{"pageNumber":"304","pageRowStart":"7575","pageSize":"25","recordCount":40783,"records":[{"id":70227253,"text":"70227253 - 2019 - Identifying and characterizing extrapolation in multivariate response data","interactions":[],"lastModifiedDate":"2022-01-05T14:32:01.511268","indexId":"70227253","displayToPublicDate":"2019-12-05T08:19:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Identifying and characterizing extrapolation in multivariate response data","docAbstract":"<p><span>Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0225715","usgsCitation":"Bartley, M., Hanks, E.M., Schliep, E.M., Soranno, P.A., and Wagner, T., 2019, Identifying and characterizing extrapolation in multivariate response data: PLoS ONE, v. 14, no. 12, e0225715, 20 p., https://doi.org/10.1371/journal.pone.0225715.","productDescription":"e0225715, 20 p.","ipdsId":"IP-107783","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":459016,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0225715","text":"Publisher Index Page"},{"id":393911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bartley, Meridith L.","contributorId":270913,"corporation":false,"usgs":false,"family":"Bartley","given":"Meridith L.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":830122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, Ephraim M.","contributorId":178093,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":830123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schliep, Erin M.","contributorId":171525,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":830124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soranno, Patricia A.","contributorId":172104,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":830121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207037,"text":"70207037 - 2019 - Evaluation of stormwater treatment vault with Coanda-effect screen for removal of solids and phosphorus in urban runoff","interactions":[],"lastModifiedDate":"2019-12-04T15:38:49","indexId":"70207037","displayToPublicDate":"2019-12-04T15:37:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5896,"text":"Journal of Sustainable Water in the Built Environment","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of stormwater treatment vault with Coanda-effect screen for removal of solids and phosphorus in urban runoff","docAbstract":"Catch basins commonly are used by cities as part of a stormwater management plan to remove sediment and associated contaminants from stormwater, keeping them in compliance with regulations. Recently, the city of Madison, Wisconsin modified traditional catch basins by incorporating a fine-mesh (1-mm) Coanda-effect screen into the design with the goal of increasing removal of sediment and organic matter from stormwater. The US Geological Survey (USGS), in cooperation with the City of Madison, installed a water-quality monitoring station at such a catch basin to quantify reductions in total suspended solids (TSS), volatile suspended solids (VSS), suspended sediment concentration (SSC), total phosphorus (TP), and dissolved phosphorus (DP) from urban stormwater before entering Lake Monona. A comparison of the cumulative load from 33 samples collected during the summers of 2016 and 2017 showed 23% and 45% reductions in TSS and SSC, respectively. A smaller reduction was observed for TP, 16%, whereas DP remained unchanged. Reported traditional catch basin sediment removal varies greatly, although typical removal rates are similar. Results from this study will help regulated municipalities determine whether the use of screened catch basins can help meet water-quality goals.","language":"English","publisher":"ASCE","doi":"10.1061/JSWBAY.0000892","usgsCitation":"Buer, N., and Selbig, W.R., 2019, Evaluation of stormwater treatment vault with Coanda-effect screen for removal of solids and phosphorus in urban runoff: Journal of Sustainable Water in the Built Environment, v. 6, no. 1, 04019013, 10 p., https://doi.org/10.1061/JSWBAY.0000892.","productDescription":"04019013, 10 p.","ipdsId":"IP-096348","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":459017,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1061/jswbay.0000892","text":"Publisher Index Page"},{"id":369921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Dane County","city":"Madison","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.0094,43.286],[-89.0084,43.2555],[-89.0094,43.2],[-89.01,43.1131],[-89.0109,43.0849],[-89.0107,43.0271],[-89.0132,42.9353],[-89.013,42.8762],[-89.0119,42.8471],[-89.132,42.8479],[-89.2488,42.8478],[-89.3689,42.8484],[-89.3688,42.8575],[-89.4832,42.858],[-89.6026,42.8575],[-89.7196,42.8587],[-89.8377,42.8598],[-89.8375,42.9471],[-89.8386,43.0317],[-89.8384,43.1181],[-89.8394,43.205],[-89.8325,43.2123],[-89.825,43.2187],[-89.8175,43.226],[-89.8125,43.2342],[-89.8088,43.2369],[-89.8012,43.2365],[-89.7874,43.2356],[-89.771,43.237],[-89.7579,43.2379],[-89.7529,43.2443],[-89.7485,43.2507],[-89.7391,43.2548],[-89.7259,43.2644],[-89.7171,43.2739],[-89.714,43.2821],[-89.7165,43.2867],[-89.7235,43.2935],[-89.7209,43.2935],[-89.6008,43.2932],[-89.4819,43.2942],[-89.3617,43.2954],[-89.3624,43.2832],[-89.246,43.2834],[-89.1271,43.2827],[-89.0094,43.286]]]},\"properties\":{\"name\":\"Dane\",\"state\":\"WI\"}}]}","volume":"6","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buer, Nicolas 0000-0002-4369-8715","orcid":"https://orcid.org/0000-0002-4369-8715","contributorId":204808,"corporation":false,"usgs":true,"family":"Buer","given":"Nicolas","email":"","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776593,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70215094,"text":"70215094 - 2019 - Improving predictions of fine particle immobilization in streams","interactions":[],"lastModifiedDate":"2020-10-07T20:12:43.002013","indexId":"70215094","displayToPublicDate":"2019-12-04T15:06:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of fine particle immobilization in streams","docAbstract":"Fine particles are critical to stream ecosystem functioning, influencing in-stream processes from pathogen transmission to carbon cycling, all of which depend on particle immobilization.  However, our ability to predict particle immobilization is limited by: (1) availability of combined solute and particle tracer data and (2) identifying parameters that appropriately represent fine particle immobilization, due to the myriad of objective functions and model formulations.  We found that improved predictions of the full distribution of possible fine particle residence times requires using an objective function that assesses both the peak and tailing together with solute tracers to constrain in-stream transport processes. The representation of immobilization processes was significantly improved when solute tracer data were combined with a particle model, starkly contrasting the common assumption that fine particles transport as washload.  We develop a clear strategy for improving fine particle transport predictions, reshaping the potential role of fine particles in water quality management.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL085849","usgsCitation":"Drummond, J.D., Schmadel, N., Kelleher, C., Packman, A.I., and Ward, A.S., 2019, Improving predictions of fine particle immobilization in streams: Geophysical Research Letters, v. 46, no. 23, p. 13,853-13,861, https://doi.org/10.1029/2019GL085849.","productDescription":"9 p.","startPage":"13,853","endPage":"13,861","ipdsId":"IP-114105","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":459021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gl085849","text":"Publisher Index Page"},{"id":379197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"23","noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Drummond, Jennifer D.","contributorId":191390,"corporation":false,"usgs":false,"family":"Drummond","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":800820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":800821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelleher, Christa","contributorId":242798,"corporation":false,"usgs":false,"family":"Kelleher","given":"Christa","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":800822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Packman, Aaron I.","contributorId":124517,"corporation":false,"usgs":false,"family":"Packman","given":"Aaron","email":"","middleInitial":"I.","affiliations":[{"id":5041,"text":"Department of Civil and Environmental Engineering, Northwestern University, Evanston, Illinois, USA","active":true,"usgs":false}],"preferred":false,"id":800823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ward, Adam S","contributorId":191363,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":800824,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70212499,"text":"70212499 - 2019 - Offset channels may not accurately record strike-slip fault displacement: Evidence from landscape evolution models","interactions":[],"lastModifiedDate":"2020-12-18T21:19:35.5406","indexId":"70212499","displayToPublicDate":"2019-12-04T09:25:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6453,"text":"Journal of Geophysical Research Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Offset channels may not accurately record strike-slip fault displacement: Evidence from landscape evolution models","docAbstract":"<p><span>Slip distribution, slip rate, and slip per event for strike‐slip faults are commonly determined by correlating offset stream channels—under the assumption that they record seismic slip—but offset channels are formed by the interplay of tectonic and geomorphic processes. To constrain offset channel development under known tectonic and geomorphic conditions, we use numerical landscape evolution simulations along a theoretical strike‐slip fault with uniform and steady uplift, erosion, and diffusion. We investigate the influence of four tectonic parameters (fault zone width, earthquake recurrence interval, variance of the recurrence interval, and total slip relative to channel spacing) on offset channel development through multiple earthquake cycles. Analysis of &gt;3,000 automatically measured offsets from &gt;135 simulations suggests ~30% variability in individual measurements, but modeled displacement is recovered by averaging multiple measurements. However, the average of multiple offset measurements systematically underestimates modeled slip except when the fault zone is less than ~5 m wide, total slip is less than channel spacing, and offsets are measured shortly after an earthquake. In these simulations, postearthquake landscape evolution widens the geomorphic expression of the fault zone and modifies apparent channel offsets. We distinguish this “geomorphic fault zone” from the tectonic fault zone (zone of coseismic distributed deformation). This study highlights the capability of landscape evolution models to explore a range of conditions not easily defined in natural examples and the importance of averaging multiple measurements. Our results verify that paleoseismic studies must consider how geomorphic change has modified offset markers and use caution interpreting slip histories with multiple earthquakes.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB018596","usgsCitation":"Reitman, N.G., Mueller, K.J., Tucker, G.E., Gold, R.D., Briggs, R.W., and Barnhart, K., 2019, Offset channels may not accurately record strike-slip fault displacement: Evidence from landscape evolution models: Journal of Geophysical Research Solid Earth, v. 124, no. 12, p. 13427-13451, https://doi.org/10.1029/2019JB018596.","productDescription":"25 p.","startPage":"13427","endPage":"13451","ipdsId":"IP-113549","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"124","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mueller, Karl J.","contributorId":238813,"corporation":false,"usgs":false,"family":"Mueller","given":"Karl","email":"","middleInitial":"J.","affiliations":[{"id":47791,"text":"Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":796592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Gregory E.","contributorId":177811,"corporation":false,"usgs":false,"family":"Tucker","given":"Gregory","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796593,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796594,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":139002,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796595,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnhart, Katherine R.","contributorId":238814,"corporation":false,"usgs":false,"family":"Barnhart","given":"Katherine R.","affiliations":[{"id":47792,"text":"Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA; Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":796596,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208455,"text":"70208455 - 2019 - Measurement of cyanobacteria bloom magnitude using satellite remote sensing","interactions":[],"lastModifiedDate":"2020-02-11T07:47:17","indexId":"70208455","displayToPublicDate":"2019-12-04T07:43:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Measurement of cyanobacteria bloom magnitude using satellite remote sensing","docAbstract":"Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.","language":"English","publisher":"Nature","doi":"10.1038/s41598-019-54453-y","usgsCitation":"Mishra, S., Stumpf, R.P., Schaeffer, B., Werdell, P.J., Loftin, K., and Meredith, A., 2019, Measurement of cyanobacteria bloom magnitude using satellite remote sensing: Scientific Reports, no. 1, 18310, 17 p., https://doi.org/10.1038/s41598-019-54453-y.","productDescription":"18310, 17 p.","ipdsId":"IP-111006","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":459026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-019-54453-y","text":"Publisher Index Page"},{"id":372207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, 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","contributorId":214109,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake ","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":781961,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Werdell, P. Jeremy 0000-0002-3592-0152","orcid":"https://orcid.org/0000-0002-3592-0152","contributorId":222358,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":781962,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":205662,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":781958,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meredith, Andrew 0000-0001-9651-7132","orcid":"https://orcid.org/0000-0001-9651-7132","contributorId":222359,"corporation":false,"usgs":false,"family":"Meredith","given":"Andrew","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":781963,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70207032,"text":"70207032 - 2019 - Environmental and biological factors influence migratory Sea Lamprey catchability: Implications for tracking abundance in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2020-07-09T14:33:55.941943","indexId":"70207032","displayToPublicDate":"2019-12-03T18:57:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Environmental and biological factors influence migratory Sea Lamprey catchability: Implications for tracking abundance in the Laurentian Great Lakes","docAbstract":"Sea Lamprey Petromyzon marinus population trends in the Great Lakes are tracked by trapping migratory adults in tributaries and using mark and recapture techniques to estimate abundance.  Understanding what environmental and biological factors influence Sea Lamprey capture in tributaries is crucial to developing efficient trapping methods and reliable abundance estimates.  We analyzed data from trapping sites located on eight Great Lakes tributaries using Cormack-Jolly-Seber models and examined how water temperature, discharge, sex, and length influenced Sea Lamprey apparent survival and capture probability.  Sea Lamprey apparent survival was negatively associated with water temperature in all tributaries.  Additionally, the odds of small Sea Lamprey (≤45 cm) remaining available to capture were 39% less (95% CI: 63% decrease – 1% increase) than large (>45 cm) lamprey odds.  These observed relationships were used to investigate if bias in abundance estimates using the pooled-Petersen estimator and Jolly-Seber models was expected to be similar across trapping locations or influenced by variable environmental conditions and biological traits.  Pooled-Petersen abundance estimates had a positive bias when datasets were generated from simulated populations with empirical relationships between environmental characteristics and catchability.  The degree of bias depended upon changes in stream warming patterns and was not consistent among trapping locations.  Jolly-Seber models using data from either weekly-batch-marked or uniquely-marked individuals generated abundance estimate with low bias when data quality was high, but performed poorly in scenarios with few recaptured Sea Lamprey.  This research can promote improved Sea Lamprey monitoring efforts by providing insight into the reliability of the pooled-Petersen abundance estimator as a tool for tracking Sea Lamprey populations and demonstrating the limitations of adopting more robust methods when data are sparse.","language":"English","publisher":"U.S. Fish and Wildlife Scientific Journals","doi":"10.3996/022019-JFWM-013","usgsCitation":"Lewandoski, S.A., Bravener, G.A., Hrodey, P.J., and Miehls, S.M., 2019, Environmental and biological factors influence migratory Sea Lamprey catchability: Implications for tracking abundance in the Laurentian Great Lakes: Journal of Fish and Wildlife Management, v. 11, no. 1, p. 68-79, https://doi.org/10.3996/022019-JFWM-013.","productDescription":"12 p.","startPage":"68","endPage":"79","ipdsId":"IP-112927","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":459029,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70227628,"text":"70227628 - 2019 - Detection probability and occupancy of American woodcock during Singing-ground surveys","interactions":[],"lastModifiedDate":"2022-01-21T15:17:00.679985","indexId":"70227628","displayToPublicDate":"2019-12-03T09:08:52","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Detection probability and occupancy of American woodcock during Singing-ground surveys","docAbstract":"<p><span>The Singing-ground Survey (SGS) was designed to exploit the conspicuous breeding-season display of male American woodcock (</span><i>Scolopax minor</i><span>; hereafter, woodcock) to monitor these otherwise inconspicuous birds. The SGS was standardized in 1968 and has since been conducted annually to derive an index of abundance and population trend. Counts of singing male woodcock on the SGS have generally declined through time, but without knowledge of the relationship among counts, woodcock abundance, and the factors affecting detection, considerable uncertainty remains in interpretation of SGS data. Using modified SGS protocols, we surveyed SGS routes in Pine County, Minnesota, in 2009 and 2010 and developed models to assess factors associated with detection probability and estimated occupancy. The intercept-only model (i.e., constant detection and occupancy probabilities across sites and no covariates) included overall detection probability of 0.59 (SE = 0.018) in 2009 and 0.66 (SE = 0.017) in 2010 with an occupancy estimate of 0.74 (SE = 0.049) in 2009 and 0.81 (SE = 0.044) in 2010. The best-supported model of detection probability for both years combined included detection as a function of woodcock abundance, observer, date, disturbance level (i.e., ambient noise that interfered with detecting woodcock), and wind speed. High wind speeds were negatively related to detection, different observers had different detection probabilities, date was quadratically related to detection (indicating a mid-period peak in detection), and high woodcock abundance and low disturbance levels were positively related to detection. We provide suggestions for incorporating these resulting into SGS protocol and analyses.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"Oct 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","doi":"10.24926/AWS.0126","usgsCitation":"Bergh, S.M., and Andersen, D.E., 2019, Detection probability and occupancy of American woodcock during Singing-ground surveys, <i>in</i> Proceedings of the eleventh American woodcock symposium, Roscommon, MI, Oct 24-27, 2017, p. 200-208, https://doi.org/10.24926/AWS.0126.","productDescription":"9 p.","startPage":"200","endPage":"208","ipdsId":"IP-043992","costCenters":[{"id":199,"text":"Coop Res Unit 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,{"id":70206996,"text":"fs20193073 - 2019 - Reach-scale monitoring and modeling of rivers--Expanding hydraulic data collection beyond the cross section","interactions":[],"lastModifiedDate":"2019-12-10T09:20:15","indexId":"fs20193073","displayToPublicDate":"2019-12-02T14:19:38","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3073","displayTitle":"Reach-Scale Monitoring and Modeling of Rivers—Expanding Hydraulic Data Collection Beyond the Cross Section","title":"Reach-scale monitoring and modeling of rivers--Expanding hydraulic data collection beyond the cross section","docAbstract":"For over 125 years, the U.S. Geological Survey streamgage network has provided important\nhydrologic information about rivers and streams throughout the Nation. Traditional streamgage\nmethods provide reliable stage and streamflow data but typically only monitor stage at a single location in a river and require frequent calibration streamflow measurements. Direct measurements are not always feasible, therefore improved sensors and methods\nare being deployed at gages to better document streamflow conditions between measurements. The technology and techniques of reach-scale monitoring allow the U.S. Geological Survey to collect more data across the full range of streamflow without requiring that a hydrographer be present. The U.S. Geological Survey Arizona Water Science Center’s reach-scale monitoring program will enhance the Arizona streamgage network with more accurate streamflow measurements and provide more extensive streamflow records and geomorphological\ndatasets for our agency partners and the public. Reach-scale monitoring installations and techniques are applicable to streams of the western United States and likely throughout the Nation.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193073","collaboration":"Prepared in cooperation with Arizona Department of Transportation","usgsCitation":"Forbes, B.T., Bunch, C.E., DeBenedetto, G., Shaw, C.J., and Gungle, B., 2019, Reach-scale monitoring and modeling of rivers—Expanding hydraulic data collection beyond the cross section: U.S. Geological Survey Fact Sheet 2019–3073, 6p., https://doi.org/10.3133/fs20193073.","productDescription":"6 p.","ipdsId":"IP-075529","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":369839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3073/fs20193073.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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 \"}}]}","contact":"<p><a href=\"mailto:dc_az@usgs.gov\" data-mce-href=\"mailto:dc_az@usgs.gov\">Director</a>, <a href=\"http://az.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"http://az.water.usgs.gov/\">Arizona Water Science Center</a><br>U.S. Geological Survey<br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Why Look Beyond the Cross Section?</li><li>Traditional Monitoring</li><li>Streamgaging</li><li>Indirect Measurement of Peak Streamflow</li><li>What is Reach-Scale Monitoring?</li><li>Data Packages for Advanced Streamflow Modeling</li><li>Transportation and Reach-Scale Monitoring</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-12-02","noUsgsAuthors":false,"publicationDate":"2019-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Forbes, Brandon T. 0000-0003-4051-0593 bforbes@usgs.gov","orcid":"https://orcid.org/0000-0003-4051-0593","contributorId":213549,"corporation":false,"usgs":true,"family":"Forbes","given":"Brandon","email":"bforbes@usgs.gov","middleInitial":"T.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunch, Claire E. 0000-0002-1360-8598 cebunch@usgs.gov","orcid":"https://orcid.org/0000-0002-1360-8598","contributorId":150240,"corporation":false,"usgs":true,"family":"Bunch","given":"Claire E.","email":"cebunch@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":776488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeBenedetto, Geoffrey 0000-0003-0696-4567 gdebened@usgs.gov","orcid":"https://orcid.org/0000-0003-0696-4567","contributorId":220988,"corporation":false,"usgs":true,"family":"DeBenedetto","given":"Geoffrey","email":"gdebened@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":776490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shaw, Corey J. 0000-0002-7794-7513","orcid":"https://orcid.org/0000-0002-7794-7513","contributorId":220989,"corporation":false,"usgs":false,"family":"Shaw","given":"Corey","email":"","middleInitial":"J.","affiliations":[{"id":38050,"text":"Contractor","active":true,"usgs":false}],"preferred":false,"id":776491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gungle, Bruce 0000-0001-6406-1206 bgungle@usgs.gov","orcid":"https://orcid.org/0000-0001-6406-1206","contributorId":107628,"corporation":false,"usgs":true,"family":"Gungle","given":"Bruce","email":"bgungle@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":776489,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207142,"text":"70207142 - 2019 - Estimating the degree to which distance and temperature differences drive changes in fish community composition over time in the upper Mississippi River","interactions":[],"lastModifiedDate":"2020-06-19T16:15:06.610147","indexId":"70207142","displayToPublicDate":"2019-12-02T12:10:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the degree to which distance and temperature differences drive changes in fish community composition over time in the upper Mississippi River","docAbstract":"Similarity in community composition declines as distance between locations increases, a phenomenon that has been observed in a wide variety of freshwater, marine and terrestrial ecosystems.  One driver of the distance-similarity relationship is the presence of environmental gradients that alter the suitability of sites for particular species.  Although some environmental gradients, such as geology, do not change on a year-to-year basis, others, such as temperature, vary annually and over longer time periods.  Here, we used a 21-year dataset of fish communities in the upper Mississippi River to identify the effect of distance on variation in community composition and to assess whether the effect of distance is primarily due to its effect on thermal regime.   Because the Mississippi River is aligned mostly north-to-south, larger distances along the river roughly correspond to larger differences in latitude and therefore temperature.  As expected, there was a moderate distance-similarity relationship, suggesting greater distance leads to less similarity.  The effect of distance appeared to increase slightly over time.  Using a subset of data for which air temperature was available, we found that difference among sites in degree days (a surrogate for thermal regime) was more strongly associated with similarity in community composition than physical distance (river km).  Although physical distance presumably incorporates more environmental gradients than just temperature (and other potential mechanisms), temperature alone appears to be more strongly associated with differences in the Mississippi River fish community.","language":"English","publisher":"Public Library of Science (PLOS)","doi":"10.1371/journal.pone.0225630","usgsCitation":"Larson, J.H., Vallazza, J.M., and Knights, B.C., 2019, Estimating the degree to which distance and temperature differences drive changes in fish community composition over time in the upper Mississippi River: PLoS ONE, v. 14, no. 12, e0225630, 13 p., https://doi.org/10.1371/journal.pone.0225630.","productDescription":"e0225630, 13 p.","ipdsId":"IP-098122","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":459037,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0225630","text":"Publisher Index Page"},{"id":437267,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P956DF36","text":"USGS data release","linkHelpText":"R Code for Comparison of Fish Community Structure among River Reaches of the Upper Mississippi River: Potential Influence of Lock and Dam 19"},{"id":437266,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MNCH0W","text":"USGS data release","linkHelpText":"Influence of a high head dam as a dispersal barrier to fish community structure of the Upper Mississippi River: Data"},{"id":370110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -95.69091796875,\n              36.63316209558658\n            ],\n            [\n              -87.64892578125,\n              36.63316209558658\n            ],\n            [\n              -87.64892578125,\n              45.84410779560204\n            ],\n            [\n              -95.69091796875,\n              45.84410779560204\n            ],\n            [\n              -95.69091796875,\n              36.63316209558658\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"14","issue":"12","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":776942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vallazza, Jonathan M. 0000-0003-2367-4887 jvallazza@usgs.gov","orcid":"https://orcid.org/0000-0003-2367-4887","contributorId":149362,"corporation":false,"usgs":true,"family":"Vallazza","given":"Jonathan","email":"jvallazza@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":776943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":776944,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70203227,"text":"70203227 - 2019 - Survival rates and stopover persistence of American Woodcock using Cape May, New Jersey during fall migration","interactions":[],"lastModifiedDate":"2020-03-09T06:18:12","indexId":"70203227","displayToPublicDate":"2019-12-02T10:03:58","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Survival rates and stopover persistence of American Woodcock using Cape May, New Jersey during fall migration","docAbstract":"<p>Cape May, New Jersey is an important stopover area for American woodcock (<i>Scolopax minor</i>, hereafter woodcock) during fall migration along the Atlantic Coast of the United States. Previous research has indicated that many woodcock stop at Cape May prior to crossing Delaware Bay; however, little is known about survival of woodcock while using Cape May. To better understand woodcock survival on Cape May during fall migration and estimate emigration rates for woodcock migrating through Cape May, we captured and marked a total of 271 woodcock with VHF transmitters and radio-tracked them weekly from November through early January, 2010-2013. Of the 271 marked woodcock, our radio-tracking efforts indicated that 131 migrated from Cape May, 57 remained on Cape May, 72 died, and 11 were censored. We used a multi-state model within Program MARK to estimate weekly survival and emigration probabilities for marked woodcock. Our best-supported model indicated that survival rate varied by year, but was constant by week within years. Weekly survival rate estimates ranged from 0.894 (95% CI = 0.834 – 0.934) in 2010 to 0.962 (95% CI = 0.928 – 0.981) in 2011, which equates to a 9-week period survival rate ranging from 0.365 (95% CI = 0.185 – 0.545) to 0.706 (95% CI = 0.541 – 0.870), respectively. The 2010-2011 field season was marked by several large snowstorms during which a large percentage of marked woodcock died, whereas the other 3 years had more mild conditions and higher woodcock survival rates. Our best-supported model indicated that weekly emigration rates varied by year and week with each year showing a different pattern of emigration from Cape May. Survival and emigration information will be useful in the development of future demographic-based population models for woodcock migrating along the Atlantic Coast.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American Woodcock symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh American Woodcock Symposium","conferenceDate":"October 24-27, 2017","conferenceLocation":"Roscommon, MI","language":"English","publisher":"University of Minnesota Libraries Publishing","doi":"10.24926/AWS.0121","collaboration":"U.S. Fish and Wildlife Service","usgsCitation":"McAuley, D., Zimmerman, G.S., Allen, B.L., Dwyer, C., and Cooper, T., 2019, Survival rates and stopover persistence of American Woodcock using Cape May, New Jersey during fall migration, <i>in</i> Proceedings of the eleventh American Woodcock symposium, v. 11, Roscommon, MI, October 24-27, 2017, p. 146-153, https://doi.org/10.24926/AWS.0121.","productDescription":"8 p.","startPage":"146","endPage":"153","ipdsId":"IP-091382","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.24926/aws.0121","text":"Publisher Index Page"},{"id":372993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","city":"Cape May","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.97241973876953,\n              38.91027022759443\n            ],\n            [\n              -74.86736297607422,\n              38.91027022759443\n            ],\n            [\n              -74.86736297607422,\n              38.974357249228206\n            ],\n            [\n              -74.97241973876953,\n              38.974357249228206\n            ],\n            [\n              -74.97241973876953,\n              38.91027022759443\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McAuley, Daniel 0000-0003-3674-6392 dmcauley@usgs.gov","orcid":"https://orcid.org/0000-0003-3674-6392","contributorId":215182,"corporation":false,"usgs":true,"family":"McAuley","given":"Daniel","email":"dmcauley@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":761786,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":761787,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, B. L.","contributorId":201458,"corporation":false,"usgs":false,"family":"Allen","given":"B.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":761788,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dwyer, C.","contributorId":215183,"corporation":false,"usgs":false,"family":"Dwyer","given":"C.","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":761789,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, T.R.","contributorId":215184,"corporation":false,"usgs":false,"family":"Cooper","given":"T.R.","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":761790,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70227757,"text":"70227757 - 2019 - Using pointing dogs and hierarchical models to evaluate American woodcock winter occupancy and densities","interactions":[],"lastModifiedDate":"2022-01-28T14:59:18.950521","indexId":"70227757","displayToPublicDate":"2019-12-02T08:35:12","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using pointing dogs and hierarchical models to evaluate American woodcock winter occupancy and densities","docAbstract":"<p>Use of dogs has increased for multiple wildlife research purposes ranging from carnivore scat detection to estimation of reptile abundance. Use of dogs is not particularly novel for upland gamebird biologists, and pointing dogs have been long considered an important research tool. However, recent advances in Global Positioning System (GPS) technology and the development of hierarchical modeling approaches that account for imperfect detection may improve estimates of occupancy and density of cryptic species such as the American woodcock (Scolopax minor; hereafter, woodcock). We conducted surveys for woodcock using a trained pointing dog wearing a GPS collar during the winters of 2010–2011 and 2011–2012 in East Texas, USA. We surveyed 0.5-km-radius circular plots (<i>n</i><span>&nbsp;</span>= 24; survey sites) randomly placed along secondary roads in Davy Crockett National Forest and on private timber property. Surveys lasted 1.5 hrs and were repeated 3–5 times each winter. We estimated woodcock occupancy and density using multiple modeling approaches at the survey site and forest stand scales within survey sites. Woodcock occupied 88% (21/24) of survey sites and 48% (39/82) of forest stands (i.e., unique cover types) within sites. Using a modified distance sampling technique, we estimated an average density of 0.16 birds/ha (SE = 0.13) throughout both study areas. We describe the first attempt to blend use of pointing dogs with hierarchical modeling approaches to derive estimates of regional diurnal woodcock occupancy and density, and describe relationships between these estimates of abundance and habitat covariates. Although forest stand occupancy estimates had the lowest coefficients of variation, our estimates of density provided the most useful inference of habitat use. Surveys using pointing dogs paired with hierarchical models of occupancy and density may provide a cost-efficient and effective approach to estimate habitat abundance at broad spatial scales.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the eleventh American Woodcock Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"American Woodcock Symposium","conferenceDate":"2017","conferenceLocation":"Michigan, United States","language":"English","publisher":"University of Minnesota Press","doi":"10.24926/AWS.0122","usgsCitation":"Sullins, D.S., Conway, W.C., Haukos, D.A., and Comer, C.E., 2019, Using pointing dogs and hierarchical models to evaluate American woodcock winter occupancy and densities, <i>in</i> Proceedings of the eleventh American Woodcock Symposium, Michigan, United States, 2017, p. 154-167, https://doi.org/10.24926/AWS.0122.","productDescription":"14 p.","startPage":"154","endPage":"167","ipdsId":"IP-090750","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459048,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.24926/aws.0122","text":"Publisher Index Page"},{"id":395048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Houston County, San Augustine County, Trinity County","otherGeospatial":"Davy Crockett National Forest, West Gulf Coastal Plain Bird Conservation Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -94.24793243408203,\n              31.45473238771609\n            ],\n            [\n              -94.14974212646484,\n              31.45473238771609\n            ],\n            [\n              -94.14974212646484,\n              31.511532395628638\n            ],\n            [\n              -94.24793243408203,\n              31.511532395628638\n            ],\n            [\n              -94.24793243408203,\n              31.45473238771609\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -95.44509887695312,\n              30.935212690426727\n            ],\n            [\n              -94.75296020507811,\n              30.935212690426727\n            ],\n            [\n              -94.75296020507811,\n              31.67675841879551\n            ],\n            [\n              -95.44509887695312,\n              31.67675841879551\n            ],\n            [\n              -95.44509887695312,\n              30.935212690426727\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sullins, Daniel S.","contributorId":166689,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":832103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":832104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comer, Christopher E.","contributorId":166690,"corporation":false,"usgs":false,"family":"Comer","given":"Christopher","email":"","middleInitial":"E.","affiliations":[{"id":32360,"text":"Stephen F. Austin State University, Nacogdoches, TX","active":true,"usgs":false}],"preferred":false,"id":832105,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70203103,"text":"70203103 - 2019 - Aquatic cycling of mercury","interactions":[],"lastModifiedDate":"2019-12-04T17:16:12","indexId":"70203103","displayToPublicDate":"2019-12-01T17:13:56","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"1","title":"Aquatic cycling of mercury","docAbstract":"<p><span>This chapter examines crucial processes in the aquatic cycling of mercury (Hg) that may lead to microbial production of neurotoxic and bioaccumulative methylmercury (MeHg), and highlights environmental conditions in the Everglades that make it ideal for MeHg production and bioaccumulation. The role of complexation of Hg</span><sup>2+</sup><span>&nbsp;in surface water, especially by dissolved organic matter (DOM), in the transport of mercury to sites of microbial methylation are discussed. Photochemical reactions important in Hg cycling in surface water are also discussed. A principal focus of the chapter is on the environmental conditions that promote MeHg production, especially the role of sulfide and DOM in transport of inorganic Hg into bacteria for methylation, and the types of bacteria that have the ability to methylate Hg. Finally, perturbations to the ecosystem (e.g., fire and drought) that have important effects on Hg cycling are discussed.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mercury and the Everglades. A synthesis and model for complex ecosystem restoration","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-32057-7_1","usgsCitation":"Orem, W.H., Krabbenhoft, D.P., Poulin, B., and Aiken, G.A., 2019, Aquatic cycling of mercury, chap. 1 <i>of</i> Mercury and the Everglades. A synthesis and model for complex ecosystem restoration, p. 1-12, https://doi.org/10.1007/978-3-030-32057-7_1.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-106651","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":369934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":761183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aiken, George A 0000-0001-8454-0984","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":206312,"corporation":false,"usgs":false,"family":"Aiken","given":"George","email":"","middleInitial":"A","affiliations":[{"id":37307,"text":"formerly USGS, deceased","active":true,"usgs":false}],"preferred":false,"id":761182,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70208117,"text":"70208117 - 2019 - Geochemistry and geophysics of iron oxide-apatite deposits and associated waste piles with implications for potential rare earth element resources from ore and historic mine waste in the eastern Adirondack Highlands, New York, USA","interactions":[],"lastModifiedDate":"2020-01-28T15:40:48","indexId":"70208117","displayToPublicDate":"2019-12-01T15:29:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry and geophysics of iron oxide-apatite deposits and associated waste piles with implications for potential rare earth element resources from ore and historic mine waste in the eastern Adirondack Highlands, New York, USA","docAbstract":"<div class=\"article-section-wrapper \"><p>The iron oxide-apatite (IOA) deposits of the eastern Adirondack Highlands, New York, are historical high-grade magnetite mines that contain variable concentrations of rare earth element (REE)-bearing apatite crystals. The majority of the deposits are hosted within sodically altered Lyon Mountain granite gneiss, although some deposits occur within paragneiss, gabbro, anorthosite, or potassically altered Lyon Mountain granite gneiss. The IOA deposits and the waste and/or tailings piles associated with them have potential as an unconventional resource for REEs. Reprocessing of these piles would have the advantage of partial recycling of the waste material to produce a set of critical elements.</p><p>Thirty-four ore, nine rock, 25 waste-pile, and four tailings-pile samples were collected and analyzed for major, minor, and trace elements. At the tailings- and waste-pile sites, composite samples were collected by combining 30 to &gt;50 subsamples randomly distributed over each pile. The total REE content of the waste and tailings piles varied from approximately 10 to 22,000 ppm, whereas the ore sample concentrations ranged from approximately 15 to 48,000 ppm total REEs. A positive correlation exists between the total REE content of ore and its associated waste pile. Median light REE/heavy REE values were 2.14 for waste/tailings piles and 2.25 for ore, which is a substantial relative enrichment in the heavy REEs in comparison to many developed REE mines, such as the mined carbonatites of Bayan Obo, China, and Mountain Pass, California. Importantly, the ore and waste samples are significantly enriched in both Y and Nd compared to other REEs in the samples. Other minor components such as Th are also elevated. Airborne radiometric surveys show large positive eTh and eU anomalies corresponding to tailings piles.</p><p>Although it is a limited data set, geochemical data of unaltered and altered host rocks suggest a speculative new model for IOA ore formation in the Adirondack Highlands that is consistent with the geology and previously published data. The ferroan ore-hosting Lyon Mountain granite gneiss underwent localized potassic alteration that enriched the altered rock in Fe, REEs, Th, and other metals. A later sodic alteration event affected the previously potassically altered Lyon Mountain granite gneiss, which increased rock porosity and remobilized Fe, REEs, and other elements from the host rock into the iron ore seams. The sodic fluids responsible for ore formation were enriched in F and Cl.</p></div>","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.4689","usgsCitation":"Taylor, R., Shah, A.K., Walsh, G.J., and Taylor, C.D., 2019, Geochemistry and geophysics of iron oxide-apatite deposits and associated waste piles with implications for potential rare earth element resources from ore and historic mine waste in the eastern Adirondack Highlands, New York, USA: Economic Geology, v. 114, no. 8, p. 1569-1598, https://doi.org/10.5382/econgeo.4689.","productDescription":"30 p.","startPage":"1569","endPage":"1598","ipdsId":"IP-105561","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":371659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Highlands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.35498046875,\n              42.827638636242284\n            ],\n            [\n              -73.2568359375,\n              42.827638636242284\n            ],\n            [\n              -73.2568359375,\n              45.24395342262324\n            ],\n            [\n              -76.35498046875,\n              45.24395342262324\n            ],\n            [\n              -76.35498046875,\n              42.827638636242284\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"114","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":201948,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":780544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":780545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Gregory J. 0000-0003-4264-8836 gwalsh@usgs.gov","orcid":"https://orcid.org/0000-0003-4264-8836","contributorId":873,"corporation":false,"usgs":true,"family":"Walsh","given":"Gregory","email":"gwalsh@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":780546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":780547,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70203511,"text":"70203511 - 2019 - Sulfur contamination in the Everglades, a major control on mercury methylation","interactions":[],"lastModifiedDate":"2019-12-03T12:03:22","indexId":"70203511","displayToPublicDate":"2019-12-01T11:59:49","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"2","title":"Sulfur contamination in the Everglades, a major control on mercury methylation","docAbstract":"<p id=\"Par1\" class=\"Para\">In this chapter sulfur contamination of the Everglades and its role as a major control on methylmercury (MeHg) production is examined. Sulfate concentrations over large portions of the Everglades (60% of the ecosystem) are elevated or greatly elevated compared to background conditions of &lt;1&nbsp;mg/L. Land and water management practices in south Florida are the primary reason for the high levels of sulfate loading to the Everglades. Marshes in the northern Everglades that are highly enriched in sulfate have average concentrations of 60&nbsp;mg/L, but water in canals in the Everglades Agricultural Area (EAA) contain the highest concentrations of sulfate averaging 60–70&nbsp;mg/L. Studies that examined the mass balance of sulfur to the Everglades have determined that the primary sources of sulfate include: sulfur currently used in agriculture, and natural and legacy agricultural sulfur released by oxidation of organic soil within the EAA. The extensive loading of sulfate to the ecosystem increases microbial sulfate reduction, the dominant microbial process driving mercury methylation and MeHg production. The biogeochemical processes linking sulfate loading and MeHg production, however, are complex. MeHg production increases as sulfate levels rise from levels &lt;1&nbsp;mg/L up to about 20&nbsp;mg/L. However, production of sulfide (a byproduct of microbial sulfate reduction) starts to inhibit MeHg production above 20&nbsp;mg/L. Sulfate loading to canals in the EAA has impacted the northern Everglades the most, but the Everglades canal system can transport sulfate as far as Everglades National Park (ENP), 80&nbsp;km further south. Plans to deliver more water to ENP as part of restoration may increase overall sulfate loads to the southern Everglades.</p><p id=\"Par2\" class=\"Para\">Reduction of sulfate loading should be a major goal of Everglades restoration because of the many negative effects of sulfate on the ecosystem. The ecosystem has been shown to respond quickly to reductions in sulfate loading, and strategies for reducing sulfate loading may produce positive outcomes for the Everglades in the near-term. Strategies for reducing sulfate loading will need to include: best management practices for agricultural use of sulfate, approaches to minimize soil oxidation in the EAA, and modifications to stormwater treatment areas to improve sulfate retention.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mercury and the Everglades. A Synthesis and Model for Complex Ecosystem Restoration","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-32057-7_2","usgsCitation":"Orem, W.H., Krabbenhoft, D.P., Poulin, B., and George Aiken, 2019, Sulfur contamination in the Everglades, a major control on mercury methylation, chap. 2 <i>of</i> Mercury and the Everglades. A Synthesis and Model for Complex Ecosystem Restoration, p. 13-48, https://doi.org/10.1007/978-3-030-32057-7_2.","productDescription":"36 p.","startPage":"13","endPage":"48","ipdsId":"IP-106372","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":369873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.3153076171875,\n              25.075648445630527\n            ],\n            [\n              -80.33752441406249,\n              25.075648445630527\n            ],\n            [\n              -80.33752441406249,\n              25.854280326572407\n            ],\n            [\n              -81.3153076171875,\n              25.854280326572407\n            ],\n            [\n              -81.3153076171875,\n              25.075648445630527\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":762941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":762942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":762943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George Aiken","contributorId":215670,"corporation":false,"usgs":false,"family":"George Aiken","affiliations":[{"id":39302,"text":"USGS WMA Boulder (Deceased)","active":true,"usgs":false}],"preferred":false,"id":762944,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70260153,"text":"70260153 - 2019 - Impacts of the 2015 eruption of Calbuco volcano on Chilean infrastructure, utilities, agriculture, and health","interactions":[],"lastModifiedDate":"2024-10-30T14:39:24.653338","indexId":"70260153","displayToPublicDate":"2019-12-01T09:33:11","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":18999,"text":"GNS Science Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"2019/04","title":"Impacts of the 2015 eruption of Calbuco volcano on Chilean infrastructure, utilities, agriculture, and health","docAbstract":"<p><span>This report presents data and summarises the findings of a reconnaissance trip investigating the impacts of the April 2015 eruption of Calbuco volcano, Chile, undertaken in November-December 2016. This study is mostly focused on the Los Lagos region, focusing on impacts occurring within ~30 km of the volcano, which includes the tourism town of Puerto Varas and port city of Puerto Montt. Eruption impacts and response strategies may be similar for moderate size eruptions from other stratovolcanoes in temperate regions. As such, this study provides useful information for development of contingency plans at active volcanoes around the world. The 2015 eruption of Calbuco volcano began at 18:04 (local time) on 22 April 2015 and consisted of three eruptive phases. The first lasted for 1.5 hours and generated a 15-km eruption column and plume that was directed towards the northeast. Pyroclastic density currents (PDCs) were generated locally and distributed radially, affecting many of the major rivers. A second phase began at 01:00 (local time) on 23 April 2015, lasted six hours and generated a 17-km high eruption column and plume that was dispersed towards the north-northeast, and the most voluminous pyroclastic density currents of the sequence. A third eruptive phase occurred on 30 April 12:10 (local time) resulting in a 5-km column and plume dispersed towards the east.</span></p>","language":"English","publisher":"The Institute of Geological and Nuclear Sciences Limited (GNS Science)","doi":"10.21420/02YC-VX66","usgsCitation":"Hayes, J., Deligne, N., Bertin, L., Calderon, R., Wardman, J., Wilson, T.J., Leonard, G., C., S., Wallace, K.L., and Baxter, P., 2019, Impacts of the 2015 eruption of Calbuco volcano on Chilean infrastructure, utilities, agriculture, and health: GNS Science Report 2019/04, 102 p., https://doi.org/10.21420/02YC-VX66.","productDescription":"102 p.","ipdsId":"IP-105929","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463432,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Calbuco volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -73.48307304563127,\n              -40.573371240910895\n            ],\n            [\n              -73.48307304563127,\n              -42.33035124814165\n            ],\n            [\n              -71.89602918061058,\n              -42.33035124814165\n            ],\n            [\n              -71.89602918061058,\n              -40.573371240910895\n            ],\n            [\n              -73.48307304563127,\n              -40.573371240910895\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hayes, J.","contributorId":345669,"corporation":false,"usgs":false,"family":"Hayes","given":"J.","affiliations":[{"id":82688,"text":"University of Canterbury, NZ","active":true,"usgs":false}],"preferred":false,"id":917219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deligne, N. I.","contributorId":149573,"corporation":false,"usgs":false,"family":"Deligne","given":"N. I.","affiliations":[],"preferred":false,"id":917221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bertin, L","contributorId":345670,"corporation":false,"usgs":false,"family":"Bertin","given":"L","email":"","affiliations":[{"id":82689,"text":"SERNAGEOMIN, Chile","active":true,"usgs":false}],"preferred":false,"id":917223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calderon, Rodrigo","contributorId":270274,"corporation":false,"usgs":false,"family":"Calderon","given":"Rodrigo","email":"","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":true,"id":917224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wardman, J.","contributorId":345671,"corporation":false,"usgs":false,"family":"Wardman","given":"J.","affiliations":[{"id":16634,"text":"Bermuda Institute of Ocean Sciences","active":true,"usgs":false}],"preferred":false,"id":917225,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, T. J.","contributorId":31942,"corporation":false,"usgs":false,"family":"Wilson","given":"T.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":917220,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Leonard, G.","contributorId":149590,"corporation":false,"usgs":false,"family":"Leonard","given":"G.","email":"","affiliations":[],"preferred":false,"id":917222,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"C., Stewart.","contributorId":345672,"corporation":false,"usgs":false,"family":"C.","given":"Stewart.","email":"","affiliations":[{"id":82690,"text":"GNS Science / Massey University, NZ","active":true,"usgs":false}],"preferred":false,"id":917226,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wallace, Kristi L. 0000-0002-0962-048X kwallace@usgs.gov","orcid":"https://orcid.org/0000-0002-0962-048X","contributorId":3454,"corporation":false,"usgs":true,"family":"Wallace","given":"Kristi","email":"kwallace@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917227,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baxter, P.","contributorId":149588,"corporation":false,"usgs":false,"family":"Baxter","given":"P.","email":"","affiliations":[],"preferred":false,"id":917228,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70227737,"text":"70227737 - 2019 - Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction","interactions":[],"lastModifiedDate":"2022-01-28T15:34:56.321091","indexId":"70227737","displayToPublicDate":"2019-12-01T09:31:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":787,"text":"Annals of Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction","docAbstract":"<p><span>Multivariate compositional count data arise in many applications including ecology, microbiology, genetics and paleoclimate. A frequent question in the analysis of multivariate compositional count data is what underlying values of a covariate(s) give rise to the observed composition. Learning the relationship between covariates and the compositional count allows for inverse prediction of unobserved covariates given compositional count observations. Gaussian processes provide a flexible framework for modeling functional responses with respect to a covariate without assuming a functional form. Many scientific disciplines use Gaussian process approximations to improve prediction and make inference on latent processes and parameters. When prediction is desired on unobserved covariates given realizations of the response variable, this is called inverse prediction. Because inverse prediction is often mathematically and computationally challenging, predicting unobserved covariates often requires fitting models that are different from the hypothesized generative model. We present a novel computational framework that allows for efficient inverse prediction using a Gaussian process approximation to generative models. Our framework enables scientific learning about how the latent processes co-vary with respect to covariates while simultaneously providing predictions of missing covariates. The proposed framework is capable of efficiently exploring the high dimensional, multi-modal latent spaces that arise in the inverse problem. To demonstrate flexibility, we apply our method in a generalized linear model framework to predict latent climate states given multivariate count data. Based on cross-validation, our model has predictive skill competitive with current methods while simultaneously providing formal, statistical inference on the underlying community dynamics of the biological system previously not available.</span></p>","language":"English","publisher":"Institute of Mathematical Statistics","doi":"10.1214/19-AOAS1281","usgsCitation":"Tipton, J.R., Hooten, M., Nolan, C., Booth, R.K., and McLachlan, J., 2019, Predicting paleoclimate from compositional data using multivariate Gaussian process inverse prediction: Annals of Applied Statistics, v. 13, no. 4, p. 2363-2388, https://doi.org/10.1214/19-AOAS1281.","productDescription":"26 p.","startPage":"2363","endPage":"2388","ipdsId":"IP-089036","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1214/19-aoas1281","text":"Publisher Index Page"},{"id":395052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tipton, John R.","contributorId":272496,"corporation":false,"usgs":false,"family":"Tipton","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":56379,"text":"u ark","active":true,"usgs":false}],"preferred":false,"id":831989,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":831988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolan, Connor","contributorId":272497,"corporation":false,"usgs":false,"family":"Nolan","given":"Connor","affiliations":[{"id":56380,"text":"u az","active":true,"usgs":false}],"preferred":false,"id":831990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booth, Robert K.","contributorId":272498,"corporation":false,"usgs":false,"family":"Booth","given":"Robert","email":"","middleInitial":"K.","affiliations":[{"id":56381,"text":"lehigh  u","active":true,"usgs":false}],"preferred":false,"id":831991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLachlan, Jason","contributorId":272499,"corporation":false,"usgs":false,"family":"McLachlan","given":"Jason","affiliations":[{"id":36611,"text":"Notre Dame","active":true,"usgs":false}],"preferred":false,"id":831992,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70215325,"text":"70215325 - 2019 - Population characteristics and the potential suppression of common carp in Lake Spokane, Washington","interactions":[],"lastModifiedDate":"2020-10-16T14:10:58.363964","indexId":"70215325","displayToPublicDate":"2019-12-01T09:06:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population characteristics and the potential suppression of common carp in Lake Spokane, Washington","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>Common Carp<span>&nbsp;</span><i>Cyprinus carpio</i><span>&nbsp;</span>is a nonnative species that often has deleterious effects on aquatic systems. As such, there is interest in suppressing nonnative Common Carp populations in areas where humans have introduced them. The objectives of this study were to 1) provide insight on efficient techniques for capturing Common Carp, 2) describe their population demographics and dynamics, 3) evaluate whether temperature and water elevation were related to growth and recruitment, and 4) develop an age-structured population model for evaluating different management scenarios of Common Carp removal in Lake Spokane, Washington. Catch rates of Common Carp varied among sampling gears with slightly higher catch rates in monofilament (mean ± SD; 15.5 ± 9.8 fish/net night) vs. multifilament (12.7 ± 7.3 fish/net night) gill nets. Catch rates of Common Carp with nighttime electrofishing (0.3 ± 0.4 fish/min) were higher than daytime electrofishing (0.1 ± 0.2 fish/min). Common Carp in Lake Spokane exhibited variable recruitment, rapid growth, large-length structure, high longevity (i.e., age 18 y), and low total annual mortality (17.0%). Air temperature was positively associated with annual growth increments (<i>R</i><sup>2</sup><span>&nbsp;</span>≤ 0.25). Neither air temperature nor water elevation was highly correlated (<i>R</i><sup>2</sup><span>&nbsp;</span>≤ 0.20) to recruitment of Common Carp. A Beverton–Holt yield-per-recruit model suggested that yield declined with increasing exploitation. Recruitment overfishing would occur at exploitation rates of 20–40% for all targeted minimum length categories (i.e., 150, 300, 450 mm) except 600 mm. Results from this study provide important information on the ecology of Common Carp that can be used to guide management efforts (e.g., suppression) in western systems.</p></div>","language":"English","publisher":"Allen Press","doi":"10.3996/122018-JFWM-114","usgsCitation":"Feeken, S., Klein, Z.B., Quist, M.C., and Horner, N., 2019, Population characteristics and the potential suppression of common carp in Lake Spokane, Washington: Journal of Fish and Wildlife Management, v. 10, no. 2, p. 362-374, https://doi.org/10.3996/122018-JFWM-114.","productDescription":"13 p.","startPage":"362","endPage":"374","ipdsId":"IP-103296","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459068,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/122018-jfwm-114","text":"Publisher Index Page"},{"id":379465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Spokane","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.87094116210936,\n              47.75917578613814\n            ],\n            [\n              -117.52143859863281,\n              47.75917578613814\n            ],\n            [\n              -117.52143859863281,\n              47.91542163178686\n            ],\n            [\n              -117.87094116210936,\n              47.91542163178686\n            ],\n            [\n              -117.87094116210936,\n              47.75917578613814\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Feeken, Stacey","contributorId":243200,"corporation":false,"usgs":false,"family":"Feeken","given":"Stacey","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":801726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":801727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":801725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horner, Ned","contributorId":243201,"corporation":false,"usgs":false,"family":"Horner","given":"Ned","affiliations":[{"id":48661,"text":"Private","active":true,"usgs":false}],"preferred":false,"id":801728,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70207160,"text":"70207160 - 2019 - Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands","interactions":[],"lastModifiedDate":"2020-12-08T16:49:59.298008","indexId":"70207160","displayToPublicDate":"2019-12-01T08:14:15","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"WO-98","chapter":"5","title":"Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands","docAbstract":"<p>How is drought expressed in Hawai‘i &amp; USAPI? Drought is a significant climate feature in Hawai‘i and the U.S.-Affiliated Pacific Islands (USAPI), at times causing severe impacts across multiple sectors. Below average precipitation anomalies are often accompanied by higher than average temperatures and reduced cloud cover. The resulting higher insolation and evapotranspiration can magnify the effects of rainfall deficits. These altered meteorological conditions lead to decreased soil moisture, which, depending on the persistence and severity of the conditions, can cause plant stress, affecting both agricultural and natural systems. The hydrological effects of drought include reductions in streamflow, groundwater recharge, and groundwater discharge to springs, streams, and the ocean. Drought also has socioeconomic impacts, where reduced water supply and other effects of drought have negative financial consequences. For these reasons, drought has been defined from at least five different perspectives: meteorological, ecological, agricultural, hydrological, and socioeconomic drought. In this chapter, we explore how these five faces of drought are expressed in Hawai‘i and the USAPI, and how managers operating within one or more these five perspectives address drought-related stressors to their systems. Not all droughts are the same, varying with respect to duration, frequency, extent, and severity. For example, the region receives severe episodic droughts during which an area will have little or no rainfall for months, even in areas that normally have no dry season. El Niño events fall into this category, and these moderate frequency events are typically responsible for shorter-lived but intense drought events that affect large areas. Drought can also be expressed as infrequent but long duration events of moderate severity, or long-term rainfall decline where the baseline condition appears to be changing when examined on longer time scales. From the perspective of the manager, understanding drought duration, frequency, extent, and severity is critical to understanding the duration, frequency, extent and severity of the response. For example, how an agency responds to El Niño events, with a focus on large-scale but short-lived emergency response campaigns, may differ from how an agency responds to baseline change or an increase in the frequency of extended dry periods, with a focus on longer-lived institutional, infrastructure, and personnel responses. The legislative and policy environment will also respond differently to different types of drought. Understanding and characterizing meteorological drought relies on a long-term network of climate stations. Rainfall has been extensively monitored in Hawai‘i since the early 1900s owing to the expansion of plantation agriculture (Giambelluca and others 1986), while rainfall monitoring for most of the USAPI began in earnest after World War II (Polhemus 2017). Due to prevailing winds, most of Hawai‘i’s land area is characterized by a wet season from November to April and a dry season from May to October. However, important dynamic features affect climate systems of the Pacific. For example, due to their tropical location, rainfall patterns in both Hawai‘i and the USAPI are strongly controlled by large-scale modes of climate variability, including the El Niño-Southern Oscillation (ENSO). El Niño events are typically associated with drier than average winter wet seasons and wetter dry seasons, while La Niña events often result in a wetter than average wet season and a drier dry season. Many historical drought events have been attributed to El Niño events, which produce atmospheric conditions that are unfavorable for rainfall (Chu 1995). However, not all El Niño events result in drought, and effects differ depending on whether the El Niño is classified as Central Pacific (CP) or Eastern Pacific (EP) (Bai 2017; Polhemus 2017).&nbsp;</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Effects of drought on forests and rangelands in the United States: Translating science into management responses","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"USDA","doi":"10.2737/WO-GTR-98","usgsCitation":"Frazier, A.G., Deenik, J., Fujii, N., Funderburk, G., Giambelluca, T., Giardina, C., Helweg, D., Keener, V., Mair, D., Marra, J., McDaniel, S., Ohye, L., Oki, D.S., Parsons, E., Strauch, A., and Trauernicht, C., 2019, Managing effects of drought in Hawai’i and U.S.-affiliated Pacific Islands: General Technical Report WO-98, 27 p., https://doi.org/10.2737/WO-GTR-98.","productDescription":"27 p.","startPage":"95","endPage":"121","ipdsId":"IP-105580","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":467312,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2737/wo-gtr-98","text":"Publisher Index Page"},{"id":370145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"U.S. Pacific Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -161.7626953125,\n              17.895114303749143\n            ],\n            [\n              -153.5009765625,\n              17.895114303749143\n            ],\n            [\n              -153.5009765625,\n              23.52370005882413\n            ],\n            [\n              -161.7626953125,\n              23.52370005882413\n            ],\n            [\n              -161.7626953125,\n              17.895114303749143\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":777050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deenik, Jonathan","contributorId":221113,"corporation":false,"usgs":false,"family":"Deenik","given":"Jonathan","email":"","affiliations":[{"id":40322,"text":"East-West Center, Honolulu, HI","active":true,"usgs":false}],"preferred":false,"id":777051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fujii, Neal","contributorId":221114,"corporation":false,"usgs":false,"family":"Fujii","given":"Neal","email":"","affiliations":[{"id":40323,"text":"University of Hawai‘i at Mānoa, Department of Tropical Plant and Soil Sciences","active":true,"usgs":false}],"preferred":false,"id":777052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Funderburk, Greg","contributorId":221115,"corporation":false,"usgs":false,"family":"Funderburk","given":"Greg","email":"","affiliations":[{"id":40324,"text":"Hawai‘i Volcanoes National Park, Hawai‘i, USA","active":true,"usgs":false}],"preferred":false,"id":777053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giambelluca, Thomas ","contributorId":221116,"corporation":false,"usgs":false,"family":"Giambelluca","given":"Thomas ","affiliations":[{"id":40325,"text":"University of Hawai‘i at Mānoa, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":777054,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giardina, Christian ","contributorId":221117,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian ","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":777055,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Helweg, David A. 0000-0002-8640-9856","orcid":"https://orcid.org/0000-0002-8640-9856","contributorId":221111,"corporation":false,"usgs":true,"family":"Helweg","given":"David A.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":777049,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Keener, Victoria ","contributorId":196013,"corporation":false,"usgs":false,"family":"Keener","given":"Victoria ","affiliations":[],"preferred":false,"id":777056,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mair, Donald","contributorId":221118,"corporation":false,"usgs":true,"family":"Mair","given":"Donald","email":"","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777057,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marra, John ","contributorId":221119,"corporation":false,"usgs":false,"family":"Marra","given":"John ","affiliations":[{"id":40326,"text":"NOAA, National Environmental Satellite, Data, and Information Service","active":true,"usgs":false}],"preferred":false,"id":777058,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McDaniel, Sierra","contributorId":221120,"corporation":false,"usgs":false,"family":"McDaniel","given":"Sierra","email":"","affiliations":[{"id":40324,"text":"Hawai‘i Volcanoes National Park, Hawai‘i, USA","active":true,"usgs":false}],"preferred":false,"id":777059,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ohye, Lenore","contributorId":221121,"corporation":false,"usgs":false,"family":"Ohye","given":"Lenore","email":"","affiliations":[{"id":40327,"text":"State of Hawai‘i, Department of Land and Natural Resources, Commission on Water Resource Management","active":true,"usgs":false}],"preferred":false,"id":777060,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":221122,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn","email":"","middleInitial":"S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":777061,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parsons, Elliott","contributorId":221123,"corporation":false,"usgs":false,"family":"Parsons","given":"Elliott","affiliations":[{"id":40328,"text":"State of Hawai‘i Division of Forestry and Wildlife, Pu‘u Wa‘awa‘a Forest Reserve","active":true,"usgs":false}],"preferred":false,"id":777062,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Strauch, Ayron","contributorId":221124,"corporation":false,"usgs":false,"family":"Strauch","given":"Ayron","email":"","affiliations":[{"id":40327,"text":"State of Hawai‘i, Department of Land and Natural Resources, Commission on Water Resource Management","active":true,"usgs":false}],"preferred":false,"id":777063,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Trauernicht, Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":777064,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70216464,"text":"70216464 - 2019 - A shallow rift basin segmented in space and time: The southern San Luis Basin, Rio Grande rift, northern New Mexico, U.S.A.","interactions":[],"lastModifiedDate":"2020-11-20T14:11:15.783736","indexId":"70216464","displayToPublicDate":"2019-12-01T07:59:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3310,"text":"Rocky Mountain Geology","active":true,"publicationSubtype":{"id":10}},"title":"A shallow rift basin segmented in space and time: The southern San Luis Basin, Rio Grande rift, northern New Mexico, U.S.A.","docAbstract":"<p><span>Interpretation of gravity, magnetotelluric, and aeromagnetic data in conjunction with geologic constraints reveals details of basin geometry, thickness, and spatiotemporal evolution of the southern San Luis Basin, one of the major basins of the northern Rio Grande rift. Spatial variations of low-density basin-fill thickness are estimated primarily using a 3D gravity inversion method that improves on previous modeling efforts by separating the effects of the low-density basin fill from the effects of pre-rift rocks. The basin is found to be significantly narrower—and more complex in the subsurface—than indicated or implied by previous modeling efforts. The basin is also estimated to be significantly shallower than previously estimated. Five distinct subbasins are recognized within the broader southern San Luis Basin. The oldest and shallowest subbasin is the Las Mesitas graben along the northwestern basin margin, formed during the Oligocene transition from Southern Rocky Mountain volcanic field magmatism to rifting. In this subbasin, sediments are estimated to reach a maximum thickness of ~400 m within a north–south elongated structural depression. Other subbasins that likely initially developed during the Miocene are the dominant tectonic features in the southern San Luis Basin. This includes the Tres Orejas subbasin, which formed in the southwestern portion of the basin by the Embudo fault zone and a hypothesized fault zone along its western margin. This subbasin reaches a maximum thickness of ~2 km, as indicated by magnetotelluric and gravity modeling. The Sunshine Valley, Questa, and Taos subbasins occupy the eastern part of the southern San Luis Basin. The southern Sangre de Cristo fault zone is the dominant tectonic feature that controlled their development after ~20 Ma. The east-down Gorge fault zone controlled the western margins of significant parts of these eastern subbasins, although much of the Taos subbasin may be superimposed on the Tres Orejas subbasin. Maximum low-density basin-fill thicknesses are estimated to be 1.2 km for the Sunshine Valley subbasin, 800 m for the Questa subbasin, and 1.8 km for the Taos subbasin. Subbasin-forming tectonic activity along the Gorge fault zone and within the Tres Orejas subbasin ceased by the end of the development of the largely Pliocene Taos Plateau volcanic field. After that, rift-related subsidence became more narrowly centered on the eastern margin of the basin, controlled mainly by the linked Embudo and southern Sangre de Cristo fault zones.</span></p>","language":"English","publisher":"Rocky Mountain Geology","doi":"10.24872/rmgjournal.54.2.97","usgsCitation":"Drenth, B.J., Grauch, V.J., Turner, K.J., Rodriguez, B.D., Thompson, R., and Bauer, P.W., 2019, A shallow rift basin segmented in space and time: The southern San Luis Basin, Rio Grande rift, northern New Mexico, U.S.A.: Rocky Mountain Geology, v. 54, no. 2, p. 97-131, https://doi.org/10.24872/rmgjournal.54.2.97.","productDescription":"35 p.","startPage":"97","endPage":"131","ipdsId":"IP-104797","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":459077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.24872/rmgjournal.54.2.97","text":"Publisher Index Page"},{"id":380645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"New Mexico","otherGeospatial":"San Luis Basin, Rio Grande rift","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.45751953125,\n              35.817813158696616\n            ],\n            [\n              -104.78759765625,\n              35.817813158696616\n            ],\n            [\n              -104.78759765625,\n              37.01132594307015\n            ],\n            [\n              -106.45751953125,\n              37.01132594307015\n            ],\n            [\n              -106.45751953125,\n              35.817813158696616\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"54","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grauch, V. J. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":152256,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Kenzie J. 0000-0002-4940-3981 kturner@usgs.gov","orcid":"https://orcid.org/0000-0002-4940-3981","contributorId":496,"corporation":false,"usgs":true,"family":"Turner","given":"Kenzie","email":"kturner@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":805197,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodriguez, Brian D. 0000-0002-2263-611X brod@usgs.gov","orcid":"https://orcid.org/0000-0002-2263-611X","contributorId":836,"corporation":false,"usgs":true,"family":"Rodriguez","given":"Brian","email":"brod@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Ren A. 0000-0002-3044-3043","orcid":"https://orcid.org/0000-0002-3044-3043","contributorId":207982,"corporation":false,"usgs":true,"family":"Thompson","given":"Ren A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":805199,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bauer, Paul W.","contributorId":145562,"corporation":false,"usgs":false,"family":"Bauer","given":"Paul","email":"","middleInitial":"W.","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":805200,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208734,"text":"70208734 - 2019 - Frequent use of upland habitats by the endangered Hawaiian stilt (Himantopus mexicanus knudseni)","interactions":[],"lastModifiedDate":"2020-02-27T06:44:37","indexId":"70208734","displayToPublicDate":"2019-12-01T06:43:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Frequent use of upland habitats by the endangered Hawaiian stilt (Himantopus mexicanus knudseni)","docAbstract":"The  Hawaiian Stilt, or Ae’o (Himantopus mexicanus knudseni), is an endangered waterbird endemic to the Hawaiian Islands. Loss of suitable wetland habitats due to anthropogenic development is a leading cause for decline, as well as the introduction of non-native predators and invasive wetland plants. This study fitted four Hawaiian Stilts with GPS satellite tags to document their use of wetland and surrounding habitats on Oahu Island. While other Hawaiian waterbirds are largely restricted to wetlands, we detected Hawaiian Stilts using heavily modified upland habitats, including developed areas, undeveloped fields, sports fields, as well as wetland habitats over a 6-month period. Overall, a high use of non-wetland habitat was observed (up to 58%), with significant differences in habitat occupancy among individual stilts and across different times of day. Wetlands were the dominant habitat occupied from morning to early afternoon, but non-wetland habitats were occupied at higher frequencies in the evening and early morning hours. Although only four birds were tracked, the extensive use of non-wetland habitat by these individuals indicates management of non-wetland habitats may be important for the conservation of this subspecies. However, more research is needed to understand how applicable these results are to other stilt popualtions across the Hawaiian Islands, and better understand the potential risks and benefits of these non-wetland habitats to stilt populations.","language":"English","publisher":"BioOne","doi":"10.1675/063.042.0408","usgsCitation":"Kawasaki, M., Hart, P.J., and Paxton, E., 2019, Frequent use of upland habitats by the endangered Hawaiian stilt (Himantopus mexicanus knudseni): Waterbirds, v. 42, no. 4, p. 431-438, https://doi.org/10.1675/063.042.0408.","productDescription":"8 p.","startPage":"431","endPage":"438","ipdsId":"IP-106610","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":437271,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RPU1X1","text":"USGS data release","linkHelpText":"Oahu Island Hawaiian stilt GPS satellite tracking data, 2017-2018"},{"id":372676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"42","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kawasaki, Martha","contributorId":222802,"corporation":false,"usgs":false,"family":"Kawasaki","given":"Martha","email":"","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":783211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":783212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689 epaxton@usgs.gov","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":438,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben H.","email":"epaxton@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":783210,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70182770,"text":"70182770 - 2019 - Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa","interactions":[],"lastModifiedDate":"2024-05-17T14:50:31.723618","indexId":"70182770","displayToPublicDate":"2019-12-01T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1248,"text":"Climate Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa","docAbstract":"<p id=\"Par1\" class=\"Para\">The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.</p>","language":"English","publisher":"Springer","doi":"10.1007/s00382-016-3296-z","usgsCitation":"Shukla, S., Roberts, J., Hoell. Andrew, Funk, C., Robertson, F.R., and Kirtmann, B., 2019, Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa: Climate Dynamics, v. 15, no. 12, p. 7411-7427, https://doi.org/10.1007/s00382-016-3296-z.","productDescription":"17 p.","startPage":"7411","endPage":"7427","ipdsId":"IP-069889","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":336787,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"East Africa","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              22,\n             -8\n            ],\n            [\n              52,\n              -8\n            ],\n            [\n              52,\n              23\n            ],\n            [\n              22,\n              23\n            ],\n            [\n              22,\n              -8\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-29","publicationStatus":"PW","scienceBaseUri":"58b7eba3e4b01ccd5500bae3","chorus":{"doi":"10.1007/s00382-016-3296-z","url":"http://dx.doi.org/10.1007/s00382-016-3296-z","publisher":"Springer Nature","authors":"Shukla Shraddhanand, Roberts Jason, Hoell Andrew, Funk Christopher C., Robertson Franklin, Kirtman Ben","journalName":"Climate Dynamics","publicationDate":"7/29/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"7/29/2016"},"contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145802,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":673688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Jason B.","contributorId":145808,"corporation":false,"usgs":false,"family":"Roberts","given":"Jason B.","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":673689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoell. Andrew","contributorId":145831,"corporation":false,"usgs":false,"family":"Hoell. Andrew","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":673690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":673687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robertson, Franklin R.","contributorId":145809,"corporation":false,"usgs":false,"family":"Robertson","given":"Franklin","email":"","middleInitial":"R.","affiliations":[{"id":16239,"text":"NASA Marshall Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":673691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kirtmann, Benjamin","contributorId":184160,"corporation":false,"usgs":false,"family":"Kirtmann","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":673692,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70228105,"text":"70228105 - 2019 - Public access for pheasant hunters: Understanding an emerging need","interactions":[],"lastModifiedDate":"2022-02-04T20:44:13.320011","indexId":"70228105","displayToPublicDate":"2019-11-30T14:35:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Public access for pheasant hunters: Understanding an emerging need","docAbstract":"<p><span>Ring-necked pheasant (</span><i>Phasianus colchicus</i><span>; i.e., pheasant) hunting participation is declining across North America, reflecting a larger downward trend in American hunting participation and threatening benefits to grassland conservation and rural economies. To stabilize and expand the pheasant hunting population, we must first identify factors that influence pheasant hunter participation. We used an extensive in-person hunter survey to test the hypothesis that hunter demographics interact with social-ecological traits of hunting locations to affect hunter decisions, outcomes, and perceptions. We built a series of Bayesian mixed effects models to parse variation in demographics, perceptions, and hunt outcomes of pheasant hunters interviewed at public access hunting sites across 3 regions in Nebraska, USA, that varied in pheasant abundance and proximity to urban population centers. Among pheasant hunters in Nebraska, access to private lands was negatively related to the human population density of a pheasant hunter's home ZIP code and the distance a hunter had traveled to reach a hunting location. Pheasant hunters interviewed closer to metropolitan areas tended to be more urban and travel shorter distances, and their parties were more likely to include youth but less likely to include dogs. Hunter satisfaction was positively associated with seeing and harvesting pheasants and hunting with youth. Whereas youth participation and the number of pheasants seen varied by study region, hunter satisfaction did not differ across regions, suggesting that hunters may calibrate their expectations and build their parties based on where they plan to hunt. The variation in hunter demographics across hunting locations and disconnects between social and ecological correlates of hunter satisfaction suggests that diverse pheasant hunting constituencies will be best served by diverse pheasant hunting opportunities.</span></p>","language":"English","doi":"10.1002/jwmg.21785","usgsCitation":"Wszola, L., Madsen, A., Stuber, E., Chizinski, C., Lusk, J., Taylor, J., Pope, K.L., and Fontaine, J.J., 2019, Public access for pheasant hunters: Understanding an emerging need: Journal of Wildlife Management, v. 84, no. 1, p. 45-55, https://doi.org/10.1002/jwmg.21785.","productDescription":"11 p.","startPage":"45","endPage":"55","ipdsId":"IP-097657","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -101.90917968749999,\n              39.9434364619742\n            ],\n            [\n              -95.712890625,\n              39.9434364619742\n            ],\n            [\n              -95.712890625,\n              40.68063802521456\n            ],\n            [\n              -101.90917968749999,\n              40.68063802521456\n            ],\n            [\n              -101.90917968749999,\n              39.9434364619742\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"84","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Wszola, L.S.","contributorId":274556,"corporation":false,"usgs":false,"family":"Wszola","given":"L.S.","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":833126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madsen, A.L.","contributorId":274557,"corporation":false,"usgs":false,"family":"Madsen","given":"A.L.","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":833127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stuber, E.F.","contributorId":205137,"corporation":false,"usgs":false,"family":"Stuber","given":"E.F.","email":"","affiliations":[{"id":37031,"text":"Nebraska Cooperative Fish & Wildlife Research Unit, University of Nebraska-Lincoln, Lincoln, Nebraska","active":true,"usgs":false}],"preferred":false,"id":833128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chizinski, Christopher J.","contributorId":274559,"corporation":false,"usgs":false,"family":"Chizinski","given":"Christopher J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":833129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lusk, J.J.","contributorId":205141,"corporation":false,"usgs":false,"family":"Lusk","given":"J.J.","email":"","affiliations":[{"id":18961,"text":"Nebraska Game and Parks Commission, Lincoln, Nebraska","active":true,"usgs":false}],"preferred":false,"id":833130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Taylor, J.S.","contributorId":274563,"corporation":false,"usgs":false,"family":"Taylor","given":"J.S.","email":"","affiliations":[{"id":56624,"text":"Pheasants Forever Inc. & Midwest Association of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":833131,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":833132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fontaine, Joseph J. 0000-0002-7639-9156 jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833133,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70214574,"text":"70214574 - 2019 - Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream","interactions":[],"lastModifiedDate":"2020-09-30T14:08:22.311228","indexId":"70214574","displayToPublicDate":"2019-11-29T09:03:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2426,"text":"Journal of Physical Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream","docAbstract":"Hurricanes interact with the Gulf Stream in the South Atlantic Bight (SAB) through a wide variety of processes, which are crucial to understand for prediction of open-ocean and coastal hazards during storms. However, it remains unclear how waves are modified by large-scale ocean currents under storm conditions, when waves are aligned with the storm-driven circulation and tightly coupled to the overlying wind field. Hurricane Matthew (2016) impacted the US southeast coast, causing extensive coastal change due to large waves and elevated water levels. The hurricane traveled on the continental shelf parallel to the SAB coastline, with the right side of the hurricane directly over the Gulf Stream. Using the Coupled Ocean Atmosphere Wave Sediment Transport Modeling System, we investigate wave-current interaction between Hurricane Matthew and the Gulf Stream. The model simulates ocean currents and waves over a grid encompassing the US east coast, with varied coupling of the hydrodynamic and wave components to isolate the effect of the currents on the waves, and the effect of the Gulf Stream relative to storm-driven circulation. The Gulf Stream modifies the direction of the storm-driven currents beneath the right side of the hurricane. Waves transitioned from following currents that result in wave lengthening, through negative current gradients that result in wave steepening and dissipation. Wave-current interaction over the Gulf Stream modified maximum coastal total water levels, and changed incident wave directions at the coast by up to 20°, with strong implications for the morphodynamic response and stability of the coast to the hurricane.","language":"English","publisher":"American Meteorology Society","doi":"10.1175/JPO-D-19-0124.1","usgsCitation":"Hegermiller, C., Warner, J., Olabarrieta, M., and Sherwood, C.R., 2019, Wave-current interaction between Hurricane Matthew wave fields and the Gulf Stream: Journal of Physical Oceanography, v. 49, no. 11, p. 2883-2900, https://doi.org/10.1175/JPO-D-19-0124.1.","productDescription":"18 p.","startPage":"2883","endPage":"2900","ipdsId":"IP-109198","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jpo-d-19-0124.1","text":"Publisher Index Page"},{"id":378900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"East Coast, Gulf Coast","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.12109375,\n              16.97274101999902\n            ],\n            [\n              -78.046875,\n              24.5271348225978\n            ],\n            [\n              -73.47656249999999,\n              36.1733569352216\n            ],\n            [\n              -65.390625,\n              44.213709909702054\n            ],\n            [\n              -68.73046875,\n              46.07323062540835\n            ],\n            [\n              -80.85937499999999,\n              34.016241889667015\n            ],\n            [\n              -92.63671875,\n              32.24997445586331\n            ],\n            [\n              -101.77734374999999,\n              25.799891182088334\n            ],\n            [\n              -97.3828125,\n              19.145168196205297\n            ],\n            [\n              -90.17578124999999,\n              16.29905101458183\n            ],\n            [\n              -89.12109375,\n              16.97274101999902\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hegermiller, Christie 0000-0002-6383-7508","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":241895,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":true,"id":800130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olabarrieta, Maitane 0000-0002-7619-7992 molabarrieta@usgs.gov","orcid":"https://orcid.org/0000-0002-7619-7992","contributorId":211373,"corporation":false,"usgs":false,"family":"Olabarrieta","given":"Maitane","email":"molabarrieta@usgs.gov","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":800132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800133,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215197,"text":"70215197 - 2019 - Separating sea and slow slip signals on the seafloor","interactions":[],"lastModifiedDate":"2020-10-12T13:02:09.265924","indexId":"70215197","displayToPublicDate":"2019-11-29T08:00:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5999,"text":"Journal of Geophysical Research- Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Separating sea and slow slip signals on the seafloor","docAbstract":"<div class=\"article-section__content en main\"><p>Seafloor pressure measurements hold promise for estimating vertical displacements from transient slow slip events on submarine faults. We assess the accuracy of pressure offset estimates that evolve over days to weeks and the confidence with which they may be attributed to tectonic deformation or to the ocean water column. One common approach to resolve this ambiguity assumes water column pressures vary insignificantly over the study region and are represented by stable reference site pressures. Assessing the validity of this assumption requires independent evidence. Correlations between pressures and colocated temperatures collected during the Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip experiment suggest temperatures might provide a useful independent proxy for water column pressures. We compared offsets estimated using several methods, with temperature and other proxies. The use of a temperature proxy was unsuccessful, because seafloor temperatures did not track the seasonal signal that contributes significantly to seafloor pressure changes over the slow slip event period. Regardless of the&nbsp;estimation method, offsets varied within a few cm around some uncertain reference level. Commonly used statistical measures are shown not to be reliable indicators of offset accuracy since offsets contribute minimally to the total variance. Offsets estimated using identical methods but with seafloor pressures simulated using a regional ocean model were larger than those derived from the data but had a similar pattern. Since the model simulates only water column processes, this suggests a significant fraction of the estimated pressure offsets are due to seasonal water column signal and are&nbsp;not of tectonic origin.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB018285","usgsCitation":"Gomberg, J.S., Hautala, S., Johnson, P., and Chiswell, S., 2019, Separating sea and slow slip signals on the seafloor: Journal of Geophysical Research- Solid Earth, v. 124, no. 12, p. 13486-13503, https://doi.org/10.1029/2019JB018285.","productDescription":"18 p.","startPage":"13486","endPage":"13503","ipdsId":"IP-109456","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":498872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019jb018285","text":"Publisher Index Page"},{"id":379298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              177.418212890625,\n              -39.82541310342477\n            ],\n            [\n              179.219970703125,\n              -39.82541310342477\n            ],\n            [\n              179.219970703125,\n              -38.54816542304657\n            ],\n            [\n              177.418212890625,\n              -38.54816542304657\n            ],\n            [\n              177.418212890625,\n              -39.82541310342477\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"124","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":801139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hautala, Susan","contributorId":194235,"corporation":false,"usgs":false,"family":"Hautala","given":"Susan","email":"","affiliations":[],"preferred":false,"id":801140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Paul","contributorId":189150,"corporation":false,"usgs":false,"family":"Johnson","given":"Paul","email":"","affiliations":[],"preferred":false,"id":801141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chiswell, Steve","contributorId":242932,"corporation":false,"usgs":false,"family":"Chiswell","given":"Steve","email":"","affiliations":[{"id":48587,"text":"National Institute of Water & Atmospheric Research Ltd","active":true,"usgs":false}],"preferred":false,"id":801142,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70211034,"text":"70211034 - 2019 - The 2018 update of the US National Seismic Hazard Model: Overview of model and implications","interactions":[],"lastModifiedDate":"2020-07-13T12:34:13.1599","indexId":"70211034","displayToPublicDate":"2019-11-28T15:52:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Overview of model and implications","docAbstract":"<p><span>During 2017–2018, the National Seismic Hazard Model for the conterminous United States was updated as follows: (1) an updated seismicity catalog was incorporated, which includes new earthquakes that occurred from 2013 to 2017; (2) in the central and eastern United States (CEUS), new ground motion models were updated that incorporate updated median estimates, modified assessments of the associated epistemic uncertainties and aleatory variabilities, and new soil amplification factors; (3) in the western United States (WUS), amplified shaking estimates of long-period ground motions at sites overlying deep sedimentary basins in the Los Angeles, San Francisco, Seattle, and Salt Lake City areas were incorporated; and (4) in the conterminous United States, seismic hazard is calculated for 22 periods (from 0.01 to 10 s) and 8 uniform V</span><sub>S30</sub><span>&nbsp;maps (ranging from 1500 to 150 m/s). We also include a description of updated computer codes and modeling details. Results show increased ground shaking in many (but not all) locations across the CEUS (up to ~30%), as well as near the four urban areas overlying deep sedimentary basins in the WUS (up to ~50%). Due to population growth and these increased hazard estimates, more people live or work in areas of high or moderate seismic hazard than ever before, leading to higher risk of undesirable consequences from forecasted future ground shaking.</span></p>","language":"English","publisher":"Sage","doi":"10.1177/8755293019878199","usgsCitation":"Petersen, M.D., Shumway, A., Powers, P.M., Mueller, C., Moschetti, M.P., Frankel, A.D., Rezaeian, S., McNamara, D.E., Luco, N., Boyd, O.S., Rukstales, K.S., Jaiswal, K.S., Thompson, E.M., Hoover, S.M., Clayton, B., Field, E., and Zeng, Y., 2019, The 2018 update of the US National Seismic Hazard Model: Overview of model and implications: Earthquake Spectra, v. 36, no. 1, p. 5-41, https://doi.org/10.1177/8755293019878199.","productDescription":"37 p.","startPage":"5","endPage":"41","ipdsId":"IP-109680","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437273,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RQMREV","text":"USGS data release","linkHelpText":"Data Release for Additional Period and Site Class Data for the 2018 National Seismic Hazard Model for the Conterminous United States (ver. 1.1, February 2020)"},{"id":437272,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WT5OVB","text":"USGS data release","linkHelpText":"Data Release for 2018 Update of the U.S. National Seismic Hazard Model"},{"id":376269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n      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