Changes in population, agricultural development and practices (including shifts to more water-intensive crops), and climate variability are increasing demands on available water resources, particularly groundwater, in one of the most productive agricultural regions in the Southwest—the Rincon and Mesilla Valley parts of Rio Grande Valley, Doña Ana and Sierra Counties, New Mexico, and El Paso County, Texas. The goal of this study was to produce an integrated hydrological simulation model to help evaluate water-management strategies, including conjunctive use of surface water and groundwater for historical conditions, and to support long-term planning for the Rio Grande Project. This report describes model construction and applications by the U.S. Geological Survey, working in cooperation and collaboration with the Bureau of Reclamation.
This model, the Rio Grande Transboundary Integrated Hydrologic Model, simulates the most important natural and human components of the hydrologic system, including selected components related to variations in climate, thereby providing a reliable assessment of surface-water and groundwater conditions and processes that can inform water users and help improve planning for future conditions and sustained operations of the Rio Grande Project (RGP) by the Bureau of Reclamation. Model development included a revision of the conceptual model of the flow system, construction of a Transboundary Rio Grande Watershed Model (TRGWM) water-balance model using the Basin Characterization Model, and construction of an integrated hydrologic flow model with MODFLOW-One-Water Hydrologic Flow Model version 2 (referred to as MF-OWHM2). The hydrologic models were developed for and calibrated to historical conditions of water and land use, and parameters were adjusted so that simulated values closely matched available measurements (calibration). The calibrated model was then used to assess the use and movement of water in the Rincon Valley, Mesilla Basin, and northern part of the Conejos-Médanos Basin, with the entire region referred to as the “Transboundary Rio Grande” or TRG. These tools provide a means to understand hydrologic system response to the evolution of water use in the region, its availability, and potential operational constraints of the RGP.
The conceptual model identified surface-water and groundwater inflows and outflows that included the movement and use of water both in natural and in anthropogenic systems. The groundwater-flow system is characterized by a layered geologic sedimentary sequence combined with the effects of groundwater pumping, operation of the RGP, natural runoff and recharge, and the application of irrigation water at the land surface that is captured and reused in an extensive network of canals and drains as part of the conjunctive use of water in the region.
Historical groundwater-level fluctuations followed a cyclic pattern that were aligned with climate cycles, which collectively resulted in alternating periods of wet or dry years. Periods of drought that persisted for one or more years are associated with low surface-water availability that resulted in higher rates of groundwater-level decline. Rates of groundwater-level decline also increased during periods of agricultural intensification, which necessitated increasing use of groundwater as a source of irrigation water. Agriculture in the area was initially dominated by alfalfa and cotton, but since 1970 more water-intensive pecan orchards and vegetable production have become more common. Groundwater levels substantially declined in subregions where drier climate combined with increased demand, resulting in periods of reduced streamflows.
Most of the groundwater was recharged in the Rio Grande Valley floor, and most of the pumpage and aquifer storage depletion was in Mesilla Basin agricultural subregions. A cyclic imbalance between inflows and outflows resulted in the modeled cyclic depletion (groundwater withdrawals in excess of natural recharge) of the groundwater basin during the 75-year simulation period of 1940–2014. Changes in groundwater storage can vary considerably from year to year, depending on land use, pumpage, and climate conditions. Climatic drivers of wet and dry years can greatly affect all inflows, outflows, and water use. Although streamflow and, to a minor extent, precipitation during inter-decadal wet-year periods replenished the groundwater historically, contemporary water use and storage depletion could have reduced the effects of these major recharge events. The average net groundwater flow-rate deficit for 1953–2014 was estimated to be about 1,090 acre-feet per year.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Groundwater in eastern Abu Dhabi in the United Arab Emirates is an important resource that is widely used for irrigation and domestic supplies in rural areas. The U.S. Geological Survey and the Environment Agency—Abu Dhabi cooperated on an investigation to integrate existing hydrogeologic information and to answer questions about regional groundwater resources in Abu Dhabi by developing a numerical groundwater flow model based on MODFLOW–2005 software. The groundwater flow model developed in this investigation provides an improved understanding of groundwater conditions in the eastern region of the Emirate of Abu Dhabi. The flow model simulates steady-state predevelopment conditions from before the rapid growth of modern pumping in the 1980s and was calibrated with 1,342 groundwater-level observations by use of automated and manual calibration techniques. The calibrated model provides good accuracy, with a mean error of 0.50 meters and a standard error of 5.92 meters for simulated groundwater levels. The results of the regional water budget simulation show that gap recharge, which is groundwater inflow through mountain-front gap alluvium, is the greatest source of water to the aquifer. In the base simulation scenario, gap recharge represents 80 percent of total inflow (119,470 of 149,403 cubic meters per day) and the greatest outflow from the aquifer is from evapotranspiration (93 percent of total outflow). Model scenario and sensitivity results reveal a need for data that more thoroughly and more accurately describe aquifer hydraulic conductivity, inflow to the aquifer from the Oman Mountains, and recharge from precipitation on the piedmont. Additional long-term aquifer pumping test observations would improve understanding of aquifer hydraulic conductivity, which would also improve model accuracy. Future studies can modify the model to understand the effect of land-use change and water use on groundwater supplies and simulate more complex groundwater flow conditions in a predictive mode.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185158","collaboration":"Prepared in cooperation with the Environment Agency—Abu Dhabi","usgsCitation":"Eggleston, J.R., Mack, T.J., Imes, J.L., Kress, W., Woodward, D.W., and Bright, D.J., 2020, Hydrogeologic framework and simulation of predevelopment groundwater flow, eastern Abu Dhabi Emirate, United Arab Emirates: U.S. Geological Survey Scientific Investigations Report 2018–5158, 48 p., https://doi.org/10.3133/sir20185158.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088658","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":373295,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5158/coverthb.jpg"},{"id":373296,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5158/sir20185158.pdf","text":"Report","size":"6.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5158"},{"id":373297,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZWZISB","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 Groundwater Flow Model to Simulate Predevelopment Groundwater Flow in the Eastern Abu Dhabi Emirate, United Arab Emirates"}],"country":"United Arab Emirates","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[51.57952,24.2455],[51.75744,24.29407],[51.79439,24.01983],[52.57708,24.17744],[53.40401,24.15132],[54.008,24.12176],[54.69302,24.79789],[55.43902,25.43915],[56.07082,26.05546],[56.26104,25.71461],[56.39685,24.92473],[55.88623,24.92083],[55.80412,24.2696],[55.98121,24.13054],[55.52863,23.9336],[55.52584,23.52487],[55.23449,23.11099],[55.20834,22.70833],[55.0068,22.49695],[52.00073,23.00115],[51.61771,24.01422],[51.57952,24.2455]]]},\"properties\":{\"name\":\"United Arab Emirates\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2019.81","usgsCitation":"Trachsel, M., Dawson, A., Paciorek, C.J., Williams, J.W., McLachlan, J.S., Cogbill, C.V., Foster, D.R., Goring, S.J., Jackson, S., Oswald, W.W., and Shuman, B.N., 2020, Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States: Quaternary Research, v. 95, p. 23-42, https://doi.org/10.1017/qua.2019.81.","productDescription":"20 p.","startPage":"23","endPage":"42","ipdsId":"IP-111664","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":497090,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":381131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.884765625,\n 44.77793589631623\n ],\n [\n -67.25830078125,\n 45.19752230305682\n ],\n [\n -67.43408203124999,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.62940492064501\n ],\n [\n -67.74169921875,\n 45.72152152227954\n ],\n [\n -67.82958984375,\n 47.07012182383309\n ],\n [\n -68.31298828125,\n 47.42808726171425\n ],\n [\n -68.994140625,\n 47.234489635299184\n ],\n [\n -69.12597656249999,\n 47.47266286861342\n ],\n [\n -70.1806640625,\n 46.45299704748289\n ],\n [\n -70.81787109374999,\n 45.336701909968134\n ],\n [\n -71.05957031249999,\n 45.321254361171476\n ],\n [\n -71.30126953124999,\n 45.30580259943578\n ],\n [\n -71.4990234375,\n 44.96479793033101\n ],\n [\n -74.8828125,\n 44.99588261816546\n ],\n [\n 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]\n ]\n }\n }\n ]\n}","volume":"95","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Trachsel, Mathias","contributorId":245526,"corporation":false,"usgs":false,"family":"Trachsel","given":"Mathias","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":806372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Andria","contributorId":245527,"corporation":false,"usgs":false,"family":"Dawson","given":"Andria","affiliations":[{"id":40107,"text":"Mount Royal University","active":true,"usgs":false}],"preferred":false,"id":806373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paciorek, Christopher J.","contributorId":245528,"corporation":false,"usgs":false,"family":"Paciorek","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":806374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John W.","contributorId":245534,"corporation":false,"usgs":false,"family":"Williams","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":806381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLachlan, Jason S.","contributorId":245535,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":39516,"text":"University of Notre Dame","active":true,"usgs":false}],"preferred":false,"id":806382,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cogbill, Charles V.","contributorId":245529,"corporation":false,"usgs":false,"family":"Cogbill","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":806375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foster, David R.","contributorId":245530,"corporation":false,"usgs":false,"family":"Foster","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":806376,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goring, Simon J.","contributorId":245531,"corporation":false,"usgs":false,"family":"Goring","given":"Simon","email":"","middleInitial":"J.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":806377,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":806378,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Oswald, W. Wyatt","contributorId":245532,"corporation":false,"usgs":false,"family":"Oswald","given":"W.","email":"","middleInitial":"Wyatt","affiliations":[{"id":33637,"text":"Emerson College","active":true,"usgs":false}],"preferred":false,"id":806379,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shuman, Bryan N.","contributorId":245533,"corporation":false,"usgs":false,"family":"Shuman","given":"Bryan","email":"","middleInitial":"N.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":806380,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70208710,"text":"fs20203016 - 2020 - Assessing geohazards to the Denali National Park road with geologic mapping","interactions":[],"lastModifiedDate":"2022-04-20T18:43:23.96299","indexId":"fs20203016","displayToPublicDate":"2020-04-07T11:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3016","displayTitle":"Assessing Geohazards to the Denali National Park Road with Geologic Mapping","title":"Assessing geohazards to the Denali National Park road with geologic mapping","docAbstract":"Denali National Park (DENA) is home to iconic and breathtaking landscapes surrounding the tallest mountain range in North America, the Alaska Range. The park, which covers 6 million acres, is a major draw for tourism and recreation, making it an important economic engine for central Alaska. However, the geologic forces that created the beautiful, steep landscape of DENA also make it prone to geologic hazards (geohazards) like landslides, debris flows, and earthquakes. DENA has only one major road, called the Park Road, that serves nearly all of its infrastructure. The success of DENA as a visitor destination, an economic engine, and a safe environment for visitors, residents, and staff relies on the resilience of this road, making it a major transportation lifeline for the region.
Since 2017, the National Park Service and the U.S. Geological Survey National Cooperative Geologic Mapping Program have partnered to produce a new high-resolution geologic map of the Park Road corridor to identify and address ongoing geohazards affecting DENA infrastructure. In the area of Polychrome Overlook, this map is being used to guide a new route for the Park Road around an area of landslide-prone slopes, where ongoing slumping is costing National Park Service millions of dollars in annual road maintenance costs. Beyond this area, the map serves as a park resource to assess geohazard risk in future infrastructure and management decisions. Geologic mapping is also fueling new research in understanding the geologic and tectonic history of DENA, while training a new generation of geologic mappers through the USGS EDMAP program.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
We systematically evaluate datasets, functional forms, independent parameters of estimation, and resulting ground-motion predictions (as median and aleatory variability) of the Graizer and Kalkan (2015, 2016) (GK15) ground-motion prediction equation (GMPE) with the next generation of attenuation project (NGA-West2) models of Abrahamson and others (2014) (ASK14) and Boore and others (2014) (BSSA14) for application to earthquakes in California. This evaluation is performed in three stages: (1) by comparing attenuation, magnitude scaling, style-of-faulting effects, site response, response-spectral shape and amplitude, and standard deviations; (2) by comparing median predictions, standard deviations, and analyses of residuals with respect to near-field (within 20 kilometers [km] of the fault) and intermediate-field (50 to 70 km from the fault) records from major earthquakes in California, and (3) by comparing total, intra-event, and inter-event residual distributions among the GMPEs with respect to a near-source (within 80 km of the fault) subset of the NGA-West2 database covering 975 ground motions from 73 events in California ranging from moment magnitude 5 to 7.36. The results reveal that the scaling features of the GK15 GMPE and the ASK14 and BSSA14 GMPEs are, in general, similar in terms of distance attenuation but differ in terms of scaling with magnitude, style of faulting, and site effects. The original standard deviations of GMPEs are also different. For the near-source California subset, the three GMPEs result in standard deviations that are similar to each other. The mixed-effect residuals analysis shows that the GK15 GMPE has no perceptible trend with respect to the independent predictors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201028","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Kalkan, E., and Graizer, V., 2020, Ground-motion predictions for California — Comparisons of three prediction equations: U.S. Geological Survey Open-File Report 2020–1028, 28 p., https://doi.org/10.3133/ofr20201028.","productDescription":"vii, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-087031","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":373754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1028/coverthb.jpg"},{"id":373755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1028/ofr20201028.pdf","text":"Report"},{"id":399470,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109905.htm"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4091796875,\n 42.16340342422401\n ],\n [\n -124.91455078125,\n 41.02964338716638\n ],\n [\n -124.78271484375,\n 39.690280594818034\n ],\n [\n -123.96972656249999,\n 38.839707613545144\n ],\n [\n -122.51953124999999,\n 36.96744946416934\n ],\n [\n -121.70654296874999,\n 35.11990857099681\n ],\n [\n -118.564453125,\n 33.55970664841198\n ],\n [\n -117.22412109375,\n 32.63937487360669\n ],\n [\n -114.63134765625001,\n 32.7503226078097\n ],\n [\n -114.14794921875,\n 34.21634468843463\n ],\n [\n -114.41162109375,\n 34.77771580360469\n ],\n [\n -119.90478515625,\n 39.18117526158749\n ],\n [\n -119.86083984375,\n 42.01665183556825\n ],\n [\n -124.4091796875,\n 42.16340342422401\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Road
Moffett Field, CA 94035
NACSN (North American Commission on Stratigraphic Nomenclature) Note 70 is a summary of the activities of the Commission from October 2014-October 2017. This note is condensed from the minutes of the 69th through the 72nd meetings of the NACSN held in conjunction with the Annual Meetings of the Geological Society of America. The 69th meeting of the NACSN was held October 20, 2014, in Vancouver, British Columbia; the 70th meeting on November 2, 2015, in Baltimore, Maryland; the 71st meeting on September 26, 2016, in Denver, Colorado; and the 72nd meeting on October 23, 2017, in Seattle, Washington. Members of the NACSN who served as officers during the period 2014-2017 serve as co-authors of this note and are listed with their roles in Appendix A. The mission of the NACSN is to develop statements of stratigraphic principles, recommend procedures applicable to the classification of nomenclature of stratigraphic units, review problems in classifying and naming stratigraphic and related units, and formulate expressions of judgement on these matters. Commissioners of the NACSN represent various geoscience professional organizations from Canada, Mexico, and the United States who have an interest in maintaining a stable stratigraphic nomenclature. Commissioners and their constituencies for 2014-2017 are listed in Appendix B.
","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.17.1.59-62","usgsCitation":"Fluegeman, R.H., Brett, C.E., Brunton, F., Edwards, L.E., and Harper, H., 2020, North American Commission on Stratigraphic Nomenclature Note 70: Records of the Stratigraphic Commission 2014-2017: Stratigraphy, v. 17, no. 1, p. 57-62, https://doi.org/10.29041/strat.17.1.59-62.","productDescription":"6 p.","startPage":"57","endPage":"62","ipdsId":"IP-114840","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":389003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387370,"type":{"id":15,"text":"Index Page"},"url":"https://www.micropress.org/microaccess/stratigraphy/issue-358/article-2176"}],"volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fluegeman, Richard H.","contributorId":139942,"corporation":false,"usgs":false,"family":"Fluegeman","given":"Richard","email":"","middleInitial":"H.","affiliations":[{"id":13322,"text":"Ball State University","active":true,"usgs":false}],"preferred":false,"id":819679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brett, Carlton E.","contributorId":214141,"corporation":false,"usgs":false,"family":"Brett","given":"Carlton","email":"","middleInitial":"E.","affiliations":[{"id":7159,"text":"University of Cincinnati","active":true,"usgs":false}],"preferred":false,"id":819680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brunton, Frank","contributorId":261296,"corporation":false,"usgs":false,"family":"Brunton","given":"Frank","affiliations":[{"id":13320,"text":"Ontario Geological Survey","active":true,"usgs":false}],"preferred":false,"id":819681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Lucy E. 0000-0003-4075-3317 leedward@usgs.gov","orcid":"https://orcid.org/0000-0003-4075-3317","contributorId":2647,"corporation":false,"usgs":true,"family":"Edwards","given":"Lucy","email":"leedward@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":819682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harper, Howard","contributorId":139944,"corporation":false,"usgs":false,"family":"Harper","given":"Howard","email":"","affiliations":[{"id":13323,"text":"Society for Sedimentary Geology","active":true,"usgs":false}],"preferred":false,"id":819683,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209678,"text":"70209678 - 2020 - Depth-dependent soil mixing persists across climate zones","interactions":[],"lastModifiedDate":"2020-05-05T17:24:18.832543","indexId":"70209678","displayToPublicDate":"2020-04-07T09:51:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2982,"text":"PNAS","active":true,"publicationSubtype":{"id":10}},"title":"Depth-dependent soil mixing persists across climate zones","docAbstract":"Soil mixing over long (>102 y) timescales enhances nutrient fluxes that support soil ecology, contributes to dispersion of sediment and contaminated material, and modulates fluxes of carbon through Earth’s largest terrestrial carbon reservoir. Despite its foundational importance, we lack robust understanding of the rates and patterns of soil mixing, largely due to a lack of long-timescale data. Here we demonstrate that luminescence, a light-sensitive property of minerals used for geologic dating, can be used as a long-timescale sediment tracer in soils to reveal the structure of soil mixing. We develop a probabilistic model of transport and mixing of tracer particles and associated luminescence in soils and compare with a global compilation of luminescence versus depth in various locations. The model–data comparison reveals that soil mixing rate varies over the soil depth, with this depth dependency persisting across climate and ecological zones. The depth dependency is consistent with a model in which mixing intensity decreases linearly or exponentially with depth, although our data do not resolve between these cases. Our findings support the long-suspected idea that depth-dependent mixing is a spatially and temporally persistent feature of soils. Evidence for a climate control on the patterns and intensities of soil mixing with depth remains elusive and requires the further study of soil mixing processes.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1914140117","collaboration":"","usgsCitation":"Gray, H., Keen-Zebert, A., Furbish, D., Tucker, G.E., and Mahan, S.A., 2020, Depth-dependent soil mixing persists across climate zones: PNAS, v. 117, no. 16, p. 8750-8756, https://doi.org/10.1073/pnas.1914140117.","productDescription":"7 p.","startPage":"8750","endPage":"8756","ipdsId":"IP-114039","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7183219","text":"Publisher Index Page"},{"id":374155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"16","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keen-Zebert, Amanda","contributorId":224228,"corporation":false,"usgs":false,"family":"Keen-Zebert","given":"Amanda","email":"","affiliations":[{"id":40841,"text":"University of Nevada Reno / Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":787488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Furbish, David","contributorId":189086,"corporation":false,"usgs":false,"family":"Furbish","given":"David","affiliations":[],"preferred":false,"id":787489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tucker, Gregory E.","contributorId":177811,"corporation":false,"usgs":false,"family":"Tucker","given":"Gregory","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":787490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":787491,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209415,"text":"70209415 - 2020 - Sea turtle conservation: 10 ways you can help","interactions":[],"lastModifiedDate":"2020-05-19T14:28:59.388526","indexId":"70209415","displayToPublicDate":"2020-04-07T09:24:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5946,"text":"EDIS","active":true,"publicationSubtype":{"id":10}},"title":"Sea turtle conservation: 10 ways you can help","docAbstract":"Five species of sea turtle rely on Florida’s coastal and nearshore habitats for nesting during the summer months and foraging throughout the year (Figure 1). \n- Loggerhead turtles, named for their large, block-shaped heads with strong jaw muscles for crushing benthic invertebrates, are the most common sea turtle species on Florida’s nesting beaches. They nest on beaches throughout much of the state. \n- Green turtles are unique among sea turtles in that they are largely vegetarian, and can be spotted foraging in seagrass meadows.\n- Leatherbacks, the largest species of sea turtle, are different from other turtles in that they are covered with a somewhat flexible “leathery” shell, rather than a hard shell. Leatherbacks can be seen in Florida’s coastal waters, but nest much less frequently in the state than loggerheads and green turtles. \n- Kemp’s ridley turtles are the smallest and most endangered marine turtle. They can be seen foraging in nearshore areas, but rarely nest on Florida’s beaches. \n- Lastly, hawksbill turtles are named for their pointed beak. 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Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A60","displayTitle":"One-Water Hydrologic Flow Model: A MODFLOW Based Conjunctive-Use Simulation Software","title":"One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software","docAbstract":"The U.S. Geological Survey’s (USGS) Modular Ground-Water Flow Model (MODFLOW-2005) is a computer program that simulates groundwater flow by using finite differences. The MODFLOW-2005 framework uses a modular design that allows for the easy development and incorporation of new features called processes and packages that work with or modify inputs to the groundwater-flow equation. A process solves a flow equation or set of equations. For example, the central part of MODFLOW is the groundwater-flow process that solves the groundwater-flow equation; the surface-water routing process is an additional process that solves the surface-water flow equation. Packages are code related to the groundwater-flow process. For example, the subsidence package modifies the groundwater-flow process by including aquifer compaction effects on flow. With the development of new packages and processes, the MODFLOW-2005 base framework diverged into multiple independent versions designed for specific simulation needs. This divergence limited each independent MODFLOW release to its specific purpose, so that there was no longer a single, comprehensive, general-purpose hydraulic-simulation framework.
The MODFLOW One-Water Hydrologic Flow Model (MF-OWHM, also informally known as OneWater) is an integrated hydrologic flow model that combines multiple MODFLOW-2005 variants in one cohesive simulation software; changes were made to enable multiple capabilities in one code. This fusion of the MODFLOW-2005 versions resulted in a simulation software that can be used to address and analyze a wide class of conjunctive-use, water-management, water-food-security, and climate-crop-water scenarios. As a second core version of MODFLOW-2005, MF-OWHM maintains backward compatibility with existing MODFLOW-2005 versions, with features that include the following:
MF-OWHM is a MODFLOW-2005 based integrated hydrologic model that can simulate and analyze varying environmental conditions to allow for the evaluation of management options from many components of human and natural water movement through a physically based, supply and demand framework. The term “integrated,” in the context of this report, refers to the tight coupling of groundwater flow, surface-water flow, landscape processes, aquifer compaction and subsidence, reservoir operations, and conduit (karst) flow. Another benefit of this integrated hydrologic model is that models developed to run by MODFLOW-2005, MODFLOW-NWT, MODFLOW-CFP, or MODFLOW-FMP can also be simulated with MF-OWHM. At the time of this report’s publication, MF-OWHM version 2 (MF-OWHM2) does not include a direct internal simulation of snowmelt, advanced mountainous watershed rainfall-runoff simulation, detailed shallow soil-moisture accounting, or atmospheric moisture content. Atmospheric moisture may be accounted for indirectly by, optionally, specifying a pan-evaporation rate, reference evapotranspiration, and precipitation. These features are not included to ensure that simulation runtime remains short enough to enable the use of automated methods of calibrating model parameters to field observations, which typically require many simulation model runs. The MF-OWHM approach is to include as much detail as possible to simulate hydrological processes, providing the simulation runtimes remain reasonable enough to allow for robust parameter estimation and model calibration.
To represent both natural and human-influenced flow, MF-OWHM integrates physically based flow processes derived from MODFLOW-2005 in a supply and demand framework. From this integration, the physically based movement of groundwater, surface water, imported water, and precipitation serve as supply to meet consumptive demands associated with irrigated and non-irrigated agriculture, natural vegetation, and urban water uses. Water consumption is determined by balancing the available water supply with water demand, leading to the concept of a demand-driven, supply-constrained simulation.
The MF-OWHM Supply-and-Demand Framework is especially useful for the analysis of agricultural water use, where there are often few data available to describe changes in land-use through time, such as crop type and distribution, and the associated changes in groundwater pumpage. This framework attempts to satisfy each land-use water demand with available water supplies—that is, groundwater uptake, precipitation, and irrigation. An option provided in MF-OWHM2 is to automatically increase groundwater pumping for irrigation, which often is unknown, by the calculated residual between demand and the other available sources of supply. From large- to small-scale applications, the physically based supply and demand framework provides key capabilities for simulating and analyzing historical, current, and future conjunctive-use of surface water and groundwater.
To achieve the physically based supply and demand framework, the MODFLOW-2005 standard of no inter-package and -process communication was relaxed for MF-OWHM2. Traditional MODFLOW simulation models required that all packages and processes interact through the groundwater-flow equation or by removing the water flow from the simulation domain. For example, the MODFLOW-2005 representation of a groundwater well extracts water from the groundwater-flow equation (by subtraction) and removes it from the simulation domain. This feature is available in the MF-OWHM framework, but options have been added to allow the specification of a use or destination of pumped groundwater within the model domain, for example, it can be used for irrigation, managed aquifer recharge, or return-flow to streams.
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California Water Science Center
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6000 J Street, Placer Hall
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The first signs of sea star wasting disease (SSWD) epidemic occurred in just few months in 2013 along the entire North American Pacific coast. Disease dynamics did not manifest as the typical travelling wave of reaction-diffusion epidemiological model, suggesting that other environmental factors might have played some role. To help explore how external factors might trigger disease, we built a coupled oceanographic-epidemiological model and contrasted three hypotheses on the influence of temperature on disease transmission and pathogenicity. Models that linked mortality to sea surface temperature gave patterns more consistent with observed data on sea star wasting disease, which suggests that environmental stress could explain why some marine diseases seem to spread so fast and have region-wide impacts on host populations.
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Annual stream temperature dynamics are useful in classifying fundamental thermal regimes and can enhance process-based interpretation of stream temperature controls, including deep and shallow groundwater discharge, when paired with air signals. In this study, we investigated multi-scale variability in annual paired air-water temperature patterns using sine-wave linear regressions of multi-year daily temperature data from streams of various sizes. A total of 311 sites from two spatially intensive regional datasets (Shenandoah National Park and Olympic Experimental State Forest) and a spatially extensive national dataset spanning the contiguous United States (U.S. Geological Survey gages) were evaluated. We calculated three annual air-water thermal metrics (mean ratio, phase lag, and amplitude ratio) to deduce the influence of groundwater and other watershed processes on stream thermal regimes at multiple spatial scales. Site-specific values of the three annual air-water thermal metrics ranged from 0.69 to 5.29 (mean ratio), −9 to 40 days (phase lag), and 0.29 to 1.12 (amplitude ratio). Regional patterns in the annual thermal metrics revealed persistent yet spatially variable influences of shallow groundwater discharge and high levels of thermal variability within watersheds, indicating the importance of local hydrogeological controls on stream temperature. Furthermore, annual thermal metric patterns from the regional datasets were generally concordant with the national dataset suggesting the utility of these annual thermal metrics for analysis at multiple scales. Analysis of the national dataset showed that previously defined thermal regimes based on water temperature alone could be further refined using air-water metrics and these metrics were related to physiographic watershed characteristics such as contributing area, elevation, and slope. This research demonstrates the importance of spatial scale and heterogeneity for inferring hydrological process in streams and provides guidance for the interpretation of annual air-water temperature metrics that can be efficiently applied to the growing database of multi-year temperature records. Results from this research can aid in the prediction of future thermal habitat suitability for coldwater-adapted species at ecologically and management-relevant spatial scales with readily available data.
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Article"},"seriesTitle":{"id":3418,"text":"Soil Dynamics and Earthquake Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes","docAbstract":"Liquefaction is a major cause of coseismic damages, occurring irregularly over hundreds or thousands of square kilometers in large earthquakes. Large variations in the extent and location of liquefaction have been observed in recent earthquakes, motivating the need for prediction methods that consider the spatial heterogeneity of geologic deposits at a regional scale. Contemporary regional-scale liquefaction hazard analyses are typically performed using only surficial data, which does not address the complicated subsurface mechanics and spatial variability associated with artificial fill and natural soil deposits.
In this study, we develop a probabilistic, regional-scale, subsurface model using data from hundreds of borings to better understand subsurface conditions that could influence liquefaction. We then use this subsurface sample database to train Gaussian process models, yielding 3D independent random fields of groundwater depth, soil plasticity, and penetration resistance for each geologic unit. We incorporate the Gaussian process models into probabilistic liquefaction triggering procedures, producing 3D estimates of the probability of liquefaction for an example study area in Portland, Oregon. Near sampling locations, the variance of the Gaussian process models approaches the variance of site-specific liquefaction triggering procedures. Conversely, when no sample data are nearby to condition a Gaussian process, the variance approaches the marginal variance of the entire recorded dataset. Thus, the procedure described in this study unifies probabilistic site-specific and regional-scale liquefaction triggering procedures and provides an important step towards quantitative liquefaction hazard assessments for regionally distributed infrastructures, such as levees, pipelines, roadways, and electrical transmission facilities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.soildyn.2020.106159","collaboration":"","usgsCitation":"Greenfield, M., and Grant, A.R., 2020, Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes: Soil Dynamics and Earthquake Engineering, v. 134, 106159, 10 p., https://doi.org/10.1016/j.soildyn.2020.106159.","productDescription":"106159, 10 p.","ipdsId":"IP-110234","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","city":"Portland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.8656005859375,\n 45.31739181570158\n ],\n [\n -122.431640625,\n 45.31739181570158\n ],\n [\n -122.431640625,\n 45.74069339553309\n ],\n [\n -122.8656005859375,\n 45.74069339553309\n ],\n [\n -122.8656005859375,\n 45.31739181570158\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Greenfield, Michael","contributorId":224657,"corporation":false,"usgs":false,"family":"Greenfield","given":"Michael","affiliations":[{"id":40903,"text":"Greenfield Geotechnical, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":788985,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":788986,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210189,"text":"70210189 - 2020 - Surface methane concentrations along the mid-Atlantic bight driven by aerobic subsurface production rather than seafloor gas seeps","interactions":[],"lastModifiedDate":"2020-05-20T12:40:04.192663","indexId":"70210189","displayToPublicDate":"2020-04-04T07:26:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2315,"text":"Journal of Geophysical Research C: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Surface methane concentrations along the mid-Atlantic bight driven by aerobic subsurface production rather than seafloor gas seeps","docAbstract":"Relatively minor amounts of methane, a potent greenhouse gas, are currently emitted from the oceans to the atmosphere, but such methane emissions have been hypothesized to increase as oceans warm. Here, we investigate the source, distribution, and fate of methane released from the upper continental slope of the U.S. Mid-Atlantic Bight, where hundreds of gas seeps have been discovered between the shelf-break and ~1600 m water depth. Using physical, chemical, and isotopic analyses, we identify two main sources of methane in the water column: seafloor gas seeps and in situ aerobic methanogenesis which primarily occurs at 100 – 200 m depth in the water column. Stable isotopic analyses reveal that water samples collected at all depths were significantly impacted by aerobic methane oxidation, the dominant methane sink in this region, with more than 50% of the methane being oxidized, on average. Due to methane oxidation in the deeper water column, below 200 m depth, surface concentrations of methane are influenced more by methane sources found near the surface (0 – 10 m depth) and in the subsurface (10 - 200 m depth), rather than seafloor emissions at greater depths.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JC015989","usgsCitation":"Leonte, M., Ruppel, C.D., Ruiz-Angelo, A., and Kessler, J.D., 2020, Surface methane concentrations along the mid-Atlantic bight driven by aerobic subsurface production rather than seafloor gas seeps: Journal of Geophysical Research C: Oceans, v. 125, no. 5, e2019JC015989, 13 p., https://doi.org/10.1029/2019JC015989.","productDescription":"e2019JC015989, 13 p.","ipdsId":"IP-114697","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457161,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jc015989","text":"External Repository"},{"id":374952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic margin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.76220703125,\n 39.232253141714885\n ],\n [\n -74.794921875,\n 37.71859032558816\n ],\n [\n -75.1904296875,\n 35.7286770448517\n ],\n [\n -76.22314453125,\n 34.27083595165\n ],\n [\n -76.11328125,\n 34.17999758688084\n ],\n [\n -75.322265625,\n 33.99802726234877\n ],\n [\n -73.916015625,\n 33.97980872872457\n ],\n [\n -72.333984375,\n 36.03133177633187\n ],\n [\n -71.56494140625,\n 38.565347844885466\n ],\n [\n -71.47705078125,\n 39.842286020743394\n ],\n [\n -73.14697265625,\n 39.50404070558415\n ],\n [\n -73.76220703125,\n 39.232253141714885\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonte, Mihai 0000-0003-1582-5606","orcid":"https://orcid.org/0000-0003-1582-5606","contributorId":224782,"corporation":false,"usgs":false,"family":"Leonte","given":"Mihai","email":"","affiliations":[{"id":40676,"text":"University of Rochester, NY","active":true,"usgs":false}],"preferred":false,"id":789479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":789480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruiz-Angelo, Angel","contributorId":224783,"corporation":false,"usgs":false,"family":"Ruiz-Angelo","given":"Angel","email":"","affiliations":[{"id":40940,"text":"Icelandic Meteorological Office","active":true,"usgs":false}],"preferred":false,"id":789481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kessler, John D. 0000-0003-1097-6800","orcid":"https://orcid.org/0000-0003-1097-6800","contributorId":184241,"corporation":false,"usgs":false,"family":"Kessler","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":789482,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212842,"text":"70212842 - 2020 - The impact is in the details: Evaluating a standardized protocol and scale for determining non-native insect impact","interactions":[],"lastModifiedDate":"2020-08-31T14:31:47.909529","indexId":"70212842","displayToPublicDate":"2020-04-03T09:27:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"The impact is in the details: Evaluating a standardized protocol and scale for determining non-native insect impact","docAbstract":"Assessing the ecological and economic impacts of non-native species is crucial to providing managers and policymakers with the information necessary to respond effectively. Most non-native species have minimal impacts on the environment in which they are introduced, but a small fraction are highly deleterious. The definition of ‘damaging’ or ‘high-impact’ varies based on the factors determined to be valuable by an individual or group, but interpretations of whether non-native species meet particular definitions can be influenced by the interpreter’s bias or level of expertise, or lack of group consensus. Uncertainty or disagreement about an impact classification may delay or otherwise adversely affect policymaking on management strategies. One way to prevent these issues would be to have a detailed, nine-point impact scale that would leave little room for interpretation and then divide the scale into agreed upon categories, such as low, medium, and high impact. Following a previously conducted, exhaustive search regarding non-native, conifer-specialist insects, the authors independently read the same sources and scored the impact of 41 conifer-specialist insects to determine if any variation among assessors existed when using a detailed impact scale. Each of the authors, who were selected to participate in the working group associated with this study because of their diverse backgrounds, also provided their level of expertise and uncertainty for each insect evaluated. We observed 85% congruence in impact rating among assessors, with 27% of the insects having perfect inter-rater agreement. Variance in assessment peaked in insects with a moderate impact level, perhaps due to ambiguous information or prior assessor perceptions of these specific insect species. The authors also participated in a joint fact-finding discussion of two insects with the most divergent impact scores to isolate potential sources of variation in assessor impact scores. We identified four themes that could be experienced by impact assessors: ambiguous information, discounted details, observed versus potential impact, and prior knowledge. To improve consistency in impact decision-making, we encourage groups to establish a detailed scale that would allow all observed and published impacts to fall under a particular score, provide clear, reproducible guidelines and training, and use consensus-building techniques when necessary.
","language":"English","publisher":"PenSoft","doi":"10.3897/neobiota.55.38981","usgsCitation":"Schulz, A.N., Mech, A.M., Allen, C., Ayres, M.P., Gandhi, K., Gurevitch, J., Havill, N.P., Herms, D.A., Hufbauer, R.A., Liebhold, A.M., Raffa, K.F., Raupp, M.J., Thomas, K.A., Tobin, P.C., and Marsico, T.D., 2020, The impact is in the details: Evaluating a standardized protocol and scale for determining non-native insect impact: NeoBiota, v. 55, p. 61-83, https://doi.org/10.3897/neobiota.55.38981.","productDescription":"13 p.","startPage":"61","endPage":"83","ipdsId":"IP-099057","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":457164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.55.38981","text":"Publisher Index Page"},{"id":378028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","noUsgsAuthors":false,"publicationDate":"2020-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Schulz, Ashley N.","contributorId":219894,"corporation":false,"usgs":false,"family":"Schulz","given":"Ashley","email":"","middleInitial":"N.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":797634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, Angela M.","contributorId":219892,"corporation":false,"usgs":false,"family":"Mech","given":"Angela","email":"","middleInitial":"M.","affiliations":[{"id":40087,"text":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA. Corresponding email: ammech@wcu.edu. Present address: Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC","active":true,"usgs":false}],"preferred":false,"id":797635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig 0000-0001-8655-8227 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8227","contributorId":219896,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":797636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayres, Matthew P.","contributorId":219897,"corporation":false,"usgs":false,"family":"Ayres","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":35787,"text":"Department of Biological Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":797637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gandhi, Kamal J.K.","contributorId":219898,"corporation":false,"usgs":false,"family":"Gandhi","given":"Kamal J.K.","affiliations":[{"id":40090,"text":"D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA","active":true,"usgs":false}],"preferred":false,"id":797638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gurevitch, Jessica","contributorId":219899,"corporation":false,"usgs":false,"family":"Gurevitch","given":"Jessica","email":"","affiliations":[{"id":33447,"text":"Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":797639,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Havill, Nathan P.","contributorId":219900,"corporation":false,"usgs":false,"family":"Havill","given":"Nathan","email":"","middleInitial":"P.","affiliations":[{"id":40091,"text":"Northern Research Station, USDA Forest Service, Hamden, CT","active":true,"usgs":false}],"preferred":false,"id":797640,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Herms, Daniel A.","contributorId":219895,"corporation":false,"usgs":false,"family":"Herms","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":40089,"text":"The Davey Tree Expert Company, Kent, OH","active":true,"usgs":false}],"preferred":false,"id":797641,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hufbauer, Ruth A.","contributorId":219901,"corporation":false,"usgs":false,"family":"Hufbauer","given":"Ruth","email":"","middleInitial":"A.","affiliations":[{"id":40092,"text":"Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":797642,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Liebhold, Andrew M.","contributorId":219902,"corporation":false,"usgs":false,"family":"Liebhold","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":40093,"text":"USDA Forest Service Northern Research Station, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":797643,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Raffa, Kenneth F.","contributorId":219903,"corporation":false,"usgs":false,"family":"Raffa","given":"Kenneth","email":"","middleInitial":"F.","affiliations":[{"id":40094,"text":"Department of Entomology, University of Wisconsin, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":797644,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Raupp, Michael J.","contributorId":239692,"corporation":false,"usgs":false,"family":"Raupp","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":47979,"text":"University of Maryland, Department of Entomology, 4112 Plant Sciences Building, College Park, MD 20742, USA","active":true,"usgs":false}],"preferred":false,"id":797645,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thomas, Kathryn A. 0000-0002-7131-8564 kathryn_a_thomas@usgs.gov","orcid":"https://orcid.org/0000-0002-7131-8564","contributorId":167,"corporation":false,"usgs":true,"family":"Thomas","given":"Kathryn","email":"kathryn_a_thomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797646,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tobin, Patrick C.","contributorId":200172,"corporation":false,"usgs":false,"family":"Tobin","given":"Patrick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":797647,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Marsico, Travis D.","contributorId":219893,"corporation":false,"usgs":false,"family":"Marsico","given":"Travis","email":"","middleInitial":"D.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":797648,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70209420,"text":"70209420 - 2020 - Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river-reservoir system","interactions":[],"lastModifiedDate":"2020-06-04T17:09:46.699712","indexId":"70209420","displayToPublicDate":"2020-04-03T08:18:18","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":"Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river-reservoir system","docAbstract":"The likelihood that fish will initiate spawning, spawn successfully, or skip spawning in a given year is conditioned in part on availability of energy reserves. We evaluated the consequences of spatial heterogeneity in thermal conditions on the energy accumulation and spawning potential of migratory bull trout (Salvelinus confluentus) in a regulated river–reservoir system. Based on existing data, we identified a portfolio of thermal exposures and migratory patterns and then estimated their influence on energy reserves of female bull trout with a bioenergetics model. Spawning by females was assumed to be possible if postspawning energy reserves equaled or exceeded 4 kJ/g. Given this assumption, results suggested up to 70% of the simulated fish could spawn each year. Fish that moved seasonally between a cold river segment and a warmer reservoir downstream had a greater growth rate and higher propensity to spawn in a given year (range: 40%–70%) compared with fish that resided solely in the cold river segment (25%–40%). On average, fish that spawned lost 30% of their energy content relative to their prespawn energy. In contrast, fish that skipped spawning accumulated, on average, 16% energy gains that could be used toward future gamete production. Skipped spawning occurred when water temperatures were relatively low or high, and if upstream migration occurred relatively late (mid-July or later) or early (early-May or earlier). Overall, our modeling effort suggests the configuration of thermal exposures, and the ability of bull trout to exploit this spatially and temporally variable thermal conditions can strongly influence energy reserves and likelihood of successful spawning.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6184","usgsCitation":"Benjamin, J.R., Vidergar, D., and Dunham, J.B., 2020, Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river-reservoir system: Ecology and Evolution, v. 10, no. 9, p. 4128-4142, https://doi.org/10.1002/ece3.6184.","productDescription":"15 p.","startPage":"4128","endPage":"4142","ipdsId":"IP-111770","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457165,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.6184","text":"External Repository"},{"id":373838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Boise River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.4111328125,\n 42.97250158602597\n ],\n [\n -113.99414062499999,\n 42.97250158602597\n ],\n [\n -113.99414062499999,\n 44.4808302785626\n ],\n [\n -116.4111328125,\n 44.4808302785626\n ],\n [\n -116.4111328125,\n 42.97250158602597\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":786443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vidergar, Dmitri T","contributorId":223858,"corporation":false,"usgs":false,"family":"Vidergar","given":"Dmitri T","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":786444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":786445,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209443,"text":"70209443 - 2020 - Long-term trends of Lake Michigan benthos with emphasis on the southern basin","interactions":[],"lastModifiedDate":"2020-06-04T17:08:34.997292","indexId":"70209443","displayToPublicDate":"2020-04-03T07:50:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trends of Lake Michigan benthos with emphasis on the southern basin","docAbstract":"Lake Michigan benthic macrofauna have been studied for almost a century, allowing for a unique analysis of long-term changes in community structure. We examined changes in abundances of three major taxonomic groups of benthic macroinvertebrates (Diporeia, Oligochaeta, and Sphaeriidae) in southern Lake Michigan from 1931-2015, and identified the most likely causes for these changes. Abundances of all three groups increased during 1931-1980, with the bulk of these increases occurring in nearshore (≤ 50 m) waters and coincident with increasing loading of phosphorus (P) to the lake. Abundances of all three taxa declined during 1980-2000 again mostly in nearshore waters and coincident with decreased P loading. The quagga mussel (Dreissena rostriformis bugensis) invasion was associated with a further decline in phytoplankton primary production during 2000-2015. Both Diporeia and Sphaeriidae declined in abundance during that time, with Diporeia exhibiting the more pronounced decrease of the two groups. In contrast, Oligochaeta increased in abundance during 2000-2015. The quagga mussel has become, by far, the most abundant benthic macroinvertebrate species in terms of density and biomass. Overall, the primary driver of changes in the abundances of the three major taxa during this 85-year period appeared to be changes in phytoplankton primary production due to changing P loadings and, later in the time series, Dreissena filtering. The dreissenid mussel invasions coincided with a rapid decline of Diporeia abundance, but the mechanism of this negative effect remains unidentified. In contrast, Oligochaeta likely benefitted from the quagga mussel invasion; perhaps via quagga-generated food supplies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.03.011","usgsCitation":"Mehler, K., Burlakova, L.E., Karatayev, A.Y., Elgin, A.K., Nalepa, T.F., Madenjian, C.P., and Hinchey, E.K., 2020, Long-term trends of Lake Michigan benthos with emphasis on the southern basin: Journal of Great Lakes Research, v. 46, no. 3, p. 528-537, https://doi.org/10.1016/j.jglr.2020.03.011.","productDescription":"10 p.","startPage":"528","endPage":"537","ipdsId":"IP-111303","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457166,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2020.03.011","text":"Publisher Index Page"},{"id":373836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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F.","contributorId":211819,"corporation":false,"usgs":false,"family":"Nalepa","given":"Thomas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":786500,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":786501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hinchey, Elizabeth K.","contributorId":197957,"corporation":false,"usgs":false,"family":"Hinchey","given":"Elizabeth","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":786502,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209988,"text":"70209988 - 2020 - Understanding the golden eagle and bald eagle sensory worlds to enhance detection and response to wind turbines","interactions":[],"lastModifiedDate":"2020-05-08T12:43:14.492553","indexId":"70209988","displayToPublicDate":"2020-04-03T07:35:55","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Understanding the golden eagle and bald eagle sensory worlds to enhance detection and response to wind turbines","docAbstract":"The objective for this study was to measure the auditory and visual physiology of Golden and Bald Eagles in order to use eagle sensory capabilities to inform the design of potential deterrent stimuli that could be used to reduce eagle/turbine collisions with wind turbines. The rationale for this approach is that sensory systems of any organism will limit the capability of that organism to perceive aspects of the world around it. Moreover, species can differ dramatically in their sensory physiology so it is important to examine these characteristics in the species of concern, rather than relying on data from similar birds. Our project consisted of two main phases. The first phase was the acquisition and analysis of visual and auditory information from Golden and Bald Eagles in rehabilitation centers. This was performed in order to identify light and sound stimuli tuned to sensitive areas in the eagle’s sensory systems. The second phase of the project was to present these stimuli to both species of eagles in a behavioral experiment to identify which stimuli would be the most effective in changing the behaviors of the eagles. \nResults of phase one indicated that the visual system of the Golden Eagle strongly absorbs ultraviolet light, making it unlikely the Golden Eagle (and most likely the Bald Eagle) will detect ultraviolet light signals. The Golden and Bald Eagles have differences in the sensitivities of their visual systems to light within the eye, but mathematical models indicate that both species are able to detect indigo/blue and orange/red light produced by LEDs (light emitting diodes) very well. We also found that both species of eagles have a blind spot above their head. This blind spot is particularly large in Golden Eagles due to a pronounced brow ridge above the eyes. This blind spot will result in the inability of a Golden Eagle to see something in front of it when its head is pointed down during flight – as might happen while hunting (i.e. searching the ground for prey). As such, the blind spot may increase the chance of collision with wind turbines if the eagle is actively hunting. This problem is less pronounced in Bald Eagles because their blind spot is smaller than in the Golden Eagles and their foraging strategy is different.\nResults of phase one also indicated that the auditory systems of the Golden and Bald Eagles respond differently to a variety of sounds (static tones, static chords (i.e. stacked tones), and sounds with dynamic changes through amplitude modulation or frequency modulation). Both species’ auditory systems responded strongly to tones across a wide range of frequencies (0.5 – 5kHz ), however the Bald Eagles’ auditory system was much better at processing complex sounds with dynamic rapid changes in amplitude or frequency modulation than the Golden Eagle. All of these sounds were then played with two types of noise in the background (white or pink). White noise more closely resembles the sound of wind and pink noise more closely resembles wind turbines or other sources of anthropogenic noise. Most sounds were more strongly masked by pink noise than by white noise, but several sounds (especially sounds with rapid modulation changes) showed little or no masking, indicating these were good candidate signals. However, even though rapidly changing sounds are less subject to noise masking, they are also less strongly processed by the Golden Eagle auditory system. This tradeoff does not exist in Bald Eagles because individuals of this species are very good at processing rapidly changing sounds. Given that Golden Eagle populations are at greater risk than Bald Eagle populations, we suggest that the most efficient alerting sound stimuli used in deterrent systems should be complex sounds that do not change very rapidly. \nWe identified candidate light (indigo/blue and orange/red LED lights) and sound (sinusoidal frequency modulated sound, linear frequency sweeps, amplitude modulated sound, and a mistuned harmonic stack) stimuli that both eagle species sensory systems are highly sensitive to. Results of phase two, in which we presented these stimuli to eagles in a behavioral experiment, indicated that eagles behaviorally responded to all the stimuli presented, but at varying degrees. The Golden Eagles, especially, elicited higher rates of visual exploratory behavior with a flashing blue light stimulus and all sound stimuli. Our results suggest using these stimuli in field-testing of light/sound eagle deterrent systems on wind turbines. The eagles showed lower rates of behavior over the course of an experiment, suggesting either that they habituated to our stimuli or were initially stressed by the setup of the behavioral tests. These results underscore the need to test for habituation effects. Nonetheless, habitation to the stimuli in these field tests would likely be reduced by the use of random presentations of the four sounds and if possible random presentation of the candidate lights.","language":"English","publisher":"U.S. Department of Energy","doi":"","collaboration":"Purdue University","usgsCitation":"Fernandez-Juricic, E., Lucas, J., Katzner, T., Goller, B., Baumhardt, P., and Lovko, N., 2020, Understanding the golden eagle and bald eagle sensory worlds to enhance detection and response to wind turbines, 181 p., https://doi.org/.","productDescription":"181 p.","ipdsId":"IP-118356","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":374571,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374570,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://tethys.pnnl.gov/publications/understanding-golden-eagle-bald-eagle-sensory-worlds-enhance-detection-response-wind"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fernandez-Juricic, Esteban","contributorId":224607,"corporation":false,"usgs":false,"family":"Fernandez-Juricic","given":"Esteban","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":788720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Jeffrey","contributorId":224608,"corporation":false,"usgs":false,"family":"Lucas","given":"Jeffrey","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":788721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":788722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goller, B.","contributorId":224609,"corporation":false,"usgs":false,"family":"Goller","given":"B.","affiliations":[],"preferred":false,"id":788739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumhardt, P.","contributorId":224610,"corporation":false,"usgs":false,"family":"Baumhardt","given":"P.","affiliations":[],"preferred":false,"id":788740,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lovko, N.","contributorId":224611,"corporation":false,"usgs":false,"family":"Lovko","given":"N.","email":"","affiliations":[],"preferred":false,"id":788741,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263118,"text":"70263118 - 2020 - Investigating population genetics of invasive rainbow smelt in the Great Lakes Region","interactions":[],"lastModifiedDate":"2025-01-30T15:34:14.883877","indexId":"70263118","displayToPublicDate":"2020-04-03T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Investigating population genetics of invasive rainbow smelt in the Great Lakes Region","docAbstract":"Increasing our understanding of invasive species is important because of the negative impacts they can have on the economies and ecosystems of invaded regions. There is growing interest in how environmental variability (e.g. temperature) and stochastic invasion events (e.g. founder effects) affect the genetic composition of populations of invasive species. Rainbow smelt (Osmerus mordax) are a cold-water, planktivorous fish that spread into the Great Lakes basin in the early 1900s. We performed genetic analyses using microsatellites (N = 10) to investigate the influence stochastic invasion events have had on the genetic composition of invasive rainbow smelt populations across a broad geographic range. Genetic analyses were conducted on rainbow smelt populations (N = 30/population) from Lake Ontario, Lake Michigan, Lake Superior, and four inland lakes in Northern Wisconsin. Populations from the Great Lakes were generally less differentiated than inland populations. Additionally, we found evidence of a significant bottleneck in two inland populations and evidence for two distinct genetic strains of rainbow smelt in Lake Ontario. We also performed genetic analyses using microsatellites to determine if a thermally-induced extreme mortality event had an effect on a population of rainbow smelt and found that there was no measurable genetic effect on the population. Overall, this study provides evidence that the genetic structure and diversity of introduced populations can vary significantly, and are likely influenced by factors such as the frequency and magnitude of introductions. Also the resiliency of an invasive species can be high despite a history of bottlenecks and low genetic diversity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.01.016","usgsCitation":"Dobosenski, J., Strasburg, J., Larson, W., and Hrabik, T., 2020, Investigating population genetics of invasive rainbow smelt in the Great Lakes Region: Journal of Great Lakes Research, v. 46, no. 2, p. 382-390, https://doi.org/10.1016/j.jglr.2020.01.016.","productDescription":"9 p.","startPage":"382","endPage":"390","ipdsId":"IP-109675","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481504,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.74064917677558,\n 47.29910280069214\n ],\n [\n -88.72180472353388,\n 45.92694839046345\n ],\n [\n -87.48882297041533,\n 41.600893566227285\n ],\n [\n -80.91070356084862,\n 41.48254481239488\n ],\n [\n -78.20297539830352,\n 42.832826145165996\n ],\n [\n -76.60960173259036,\n 42.98668563895146\n ],\n [\n -75.64453839868747,\n 44.260789106848705\n ],\n [\n -81.81944877789577,\n 46.04033704847711\n ],\n [\n -88.97252426064546,\n 48.19838227578134\n ],\n [\n -90.15695774500598,\n 47.35524992499353\n ],\n [\n -91.74064917677558,\n 47.29910280069214\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dobosenski, Jamie A.","contributorId":350280,"corporation":false,"usgs":false,"family":"Dobosenski","given":"Jamie A.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":925612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strasburg, Jared L.","contributorId":350281,"corporation":false,"usgs":false,"family":"Strasburg","given":"Jared L.","affiliations":[{"id":34699,"text":"University of Minnesota-Duluth","active":true,"usgs":false}],"preferred":false,"id":925613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":925611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hrabik, Thomas R.","contributorId":350283,"corporation":false,"usgs":false,"family":"Hrabik","given":"Thomas R.","affiliations":[{"id":34699,"text":"University of Minnesota-Duluth","active":true,"usgs":false}],"preferred":false,"id":925614,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224951,"text":"70224951 - 2020 - Using ultrasonic acoustics to detect cryptic flying squirrels: Effects of season and habitat suitability","interactions":[],"lastModifiedDate":"2021-10-11T16:33:51.598504","indexId":"70224951","displayToPublicDate":"2020-04-02T11:29:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Using ultrasonic acoustics to detect cryptic flying squirrels: Effects of season and habitat suitability","docAbstract":"New technologies allow for more efficient and effective monitoring of rare or elusive species. However, standardizing protocol to ensure high detection rates is important prior to widespread use of a new technique. The use of ultrasonic acoustic detectors to survey for flying squirrels (Glaucomys spp.) is a novel method that is more efficient than traditional methods. However, certain methodologies for this technique still need to be refined. During 2015, we conducted a seasonal and habitat quality study on the endangered Carolina northern flying squirrel (G. sabrinus coloratus) in western North Carolina, USA. Our seasonal study examined differences in probability of detection (POD) and latency to detection (LTD) at 30 high-quality sites across 10 survey nights in spring, summer, and autumn. The habitat quality study focused on POD and LTD among 15 sites with varying habitat quality (5 High, 5 Medium, 5 Low) across 20 survey nights. We found POD similar between seasons, with POD 15–20% greater during spring. The LTD was comparable among seasons. We found that POD and LTD varied at sites with different habitat quality. The POD was similar between High and Medium sites (0.26 ± 0.04 SE and 0.29 ± 0.05, respectively), but greater than Low sites (0.02 ± 0.02). The LTD was not different among sites with differing habitat quality, although LTD at High sites was 2.7 and 4.5 times lower than Medium and Low sites, respectively. Trill calls, the most distinctive species-specific call type produced by species of flying squirrels, was recorded at greater rates in spring versus other times of the year. Our results indicate flying squirrels can be surveyed during any season, although habitat quality needs to be considered when determining survey length. For Carolina northern flying squirrel, the optimal time to perform acoustic surveys is during the spring season for 6–10 survey nights at sites with high or medium habitat quality.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1083","usgsCitation":"Diggins, C.A., Gilley, L.M., Kelly, C.A., and Ford, W., 2020, Using ultrasonic acoustics to detect cryptic flying squirrels: Effects of season and habitat suitability: Wildlife Society Bulletin, v. 44, no. 2, p. 300-308, https://doi.org/10.1002/wsb.1083.","productDescription":"9 p.","startPage":"300","endPage":"308","ipdsId":"IP-105755","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457169,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/98682","text":"External Repository"},{"id":390395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.87811279296875,\n 35.252348097623354\n ],\n [\n -82.69683837890625,\n 35.40696093270201\n ],\n [\n -82.1942138671875,\n 35.66622234103479\n ],\n [\n -81.87286376953124,\n 36.219902972702606\n ],\n [\n -81.9305419921875,\n 36.357163062654365\n ],\n [\n -82.177734375,\n 36.37264499608118\n ],\n [\n -83.02642822265625,\n 35.92909271208457\n ],\n [\n -83.74603271484375,\n 35.68184060244453\n ],\n [\n -83.9630126953125,\n 35.639441068973944\n ],\n [\n -84.04541015625,\n 35.507635947037855\n ],\n [\n -82.94952392578125,\n 35.191766965947394\n ],\n [\n -82.87811279296875,\n 35.252348097623354\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Diggins, Corinne A.","contributorId":171667,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne","email":"","middleInitial":"A.","affiliations":[{"id":33131,"text":"Dept of Fish and Wildlife Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":824817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilley, L. Michelle","contributorId":171670,"corporation":false,"usgs":false,"family":"Gilley","given":"L.","email":"","middleInitial":"Michelle","affiliations":[{"id":35652,"text":"Mars Hill University, Mars Hill, NC","active":true,"usgs":false}],"preferred":false,"id":824818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Christine A.","contributorId":171661,"corporation":false,"usgs":false,"family":"Kelly","given":"Christine","email":"","middleInitial":"A.","affiliations":[{"id":35598,"text":"North Carolina Wildlife Resources Commission ","active":true,"usgs":false}],"preferred":false,"id":824819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":824816,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210178,"text":"70210178 - 2020 - Low threshold for nitrogen concentration saturation in headwaters increases regional and coastal delivery","interactions":[],"lastModifiedDate":"2020-09-01T13:53:32.395533","indexId":"70210178","displayToPublicDate":"2020-04-02T08:01:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Low threshold for nitrogen concentration saturation in headwaters increases regional and coastal delivery","docAbstract":"River corridors store, convey, and process nutrients from terrestrial and upstream sources, regulating delivery from headwaters to estuaries. A consequence of chronic excess nitrogen loading, as supported by theory and field studies in specific areas, is saturation of the biogeochemically-mediated nitrogen removal processes that weakens the capacity of the river corridor to remove nitrogen. Regional nitrogen models typically assume that removal capacity exhibits first-order behavior, scaling positively and linearly with increasing concentration, which may bias the estimation of where and at what rate nitrogen is removed by river corridors. Here we estimate the nitrogen concentration saturation threshold and its effects on nitrogen export from the Northeastern United States, revealing an average 42% concentration-induced reduction in headwater removal capacity. The weakened capacity caused an average 10% increase in the predicted delivery of riverine nitrogen from urban and agricultural watersheds compared to estimates using first-order process assumptions. Our results suggest that nitrogen removal may fall below a first-order process at a low riverine threshold concentration of 0.5 mg N L-1. Threshold behavior indicates that even modest mitigation of nitrogen concentration in river corridors above the threshold can cause a self-reinforcing boost to nitrogen removal.","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ab751b","usgsCitation":"Schmadel, N., Harvey, J., Alexander, R., Boyer, E.W., Schwarz, G.E., Gomez-Velez, J., Scott, D., and Konrad, C., 2020, Low threshold for nitrogen concentration saturation in headwaters increases regional and coastal delivery: Environmental Research Letters, v. 15, no. 4, 044018, 10 p., https://doi.org/10.1088/1748-9326/ab751b.","productDescription":"044018, 10 p.","ipdsId":"IP-114890","costCenters":[{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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\"}}]}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":789436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":789437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":219107,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":789438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":789439,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":789440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gomez-Velez, Jesus D.","contributorId":219103,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus D.","affiliations":[{"id":39962,"text":"Department of Earth & Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA","active":true,"usgs":false}],"preferred":false,"id":789441,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Durelle","contributorId":219088,"corporation":false,"usgs":false,"family":"Scott","given":"Durelle","affiliations":[{"id":39959,"text":"Virginia 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Comparing the effect of risk aversion on objective and self-reported mitigation measures","interactions":[],"lastModifiedDate":"2021-04-23T21:29:00.686727","indexId":"70220191","displayToPublicDate":"2020-04-01T16:27:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8572,"text":"Journal of Environmental Behavior and Organization","active":true,"publicationSubtype":{"id":10}},"title":"Do actions speak louder than words? Comparing the effect of risk aversion on objective and self-reported mitigation measures","docAbstract":"Risky behaviors are of public concern when they are associated with negative externalities. Public programs and policy seek to incentivize less risky behaviors in an effort to reduce or eliminate such social costs. It is in this context that the relationship between risk aversion and risky behaviors is of particular interest. However, the literature on risk aversion and risky behaviors has largely relied on self-reported behaviors. Whether intentional or unintentional, self-reported behaviors may differ from objective measures of behavior. Because policies and programs are often based on objective measures of behavior, rather than self-reports, we ask the question of whether observed relationships between risk preferences and self-reported behaviors extend to objective measures of behavior.
","language":"English","doi":"10.1016/j.jebo.2019.11.019","usgsCitation":"Champ, P.A., Meldrum, J., Brenkert-Smith, H., Warziniack, T., Barth, C.M., Falk, L.C., and Gomez, J., 2020, Do actions speak louder than words? Comparing the effect of risk aversion on objective and self-reported mitigation measures: Journal of Environmental Behavior and Organization, v. 169, p. 301-313, https://doi.org/10.1016/j.jebo.2019.11.019.","productDescription":"13 p.","startPage":"301","endPage":"313","ipdsId":"IP-083207","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":385296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"169","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Champ, Patricia A.","contributorId":195486,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":814680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":814682,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warziniack, Travis 0000-0002-9431-2288","orcid":"https://orcid.org/0000-0002-9431-2288","contributorId":217841,"corporation":false,"usgs":false,"family":"Warziniack","given":"Travis","email":"","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":814683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barth, Christopher M.","contributorId":195487,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":814684,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Falk, Lilia C.","contributorId":210655,"corporation":false,"usgs":false,"family":"Falk","given":"Lilia","email":"","middleInitial":"C.","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":814685,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomez, Jamie","contributorId":218078,"corporation":false,"usgs":false,"family":"Gomez","given":"Jamie","email":"","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":814686,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211966,"text":"70211966 - 2020 - Runoff-initiated post-fire debris flow Western Cascades, Oregon","interactions":[],"lastModifiedDate":"2020-08-12T20:57:19.586297","indexId":"70211966","displayToPublicDate":"2020-04-01T15:54:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Runoff-initiated post-fire debris flow Western Cascades, Oregon","docAbstract":"Wildfires dramatically alter the hydraulics and root reinforcement of soil on forested hillslopes, which can promote the generation of debris flows. In the Pacific Northwest, post-fire shallow landsliding has been well documented and studied, but the potential role of runoff-initiated debris flows is not well understood and only one previous to 2018 had been documented in the region. On 20 June 2018, approximately 1 year after the Milli fire burned 24,000 acres, a runoff-initiated debris flow occurred on the flanks of Black Crater in the Oregon Cascade Range. The debris flow was initiated via dispersed rilling on > 30-degree slopes near the crater rim and traveled > 1.5 km downslope. We measured exceptionally low soil infiltration rates at the study site, likely due to high burn severity during the Milli fire. Based on nearby 5-min rain gage data, we quantified rainfall rates for the storm event that triggered the debris flow. Our results show that peak 15-min rainfall rates were 25.4 mmh−1, equaling or exceeding the measured infiltration rates at the study site, which had a geometric mean of ~ 24 mmh−1. Field mapping shows that high burn severity resulted in the initiation of the debris flow and that convergent and steep topography promoted the development of a debris flow at this site. As wildfires increase in frequency and intensity across the western USA, the Pacific Northwest could become more susceptible to runoff-initiated debris flows. Therefore, characterization of the conditions that resulted in this debris flow is crucial for understanding how runoff-initiated debris flows may shape terrain and impact hazards in the Pacific Northwest.
","language":"English","publisher":"Springerlink","doi":"10.1007/s10346-020-01376-9","usgsCitation":"Wall, S., Roering, J., and Rengers, F.K., 2020, Runoff-initiated post-fire debris flow Western Cascades, Oregon: Landslides, v. 17, p. 1649-1661, https://doi.org/10.1007/s10346-020-01376-9.","productDescription":"13 p.","startPage":"1649","endPage":"1661","ipdsId":"IP-114420","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Western Cascades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.882080078125,\n 43.23719944365308\n ],\n [\n -121.695556640625,\n 43.23719944365308\n ],\n [\n -121.695556640625,\n 45.26715476332791\n ],\n [\n -122.882080078125,\n 45.26715476332791\n ],\n [\n -122.882080078125,\n 43.23719944365308\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2020-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Wall, Sara","contributorId":238092,"corporation":false,"usgs":false,"family":"Wall","given":"Sara","email":"","affiliations":[{"id":33615,"text":"Carleton College","active":true,"usgs":false}],"preferred":false,"id":796002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roering, J.J.","contributorId":238093,"corporation":false,"usgs":false,"family":"Roering","given":"J.J.","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":796003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796004,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228481,"text":"70228481 - 2020 - Can genetic assignment tests provide insight on the influence of captive egression on epizootiology of chronic wasting disease?","interactions":[],"lastModifiedDate":"2022-02-11T19:21:28.963805","indexId":"70228481","displayToPublicDate":"2020-04-01T13:15:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1601,"text":"Evolutionary Applications","active":true,"publicationSubtype":{"id":10}},"title":"Can genetic assignment tests provide insight on the influence of captive egression on epizootiology of chronic wasting disease?","docAbstract":"Identifying the sources of ongoing and novel disease outbreaks is critical for understanding the diffusion of epizootic diseases. Identifying infection sources is difficult when few physical differences separate individuals with different origins. Genetic assignment procedures show great promise for assessing transmission dynamics in such situations. Here, we use genetic assignment tests to determine the source of chronic wasting disease infections in free-ranging white-tailed deer (Odocoileus virginianus) populations. Natural dispersal is thought to facilitate the geographic diffusion of chronic wasting disease, but egression from captive cervid populations represents an alternative source of infection that is difficult to detect due to physical similarities with wild deer. Simulated reference populations were created based on allele frequencies from 1,912 empirical microsatellite genotypes collected in four sampling subregions and five captive facilities. These reference populations were used to assess the likelihood of ancestry and assignment of 1,861 free-ranging deer (1,834 noninfected and 27 infected) and 51 captive individuals to captive or wild populations. The ancestry (Q) and assignment scores (A) for free-ranging deer to wild populations were high (average Qwild = 0.913 and average Awild = 0.951, respectively), but varied among subregions (Qwild = 0.800–0.947, Awild = 0.857–0.976). These findings suggest that captive egression and admixture are rare, but risk may not be spatially uniform. Ancestry and assignment scores for two free-ranging deer with chronic wasting disease sampled in an area where chronic wasting disease was previously unobserved in free-ranging herds indicated a higher likelihood of assignment and proportion of ancestry attributable to captive populations. While we cannot directly assign these individuals to infected facilities, these findings suggest that rare egression events may influence the epizootiology of chronic wasting disease in free-ranging populations. Continued disease surveillance and genetic analyses may further elucidate the relative disease risk attributable to captive and wild sources.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.12895","usgsCitation":"Miller, W.L., and Walter, W., 2020, Can genetic assignment tests provide insight on the influence of captive egression on epizootiology of chronic wasting disease?: Evolutionary Applications, v. 13, no. 4, p. 715-726, https://doi.org/10.1111/eva.12895.","productDescription":"12 p.","startPage":"715","endPage":"726","ipdsId":"IP-111530","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457178,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/eva.12895","text":"External Repository"},{"id":395859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.870849609375,\n 38.77978137804918\n ],\n [\n -77.080078125,\n 38.8824811975508\n ],\n [\n -76.3330078125,\n 39.73253798438173\n ],\n [\n -76.32202148437499,\n 41.40153558289846\n ],\n [\n -79.925537109375,\n 41.30257109430557\n ],\n [\n -78.870849609375,\n 38.77978137804918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, William L.","contributorId":200356,"corporation":false,"usgs":false,"family":"Miller","given":"William","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":834407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834406,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208771,"text":"sir20205021 - 2020 - Effects of box culverts on stream habitat, channel morphology, and fish and macroinvertebrate communities at selected sites in South Carolina, 2016–18","interactions":[],"lastModifiedDate":"2022-04-26T18:43:50.872109","indexId":"sir20205021","displayToPublicDate":"2020-04-01T11:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5021","displayTitle":"Effects of Box Culverts on Stream Habitat, Channel Morphology, and Fish and Macroinvertebrate Communities at Selected Sites in South Carolina, 2016–18","title":"Effects of box culverts on stream habitat, channel morphology, and fish and macroinvertebrate communities at selected sites in South Carolina, 2016–18","docAbstract":"Much attention has been placed on the role that under-roadway culverts may have in inhibiting upstream fish movement because of altered hydrology and unsuitable conditions for accessing or swimming through the culvert. Other culvert effects related to habitat alterations or disturbance to macroinvertebrate communities have received relatively little attention. Entities responsible for culverts or other stream crossing structures are required to follow the U.S. Army Corps of Engineers guidelines for compensatory mitigation should any disturbance result from an engineering activity. One factor considered in the scoring of mitigation requirements is culvert length. Except for shading a longer length of stream, it is unknown whether longer culverts result in greater disturbance to stream habitat or the biotic communities than shorter culverts. The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, evaluated the role of culverts in altering physical habitat and community structure of fish and macroinvertebrates at 20 sites in South Carolina. Culvert sites were categorized by length (either greater than 30.5 meters or less than or equal to 30.5 meters) and physiographic province (Piedmont or upper Coastal Plain). This study design allowed for a regional assessment to determine if culverts may have different effects on habitat and biotic communities in different physical settings. The results indicated considerable variation in physical habitat characteristics within and among the culvert sites from all categories. A consistent finding was that channel cross-sectional area tended to increase in reaches downstream from culverts in the upper Coastal Plain. The primary dimension of change was vertical, that is, incision of the streambed. This change, however, did not seem to coincide with a deleterious effect on the fish community. Increased habitat complexity and greater taxonomic richness were observed at most sites with downstream incision. Macroinvertebrate communities were highly variable and did not tend to cluster along any of the culvert categories, which may reflect the variability of microhabitats within each site. In contrast, fish communities were largely segregated by physiographic province but did not show any other significant clustering on the basis of upstream or downstream reach or culvert length. Given the small within-group sample size, extrapolation of results should be done carefully, acknowledging the physiographic and group characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205021","collaboration":"Prepared in cooperation with the South Carolina Department of Transportation","usgsCitation":"Riley, J.W., Beaulieu, K.M., Walsh, S.J., and Journey, C.A., 2020, Effects of box culverts on stream habitat, channel morphology, and fish and macroinvertebrate communities at selected sites in South Carolina, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5021, 51 p., https://doi.org/10.3133/sir20205021.","productDescription":"viii, 51 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-104418","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437037,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VD9AYY","text":"USGS data release","linkHelpText":"Stream habitat and geomorphic characteristics above and below 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U.S. Geological Survey
720 Gracern Road
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White-nose syndrome is an emerging fungal disease that has devastated hibernating bat populations across eastern North America. The causal pathogen, Pseudogymnoascus destructans (PD), is a psychrophilic fungus with a known maximal growth temperature of 20 C. Although it is widely speculated that PD is primarily spread between hibernacula by the movement of bats, experimental evidence is lacking to demonstrate that PD can endure temperatures experienced by active bats for periods of time that would facilitate dispersal of viable fungus. We used an in vitro culture-based approach to study the survival of PD conidia on three artificial growth media and bat fur. The fungus was incubated at three temperatures it might realistically be exposed to on nonhibernating bats or in the environment outside of caves and mines (24 C, 30 C, and 37 C). When incubated on artificial media, we found that PD conidia were able to survive for a maximum of 150 d when exposed to temperatures of 24 C, 60 d at 30 C, and 15 d at 37 C. At all temperatures, maximal survival duration was recorded when conidia were incubated on brain–heart infusion agar with 10% volume of sheep (Ovis aries) blood. When incubated on bat fur, viable PD was recovered at 180 d, 60 d, and 5 d when exposed to temperatures of 24 C, 30 C, and 37 C, respectively. Our results suggest that viable PD conidia may be able to survive on or within the bodies of bats, which may facilitate long-distance dispersal. The long-term viability of the fungus on various fomites may differ, and therefore must be assessed for each potential substrate.
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