No abstract available.
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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Persons, Trevor","contributorId":247703,"corporation":false,"usgs":false,"family":"Persons","given":"Trevor","affiliations":[{"id":49619,"text":"Field Herpetologist, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":808137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drost, Charles A. 0000-0002-4792-7095 charles_drost@usgs.gov","orcid":"https://orcid.org/0000-0002-4792-7095","contributorId":3151,"corporation":false,"usgs":true,"family":"Drost","given":"Charles","email":"charles_drost@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":808138,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217097,"text":"70217097 - 2020 - Using hair cortisol to assess physiological stress in Alaska polar bears","interactions":[],"lastModifiedDate":"2025-03-07T15:42:57.149246","indexId":"70217097","displayToPublicDate":"2020-10-31T08:27:38","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"Using hair cortisol to assess physiological stress in Alaska polar bears","docAbstract":"The concentration of cortisol in hair (HCC) of polar bears (Ursus maritimus) may provide a retrospective view of physiological stress they experience and a link to their response to environmental change. To understand this relationship, we assayed HCC from polar bears captured in the Alaska Beaufort, Bering and Chukchi seas during 1983–1989 and 2004–2016. Cortisol accumulated in hair through summer and autumn and into the subsequent winter. HCC was similar between adult males and adult females. No difference in HCC across regions suggested all bears responded similarly to the environment. HCC in spring was elevated following years with a high winter Arctic Oscillation index and highly variable wind speed. HCC increased non-linearly with increasing duration of the continental shelf summer open water period up to 50 days and then decreased. HCC of spring samples declined with increasing body size, indicating that the stress response was more active in smaller bears or those in poor body condition. HCC of spring samples was greater and more variable in 2004–2006 than during either 1983–1989 or 2008–2016, and significantly so for females with 1st year cubs and subadult females. Elevated HCC in 2004–2006 coincided with years of reduced survival of southern Beaufort Sea polar bears and suggests that unidentified environmental perturbations impacted Alaska polar bears. Because HCC may be obtained by relatively non-invasive means, it has potential use for assessing polar bear populations that are difficult to study by capturing. Hence, information gained from HCC can inform polar bear conservation, especially on the vulnerability of subadult females and adult females with new cubs, and possible future environmental perturbations impacts on bear physiology.","language":"English","publisher":"Northern Pacific Research Board","usgsCitation":"Durner, G.M., 2020, Using hair cortisol to assess physiological stress in Alaska polar bears: Final Report, 79 p.","productDescription":"79 p.","ipdsId":"IP-123453","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":381912,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://nprb.org/project-search/#metadata/9be4eee1-a9a4-4026-a477-02da9460d0d3/project/files"},{"id":381946,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea, Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.1083984375,\n 69.61120561869633\n ],\n [\n -135.17578125,\n 70.40734767606811\n ],\n [\n -140.18554687499997,\n 72.3157853052617\n ],\n [\n -154.3798828125,\n 72.93253803407026\n ],\n [\n -160.576171875,\n 73.18861179538935\n ],\n [\n -162.509765625,\n 73.20131708476698\n ],\n [\n -164.2236328125,\n 72.79008827319015\n ],\n [\n -160.7080078125,\n 71.00265967789278\n ],\n [\n -159.2578125,\n 70.8446726342528\n ],\n [\n -156.8408203125,\n 70.91664110709776\n ],\n [\n -152.0068359375,\n 70.34831755984779\n ],\n [\n -141.1083984375,\n 69.54987728327795\n ],\n [\n -141.1083984375,\n 69.61120561869633\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.1787109375,\n 65.6582745198266\n ],\n [\n -164.00390625,\n 66.60067571342496\n ],\n [\n -163.6083984375,\n 67.13582938531948\n ],\n [\n -166.6845703125,\n 68.39918004344189\n ],\n [\n -166.201171875,\n 69.06856318696033\n ],\n [\n -165.05859375,\n 68.95839084822076\n ],\n [\n -163.4765625,\n 69.4421276134176\n ],\n [\n -162.9052734375,\n 70.24460360904779\n ],\n [\n -167.5634765625,\n 70.88788500718185\n ],\n [\n -170.244140625,\n 70.17020068549206\n ],\n [\n -168.1787109375,\n 65.6582745198266\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.650390625,\n 63.68524808030715\n ],\n [\n -168.046875,\n 63.11463763252091\n ],\n [\n -167.51953124999997,\n 63.87939001720202\n ],\n [\n -169.541015625,\n 64.4348920430406\n ],\n [\n -171.38671874999997,\n 64.41592147626879\n ],\n [\n -172.3095703125,\n 64.03374392176401\n ],\n [\n -171.650390625,\n 63.68524808030715\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":807603,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70268708,"text":"70268708 - 2020 - On the robustness of annual daily precipitation maxima estimates over Monsoon Asia","interactions":[],"lastModifiedDate":"2025-07-07T16:11:01.608215","indexId":"70268708","displayToPublicDate":"2020-10-30T11:09:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21978,"text":"Frontiers in Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"On the robustness of annual daily precipitation maxima estimates over Monsoon Asia","docAbstract":"Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India, and the Maritime Continent based on their different station density, orography, and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g., Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.578785","usgsCitation":"Nguyen, P., Bador, M., Alexander, L., Lane, T., and Funk, C., 2020, On the robustness of annual daily precipitation maxima estimates over Monsoon Asia: Frontiers in Climate Services, v. 2, 578785, 19 p., https://doi.org/10.3389/fclim.2020.578785.","productDescription":"578785, 19 p.","ipdsId":"IP-121958","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492046,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.578785","text":"Publisher Index Page"},{"id":491743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Monsoon Asia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 67.82681208821384,\n 28.08149631086141\n ],\n [\n 67.82681208821384,\n 4.541379126404635\n ],\n [\n 88.69639230102973,\n 4.541379126404635\n ],\n [\n 88.69639230102973,\n 28.08149631086141\n ],\n [\n 67.82681208821384,\n 28.08149631086141\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 148.23729356007158,\n 45.2939171288528\n ],\n [\n 129.57288839651233,\n 45.2939171288528\n ],\n [\n 129.57288839651233,\n 29.638462684082825\n ],\n [\n 148.23729356007158,\n 29.638462684082825\n ],\n [\n 148.23729356007158,\n 45.2939171288528\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 90.4186427916805,\n 10.095537024904786\n ],\n [\n 90.4186427916805,\n -11.16935497577198\n ],\n [\n 155.06835956423896,\n -11.16935497577198\n ],\n [\n 155.06835956423896,\n 10.095537024904786\n ],\n [\n 90.4186427916805,\n 10.095537024904786\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Nguyen, Phuong-Loan","contributorId":357544,"corporation":false,"usgs":false,"family":"Nguyen","given":"Phuong-Loan","affiliations":[{"id":85452,"text":"ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Sydney, New South Wales, Australia","active":true,"usgs":false}],"preferred":false,"id":941695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bador, Margot","contributorId":223056,"corporation":false,"usgs":false,"family":"Bador","given":"Margot","email":"","affiliations":[{"id":40656,"text":"Climate Change Research Centre, UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":941696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Lisa","contributorId":223054,"corporation":false,"usgs":false,"family":"Alexander","given":"Lisa","email":"","affiliations":[{"id":40656,"text":"Climate Change Research Centre, UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":941697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, Todd P.","contributorId":357545,"corporation":false,"usgs":false,"family":"Lane","given":"Todd P.","affiliations":[{"id":85454,"text":"2School of Earth Science and ARC Centre of Excellence for Climate Extremes, The University of Melbourne, Melbourne, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":941698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":941699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227642,"text":"70227642 - 2020 - Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data","interactions":[],"lastModifiedDate":"2022-01-24T14:57:06.932262","indexId":"70227642","displayToPublicDate":"2020-10-30T08:48:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data","docAbstract":"Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R2, RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.
No abstract available.
","language":"English","publisher":"Bonneville Power Administration","collaboration":"Bonneville Power Administration","usgsCitation":"Jezorek, I., 2020, Wind River Subbasin Restoration annual report of U.S. Geological Survey activities January 2019 through December 2019, 74 p.","productDescription":"74 p.","ipdsId":"IP-121318","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":382093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382077,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/DocumentViewer/P179251/83769-2.pdf"}],"country":"United States","state":"Washington","otherGeospatial":"Wind River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.2119140625,\n 45.644768217751924\n ],\n [\n -121.09130859375,\n 45.644768217751924\n ],\n [\n -121.09130859375,\n 46.195042108660154\n ],\n [\n -122.2119140625,\n 46.195042108660154\n ],\n [\n -122.2119140625,\n 45.644768217751924\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217811,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":808006,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217144,"text":"70217144 - 2020 - Riparian plant communities remain stable in response to a second cycle of Tamarix biocontrol defoliation","interactions":[],"lastModifiedDate":"2021-01-07T13:24:15.179148","indexId":"70217144","displayToPublicDate":"2020-10-30T07:20:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Riparian plant communities remain stable in response to a second cycle of Tamarix biocontrol defoliation","docAbstract":"Reduced abundance of non-native Tamarix shrubs in western U.S. riparian systems following biological control by a defoliating beetle has led to concerns that replacement plant communities could be dominated by other invasive species and/or not provide some of the ecosystem services that Tamarix was providing. In previous studies, Tamarix decline following biocontrol was accompanied by small increases in native and non-native herbaceous species, with variable responses of woody vegetation. However, none of these studies spanned periods longer than a decade since beetle release. This is an important caveat, given the cyclical nature of plant-herbivore interactions and potential lags in vegetation recovery. We report plant community response to an eight-year-long second cycle of Tamarix defoliation-refoliation in two reaches of the upper Colorado River in eastern Utah, 11–13 years after beetle arrival. Tamarix cover across sites initially declined an average of ca. 50% in response to the beetle, but then recovered. Changes in the associated plant community were small but supported common management goals, including a 47% average increase in cover of a native shrub (Salix exigua), and no secondary invasions by other non-native plants. We suggest that the effectiveness of biocontrol programs must be assessed case-by-case, and on a long-term basis.
A geochemical characterization of groundwater in the Big Chino subbasin of Arizona was conducted by the U.S. Geological Survey, in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project, to understand groundwater evolution through the study area and the source of water to springs along the gaining reach of the Verde River just downstream from its confluence with Granite Creek. Samples were collected between 2011 and 2018 in groundwater wells completed in basin-fill and carbonate aquifers and at selected springs, including two discrete springs discharging along the aforementioned stretch of the Verde River. Five newly installed monitoring wells completed in the carbonate aquifer were sampled in 2018. Water-quality results obtained from these samples include the first known geochemical data for carbonate groundwater beneath the basin-fill in the Big Chino subbasin downgradient from Walnut Creek near Paulden, Arizona, as well as other parts of the study area without previous data. Groundwater samples were collected and analyzed for major ions, arsenic, nutrients, stable isotopes of oxygen and hydrogen (δ18O and δ2H), strontium isotopes (87Sr/86Sr), carbon-14, isotopes of carbon (δ13C), and noble gases.
Significant differences in groundwater geochemistry between the basin-fill and carbonate aquifers were driven primarily by higher pH, tritium, and δ18O and δ2H in the basin-fill aquifer samples and higher specific conductance and higher concentrations of calcium, sodium, bicarbonate, fluoride, and arsenic in the carbonate aquifer samples. All but one sample from the carbonate aquifer and two samples from the basin-fill aquifer exceeded the U.S. Environmental Protection Agency (EPA) drinking water standard for arsenic of 10 micrograms per liter. One basin-fill aquifer sample exceeded the EPA drinking water standard for fluoride of 4 milligrams per liter, and one carbonate aquifer sample exceeded the EPA secondary drinking water standard for fluoride of 2 milligrams per liter. A component of modern groundwater recharged following aboveground nuclear testing beginning in the mid-1950s is present in some basin-fill and spring groundwater from this study. Groundwater that can be dated using radiocarbon decay is also present in the study area, with four groundwater samples indicating possible recharge during the Pleistocene with groundwater ages ranging from approximately 34,600 to 13,300 years before present. Other groundwater sampled during this study that can dated using radiocarbon decay ranged in age from about 7,500 to 1,100 years before present, indicating possible recharge during the Holocene.
The gaining reach of the Verde River downstream from the confluence with Granite Creek shows areal changes in temperature, pH, and specific conductance, indicating multiple zones of groundwater input. Surface-water samples for analyses of δ18O and δ2H have been collected at the Verde River near Paulden, Ariz. streamgage (09503700) during discharge measurements since 2009, and a trend analysis of the δ18O and δ2H data indicated no significant trend exists for the 10-year period of record. Additional groundwater samples from the carbonate aquifer beneath the basin-fill upgradient and downgradient from Walnut Creek would provide valuable information to understand groundwater evolution along the Big Chino subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205094","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project","usgsCitation":"Beisner, K.R., and Jones, C.J.R., 2020, Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18: U.S. Geological Survey Scientific Investigations Report 2020–5094, 49 p., https://doi.org/10.3133/sir20205094.","productDescription":"Report: viii, 49 p.; 2 Appendixes; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113409","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":379927,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HMZNIK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Carbon and strontium isotopic data for rock, soil, and soil gas from the Big Chino Sub-Basin, Arizona, 2017 and 2018"},{"id":379924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.csv","text":"Appendix 1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"},{"id":379923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094.pdf","text":"Report","size":"37.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5094"},{"id":379926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P909LD47","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water quality parameters in the Verde River below Granite Creek, Arizona, June 2018"},{"id":379922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5094/coverthb.jpg"},{"id":379925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.xlsx","text":"Appendix 1","size":"33.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"}],"country":"United States","state":"Arizona","otherGeospatial":"Big Chino subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.18389892578125,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 34.3366324743773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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Albuquerque, NM 87111
We present measurements of the density, hydraulic conductivity, and specific discharge of a widespread firn aquifer in Antarctica, within the Wilkins Ice Shelf. At the field site, the aquifer is 16.2 m thick, starting at 13.4 m from the snow surface and transitioning from water‐saturated firn to ice at 29.6 m. Hydraulic conductivity derived from slug tests show a geometric mean value of 1.4 ± 1.2 × 10−4 m s−1, equivalent to permeability of 2.6 ± 2.2 × 10−11 m2. A borehole dilution test indicates an average specific discharge value of 1.9 ± 2.8 × 10−6 m s−1. Ground‐penetrating radar profiles and a groundwater flow model show the aquifer is draining laterally into a large nearby rift. Our findings indicate that the firn aquifer in the vicinity of the field site is likely not in a steady state and its presence likely contributed to past ice shelf instability.
","language":"English","publisher":"Wiley","doi":"10.1029/2020GL089552","usgsCitation":"Montgomery, L., Miege, C., MIller, J., Wallin, B., Miller, O.L., Scambos, T.A., Solomon, D., Forster, R., and Koenig, L., 2020, Hydrologic properties of a highly permeable firn aquifer in the Wilkins Ice Shelf, Antarctica: Geophysical Research Letters, v. 47, e2020GL089552, 10 p., https://doi.org/10.1029/2020GL089552.","productDescription":"e2020GL089552, 10 p.","ipdsId":"IP-119455","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":454923,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl089552","text":"External Repository"},{"id":382525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Wilkins Ice Sheet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.54022216796875,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.79883675782347\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Montgomery, Lynn","contributorId":244036,"corporation":false,"usgs":false,"family":"Montgomery","given":"Lynn","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":803105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miege, C.","contributorId":248303,"corporation":false,"usgs":false,"family":"Miege","given":"C.","email":"","affiliations":[],"preferred":false,"id":808855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MIller, Julie","contributorId":248311,"corporation":false,"usgs":false,"family":"MIller","given":"Julie","email":"","affiliations":[],"preferred":false,"id":808856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallin, Bruce","contributorId":248312,"corporation":false,"usgs":false,"family":"Wallin","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":808857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scambos, Ted A.","contributorId":57367,"corporation":false,"usgs":true,"family":"Scambos","given":"Ted","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":216556,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803106,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solomon, D Kip","contributorId":146290,"corporation":false,"usgs":false,"family":"Solomon","given":"D Kip","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":808859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Forster, Richard","contributorId":172149,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","affiliations":[{"id":26993,"text":"University of Utah, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":808860,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Koenig, Lora","contributorId":248313,"corporation":false,"usgs":false,"family":"Koenig","given":"Lora","affiliations":[],"preferred":false,"id":808861,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215786,"text":"ds1131 - 2020 - Fish assemblages in eelgrass beds of Bellingham Bay, Washington, Northern Puget Sound, 2019","interactions":[],"lastModifiedDate":"2020-10-30T15:31:44.761418","indexId":"ds1131","displayToPublicDate":"2020-10-29T11:53:31","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1131","displayTitle":"Fish Assemblages in Eelgrass Beds of Bellingham Bay, Washington, Northern Puget Sound, 2019","title":"Fish assemblages in eelgrass beds of Bellingham Bay, Washington, Northern Puget Sound, 2019","docAbstract":"Puget Sound is a critical part of the Pacific Northwest, both culturally and economically. Eelgrass beds are an important feature of Puget Sound and are known to influence fish assemblages. As part of a larger site-characterization effort, and to gain a better understanding of the fish assemblages in Bellingham Bay, Washington, four eelgrass beds (Zostera marina) along the shoreline were surveyed. Fish were captured from 24 through 26 September 2019 by using three beach-seine hauls per eelgrass bed. In total, 12 hauls yielded 2,135 fish that comprised 20 species from 14 families. Shiner perch (Cymatogaster aggregata) accounted for 52 percent of the total catch. The other common species included three-spine stickleback (Gasterosteus aculeatus), bay pipefish (Syngnathus leptorhynchus), saddleback gunnel (Pholis ornata), Pacific staghorn sculpin (Leptocottus armatus), and Pacific sand lance (Ammodytes personatus). Total catch and species richness were highest at the two locations closest to the urban center of Bellingham; however, species diversity and evenness were highest at the two eelgrass beds farthest from the city center. Descriptions of fish assemblages in eelgrass beds are expected to be useful in the development of future process-based investigations by study partners and will focus on the movements of sediments and contaminants and their influence on biota.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1131","usgsCitation":"Andrews, M.I., and Liedtke, T.L., 2020, Fish assemblages in eelgrass beds of Bellingham Bay, Washington, Northern Puget Sound, 2019: U.S. Geological Survey Data Series 1131, 11 p., https://doi.org/10.3133/ds1131.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-117153","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":379932,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1131/ds1131.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1131"},{"id":379931,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1131/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Bellingham Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.68020629882812,\n 48.45561965661709\n ],\n [\n -122.42752075195314,\n 48.45561965661709\n ],\n [\n -122.42752075195314,\n 48.79510425169179\n ],\n [\n -122.68020629882812,\n 48.79510425169179\n ],\n [\n -122.68020629882812,\n 48.45561965661709\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Widespread application of neonicotinoids has led to their proliferation in waters. Despite low neonicotinoid hydrophobicity, our prior studies implicated granular activated carbon (GAC) in neonicotinoid removal. Based on known receptor binding characteristics, we hypothesized that the insecticidal pharmacophore influences neonicotinoid sorption. Our objectives were to illuminate drivers of neonicotinoid sorption for parent neonicotinoids (imidacloprid, clothianidin, thiamethoxam, and thiacloprid) and pharmacophore-altered metabolites (desnitro-imidacloprid and imidacloprid urea) to GAC, powdered activated carbon, and carbon nanotubes (CNTs). Neonicotinoid sorption to GAC was extensive and largely irreversible, with significantly greater sorption of imidacloprid than desnitro-imidacloprid. Imidacloprid and imidacloprid urea (electronegative pharmacophores) sorbed most extensively to nonfunctionalized CNTs, whereas desnitro-imidacloprid (positive pharmacophore) sorbed most to COOH-CNTs, indicating the importance of charge interactions and/or hydrogen bonding between the pharmacophore and carbon surface. Water chemistry parameters (temperature, alkalinity, ionic strength, and humic acid) inhibited overall neonicotinoid sorption, suggesting that pharmacophore-driven sorption in real waters may be diminished. Analysis of a full-scale drinking water treatment plant GAC filter influent, effluent, and spent GAC attributes neonicotinoid/metabolite removal to GAC under real-world conditions for the first time. Our results demonstrate that the neonicotinoid pharmacophore not only confers insecticide selectivity but also impacts sorption behavior, leading to less effective removal of metabolites by GAC filters in water treatment.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c04187","usgsCitation":"Webb, D.T., Nagorzanski, M.R., Powers, M.M., Cwiertny, D.M., Hladik, M.L., and LeFevre, G.H., 2020, Differences in neonicotinoid and metabolite sorption to activated carbon are driven by alterations to the insecticidal pharmacophore: Environmental Science and Technology, v. 54, no. 22, p. 14694-14705, https://doi.org/10.1021/acs.est.0c04187.","productDescription":"10 p.","startPage":"14694","endPage":"14705","onlineOnly":"N","ipdsId":"IP-119721","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":379964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Danielle T.","contributorId":211879,"corporation":false,"usgs":false,"family":"Webb","given":"Danielle","email":"","middleInitial":"T.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":803520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagorzanski, Matthew R.","contributorId":211881,"corporation":false,"usgs":false,"family":"Nagorzanski","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":803521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powers, Megan M","contributorId":244212,"corporation":false,"usgs":false,"family":"Powers","given":"Megan","email":"","middleInitial":"M","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":803522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cwiertny, David M.","contributorId":190557,"corporation":false,"usgs":false,"family":"Cwiertny","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":803523,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":205314,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803524,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":803525,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215981,"text":"70215981 - 2020 - Harvester ant seed removal in an invaded sagebrush ecosystem: Implications for restoration","interactions":[],"lastModifiedDate":"2020-12-29T21:46:00.683246","indexId":"70215981","displayToPublicDate":"2020-10-29T08:11:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Harvester ant seed removal in an invaded sagebrush ecosystem: Implications for restoration","docAbstract":"A better understanding of seed movement in plant community dynamics is needed, especially in light of disturbance‐driven changes and investments into restoring degraded plant communities. A primary agent of change within the sagebrush‐steppe is wildfire and invasion by non‐native forbs and grasses, primarily cheatgrass (Bromus tectorum). Our objectives were to quantify seed removal and evaluate ecological factors influencing seed removal within degraded sagebrush‐steppe by granivorous Owyhee harvester ants (Pogonomyrmex salinus Olsen). In 2014, we sampled 76 harvester ant nests across 11 plots spanning a gradient of cheatgrass invasion (40%–91% cover) in southwestern Idaho, United States. We presented seeds from four plant species commonly used in postfire restoration at 1.5 and 3.0 m from each nest to quantify seed removal. We evaluated seed selection for presented species, monthly removal, and whether biotic and abiotic factors (e.g., distance to nearest nest, temperature) influenced seed removal. Our top model indicated seed removal was positively correlated with nest height, an indicator of colony size. Distance to seeds and cheatgrass canopy cover reduced seed removal, likely due to increased search and handling time. Harvester ants were selective, removing Indian ricegrass (Achnatherum hymenoides) more than any other species presented. We suspect this was due to ease of seed handling and low weight variability. Nest density influenced monthly seed removal, as we estimated monthly removal of 1,890 seeds for 0.25 ha plots with 1 nest and 29,850 seeds for plots with 15 nests. Applying monthly seed removal to historical restoration treatments across the western United States showed harvester ants can greatly reduce seed availability at degraded sagebrush sites; for instance, fourwing saltbush (Atriplex canescens) seeds could be removed in <2 months. Collectively, these results shed light on seed removal by harvester ants and emphasize their potential influence on postfire restoration within invaded sagebrush communities.
Plant productivity is central to numerous ecosystem functions in tidal wetlands. We examined how productivity of brackish marsh plants in northern California responded to abiotic stress gradients of inundation and salinity using two experimental approaches. In a greenhouse study with varying salinity, shoot production and biomass of Juncus balticus, Schoenoplectus acutus and S. americanus all declined monotonically with higher salinity, with evidence of differences in sensitivity among species by their varied functional responses. Salinity also negatively affected fecundity for the one species (S. americanus) that produced enough inflorescences during the experiment for analysis. In a field manipulation of inundation and initial pore water salinity, total end-of-season biomass and other metrics of growth in the high marsh species, J. balticus, had unimodal relationships with inundation. Root production tended to be greater strongly impacted by greater inundation than shoot production. The salinity treatment quickly dissipated for treatments that were flooded more frequently but persisted at a higher marsh elevation where it suppressed plant growth. These results suggest that both increased flooding and salinity associated with climate change and sea-level rise may negatively impact productivity of brackish marsh species, but with variable effects by species and stressor.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-020-04419-3","usgsCitation":"Janousek, C.N., Dugger, B.D., Drucker, B.M., and Thorne, K., 2020, Salinity and inundation effects on productivity of brackish tidal marsh plants in the San Francisco Bay-Delta Estuary: Hydrobiologia, v. 847, p. 4311-4323, https://doi.org/10.1007/s10750-020-04419-3.","productDescription":"13 p.","startPage":"4311","endPage":"4323","ipdsId":"IP-122239","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":385759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.134765625,\n 36.84446074079564\n ],\n [\n -120.9814453125,\n 36.84446074079564\n ],\n [\n -120.9814453125,\n 39.232253141714885\n ],\n [\n -123.134765625,\n 39.232253141714885\n ],\n [\n -123.134765625,\n 36.84446074079564\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"847","noUsgsAuthors":false,"publicationDate":"2020-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815986,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Bruce D.","contributorId":176167,"corporation":false,"usgs":false,"family":"Dugger","given":"Bruce","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":815987,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drucker, Brandon M","contributorId":258214,"corporation":false,"usgs":false,"family":"Drucker","given":"Brandon","email":"","middleInitial":"M","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":815988,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815989,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216159,"text":"70216159 - 2020 - Detection and assessment of a large and potentially tsunamigenic periglacial landslide in Barry Arm, Alaska","interactions":[],"lastModifiedDate":"2023-11-02T16:54:18.814614","indexId":"70216159","displayToPublicDate":"2020-10-29T07:50:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Detection and assessment of a large and potentially tsunamigenic periglacial landslide in Barry Arm, Alaska","docAbstract":"The retreat of glaciers in response to global warming has the potential to trigger landslides in glaciated regions around the globe. Landslides that enter fjords or lakes can cause tsunamis, which endanger people and infrastructure far from the landslide itself. Here we document the ongoing movement of an unstable slope (total volume of 455 million m3) in Barry Arm, a fjord in Prince William Sound, Alaska. The slope moved rapidly between 2010 and 2017, yielding a horizontal displacement of 120 m, which is highly correlated with the rapid retreat and thinning of Barry Glacier. Should the entire unstable slope collapse at once, preliminary tsunami modeling suggests a maximum runup of 300 m near the landslide, which may have devastating impacts on local communities. Our findings highlight the need for interdisciplinary studies of recently deglaciated fjords to refine our understanding of the impact of climate change on landslides and tsunamis.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089800","usgsCitation":"Dai, C., Higman, B., Lynett, P.J., Jacquemart, M., Howat, I., Liljedahl, A.K., Dufresne, A., Freymueller, J.T., Geertsema, M., Jones, M.W., and Haeussler, P., 2020, Detection and assessment of a large and potentially tsunamigenic periglacial landslide in Barry Arm, Alaska: Geophysical Research Letters, e2020GL089800, 9 p., https://doi.org/10.1029/2020GL089800.","productDescription":"e2020GL089800, 9 p.","ipdsId":"IP-122213","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":454932,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl089800","text":"Publisher Index Page"},{"id":380255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.18436382537658,\n 61.20521067964421\n ],\n [\n -148.18436382537658,\n 61.11202000604544\n ],\n [\n -148.0222489470055,\n 61.11202000604544\n ],\n [\n -148.0222489470055,\n 61.20521067964421\n ],\n [\n -148.18436382537658,\n 61.20521067964421\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dai, Chunli 0000-0003-1840-8699","orcid":"https://orcid.org/0000-0003-1840-8699","contributorId":244604,"corporation":false,"usgs":false,"family":"Dai","given":"Chunli","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":804250,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Higman, Bretwood","contributorId":224587,"corporation":false,"usgs":false,"family":"Higman","given":"Bretwood","affiliations":[{"id":40893,"text":"Ground Truth Trekking, Seldovia, AK, USA","active":true,"usgs":false}],"preferred":false,"id":804251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynett, Patrick J. 0000-0002-2856-9405","orcid":"https://orcid.org/0000-0002-2856-9405","contributorId":244605,"corporation":false,"usgs":false,"family":"Lynett","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":804252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacquemart, Mylene 0000-0003-2501-7645","orcid":"https://orcid.org/0000-0003-2501-7645","contributorId":244606,"corporation":false,"usgs":false,"family":"Jacquemart","given":"Mylene","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":804253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howat, Ian 0000-0002-8072-6260","orcid":"https://orcid.org/0000-0002-8072-6260","contributorId":244607,"corporation":false,"usgs":false,"family":"Howat","given":"Ian","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":804254,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liljedahl, Anna K. 0000-0001-7114-6443","orcid":"https://orcid.org/0000-0001-7114-6443","contributorId":150135,"corporation":false,"usgs":false,"family":"Liljedahl","given":"Anna","email":"","middleInitial":"K.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":804255,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dufresne, Anja 0000-0001-7777-3317","orcid":"https://orcid.org/0000-0001-7777-3317","contributorId":244608,"corporation":false,"usgs":false,"family":"Dufresne","given":"Anja","email":"","affiliations":[{"id":48946,"text":"Aachen University, Germany","active":true,"usgs":false}],"preferred":false,"id":804256,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freymueller, Jeffery T. 0000-0003-0614-0306","orcid":"https://orcid.org/0000-0003-0614-0306","contributorId":244609,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffery","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":804257,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Geertsema, Marten","contributorId":197464,"corporation":false,"usgs":false,"family":"Geertsema","given":"Marten","email":"","affiliations":[],"preferred":false,"id":804258,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jones, Melissa Ward 0000-0002-3401-2515","orcid":"https://orcid.org/0000-0002-3401-2515","contributorId":244610,"corporation":false,"usgs":false,"family":"Jones","given":"Melissa","email":"","middleInitial":"Ward","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":804259,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":804260,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70222578,"text":"70222578 - 2020 - On the size of the flare associated with the solar proton event in 774 AD","interactions":[],"lastModifiedDate":"2021-08-05T12:52:31.307","indexId":"70222578","displayToPublicDate":"2020-10-29T07:48:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":917,"text":"Astrophysical Journal","active":true,"publicationSubtype":{"id":10}},"title":"On the size of the flare associated with the solar proton event in 774 AD","docAbstract":"The 774 AD solar proton event (SPE) detected in cosmogenic nuclides had an inferred >1 GV (>430 MeV) fluence estimated to have been ~30–70 times larger than that of the 1956 February 23 ground level event (GLE). The 1956 GLE was itself ~2.5 times larger at >430 MeV than the episode of strong GLE activity from 1989 August–October. We use an inferred soft X-ray (SXR) class of X20 ± 10 for the 1956 February 23 eruptive flare as a bridge to the source flare for the 774 SPE. A correlation of the >200 MeV proton fluences of hard-spectra post-1975 GLEs with the SXR peak fluxes of their associated flares yields an SXR flare class of X285 ± 140 (bolometric energy of ~(1.9 ± 0.7) × 1033 erg) for the 774 flare. This estimate is within theoretical determinations of the largest flare the Sun could produce based on the largest spot group yet observed. Assuming a single eruptive flare source for the 774 SPE, the above estimate indicates that the Sun can produce a threshold-level 1033 erg superflare. If the 774 event originated in two closely timed, equal-fluence SPEs, the inferred flare size drops to X180 ± 90 (~(1.4 ± 0.5) × 1033 erg). We speculate on favorable solar conditions that can lead to enhanced shock acceleration of high-energy protons in eruptive flares.
","language":"English","publisher":"American Astronomical Society","doi":"10.3847/1538-4357/abad93","usgsCitation":"Cliver, E., Hayakawa, H., Love, J.J., and Neidig, D.F., 2020, On the size of the flare associated with the solar proton event in 774 AD: Astrophysical Journal, v. 903, no. 1, https://doi.org/10.3847/1538-4357/abad93.","ipdsId":"IP-120688","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":454933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/1538-4357/abad93","text":"Publisher Index Page"},{"id":387711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"903","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cliver, E. W. 0000-0002-4342-6728","orcid":"https://orcid.org/0000-0002-4342-6728","contributorId":261774,"corporation":false,"usgs":false,"family":"Cliver","given":"E. W.","affiliations":[{"id":53008,"text":"School of Physics & Astronomy, University of Glasgow, Glasgow G12 8QQ, UK National Solar Observatory, 3665 Discovery Drive, Boulder, CO, 80304, USA","active":true,"usgs":false}],"preferred":false,"id":820617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayakawa, H. 0000-0001-5370-3365","orcid":"https://orcid.org/0000-0001-5370-3365","contributorId":261775,"corporation":false,"usgs":false,"family":"Hayakawa","given":"H.","email":"","affiliations":[{"id":53009,"text":"Nagoya University, Rutherford Appleton Laboratory, Nishina Center","active":true,"usgs":false}],"preferred":false,"id":820618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neidig, D. F.","contributorId":261776,"corporation":false,"usgs":false,"family":"Neidig","given":"D.","email":"","middleInitial":"F.","affiliations":[{"id":53010,"text":"U.S. Air Force Research Laboratory (retired)","active":true,"usgs":false}],"preferred":false,"id":820620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215985,"text":"70215985 - 2020 - Nitrate in streams during winter low‐flow conditions as an indicator of legacy nitrate","interactions":[],"lastModifiedDate":"2020-11-30T16:30:57.387972","indexId":"70215985","displayToPublicDate":"2020-10-29T07:48:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Nitrate in streams during winter low‐flow conditions as an indicator of legacy nitrate","docAbstract":"Winter low‐flow (LF) conditions in streams provide a potential opportunity to evaluate the importance of legacy nitrate in catchments due to the dominance of slow‐flow transport pathways and lowered biotic activity. In this study, the concentration, flux, and trend of nitrate in streams during winter low‐flow conditions were analyzed at 320 sites in the conterminous United States. LF flow‐normalized nitrate concentrations varied from <0.1 to >20 mg‐N L‐1 and LF conditions contributed between 2% and 98% of the winter nitrate flux. LF nitrate concentrations generally exceeded 2.5 mg‐N L‐1 in the upper Midwest, with smaller regions of high LF nitrate concentrations in eastern Texas and along the northern mid‐Atlantic coast. Groundwater was inferred to be the primary or sole contributor of nitrate to streams during winter LF conditions at 140 of our 320 sites. Among these 140 sites, nitrate from groundwater comprised 45% or more of the winter nitrate flux at a quarter of the sites. Among the same 140 sites, concentrations of nitrate in streams during winter LF conditions generally increased between 2002 and 2012 at sites where 40% or more of the winter flux was from groundwater, suggesting that concentrations of nitrate in the contributing groundwater system were increasing. Using metrics developed herein, we characterize the potential importance of legacy nitrate at sites in this study and discuss methods to characterize sites with fewer samples than required by our models or at sites without continuous stream discharge measurements.
Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.
In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027721","usgsCitation":"Fu, B., Horsburgh, J., Jakeman, A.J., Gaultieri, C., Arnold, T.W., Marshall, L.A., Green, T.R., Quinn, N.W., Volk, M., Hunt, R., Vezzaro, L., Croke, B., Jakeman, J., Snow, V.O., and Rashleigh, B., 2020, Modeling water quality in watersheds: From here to the next generation: Water Resources Research, v. 56, no. 11, e2020WR027721, 28 p., https://doi.org/10.1029/2020WR027721.","productDescription":"e2020WR027721, 28 p.","ipdsId":"IP-123332","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":454942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr027721","text":"Publisher Index Page"},{"id":383140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Fu, Baihua 0000-0003-2494-0518","orcid":"https://orcid.org/0000-0003-2494-0518","contributorId":174165,"corporation":false,"usgs":false,"family":"Fu","given":"Baihua","email":"","affiliations":[],"preferred":false,"id":810074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horsburgh, J. S. 0000-0002-0768-3196","orcid":"https://orcid.org/0000-0002-0768-3196","contributorId":248851,"corporation":false,"usgs":false,"family":"Horsburgh","given":"J. S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":810075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jakeman, Anthony J. 0000-0001-5282-2215","orcid":"https://orcid.org/0000-0001-5282-2215","contributorId":173848,"corporation":false,"usgs":false,"family":"Jakeman","given":"Anthony","email":"","middleInitial":"J.","affiliations":[{"id":17939,"text":"The Australian National University","active":true,"usgs":false}],"preferred":false,"id":810076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gaultieri, C 0000-0002-3717-1618","orcid":"https://orcid.org/0000-0002-3717-1618","contributorId":248852,"corporation":false,"usgs":false,"family":"Gaultieri","given":"C","email":"","affiliations":[{"id":50045,"text":"University of Napoli","active":true,"usgs":false}],"preferred":false,"id":810077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arnold, Todd W.","contributorId":36058,"corporation":false,"usgs":false,"family":"Arnold","given":"Todd","email":"","middleInitial":"W.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":810078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marshall, Lucy A. 0000-0003-0450-4292","orcid":"https://orcid.org/0000-0003-0450-4292","contributorId":198080,"corporation":false,"usgs":false,"family":"Marshall","given":"Lucy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":810079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Green, Tim R 0000-0002-1441-8008","orcid":"https://orcid.org/0000-0002-1441-8008","contributorId":248853,"corporation":false,"usgs":false,"family":"Green","given":"Tim","email":"","middleInitial":"R","affiliations":[{"id":39550,"text":"U.S. Department of Agriculture, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":810080,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quinn, Nigel W. T. 0000-0003-3333-4763","orcid":"https://orcid.org/0000-0003-3333-4763","contributorId":248854,"corporation":false,"usgs":false,"family":"Quinn","given":"Nigel","email":"","middleInitial":"W. T.","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":810081,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Volk, Martin 0000-0003-0064-8133","orcid":"https://orcid.org/0000-0003-0064-8133","contributorId":247479,"corporation":false,"usgs":false,"family":"Volk","given":"Martin","email":"","affiliations":[{"id":13477,"text":"Washington Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":810082,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810083,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vezzaro, L. 0000-0001-6344-7131","orcid":"https://orcid.org/0000-0001-6344-7131","contributorId":248855,"corporation":false,"usgs":false,"family":"Vezzaro","given":"L.","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":810084,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Croke, Barry 0000-0001-9216-1554","orcid":"https://orcid.org/0000-0001-9216-1554","contributorId":248856,"corporation":false,"usgs":false,"family":"Croke","given":"Barry","email":"","affiliations":[{"id":27305,"text":"Australia National University","active":true,"usgs":false}],"preferred":false,"id":810085,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Jakeman, John 0000-0002-3517-337X","orcid":"https://orcid.org/0000-0002-3517-337X","contributorId":248857,"corporation":false,"usgs":false,"family":"Jakeman","given":"John","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":810086,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Snow, Valerie O 0000-0002-6911-8184","orcid":"https://orcid.org/0000-0002-6911-8184","contributorId":248846,"corporation":false,"usgs":false,"family":"Snow","given":"Valerie","email":"","middleInitial":"O","affiliations":[{"id":50044,"text":"AgResearch","active":true,"usgs":false}],"preferred":false,"id":810087,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rashleigh, Brenda 0000-0002-0806-686X","orcid":"https://orcid.org/0000-0002-0806-686X","contributorId":242708,"corporation":false,"usgs":false,"family":"Rashleigh","given":"Brenda","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":810088,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70259932,"text":"70259932 - 2020 - The interaction between concentrated pyroclastic density currents and snow: a case study from the 2008 mixed-avalanche from Volcán Llaima (Chile)","interactions":[],"lastModifiedDate":"2024-10-28T11:25:13.583953","indexId":"70259932","displayToPublicDate":"2020-10-29T06:23:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"The interaction between concentrated pyroclastic density currents and snow: a case study from the 2008 mixed-avalanche from Volcán Llaima (Chile)","docAbstract":"The incorporation of snow and ice by pyroclastic density currents (PDCs) can generate mixed-avalanches and pose significant hazards at snow-clad volcanoes. Commonly, the poor preservation of these thin deposits, combined with the subtle characteristics of PDC-snow interaction, has limited their recognition in the geological record. A small-volume (2.5 × 105 m3), basaltic-andesite, mixed-avalanche deposit formed during the 2008 eruption of Volcán Llaima (Chile) provides insight into PDC and snow interactions. Pyroclasts accumulated on the crater rim and collapsed to form a flow that swept up to 2.8 km from source and spread across 6.09 × 105 m2 of the snow-clad slopes. The PDC-snow interaction at the crater rim or during flow propagation resulted in distinct deposit and pyroclast textures. These included abundant blocky non-vesicular cauliflower clasts and blocky poorly vesicular scoria. The thin and sheet-like mixed-avalanche deposit had a lumpy surface, lacked marginal levees, was very loose, and fine ash depleted. Although the flow likely incorporated snow and/or ice mechanically, the overall coarseness of the mixture precluded effective fluidization related to vaporization. Many of the features described herein are distinctive features of other mixed-avalanche deposits worldwide and should be considered key indicators of PDC-snow interaction.
Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails (Rallus obsoletus yumanensis) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108734","usgsCitation":"Harrity, E.J., Stevens, B., and Conway, C.J., 2020, Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment: Biological Conservation, v. 250, 108734,10 p., https://doi.org/10.1016/j.biocon.2020.108734.","productDescription":"108734,10 p.","ipdsId":"IP-116214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River Basin, Salton Sink","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.840087890625,\n 32.52828936482526\n ],\n [\n -114.796142578125,\n 32.44024912337551\n ],\n [\n -114.08752441406249,\n 32.697177359290635\n ],\n [\n -114.246826171875,\n 33.55055114384406\n ],\n [\n -114.29077148437499,\n 33.8247936182649\n ],\n [\n -113.873291015625,\n 34.31621838080741\n ],\n [\n -114.356689453125,\n 34.88593094075317\n ],\n [\n -114.63134765625001,\n 34.858890491257796\n ],\n [\n -115.916748046875,\n 33.128351191631566\n ],\n [\n -114.840087890625,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"250","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrity, Eamon J.","contributorId":275852,"corporation":false,"usgs":false,"family":"Harrity","given":"Eamon","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216654,"text":"70216654 - 2020 - Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling","interactions":[],"lastModifiedDate":"2020-11-27T17:09:15.373856","indexId":"70216654","displayToPublicDate":"2020-10-28T11:04:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7439,"text":"Frontiers in Ecology and Evolution Section Biogeography and Macroecology","active":true,"publicationSubtype":{"id":10}},"title":"Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling","docAbstract":"Biological soil crusts (biocrusts) on the Colorado Plateau may fuel carbon (C) and nitrogen (N) cycling of soil heterotrophic organisms throughout the region. Late successional moss and lichen biocrusts, in particular, can increase soil C and N availability, but some data suggest these biocrust types will be replaced by early successional cyanobacterial biocrusts as the region undergoes warming and aridification. In this study, we evaluated the short-term interactive effects of biocrust successional state and elevated temperature on soil heterotrophic C and N cycling (specifically, soil respiration, N2O emissions, microbial biomass C and N, and soluble C and N). We collected soils following an 87-day greenhouse mesocosm experiment where the soils had been topped with different biocrust successional states (moss-dominated, cyanobacteria-dominated, or no biocrust) and had experienced different temperatures (ambient and warmed), under an artificial precipitation regime. Following this pre-incubation mesocosm phase, the soils were assessed using a short-term (2-day) laboratory incubation to determine the cumulative effect of the elevated temperature and altered biocrust successional state on the temperature sensitivity of soil heterotrophic C and N cycling. We found that there were interactive effects of biocrust successional state and exposure to warmer temperatures during the mesocosm phase under greenhouse conditions on the rate and temperature sensitivity of soil heterotrophic C and N cycling in laboratory incubations. Soils collected from beneath late successional biocrusts exhibited higher C and N cycling rates than those from beneath early successional crusts, while warming reduced both the magnitude and the temperature sensitivity of C and N cycling. The inhibiting effect of warming, was most evident in soils from beneath late successional biocrusts, which, during the mesocosm phase, also exhibited the greatest reductions in gross primary production and respiration in response to the warming treatment. Taken together, these data suggest that an overall effect of climate warming may be increasing resource limitation of the soil heterotrophic C and N cycles in the region, which may magnify alterations associated with the changes in biocrust community structure documented in previous studies. Overall, results from this study suggest that soil heterotrophic biogeochemical cycling is affected by interactions between temperature and the biocrust community that lives atop the mineral soil, with important implications for C and N cycling into the future.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.467157","usgsCitation":"Tucker, C., Ferrenberg, S., and Reed, S., 2020, Modest residual effects of short-term warming, altered hydration, and biocrust successional state on dryland soil heterotrophic carbon and nitrogen cycling: Frontiers in Ecology and Evolution Section Biogeography and Macroecology, v. 8, 467157, 17 p., https://doi.org/10.3389/fevo.2020.467157.","productDescription":"467157, 17 p.","ipdsId":"IP-110869","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.467157","text":"Publisher Index Page"},{"id":380845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","city":"Castle Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.66140747070312,\n 38.50519140240356\n ],\n [\n -109.26040649414062,\n 38.50519140240356\n ],\n [\n -109.26040649414062,\n 38.78085193143006\n ],\n [\n -109.66140747070312,\n 38.78085193143006\n ],\n [\n -109.66140747070312,\n 38.50519140240356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrenberg, Scott","contributorId":217143,"corporation":false,"usgs":false,"family":"Ferrenberg","given":"Scott","affiliations":[{"id":39569,"text":"Department of Biology, New Mexico State University, Las Cruces, NM 88001, USA","active":true,"usgs":false}],"preferred":false,"id":805740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":805741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255170,"text":"70255170 - 2020 - Daily nest predation rates decrease with body size in passerine birds","interactions":[],"lastModifiedDate":"2024-06-13T14:46:53.839237","indexId":"70255170","displayToPublicDate":"2020-10-28T09:42:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5500,"text":"The American Naturalist","onlineIssn":"1537-5323","printIssn":" 0003-014","active":true,"publicationSubtype":{"id":10}},"title":"Daily nest predation rates decrease with body size in passerine birds","docAbstract":"Body size evolution is generally framed by the benefits of being large, while costs are largely overlooked. An important putative cost of being large is the need to extend development periods, which should increase exposure to predation and potentially select against larger size. In birds, this selection pressure can be important because predation is the main source of offspring mortality and predators should more readily detect the larger nests associated with larger body sizes. Here, we show for diverse passerine birds across the world that counter to expectations, larger species suffer lower daily nest predation rates than smaller species. This pattern is consistent despite latitudinal variation in predation and does not seem to reflect a tendency of larger species to use more protected nests or less exposed nest locations. Evidence instead suggests that larger species attack a wider array of predator sizes, which could reduce predation rates in nests of large-bodied species. Regardless of the mechanism, the lower daily nest predation rates of larger species yield slightly lower predation rates over the entire development period compared with smaller species. These results highlight the importance of behavior as a mechanism to alter selection pressures and have implications for body size evolution.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/711413","usgsCitation":"Unzeta, M., Martin, T.E., and Sol, D., 2020, Daily nest predation rates decrease with body size in passerine birds: The American Naturalist, v. 196, no. 6, p. 743-754, https://doi.org/10.1086/711413.","productDescription":"12 p.","startPage":"743","endPage":"754","ipdsId":"IP-113054","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"196","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Unzeta, Mar","contributorId":338887,"corporation":false,"usgs":false,"family":"Unzeta","given":"Mar","email":"","affiliations":[{"id":81202,"text":"creaf","active":true,"usgs":false}],"preferred":false,"id":903655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903654,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sol, Daniel","contributorId":338888,"corporation":false,"usgs":false,"family":"Sol","given":"Daniel","email":"","affiliations":[{"id":81202,"text":"creaf","active":true,"usgs":false}],"preferred":false,"id":903656,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216386,"text":"70216386 - 2020 - Mussel community assessment tool for the Upper Mississippi River system","interactions":[],"lastModifiedDate":"2020-11-13T14:53:45.631508","indexId":"70216386","displayToPublicDate":"2020-10-28T08:47:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Mussel community assessment tool for the Upper Mississippi River system","docAbstract":"Upper Mississippi River (UMR) resource managers need a quantitative means of evaluating the health of mussel assemblages to measure effects of management and regulatory actions, assess restoration techniques, and inform regulatory tasks. Our objective was to create a mussel community assessment tool (MCAT), consisting of a suite of metrics and scoring criteria, to consistently compare the relative health of UMR mussel assemblages. We developed an initial MCAT using quantitative data from 25 sites and 10 metrics. Metrics fell in five broad groups: conservation status and environmental sensitivity, taxonomic composition, population processes, abundance, and diversity. Metric scoring categories were based on quartile analysis: 25% scoring as good, 50% scoring as fair, and 25% scoring as poor. Scores were meant to facilitate establishing management priorities and mitigation options for the conservation of mussels. Scoring categories assumed that a healthy mussel assemblage consists of species with a variety of reproductive and life-history strategies, a low percentage of tolerant species, and a high percentage of sensitive species; shows evidence of adequate recruitment, a variety of age classes, and low mortality; and has high abundance, species richness, and species and tribe evenness. Metrics were validated using a modified Delphi technique. MCAT metrics generally reflected the professional opinions of UMR resource managers and provided a consistent evaluation technique with uniform definitions that managers could use to evaluate mussel assemblages. Additional data sets scored a priori by UMR resource managers were used to further validate metrics, resulting in data from 33 sites spanning over 980 km of the UMR. Initial and revised MCAT scores were similar, indicating that data represent the range of mussel assemblages in the UMR. Mussel assemblages could be evaluated using individual metrics or a composite score to suit management purposes. With additional data, metrics could be calibrated on a local scale or applied to other river systems.
","language":"English","publisher":"BioOne","doi":"10.31931/fmbc.v23i2.2020.109-123","usgsCitation":"Dunn, H.L., Zigler, S.J., and Newton, T., 2020, Mussel community assessment tool for the Upper Mississippi River system: Freshwater Mollusk Biology and Conservation, v. 23, no. 2, p. 109-123, https://doi.org/10.31931/fmbc.v23i2.2020.109-123.","productDescription":"15 p.","startPage":"109","endPage":"123","ipdsId":"IP-100031","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":454949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc.v23i2.2020.109-123","text":"Publisher Index Page"},{"id":380503,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.9560546875,\n 38.85682013474361\n ],\n [\n -90.3076171875,\n 39.13006024213511\n ],\n [\n -91.043701171875,\n 39.690280594818034\n ],\n [\n -91.307373046875,\n 40.26276066437183\n ],\n [\n -90.791015625,\n 41.1290213474951\n ],\n [\n -90.87890625,\n 41.31907562295139\n ],\n [\n -90.2197265625,\n 41.566141964768384\n ],\n [\n -89.9560546875,\n 42.032974332441405\n ],\n [\n -90.50537109375,\n 42.54498667313236\n ],\n [\n -90.59326171875,\n 42.70665956351041\n ],\n [\n -90.90087890624999,\n 42.867912483915305\n ],\n [\n -90.966796875,\n 43.16512263158296\n ],\n [\n -90.999755859375,\n 43.492782808225\n ],\n [\n -91.1865234375,\n 43.96909818325171\n ],\n [\n -91.790771484375,\n 44.35527821160296\n ],\n [\n -92.1533203125,\n 44.53567453241317\n ],\n [\n -92.779541015625,\n 44.87144275016589\n ],\n [\n -93.087158203125,\n 44.68427737181225\n ],\n [\n -92.2412109375,\n 44.315987905196906\n ],\n [\n -91.834716796875,\n 44.02442151965934\n ],\n [\n -91.5380859375,\n 43.8503744993026\n ],\n [\n -91.241455078125,\n 43.22118973298753\n ],\n [\n -91.25244140624999,\n 43.092960677116295\n ],\n [\n -91.1865234375,\n 42.71473218539458\n ],\n [\n -90.758056640625,\n 42.50450285299051\n ],\n [\n -90.3955078125,\n 42.09007006868398\n ],\n [\n -90.50537109375,\n 41.672911819602085\n ],\n [\n -91.19750976562499,\n 41.50034959128928\n ],\n [\n -91.29638671875,\n 41.19518982948959\n ],\n [\n -91.20849609375,\n 41.03793062246529\n ],\n [\n -91.395263671875,\n 40.73893324113601\n ],\n [\n -91.62597656249999,\n 40.413496049701955\n ],\n [\n -91.746826171875,\n 40.027614437486655\n ],\n [\n -91.483154296875,\n 39.42770738465604\n ],\n [\n -90.966796875,\n 39.172658670429946\n ],\n [\n -90.703125,\n 38.79690830348427\n ],\n [\n -89.9560546875,\n 38.85682013474361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Heidi L.","contributorId":244888,"corporation":false,"usgs":false,"family":"Dunn","given":"Heidi","email":"","middleInitial":"L.","affiliations":[{"id":49009,"text":"EcoAnalysts, Inc.","active":true,"usgs":false}],"preferred":false,"id":804848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zigler, Steven J. 0000-0002-4153-0652 szigler@usgs.gov","orcid":"https://orcid.org/0000-0002-4153-0652","contributorId":2410,"corporation":false,"usgs":true,"family":"Zigler","given":"Steven","email":"szigler@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Teresa 0000-0001-9351-5852 tnewton@usgs.gov","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":150098,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa","email":"tnewton@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804850,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216104,"text":"70216104 - 2020 - Assessment of burrowing behavior of freshwater juvenile mussels in sediment","interactions":[],"lastModifiedDate":"2020-11-06T12:49:54.295425","indexId":"70216104","displayToPublicDate":"2020-10-28T08:23:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of burrowing behavior of freshwater juvenile mussels in sediment","docAbstract":"Standard laboratory sediment toxicity methods have been adapted for conducting toxicity tests with juvenile freshwater mussels. However, studies looking at juvenile mussel burrowing behavior at the water-sediment interface are limited. Juvenile mussels burrow in sediment for the first 0 to 4 yr of life but also may inhabit the sediment-water interface. The objective of this study was to evaluate burrowing behavior of various species and ages of juvenile freshwater mussels in three control sediments: West Bearskin Lake, Spring River, and coarse commercial sand. Species tested included (1) Fatmucket (Lampsilis siliquoidea), (2) Notched Rainbow (Villosa constricta), (3) Washboard (Megalonaias nervosa), (4) Rainbow (Villosa iris), (5) Arkansas Fatmucket (Lampsilis powellii), and (6) Oregon Floater (Anodonta oregonensis). Greater than 95% of the mussels burrowed into test sediment within 15 min. Across species, life stage, and substrate type, most mussels were recovered from the upper layers of sediment (91% at a sediment depth of 3.4 mm or less), and only 2% of the mussels were recovered at a depth >5.1 mm. No mussels were recovered from a depth >6.8 mm. There was no difference in mussel burrowing depth at 4 h versus 24 h across species, age, and sediment type. Two ages of Fatmucket burrowed to a significantly greater depth in the West Bearskin Lake sediment compared to the Spring River sediment or Coarse Sand. However, there was no significant difference in mean depth across sediment type with the other five species of mussels tested. Based on species and age of mussels tested, juvenile mussels up to an age of at least 20 wk and a length of at least 5 mm readily burrow into sediment and likely would be exposed to contaminants in whole sediment and associated pore water throughout a laboratory sediment toxicity test.
","language":"English","publisher":"BioOne","doi":"10.31931/fmbc.v23i2.2020.69-81","usgsCitation":"Kemble, N.E., Besser, J.M., Steevens, J.A., and Hughes, J., 2020, Assessment of burrowing behavior of freshwater juvenile mussels in sediment: Freshwater Mollusk Biology and Conservation, v. 23, no. 2, p. 69-81, https://doi.org/10.31931/fmbc.v23i2.2020.69-81.","productDescription":"13 p.","startPage":"69","endPage":"81","ipdsId":"IP-105537","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":454951,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc.v23i2.2020.69-81","text":"Publisher Index Page"},{"id":436739,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NTLG30","text":"USGS data release","linkHelpText":"Burrowing behavior of freshwater mussels"},{"id":380186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kemble, Nile E. 0000-0002-3608-0538 nkemble@usgs.gov","orcid":"https://orcid.org/0000-0002-3608-0538","contributorId":2626,"corporation":false,"usgs":true,"family":"Kemble","given":"Nile","email":"nkemble@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Jamie P.","contributorId":244522,"corporation":false,"usgs":false,"family":"Hughes","given":"Jamie P.","affiliations":[{"id":48808,"text":"Veterans United, Columbia MO","active":true,"usgs":false}],"preferred":false,"id":804106,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}