Power spectral density (PSD) estimates are widely used in seismological studies to characterize background noise conditions, assess instrument performance, and study quasi‐stationary signals that are difficult to observe in the time domain. However, these studies often utilize different processing techniques, each of which can inherently bias the resulting PSD estimates. The level of smoothing, the size of the data window, and the method used for actually estimating the spectral content can all have strong influences on PSD estimates and background noise statistics. We show that although smoothing reduces the variance of the PSD estimate, the corresponding decrease in frequency resolution can eliminate or distort features of interest. For instance, popular software packages such as Incorporated Research Institutions for Seismology Modular Utility for STAatistical kNowledge Gathering (MUSTANG) and earlier versions of Portable Array Seismic Studies of the Continental Lithosphere Quick Look eXtended (PQLX), which were designed for data quality control and are effective in that regard, are less suitable for scientific studies that require accurate resolution of spectral peaks, even for peaks as broad as the primary (
Endangered whooping cranes (Grus americana ) have been produced in captivity for reintroduction programs since the 1980s, using techniques such as artificial insemination, multiple clutching, and captive‐rearing to speed recovery efforts. Chicks are often hand‐reared (HR) by caretakers in crane costumes, socialized into groups and released together, unlike parent‐reared (PR) cranes that are raised individually by a male/female crane pair and released singly. HR cranes historically exhibit greater morbidity rates during development than PR cranes, involving musculoskeletal and respiratory system disease, among others. We hypothesized that HR crane chicks exhibit a higher baseline fecal glucocorticoid metabolite (FGM) concentrations during the development compared with PR chicks. Fecal samples were collected between 15 and 70 days of age from HR (n = 15) and PR (n = 8) chicks to test for differences in FGM concentrations using a radioimmunoassay technique following ethanol extraction for steroids. Linear mixed model analysis suggests increasing age of the chick was associated with an increase in FGM (p < .001). Analysis also supported the interaction between rearing strategy and sex of the crane chick (p < .01). Female PR chicks had greater FGM concentrations than all other groups (PR male, p < .01; HR female, p < .001; and HR male, p < .001). This result suggests that there may be an effect of rearing strategy on stress physiology of whooping crane chicks, especially among females. Further research is needed to investigate whether the FGM concentrations are reflective of true differences in stress physiology of young cranes and whether this may impact health and conservation success.zo
The 3D Elevation Program (3DEP) is an acquisition strategy that uses data from commercial remote sensing technologies to create three-dimensional maps of the United States and U.S. territories. Currently, light detection and ranging and interferometric synthetic aperture radar are the two commercial technologies being used to provide three-dimensional information to meet the program’s operational requirements. This is because there is not a well-established process for vendors of new and novel instruments to know when and how 3DEP will accept their technologies into the 3DEP portfolio. The purpose of this plan is to provide a strategy and rules for communication between 3DEP and commercial partners interested in proposing their modalities for use in the program. To accomplish this, 3DEP will also consider how it invests in new technologies and how it disseminates data to and categorizes data for the broader community and the public.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201015","collaboration":"","usgsCitation":"Stoker, J.M., 2020, Defining technology operational readiness for the 3D Elevation Program—A plan for investment, incubation, and adoption: U.S. Geological Survey Open-File Report 2020–1015, 7 p., \nhttps://doi.org/ 10.3133/ ofr20201015.","productDescription":"iv, 7 p.","onlineOnly":"Y","ipdsId":"IP-110726","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":373798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1015/ofr20201015.pdf","text":"Report","size":"932 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1015"},{"id":373797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1015/coverthb2.jpg"}],"contact":"Director, National Geospatial Program
U.S. Geological Survey
MS-511
12201 Sunrise Valley Drive
Reston, VA 20192
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":70209098,"text":"ofr20201028 - 2020 - Ground-motion predictions for California — Comparisons of three prediction equations","interactions":[],"lastModifiedDate":"2022-04-21T21:00:24.232588","indexId":"ofr20201028","displayToPublicDate":"2020-04-07T10:41:29","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1028","displayTitle":"Ground-Motion Predictions for California — Comparisons of Three Prediction Equations","title":"Ground-motion predictions for California — Comparisons of three prediction equations","docAbstract":"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
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":70209327,"text":"tm6A60 - 2020 - One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software","interactions":[],"lastModifiedDate":"2023-03-31T18:33:38.4397","indexId":"tm6A60","displayToPublicDate":"2020-04-07T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and 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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A60","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Boyce, S.E., Hanson, R.T., Ferguson, I., Schmid, W., Henson, W., Reimann, T., Mehl, S.M., and Earll, M.M., 2020, One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software: U.S. Geological Survey Techniques and Methods 6–A60, 435 p., https://doi.org/10.3133/tm6A60.","productDescription":"Report: xvii, 435 p.; Application Site","numberOfPages":"435","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071159","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437036,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K2IQ6Y","text":"USGS data release","linkHelpText":"Batteries Included Fortran Library (BiF-lib), version 1.0.0"},{"id":437035,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8I8GS","text":"USGS data release","linkHelpText":"MODFLOW One-Water Hydrologic Flow Model (MF-OWHM) Conjunctive Use and Integrated Hydrologic Flow Modeling Software"},{"id":374113,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix8.pdf","text":"Appendix 8","size":"300 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Conduit Flow Process (CFP2) Input File Documentation for New Capabilities of CFP2 Mode 1—Discrete Conduits"},{"id":374112,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix7.pdf","text":"Appendix 7","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Conduit Flow Process Updates and Upgrades (CFP2)"},{"id":374111,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix6.pdf","text":"Appendix 6","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Farm Process Version 4 (FMP)"},{"id":374110,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix5.pdf","text":"Appendix 5","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Landscape and Root-Zone Processes and Water Demand and Supply"},{"id":374109,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix4.pdf","text":"Appendix 4","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Consumptive Use and Evapotranspiration in the Farm Process"},{"id":374108,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix3.pdf","text":"Appendix 3","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Modflow Upgrades and Updates"},{"id":374107,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix2.pdf","text":"Appendix 2","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Separation of Spatial and Temporal Input Options"},{"id":374106,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix1.pdf","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- New Input Formats and Utilities"},{"id":374105,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_appendix0.pdf","text":"Appendix 0","size":"500 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60","linkHelpText":"- Report Syntax Highlighting and Custom Font Styles"},{"id":374104,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60_body.pdf","text":"Main body","size":"3 MB - Main body","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60 Main body"},{"id":373682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a60/coverthb.jpg"},{"id":373683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a60/tm6A60.pdf","text":"Full report","size":"30 MB - Full report","linkFileType":{"id":1,"text":"pdf"},"description":"Techniques and Methods A6-60 Full report"},{"id":373696,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://www.usgs.gov/software/modflow-owhm-one-water-hydrologic-flow-model"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
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.
","language":"English","publisher":"Springer Nature Limited","doi":"10.1038/s41598-020-62118-4","usgsCitation":"Aalto, E.A., Lafferty, K.D., Sokolow, S.H., Grewelle, R.E., Ben-Horin, T., Boch, C., Raimondi, P.T., Bograd, S.J., Hazen, E.L., Jacox, M., Micheli, F., and De Leo, G., 2020, Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms: Scientific Reports, v. 10, 5975, 10 p., https://doi.org/10.1038/s41598-020-62118-4.","productDescription":"5975, 10 p.","ipdsId":"IP-086788","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-62118-4","text":"Publisher Index Page"},{"id":376468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","state":"Baja California, British Columbia, California, Oregon, Washington","otherGeospatial":"Pacific Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.64257812499999,\n 28.536274512989916\n ],\n [\n -119.70703125,\n 35.88905007936091\n ],\n [\n -123.134765625,\n 39.16414104768742\n ],\n [\n -123.662109375,\n 43.32517767999296\n ],\n [\n -121.025390625,\n 48.40003249610685\n ],\n [\n -123.48632812499999,\n 50.62507306341435\n ],\n [\n -130.25390625,\n 55.02802211299252\n ],\n [\n -134.6484375,\n 54.97761367069628\n ],\n [\n -132.890625,\n 51.83577752045248\n ],\n [\n -126.298828125,\n 47.931066347509784\n ],\n [\n -125.771484375,\n 42.74701217318067\n ],\n [\n -124.892578125,\n 38.54816542304656\n ],\n [\n -120.673828125,\n 33.358061612778876\n ],\n [\n -115.13671875,\n 28.14950321154457\n ],\n [\n -113.466796875,\n 26.194876675795218\n ],\n [\n -113.64257812499999,\n 28.536274512989916\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Aalto, E. A.","contributorId":229403,"corporation":false,"usgs":false,"family":"Aalto","given":"E.","email":"","middleInitial":"A.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":793142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":793143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sokolow, S. H.","contributorId":229404,"corporation":false,"usgs":false,"family":"Sokolow","given":"S.","email":"","middleInitial":"H.","affiliations":[{"id":40446,"text":"Hopkins Marine Station, Stanford University","active":true,"usgs":false}],"preferred":false,"id":793144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grewelle, R. E.","contributorId":229405,"corporation":false,"usgs":false,"family":"Grewelle","given":"R.","email":"","middleInitial":"E.","affiliations":[{"id":40446,"text":"Hopkins Marine Station, Stanford University","active":true,"usgs":false}],"preferred":false,"id":793145,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ben-Horin, Tal","contributorId":58137,"corporation":false,"usgs":false,"family":"Ben-Horin","given":"Tal","email":"","affiliations":[],"preferred":false,"id":793146,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boch, C. A.","contributorId":229406,"corporation":false,"usgs":false,"family":"Boch","given":"C. A.","affiliations":[{"id":41634,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793147,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raimondi, P. T.","contributorId":229407,"corporation":false,"usgs":false,"family":"Raimondi","given":"P.","email":"","middleInitial":"T.","affiliations":[{"id":41635,"text":"University of California, Santa Cruz, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793148,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bograd, S. J.","contributorId":229408,"corporation":false,"usgs":false,"family":"Bograd","given":"S.","email":"","middleInitial":"J.","affiliations":[{"id":41636,"text":"NOAA Southwest Fisheries Science Center, Monterey, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793149,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hazen, E. L.","contributorId":229409,"corporation":false,"usgs":false,"family":"Hazen","given":"E.","email":"","middleInitial":"L.","affiliations":[{"id":41636,"text":"NOAA Southwest Fisheries Science Center, Monterey, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793150,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jacox, M. G.","contributorId":229410,"corporation":false,"usgs":false,"family":"Jacox","given":"M. G.","affiliations":[{"id":41636,"text":"NOAA Southwest Fisheries Science Center, Monterey, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793151,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Micheli, F.","contributorId":229411,"corporation":false,"usgs":false,"family":"Micheli","given":"F.","email":"","affiliations":[{"id":41637,"text":"Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793152,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"De Leo, G. A.","contributorId":229412,"corporation":false,"usgs":false,"family":"De Leo","given":"G. A.","affiliations":[{"id":41637,"text":"Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA","active":true,"usgs":false}],"preferred":false,"id":793153,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70210071,"text":"70210071 - 2020 - Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes","interactions":[],"lastModifiedDate":"2020-05-13T14:18:36.270936","indexId":"70210071","displayToPublicDate":"2020-04-06T09:15:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal 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":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":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":786445,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"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":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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bioaccumulation in freshwater fishes of the Chesapeake Bay watershed","docAbstract":"Chemical contaminants are a threat to the Chesapeake Bay watershed, with mercury (Hg) among the most prevalent causes of impairment. Despite this, large-scale patterns of Hg concentrations, and the potential risks to fish, wildlife, and humans across the watershed, are poorly understood. We compiled fish Hg data from state monitoring programs and recent research efforts to address this knowledge gap and provide a comprehensive assessment of fish Hg concentrations in the watershed’s freshwater habitats. The resulting dataset consisted of nearly 8000 total Hg (THg) concentrations from 600 locations. Across the watershed, fish THg concentrations spanned a 44-fold range, with mean concentrations varying by 2.6- and 8.8-fold among major sub-watersheds and individual 8-digit hydrological units, respectively. Although, mean THg concentrations tended to be moderate, fish frequently exceeded benchmarks for potential adverse health effects, with 45, 48, and 36% of all samples exceeding benchmarks for human, avian piscivore, and fish risk, respectively. Importantly, the percentage of fish exceeding these benchmarks was not uniform among species or locations. The variation in fish THg concentrations among species and sites highlights the roles of waterbody, landscape, and ecological processes in shaping broad patterns in Hg risk across the watershed. We outline an integrated Hg monitoring program that could identify key factors influencing Hg concentrations across the watershed and facilitate the implementation of management strategies to mitigate the risks posed by Hg.","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02193-5","usgsCitation":"Willacker, J., Eagles-Smith, C., and Blazer, V., 2020, Mercury bioaccumulation in freshwater fishes of the Chesapeake Bay watershed: Ecotoxicology, v. 29, p. 459-484, https://doi.org/10.1007/s10646-020-02193-5.","productDescription":"26 p.","startPage":"459","endPage":"484","ipdsId":"IP-111752","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":437039,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T2N1UT","text":"USGS data release","linkHelpText":"Total Mercury Concentrations in Smallmouth Bass from 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]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":795670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":795671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":795672,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70214307,"text":"70214307 - 2020 - Trait integration and functional differentiation among co-existing plant species","interactions":[],"lastModifiedDate":"2020-09-25T14:10:30.6953","indexId":"70214307","displayToPublicDate":"2020-04-01T09:06:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Trait integration and functional differentiation among co-existing plant species","docAbstract":"Determining which traits characterize strategies of coexisting species is important to developing trait‐based models of plant communities. First, global dimensions may not exist locally. Second, the degree to which traits and trait spectra constitute independent dimensions of functional variation at various scales continues to be refined. Finally, traits may be associated with existing categorical groupings.
We assessed trait integration and differentiation across 57 forest understory plant species in Douglas‐fir forests of western Oregon, United States. We combined measurements for a range of traits with literature‐based estimates of seed mass and species groupings. We used network analysis and nonmetric multidimensional scaling ordination (NMS) to determine the degree of integration.
We observed a strong leaf economics spectrum (LES) integrated with stem but not root traits. However, stem traits and intrinsic water‐use efficiency integrated LES and root traits. Network analyses indicated a modest grouping of a priori trait dimensions. NMS indicated that multivariate differences among species were related primarily to (1) rooting depth and plant height vs. specific root length, (2) the LES, and (3) leaf size vs. seed mass. These differences were related to species groupings associated with growth and life form, leaf lifespan and seed dispersal mechanisms.
The strategies of coexisting understory plant species could not be reduced to a single dimension. Yet, species can be characterized efficiently and effectively for trait‐based studies of plant communities by measuring four common traits: plant height, specific leaf area, leaf size, and seed mass.
","language":"English","publisher":"Wiley","doi":"10.1002/ajb2.1451","usgsCitation":"Burton, J.I., Perakis, S.S., Brooks, J.R., and Puettmann, K.J., 2020, Trait integration and functional differentiation among co-existing plant species: American Journal of Botany, v. 107, no. 4, p. 628-638, https://doi.org/10.1002/ajb2.1451.","productDescription":"11 p.","startPage":"628","endPage":"638","ipdsId":"IP-095474","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8108537","text":"Publisher Index Page"},{"id":378745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.1455078125,\n 41.983994270935625\n ],\n [\n -120.25634765624999,\n 41.983994270935625\n ],\n [\n -120.25634765624999,\n 45.78284835197676\n ],\n [\n -124.1455078125,\n 45.78284835197676\n ],\n [\n -124.1455078125,\n 41.983994270935625\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"107","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burton, Julia I. 0000-0002-3205-8819","orcid":"https://orcid.org/0000-0002-3205-8819","contributorId":192599,"corporation":false,"usgs":false,"family":"Burton","given":"Julia","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":799605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":799606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooks, J. Renee","contributorId":241131,"corporation":false,"usgs":false,"family":"Brooks","given":"J.","email":"","middleInitial":"Renee","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":799607,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Puettmann, Klaus J.","contributorId":192602,"corporation":false,"usgs":false,"family":"Puettmann","given":"Klaus","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":799608,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209458,"text":"70209458 - 2020 - Cooperatively improving tallgrass prairie with adaptive management","interactions":[],"lastModifiedDate":"2020-04-10T16:04:37.654404","indexId":"70209458","displayToPublicDate":"2020-04-01T08:19:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Cooperatively improving tallgrass prairie with adaptive management","docAbstract":"Adaptive management (AM) is widely recommended as an approach for learning to improve resource management, but successful AM projects remain relatively uncommon, with few documented examples applied by natural resource management agencies. We used AM to make recommendations for the management of native tallgrass prairie plant communities in western Minnesota and eastern North and South Dakota, USA. After nine years of data collection and learning, we report on whether the condition of the prairie improved with management and which actions and frequency of action allowed improvement. Our approach to AM employed Bayesian updating to generate annual management recommendations at a site and state-dependent scale. We also used a logistic regression approach to complement the output from the AM model and evaluate the more general conditions which led to attaining management goals. Overall, the cover of native plants increased for low-quality sites, and among the management practices considered, we found that burning most effectively enhanced the native prairie plant community and increased the dominance of native indicator species. Contrary to expectations, the results also suggest that grazing on sites that started in a poor condition were less likely to show improvements in the native plant community. Complementing AM with more traditional statistical analyses can help inform the iterative doubleloop learning phase of the AM framework. AM has many challenges, but we demonstrate that multi-agency AM can be successful. Keys to success include starting the project with an in-person, in-depth workshop; standardized protocols and a centralized database; a core project team with multi-disciplinary backgrounds; stability in project leadership; and regular communication to meet annual deadlines.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3095","collaboration":"","usgsCitation":"Ahlering, M., Carlson, D., Vacek, S., Jacobi, S., Hunt, V., Stanton, J.C., Knutson, M.G., and Lonsdorf, E.V., 2020, Cooperatively improving tallgrass prairie with adaptive management: Ecosphere, v. 11, no. 4, e03095, 21 p., https://doi.org/10.1002/ecs2.3095.","productDescription":"e03095, 21 p.","ipdsId":"IP-106974","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":457188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3095","text":"Publisher Index Page"},{"id":373859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.57958984375,\n 42.58544425738491\n ],\n [\n -93.42773437499999,\n 42.58544425738491\n ],\n [\n -93.42773437499999,\n 48.951366470947725\n ],\n [\n -101.57958984375,\n 48.951366470947725\n ],\n [\n -101.57958984375,\n 42.58544425738491\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlering, Marissa 0000-0002-3913-428X","orcid":"https://orcid.org/0000-0002-3913-428X","contributorId":171943,"corporation":false,"usgs":false,"family":"Ahlering","given":"Marissa","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":786553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlson, Daren","contributorId":219541,"corporation":false,"usgs":false,"family":"Carlson","given":"Daren","email":"","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":786554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vacek, Sara","contributorId":178445,"corporation":false,"usgs":false,"family":"Vacek","given":"Sara","email":"","affiliations":[],"preferred":false,"id":786555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobi, Sarah","contributorId":149496,"corporation":false,"usgs":false,"family":"Jacobi","given":"Sarah","email":"","affiliations":[{"id":17752,"text":"Chicago Botanic Garden","active":true,"usgs":false}],"preferred":false,"id":786556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Vicky","contributorId":219542,"corporation":false,"usgs":false,"family":"Hunt","given":"Vicky","email":"","affiliations":[{"id":17752,"text":"Chicago Botanic Garden","active":true,"usgs":false}],"preferred":false,"id":786557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":786558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Knutson, Melinda G.","contributorId":205325,"corporation":false,"usgs":false,"family":"Knutson","given":"Melinda","email":"","middleInitial":"G.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":786559,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lonsdorf, Eric V.","contributorId":149495,"corporation":false,"usgs":false,"family":"Lonsdorf","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":17752,"text":"Chicago Botanic Garden","active":true,"usgs":false}],"preferred":false,"id":786560,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209474,"text":"70209474 - 2020 - Through thick and thin: Sexing Bristle-thighed Curlews Numenius tahitiensis using measures of bill depth","interactions":[],"lastModifiedDate":"2020-05-01T13:15:22.500537","indexId":"70209474","displayToPublicDate":"2020-04-01T07:26:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5557,"text":"Wader Study","active":true,"publicationSubtype":{"id":10}},"title":"Through thick and thin: Sexing Bristle-thighed Curlews Numenius tahitiensis using measures of bill depth","docAbstract":"Birds often exhibit diagnostic traits that differ among individuals of the same species with regard to factors like sex, age, or breeding status. Shorebirds exhibit a wide diversity of colors, shapes, and sizes of their bills, and these traits are commonly used to determine the sex of individuals. In curlews (genus Numenius), length alone accurately separates the sexes in some species, but the shape of the bill has not typically been assessed for this purpose. We collected a suite of measurements on the bills of known-sex Bristle-thighed Curlews N. tahitiensis and determined that standardized measurements of bill depth separated the sexes with high accuracy. A model incorporating the length of a bird’s diagonal tarsus and multiple measurements of the bill was 93.1% accurate in predicting the sex of individual Bristle-thighed Curlews. Simpler models involving only the values of the bill depth near the tip and the base of the bill, however, produced similarly accurate results and are preferred for their parsimony. We advocate the use of one such model that is 93.4% accurate in determining the sex of Bristle-thighed Curlews. As a simple heuristic, a value for the ratio of the bill depth near the tip to that at the base of >0.5 indicated a female, providing an easy field calculation to help determine the sex of Bristle-thighed Curlews. In general, the bills of female Bristle-thighed Curlews are deeper and taper relatively less than those of males. Other observers have qualitatively noted apparent sex-specific differences in the shape of curlew bills, but the generality of our quantitative findings remains to be examined in other curlew species.","language":"English","publisher":"International Wader Study Group","doi":"10.18194/ws.00171","collaboration":"","usgsCitation":"Ruthrauff, D.R., Handel, C.M., Tibbitts, T.L., and Gill, R., 2020, Through thick and thin: Sexing Bristle-thighed Curlews Numenius tahitiensis using measures of bill depth: Wader Study, v. 127, no. 1, p. 31-36, https://doi.org/10.18194/ws.00171.","productDescription":"6 p.","startPage":"31","endPage":"36","ipdsId":"IP-111718","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437040,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KNRWXB","text":"USGS data release","linkHelpText":"USGS Alaska Science Center Adult Shorebird Morphological Measurement Data"},{"id":373886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruthrauff, Daniel R. 0000-0003-1355-9156 druthrauff@usgs.gov","orcid":"https://orcid.org/0000-0003-1355-9156","contributorId":4181,"corporation":false,"usgs":true,"family":"Ruthrauff","given":"Daniel","email":"druthrauff@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":786674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":786675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592 ltibbitts@usgs.gov","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":102185,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T.","email":"ltibbitts@usgs.gov","middleInitial":"Lee","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":786676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gill, Robert E. Jr. 0000-0002-6385-4500 rgill@usgs.gov","orcid":"https://orcid.org/0000-0002-6385-4500","contributorId":171747,"corporation":false,"usgs":true,"family":"Gill","given":"Robert E.","suffix":"Jr.","email":"rgill@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":786677,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209472,"text":"70209472 - 2020 - Climate and local environment structure asynchrony and the stability of primary production in grasslands","interactions":[],"lastModifiedDate":"2020-06-04T17:10:49.928549","indexId":"70209472","displayToPublicDate":"2020-04-01T06:54:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Climate and local environment structure asynchrony and the stability of primary production in grasslands","docAbstract":"Aim\nClimate variability threatens to destabilize production in many ecosystems. Asynchronous species dynamics may buffer against such variability when a decrease in performance by some species is offset by an increase in performance of others. However, high climatic variability can eliminate species through stochastic extinctions or cause similar stress responses among species that reduce buffering. Local conditions, such as soil nutrients, can also alter production stability directly or by influencing asynchrony. We test these hypotheses using a globally distributed sampling experiment.\n\nLocation\nGrasslands in North America, Europe and Australia.\n\nTime period\nAnnual surveys over 5 year intervals occurring between 2007 and 2014.\n\nMajor taxa studied\nHerbaceous plants.\n\nMethods\nWe sampled annually the per species cover and aboveground community biomass [net primary productivity (NPP)], plus soil chemical properties, in 29 grasslands. We tested how soil conditions, combined with variability in precipitation and temperature, affect species richness, asynchrony and temporal stability of primary productivity. We used bivariate relationships and structural equation modelling to examine proximate and ultimate relationships.\n\nResults\nClimate variability strongly predicted asynchrony, whereas NPP stability was more related to soil conditions. Species richness was structured by both climate variability and soils and, in turn, increased asynchrony. Variability in temperature and precipitation caused a unimodal asynchrony response, with asynchrony being lowest at low and high climate variability. Climate impacted stability indirectly, through its effect on asynchrony, with stability increasing at higher asynchrony owing to lower inter‐annual variability in NPP. Soil conditions had no detectable effect on asynchrony but increased stability by increasing the mean NPP, especially when soil organic matter was high.\n\nMain conclusions\nWe found globally consistent evidence that climate modulates species asynchrony but that the direct effect on stability is low relative to local soil conditions. Nonetheless, our observed unimodal responses to variability in temperature and precipitation suggest asynchrony thresholds, beyond which there are detectable destabilizing impacts of climate on primary productivity.","language":"English","publisher":"Wiley","doi":"10.1111/geb.13094","usgsCitation":"Gilbert, B., MacDougall, A., Kadoya, T., Akasaka, M., Bennett, J.R., Lind, E., Flores-Moreno, H., Firn, J., Hautier, Y., Borer, E., Seabloom, E., Adler, P., Cleland, E., Grace, J., Harpole, W., Esch, E., Moore, J., Knops, J., McCulley, R., Mortensen, B., Bakker, J., and Fay, P., 2020, Climate and local environment structure asynchrony and the stability of primary production in grasslands: Global Ecology and Biogeography, v. 29, no. 7, p. 1177-1188, https://doi.org/10.1111/geb.13094.","productDescription":"12 p.","startPage":"1177","endPage":"1188","ipdsId":"IP-096717","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457195,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/geb.13094","text":"External Repository"},{"id":373885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, B.","contributorId":223928,"corporation":false,"usgs":false,"family":"Gilbert","given":"B.","affiliations":[{"id":40795,"text":"Department of Ecology and Evolutionary Biology, University of Toronto","active":true,"usgs":false}],"preferred":false,"id":786648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacDougall, A.S.","contributorId":203183,"corporation":false,"usgs":false,"family":"MacDougall","given":"A.S.","email":"","affiliations":[{"id":36573,"text":"Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":786649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kadoya, T.","contributorId":223929,"corporation":false,"usgs":false,"family":"Kadoya","given":"T.","affiliations":[{"id":40796,"text":"Environmental Biology Division, National Institute for Environmental Studies, Tsukuba, Japan","active":true,"usgs":false}],"preferred":false,"id":786650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Akasaka, M.","contributorId":223930,"corporation":false,"usgs":false,"family":"Akasaka","given":"M.","email":"","affiliations":[{"id":40797,"text":"Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tsukuba, Japan","active":true,"usgs":false}],"preferred":false,"id":786651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennett, J. R.","contributorId":223931,"corporation":false,"usgs":false,"family":"Bennett","given":"J.","email":"","middleInitial":"R.","affiliations":[{"id":40798,"text":"Department of Biology, Carleton University, Ottawa, Ontario Canada","active":true,"usgs":false}],"preferred":false,"id":786652,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lind, E.M.","contributorId":223932,"corporation":false,"usgs":false,"family":"Lind","given":"E.M.","email":"","affiliations":[{"id":40799,"text":"Department of Ecology, Evolution & Behavior, University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":786653,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flores-Moreno, H.","contributorId":223933,"corporation":false,"usgs":false,"family":"Flores-Moreno","given":"H.","email":"","affiliations":[{"id":40799,"text":"Department of Ecology, Evolution & Behavior, University of 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The project had three main components. First, a systematic literature\nreview studied 20 peer-reviewed publications and 612 reports from other nonreviewed\nsources from 231 wind and solar facilities in North America. Within California, 50\npercent of facilities collected pre- and post-construction data, 30 percent had\nexperimental study designs, and fewer than 7 percent estimated detection probability\nduring habitat use surveys. Mitigation at wind power plants focused on repowering to\nreduce risk to soaring birds and at solar facilities emphasized wildlife deterrence and\ncompensatory mitigation. Second, the authors developed a best-practices approach to\nemploy environmental isotopes (for example, hydrogen obtained from animal tissue)\nand rescaling functions (a statistical approach to modeling the relationship between\nvariables) to assign individual birds or bats to their place of origin. The team applied\nthis approach to feathers from 411 individuals of 12 species killed at wind facilities and\n515 individuals of 19 species killed at solar facilities. From 24 percent to 100 percent\n(mean +/- SD = 49 percent +/- 33 percent) and 25 percent to 100 percent (73 percent +/-\n25 percent) of birds grew feathers at a location outside the collection site at wind and\nsolar facilities, respectively. Third, the authors constructed Bayesian integrated\npopulation models (probability models) for 29 focal species affected by wind or solar\nenergy generation in California. Species predominantly local in origin generally had\nlower population growth rates than did species that were predominantly nonlocal in\norigin. These patterns illustrate the complex linkages between behavioral ecology,\nvulnerability to mortality, and population-level impacts to wildlife from fatalities at\nrenewable energy facilities. This project benefits the renewable energy sector by\nproviding a framework and specific tools for understanding environmental impacts of\nrenewable energy generation.","language":"English","publisher":"California Energy Commission","usgsCitation":"Conkling, T., Vander Zanden, H.B., Poessel, S.A., Loss, S., Allison, T.D., Diffendorfer, J., Duerr, A.E., Nelson, D.M., Yee, J.L., and Katzner, T., 2020, Learning from real-world experience to understand renewable energy impacts to wildlife: Energy Research ad Development Division Final Research Report CEC-500-2020-012, 132 p.","productDescription":"132 p.","ipdsId":"IP-106403","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science 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Arizona"},"id":3},{"subject":{"id":70208724,"text":"sir20205017D - 2020 - Major hydrostratigraphic contacts of the Truxton basin and Hualapai Plateau, northwestern Arizona, developed from airborne electromagnetic data","indexId":"sir20205017D","publicationYear":"2020","noYear":false,"chapter":"D","displayTitle":"Major Hydrostratigraphic Contacts of the Truxton Basin and Hualapai Plateau, Northwestern Arizona, Developed from Airborne Electromagnetic Data","title":"Major hydrostratigraphic contacts of the Truxton basin and Hualapai Plateau, northwestern Arizona, developed from airborne electromagnetic data"},"predicate":"IS_PART_OF","object":{"id":70209317,"text":"sir20205017 - 2020 - Geophysical surveys, hydrogeologic characterization, and groundwater flow model for the Truxton basin and Hualapai Plateau, northwestern Arizona","indexId":"sir20205017","publicationYear":"2020","noYear":false,"title":"Geophysical surveys, hydrogeologic characterization, and groundwater flow model for the 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Hualapai Plateau, northwestern Arizona"},"id":5}],"lastModifiedDate":"2020-04-07T16:55:08.827197","indexId":"sir20205017","displayToPublicDate":"2020-03-31T17:47:07","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":"2020-5017","displayTitle":"Geophysical Surveys, Hydrogeologic Characterization, and Groundwater Flow Model for the Truxton Basin and Hualapai Plateau, Northwestern Arizona","title":"Geophysical surveys, hydrogeologic characterization, and groundwater flow model for the Truxton basin and Hualapai Plateau, northwestern Arizona","docAbstract":"This is the third and final report in a series that describe the groundwater resources of the Hualapai Indian Reservation. These reports document the findings of a comprehensive groundwater study conducted on the reservation and adjacent areas from 2015 through 2018 by the U.S. Geological Survey in cooperation with the Bureau of Reclamation. The first report described the hydrologic framework and characterization of the Truxton aquifer on the Hualapai Indian Reservation (Bills and Macy, 2016). The second report described the hydrogeologic characterization of the Hualapai Plateau part of the reservation (Mason, Macy, and others, 2020).
This report includes five chapters. Chapter A (Mason, Knight, and others, 2020) is a summary of this multichapter volume and briefly describes the study area. Chapter B (Mason, Bills, and Macy, 2020) describes the geology and hydrology of the Truxton basin and Hualapai Plateau. Chapter C (Kennedy, 2020) describes the results of a gravity geophysical survey of the Truxton basin. Chapter D (Ball, 2020) describes the findings of an airborne electromagnetic survey of the Truxton aquifer and Hualapai Plateau. Chapter E (Knight, 2020) describes the results of a transient groundwater model created for the entire Truxton aquifer both on and off the reservation. The groundwater-flow model is used to estimate projected groundwater levels based on future groundwater withdrawal scenarios.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201