location approximately 350 miles from the Pacific Ocean and its 80 recognized mountain ranges, the State’s climate varies widely, with maritime influence in the northern and western parts of Idaho and continental influence on the eastern side. The weather in the abundant mountains is unpredictable and often associated with natural hazards such as severe thunder and lightning storms leading to flooding, landslides, and wildfires. Issues important to Idaho’s economy include river, stream, and forest resource management, and infrastructure and construction management. Idaho participated in the U.S. Geological Survey 3D Elevation Program (3DEP) in 2016, the State’s first 3DEP project. The success of this project led to development of the Idaho Statewide Lidar Plan. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"