{"pageNumber":"224","pageRowStart":"5575","pageSize":"25","recordCount":40783,"records":[{"id":70220499,"text":"sir20205151 - 2021 - Characterization of factors affecting groundwater levels in and near the former Lake Traverse Indian Reservation, South Dakota, water years 1956–2017","interactions":[],"lastModifiedDate":"2021-05-20T11:45:53.918338","indexId":"sir20205151","displayToPublicDate":"2021-05-19T09:12:25","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5151","displayTitle":"Characterization of Factors Affecting Groundwater Levels in and near the Former Lake Traverse Indian Reservation, South Dakota, Water Years 1956–2017","title":"Characterization of factors affecting groundwater levels in and near the former Lake Traverse Indian Reservation, South Dakota, water years 1956–2017","docAbstract":"<p>The U.S. Geological Survey (USGS), in cooperation with the Sisseton Wahpeton Oyate, completed a study to characterize water-level fluctuations in observation wells relative to driving factors that affect water levels in and near the historical 1867 boundary of the Lake Traverse Indian Reservation. The study investigated concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes the historical boundary of the reservation and a surrounding area that extends 10 miles in all directions within South Dakota. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis.</p><p>Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature (<i>T<sub>min</sub></i>), and maximum air temperature (<i>T<sub>max</sub></i>) for the study area and a surrounding buffer area. Trend tests for gridded data for total precipitation, <i>T<sub>min</sub></i>, and <i>T<sub>max</sub></i> were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual <i>T<sub>min</sub></i> for most of the study area and the surrounding buffer area. Statistically significant downward trends in <i>T<sub>max</sub></i> were detected for only a few 2.5 arc-minute grid cells; however, the sparsity of the spatial coverage reduces confidence that these are true trends, in contrast to the near completeness of the spatial coverage in upward trends for <i>T<sub>min</sub></i>. Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends.</p><p>Potential interactions among water levels in observation wells and streamflow were examined through correlation analyses of the annual median water level for each of 76 observation wells versus the annual mean streamflow for each of four area streamgages. Potential interactions among water levels in observation wells and lake levels were examined through correlation analyses involving 25 area lakes. Resulting correlation coefficients were used as part of an approach for selecting a lake to be plotted in conjunction with water-level and precipitation data for each observation well.</p><p>Groundwater trends for 76 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically, and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall <i>p</i>-values were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes with time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1.0, 2.0, 3.0, 4.0, and 5.0 miles) of each well as a qualitative indicator of proximal groundwater demand.</p><p>Trends in groundwater levels in observation wells in the study area are predominantly upward, with 43 of 76 wells having significant upward trends for at least one of the three water-level parameters and only 8 wells having significant downward trends for at least one water-level parameter. The upward groundwater trends are driven by predominantly upward precipitation trends, with 43 wells (not all the same wells) also having significant upward trends and no wells having significant downward trends. Significant upward precipitation trends were detected for only two of the eight wells with significant downward groundwater trends. Groundwater levels in some observation wells likely are also substantially affected by interactions with surface water, especially with lakes. Water levels in many area lakes increased in response to wet conditions of the early 1990s and have maintained high water levels ever since. It is recognized that in many cases lakes that were selected for plotting with groundwater hydrographs likely are not hydraulically connected with a groundwater system or aquifer associated with an individual well; however, interactions also are plausible for numerous other lakes for which water-level records are not available.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205151","collaboration":"Prepared in cooperation with the Sisseton Wahpeton Oyate","usgsCitation":"Valseth, K.J., and Driscoll, D.G., 2021, Characterization of factors affecting groundwater levels in and near the former Lake Traverse Indian Reservation, South Dakota, water years 1956–2017: U.S. Geological Survey Scientific Investigations Report 2020–5151, 64 p., https://doi.org/10.3133/sir20205151.","productDescription":"Report: vi, 64 p.; 2 Appendixes; Dataset","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114147","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":385692,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5151/sir20205151_appendix1.pdf","text":"Appendix 1","size":"957 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5151 Appendix 1","linkHelpText":"— Figure 1.1 Graphs showing trends in annual precipitation totals, trends in measured groundwater levels, lake levels for a selected lake, and proximal groundwater withdrawals"},{"id":385685,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5151/coverthb.jpg"},{"id":385686,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5151/sir20205151.pdf","text":"Report","size":"4.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5151"},{"id":385689,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":385693,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5151/sir20205151_appendix2.pdf","text":"Appendix 2","size":"165 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5151 Appendix 2","linkHelpText":"— Figure 2.1 Graphs showing autocorrelation function values for annual total precipitation, annual mean maximum temperature, and annual mean minimum temperature for the study area from 1956 to 2017"}],"country":"United States","state":"South Dakota","otherGeospatial":"Lake Traverse Indian Reservation","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.72338867187499,\n              45.034714778688624\n            ],\n            [\n              -96.43798828125,\n              45.034714778688624\n            ],\n            [\n              -96.43798828125,\n              45.9511496866914\n            ],\n            [\n              -97.72338867187499,\n              45.9511496866914\n            ],\n            [\n              -97.72338867187499,\n              45.034714778688624\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a><br>U.S. Geological Survey<br>821 East Interstate Avenue, Bismarck, ND 58503<br>1608 Mountain View Road, Rapid City, SD 57702</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data Sources and Analytical Methods</li><li>Characterization of Factors Affecting Groundwater Levels</li><li>Summary</li><li>References Cited</li><li>Appendixes</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-05-19","noUsgsAuthors":false,"publicationDate":"2021-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Valseth, Kristen J. 0000-0003-4257-6094","orcid":"https://orcid.org/0000-0003-4257-6094","contributorId":203447,"corporation":false,"usgs":true,"family":"Valseth","given":"Kristen","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Daniel G. 0000-0003-0016-8535 dgdrisco@usgs.gov","orcid":"https://orcid.org/0000-0003-0016-8535","contributorId":207583,"corporation":false,"usgs":true,"family":"Driscoll","given":"Daniel","email":"dgdrisco@usgs.gov","middleInitial":"G.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815836,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70221155,"text":"70221155 - 2021 - Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States","interactions":[],"lastModifiedDate":"2022-04-01T22:14:57.190942","indexId":"70221155","displayToPublicDate":"2021-05-19T08:12:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States","docAbstract":"<p>Land system change has been identified as one of four major Earth system processes where change has passed a destabilizing threshold. A historical record of landscape change is required to understand the impacts change has had on human and natural systems, while scenarios of future landscape change are required to facilitate planning and mitigation efforts. A methodology for modeling long-term historical and future landscape change was applied in the Delaware River Basin of the United States. A parcel-based modeling framework was used to reconstruct historical landscapes back to 1680, parameterized with a variety of spatial and nonspatial historical datasets. Similarly, scenarios of future landscape change were modeled for multiple scenarios out to 2100. Results demonstrate the ability to represent historical land cover proportions and general patterns at broad spatial scales and model multiple potential future landscape trajectories. The resulting land cover collection provides consistent data from 1680 through 2100, at a 30-m spatial resolution, 10-year intervals, and high thematic resolution. The data are consistent with the spatial and thematic characteristics of widely used national-scale land cover datasets, facilitating use within existing land management and research workflows. The methodology demonstrated in the Delaware River Basin is extensible and scalable, with potential applications at national scales for the United States.</p>","language":"English","publisher":"MDPI","doi":"10.3390/land10050536","usgsCitation":"Dornbierer, J., Wika, S., Robison, C., Rouze, G., and Sohl, T.L., 2021, Prototyping a methodology for long-term (1680-2100) historical-to-future landscape modeling for the conterminous United States: Land, v. 10, no. 5, 536, 31 p.; Data Release, https://doi.org/10.3390/land10050536.","productDescription":"536, 31 p.; Data Release","ipdsId":"IP-127950","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452199,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land10050536","text":"Publisher Index Page"},{"id":386174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397938,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93J4Z2W"}],"country":"United States","state":"Delaware, Maryland, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.728759765625,\n              38.676933444637925\n            ],\n            [\n              -75.333251953125,\n              38.46219172306828\n            ],\n            [\n              -74.827880859375,\n              39.06184913429154\n            ],\n            [\n              -75.025634765625,\n              39.38526381099774\n            ],\n            [\n              -74.2236328125,\n              40.212440718286466\n            ],\n            [\n              -74.696044921875,\n              40.78885994449482\n            ],\n            [\n              -73.58642578125,\n              41.5579215778042\n            ],\n            [\n              -74.278564453125,\n              42.27730877423709\n            ],\n            [\n              -76.83837890625,\n              40.538851525354666\n            ],\n            [\n              -75.728759765625,\n              38.676933444637925\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dornbierer, Jordan 0000-0003-2099-5095","orcid":"https://orcid.org/0000-0003-2099-5095","contributorId":213067,"corporation":false,"usgs":false,"family":"Dornbierer","given":"Jordan","affiliations":[{"id":38270,"text":"SGT Inc., contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":816876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wika, Steve 0000-0001-9992-8973","orcid":"https://orcid.org/0000-0001-9992-8973","contributorId":213068,"corporation":false,"usgs":false,"family":"Wika","given":"Steve","affiliations":[{"id":38700,"text":"SGT Inc.","active":true,"usgs":false}],"preferred":false,"id":816877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robison, Charles 0000-0002-7623-2380","orcid":"https://orcid.org/0000-0002-7623-2380","contributorId":217916,"corporation":false,"usgs":false,"family":"Robison","given":"Charles","email":"","affiliations":[{"id":39714,"text":"SGT Inc. (USGS Contractor)","active":true,"usgs":false}],"preferred":false,"id":816878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouze, Gregory 0000-0002-3344-2708","orcid":"https://orcid.org/0000-0002-3344-2708","contributorId":259239,"corporation":false,"usgs":false,"family":"Rouze","given":"Gregory","email":"","affiliations":[{"id":52337,"text":"TSSC contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":816879,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":816880,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220861,"text":"70220861 - 2021 - Incorporating climate change in a harvest risk assessment for polar bears Ursus maritimus in Southern Hudson Bay","interactions":[],"lastModifiedDate":"2021-05-26T12:28:44.821314","indexId":"70220861","displayToPublicDate":"2021-05-19T07:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating climate change in a harvest risk assessment for polar bears Ursus maritimus in Southern Hudson Bay","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0035\"><span>Arctic&nbsp;marine mammals&nbsp;are harvested by Indigenous people for subsistence and are socially and culturally important. For ice-dependent species like the polar bear&nbsp;</span><i>Ursus maritimus</i>, management and conservation require understanding interactions between harvest and sea-ice loss due to climate change. We developed a demographic model to evaluate harvest risk for polar bears in Southern Hudson Bay, Canada, where the annual ice-free season has increased by approximately one month in recent decades. The model was based on the theta-logistic equation and allowed for density-dependent changes (through carrying capacity [<i>K</i>]) and density-independent changes (through population growth rate [<i>r</i><span>]). Model parameters were estimated using a Bayesian&nbsp;Monte Carlo method&nbsp;that included capture-recapture,&nbsp;aerial survey, and harvest data. Harvest management followed a state-dependent approach under which new estimates of abundance were used to update the harvest level every five years. Under a middle-of-the-road environmental scenario that assumed&nbsp;</span><i>K</i><span>&nbsp;</span>and<span>&nbsp;</span><i>r</i><span>&nbsp;would decline in proportion to projected sea-ice declines, annual removal of 0.02–0.03 of females resulted in a 0.8 probability of maintaining subpopulation abundance above maximum net productivity level for three polar bear generations (~34&nbsp;years), our primary criterion for sustainability. Under more pessimistic and optimistic environmental scenarios, comparable female harvest rates were 0.01 and 0.055, respectively. Our coupled modeling-management framework can be used to inform&nbsp;tradeoffs&nbsp;between protection and sustainable use for wildlife populations experiencing habitat loss.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2021.109128","usgsCitation":"Regehr, E.V., Dyck, M., Iverson, S.A., Lee, D.S., Lunn, N.J., Northrup, J.M., Richer, M., Szor, G., and Runge, M.C., 2021, Incorporating climate change in a harvest risk assessment for polar bears Ursus maritimus in Southern Hudson Bay: Biological Conservation, v. 258, 109128, 12 p., https://doi.org/10.1016/j.biocon.2021.109128.","productDescription":"109128, 12 p.","ipdsId":"IP-119919","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488581,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2021.109128","text":"Publisher Index Page"},{"id":385979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","otherGeospatial":"Southern Hudson Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -86.8359375,\n              50.51342652633956\n            ],\n            [\n              -72.0703125,\n              50.51342652633956\n            ],\n            [\n              -73.125,\n              58.90464570302001\n            ],\n            [\n              -91.93359375,\n              57.70414723434193\n            ],\n            [\n              -86.8359375,\n              50.51342652633956\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"258","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":816479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dyck, Markus","contributorId":173868,"corporation":false,"usgs":false,"family":"Dyck","given":"Markus","affiliations":[],"preferred":false,"id":816480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iverson, Samuel A.","contributorId":52308,"corporation":false,"usgs":false,"family":"Iverson","given":"Samuel","email":"","middleInitial":"A.","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":false,"id":816481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, David S.","contributorId":257883,"corporation":false,"usgs":false,"family":"Lee","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":52159,"text":"Department of Wildlife and Environment, Nunavut Tunngavik Inc., Ottawa, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":816482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lunn, Nicholas J","contributorId":198991,"corporation":false,"usgs":false,"family":"Lunn","given":"Nicholas","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":816483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Northrup, Joseph M","contributorId":258776,"corporation":false,"usgs":false,"family":"Northrup","given":"Joseph","email":"","middleInitial":"M","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":816484,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Richer, Marie-Claude","contributorId":258777,"corporation":false,"usgs":false,"family":"Richer","given":"Marie-Claude","email":"","affiliations":[{"id":52285,"text":"Ministere des Forets, de la Faune et des Parcs, Gourvernement du Quebec","active":true,"usgs":false}],"preferred":false,"id":816485,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Szor, Guillaume","contributorId":258778,"corporation":false,"usgs":false,"family":"Szor","given":"Guillaume","email":"","affiliations":[{"id":52286,"text":"Ministere des Forets, de la Faune et des Parcs, Gouvernement du Quebec","active":true,"usgs":false}],"preferred":false,"id":816486,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":816487,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70220681,"text":"70220681 - 2021 - The 2008-2010 subsidence of Dallol volcano on the 2 spreading Erta Ale ridge: InSAR observations and source models","interactions":[],"lastModifiedDate":"2021-05-25T12:19:14.37282","indexId":"70220681","displayToPublicDate":"2021-05-19T07:14:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The 2008-2010 subsidence of Dallol volcano on the 2 spreading Erta Ale ridge: InSAR observations and source models","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">In this work, we study the subsidence of Dallol, an explosive crater and hydrothermal area along the spreading Erta Ale ridge of Afar (Ethiopia). No volcanic products exist at the surface. However, a diking episode in 2004, accompanied by dike-induced faulting, indicates that Dallol is an active volcanic area. The 2004 diking episode was followed by quiescence until subsidence started in 2008. We use InSAR to measure the deformation, and inverse, thermoelastic and poroelastic modelling to understand the possible causes of the subsidence. Analysis of InSAR data from 2004–2010 shows that subsidence, centered at Dallol, initiated in October 2008, and continued at least until February 2010 at an approximately regular rate of up to 10 cm/year. The inversion of InSAR average velocities finds that the source causing the subsidence is shallow (depth between 0.5 and 1.5 km), located under Dallol and with a volume decrease between −0.63 and −0.26 × 10<sup>6</sup><span>&nbsp;</span>km<sup>3</sup>/year. The most likely explanation for the subsidence of Dallol volcano is a combination of outgassing (depressurization), cooling and contraction of the roof of a shallow crustal magma chamber or of the hydrothermal system.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13101991","usgsCitation":"Battaglia, M., Paglia, C., and Meuti, S., 2021, The 2008-2010 subsidence of Dallol volcano on the 2 spreading Erta Ale ridge: InSAR observations and source models: Remote Sensing, v. 13, no. 10, 1991, 14 p., https://doi.org/10.3390/rs13101991.","productDescription":"1991, 14 p.","ipdsId":"IP-128962","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":452204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13101991","text":"Publisher Index Page"},{"id":385913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","otherGeospatial":"Dallol volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              40.12481689453125,\n              14.064652358057725\n            ],\n            [\n              40.450286865234375,\n              14.064652358057725\n            ],\n            [\n              40.450286865234375,\n              14.368173317117904\n            ],\n            [\n              40.12481689453125,\n              14.368173317117904\n            ],\n            [\n              40.12481689453125,\n              14.064652358057725\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":816417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paglia, Carolina 0000-0002-9072-3004","orcid":"https://orcid.org/0000-0002-9072-3004","contributorId":258325,"corporation":false,"usgs":false,"family":"Paglia","given":"Carolina","email":"","affiliations":[{"id":52280,"text":"Universita' di Pisa","active":true,"usgs":false}],"preferred":false,"id":816418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meuti, Stefano","contributorId":258326,"corporation":false,"usgs":false,"family":"Meuti","given":"Stefano","email":"","affiliations":[{"id":52281,"text":"University of Rome","active":true,"usgs":false}],"preferred":false,"id":816422,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70233540,"text":"70233540 - 2021 - American crocodiles (Crocodylus acutus) as restoration bioindicators in the Florida Everglades","interactions":[],"lastModifiedDate":"2022-07-25T12:07:10.672213","indexId":"70233540","displayToPublicDate":"2021-05-19T07:04:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"American crocodiles (Crocodylus acutus) as restoration bioindicators in the Florida Everglades","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>The federally threatened American crocodile (<i>Crocodylus acutus</i>) is a flagship species and ecological indicator of hydrologic restoration in the Florida Everglades. We conducted a long-term capture-recapture study on the South Florida population of American crocodiles from 1978 to 2015 to evaluate the effects of restoration efforts to more historic hydrologic conditions. The study produced 10,040 crocodile capture events of 9,865 individuals and more than 90% of captures were of hatchlings. Body condition and growth rates of crocodiles were highly age-structured with younger crocodiles presenting with the poorest body condition and highest growth rates. Mean crocodile body condition in this study was 2.14±0.35 SD across the South Florida population. Crocodiles exposed to hypersaline conditions (&gt; 40 psu) during the dry season maintained lower body condition scores and reduced growth rate by 13% after one year, by 24% after five years, and by 29% after ten years. Estimated hatchling survival for the South Florida population was 25% increasing with ontogeny and reaching near 90% survival at year six. Hatchling survival was 34% in NE Florida Bay relative to a 69% hatchling survival at Crocodile Lake National Wildlife Refuge and 53% in Flamingo area of Everglades National Park. Hypersaline conditions negatively affected survival, growth and body condition and was most pronounced in NE Florida Bay, where the hydrologic conditions have been most disturbed. The American crocodile, a long-lived animal, with relatively slow growth rate provides an excellent model system to measure the effects of altered hydropatterns in the Everglades landscape. These results illustrate the need for continued long-term monitoring to assess system-wide restoration outcomes and inform resource managers.</p></div></div>","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0250510","usgsCitation":"Briggs-Gonzalez, V.S., Basille, M., Cherkiss, M., and Mazzotti, F., 2021, American crocodiles (Crocodylus acutus) as restoration bioindicators in the Florida Everglades: PLoS ONE, v. 16, no. 5, e0250510, 23 p., https://doi.org/10.1371/journal.pone.0250510.","productDescription":"e0250510, 23 p.","ipdsId":"IP-095038","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452206,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0250510","text":"Publisher Index Page"},{"id":404415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.76025390624999,\n              24.996015742094006\n            ],\n            [\n              -80.16723632812499,\n              24.996015742094006\n            ],\n            [\n              -80.16723632812499,\n              26.716173757934094\n            ],\n            [\n              -81.76025390624999,\n              26.716173757934094\n            ],\n            [\n              -81.76025390624999,\n              24.996015742094006\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"16","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs-Gonzalez, Venetia S.","contributorId":293592,"corporation":false,"usgs":false,"family":"Briggs-Gonzalez","given":"Venetia","email":"","middleInitial":"S.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":847369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Basille, Mathieu","contributorId":175274,"corporation":false,"usgs":false,"family":"Basille","given":"Mathieu","email":"","affiliations":[],"preferred":false,"id":847370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":222180,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":847371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazzotti, Frank J.","contributorId":100018,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":847372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70220616,"text":"70220616 - 2021 - Forest evapotranspiration dynamics over a fragmented forest landscape under drought in southwestern Amazonia","interactions":[],"lastModifiedDate":"2021-05-24T11:56:27.126561","indexId":"70220616","displayToPublicDate":"2021-05-19T06:45:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Forest evapotranspiration dynamics over a fragmented forest landscape under drought in southwestern Amazonia","docAbstract":"<p id=\"spara011\">Ongoing climate change and human conversion of forests to other land uses alter regional evapotranspiration dynamics and, consequently, impact associated hydrological systems in Amazonia. We studied the effects of drought and fragmentation on forest evapotranspiration using the surface energy balance-based model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) for a fragmented forest landscape in Brazil's Amazonian state of Rondônia.</p><p id=\"spara012\">Dry season (June-August) forest evapotranspiration estimates were produced for the 2009-2011 period that encompassed the 2010 drought event, one of the extreme droughts in the Amazon. METRIC evapotranspiration data were analyzed in relation to climate (monthly precipitation and cumulative water deficit) and forest fragmentation (edge distance from 100m to 1000m from forest edge and edge density). During the dry season of 2009, pre-drought, forest evapotranspiration did not fall below 110mm/month. However, the 2010 drought year showed a drastic decline in evapotranspiration by 32%, to 75mm/month, from July to August. In 2011, evapotranspiration rates were still depressed with August rates dropping as low as 85mm/month. Forest evapotranspiration dynamics were driven mainly by precipitation and corresponding water deficits in the drier years (2010 and 2011), although evapotranspiration deficits along the edges of forest fragments were locally significant, at the landscape scale. The forests near edges (to 100m) had progressively lower evapotranspiration levels than interior forests as dry seasons progressed and these differences were greatest in the 2010 drought year, reaching almost 5%.</p><p id=\"spara013\">Our results suggest that during the driest months, fragmentation exacerbated both the rate and extent of evapotranspiration reductions over forest areas up to 100m from edges, equivalent to ~20% of the forested landscape in our study area.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2021.108446","usgsCitation":"Numata, I., Khand, K.B., Kjaersgaard, J., Cochrane, M.A., and Silva, S.S., 2021, Forest evapotranspiration dynamics over a fragmented forest landscape under drought in southwestern Amazonia: Agricultural and Forest Meteorology, v. 306, 108446, 9 p., https://doi.org/10.1016/j.agrformet.2021.108446.","productDescription":"108446, 9 p.","ipdsId":"IP-122348","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2021.108446","text":"Publisher Index Page"},{"id":385833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Rondonia","otherGeospatial":"Amazon Rain Forest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -66.97265625,\n              -2.4162756547063857\n            ],\n            [\n              -56.6455078125,\n              -2.4162756547063857\n            ],\n            [\n              -56.6455078125,\n              6.18424616128059\n            ],\n            [\n              -66.97265625,\n              6.18424616128059\n            ],\n            [\n              -66.97265625,\n              -2.4162756547063857\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"306","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Numata, Izaya","contributorId":219508,"corporation":false,"usgs":false,"family":"Numata","given":"Izaya","email":"","affiliations":[],"preferred":false,"id":816235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khand, Kul Bikram 0000-0002-1593-1508","orcid":"https://orcid.org/0000-0002-1593-1508","contributorId":242921,"corporation":false,"usgs":true,"family":"Khand","given":"Kul","email":"","middleInitial":"Bikram","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":816236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kjaersgaard, Jeppe","contributorId":258261,"corporation":false,"usgs":false,"family":"Kjaersgaard","given":"Jeppe","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":816237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochrane, Mark A.","contributorId":20884,"corporation":false,"usgs":false,"family":"Cochrane","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816238,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silva, Sonaira S.","contributorId":258262,"corporation":false,"usgs":false,"family":"Silva","given":"Sonaira","email":"","middleInitial":"S.","affiliations":[{"id":52266,"text":"Federal University of Acre","active":true,"usgs":false}],"preferred":false,"id":816239,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220617,"text":"70220617 - 2021 - Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models","interactions":[],"lastModifiedDate":"2021-06-30T18:58:48.8679","indexId":"70220617","displayToPublicDate":"2021-05-19T06:41:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8605,"text":"JGR-Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models","docAbstract":"<p><span>Re-examination of previously published dissolved iron time-series data from Ocean Station Papa in the central Gulf of Alaska (GoA) reveals 33-70% increases in the dissolved iron inventories occurring between September and February of successive years, implying a source of Fe to this region during autumn or early winter. Because I can virtually rule out many possible iron sources at this time of year, I suggest Alaskan glacial dust is the likely iron source. Large plumes of such dust are known to be generated regularly in the autumn by anomalous offshore winds and channelled through mountain gaps, simultaneously from several locations spanning ∼1000 km of the northern Gulf of Alaska coastline. Large dust flux events occur when below-freezing, low-humidity air temperatures persist for many days during the autumn. I suggest that existing state-of-the-art global dust models fail to reproduce this Alaskan dust flux because the model spatial resolution is too coarse to resolve the high winds through the narrow mountain gaps that generate the dust. Future work that could help to confirm this Fe source to the central GoA includes time-series profiles of iron concentrations, and ancillary information from sensor-equipped profiling floats. If this mechanism of Fe supply to the central GoA were confirmed, it would imply this Alaskan dust is transported ≥ 1100 km from the coast, more than twice as far as has been visually documented from satellite observations.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006323","usgsCitation":"Crusius, J., 2021, Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models: JGR-Biogeosciences, v. 126, e2021JG006323, 13 p., https://doi.org/10.1029/2021JG006323.","productDescription":"e2021JG006323, 13 p.","ipdsId":"IP-102176","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":385832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -153.28125,\n              51.39920565355378\n            ],\n            [\n              -131.1328125,\n              51.39920565355378\n            ],\n            [\n              -131.1328125,\n              59.5343180010956\n            ],\n            [\n              -153.28125,\n              59.5343180010956\n            ],\n            [\n              -153.28125,\n              51.39920565355378\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":816240,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70220525,"text":"ofr20211043 - 2021 - Dynamics of endangered sucker populations in Clear Lake Reservoir, California","interactions":[],"lastModifiedDate":"2021-05-19T12:01:59.893466","indexId":"ofr20211043","displayToPublicDate":"2021-05-18T16:18:22","publicationYear":"2021","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":"2021-1043","displayTitle":"Dynamics of Endangered Sucker Populations in Clear Lake Reservoir, California","title":"Dynamics of endangered sucker populations in Clear Lake Reservoir, California","docAbstract":"<h1>Executive Summary</h1><p class=\"p1\">In collaboration with the Bureau of Reclamation, the U.S. Geological Survey began a consistent monitoring program for endangered Lost River suckers (<i>Deltistes luxatus</i>) and shortnose suckers (<i>Chasmistes brevirostris</i>) in Clear Lake Reservoir, California, in fall 2004. The program was intended to improve understanding of the Clear Lake Reservoir populations because they are important to recovery efforts for these species. We report results from the ongoing program and include sampling efforts through fall 2019. We summarize catches and passive integrated transponder (PIT) tagging efforts from trammel net sampling in the fall seasons (September–October each year) and detections of PIT-tagged suckers on remote antennas in the spring in each year from 2006 to 2019. We also combine the data from physical captures and remote detections in capture-recapture models to provide estimates of annual survival for suckers in the reservoir.</p><p class=\"p1\">A lack of genetic distinctiveness between shortnose suckers and Klamath largescale suckers (<i>Catostomus snyderi</i>) in the Lost River subbasin, including Clear Lake Reservoir, is a likely cause of past difficulty in identification of these species. Field identification can be subjective for many captured individuals, and very few individuals were identified as Klamath largescale suckers in the most recent years of our monitoring program. For this report, we combine individuals that were identified as either shortnose sucker (SNS) or Klamath largescale sucker (KLS) into a single “SNS-KLS” group for most analyses. Identification of Lost River suckers (LRS) is based on external morphological characteristics.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211043","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Hayes, B.S., Harris, A.C., Janney, E.C., Kelsey, C.M., Perry, R.W., and Burdick, S.M., 2021, Dynamics of endangered sucker populations in Clear Lake Reservoir, California: U.S. Geological Survey Open-File Report 2021–1043, 59 p., https://doi.org/10.3133/ofr20211043.","productDescription":"v, 59 p.","onlineOnly":"Y","ipdsId":"IP-108970","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":385709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1043/coverthb.jpg"},{"id":385710,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1043/ofr20211043.pdf","text":"Report","size":"12.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1043"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake Reservoir","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.25747680664064,\n              41.78360106648078\n            ],\n            [\n              -121.01852416992186,\n              41.78360106648078\n            ],\n            [\n              -121.01852416992186,\n              41.96663812286332\n            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C.","email":"aharris@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":815893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janney, Eric C. 0000-0002-0228-2174","orcid":"https://orcid.org/0000-0002-0228-2174","contributorId":83629,"corporation":false,"usgs":true,"family":"Janney","given":"Eric","email":"","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":815894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelsey, Caylen M. 0000-0003-0470-0963 ckelsey@usgs.gov","orcid":"https://orcid.org/0000-0003-0470-0963","contributorId":258179,"corporation":false,"usgs":true,"family":"Kelsey","given":"Caylen","email":"ckelsey@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research 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,{"id":70220247,"text":"ofr20211003 - 2021 - Sediment characteristics of northwestern Wisconsin’s Nemadji River, 1973–2016","interactions":[],"lastModifiedDate":"2021-05-19T11:51:06.797744","indexId":"ofr20211003","displayToPublicDate":"2021-05-18T16:16:58","publicationYear":"2021","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":"2021-1003","displayTitle":"Sediment Characteristics of Northwestern Wisconsin’s Nemadji River, 1973–2016","title":"Sediment characteristics of northwestern Wisconsin’s Nemadji River, 1973–2016","docAbstract":"<p>In 2015–16, a comparison study of stream sediment collection techniques was done for a U.S. Geological Survey streamgage on the Nemadji River near South Superior, Wisconsin (U.S. Geological Survey station number 04024430) to provide an adjustment factor for comparing suspended-sediment rating curves for two historical periods 1973–86 and 2006–16. During 1973–1986, the U.S. Geological Survey used the equal-width-increment technique to collect suspended-sediment concentration data (EWI SSC). The Wisconsin Department of Natural Resources and Minnesota Pollution Control Agency collected grab samples for total suspended solids (grab TSS) concentration starting in 2006 and continuing beyond 2016. In addition to the comparison study of suspended-sediment concentrations, bedload and bed material samples were collected in 2015–16, and the modified Einstein procedure was run to further characterize total sediment loads. The 2015–16 study indicated that the EWI SSC and grab TSS concentrations were different, but not as much as expected, especially on the high end where grab TSS concentrations were sometimes higher than EWI SSC concentrations, possibly due to a combination of a high percentage of fines in suspension and higher concentrations in the center of the channel than the margins. The 2015–16 measured bedload made up a small percentage of total sediment load, and bedload and streambed particle sizes are 90 to 100 percent sand sized or smaller. The relative proportion of measured bedload to total load decreased with increased streamflow, and for streamflows greater than 1,800 cubic feet per second, the suspended load made up 98 percent of the total load. Calculated 2015–16 instantaneous total sediment loads from the modified Einstein procedure were up to 70 percent of the measured loads for flows less than 1,000 cubic feet per second and near or more than 100 percent for flows greater than 1,000 cubic feet per second. The sediment rating curve developed for the 2006–16 adjusted grab TSS data had a similar slope but a lower intercept than its 1973–86 EWI SSC counterpart, indicating that for a given streamflow, suspended-sediment concentrations were lower for 2006–16 compared to 1973–86. The negative offset equates to estimates of annual suspended-sediment loads in 2006–16 being on average 87 percent of the 1973–86 loads. Over the period 2009–16, annual suspended-sediment loads ranged from a low of about 21,000 tons per year in 2015 to a high of 167,000 tons per year in 2012 with a mean of 85,000 tons per year. However, reductions in suspended-sediment concentrations are likely obscured by large loads during years with flooding.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211003","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Fitzpatrick, F.A., 2021, Sediment characteristics of northwestern Wisconsin’s Nemadji River, 1973–2016: U.S. Geological Survey Open-File Report 2021–1003, 27 p., https://doi.org/10.3133/ofr20211003.","productDescription":"Report: viii, 27 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-085024","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385361,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FX0X6Y","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selected sediment data and results from regression models, modified Einstein Procedure, and loads estimation for the Nemadji River, 1973–2016"},{"id":385360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1003/ofr20211003.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1003"},{"id":385359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1003/coverthb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Nemadji River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.55157470703125,\n              46.38672781370433\n            ],\n            [\n              -92.01599121093749,\n              46.38672781370433\n            ],\n            [\n              -92.01599121093749,\n              46.65697731621612\n            ],\n            [\n              -92.55157470703125,\n              46.65697731621612\n            ],\n            [\n              -92.55157470703125,\n              46.38672781370433\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umid-water/locations\" href=\"https://www.usgs.gov/centers/umid-water/locations\">Upper Midwest Water Science Center</a><br>U.S. Geological Survey<br>8505 Research Way<br>Middleton, WI 53562</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Streamflow Characteristics 1973–2016</li><li>Sediment Characteristics 2015–16</li><li>Comparison of Suspended-Sediment Rating Curves 1973–86 and 2006–16</li><li>Estimates of Annual Suspended and Total Sediment Loads 2009–16</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-05-18","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":150164,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","email":"fafitzpa@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":814884,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70263925,"text":"70263925 - 2021 - Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","interactions":[],"lastModifiedDate":"2025-02-28T16:13:52.073178","indexId":"70263925","displayToPublicDate":"2021-05-18T10:10:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","docAbstract":"<p><span>Earthquake early warning (EEW) systems can quickly identify the beginning of a significant earthquake rupture, but the first seconds of seismic data have not been found to predict the final rupture length. We present two approaches for estimating probabilities of rupture length given the rupture initiation from an EEW system. In the first approach, bends and steps on the fault are interpreted as physical mechanisms for rupture arrest. Arrest probability relations are developed from empirical observations and depend on bend angle and step size. Probability of arrest compounds serially with increasing rupture length as bends or steps are encountered. In the second approach, time‐independent rates among ruptures from the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), are interpreted to apply to the time‐dependent condition in which rupture grows from a known starting point. Length probabilities from a Gutenberg–Richter magnitude–frequency relation provide a reference of comparison. We illustrate the new approach using the discretized fault model for California developed for UCERF3. For the case of rupture initiating on the southeast end of the San Andreas fault we find the geometric complexity of the Mill Creek section impedes most ruptures, and only&nbsp;</span><span class=\"inline-formula no-formula-id\">∼5%</span><span>&nbsp;are predicted to reach to San Bernardino on the eastern edge of the greater Los Angeles region. Conditional probabilities of length can be precompiled in this manner for any initiation point on the fault system and thus are of potential value in seismic hazard and EEW applications.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200370","usgsCitation":"Biasi, G., and Wesnousky, S.G., 2021, Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning: Bulletin of the Seismological Society of America, v. 111, no. 4, p. 2235-2247, https://doi.org/10.1785/0120200370.","productDescription":"13 p.","startPage":"2235","endPage":"2247","ipdsId":"IP-116890","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"111","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wesnousky, Steven G.","contributorId":193416,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":33746,"text":"Center for Neotectonic Studies, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929128,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70221794,"text":"70221794 - 2021 - Wave-driven flood-forecasting on reef-lined coasts early warning system (WaveFoRCE)","interactions":[],"lastModifiedDate":"2021-07-07T12:06:55.985933","indexId":"70221794","displayToPublicDate":"2021-05-18T07:06:35","publicationYear":"2021","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"title":"Wave-driven flood-forecasting on reef-lined coasts early warning system (WaveFoRCE)","docAbstract":"<p>Increasing the resilience of coastal communities while decreasing the risk to them are key to the continued inhabitance and sustainability of these areas. Low-lying coral reef-lined islands are experiencing storm wave-driven flood events that currently strike with little to no warning. These events are occurring more frequently and with increasing severity. There is a need along the world’s coral reef-lined coasts for a tool that can forecast the timing and severity of wave-driven flooding events. Without this tool, coastal communities are vulnerable to: </p><p>loss of life from drowning • loss of, and damage to, property and infrastructure • decreasing viability of communities via loss of, and damage to crops, fishing (via decreased water quality and wave-damaged reefs), and freshwater resources • reduction of livable land due to increased erosion and salt intrusion. The currently available tools were developed for sandy shorelines and do not accurately predict wave-driven flooding on reef-lined coasts, leaving inhabitants without accurate and timely warnings. In addition, the flood models that do exist for reef-lined coasts have only been implemented on a small number of areas throughout the world because running these models is costly and requires a high level of computing power. Using these existing models and techniques to generate high-resolution forecasts for wave-driven flooding for all reef-lined coasts would cost approximately US$1 billion. To remedy this issue, an international team associated with the GEO Blue Planet initiative is working to develop a wave-driven flood-forecasting early-warning system (EWS) for coral reef-lined coasts known as WaveFoRCE. The system aims to provide all nations and people living on a coral reef-lined coast anywhere in the world with an up to 7.5-day forecast of storm wave-driven flood events.</p>","largerWorkType":{"id":25,"text":"Newsletter"},"largerWorkTitle":"Environment Coastal & Offshore (ECO)","language":"English","publisher":"United Nations","usgsCitation":"Skirving, W., Storlazzi, C.D., and Smail, E.A., 2021, Wave-driven flood-forecasting on reef-lined coasts early warning system (WaveFoRCE), p. 144-147.","productDescription":"4 p.","startPage":"144","endPage":"147","ipdsId":"IP-127710","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386977,"type":{"id":15,"text":"Index Page"},"url":"https://www.oceandecade.org/news/128/ECO-Magazine--special-digital-issue-on-the-Ocean-Decade-May-2021"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skirving, William","contributorId":224303,"corporation":false,"usgs":false,"family":"Skirving","given":"William","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smail, Emily A","contributorId":217219,"corporation":false,"usgs":false,"family":"Smail","given":"Emily","email":"","middleInitial":"A","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818747,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70221150,"text":"70221150 - 2021 - Aeolian sediments in paleowetland deposits of the Las Vegas Formation","interactions":[],"lastModifiedDate":"2022-01-06T17:13:04.657899","indexId":"70221150","displayToPublicDate":"2021-05-17T08:21:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Aeolian sediments in paleowetland deposits of the Las Vegas Formation","docAbstract":"<div class=\"abstract-content\"><div class=\"abstract\" data-abstract-type=\"normal\"><p>The Las Vegas Formation (LVF) is a well-characterized sequence of groundwater discharge (GWD) deposits exposed in and around the Las Vegas Valley in southern Nevada. Nearly monolithologic bedrock surrounds the valley, which provides an excellent opportunity to test the hypothesis that GWD deposits include an aeolian component. Mineralogical data indicate that the LVF sediments are dominated by carbonate minerals, similar to the local bedrock, but silicate minerals are also present. The median particle size is ~35 μm, consistent with modern dust in the region, and magnetic properties contrast strongly with local bedrock, implying an extralocal origin. By combining geochemical data from the LVF sediments and modern dust, we found that an average of ~25% of the LVF deposits were introduced by aeolian processes. The remainder consists primarily of authigenic groundwater carbonate as well as minor amounts of alluvial material and soil carbonate. Our data also show that the aeolian sediments accumulated in spring ecosystems in the Las Vegas Valley in a manner that was independent of both time and the specific hydrologic environment. These results have broad implications for investigations of GWD deposits located elsewhere in the southwestern U.S. and worldwide.</p></div></div>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2021.14","usgsCitation":"Goldstein, H.L., Springer, K.B., Pigati, J.S., Reheis, M.C., and Skipp, G.L., 2021, Aeolian sediments in paleowetland deposits of the Las Vegas Formation: Quaternary Research, v. 104, p. 1-13, https://doi.org/10.1017/qua.2021.14.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-121674","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":436360,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96578BP","text":"USGS data release","linkHelpText":"Supporting data for Physical and chemical evidence for an aeolian component of paleowetland deposits"},{"id":386175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Las Vegas Valley watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.71899414062499,\n              35.68407153314097\n            ],\n            [\n              -114.49951171875,\n              35.68407153314097\n            ],\n            [\n              -114.49951171875,\n              36.465471886798134\n            ],\n            [\n              -115.71899414062499,\n              36.465471886798134\n            ],\n            [\n              -115.71899414062499,\n              35.68407153314097\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"104","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Goldstein, Harland L. 0000-0002-6092-8818 hgoldstein@usgs.gov","orcid":"https://orcid.org/0000-0002-6092-8818","contributorId":807,"corporation":false,"usgs":true,"family":"Goldstein","given":"Harland","email":"hgoldstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reheis, Marith C. 0000-0002-8359-323X mreheis@usgs.gov","orcid":"https://orcid.org/0000-0002-8359-323X","contributorId":138571,"corporation":false,"usgs":true,"family":"Reheis","given":"Marith","email":"mreheis@usgs.gov","middleInitial":"C.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skipp, Gary L. 0000-0002-9404-0980","orcid":"https://orcid.org/0000-0002-9404-0980","contributorId":201777,"corporation":false,"usgs":true,"family":"Skipp","given":"Gary","email":"","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816856,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70221168,"text":"70221168 - 2021 - Quantifying slopes as a driver of forest to marsh conversion using geospatial techniques: Application to Chesapeake Bay coastal-plain, USA","interactions":[],"lastModifiedDate":"2021-06-04T12:58:00.496999","indexId":"70221168","displayToPublicDate":"2021-05-17T07:49:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying slopes as a driver of forest to marsh conversion using geospatial techniques: Application to Chesapeake Bay coastal-plain, USA","docAbstract":"<div class=\"JournalAbstract\"><p class=\"mb15\">Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fenvs.2021.616319","usgsCitation":"Molino, G.D., Defne, Z., Aretxabaleta, A., Ganju, N., and Carr, J., 2021, Quantifying slopes as a driver of forest to marsh conversion using geospatial techniques: Application to Chesapeake Bay coastal-plain, USA: Frontiers in Environmental Science, v. 9, 616319, 13 p., https://doi.org/10.3389/fenvs.2021.616319.","productDescription":"616319, 13 p.","ipdsId":"IP-120435","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":452230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2021.616319","text":"Publisher Index Page"},{"id":386201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay coastal-plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.87109375,\n              36.84446074079564\n            ],\n            [\n              -75.12451171875,\n              36.84446074079564\n            ],\n            [\n              -75.12451171875,\n              39.740986355883564\n            ],\n            [\n              -77.87109375,\n              39.740986355883564\n            ],\n            [\n              -77.87109375,\n              36.84446074079564\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Molino, Grace Damore 0000-0001-7345-8619","orcid":"https://orcid.org/0000-0001-7345-8619","contributorId":225086,"corporation":false,"usgs":true,"family":"Molino","given":"Grace","email":"","middleInitial":"Damore","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816930,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":816931,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220514,"text":"70220514 - 2021 - Monitoring long-term riparian vegetation trends to inform local habitat management in a mountainous environment","interactions":[],"lastModifiedDate":"2021-05-19T12:04:52.977459","indexId":"70220514","displayToPublicDate":"2021-05-17T07:22:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring long-term riparian vegetation trends to inform local habitat management in a mountainous environment","docAbstract":"<div id=\"ab015\" class=\"abstract author\" lang=\"en\"><div id=\"as015\"><p id=\"sp0015\">Riparian ecosystems provide critical habitat for many species, yet assessment of vegetation condition at local scales is difficult to measure when considering large areas over long time periods. We present a framework to map and monitor two deciduous cover types, upland and riparian, occupying a small fraction of an expansive, mountainous landscape in north-central Wyoming. Initially, we developed broad-scale predictions of predominant woody vegetation types by integrating Landsat data into species distribution models and combining subsequent outputs into a synthesis map. Then, we evaluated a 35-year Landsat time series (1985–2019) using the Mann-Kendall test to identify significant trends in the condition of upland and riparian deciduous vegetation and assessed the rate and direction of change using the Theil-Sen estimator. Finally, we used plot level data to assess the utility of the framework to detect bottom-up controls (ungulate browse pressure and management actions) on vegetation condition. The synthesis map had an overall correct classification rate of 87% and field data indicated deciduous vegetation within 45&nbsp;m of coniferous forest faces increased pressure of conifer expansion. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12&nbsp;years followed by a 9-year browning trend, before switching back to a greening trend for the last 13&nbsp;years of the study. Our results indicate trends are driven by the climate of the measurement period at the landscape scale. Although we did not find conclusive evidence to establish a strong link between browse pressure and satellite data, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. This framework is transferable to other understudied riparian environments throughout western North America to provide insight on ecohydrological processes and assess global and local stressors across broad spatiotemporal scales.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107807","usgsCitation":"Assal, T.J., Steen, V., Caltrider, T., Cundy, T., Stewart, C., Manning, N., and Anderson, P.J., 2021, Monitoring long-term riparian vegetation trends to inform local habitat management in a mountainous environment: Ecological Indicators, v. 127, 107807, 11 p., https://doi.org/10.1016/j.ecolind.2021.107807.","productDescription":"107807, 11 p.","ipdsId":"IP-124139","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452231,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107807","text":"Publisher Index Page"},{"id":385700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.8193359375,\n              42.261049162113856\n            ],\n            [\n              -106.083984375,\n              42.261049162113856\n            ],\n            [\n              -106.083984375,\n              45.02695045318546\n            ],\n            [\n              -109.8193359375,\n              45.02695045318546\n            ],\n            [\n              -109.8193359375,\n              42.261049162113856\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"127","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Assal, Timothy J. 0000-0001-6342-2954","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":258157,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":815864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steen, Valerie A. 0000-0002-1417-8139","orcid":"https://orcid.org/0000-0002-1417-8139","contributorId":205994,"corporation":false,"usgs":false,"family":"Steen","given":"Valerie A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":815865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caltrider, Todd","contributorId":258158,"corporation":false,"usgs":false,"family":"Caltrider","given":"Todd","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":815866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cundy, Travis","contributorId":258159,"corporation":false,"usgs":false,"family":"Cundy","given":"Travis","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":815867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Cheyenne","contributorId":258161,"corporation":false,"usgs":false,"family":"Stewart","given":"Cheyenne","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":815868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manning, Nicholas","contributorId":258163,"corporation":false,"usgs":false,"family":"Manning","given":"Nicholas","email":"","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":815869,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":815870,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70221223,"text":"70221223 - 2021 - Recovering individual-level spatial inference from aggregated binary data","interactions":[],"lastModifiedDate":"2023-06-23T13:23:33.186445","indexId":"70221223","displayToPublicDate":"2021-05-17T06:56:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5548,"text":"Spatial Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Recovering individual-level spatial inference from aggregated binary data","docAbstract":"<p><span>Binary regression models are commonly used in disciplines such as epidemiology and ecology to determine how spatial covariates influence individuals. In many studies, binary data are shared in a spatially aggregated form to protect privacy. For example, rather than reporting the location and result for each individual that was tested for a disease, researchers may report that a disease was detected or not detected within geopolitical units. Often, the&nbsp;</span>spatial aggregation<span>&nbsp;process obscures the values of response variables, spatial covariates, and locations of each individual, which makes recovering individual-level inference difficult. We show that applying a series of transformations, including a change of support, to a&nbsp;bivariate&nbsp;point process model allows researchers to recover individual-level inference for spatial covariates from spatially aggregated binary data. The series of transformations preserves the convenient interpretation of desirable binary regression models that are commonly applied to individual-level data. Using a simulation experiment, we compare the performance of our proposed method under varying types of spatial aggregation against the performance of standard approaches using the original individual-level data. We illustrate our method by modeling individual-level probability of infection using a data set that has been aggregated to protect an at-risk and endangered species of bats. Our simulation experiment and data illustration demonstrate the utility of the proposed method when access to original non-aggregated data is impractical or prohibited.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2021.100514","usgsCitation":"Walker, N., Hefley, T.J., Ballmann, A., Russell, R., and Walsh, D.P., 2021, Recovering individual-level spatial inference from aggregated binary data: Spatial Statistics, v. 44, 100514, 14 p.; Data release, https://doi.org/10.1016/j.spasta.2021.100514.","productDescription":"100514, 14 p.; Data release","ipdsId":"IP-118748","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":452237,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://arxiv.org/abs/2004.12013","text":"Publisher Index Page"},{"id":386279,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418318,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XUPDIB","text":"USGS data release","description":"USGS data release","linkHelpText":"Pseudogymnoascus destructans detections by US county (2008-2012)"}],"country":"United  States","otherGeospatial":"Northeast and Midwest United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.20703125,\n              36.87962060502676\n            ],\n            [\n              -66.26953125,\n              36.87962060502676\n            ],\n            [\n              -66.26953125,\n              49.15296965617042\n            ],\n            [\n              -97.20703125,\n              49.15296965617042\n            ],\n            [\n              -97.20703125,\n              36.87962060502676\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Nelson","contributorId":259320,"corporation":false,"usgs":false,"family":"Walker","given":"Nelson","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":817117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":817118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":817121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70220612,"text":"70220612 - 2021 - Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest","interactions":[],"lastModifiedDate":"2021-12-10T16:26:48.54244","indexId":"70220612","displayToPublicDate":"2021-05-17T06:49:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest","docAbstract":"<p><span>Recent studies have shown the oxygen isotopic composition (δ</span><span class=\"sup\">18</span><span>O) of modern terrestrial gastropod shells is determined largely by the δ</span><span class=\"sup\">18</span><span>O of precipitation. This implies that fossil shells could be used to reconstruct the δ</span><span class=\"sup\">18</span><span>O of paleo-precipitation as long as the isotopic system, including the hydrologic pathways of the local watershed and the gastropod systematics, is well understood. In this study, we measured the δ</span><span class=\"sup\">18</span><span>O values of 456 individual gastropod shells collected from paleowetland deposits in the San Pedro Valley, Arizona that range in age from ca. 29.1 to 9.8 ka. Isotopic differences of up to 2‰ were identified among the four taxa analyzed (Succineidae,&nbsp;</span><span class=\"italic\">Pupilla hebes</span><span>,&nbsp;</span><span class=\"italic\">Gastrocopta tappaniana</span><span>, and&nbsp;</span><span class=\"italic\">Vallonia gracilicosta</span><span>), with Succineidae shells yielding the highest values and&nbsp;</span><span class=\"italic\">V</span><span>.&nbsp;</span><span class=\"italic\">gracilicosta</span><span>&nbsp;shells exhibiting the lowest values. We used these data to construct a composite isotopic record that incorporates these taxonomic offsets, and found shell δ</span><span class=\"sup\">18</span><span>O values increased by ~4‰ between the last glacial maximum and early Holocene, which is similar to the magnitude, direction, and rate of isotopic change recorded by speleothems in the region. These results suggest the terrestrial gastropods analyzed here may be used as a proxy for past climate in a manner that is complementary to speleothems, but potentially with much greater spatial coverage.</span></p>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2021.18","usgsCitation":"Rech, J.A., Pigati, J.S., Springer, K.B., Bosch, S., Nekola, J.C., and Yanes, Y., 2021, Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest: Quaternary Research, v. 104, p. 43-53, https://doi.org/10.1017/qua.2021.18.","productDescription":"11 p.","startPage":"43","endPage":"53","onlineOnly":"N","ipdsId":"IP-122769","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":436362,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EISWFZ","text":"USGS data release","linkHelpText":"Data release for Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest"},{"id":385834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Arizona, Colorado, Nevada, New Mexico, Utah","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.1904296875,\n              42.032974332441405\n            ],\n            [\n              -119.92675781249999,\n              39.16414104768742\n            ],\n            [\n              -114.9169921875,\n              35.35321610123823\n            ],\n            [\n              -114.9609375,\n              32.731840896865684\n            ],\n            [\n              -111.005859375,\n              31.240985378021307\n            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  }\n  ]\n}","volume":"104","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rech, Jason A.","contributorId":117323,"corporation":false,"usgs":false,"family":"Rech","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":816201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bosch, Stephanie","contributorId":258260,"corporation":false,"usgs":false,"family":"Bosch","given":"Stephanie","email":"","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":816202,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nekola, Jeffrey C.","contributorId":26214,"corporation":false,"usgs":false,"family":"Nekola","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":816203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yanes, Yurena","contributorId":197219,"corporation":false,"usgs":false,"family":"Yanes","given":"Yurena","email":"","affiliations":[],"preferred":false,"id":816204,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70229404,"text":"70229404 - 2021 - Moose habitat selection and fitness consequences during two critical winter tick life stages in Vermont, United States","interactions":[],"lastModifiedDate":"2022-03-07T12:54:30.254398","indexId":"70229404","displayToPublicDate":"2021-05-17T06:41:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Moose habitat selection and fitness consequences during two critical winter tick life stages in Vermont, United States","docAbstract":"<div class=\"JournalAbstract\"><p class=\"mb0\">The moose (<i>Alces alces</i>) is a charismatic species in decline across much of their southern distribution in North America. In the northeastern United States, much of the reduction has been attributed to winter tick (<i>Dermacentor albipictus</i>) infestations. Winter ticks are fairly immobile throughout all life stages, and therefore their distribution patterns at any given time are shaped largely by the occurrence of moose across the landscape during the peak of two critical time periods: fall questing (when ticks latch onto moose) and spring drop-off (when engorged female ticks detach from moose). We used recent land cover and lidar data within a dynamic occupancy modeling framework to estimate first-order habitat selection (use vs. non-use) of female moose (<i>n</i><span>&nbsp;</span>= 74) during the tick questing and drop-off periods. Patch extinction and colonization rates between the fall questing and spring drop-off periods were strongly influenced by habitat and elevation, but these effects were diminished during the fall questing period when moose were more active across the landscape. From the fall questing period to the spring drop-off period, patches where colonization was high and extinction was low had higher proportions of young (shrub/forage) mixed forest at higher elevations. Further, we evaluated the fitness consequences of habitat selection by adult females during the fall questing period, when females and their calves acquire ticks. We compared Resource Selection Functions (RSF) for five females that successfully reared a calf to age 1 with five females whose calves perished due to ticks. Adult female moose whose offspring perished selected habitats in the fall that spatially coincided with areas of high occupancy probability during the spring tick drop-off period. In contrast, adult female moose whose offspring survived selected areas where the probability of occupancy during the spring drop-off was low; at present, natural selection may favor female adults who do not select the same habitats in fall as in spring. Our model coefficients and mapped results define “hotspots” that are likely encouraging the deleterious effects of the tick-moose cycle. These findings fill knowledge gaps about moose habitat selection that may improve the effectiveness of management aimed at reversing declining population trends.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2021.642276","usgsCitation":"Blouin, J., Debow, J., Rosenblatt, E., Hines, J.E., Alexander, C., Gieder, K., Fortin, N., Murdoch, J., and Donovan, T.M., 2021, Moose habitat selection and fitness consequences during two critical winter tick life stages in Vermont, United States: Frontiers in Ecology and Evolution, v. 9, 642276, 17 p., https://doi.org/10.3389/fevo.2021.642276.","productDescription":"642276, 17 p.","ipdsId":"IP-124909","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":452240,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.642276","text":"Publisher Index Page"},{"id":396778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -72.24609375,\n              44.22945656830167\n            ],\n            [\n              -71.455078125,\n              44.22945656830167\n            ],\n            [\n              -71.455078125,\n              45.02695045318546\n            ],\n            [\n              -72.24609375,\n              45.02695045318546\n            ],\n            [\n              -72.24609375,\n              44.22945656830167\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Blouin, Joshua","contributorId":276322,"corporation":false,"usgs":false,"family":"Blouin","given":"Joshua","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":837286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Debow, Jacob","contributorId":276321,"corporation":false,"usgs":false,"family":"Debow","given":"Jacob","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":837287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenblatt, Elias","contributorId":276324,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":837288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":837289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexander, Cedric","contributorId":280058,"corporation":false,"usgs":false,"family":"Alexander","given":"Cedric","email":"","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":837290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gieder, Katherina","contributorId":288028,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":837291,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fortin, Nicholas","contributorId":288029,"corporation":false,"usgs":false,"family":"Fortin","given":"Nicholas","email":"","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":837292,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Murdoch, James","contributorId":276325,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":837293,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":837285,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70223143,"text":"70223143 - 2021 - Spring phenology drives range shifts in a migratory Arctic ungulate with key implications for the future","interactions":[],"lastModifiedDate":"2021-09-14T16:51:40.1867","indexId":"70223143","displayToPublicDate":"2021-05-16T07:48:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Spring phenology drives range shifts in a migratory Arctic ungulate with key implications for the future","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Annual variation in phenology can have profound effects on the behavior of animals. As climate change advances spring phenology in ecosystems around the globe, it is becoming increasingly important to understand how animals respond to variation in the timing of seasonal events and how their responses may shift in the future. We investigated the influence of spring phenology on the behavior of migratory, barren-ground caribou (<i>Rangifer tarandus</i>), a species that has evolved to cope with short Arctic summers. Specifically, we examined the effect of spring snow melt and vegetation growth on the current and potential future space-use patterns of the Porcupine Caribou Herd (PCH), which exhibits large, inter-annual shifts in their calving and post-calving distributions across the U.S.–Canadian border. We quantified PCH selection for snow melt and vegetation phenology using machine learning models, determined how selection resulted in annual shifts in space-use, and then projected future distributions based on climate-driven phenology models. Caribou exhibited strong, scale-dependent selection for both snow melt and vegetation growth. During the calving season, caribou selected areas at finer scales where the snow had melted and vegetation was greening, but within broader landscapes that were still brown or snow covered. During the post-calving season, they selected vegetation with intermediate biomass expected to have high forage quality. Annual variation in spring phenology predicted major shifts in PCH space-use. In years with early spring phenology, PCH predominately used habitat in Alaska, while in years with late phenology, they spent more time in Yukon. Future climate conditions were projected to advance spring phenology, shifting PCH calving and post-calving distributions further west into Alaska. Our results demonstrate that caribou selection for habitat in specific phenological stages drive dramatic shifts in annual space-use patterns, and will likely affect future distributions, underscoring the importance of maintaining sufficient suitable habitat to allow for behavioral plasticity.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15682","usgsCitation":"Severson, J.P., Johnson, H.E., Arthur, S.M., Leacock, W., and Suitor, M.J., 2021, Spring phenology drives range shifts in a migratory Arctic ungulate with key implications for the future: Global Change Biology, v. 27, no. 19, p. 4546-4563, https://doi.org/10.1111/gcb.15682.","productDescription":"18 p.","startPage":"4546","endPage":"4563","ipdsId":"IP-127583","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":452243,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.15682","text":"External Repository"},{"id":436363,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TTRPAC","text":"USGS data release","linkHelpText":"Predicted Calving and Post-calving Season Resource Use of the Porcupine Caribou Herd During 2012-2018 With Future Projections for the 2030s, 2040s, and 2050s"},{"id":387896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -144.0087890625,\n              68.79209388053664\n            ],\n            [\n              -138.076171875,\n              68.79209388053664\n            ],\n            [\n              -138.076171875,\n              70.57611174177543\n            ],\n            [\n              -144.0087890625,\n              70.57611174177543\n            ],\n            [\n              -144.0087890625,\n              68.79209388053664\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"27","issue":"19","noUsgsAuthors":false,"publicationDate":"2021-07-12","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":821103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arthur, Stephen M.","contributorId":189438,"corporation":false,"usgs":false,"family":"Arthur","given":"Stephen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":821105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leacock, William","contributorId":192123,"corporation":false,"usgs":false,"family":"Leacock","given":"William","affiliations":[],"preferred":false,"id":821106,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suitor, Michael J.","contributorId":264206,"corporation":false,"usgs":false,"family":"Suitor","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":821107,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70259396,"text":"70259396 - 2021 - Repeating earthquakes during multiple phases of unrest and eruption at Mount Agung, Bali, Indonesia, 2017","interactions":[],"lastModifiedDate":"2024-10-07T12:23:05.595645","indexId":"70259396","displayToPublicDate":"2021-05-16T07:20:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18735,"text":"Frontiers in Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Repeating earthquakes during multiple phases of unrest and eruption at Mount Agung, Bali, Indonesia, 2017","docAbstract":"<div class=\"JournalAbstract\"><p class=\"mb15\">In 2017, Mount Agung produced a small (VEI 2) eruption that was preceded by an energetic volcano-tectonic (VT) swarm (&gt;800 earthquakes per day up to M4.9) and two months of declining activity. The period of decreased seismic activity complicated forecasting efforts for scientists monitoring the volcano. We examine the time history of earthquake families at Mount Agung in search of additional insight into the temporal changes in the shallow crust prior to eruption. Specifically, we analyze the period of declining seismic activity about five weeks prior to the eruption when forecasting uncertainty was greatest. We use REDPy (Hotovec-Ellis and Jeffries, 2016) to build a catalog of 6,508 earthquakes from 18 October 2017–15 February 2018 and group them into families of repeating earthquakes based on waveform similarity using a cross-correlation coefficient threshold of 0.8. We show that the evolution of earthquake families provides evidence that Mount Agung was progressing toward eruption even though overall earthquake rates and seismic-energy-release declined. We find that earthquake families that dominated seismicity during the beginning of the crisis ceased near the onset of tremor on 12 November 2017. Then, earthquake families took on characteristics commonly observed during effusive phases of eruptions on 15 November—a full six days before the first phreatomagmatic eruption on 21 November 2017 and a full ten days before the actual onset of lava effusion on 25 November 2017. We interpret the transitions in seismicity as the manifestation of a three-phase physical model including an Intrusion Phase, a Transition Phase, and a Eruptive Phase. During the Intrusion Phase, seismicity was dominated by VT earthquakes with a relatively high percentage of repeaters (59%) grouped into numerous (65) simultaneous families. During the Eruptive Phase, seismicity included both VT and low frequency earthquakes that grouped into relatively long-lived families despite a low overall percentage of repeaters (14%). The Transition Phase exhibited characteristics of earthquake families between the Intrusion Phase and Eruptive Phase. We conclude that the time history of earthquake families provides insight into the evolution of the stress distribution in the volcanic edifice, the development of the volcanic conduit, and seismogenesis of magma effusion. Finally, we discuss the role that repeating earthquakes could play in real-time monitoring at restless volcanoes. Our work suggests eruption forecasts can be improved by incorporating automatic processing codes to assist seismologists during sustained periods of high earthquake rates, even at sparsely monitored volcanoes.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2021.653164","usgsCitation":"Wellik, J., Prejean, S., and Syahbana, D.K., 2021, Repeating earthquakes during multiple phases of unrest and eruption at Mount Agung, Bali, Indonesia, 2017: Frontiers in Volcanology, v. 9, 653164, 11 p., https://doi.org/10.3389/feart.2021.653164.","productDescription":"653164, 11 p.","ipdsId":"IP-126621","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467245,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2021.653164","text":"Publisher Index Page"},{"id":462660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Bali, Mount Agung","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              113.79164923802762,\n              -7.308781448779953\n            ],\n            [\n              113.79164923802762,\n              -9.308941309600499\n            ],\n            [\n              116.32949103490353,\n              -9.308941309600499\n            ],\n            [\n              116.32949103490353,\n              -7.308781448779953\n            ],\n            [\n              113.79164923802762,\n              -7.308781448779953\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Wellik, John 0000-0002-8099-5794","orcid":"https://orcid.org/0000-0002-8099-5794","contributorId":204753,"corporation":false,"usgs":true,"family":"Wellik","given":"John","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prejean, Stephanie 0000-0003-0510-1989 sprejean@usgs.gov","orcid":"https://orcid.org/0000-0003-0510-1989","contributorId":172404,"corporation":false,"usgs":true,"family":"Prejean","given":"Stephanie","email":"sprejean@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Syahbana, Devy K.","contributorId":194994,"corporation":false,"usgs":false,"family":"Syahbana","given":"Devy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":915151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70220496,"text":"70220496 - 2021 - Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios — United States, April–September 2021","interactions":[],"lastModifiedDate":"2021-05-17T15:55:08.190065","indexId":"70220496","displayToPublicDate":"2021-05-14T11:54:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8598,"text":"Morbidity and Mortality Weekly Report","active":true,"publicationSubtype":{"id":10}},"title":"Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios — United States, April–September 2021","docAbstract":"<p><strong>What is already known about this topic?</strong></p><p>Increases in COVID-19 cases in March and early April occurred despite a large-scale vaccination program. Increases coincided with the spread of SARS-CoV-2 variants and relaxation of nonpharmaceutical interventions (NPIs).</p><p><strong>What is added by this report?</strong></p><p>Data from six models indicate that with high vaccination coverage and moderate NPI adherence, hospitalizations and deaths will likely remain low nationally, with a sharp decline in cases projected by July 2021. Lower NPI adherence could lead to substantial increases in severe COVID-19 outcomes, even with improved vaccination coverage.</p><p><strong>What are the implications for public health practice?</strong></p><p>High vaccination coverage and compliance with NPIs are essential to control COVID-19 and prevent surges in hospitalizations and deaths in the coming months.</p>","language":"English","publisher":"Centers for Disease Control and Prevention","doi":"10.15585/mmwr.mm7019e3","usgsCitation":"Borchering, R.K., Viboud, C., Howerton, E., Smith, C.P., Truelove, S., Runge, M.C., Reich, N.G., Contamin, L., Levander, J., Salerno, J., van Panhuis, W., Kinsey, M., Tallaksen, K., Obrecht, R.F., Asher, L., Costello, C., Kelbaugh, M., Wilson, S., Shin, L., Gallagher, M., Mullany, L., Rainwater-Lovett, K., Lemaitre, J., Dent, J., Grantz, K., Kaminsky, J., Lauer, S., Lee, E., Meredith, H., Perez-Saez, J., Keegan, L.T., Karlen, D., Chinazzi, M., Davis, J., Mu, K., Xiong, X., Pastore y Piontti, A., Vespignani, A., Srivastava, A., Porebski, P., Venkatramanan, S., Adiga, A., Lewis, B., Klahn, B., Outten, J., Schlitt, J., Corbett, P., Telionis, P.A., Wang, L., Peddireddy, A.S., Hurt, B., Chen, J., Vullikanti, A., Marathe, M., Healy, J., Slayton, R.B., Biggerstaff, M., Johansson, M.A., Shea, K., and Lessler, J., 2021, Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios — United States, April–September 2021: Morbidity and Mortality Weekly Report, v. 70, no. 19, p. 719-724, https://doi.org/10.15585/mmwr.mm7019e3.","productDescription":"6 p.","startPage":"719","endPage":"724","ipdsId":"IP-128781","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":452251,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15585/mmwr.mm7019e3","text":"Publisher Index 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Lijing","contributorId":258127,"corporation":false,"usgs":false,"family":"Wang","given":"Lijing","email":"","affiliations":[],"preferred":false,"id":815821,"contributorType":{"id":1,"text":"Authors"},"rank":49},{"text":"Peddireddy, Akhil S.","contributorId":258128,"corporation":false,"usgs":false,"family":"Peddireddy","given":"Akhil","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":815822,"contributorType":{"id":1,"text":"Authors"},"rank":50},{"text":"Hurt, Benjamin","contributorId":258129,"corporation":false,"usgs":false,"family":"Hurt","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":815823,"contributorType":{"id":1,"text":"Authors"},"rank":51},{"text":"Chen, Jiangzhuo","contributorId":258130,"corporation":false,"usgs":false,"family":"Chen","given":"Jiangzhuo","email":"","affiliations":[],"preferred":false,"id":815824,"contributorType":{"id":1,"text":"Authors"},"rank":52},{"text":"Vullikanti, 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Prevention","active":true,"usgs":false}],"preferred":false,"id":815828,"contributorType":{"id":1,"text":"Authors"},"rank":56},{"text":"Biggerstaff, Matthew","contributorId":258040,"corporation":false,"usgs":false,"family":"Biggerstaff","given":"Matthew","email":"","affiliations":[{"id":27265,"text":"Centers for Disease Control and Prevention","active":true,"usgs":false}],"preferred":false,"id":815829,"contributorType":{"id":1,"text":"Authors"},"rank":57},{"text":"Johansson, Michael A","contributorId":258041,"corporation":false,"usgs":false,"family":"Johansson","given":"Michael","email":"","middleInitial":"A","affiliations":[{"id":27265,"text":"Centers for Disease Control and Prevention","active":true,"usgs":false}],"preferred":false,"id":815830,"contributorType":{"id":1,"text":"Authors"},"rank":58},{"text":"Shea, Katriona","contributorId":8783,"corporation":false,"usgs":true,"family":"Shea","given":"Katriona","affiliations":[],"preferred":false,"id":815831,"contributorType":{"id":1,"text":"Authors"},"rank":59},{"text":"Lessler, Justin","contributorId":258042,"corporation":false,"usgs":false,"family":"Lessler","given":"Justin","email":"","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":815832,"contributorType":{"id":1,"text":"Authors"},"rank":60}]}}
,{"id":70228936,"text":"70228936 - 2021 - Effects of elk and bison herbivory on narrowleaf cottonwood","interactions":[],"lastModifiedDate":"2022-02-24T16:30:26.288537","indexId":"70228936","displayToPublicDate":"2021-05-14T10:28:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"title":"Effects of elk and bison herbivory on narrowleaf cottonwood","docAbstract":"<p><span>Ungulate browsing influences the structure and composition of woody plant communities, including species composition and biomass production as well as age distribution, recruitment, and mortality. We evaluated effects of elk and bison herbivory on narrowleaf cottonwood (</span><i>Populus angustifolia</i><span>) communities in a semiarid ecosystem in southern Colorado. Cottonwoods in this ecosystem have been aged at ≥300 years old and are among the oldest cottonwood trees in North America. We compared browsing intensity and structural and productivity responses of cottonwood to ungulate herbivory. We compared responses in sites with elk and bison, sites with elk but no bison, and sites where both ungulates were excluded. We found that the majority of browsing on cottonwood occurred during summer in this high desert ecosystem. Areas with both elk and bison had higher browse utilization than areas with only elk, but diet data indicated that elk consumed a much greater proportion of cottonwood than bison. Overall, browse utilization observed in this study was low to moderate compared to other studies, and our results may not be representative of sites experiencing intense year-round herbivory. Removal of all ungulate herbivory led to taller and denser cottonwood suckers; however, other environmental factors, in addition to herbivory, still strongly limit cottonwood growth and recruitment in this ecosystem.</span></p>","language":"English","publisher":"Brigham Young University","doi":"10.3398/064.081.0109","usgsCitation":"Zeigenfuss, L.C., and Schoenecker, K., 2021, Effects of elk and bison herbivory on narrowleaf cottonwood: Western North American Naturalist, v. 81, no. 1, p. 97-112, https://doi.org/10.3398/064.081.0109.","productDescription":"16 p.","startPage":"97","endPage":"112","ipdsId":"IP-080677","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":396429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Great Sand Dunes ecosystem of the San Luis Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.80795288085936,\n              37.615319243559085\n            ],\n            [\n              -105.40557861328125,\n              37.615319243559085\n            ],\n            [\n              -105.40557861328125,\n              37.996162679728116\n            ],\n            [\n              -105.80795288085936,\n              37.996162679728116\n            ],\n            [\n              -105.80795288085936,\n              37.615319243559085\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"81","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigenfuss, Linda C.","contributorId":280062,"corporation":false,"usgs":false,"family":"Zeigenfuss","given":"Linda","email":"","middleInitial":"C.","affiliations":[{"id":57415,"text":"LZ Ecology","active":true,"usgs":false}],"preferred":false,"id":835965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835966,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70220504,"text":"70220504 - 2021 - Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes","interactions":[],"lastModifiedDate":"2025-05-13T16:07:15.741437","indexId":"70220504","displayToPublicDate":"2021-05-14T07:25:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8601,"text":"Estuarine, Coastal, and Shelf Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Emerging dominance of <i>Paratrochammina simplissima</i> (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes","title":"Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes","docAbstract":"<p><span>Grand Bay&nbsp;estuary&nbsp;in coastal Mississippi and Alabama (USA) has undergone significant&nbsp;geomorphic changes&nbsp;over the last few centuries as a result of anthropogenic (bridge, road, and hardened&nbsp;shoreline&nbsp;construction) and climatic (extreme storm events) processes, which reduce&nbsp;freshwater input, sediment supply, and degrade barrier islands. To investigate how geomorphic changes may have altered the Grand Bay estuary, sediment push cores were collected for foraminiferal, sedimentological (organic matter content, grain-size distribution), and radiochemical (</span><sup>210</sup><span>Pb,</span><sup>137</sup><span>Cs, and&nbsp;</span><sup>7</sup><span>Be) analyses. Clay normalized&nbsp;geochronologies&nbsp;were determined with a constant rate of supply model. Based on downcore age-depth relationships, select intervals were analyzed for&nbsp;foraminifera&nbsp;in order to assess alterations in the&nbsp;microfossil&nbsp;assemblage in Grand Bay estuary over the 20th Century. All estuarine samples were low diversity (species richness: 1–10; Fisher's alpha diversity: 0.14–1.75); two species,&nbsp;</span><i>Ammotium salsum</i><span>&nbsp;and&nbsp;</span><i>Paratrochammina simplissima,</i><span>&nbsp;dominated all downcore assemblages.&nbsp;</span><i>Paratrochammina simplissima</i><span>&nbsp;increased in abundance up-core from a minor subsidiary species (median&nbsp;=&nbsp;4.7% at 19–20&nbsp;cm) to dominant or co-dominant with&nbsp;</span><i>A. salsum</i><span>&nbsp;over the 20th and early 21st Centuries in six cores, comprising up to 60.7% of a single sample. The emerging dominance of&nbsp;</span><i>P. simplissima</i><span>&nbsp;since ~1950 along with the reduction of brackish-estuarine taxa and introduction of calcareous species signifies increased&nbsp;salinity&nbsp;and less&nbsp;marsh&nbsp;organic matter preserved in the sediments. While seasonal dissolution limits our ability to chronologically constrain the introduction of calcareous species,&nbsp;</span><i>P. simplissima</i><span>, a species not referenced in taxonomic data from the northern&nbsp;Gulf of Mexico&nbsp;until 2012, is well constrained, following its first occurrence in the 1930s.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2021.107312","usgsCitation":"Ellis, A.M., and Smith, C., 2021, Emerging dominance of Paratrochammina simplissima (Cushman and McCulloch) in the northern Gulf of Mexico following hydrologic and geomorphic changes: Estuarine, Coastal, and Shelf Science, v. 255, 107312, 15 p., https://doi.org/10.1016/j.ecss.2021.107312.","productDescription":"107312, 15 p.","ipdsId":"IP-123715","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":385701,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Mississippi","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.52484130859375,\n              29.99062347853047\n            ],\n            [\n              -88.363037109375,\n              29.99062347853047\n            ],\n            [\n              -88.363037109375,\n              30.38709188778112\n            ],\n            [\n              -89.52484130859375,\n              30.38709188778112\n            ],\n            [\n              -89.52484130859375,\n              29.99062347853047\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"255","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ellis, Alisha M. 0000-0002-1785-020X aellis@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-020X","contributorId":192957,"corporation":false,"usgs":true,"family":"Ellis","given":"Alisha","email":"aellis@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815846,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70231206,"text":"70231206 - 2021 - The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US","interactions":[],"lastModifiedDate":"2022-05-03T11:58:19.866725","indexId":"70231206","displayToPublicDate":"2021-05-14T06:51:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>The U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) is the scientific foundation of seismic design regulations in the United States and is regularly updated to consider the best available science and data. The 2018 update of the conterminous U.S. NSHM includes significant changes to the underlying ground motion models (GMMs), most of which are necessary to enable the new multi-period response spectra (MPRS) requirements of seismic design regulations that use hazard results for 22 spectral periods and eight site classes. This article focuses on the GMMs used in the western United States (WUS) and is a companion to a recent article on the GMMs used in the central and eastern United States (CEUS). In the WUS, for crustal and subduction earthquakes, two models used in previous versions of the NSHM are excluded to provide consistency over all considered periods and site classes. To more accurately estimate ground motions at long periods in the vicinity of Los Angeles, San Francisco, Salt Lake City, and Seattle, the 2018 NSHM incorporates deep sedimentary basin depth from local seismic velocity models. The subduction GMMs considered lack basin depth terms and are modified to include an additional scale factor to account for this. This article documents the WUS GMMs used in the 2018 NSHM update and provides detail on the changes to GMM medians, aleatory variability, epistemic uncertainty, and site-effect models. It compares each of these components with those considered in prior NSHMs and discusses their total effect on hazard.</p></div></div>","language":"English","publisher":"Sage Publications","doi":"10.1177/87552930211011200","usgsCitation":"Powers, P.M., Rezaeian, S., Shumway, A., Petersen, M.D., Luco, N., Boyd, O.S., Moschetti, M.P., Frankel, A.D., and Thompson, E.M., 2021, The 2018 update of the US National Seismic Hazard Model: Ground motion models in the western US: Earthquake Spectra, v. 37, no. 4, p. 2315-2341, https://doi.org/10.1177/87552930211011200.","productDescription":"28 p.","startPage":"2315","endPage":"2341","ipdsId":"IP-127011","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930211011200","text":"Publisher Index 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,{"id":70223898,"text":"70223898 - 2021 - What's left before participatory modeling can fully support real-world environmental planning processes: A case study review","interactions":[],"lastModifiedDate":"2021-09-13T15:31:13.233129","indexId":"70223898","displayToPublicDate":"2021-05-13T10:08:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"What's left before participatory modeling can fully support real-world environmental planning processes: A case study review","docAbstract":"<p><span>In environmental participatory modeling (PM), both computer and non-computer-based modeling techniques are used to aid participatory problem description, solution, and decision-making actions in environmental contexts. Although many PM case studies have been published, few efforts have sought to systematically describe and understand dominant PM processes or establish best practices for PM. As a first step, we have reviewed a random sample of environmental PM case study articles (n&nbsp;=&nbsp;60) using a novel PM process evaluation instrument. We found that significant work likely remains for PM to fully support participatory and integrated planning processes. While PM reports systematically address knowledge integration and learning, they often neglect the facilitation of a multi-value perspective within a&nbsp;</span>democratic process<span>, and the integration across organizations within a governance system. If not reported, we suspect these aspects are also neglected in practice. We conclude with key research and practice issues for improving PM as an approach for real-world participatory planning and governance.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105073","usgsCitation":"Hedelin, B., Gray, S., Woehlke, S., BenDor, T., Singer, A., Jordan, R., Zellner, M., Giabbanelli, P., Glynn, P., Jenni, K., Jetter, A., Kolgani, N., Laursen, B., Leong, K.M., Schmitt Olabisi, L., and Sterling, E., 2021, What's left before participatory modeling can fully support real-world environmental planning processes: A case study review: Environmental Modelling & Software, v. 143, 105073, 15 p., https://doi.org/10.1016/j.envsoft.2021.105073.","productDescription":"105073, 15 p.","ipdsId":"IP-129309","costCenters":[],"links":[{"id":452273,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105073","text":"Publisher Index Page"},{"id":389153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hedelin, B.","contributorId":265685,"corporation":false,"usgs":false,"family":"Hedelin","given":"B.","affiliations":[],"preferred":false,"id":823192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, S.","contributorId":265686,"corporation":false,"usgs":false,"family":"Gray","given":"S.","email":"","affiliations":[],"preferred":false,"id":823193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woehlke, S.","contributorId":265687,"corporation":false,"usgs":false,"family":"Woehlke","given":"S.","email":"","affiliations":[],"preferred":false,"id":823194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"BenDor, T. K.","contributorId":19011,"corporation":false,"usgs":true,"family":"BenDor","given":"T. K.","affiliations":[],"preferred":false,"id":823195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singer, A.","contributorId":265688,"corporation":false,"usgs":false,"family":"Singer","given":"A.","affiliations":[],"preferred":false,"id":823196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jordan, R.","contributorId":62742,"corporation":false,"usgs":true,"family":"Jordan","given":"R.","email":"","affiliations":[],"preferred":false,"id":823197,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zellner, M.","contributorId":265689,"corporation":false,"usgs":false,"family":"Zellner","given":"M.","email":"","affiliations":[],"preferred":false,"id":823198,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Giabbanelli, P.","contributorId":265690,"corporation":false,"usgs":false,"family":"Giabbanelli","given":"P.","affiliations":[],"preferred":false,"id":823199,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Glynn, P.","contributorId":56394,"corporation":false,"usgs":true,"family":"Glynn","given":"P.","affiliations":[],"preferred":false,"id":823200,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jenni, K.","contributorId":131113,"corporation":false,"usgs":false,"family":"Jenni","given":"K.","email":"","affiliations":[{"id":7250,"text":"Insight Decisions LCC, 2200 Quitman Street, Denver, CO 80212","active":true,"usgs":false}],"preferred":false,"id":823201,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jetter, A","contributorId":265691,"corporation":false,"usgs":false,"family":"Jetter","given":"A","email":"","affiliations":[],"preferred":false,"id":823202,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kolgani, N.","contributorId":265692,"corporation":false,"usgs":false,"family":"Kolgani","given":"N.","email":"","affiliations":[],"preferred":false,"id":823203,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Laursen, B.","contributorId":265693,"corporation":false,"usgs":false,"family":"Laursen","given":"B.","email":"","affiliations":[],"preferred":false,"id":823204,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Leong, K. M.","contributorId":265694,"corporation":false,"usgs":false,"family":"Leong","given":"K.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":823205,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schmitt Olabisi, L.","contributorId":265695,"corporation":false,"usgs":false,"family":"Schmitt Olabisi","given":"L.","email":"","affiliations":[],"preferred":false,"id":823206,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Sterling, E.","contributorId":265696,"corporation":false,"usgs":false,"family":"Sterling","given":"E.","email":"","affiliations":[],"preferred":false,"id":823207,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70220443,"text":"70220443 - 2021 - Trophic transfer efficiency in the Lake Superior food web: Assessing the impacts of non-native species","interactions":[],"lastModifiedDate":"2021-08-03T16:11:08.337078","indexId":"70220443","displayToPublicDate":"2021-05-13T08:05:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Trophic transfer efficiency in the Lake Superior food web: Assessing the impacts of non-native species","docAbstract":"<p><span>Ecosystem-based management relies on understanding how perturbations influence ecosystem structure and function (e.g.,&nbsp;invasive species, exploitation, abiotic changes). However, data on unimpacted systems are scarce; therefore, we often rely on impacted systems to make inferences about ‘natural states.’ Among the Laurentian Great Lakes,&nbsp;</span>Lake Superior<span>&nbsp;provides a unique case study to address non-native species impacts because the food web is dominated by native species. Additionally, Lake Superior is both vertically (benthic versus pelagic) and horizontally (nearshore versus offshore) structured by depth, providing an opportunity to compare the function of these sub-food webs. We developed an updated Lake Superior EcoPath model using data from the 2005/2006 lake-wide multi-agency surveys covering multiple&nbsp;trophic levels. We then compared trophic transfer efficiency (TTE) to previously published EcoPath models. Finally, we compared ecosystem function of the 2005/2006 ecosystem to that with non-native linkages removed and compared native versus non-native species-specific approximations of TTE and trophic flow. Lake Superior was relatively efficient (TTE&nbsp;=&nbsp;0.14) compared to systems reported in a global review (average TTE&nbsp;=&nbsp;0.09), and the&nbsp;microbial loop&nbsp;was highly efficient (TTE&nbsp;&gt;&nbsp;0.20). Non-native species represented a very small proportion (&lt;0.01%) of total biomass and were generally more efficient and had higher trophic flow compared to native species. Our results provide valuable insight into the importance of the microbial loop and represent a baseline estimate of non-native species impacts on Lake Superior. Finally, this work is a starting point for further model development to predict future changes in the Lake Superior ecosystem.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.04.010","usgsCitation":"Mathias, B.G., Hrabik, T.R., Hoffman, J.C., Gorman, O., Seider, M., Sierszen, M.E., Vinson, M., Yule, D.L., and Yurista, P.M., 2021, Trophic transfer efficiency in the Lake Superior food web: Assessing the impacts of non-native species: Journal of Great Lakes Research, v. 47, no. 4, p. 1146-1158, https://doi.org/10.1016/j.jglr.2021.04.010.","productDescription":"13 p.","startPage":"1146","endPage":"1158","ipdsId":"IP-115192","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452278,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9067395","text":"External Repository"},{"id":436366,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W93YXH","text":"USGS data release","linkHelpText":"Compilation of Data for Parameterization of an Ecopath Model of Lake Superior at the Beginning of the 21st Century (2001-2016)"},{"id":385642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","otherGeospatial":"Lake Superior","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.63671874999997,\n              46.195042108660154\n            ],\n            [\n              -83.84765624999997,\n              46.195042108660154\n            ],\n            [\n              -83.84765624999997,\n              49.83798245308484\n            ],\n            [\n              -92.63671874999997,\n              49.83798245308484\n            ],\n            [\n              -92.63671874999997,\n              46.195042108660154\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mathias, Bryan G.","contributorId":240743,"corporation":false,"usgs":false,"family":"Mathias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":815547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hrabik, Thomas R.","contributorId":35614,"corporation":false,"usgs":false,"family":"Hrabik","given":"Thomas","email":"","middleInitial":"R.","affiliations":[{"id":6915,"text":"University of Minnesota - Duluth","active":true,"usgs":false}],"preferred":false,"id":815548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoffman, Joel C.","contributorId":84244,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman, Owen 0000-0003-0451-110X","orcid":"https://orcid.org/0000-0003-0451-110X","contributorId":216889,"corporation":false,"usgs":true,"family":"Gorman","given":"Owen","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seider, Michael J.","contributorId":258016,"corporation":false,"usgs":false,"family":"Seider","given":"Michael J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sierszen, Michael E.","contributorId":63320,"corporation":false,"usgs":false,"family":"Sierszen","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815554,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yurista, Peder M.","contributorId":127358,"corporation":false,"usgs":false,"family":"Yurista","given":"Peder","email":"","middleInitial":"M.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815555,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
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