{"pageNumber":"328","pageRowStart":"8175","pageSize":"25","recordCount":41075,"records":[{"id":70204989,"text":"70204989 - 2019 - Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters","interactions":[],"lastModifiedDate":"2019-08-28T12:09:20","indexId":"70204989","displayToPublicDate":"2019-08-18T12:02:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters","docAbstract":"This article aims to understand decision making under uncertainty and risk, with a case study on Cape Cod, Massachusetts. Decision makers need to consider imperfect information on the cost and effectiveness of advanced nitrogen-removing on-site wastewater treatment systems as options to mitigate water quality degradation. Research included modeling nitrogen load reduction to impaired coastal waters from seven treatment system technologies and eliciting expert knowledge on their costs. Predictions of nitrogen load removal and cost for each technology incorporated variation in effectiveness and uncertainty in household water use, costs, and expert confidence in costs. The predictions were evaluated using the Pareto efficiency concept to reveal tradeoffs between cost and effectiveness. The stochastic dominance index was used to identify preferred technologies for risk-averse decision making, assuming no further learning is possible. Lastly, the predictions were combined into a cost-effectiveness metric to estimate the expected payoff of implementing the best treatment system in the face of uncertainty and the expected payoff of learning which treatment systems are most cost-effective over time. The expected value of perfect information was calculated as the difference between the expected payoffs. Three technologies revealed Pareto efficient tradeoffs between cost and effectiveness, whereas one technology was the preferred risk-averse option in the absence of future learning. There was a high expected value of perfect information, which could motivate adaptive management on Cape Cod. This research demonstrated decision analysis methods to guide future research and decision making toward meeting water quality objectives and reducing uncertainty.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2019.109380","usgsCitation":"Martin, D.M., and Johnson, F., 2019, Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters: Journal of Environmental Management, v. 249, 109380, 9 p., https://doi.org/10.1016/j.jenvman.2019.109380.","productDescription":"109380, 9 p.","ipdsId":"IP-103926","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467362,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7901712","text":"External Repository"},{"id":367010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.48004150390625,\n              41.79691191119474\n            ],\n            [\n              -70.6640625,\n              41.713930073371294\n            ],\n            [\n              -70.68878173828125,\n              41.572306568724365\n            ],\n            [\n              -70.96343994140625,\n              41.44684402008925\n            ],\n            [\n              -70.97305297851562,\n              41.395354710280166\n            ],\n            [\n              -70.53085327148438,\n              41.52811390935743\n            ],\n            [\n              -70.41412353515625,\n              41.558949183362564\n            ],\n            [\n              -70.301513671875,\n              41.61646901513335\n            ],\n            [\n              -70.23422241210938,\n              41.58771550500517\n            ],\n            [\n              -70.03097534179688,\n              41.64623592868676\n            ],\n            [\n              -70.04470825195312,\n              41.51680395810118\n            ],\n            [\n              -69.98016357421875,\n              41.509605687197975\n            ],\n            [\n              -69.87442016601561,\n              41.712904935827744\n            ],\n            [\n              -69.94583129882812,\n              41.95540515378059\n            ],\n            [\n              -70.06668090820312,\n              42.07783959017503\n            ],\n            [\n              -70.22598266601562,\n              42.09618442380296\n            ],\n            [\n              -70.26443481445311,\n              42.07783959017503\n            ],\n            [\n              -70.21499633789062,\n              41.9942015603157\n            ],\n            [\n              -70.13534545898438,\n              42.02685388718981\n            ],\n            [\n              -70.09689331054688,\n              41.89716623689334\n            ],\n            [\n              -70.05157470703125,\n              41.87160608117939\n            ],\n            [\n              -70.0543212890625,\n              41.80919639152055\n            ],\n            [\n              -70.28366088867188,\n              41.76721469421018\n            ],\n            [\n              -70.48004150390625,\n              41.79691191119474\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"249","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, David M. 0000-0002-1514-5734","orcid":"https://orcid.org/0000-0002-1514-5734","contributorId":210575,"corporation":false,"usgs":false,"family":"Martin","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":769462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Fred 0000-0002-5854-3695","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":218516,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70205015,"text":"70205015 - 2019 - Air pollution and respiratory hospital admissions in Shiraz, Iran, 2009 to 2015","interactions":[],"lastModifiedDate":"2019-12-22T14:56:07","indexId":"70205015","displayToPublicDate":"2019-08-16T11:37:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":924,"text":"Atmospheric Environment","active":true,"publicationSubtype":{"id":10}},"title":"Air pollution and respiratory hospital admissions in Shiraz, Iran, 2009 to 2015","docAbstract":"<div id=\"abssec0010\"><p id=\"abspara0010\">Air pollution has been identified as one of the most challenging health issues in urban areas worldwide.</p><p id=\"abspara0015\"><span>The aim of this study was to investigate the association between short-term exposure to ambient air pollution and&nbsp;respiratory disease&nbsp;over a long-term period in Shiraz, one of the largest cities in Iran. Methods: hospital admissions due to respiratory diseases (asthma,&nbsp;pneumonia, chronic obstructive&nbsp;pulmonary disease(COPD) and pleural effusion) in residents of Shiraz between March 21, 2009 and March 20, 2015 were included. Demographics for each patient and&nbsp;meta data&nbsp;to include principal meteorological variables (temperature and relative humidity) and five ambient&nbsp;pollutants&nbsp;(CO, O</span><sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, and PM<sub>10</sub>) were also included. Statistical analysis was performed by Poisson regression in single-pollutant generalized linear model with principal component analysis (GLPCA) to analyze the relationship between pollutants and hospital admissions at the 0–9 cumulative lag day period. Pearson correlation test was used to determine the relationship between different pollutants, temperature and humidity.</p></div><div id=\"abssec0015\"><h3 id=\"sectitle0015\" class=\"u-h4 u-margin-m-top u-margin-xs-bottom\">Results</h3><p id=\"abspara0020\">It was found that the highest increase in asthma admission was related to PM<sub>10</sub>(relative risk (RR) = 1.31, 95% CI = 1.17, 1.47). For COPD, the rate of hospital visits significantly increased with the increase in NO<sub>2</sub><span>&nbsp;</span>concentration (RR = 1.17, 95% CI = 1.09.1.27). In the children's hospital, O<sub>3</sub><span>&nbsp;</span>(RR = 1.25, 95% CI = 1.06, 1.47) and SO<sub>2</sub>(RR = 1.17, 95% CI = 1.07, 1.28) affected the asthma admissions and all contaminants (highest RR observed was for NO<sub>2</sub><span>&nbsp;(RR = 1.28 95% CI = 1.18, 1.40) affected pneumonia admissions on cumulative lag days of 0–9. Conclusions: These data confirm an association between ambient&nbsp;air pollutants&nbsp;and hospital admissions due to respiratory disease.</span></p></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.atmosenv.2019.04.030","usgsCitation":"Soleimani, Z., Darvishi Boloorani, A., Khalifeh, R., Teymouri, P., Mesdaghinia, A., and Griffin, D.W., 2019, Air pollution and respiratory hospital admissions in Shiraz, Iran, 2009 to 2015: Atmospheric Environment, v. 209, p. 233-239, https://doi.org/10.1016/j.atmosenv.2019.04.030.","productDescription":"7 p.","startPage":"233","endPage":"239","ipdsId":"IP-098429","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":367007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","city":"Shiraz","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[53.9216,37.19892],[54.8003,37.39242],[55.51158,37.96412],[56.18037,37.93513],[56.61937,38.12139],[57.33043,38.02923],[58.43615,37.52231],[59.23476,37.41299],[60.37764,36.52738],[61.12307,36.4916],[61.21082,35.65007],[60.80319,34.4041],[60.52843,33.67645],[60.9637,33.52883],[60.53608,32.98127],[60.86365,32.18292],[60.94194,31.54807],[61.69931,31.37951],[61.78122,30.73585],[60.87425,29.82924],[61.36931,29.30328],[61.77187,28.69933],[62.72783,28.25964],[62.75543,27.37892],[63.2339,27.21705],[63.31663,26.75653],[61.87419,26.23997],[61.49736,25.07824],[59.61613,25.38016],[58.52576,25.60996],[57.39725,25.7399],[56.97077,26.96611],[56.49214,27.1433],[55.72371,26.96463],[54.71509,26.48066],[53.4931,26.81237],[52.4836,27.58085],[51.52076,27.86569],[50.85295,28.81452],[50.11501,30.14777],[49.57685,29.98572],[48.94133,30.31709],[48.56797,29.92678],[48.01457,30.45246],[48.0047,30.98514],[47.68529,30.98485],[47.8492,31.70918],[47.33466,32.46916],[46.10936,33.01729],[45.41669,33.9678],[45.64846,34.74814],[46.15179,35.09326],[46.07634,35.67738],[45.42062,35.97755],[44.77267,37.17045],[44.22576,37.97158],[44.4214,38.28128],[44.10923,39.42814],[44.79399,39.713],[44.95269,39.33576],[45.45772,38.87414],[46.14362,38.7412],[46.50572,38.77061],[47.68508,39.50836],[48.0601,39.58224],[48.35553,39.28876],[48.01074,38.79401],[48.63438,38.27038],[48.88325,38.32025],[49.19961,37.58287],[50.14777,37.37457],[50.84235,36.87281],[52.26402,36.70042],[53.82579,36.96503],[53.9216,37.19892]]]},\"properties\":{\"name\":\"Iran\"}}]}","volume":"209","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soleimani, Zahra","contributorId":218554,"corporation":false,"usgs":false,"family":"Soleimani","given":"Zahra","email":"","affiliations":[{"id":39869,"text":"Tehran University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769556,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darvishi Boloorani, Ali","contributorId":218553,"corporation":false,"usgs":false,"family":"Darvishi Boloorani","given":"Ali","email":"","affiliations":[{"id":39868,"text":"Semnan University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Khalifeh, Reza","contributorId":218555,"corporation":false,"usgs":false,"family":"Khalifeh","given":"Reza","email":"","affiliations":[{"id":39870,"text":"Tehran University","active":true,"usgs":false}],"preferred":false,"id":769557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teymouri, Pari","contributorId":218556,"corporation":false,"usgs":false,"family":"Teymouri","given":"Pari","email":"","affiliations":[{"id":39871,"text":"Tabriz University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mesdaghinia, Alireza","contributorId":218557,"corporation":false,"usgs":false,"family":"Mesdaghinia","given":"Alireza","email":"","affiliations":[{"id":39869,"text":"Tehran University of Medical Sciences","active":true,"usgs":false}],"preferred":false,"id":769559,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769554,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204786,"text":"70204786 - 2019 - Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","interactions":[],"lastModifiedDate":"2019-08-19T13:52:04","indexId":"70204786","displayToPublicDate":"2019-08-16T06:59:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","docAbstract":"Abstract \nWildfires raise risks of floods, debris flows, major geomorphologic and sedimentologic change, and water quality and quantity shifts. A principal control on the magnitude of these changes is field-saturated hydraulic conductivity (Kfs), which dictates surface runoff generation and is a key input into numerical models. This work synthesizes 73 Kfs datasets from the literature in the first year following fire at the plot scale (≤ 10 m2). A meta-analysis using a random effects analysis showed significant differences between burned and unburned Kfs. The reductions in Kfs after fire, expressed by the ratio of Kfs Burned / Kfs Unburned, were 0.46 (95% confidence interval of 0.31-0.70) combining wildfire and prescribed fire and 0.3 (95% confidence interval of 0.13-0.71) for wildfire.  No significant differences for Kfs were observed between wildfire and prescribed fire or moderate and high fire severity. Both Kfs magnitude and variability depended more on measurement method than measurement support area at the plot scale, with methods applying head ≥0.5 cm producing larger estimates of Kfs. It is recommended that post-fire efforts to characterize Kfs for modeling or process-based interpretations use methods that reflect the dominant infiltration processes: tension infiltrometers and simulated rainfall methods when soil matrix flow dominates and ponded head methods when macropore flow is critical.","language":"English","publisher":"Wiley","doi":"10.1002/esp.4621","usgsCitation":"Ebel, B.A., 2019, Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests: Earth Surface Processes and Landforms, v. 44, no. 10, p. 1945-1956, https://doi.org/10.1002/esp.4621.","productDescription":"12 p.","startPage":"1945","endPage":"1956","ipdsId":"IP-101329","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":366581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":218151,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":768477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70227922,"text":"70227922 - 2019 - Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","interactions":[],"lastModifiedDate":"2022-02-03T12:08:31.632737","indexId":"70227922","displayToPublicDate":"2019-08-15T14:38:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","docAbstract":"Hybridization can profoundly affect the genomic composition and phenotypes of closely related species, and provides an opportunity to identify mechanisms that maintain reproductive isolation between species. Recent evidence suggests that hybridization outcomes within a species pair can vary across locations. However, we still don’t know how extensive variation in outcomes of hybridization is across geographic replicates, and what mechanisms drive that variation. In this study, we described hybridization outcomes across 27 locations in the North Fork Shoshone River basin (Wyoming, USA) where native Yellowstone cutthroat trout and introduced rainbow trout co-occur. We used genomic data and hierarchical Bayesian models to precisely identify ancestry of hybrid individuals. Hybridization outcomes varied across locations. In some locations, only rainbow trout and advanced backcrossed hybrids towards rainbow trout were present, while other locations had a broader range of ancestry, including both parental species and first-generation hybrids. Using an individual-based simulation, we found that outcomes of hybridization in the North Fork Shoshone River basin deviate substantially from what we would expect under assumptions of random mating and no selection against hybrids. Since this implies that some mechanisms of reproductive isolation function to maintain parental taxa and a diversity of hybrid types, we then modeled hybridization outcomes as a function of environmental variables and stocking history that are likely to affect prezygotic barriers to hybridization. Variables associated with history of fish stocking were the strongest predictors of hybridization outcomes, followed by environmental variables that might affect overlap in spawning time and location.","language":"English","publisher":"Wiley","doi":"10.1111/mec.15175","usgsCitation":"Mandeville, E., Walters, A.W., Nordberg, B.J., Higgins, K.H., Burckhardt, J.C., and Wagner, C.E., 2019, Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context: Molecular Ecology, v. 28, no. 16, p. 3738-3755, https://doi.org/10.1111/mec.15175.","productDescription":"18 p.","startPage":"3738","endPage":"3755","ipdsId":"IP-099309","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467366,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6775767","text":"External Repository"},{"id":395304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Buffalo Bill Reservoir, North Fork Shoshone River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.57077026367188,\n              44.26683800273895\n            ],\n            [\n              -108.96102905273438,\n              44.26683800273895\n            ],\n            [\n              -108.96102905273438,\n              44.6579085850145\n            ],\n            [\n              -109.57077026367188,\n              44.6579085850145\n            ],\n            [\n              -109.57077026367188,\n              44.26683800273895\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","issue":"16","noUsgsAuthors":false,"publicationDate":"2019-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nordberg, Brittany J.","contributorId":270690,"corporation":false,"usgs":false,"family":"Nordberg","given":"Brittany","email":"","middleInitial":"J.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Higgins, Karly H.","contributorId":273111,"corporation":false,"usgs":false,"family":"Higgins","given":"Karly","email":"","middleInitial":"H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burckhardt, Jason C.","contributorId":270692,"corporation":false,"usgs":false,"family":"Burckhardt","given":"Jason","email":"","middleInitial":"C.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":832581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832582,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204253,"text":"sir20195068 - 2019 -  Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-08-16T06:55:10","indexId":"sir20195068","displayToPublicDate":"2019-08-15T13:46:58","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5068","displayTitle":"Flood-Inundation Maps for Joachim Creek, De Soto, Missouri, 2018","title":" Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","docAbstract":"<p>Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek, De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the city of De Soto and Jefferson County, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at <a href=\"https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program\" data-mce-href=\"https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program\">https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program</a>, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at <a href=\"https://waterdata.usgs.gov/nwis\" data-mce-href=\"https://waterdata.usgs.gov/nwis\">https://waterdata.usgs.gov/nwis</a> or the National Weather Service Advanced Hydrologic Prediction Service at <a data-mce-href=\"https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&amp;gage=desm7\" href=\"https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&amp;gage=desm7\">https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&amp;gage=desm7</a>, which also forecasts flood hydrographs at this site (site DESM7).</p><p>Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013.</p><p>The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) to delineate the area flooded at each water level.</p><p>The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195068","collaboration":"Prepared in cooperation with the city of De Soto, Missouri, and Jefferson County, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2019, Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5068, 10 p., https://doi.org/10.3133/sir20195068.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-105218","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366556,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5068/sir20195068.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5068"},{"id":366557,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MD7KUL","text":"USGS data release","linkHelpText":"Geospatial datasets for the flood-inundation study of Joachim Creek, De Soto, Missouri, 2018"},{"id":366555,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5068/coverthb.jpg"}],"country":"United States","state":"Missouri","county":"Jefferson County","city":"De Soto","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.1091,39.1434],[-94.1183,38.9171],[-94.1223,38.8323],[-94.4007,38.837],[-94.5528,38.844],[-94.5705,38.8447],[-94.6082,38.8463],[-94.6075,39.0399],[-94.6075,39.0437],[-94.6082,39.1147],[-94.5966,39.1144],[-94.5889,39.115],[-94.5801,39.1188],[-94.566,39.1231],[-94.5637,39.124],[-94.5531,39.1287],[-94.5455,39.1343],[-94.5393,39.1426],[-94.5358,39.1449],[-94.5334,39.1463],[-94.5293,39.1464],[-94.5228,39.1487],[-94.5204,39.1492],[-94.5163,39.1502],[-94.5122,39.1503],[-94.508,39.1508],[-94.5039,39.1504],[-94.5015,39.1495],[-94.4985,39.1482],[-94.4931,39.1465],[-94.4883,39.1439],[-94.4834,39.1394],[-94.4797,39.1349],[-94.467,39.1242],[-94.4651,39.1229],[-94.461,39.1221],[-94.4592,39.1226],[-94.4563,39.124],[-94.4522,39.1276],[-94.4452,39.1314],[-94.4446,39.1314],[-94.4353,39.1379],[-94.4277,39.1425],[-94.4271,39.143],[-94.4266,39.1439],[-94.426,39.1448],[-94.4254,39.1453],[-94.4248,39.1453],[-94.4178,39.1517],[-94.4098,39.1609],[-94.4092,39.1614],[-94.4063,39.1655],[-94.4058,39.1678],[-94.4052,39.1687],[-94.4036,39.1755],[-94.4068,39.1823],[-94.413,39.1931],[-94.413,39.1944],[-94.4107,39.1963],[-94.406,39.1982],[-94.4054,39.1982],[-94.3989,39.1969],[-94.394,39.1943],[-94.3934,39.1938],[-94.3885,39.188],[-94.3847,39.1763],[-94.3816,39.1713],[-94.3798,39.17],[-94.3791,39.1687],[-94.3779,39.1655],[-94.376,39.1633],[-94.3748,39.1624],[-94.3718,39.1611],[-94.3695,39.162],[-94.3631,39.168],[-94.3602,39.1703],[-94.3561,39.1735],[-94.3549,39.174],[-94.3538,39.1745],[-94.3479,39.1777],[-94.3473,39.1791],[-94.3386,39.1856],[-94.3369,39.1879],[-94.3357,39.1893],[-94.3323,39.1929],[-94.3311,39.1948],[-94.3282,39.1975],[-94.3265,39.1994],[-94.3253,39.2007],[-94.3248,39.2021],[-94.3236,39.2035],[-94.3236,39.2044],[-94.3231,39.2062],[-94.3226,39.2085],[-94.318,39.2176],[-94.3134,39.2213],[-94.3128,39.2222],[-94.3122,39.2227],[-94.3111,39.2236],[-94.3099,39.2254],[-94.307,39.2291],[-94.3064,39.2296],[-94.3053,39.2305],[-94.3047,39.231],[-94.3024,39.2324],[-94.3006,39.2333],[-94.2982,39.2342],[-94.2959,39.2352],[-94.2947,39.2356],[-94.2929,39.2361],[-94.2912,39.2366],[-94.2906,39.2371],[-94.2894,39.2371],[-94.2876,39.2367],[-94.2858,39.2367],[-94.2847,39.2367],[-94.284,39.2363],[-94.2811,39.2363],[-94.2787,39.2363],[-94.2769,39.2364],[-94.2739,39.2359],[-94.2703,39.2351],[-94.2674,39.2351],[-94.2656,39.2356],[-94.2644,39.2356],[-94.2638,39.2356],[-94.2614,39.2357],[-94.2603,39.2361],[-94.2585,39.2362],[-94.2561,39.2362],[-94.2537,39.2358],[-94.2525,39.2353],[-94.2519,39.2354],[-94.2507,39.2349],[-94.2489,39.2345],[-94.2471,39.2341],[-94.2459,39.2336],[-94.2441,39.2327],[-94.2436,39.2323],[-94.2417,39.2314],[-94.2411,39.231],[-94.2405,39.2305],[-94.2399,39.2296],[-94.2393,39.2283],[-94.2387,39.2274],[-94.2368,39.2233],[-94.2349,39.2202],[-94.2331,39.2179],[-94.2313,39.2166],[-94.2295,39.2157],[-94.2283,39.2148],[-94.2277,39.2144],[-94.2265,39.2135],[-94.2252,39.2122],[-94.224,39.2117],[-94.2222,39.2099],[-94.2186,39.2091],[-94.218,39.2086],[-94.2174,39.2082],[-94.2168,39.2077],[-94.2144,39.2073],[-94.2132,39.2073],[-94.2127,39.2073],[-94.2085,39.2074],[-94.2067,39.2079],[-94.2056,39.2083],[-94.2044,39.2093],[-94.2009,39.2111],[-94.1997,39.212],[-94.1973,39.2125],[-94.1962,39.213],[-94.195,39.2135],[-94.1926,39.2144],[-94.1908,39.2144],[-94.1897,39.2145],[-94.1879,39.2145],[-94.1867,39.2145],[-94.1849,39.2145],[-94.1843,39.2145],[-94.1825,39.2146],[-94.1796,39.2141],[-94.1784,39.2137],[-94.1778,39.2132],[-94.1766,39.2124],[-94.1759,39.2119],[-94.1753,39.211],[-94.1753,39.2097],[-94.1764,39.2065],[-94.1764,39.2051],[-94.1764,39.2042],[-94.1769,39.2033],[-94.1769,39.2024],[-94.1781,39.2015],[-94.1792,39.1996],[-94.1804,39.1973],[-94.1815,39.196],[-94.1827,39.195],[-94.1844,39.1941],[-94.1862,39.1927],[-94.188,39.1918],[-94.1891,39.1904],[-94.1897,39.19],[-94.1903,39.1895],[-94.1914,39.1881],[-94.1902,39.1859],[-94.1871,39.1828],[-94.1865,39.1819],[-94.1859,39.181],[-94.1847,39.1801],[-94.1829,39.1796],[-94.1811,39.1792],[-94.1799,39.1788],[-94.1787,39.1788],[-94.1769,39.1784],[-94.1746,39.1775],[-94.1698,39.1766],[-94.1644,39.1749],[-94.159,39.1741],[-94.1501,39.1733],[-94.1489,39.1728],[-94.1466,39.1724],[-94.146,39.1724],[-94.1448,39.172],[-94.143,39.1716],[-94.1412,39.1711],[-94.1394,39.1702],[-94.1382,39.1693],[-94.1364,39.168],[-94.1351,39.1667],[-94.1345,39.1667],[-94.1327,39.1644],[-94.1315,39.1636],[-94.1303,39.1622],[-94.1296,39.1604],[-94.1284,39.1581],[-94.1187,39.1488],[-94.1091,39.1434]]]},\"properties\":{\"name\":\"Jackson\",\"state\":\"MO\"}}]}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/cm-water\" href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a> <br>U.S. Geological Survey<br>1400 Independence Road <br>Rolla, MO 65401</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Creation of Flood-Inundation-Map Library</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-08-15","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Jonathan D.","contributorId":218139,"corporation":false,"usgs":false,"family":"Voss","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":768433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":768432,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204034,"text":"sir20195064 - 2019 - Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i","interactions":[],"lastModifiedDate":"2019-08-16T06:52:34","indexId":"sir20195064","displayToPublicDate":"2019-08-15T10:51:50","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5064","displayTitle":"Estimated Groundwater Recharge from a Water-Budget Model Incorporating Selected Climate Projections, Island of Maui, Hawai‘i","title":"Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i","docAbstract":"<p><span>Demand for freshwater on the Island of Maui is expected to increase by 45 percent between 2015 and 2035. Groundwater availability on Maui is affected by changes in climate and agricultural irrigation. To evaluate the availability of fresh groundwater under projected future climate conditions and changing agricultural irrigation practices, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of recharge on Maui for one present-day and two future-climate scenarios. All three scenarios used 2017 land cover. The two future-climate scenarios, including one wetter than the present-day scenario and one drier than the present-day scenario, were developed using available high-resolution downscaled climate projections. The drier future scenario was developed using projections for a Representative Concentration Pathway warming scenario during 2071–99 with total radiative forcing of 8.5 Watts per square meter by the year 2100 (RCP8.5 2071–99 scenario), whereas the wetter future scenario was developed using projections for a “Special Report on Emissions Scenarios” A1B emission scenario during 2080–99 (A1B 2080–99 scenario). For the RCP8.5 2071–99 scenario, projected mean annual recharge decrease for Maui is about 172 million gallons per day, or about 14 percent less than present-day recharge, which is estimated to be 1,232 million gallons per day. Recharge for the RCP8.5 2071–99 scenario is projected to decrease in 22 of Maui’s 25 aquifer systems, which are defined by the Hawaiʻi Commission on Water Resource Management. For the A1B 2080–99 future scenario, projected mean annual recharge increase for Maui is about 144 million gallons per day, or about 12 percent more than present-day recharge. Recharge for the A1B 2080–99 scenario is projected to increase in 17 of Maui’s 25 aquifer systems. Between the two future scenarios, a total of 11 aquifer systems show similar direction in drying (Kahului, Kama‘ole, Lualaʻilua, Makawao, Olowalu, Pāʻia, Ukumehame, Waikapū) or wetting (Honopou, Kawaipapa, and Waikamoi) changes for recharge. Selectively modifying the climate inputs for the A1B 2080–99 scenario indicates that the projected changes in rainfall account for most of the projected changes in recharge for Maui’s 25 aquifer systems. However,&nbsp;</span><span>projected changes in reference evapotranspiration and forest-canopy evaporation also can account for a substantial part of the projected changes in recharge where changes in reference evapotranspiration are relatively large and where changes in forest-canopy evaporation extend across large forested areas. Projected changes in daily rainfall frequency have a relatively small but non-negligible impact on recharge estimates.</span></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195064","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply and the Pacific Regional Integrated Sciences and Assessments Program","usgsCitation":"Mair, A., Johnson A.G., Rotzoll, Kolja, and Oki, D.S., 2019, Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5064, 46 p., https://doi.org/10.3133/sir20195064.","productDescription":"Report: vi, 46 p., 3 data releases","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-100732","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":366544,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98W9ABX","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP5 RCP8.5 2071-99 scenario rainfall and 2017 land cover"},{"id":366541,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5064/sir20195064.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5064"},{"id":366542,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WSOFO","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2017 land cover"},{"id":366540,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5064/coverthb.jpg"},{"id":366543,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9437T2F","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP3 A1B 2080-99 scenario climate and 2017 land cover"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -156.7474365234375,\n              20.52478875041428\n            ],\n            [\n              -155.8685302734375,\n              20.52478875041428\n            ],\n            [\n              -155.8685302734375,\n              21.099875492701216\n            ],\n            [\n              -156.7474365234375,\n              21.099875492701216\n            ],\n            [\n              -156.7474365234375,\n              20.52478875041428\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://mail.google.com/mail/?view=cm&amp;fs=1&amp;tf=1&amp;to=dc_hi@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:dc_hi@usgs.gov\">Director</a>,<br><a href=\"http://hi.water.usgs.gov/\" data-mce-href=\"http://hi.water.usgs.gov/\">Pacific Islands Water Science Center</a><br><a href=\"https://www.usgs.gov/\" data-mce-href=\"https://www.usgs.gov/\">U.S. Geological Survey</a><br>Inouye Regional Center<br>1845 Wasp Blvd., B176<br>Honolulu, HI 96818</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Area</li><li>Future-Climate Scenarios</li><li>Water-Budget Model</li><li>Water-Budget and Groundwater-Recharge Estimates</li><li>Study Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-08-15","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Mair, Alan 0000-0003-0302-6647 dmair@usgs.gov","orcid":"https://orcid.org/0000-0003-0302-6647","contributorId":4975,"corporation":false,"usgs":true,"family":"Mair","given":"Alan","email":"dmair@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Adam G. 0000-0003-2448-5746 ajohnson@usgs.gov","orcid":"https://orcid.org/0000-0003-2448-5746","contributorId":4752,"corporation":false,"usgs":true,"family":"Johnson","given":"Adam","email":"ajohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rotzoll, Kolja 0000-0002-5910-888X","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":201087,"corporation":false,"usgs":false,"family":"Rotzoll","given":"Kolja","affiliations":[],"preferred":false,"id":765212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":207735,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765213,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70211576,"text":"70211576 - 2019 - Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","interactions":[],"lastModifiedDate":"2020-07-31T15:01:25.745893","indexId":"70211576","displayToPublicDate":"2019-08-15T09:24:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","docAbstract":"<p><span>The Rio Grande Cutthroat Trout&nbsp;</span><i>Oncorhynchus clarkii virginalis<span>&nbsp;</span></i><span>(RGCT ) occupies just 12% of its ancestral range. As the southernmost subspecies of Cutthroat Trout, we expect a warming climate to bring additional stressors to RGCT populations, such as increased stream temperatures, reduced streamflows, and increased incidence of wildfire. We developed a Bayesian network (BN ) model using site‐specific data, empirical research, and expert knowledge to estimate the probability of persistence for each of the 121 remaining RGCT conservation populations and to rank the severity of the threats they face. These inputs quantified the genetic risks (e.g., inbreeding risk and hybridization risk), population demographics (disease risk, habitat suitability, and survival), and probability of stochastic disturbances (stream drying risk and wildfire risk) in an uncertain future. We also created stream temperature and base flow discharge models coupled with regionally downscaled climate projections to predict future abiotic conditions at short‐term (2040s) and long‐term (2080s) time horizons. In the absence of active management, we predicted a decrease in the average probability of population persistence from 0.53 (current) to 0.31 (2040s) and 0.26 (2080s). Only 11% of these populations were predicted to have a greater than 75% chance of persisting to the 2080s. Threat of invasion by nonnative trout had the strongest effect on population persistence. Of the 78 populations that are already invaded or lacking complete barriers, 60% were estimated to be extirpated by 2080 and the remainder averaged only a 10% chance of persistence. In contrast, the effects of increased stream temperatures were predicted to affect the future persistence of only 9% of the 121 RGCT populations remaining, as most have been restricted to high‐elevation habitats that are cold enough to buffer against some stream warming. Our BN model provides a framework for evaluating threats and will be useful to guide management actions that are likely to provide the most benefit for long‐term conservation.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10320","usgsCitation":"Zeigler, M.P., Rogers, K., Roberts, J., Todd, A., and Fausch, K., 2019, Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future: North American Journal of Fisheries Management, v. 39, no. 5, p. 819-848, https://doi.org/10.1002/nafm.10320.","productDescription":"30 p.","startPage":"819","endPage":"848","ipdsId":"IP-080586","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.677001953125,\n              33.578014746143985\n            ],\n            [\n              -103.7548828125,\n              33.578014746143985\n            ],\n            [\n              -103.7548828125,\n              38.47079371120379\n            ],\n            [\n              -107.677001953125,\n              38.47079371120379\n            ],\n            [\n              -107.677001953125,\n              33.578014746143985\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigler, Mathew P.","contributorId":91006,"corporation":false,"usgs":true,"family":"Zeigler","given":"Mathew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Kevin B.","contributorId":220104,"corporation":false,"usgs":false,"family":"Rogers","given":"Kevin B.","affiliations":[],"preferred":false,"id":794699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James J. 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Todd, Andrew atodd@usgs.gov","contributorId":149790,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew","email":"atodd@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":794700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fausch, Kurt D. 0000-0001-5825-7560","orcid":"https://orcid.org/0000-0001-5825-7560","contributorId":198488,"corporation":false,"usgs":false,"family":"Fausch","given":"Kurt D.","affiliations":[],"preferred":false,"id":794701,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70207595,"text":"70207595 - 2019 - Predicting surf zone injuries along the Delaware coast using a Bayesian network","interactions":[],"lastModifiedDate":"2019-12-30T16:30:44","indexId":"70207595","displayToPublicDate":"2019-08-14T16:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Predicting surf zone injuries along the Delaware coast using a Bayesian network","docAbstract":"Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.","language":"English","publisher":"Springer","doi":"10.1007/s11069-019-03697-y","usgsCitation":"Doelp, M., Puleo, J., and Plant, N.G., 2019, Predicting surf zone injuries along the Delaware coast using a Bayesian network: Natural Hazards, v. 98, no. 2, p. 379-401, https://doi.org/10.1007/s11069-019-03697-y.","productDescription":"22 p.","startPage":"379","endPage":"401","ipdsId":"IP-100096","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":370880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-75.564927,39.583248],[-75.576271,39.588144],[-75.578719,39.591504],[-75.579615,39.598656],[-75.565823,39.590608],[-75.564927,39.583248]]],[[[-75.55587,39.605824],[-75.561934,39.605216],[-75.567694,39.613744],[-75.571759,39.623584],[-75.570798,39.626768],[-75.559446,39.629812],[-75.559102,39.629056],[-75.559614,39.624208],[-75.558446,39.617296],[-75.556878,39.612144],[-75.557502,39.609184],[-75.556734,39.606688],[-75.55587,39.605824]]],[[[-75.594846,39.837286],[-75.593666,39.837455],[-75.593082,39.8375],[-75.5799,39.838522],[-75.579849,39.838526],[-75.570464,39.839007],[-75.539346,39.838211],[-75.518444,39.836311],[-75.498843,39.833312],[-75.481242,39.829112],[-75.463341,39.823812],[-75.45374,39.820312],[-75.428038,39.809212],[-75.415041,39.801786],[-75.405337,39.796213],[-75.437938,39.783413],[-75.440909,39.780831],[-75.448639,39.774113],[-75.448135,39.773969],[-75.447339,39.773313],[-75.452339,39.769013],[-75.459439,39.765813],[-75.463339,39.761213],[-75.463039,39.758313],[-75.466249,39.750769],[-75.466263,39.750737],[-75.469239,39.743613],[-75.474168,39.735473],[-75.475384,39.731057],[-75.47544,39.728713],[-75.47724,39.724713],[-75.477432,39.720561],[-75.476888,39.718337],[-75.47764,39.715013],[-75.47894,39.713813],[-75.481741,39.714546],[-75.483141,39.715513],[-75.485241,39.715813],[-75.488553,39.714833],[-75.491341,39.711113],[-75.496241,39.701413],[-75.504042,39.698313],[-75.507162,39.696961],[-75.509042,39.694513],[-75.509742,39.686113],[-75.529744,39.692613],[-75.562246,39.656712],[-75.587147,39.651012],[-75.611969,39.621968],[-75.613153,39.62096],[-75.613377,39.620288],[-75.614065,39.61832],[-75.614929,39.615952],[-75.614273,39.61464],[-75.613345,39.613056],[-75.613665,39.61256],[-75.613233,39.607408],[-75.613477,39.606861],[-75.613473,39.606832],[-75.613793,39.606192],[-75.611905,39.597568],[-75.611873,39.597408],[-75.60464,39.58992],[-75.603584,39.58896],[-75.592224,39.583568],[-75.591984,39.583248],[-75.587744,39.580672],[-75.5872,39.580256],[-75.586608,39.57888],[-75.586016,39.578448],[-75.571599,39.567728],[-75.570783,39.56728],[-75.563034,39.56224],[-75.564649,39.559922],[-75.565636,39.558509],[-75.569359,39.540589],[-75.569418,39.539124],[-75.570362,39.527223],[-75.560728,39.520472],[-75.566933,39.508273],[-75.576436,39.509195],[-75.587729,39.496353],[-75.587729,39.495369],[-75.593068,39.479186],[-75.593068,39.477996],[-75.589901,39.462022],[-75.589439,39.460812],[-75.580185,39.450786],[-75.578914,39.44788],[-75.570985,39.442486],[-75.57183,39.438897],[-75.55589,39.430351],[-75.538512,39.416502],[-75.535977,39.409384],[-75.523583,39.391583],[-75.521682,39.387871],[-75.512996,39.366153],[-75.512372,39.365656],[-75.511788,39.365191],[-75.505276,39.359169],[-75.494158,39.354613],[-75.491797,39.351845],[-75.494122,39.34658],[-75.493148,39.345527],[-75.491688,39.343963],[-75.490377,39.342818],[-75.479845,39.337472],[-75.479963,39.336577],[-75.469324,39.33082],[-75.460423,39.328236],[-75.439027,39.313384],[-75.436936,39.309379],[-75.435551,39.297546],[-75.435374,39.296676],[-75.427953,39.285049],[-75.408376,39.264698],[-75.402964,39.254626],[-75.404823,39.245898],[-75.405927,39.243631],[-75.405716,39.223834],[-75.404745,39.222666],[-75.396892,39.216141],[-75.393015,39.204512],[-75.39479,39.188354],[-75.398584,39.186616],[-75.400144,39.186456],[-75.408266,39.174625],[-75.410625,39.156246],[-75.401193,39.088762],[-75.402035,39.066885],[-75.400294,39.065645],[-75.395806,39.059211],[-75.396277,39.057884],[-75.387914,39.051174],[-75.379873,39.04879],[-75.345763,39.024857],[-75.34089,39.01996],[-75.318354,38.988191],[-75.314951,38.980775],[-75.311607,38.967637],[-75.312546,38.951065],[-75.312546,38.94928],[-75.311923,38.945917],[-75.311882,38.945698],[-75.311542,38.944633],[-75.302552,38.939002],[-75.312282,38.924594],[-75.304078,38.91316],[-75.263115,38.877351],[-75.232029,38.844254],[-75.205329,38.823386],[-75.190552,38.806861],[-75.160748,38.791224],[-75.159022,38.790193],[-75.134022,38.782242],[-75.113331,38.782998],[-75.097103,38.788703],[-75.093654,38.793992],[-75.097197,38.803101],[-75.093805,38.803812],[-75.089473,38.797198],[-75.082153,38.772157],[-75.080217,38.750112],[-75.079221,38.738238],[-75.06551,38.66103],[-75.065217,38.632394],[-75.06192,38.608869],[-75.061259,38.608602],[-75.060478,38.608012],[-75.060032,38.607709],[-75.049748,38.486387],[-75.048939,38.451263],[-75.049268,38.451264],[-75.05251,38.451273],[-75.053483,38.451274],[-75.064719,38.451289],[-75.066327,38.451291],[-75.069909,38.451276],[-75.070356,38.451276],[-75.085814,38.451258],[-75.088281,38.451256],[-75.089649,38.451254],[-75.141894,38.451196],[-75.185413,38.451013],[-75.252723,38.451397],[-75.26035,38.451492],[-75.341247,38.45197],[-75.34125,38.45197],[-75.355797,38.452008],[-75.371054,38.452107],[-75.393563,38.452114],[-75.394786,38.45216],[-75.41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 \"}}]}","volume":"98","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Doelp, Matthew","contributorId":221558,"corporation":false,"usgs":false,"family":"Doelp","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":778659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puleo, Jack A.","contributorId":108287,"corporation":false,"usgs":true,"family":"Puleo","given":"Jack A.","affiliations":[],"preferred":false,"id":778660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":778661,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204738,"text":"sir20195082 - 2019 - Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","interactions":[],"lastModifiedDate":"2019-10-07T16:51:39","indexId":"sir20195082","displayToPublicDate":"2019-08-14T09:51:12","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5082","displayTitle":"Characterization of Big Chino Subbasin Hydrogeology near Paulden, Arizona, Using Controlled Source Audio-Frequency Magnetotelluric Surveys","title":"Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","docAbstract":"<p class=\"m_1329192573577678645m_7553462186234747730gmail-p1\">The Big Chino subbasin is located in central-northwest Arizona in the transition zone between the Colorado Plateau and the Basin and Range Province. The controlled source audio-frequency magnetotelluric (CSAMT) geophysical method, a low-impact, non-intrusive, electrical resistance sounding technique, was used to evaluate the subsurface hydrogeology of the southern third of the Big Chino subbasin. The Big Chino subbasin is a northwest-trending, late Tertiary graben bordered by the Big Chino Fault along its northeast flank where there is as much as 1,100 meters of displacement. The main water-bearing stratigraphic unit of the basin is Tertiary alluvial-fill sediment. The Devonian Martin Formation provides water to wells near Drake and the Mississippian Redwall Limestone provides water to wells east of the basin and in the Paulden area.</p><p class=\"m_1329192573577678645m_7553462186234747730gmail-p1\">The purpose of the CSAMT surveys was to improve the conceptual model of the aquifer by constraining the basin geometry and identifying stratigraphic units and their subsurface extents. CSAMT methods were used to map the subsurface along 100 kilometers (62 miles) of survey lines across the southern third of the subbasin. Of 21 survey lines, 14 were west of the town of Paulden and another 7 were east of Paulden. Data were cleaned and prepared for entry into Zonge SCS2D software and then inverted to provide a two-dimensional resistivity profile for each survey line. Final inversion models representing the best fit to measured data were compared to driller’s logs or borehole data where present.</p><p class=\"m_1329192573577678645m_7553462186234747730gmail-p1\">Data from the CSAMT lines west and north of Paulden are consistent with thicker alluvial basin deposits that range from 100 meters thick to a few hundred meters thick. Data from the CSAMT lines east of Paulden are consistent with thinner alluvial and basalt deposits overlying Paleozoic Martin Formation and Redwall Limestone, Tapeats Sandstone, and Precambrian granite and schist.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195082","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Macy, J.P., Gungle, B., and Mason, J.P., 2019, Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotellursurveys: U.S. Geological Survey Scientific Investigations Report 2019–5082, 39 p., https://doi.org/10.3133/sir20195082.\nic ","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-098264","costCenters":[{"id":128,"text":"Arizona Water Science 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href=\"mailto:leenhout@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:leenhout@usgs.gov\">Director</a>,<br><a href=\"https://az.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://az.water.usgs.gov/\">Arizona Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Methods</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2019-08-14","noUsgsAuthors":false,"publicationDate":"2019-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Macy, Jamie P. 0000-0003-3443-0079 jpmacy@usgs.gov","orcid":"https://orcid.org/0000-0003-3443-0079","contributorId":2173,"corporation":false,"usgs":true,"family":"Macy","given":"Jamie","email":"jpmacy@usgs.gov","middleInitial":"P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":768253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gungle, Bruce 0000-0001-6406-1206 bgungle@usgs.gov","orcid":"https://orcid.org/0000-0001-6406-1206","contributorId":2237,"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":true,"id":768254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mason, Jon P. 0000-0003-0576-5494 jmason@usgs.gov","orcid":"https://orcid.org/0000-0003-0576-5494","contributorId":196854,"corporation":false,"usgs":true,"family":"Mason","given":"Jon","email":"jmason@usgs.gov","middleInitial":"P.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":768255,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227931,"text":"70227931 - 2019 - Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie","interactions":[],"lastModifiedDate":"2022-02-03T12:03:54.22088","indexId":"70227931","displayToPublicDate":"2019-08-13T15:14:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Management regime and habitat response influence abundance of regal fritillary (<i>Speyeria idalia </i>) in tallgrass prairie","title":"Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie","docAbstract":"<p><span>The &gt;2,570,000-ha Flint Hills ecoregion of Kansas, USA, harbors the largest remaining contiguous tract of tallgrass prairie in North America, a unique system, as the remainder of North America's tallgrass prairie has succumbed to development and conversion. Consequently, the loss and degradation of tallgrass prairie has reduced populations of many North American prairie-obligate species including the regal fritillary (</span><i>Speyeria idalia</i><span>) butterfly. Population abundance and occupied range of regal fritillary have declined &gt;99%, restricting many populations to isolated, remnant patches of tallgrass prairie. Such extensive decline has resulted in consideration of the regal fritillary for protection under the Endangered Species Act. Although it is widely accepted that management practices such as fire, grazing, and haying are necessary to maintain prairie ecosystems, reported responses by regal fritillary to these management regimes have been ambiguous. We tested effects of prescribed fire across short, moderate, and long fire-return intervals as well as grazing and haying management treatments on regal fritillary density. We also tested the relative influence of habitat characteristics created by these management regimes by measuring density of an obligate host plant (</span><i>Viola</i><span>&nbsp;spp.) and canopy cover of woody vegetation, grasses, forbs/ferns, bare ground, and litter. We found density was at least 1.6 times greater in sites burned with a moderate fire-return interval vs. sites burned with short and long fire-return intervals. Overall management regardless of fire-return interval did not have an effect on density. Percent cover of grass had the strongest positive association, while percent cover of woody vegetation had the greatest negative effect on density. Our results indicate that patch-burning is a viable and perhaps even ideal management strategy for regal fritillary in tallgrass prairie landscapes. Additionally, these results elucidate the importance of fire, particularly when applied at moderate-return intervals to regal fritillary, and corroborate a growing suite of studies that suggest fire is perhaps not as detrimental to populations of regal fritillary as previously believed.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2845","usgsCitation":"McCullough, K., Albanese, G., Haukos, D.A., Ricketts, A., and Stratton, S., 2019, Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie: Ecosphere, v. 10, no. 8, e02845, 18 p., https://doi.org/10.1002/ecs2.2845.","productDescription":"e02845, 18 p.","ipdsId":"IP-094927","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2845","text":"Publisher Index Page"},{"id":395312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Flint Hills","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.18505859374999,\n              37.020098201368114\n            ],\n            [\n              -96.3720703125,\n              36.96744946416934\n            ],\n            [\n              -95.47119140625,\n              36.932330061503144\n            ],\n            [\n              -95.47119140625,\n              39.80853604144591\n            ],\n            [\n              -97.1630859375,\n              39.80853604144591\n            ],\n            [\n              -97.18505859374999,\n              37.020098201368114\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"McCullough, K.","contributorId":273122,"corporation":false,"usgs":false,"family":"McCullough","given":"K.","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albanese, G.","contributorId":67722,"corporation":false,"usgs":true,"family":"Albanese","given":"G.","email":"","affiliations":[],"preferred":false,"id":832759,"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":832594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricketts, A.M.","contributorId":273124,"corporation":false,"usgs":false,"family":"Ricketts","given":"A.M.","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stratton, S.","contributorId":273125,"corporation":false,"usgs":false,"family":"Stratton","given":"S.","email":"","affiliations":[{"id":56426,"text":"fort riley","active":true,"usgs":false}],"preferred":false,"id":832761,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70206000,"text":"70206000 - 2019 - Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","interactions":[],"lastModifiedDate":"2019-10-17T07:00:50","indexId":"70206000","displayToPublicDate":"2019-08-13T14:44:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","docAbstract":"The 9 month long 2011-2012 eruption of Cordon Caulle (Southern Andes, Chile) is the best instrumentally recorded rhyolitic eruption to date and the first time that the effusion of a rhyolitic flow has been observed in detail. We use Interferometric Synthetic Aperture Radar (InSAR), with time-lapse DEMs and numerical models to study the dynamics of coupled magma reservoir deflation and lava effusion. InSAR recorded ~2.2-2.5 m of deflation after the first three days of the eruption, which can be modeled using a spheroidal magma reservoir at a depth of ∼5 km, ∼20 km long, and with a pressure drop of ~20-30 MPa. The source is elongated in the NW-SE direction and its large dimensions imply a large plumbing system spanning neighboring volcanoes and active throughout the eruption, with a slight change halfway through the effusive phase. TanDEM-X DEMs record the extrusion of both the rhyolitic lava flow and the intrusion of a shallow laccolith around the eruptive vent, with a total volume of ~1.2 km3 DRE. The laccolith was emplaced during the first month of the eruption, during both the eruption explosive and effusive stages. Both the reservoir pressure drop and the extruded volume time series follow quasi-exponential trends, and can be explained by a model that couples the reservoir pres- sure decrease, time- and pressured ependent variations in the magma properties inside of the reservoir, and conduit flow. This model predicts both the temporal evolution and amplitude of both time series, and a magma compressibility of ∼10^−10 Pa−1, half the compressibility of the magma of the sub-Plinian explosive phase. Further, we estimate that the reservoir contained 1-3 wt.% dissolved H2O at the onset of lava effusion, with no exsolved CO2 and H2O in the reservoir throughout the eruption. This is in accord with a magma that was significantly degassed after the explosive phase. These remaining volatiles might have been responsible for magma fragmentation, consistent with the hybrid explosive and effusive style observed during the waning of the eruption.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.115736","usgsCitation":"Delgado, F., Julia Kubanek, Anderson, K.R., Paul Lundgren, and Pritchard, M.E., 2019, Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption: Earth and Planetary Science Letters, v. 524, p. 1-14, https://doi.org/10.1016/j.epsl.2019.115736.","productDescription":"115736, 14p.","startPage":"1","endPage":"14","ipdsId":"IP-102193","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2019.115736","text":"Publisher Index Page"},{"id":368337,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0012821X19304285"},{"id":368348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Cordón Caulle","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -72.59765625,\n              -42.779275360241904\n            ],\n            [\n              -71.69677734375,\n              -42.779275360241904\n            ],\n            [\n              -71.69677734375,\n              -41.4262531950727\n            ],\n            [\n              -72.59765625,\n              -41.4262531950727\n            ],\n            [\n              -72.59765625,\n              -42.779275360241904\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"524","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Delgado, Francisco","contributorId":219817,"corporation":false,"usgs":false,"family":"Delgado","given":"Francisco","email":"","affiliations":[{"id":40075,"text":"Institute de Physique du Globe de Paris","active":true,"usgs":false}],"preferred":false,"id":773252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Julia Kubanek","contributorId":219818,"corporation":false,"usgs":false,"family":"Julia Kubanek","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":773253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":773251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul Lundgren","contributorId":219819,"corporation":false,"usgs":false,"family":"Paul Lundgren","affiliations":[{"id":32904,"text":"Jet Propulsion Lab, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":773254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pritchard, Matthew E.","contributorId":219820,"corporation":false,"usgs":false,"family":"Pritchard","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":773255,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208120,"text":"70208120 - 2019 - Global positioning system tracking devices can decrease Greater Sage-Grouse survival","interactions":[],"lastModifiedDate":"2020-01-29T16:27:32","indexId":"70208120","displayToPublicDate":"2019-08-13T13:06:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Global positioning system tracking devices can decrease Greater Sage-Grouse survival","docAbstract":"<p><span>Reliable demographic estimates hinge on the assumption that marking animals does not alter their behavior, reproduction, or survival. Violations can bias inference and are especially egregious for species of high conservation concern. Global positioning system (GPS) devices represent a recent technological advancement that has contributed greatly to avian ecological studies compared with traditionally used very high frequency (VHF) radio transmitters, but may affect demographic rates differently than VHF transmitters. We compared survival between VHF (necklace attachment) and GPS (rump-mounted attachment) devices from &gt;1,100 Greater Sage-Grouse (</span><i>Centrocercus urophasianus</i><span>), a species of high conservation concern, across multiple populations within California and Nevada. We found lower survival for GPS-marked compared to VHF-marked sage-grouse across most sex, age, and seasonal comparisons. Estimates of annual survival for GPS-marked sage-grouse were 0.55–0.86 times that of VHF-marked birds with considerable variation among sex and age classes. Differences in survival could be attributed to features associated with GPS devices, including greater weight, position of attachment (e.g., rump-mount harness), and a semi-reflective solar panel. In a post hoc analysis, we evaluated additive and interactive effects between device type (GPS vs. VHF) and transmitter mass as a proportion of body mass (PBM). While the device type effect alone was the best model, the PBM interaction also had support. For GPS devices, survival decreased with increasing PBM, whereas PBM effects were not found for VHF. We attributed differences in PBM effect to placement of transmitters on sage-grouse, as weight of GPS devices was positioned rearward. This information can help managers and researchers weigh costs and benefits of GPS-based monitoring. Our results indicate demographic data collected from GPS devices should be interpreted with caution, and use of these devices should be tailored to specific ecological questions. Future research aimed at investigating behavioral impacts and GPS designs that reduce adverse impacts on survival would be beneficial.</span></p>","language":"English","publisher":"American Ornithological Society","doi":"10.1093/condor/duz032","usgsCitation":"Severson, J.P., Coates, P.S., Prochazka, B.G., Ricca, M.A., Casazza, M.L., and Delahunty, D.J., 2019, Global positioning system tracking devices can decrease Greater Sage-Grouse survival: The Condor, v. 121, no. 3, duz032, 15 p., https://doi.org/10.1093/condor/duz032.","productDescription":"duz032, 15 p.","ipdsId":"IP-106346","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":460307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz032","text":"Publisher Index Page"},{"id":371649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"121","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delahunty, David J","contributorId":221820,"corporation":false,"usgs":false,"family":"Delahunty","given":"David","email":"","middleInitial":"J","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":780564,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204877,"text":"70204877 - 2019 - A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","interactions":[],"lastModifiedDate":"2019-08-21T10:31:44","indexId":"70204877","displayToPublicDate":"2019-08-13T10:22:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","docAbstract":"<p><span>Worldwide, eutrophication is threatening lake ecosystems. To support lake management numerous eutrophication models have been developed. Diverse research questions in a wide range of lake ecosystems are addressed by these models. The established models are based on three key approaches: the empirical approach that employs field surveys, the theoretical approach in which models based on first principles are tested against lab experiments, and the process-based approach that uses parameters and functions representing detailed biogeochemical processes. These approaches have led to an accumulation of field-, lab- and model-based knowledge, respectively. Linking these sources of knowledge would benefit lake management by exploiting complementary information; however, the development of a simple tool that links these approaches was hampered by their large differences in scale and complexity. Here we propose a Generically Parameterized Lake eutrophication model (GPLake) that links field-, lab- and model-based knowledge and can be used to make a first diagnosis of lake water quality. We derived GPLake from consumer-resource theory by the principle that lacustrine phytoplankton is typically limited by two resources: nutrients and light. These limitations are captured in two generic parameters that shape the nutrient to chlorophyll-</span><i>a</i><span>&nbsp;relations. Next, we parameterized GPLake, using knowledge from empirical, theoretical, and process-based approaches. GPLake generic parameters were found to scale in a comparable manner across data sources. Finally, we show that GPLake can be applied as a simple tool that provides lake managers with a first diagnosis of the limiting factor and lake water quality, using only the parameters for lake depth, residence time and current nutrient loading. With this first-order assessment, lake managers can easily assess measures such as reducing nutrient load, decreasing residence time or changing depth before spending money on field-, lab- or model- experiments to support lake management.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133887","usgsCitation":"Chang, M., Teurlincx, S., DeAngelis, D.L., Janse, J.H., Troost, T.A., van Wijk, D., Mooij, W.M., and Janssen, A., 2019, A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge: Science of the Total Environment, v. 695, 133887, 11 p., https://doi.org/10.1016/j.scitotenv.2019.133887.","productDescription":"133887, 11 p.","ipdsId":"IP-104765","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.133887","text":"Publisher Index Page"},{"id":366781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"695","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chang, Manqi","contributorId":218274,"corporation":false,"usgs":false,"family":"Chang","given":"Manqi","email":"","affiliations":[],"preferred":false,"id":768853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teurlincx, Sven","contributorId":218275,"corporation":false,"usgs":false,"family":"Teurlincx","given":"Sven","email":"","affiliations":[],"preferred":false,"id":768854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":768855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janse, Jan H.","contributorId":215555,"corporation":false,"usgs":false,"family":"Janse","given":"Jan","email":"","middleInitial":"H.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Troost, Tineke A.","contributorId":218276,"corporation":false,"usgs":false,"family":"Troost","given":"Tineke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Wijk, Dianneke","contributorId":215557,"corporation":false,"usgs":false,"family":"van Wijk","given":"Dianneke","email":"","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mooij, Wolf M.","contributorId":215556,"corporation":false,"usgs":false,"family":"Mooij","given":"Wolf","email":"","middleInitial":"M.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Janssen, Annette B. G.","contributorId":200076,"corporation":false,"usgs":false,"family":"Janssen","given":"Annette B. G.","affiliations":[],"preferred":false,"id":768860,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70205307,"text":"70205307 - 2019 - Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface","interactions":[],"lastModifiedDate":"2019-09-13T14:38:35","indexId":"70205307","displayToPublicDate":"2019-08-12T14:35:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3047,"text":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface","docAbstract":"<p><span>Managing pathogen spillover at the wildlife–livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host–pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife–livestock interface.</span></p>","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2018.0343","usgsCitation":"Manlove, K., Sam, L., Borremans, B., Cassirer, E.F., Miller, R.S., Pepin, K., Besser, T.E., and Cross, P., 2019, Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 374, no. 1782, 20180343, https://doi.org/10.1098/rstb.2018.0343.","productDescription":"20180343","ipdsId":"IP-103606","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467372,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6711312","text":"Publisher Index Page"},{"id":367417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1782","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Manlove, K.R. 0000-0002-7200-5236","orcid":"https://orcid.org/0000-0002-7200-5236","contributorId":218981,"corporation":false,"usgs":false,"family":"Manlove","given":"K.R.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":770820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sam, L.","contributorId":218982,"corporation":false,"usgs":false,"family":"Sam","given":"L.","email":"","affiliations":[],"preferred":false,"id":770821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borremans, B. 0000-0002-7779-4107","orcid":"https://orcid.org/0000-0002-7779-4107","contributorId":218983,"corporation":false,"usgs":false,"family":"Borremans","given":"B.","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":770822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cassirer, E. Frances","contributorId":198303,"corporation":false,"usgs":false,"family":"Cassirer","given":"E.","email":"","middleInitial":"Frances","affiliations":[],"preferred":false,"id":770826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, R. S.","contributorId":172739,"corporation":false,"usgs":false,"family":"Miller","given":"R.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":770823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pepin, K. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":218984,"corporation":false,"usgs":false,"family":"Pepin","given":"K.","email":"","affiliations":[{"id":39647,"text":"USDA-APHIS","active":true,"usgs":false}],"preferred":false,"id":770824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Besser, T. E. 0000-0003-0449-1989","orcid":"https://orcid.org/0000-0003-0449-1989","contributorId":215110,"corporation":false,"usgs":false,"family":"Besser","given":"T.","email":"","middleInitial":"E.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":770825,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cross, Paul","contributorId":218980,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":770819,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70205208,"text":"70205208 - 2019 - Confronting models with data: The challenges of estimating disease spillover","interactions":[],"lastModifiedDate":"2019-09-06T10:33:08","indexId":"70205208","displayToPublicDate":"2019-08-12T10:29:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Confronting models with data: The challenges of estimating disease spillover","docAbstract":"For pathogens known to transmit across host species, strategic investment in disease control requires knowledge about where and when spillover transmission is likely. One approach to estimating spillover is to directly correlate observed spillover events with covariates. An alternative is to mechanistically combine information on host density, distribution, and pathogen prevalence to predict where and when spillover events are expected to occur. We use several case studies at the wildlife-livestock disease interface to highlight the challenges, and potential solutions, to estimating spatio-temporal variation in spillover risk. Datasets on multiple host species often do not align in space, time or resolution, and may have no estimates of observation error. Linking these datasets requires they be related to a common spatial and temporal resolution and appropriately propagating errors in predictions can be difficult. Hierarchical models are one potential solution, but for fine-resolution predictions at broad spatial scales many models become computationally challenging. Despite these limitations, the confrontation of mechanistic predictions with observed events is an important avenue for developing a better understanding of pathogen spillover. Systems where data have been collected at all levels in the spillover process are rare, or non-existent, and require investment and sustained effort across disciplines.","language":"English","publisher":"The Royal Society","doi":"10.1098/rstb.2018.0435","usgsCitation":"Cross, P.C., Prosser, D., Ramey, A.M., Hanks, E.M., and Pepin, K., 2019, Confronting models with data: The challenges of estimating disease spillover: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 374, no. 1782, 20180435, https://doi.org/10.1098/rstb.2018.0435.","productDescription":"20180435","ipdsId":"IP-103613","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6711303","text":"Publisher Index Page"},{"id":367254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1782","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":770369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":770371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Ephraim M. 0000-0003-0345-7164","orcid":"https://orcid.org/0000-0003-0345-7164","contributorId":210840,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","email":"","middleInitial":"M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":770372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pepin, Kim M. 0000-0002-9931-8312","orcid":"https://orcid.org/0000-0002-9931-8312","contributorId":187441,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim M.","affiliations":[],"preferred":false,"id":770373,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204739,"text":"70204739 - 2019 - Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling","interactions":[],"lastModifiedDate":"2019-08-15T09:49:41","indexId":"70204739","displayToPublicDate":"2019-08-12T09:46:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling","docAbstract":"Fire science emphasizes that mitigation actions on residential property, including structural hardening and maintaining defensible space, can reduce the risk of wildfire at a home. Accordingly, a rich body of social science literature investigates the determinants of wildfire risk mitigation behaviors of residents living in fire-prone areas. Here, we investigate relationships among wildfire hazards, residents’ risk perceptions, and conditions associated with mitigation actions using a combination of simulated wildfire conditions, household survey responses, and professionally assessed parcel characteristic data. We estimate a simultaneous model of these data that accounts for potential direct feedbacks between risk perceptions and parcel-level conditions. We also compare the use of self-reported versus assessed parcel-level data for estimating these relationships. Our analysis relies on paired survey and assessment data for approximately 2000 homes in western Colorado. Our simultaneous model demonstrates dual-directional interactions between risk perceptions and conditions associated with mitigation actions, with important implications for inference from simpler approaches. In addition to improving general understanding of decision-making about risk and natural hazards, our findings can support the effectiveness of publicly supported programs intended to encourage mitigation to reduce society’s overall wildfire risk.","language":"English","publisher":"MDPI","doi":"10.3390/fire2030046","usgsCitation":"Meldrum, J., Brenkert-Smith, H., Champ, P.A., Gomez, J., Falk, L.C., and Barth, C.M., 2019, Interactions between resident risk perceptions and wildfire risk mitigation: Evidence from simultaneous equations modeling: Fire, v. 2, no. 3, 46, 18 p., https://doi.org/10.3390/fire2030046.","productDescription":"46, 18 p.","ipdsId":"IP-109622","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire2030046","text":"Publisher Index Page"},{"id":366559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":768256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":768257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Champ, Patricia A.","contributorId":195486,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomez, Jamie","contributorId":218078,"corporation":false,"usgs":false,"family":"Gomez","given":"Jamie","email":"","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":768259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falk, Lilia C.","contributorId":210655,"corporation":false,"usgs":false,"family":"Falk","given":"Lilia","email":"","middleInitial":"C.","affiliations":[{"id":38125,"text":"West Region Wildfire Council","active":true,"usgs":false}],"preferred":false,"id":768260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barth, Christopher M.","contributorId":195487,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":768261,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70203978,"text":"sir20195063 - 2019 - Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques","interactions":[],"lastModifiedDate":"2024-01-22T22:05:30.263439","indexId":"sir20195063","displayToPublicDate":"2019-08-12T06:05:02","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5063","displayTitle":"Estimating Potential Wetland Extent along Selected River Reaches in Indiana using Streamflow Statistics and Flood-Inundation Mapping Techniques","title":"Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques","docAbstract":"<p>In this study potential wetland extents were estimated for 12 river reaches covering about 750 river miles in Indiana and parts of Illinois and Ohio. The study was completed by the U.S. Geological Survey in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service. This study follows and adds to the work completed in a pilot study and determines that potential wetland extents can be estimated using streamflow statistics, streamgage data, and flood-inundation mapping techniques.</p><p>The study was designed to assist in the Agricultural Conservation Easement Program. The Agricultural Conservation Easement Program is a voluntary program administered by the Natural Resources Conservation Service that provides technical and financial assistance to private landowners and Tribes to restore, protect, and enhance wetlands in exchange for retiring eligible land from agriculture. For a site to be eligible for wetland restoration, it should be in a zone with sustained or frequent flooding. This study calculated the flows that lasted for a period of 7 consecutive days on average at least once every 2 years (a value termed the “7MQ2”) for all the U.S. Geological Survey streamgages within the selected river reaches. These 7MQ2 flows were related to the stage-discharge tables for each streamgage, and a corresponding water-surface elevation was determined. Maps of estimated wetland extent were prepared using the 7MQ2 inundation elevation data in conjunction with bare-earth land-surface elevation data made publicly available through the online geospatial data clearinghouses of Indiana, Illinois, and Ohio. Flood-inundation mapping techniques were applied with the aid of geographic information system software to generate water-surface planes that represent inundation elevations associated with the 7MQ2 streamflow. Land-surface elevation data from high-resolution digital elevation models were subtracted from the water-surface planes to produce maps of wetland extent. The 12 map products, including datasets and geoprocessing tools, produced from this study will aid the National Resources Conservation Service and its partners with the onsite inundation-zone verification in agricultural land for potential restoration.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195063","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service","usgsCitation":"Fowler, K.K., Sperl, B.J., and Kim, M.H., 2019, Estimating potential wetland extent along selected river reaches in Indiana using streamflow statistics and flood-inundation mapping techniques: U.S. Geological Survey Scientific Investigations Report 2019–5063, 12 p., https://doi.org/10.3133/sir20195063.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097069","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":366436,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LGXDJ8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets related to wetland extent maps for 12 stream reaches covering approximately 750 river miles in Indiana"},{"id":424708,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108893.htm","linkFileType":{"id":5,"text":"html"},"description":"108893"},{"id":424707,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108892.htm","linkFileType":{"id":5,"text":"html"},"description":"108892"},{"id":366472,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://wim.usgs.gov/geonarrative/indianawetlands/","text":"USGS story map","linkHelpText":"– Geo-narrative"},{"id":366438,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5063/coverthb2.jpg"},{"id":366435,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5063/sir20195063.pdf","text":"Report","size":"2.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5063"}],"country":"United States","state":"Illinois, Indiana, Ohio","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.582763671875,\n              37.21283151445594\n            ],\n            [\n              -83.924560546875,\n              37.21283151445594\n            ],\n            [\n              -83.924560546875,\n              41.934976500546604\n            ],\n            [\n              -88.582763671875,\n              41.934976500546604\n            ],\n            [\n              -88.582763671875,\n              37.21283151445594\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/oki-water\" href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>5957 Lakeside Boulevard<br>Indianapolis, IN 46278</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Transferability of Methods</li><li>Summary</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2019-08-12","noUsgsAuthors":false,"publicationDate":"2019-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Fowler, Kathleen K. 0000-0002-0107-3848 kkfowler@usgs.gov","orcid":"https://orcid.org/0000-0002-0107-3848","contributorId":2439,"corporation":false,"usgs":true,"family":"Fowler","given":"Kathleen","email":"kkfowler@usgs.gov","middleInitial":"K.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sperl, Benjamin J. 0000-0002-3294-6189 bsperl@usgs.gov","orcid":"https://orcid.org/0000-0002-3294-6189","contributorId":196659,"corporation":false,"usgs":true,"family":"Sperl","given":"Benjamin J.","email":"bsperl@usgs.gov","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":false,"id":765067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Moon H. 0000-0002-4328-8409 mkim@usgs.gov","orcid":"https://orcid.org/0000-0002-4328-8409","contributorId":204039,"corporation":false,"usgs":true,"family":"Kim","given":"Moon H.","email":"mkim@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765068,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205112,"text":"70205112 - 2019 - The effects of restored hydrologic connectivity on floodplain trapping vs. release of phosphorus, nitrogen, and sediment along the Pocomoke River, Maryland USA","interactions":[],"lastModifiedDate":"2019-09-03T17:34:57","indexId":"70205112","displayToPublicDate":"2019-08-09T17:24:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"The effects of restored hydrologic connectivity on floodplain trapping vs. release of phosphorus, nitrogen, and sediment along the Pocomoke River, Maryland USA","docAbstract":"River channelization and artificial levees have decreased the hydrologic connectivity of river-floodplain systems around the world. In response, restoration through enhancing connectivity has been advocated to improve the functions of floodplains, but uncertain benefits and the possibility of phosphate release from re-flooded soils has limited implementation. In this study, we measured change in floodplain P, N, and sediment mass balances after restoration along channelized reaches in the lowland Pocomoke River, Maryland USA. Two floodplains (one headwater, one mainstem) restored through partial levee breaches were compared to two additional mainstem floodplains (one natural unchannelized, one unrestored channelized). Potential soluble reactive P (SRP) release from soil cores during experimental laboratory floods; soil P, Fe, and Al fractionation; and deposition and P and N content of sediment were measured before and after the restoration period, as well as in situ inputs and release of SRP and dissolved inorganic N from soils after restorations. Potential SRP release, during both the before and after restoration period, was greatest at the channelized mainstem and restored mainstem sites, lower at the restored headwater site, and small at the natural mainstem site. Both restored sites had smaller potential SRP release after restoration compared to before restoration. In situ SRP release slightly exceeded inputs to soils at connected sites during the post-restoration period, with less net release at the restored sites compared to the natural mainstem site. The magnitude of gross and net SRP release from soils in the field was smaller than, and uncorrelated with, potential SRP release estimated from laboratory experimental floods. Gross soil SRP release rates in the field were predictable using the ratio of soil oxalate-extractable P/Al. Sedimentation inputs of P and N increased at all sites during the post-restoration period, with rates at restored sites intermediate compared to the much higher rates at the natural mainstem site and somewhat lower rates at the channelized mainstem site. These sediment inputs of nutrients were much larger than rates of inorganic P and N release from soils, indicating net trapping of P and N after restoration. Restoring floodplain hydrologic connectivity showed moderate success at increasing the trapping of P, N, and sediment, with relatively little phosphate release, and therefore improving water quality.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2019.08.002","usgsCitation":"Noe, G.E., Boomer, K., Gillespie, J., Hupp, C.R., Martin-Alciati, M., Floro, K., Schenk, E.R., Jacobs, A.K., and Strano, S., 2019, The effects of restored hydrologic connectivity on floodplain trapping vs. release of phosphorus, nitrogen, and sediment along the Pocomoke River, Maryland USA: Ecological Engineering, v. 138, p. 334-352, https://doi.org/10.1016/j.ecoleng.2019.08.002.","productDescription":"19 p.","startPage":"334","endPage":"352","ipdsId":"IP-106687","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2019.08.002","text":"Publisher Index Page"},{"id":367160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Pocomoke River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.5859375,\n              38.0091482264894\n            ],\n            [\n              -75.3717041015625,\n              38.08268954483802\n            ],\n            [\n              -75.29891967773438,\n              38.13887716726548\n            ],\n            [\n              -75.19454956054688,\n              38.28885871419223\n            ],\n            [\n              -75.2838134765625,\n              38.43960662292255\n            ],\n            [\n              -75.35110473632812,\n              38.4514377951069\n            ],\n            [\n              -75.4046630859375,\n              38.4514377951069\n            ],\n            [\n              -75.43899536132812,\n              38.429925130409366\n            ],\n            [\n              -75.53237915039062,\n              38.24680876017446\n            ],\n            [\n              -75.61203002929688,\n              38.212288054388175\n            ],\n            [\n              -75.68206787109375,\n              38.04052046968823\n            ],\n            [\n              -75.66696166992186,\n              37.96152331396614\n            ],\n            [\n              -75.5859375,\n              38.0091482264894\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"138","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":770071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boomer, Kathy","contributorId":218733,"corporation":false,"usgs":false,"family":"Boomer","given":"Kathy","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":770072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gillespie, Jaimie 0000-0002-6483-0359","orcid":"https://orcid.org/0000-0002-6483-0359","contributorId":202016,"corporation":false,"usgs":true,"family":"Gillespie","given":"Jaimie","email":"","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":770073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hupp, Cliff R. 0000-0003-1853-9197 crhupp@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-9197","contributorId":2344,"corporation":false,"usgs":true,"family":"Hupp","given":"Cliff","email":"crhupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":770074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin-Alciati, Mario 0000-0003-3094-2843","orcid":"https://orcid.org/0000-0003-3094-2843","contributorId":218734,"corporation":false,"usgs":true,"family":"Martin-Alciati","given":"Mario","email":"","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":770075,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Floro, Kelly","contributorId":218735,"corporation":false,"usgs":false,"family":"Floro","given":"Kelly","email":"","affiliations":[],"preferred":false,"id":770076,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schenk, Edward R.","contributorId":202018,"corporation":false,"usgs":false,"family":"Schenk","given":"Edward","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":770077,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jacobs, Amy K.","contributorId":174754,"corporation":false,"usgs":false,"family":"Jacobs","given":"Amy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":770078,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Strano, Steve","contributorId":218736,"corporation":false,"usgs":false,"family":"Strano","given":"Steve","email":"","affiliations":[{"id":13501,"text":"USDA NRCS","active":true,"usgs":false}],"preferred":false,"id":770079,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70204890,"text":"70204890 - 2019 - Mid-piacenzian of the north Atlantic Ocean","interactions":[],"lastModifiedDate":"2020-04-04T17:14:58.403893","indexId":"70204890","displayToPublicDate":"2019-08-09T15:09:42","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Mid-piacenzian of the north Atlantic Ocean","docAbstract":"The Piacenzian Age (Pliocene) represents a past climate interval within which frequency and magnitude of environmental changes during a period of past global warmth can be analyzed, climate models can be tested, and results can be placed in a context to better prepare for future change. Here we focus on the North Atlantic region, incorporating new and existing faunal assemblage and alkenone data from Ocean Drilling Program Sites 642, 662, 982, and 999, and International Ocean Discovery Program Sites 1308 and 1313 into our paleoenvironmental reconstruction. Cores and outcrop material containing Piacenzian sediments from the Atlantic Coastal Plain of Virginia, USA, are also included. These data allow us to characterize regional changes in temperature, salinity, upwelling, surface productivity, and diversity, associated with climate transitions, and make nuanced reconstructions of mid-Piacenzian conditions within a high-resolution temporal framework between ~3.40 and ~3.15 Ma, inclusive of Marine Isotope Stages M2 through KM5. We include an initial comparison of estimated sea-surface temperature to coupled climate model simulations, which shows improvement in model adherence to paleoclimate parameters over previous data-model comparisons for the Pliocene.","language":"English","publisher":"Micropress","doi":"10.29041/strat.16.3.119-144","usgsCitation":"Dowsett, H.J., Robinson, M.M., Foley, K.M., Herbert, T.D., Otto-Bliesner, B.L., and Spivey, W., 2019, Mid-piacenzian of the north Atlantic Ocean: Stratigraphy, v. 16, no. 3, p. 119-144, https://doi.org/10.29041/strat.16.3.119-144.","productDescription":"26 p.","startPage":"119","endPage":"144","ipdsId":"IP-099499","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":366807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":768899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":2082,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":768900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":768901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herbert, Timothy D.","contributorId":192841,"corporation":false,"usgs":false,"family":"Herbert","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":768902,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Otto-Bliesner, Bette L.","contributorId":209685,"corporation":false,"usgs":false,"family":"Otto-Bliesner","given":"Bette","email":"","middleInitial":"L.","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":768904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":768903,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70203223,"text":"ofr20191046 - 2019 - Using scenarios to evaluate vulnerability of grassland communities to climate change in the Southern Great Plains of the United States","interactions":[],"lastModifiedDate":"2020-11-03T17:40:04.061442","indexId":"ofr20191046","displayToPublicDate":"2019-08-09T15:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1046","displayTitle":"Using Scenarios to Evaluate Vulnerability of Grassland Communities to Climate Change in the Southern Great Plains of the United States","title":"Using scenarios to evaluate vulnerability of grassland communities to climate change in the Southern Great Plains of the United States","docAbstract":"<p>Scenario planning is a useful tool for identifying key vulnerabilities of ecological systems to changing climates, informed by the potential outcomes for a set of divergent, plausible, and relevant climate scenarios. We evaluated potential vulnerabilities of grassland communities to changing climate in the Southern Great Plains (SGP) and the Landscape Conservation Design pilot area (LCD) for the U.S. Fish and Wildlife Service, Science Applications Program, Great Plains Landscape Conservation Cooperative. Four climate scenarios (warm-dry, warm-wet, hot-dry, and hot-wet) from atmospheric-ocean general circulation models were selected to represent a suite of plausible future climatic conditions. For each scenario, and for contemporary climatic conditions, we predicted the spatial patterns of relative productivity for indicator grass species using statistical models of relative above-ground net primary productivity (hereafter, productivity) based on temperature, precipitation, and soil texture (percent sand, silt, or clay).</p><p>Two indicator grass species were selected to represent each of four focal grassland communities: semi-desert grasslands, shortgrass prairie, mixed-grass prairie, and tallgrass prairie. Changes in spatial patterning of bioclimatic conditions conducive for each indicator species as predicted for each climate scenario relative to current land use were used to evaluate potential vulnerability and conservation opportunities for grassland communities. Specifically, the following questions were addressed for each focal grassland community: (1) Where is the productivity of each species predicted to increase, decrease, or remain stable relative to estimated contemporary productivity for the SGP and LCD pilot area, (2) where is the productivity of the two indicator species for each community predicted to increase, decrease, or remain stable, (3) which grassland communities are most vulnerable to changes in composition and vertical structure, (4) how do current land-use patterns contribute to potential vulnerabilities of grassland communities for the climate scenarios evaluated, and (5) how can managers use the vulnerabilities identified to evaluate conservation opportunities in the SGP and LCD?</p><p>Current land-use patterns, in combination with the potential effects of a changing climate, pose greater risks to mixed-grass and tallgrass prairies of the SGP compared to semi-desert grasslands and shortgrass prairie. For most climate scenarios evaluated, bioclimatic conditions conducive to the taller species were predicted to contract within some or all the current distribution of mixed-grass and tallgrass prairies within the SGP. An increase in precipitation, however, could potentially ameliorate the negative effects of increasing temperatures as evidenced by higher productivity for the hot-wet scenario compared to the other scenarios for the most vulnerable species. Compounding their greater vulnerability to increasing temperatures coupled with decreasing precipitation, the mixed-grass and tallgrass prairies have been greatly fragmented and converted, primarily by agriculture. In contrast, the climate scenarios evaluated are generally conducive to stable or increasing productivity of indicator species for semi-desert grasslands and shortgrass prairie. In addition, conversion and fragmentation of semi-desert grasslands and shortgrass prairie were relatively low. These results suggest that the synergistic effects of land use and changing climatic conditions could have the greatest effects on the composition and structure of mixed-grass and tallgrass prairies in the SGP. ScienceBase data release files that support this report are available at <a data-mce-href=\"https://doi.org/10.5066/P9DGJHEP\" href=\"https://doi.org/10.5066/P9DGJHEP\">https://doi.org/10.5066/P9DGJHEP</a> <br>(Manier and others, 2019).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191046","isbn":"978-1-4113-4297-2","collaboration":"In cooperation with the U.S. Fish and Wildlife Service, Science Applications Program,  Great Plains Landscape Conservation Cooperative","usgsCitation":"Manier, D.J., Carr, N.B., Reese, G.C., and Burris, L., 2019, Using scenarios to evaluate vulnerability of grassland communities to climate change in the Southern Great Plains of the United States: U.S. Geological Survey, Open-File Report 2019–1046, 48 p., https://doi.org/10.3133/ofr20191046.","productDescription":"Report: vii, 48 p.","onlineOnly":"N","ipdsId":"IP-092145","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":366068,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DGJHEP","text":"USGS data release","linkHelpText":"Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains"},{"id":366067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1046/ofr20191046.pdf","text":"Report","size":"3.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1046"},{"id":366066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1046/coverthb.jpg"}],"country":"United States","state":"Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, Wyoming","otherGeospatial":"Southern Great Plains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.9296875,\n              29.53522956294847\n            ],\n            [\n              -95.361328125,\n              29.53522956294847\n            ],\n            [\n              -95.361328125,\n              43.77109381775651\n            ],\n            [\n              -107.9296875,\n              43.77109381775651\n            ],\n            [\n              -107.9296875,\n              29.53522956294847\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director,&nbsp;<a href=\"https://www.usgs.gov/fort/\" data-mce-href=\"https://www.usgs.gov/fort/\">Fort Collins Science Center</a><br>U.S. Geological Survey<br>2150 Centre Ave., Building C<br>Fort Collins, CO 80526-8118</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Conclusions</li><li>References Cited</li><li>Appendix 1. Classified Relative Production Estimates Model Convergence and Uncertainty</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-08-09","noUsgsAuthors":false,"publicationDate":"2019-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Manier, Daniel J. 0000-0002-1105-1327 manierd@usgs.gov","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":127553,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","email":"manierd@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":761763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carr, Natasha B. 0000-0002-4842-0632 carrn@usgs.gov","orcid":"https://orcid.org/0000-0002-4842-0632","contributorId":1918,"corporation":false,"usgs":true,"family":"Carr","given":"Natasha","email":"carrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":761764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reese, Gordon C. 0000-0002-5191-7770 greese@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-7770","contributorId":189809,"corporation":false,"usgs":true,"family":"Reese","given":"Gordon","email":"greese@usgs.gov","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":761765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burris, Lucy 0000-0003-0308-7044 lburris@usgs.gov","orcid":"https://orcid.org/0000-0003-0308-7044","contributorId":209816,"corporation":false,"usgs":true,"family":"Burris","given":"Lucy","email":"lburris@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":767429,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228058,"text":"70228058 - 2019 - Soil chemistry, and not short-term (1–2 year) deer exclusion, explains understory plant occupancy in forests affected by acid deposition","interactions":[],"lastModifiedDate":"2022-02-03T15:35:42.120801","indexId":"70228058","displayToPublicDate":"2019-08-09T09:25:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5538,"text":"AoB PLANTS","active":true,"publicationSubtype":{"id":10}},"title":"Soil chemistry, and not short-term (1–2 year) deer exclusion, explains understory plant occupancy in forests affected by acid deposition","docAbstract":"<p><span>The loss of species diversity and plant community structure throughout the temperate deciduous forests of North America have often been attributed to overbrowsing by white-tailed deer (</span><i>Odocoileus virginanus</i><span>). Slow species recovery following removal from browsing, or reduction in deer density, has been termed a legacy effect of past deer herbivory. However, vegetation legacy effects have also coincided with changes to soil chemistry throughout the north-eastern USA. In this paper, we assess the viability of soil chemistry (i.e. pH, extractable nutrients and extractable metals) and other factors (topography, light, overstory basal area and location) as alternative explanations for a lack of vegetation recovery. We compared the relative effects of soil chemistry, site conditions and short-term (1–2 year) deer exclusion on single-species occupancy probabilities of 10 plant taxa common to oak-hickory forests in central Pennsylvania. We found detection for all modelled species was constant and high (</span><span class=\"inline-formula no-formula-id\">⁠<span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mstyle xmlns=&quot;&quot; displaystyle=&quot;false&quot;><mrow><mover accent=&quot;true&quot;><mi>p</mi><mo stretchy=&quot;false&quot;>^</mo></mover></mrow></mstyle></math>\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mstyle\"><span id=\"MathJax-Span-4\" class=\"mrow\"><span id=\"MathJax-Span-5\" class=\"mrow\"><span id=\"MathJax-Span-6\" class=\"mover\"><span id=\"MathJax-Span-7\" class=\"mi\">p</span><span id=\"MathJax-Span-8\" class=\"mo\">^</span></span></span></span></span></span></span></span><span class=\"MJX_Assistive_MathML\">p^</span></span></span><span>&nbsp;&gt; 0.65), and occupancy probability of most taxa was best explained by at least one soil chemistry parameter. Specifically, ericaceous competing vegetation was more likely to occupy acidic (pH &lt; 3.5), base cation-poor (K &lt; 0.20 cmol</span><sub>c</sub><span>&nbsp;kg</span><sup>−1</sup><span>) sites, while deer-preferred plants were less likely to occur when soil manganese exceeded 0.1 cmol</span><sub>c</sub><span>&nbsp;kg</span><sup>−1</sup><span>. Short-term deer exclusion did not explain occupancy of any plant taxon, and site conditions were of nominal importance. This study demonstrates the importance of soil chemistry in shaping plant community composition in the north-central Appalachians, and suggests soil as an alternative, or additional, explanation for deer vegetation legacy effects. We suggest that the reliance on phyto-indicators of deer browsing effects may overestimate the effects of browsing if those species are also limited by unfavourable soil conditions. Future research should consider study designs that address the complexity of deer forest interactions, especially in areas with complex site-vegetation histories.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/aobpla/plz044","usgsCitation":"Begley-Miller, D.R., Diefenbach, D.R., McDill, M.E., Drohan, P.J., Rosenberry, C.S., and Just Domoto, E.H., 2019, Soil chemistry, and not short-term (1–2 year) deer exclusion, explains understory plant occupancy in forests affected by acid deposition: AoB PLANTS, v. 11, no. 5, pls044, 15 p., https://doi.org/10.1093/aobpla/plz044.","productDescription":"pls044, 15 p.","ipdsId":"IP-099980","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aobpla/plz044","text":"Publisher Index Page"},{"id":395351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Bald Eagle State Forest, Rothrock State Forest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.15835571289062,\n              40.88963898943264\n            ],\n            [\n              -76.86172485351562,\n              41.089702205437405\n            ],\n            [\n              -76.93588256835938,\n              41.11557271185201\n            ],\n            [\n              -77.08969116210938,\n              41.12798693490564\n            ],\n            [\n              -77.22015380859375,\n              41.18692242290296\n            ],\n            [\n              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drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDill, Marc E.","contributorId":274414,"corporation":false,"usgs":false,"family":"McDill","given":"Marc","email":"","middleInitial":"E.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":832984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drohan, Patrick J.","contributorId":274416,"corporation":false,"usgs":false,"family":"Drohan","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":832985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosenberry, Christopher S.","contributorId":274418,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":56616,"text":"PA Game Commission","active":true,"usgs":false}],"preferred":false,"id":832986,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Just Domoto, Emily H.","contributorId":274421,"corporation":false,"usgs":false,"family":"Just Domoto","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":56618,"text":"PA Dept of Conservation and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":832987,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70216451,"text":"70216451 - 2019 - Genomic identity of white oak species in an eastern North American syngameon","interactions":[],"lastModifiedDate":"2020-11-18T16:23:42.581442","indexId":"70216451","displayToPublicDate":"2019-08-08T10:17:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":800,"text":"Annals of the Missouri Botanical Garden","active":true,"publicationSubtype":{"id":10}},"title":"Genomic identity of white oak species in an eastern North American syngameon","docAbstract":"<p><span>The eastern North American white oaks, a complex of approximately 16 potentially interbreeding species, have become a classic model for studying the genetic nature of species in a syngameon. Genetic work over the past two decades has demonstrated the reality of oak species, but gene flow between sympatric oaks raises the question of whether there are conserved regions of the genome that define oak species. Does gene flow homogenize the entire genome? Do the regions of the genome that distinguish a species in one part of its range differ from the regions that distinguish it in other parts of its range, where it grows in sympatry with</span><br><span>different species? Or are there regions of the genome that are relatively conserved across species ranges? In this study, we revisit seven species of the eastern North American white oak syngameon using a set of 80 single-nucleotide polymorphisms (SNPs) selected in a previous study because they show differences among, and consistency within, the species. We test the hypothesis that there exist segments of the genome that do not become homogenized by repeated introgression, but retain distinct alleles characteristic of each species. We undertake a range-wide sampling to investigate whether SNPs that appeared to be fixed based on a relatively small sample in our previous work are fixed or nearly fixed across the range of the species. Each of the seven species remains genetically distinct across its range, given our diagnostic set of markers, with relatively few individuals exhibiting admixture of multiple species. SNPs map back to all 12&nbsp;</span><i>Quercus</i><span>&nbsp;linkage groups (chromosomes) and are separated from each other by an average of 7.47 million bp (± 8.74 million bp, SD), but are significantly clustered relative to a random null distribution, suggesting that our SNP toolkit reflects genome-wide patterns of divergence while potentially being concentrated in regions of the genome that reflect a higher-than-average history of among-species divergence. This application of a DNA toolkit designed for the simple problem of identifying species in the field has two important implications. First, the eastern North American white oak syngameon is composed of entities that most taxonomists would consider “good species.” Second, and more fundamentally, species in the syngameon are genetically coherent because characteristic portions of the genome remain divergent despite a history of introgression. Understanding the conditions under which some loci diverge while others introgress is key to understanding the origins and maintenance of global tree diversity.</span></p>","language":"English","publisher":"Missouri Botanical Garden Press","doi":"10.3417/2019434","usgsCitation":"Hipp, A., Whittemore, A.T., Garner, M., Hahn, M., Fitzek, E., Guichoux, E., Cavender-Bares, J., Gugger, P.F., Manos, P., Pearse, I., and Cannon, C., 2019, Genomic identity of white oak species in an eastern North American syngameon: Annals of the Missouri Botanical Garden, v. 104, no. 3, p. 455-477, https://doi.org/10.3417/2019434.","productDescription":"23 p.","startPage":"455","endPage":"477","ipdsId":"IP-106440","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467382,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3417/2019434","text":"Publisher Index Page"},{"id":380600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Estern United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -66.62109375,\n              45.213003555993964\n            ],\n            [\n              -69.78515625,\n              47.635783590864854\n            ],\n            [\n              -78.662109375,\n              46.73986059969267\n            ],\n            [\n              -82.880859375,\n              46.437856895024204\n            ],\n            [\n              -91.14257812499999,\n              49.38237278700955\n            ],\n            [\n              -97.3828125,\n              50.401515322782366\n            ],\n 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       -70.13671875,\n              43.77109381775651\n            ],\n            [\n              -66.62109375,\n              45.213003555993964\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"104","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Hipp, Andrew","contributorId":219598,"corporation":false,"usgs":false,"family":"Hipp","given":"Andrew","email":"","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":805154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whittemore, Alan T.","contributorId":219595,"corporation":false,"usgs":false,"family":"Whittemore","given":"Alan","email":"","middleInitial":"T.","affiliations":[{"id":40034,"text":"U.S. National Arboretum","active":true,"usgs":false}],"preferred":false,"id":805155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garner, Mira","contributorId":219593,"corporation":false,"usgs":false,"family":"Garner","given":"Mira","email":"","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":805156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hahn, Marlene","contributorId":245007,"corporation":false,"usgs":false,"family":"Hahn","given":"Marlene","email":"","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":805157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzek, Elisabeth","contributorId":245008,"corporation":false,"usgs":false,"family":"Fitzek","given":"Elisabeth","email":"","affiliations":[{"id":49053,"text":"Universitaet Bielefeld","active":true,"usgs":false}],"preferred":false,"id":805158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guichoux, Erwan","contributorId":245009,"corporation":false,"usgs":false,"family":"Guichoux","given":"Erwan","email":"","affiliations":[{"id":49054,"text":"INRA - France","active":true,"usgs":false}],"preferred":false,"id":805159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cavender-Bares, Jeannine","contributorId":219596,"corporation":false,"usgs":false,"family":"Cavender-Bares","given":"Jeannine","email":"","affiliations":[{"id":40035,"text":"U Minnesota","active":true,"usgs":false}],"preferred":false,"id":805160,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gugger, Paul F.","contributorId":206006,"corporation":false,"usgs":false,"family":"Gugger","given":"Paul","email":"","middleInitial":"F.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":805161,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Manos, Paul","contributorId":219597,"corporation":false,"usgs":false,"family":"Manos","given":"Paul","email":"","affiliations":[{"id":40036,"text":"Duke U.","active":true,"usgs":false}],"preferred":false,"id":805162,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":805163,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cannon, Chuck","contributorId":245010,"corporation":false,"usgs":false,"family":"Cannon","given":"Chuck","email":"","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":805164,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70205609,"text":"70205609 - 2019 - Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model","interactions":[],"lastModifiedDate":"2019-11-13T13:41:56","indexId":"70205609","displayToPublicDate":"2019-08-08T09:53:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model","docAbstract":"A Bayesian model that uses the spatial dependence induced by the river network topology, and the leading principal components of regional tree-ring chronologies for paleo-streamflow reconstruction is presented.  In any river basin, a convergent, dendritic network of tributaries comes together to form the main stem of a river.  Consequently, it is natural to think of a spatial Markov process that recognizes this topological structure to develop a spatially consistent basin-scale streamflow reconstruction model that uses the information in streamflow and tree-ring chronology data to inform the reconstructed flows, while maintaining the space-time correlation structure of flows that is critical for water resource assessments and management. Given historical data from multiple streamflow gauges along a river, their tributaries in a watershed, and regional tree-ring chronologies, the model is fit and used to simultaneously reconstruct the full network of paleo-streamflow at all gauges in the basin progressing upstream to downstream along the river. The spatial network structure allows a substantial reduction in the uncertainty associated with paleo-streamflow as one proceeds downstream in the network and the spatial dependence structure increases the information content. Our application to eighteen streamflow gauges in the Upper Missouri River Basin shows that the mean adjusted-R2 for the basin is approximately 0.5 with good overall cross-validated skill as measured by five different skill metrics. A comparison with the traditional principal components regression shows that the spatial Bayesian model offers improvements, as downstream gauges are informed by the reconstruction of the upstream gauges, as well as the tree-ring chronologies.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR024901","usgsCitation":"Ravindranath, A., Devineni, N., Lall, U., Cook, E., Pederson, G.T., Martin, J.T., and Woodhouse, C.A., 2019, Streamflow reconstruction in the Upper Missouri River Basin using a novel Bayesian network model: Water Resources Research, v. 55, no. 9, p. 7694-7716, https://doi.org/10.1029/2019WR024901.","productDescription":"23 p.","startPage":"7694","endPage":"7716","ipdsId":"IP-104913","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr024901","text":"Publisher Index Page"},{"id":367776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Missouri River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.0,\n              48.5\n            ],\n            [\n              -104.5,\n              48.5\n            ],\n            [\n              -104.5,\n              42.0\n            ],\n            [\n              -115.0,\n              42.0\n            ],\n            [\n              -115.0,\n              48.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"55","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Ravindranath, Arun","contributorId":219272,"corporation":false,"usgs":false,"family":"Ravindranath","given":"Arun","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":771848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devineni, Naresh","contributorId":219273,"corporation":false,"usgs":false,"family":"Devineni","given":"Naresh","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":771849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lall, Upmanu 0000-0003-0529-8128","orcid":"https://orcid.org/0000-0003-0529-8128","contributorId":212142,"corporation":false,"usgs":false,"family":"Lall","given":"Upmanu","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":771850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Edward","contributorId":197078,"corporation":false,"usgs":false,"family":"Cook","given":"Edward","affiliations":[],"preferred":false,"id":771851,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":771847,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":771852,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":771853,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70204637,"text":"70204637 - 2019 - Bloom forming cyanobacteria can adversely affect zebra and quagga mussel veligers","interactions":[],"lastModifiedDate":"2019-08-12T09:28:24","indexId":"70204637","displayToPublicDate":"2019-08-08T08:55:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1480,"text":"Ecotoxicology and Environmental Safety","active":true,"publicationSubtype":{"id":10}},"title":"Bloom forming cyanobacteria can adversely affect zebra and quagga mussel veligers","docAbstract":"<p><span>Quagga (</span><i>Dreissena rostriformis bugensis</i><span>) and zebra (</span><i>D. polymorpha</i><span>) mussels are broadcast spawners that produce planktonic, free swimming veligers, a life history strategy dissimilar to native North American freshwater bivalves. Dreissenid veligers require highly nutritious food to grow and survive, and thus may be susceptible to increased mortality rates during harsh environmental conditions like cyanobacteria blooms. However, the impact of cyanobacteria and one of the toxins they can produce (microcystin) has not been evaluated in dreissenid veligers. Therefore, we exposed dreissenid veligers to eleven distinct cultures (isolates) of cyanobacteria representing&nbsp;</span><i>Anabaena, Aphanizomenon, Dolichospermum, Microcystis,</i><span>&nbsp;and&nbsp;</span><i>Planktothrix</i><span>species and the cyanotoxin microcystin to determine the lethality of cyanobacteria on dreissenid veligers. Six-day laboratory bioassays were performed in microplates using dreissenid veligers collected from the Detroit River, Michigan, USA. Veligers were exposed to increasing concentrations of cyanobacteria and microcystin using the green algae&nbsp;</span><i>Chlorella minutissima</i><span>&nbsp;as a control. Based on dose response curves formulated from a Probit model, the LC</span><sub>50</sub><span>&nbsp;values for cyanobacteria used in this study range between 15.06 and 135.06 μg/L chlorophyll-</span><i>a</i><span>, with the LC</span><sub>50</sub><span>&nbsp;for microcystin-LR at 13.03 μg/L. Because LC</span><sub>50</sub><span>&nbsp;values were within ranges observed in natural waterbodies, it is possible that dreissenid recruitment may be suppressed when veliger abundances overlap with seasonal cyanobacteria blooms. Thus, the toxicity of cyanobacteria to dreissenid veligers may be useful to include in models forecasting dreissenid mussel abundance and spread.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoenv.2019.109426","usgsCitation":"Boegehold, A.G., Johnson, N., and Kashian, D.R., 2019, Bloom forming cyanobacteria can adversely affect zebra and quagga mussel veligers: Ecotoxicology and Environmental Safety, v. 182, Article 109426, https://doi.org/10.1016/j.ecoenv.2019.109426.","productDescription":"Article 109426","ipdsId":"IP-109520","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoenv.2019.109426","text":"Publisher Index Page"},{"id":366365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"182","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boegehold, Anna G.","contributorId":205600,"corporation":false,"usgs":false,"family":"Boegehold","given":"Anna","email":"","middleInitial":"G.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":767856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":767855,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kashian, Donna R.","contributorId":205602,"corporation":false,"usgs":false,"family":"Kashian","given":"Donna","email":"","middleInitial":"R.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":767857,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70263893,"text":"70263893 - 2019 - High-frequency ground motion and source characteristics of the 2008 Wenchuan and 2013 Lushan, China, earthquakes","interactions":[],"lastModifiedDate":"2025-02-27T15:12:24.556905","indexId":"70263893","displayToPublicDate":"2019-08-08T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20190,"text":"Pure & Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"High-frequency ground motion and source characteristics of the 2008 Wenchuan and 2013 Lushan, China, earthquakes","docAbstract":"<p><span>The 2008&nbsp;</span><i>M</i><sub>W</sub><span>7.9 Wenchuan and the 2013&nbsp;</span><i>M</i><sub>W</sub><span>6.6 Lushan earthquakes, which both occurred on the Longmen Shan thrust belt, show some interesting similarities and differences. Whereas the Wenchuan earthquake entailed a rupture zone that extended about 300&nbsp;km northeastward, with fault slip extending to the surface, the Lushan earthquake was the result of a buried and much more compact zone of rupture. The high-frequency ground motions, however, for these two earthquakes, as measured by the peak ground acceleration, were evidently influenced by neither the extent of rupture nor the presence or absence of surface rupture. The source parameters for these two earthquakes tend to confirm the idea that high-frequency ground motion is controlled by stress changes in the rupture zone that give rise to the radiated ground acceleration. The apparent stresses for the Wenchuan and Lushan earthquakes are about 0.5 and 0.75&nbsp;MPa, respectively, and the stress drops, in the same order, are about 2.5 and 3.5&nbsp;MPa. The ratios of average stress drop to apparent stress are in the range 4.5–5 for both events, consistent with expectations based on the Brune (J Geophys Res 75(26):4997–5009, 1970) source model.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00024-019-02291-4","usgsCitation":"Meng, L., Zang, Y., and Zhou, L., 2019, High-frequency ground motion and source characteristics of the 2008 Wenchuan and 2013 Lushan, China, earthquakes: Pure & Applied Geophysics, v. 177, p. 81-93, https://doi.org/10.1007/s00024-019-02291-4.","productDescription":"13 p.","startPage":"81","endPage":"93","ipdsId":"IP-108067","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Lushan, 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Lingyuan","contributorId":351564,"corporation":false,"usgs":false,"family":"Meng","given":"Lingyuan","affiliations":[{"id":84008,"text":"China Earthquake Networks Center","active":true,"usgs":false}],"preferred":false,"id":928929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zang, Yang","contributorId":351563,"corporation":false,"usgs":false,"family":"Zang","given":"Yang","affiliations":[{"id":84008,"text":"China Earthquake Networks Center","active":true,"usgs":false}],"preferred":false,"id":928930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Longquan","contributorId":351562,"corporation":false,"usgs":false,"family":"Zhou","given":"Longquan","affiliations":[{"id":84008,"text":"China Earthquake Networks Center","active":true,"usgs":false}],"preferred":false,"id":928932,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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