{"pageNumber":"285","pageRowStart":"7100","pageSize":"25","recordCount":40783,"records":[{"id":70211902,"text":"70211902 - 2020 - Estimating abiotic thresholds for sagebrush condition class in the western United States","interactions":[],"lastModifiedDate":"2024-05-17T15:45:40.859631","indexId":"70211902","displayToPublicDate":"2020-03-01T14:13:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating abiotic thresholds for sagebrush condition class in the western United States","docAbstract":"<p><span>Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping areas of stability and areas of change using machine-learning algorithms allows both the identification of dominant abiotic variables that drive ecosystem dynamics and the variables’ important thresholds. We develop a decision-tree model with rulesets that estimate three classes of sagebrush condition (i.e., sagebrush recovery, tipping point [ecosystem degradation], and stable). We find rulesets that primarily drive development of the sagebrush recovery class indicate areas of midelevations (1 602 m), warm 30-yr July temperature maximums (tmax) (30.62°C), and 30-yr March precipitation (ppt) averages equal to 26.26 mm, about 10% of the 30-yr annual ppt values. Tipping point and stable classes occur at elevations that are lower (1 505 m) and higher (1 939 m), respectively, more mesic during March and annually, and experience lower 30-yr July tmax averages. These defined variable averages can be used to understand current dynamics of sagebrush condition and to predict where future transitions may occur under novel conditions.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2019.10.010","usgsCitation":"Boyte, S., Wylie, B.K., Gu, Y., and Major, D.J., 2020, Estimating abiotic thresholds for sagebrush condition class in the western United States: Rangeland Ecology & Management, v. 73, no. 2, p. 297-308, https://doi.org/10.1016/j.rama.2019.10.010.","productDescription":"12 p.","startPage":"297","endPage":"308","ipdsId":"IP-109577","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457545,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2019.10.010","text":"Publisher Index Page"},{"id":377373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.59667968749999,\n              35.71083783530009\n            ],\n            [\n              -103.0078125,\n              35.71083783530009\n            ],\n            [\n              -103.0078125,\n              47.517200697839414\n            ],\n            [\n              -121.59667968749999,\n              47.517200697839414\n            ],\n            [\n              -121.59667968749999,\n              35.71083783530009\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"73","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":795726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":795727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gu, Yingxin 0000-0002-3544-1856","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":209983,"corporation":false,"usgs":false,"family":"Gu","given":"Yingxin","affiliations":[],"preferred":false,"id":795728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Major, Donald J.","contributorId":83405,"corporation":false,"usgs":false,"family":"Major","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":795729,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227932,"text":"70227932 - 2020 - Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest","interactions":[],"lastModifiedDate":"2022-02-02T18:19:51.200058","indexId":"70227932","displayToPublicDate":"2020-03-01T11:56:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest","docAbstract":"<p>Climate-related changes in fish communities can present new challenges for fishery managers who must address declines in cool- and cold-water sportfish while dealing with increased abundance of warm-water sportfish. We used largemouth bass (<i>Micropterus salmoides</i>) in Wisconsin lakes as model populations to determine whether angler harvest provides a realistic method for reducing abundance of a popular warm-water sportfish that has become more prevalent and has prompted management concerns around the globe. Model results indicate largemouth bass will be resilient to increased fishing mortality. Furthermore, high rates of voluntary catch-and-release occurring in most largemouth bass fisheries likely preclude fishing mortality rates required to reduce bass abundance at meaningful levels (≥25% reductions). Increasing fishing mortality in these scenarios may require more “stimulus” than merely providing anglers with greater harvest opportunities via less stringent harvest regulations. Angler harvest could result in populations dominated by small fish, a scenario that may be undesirable to anglers, but could provide ecological benefits in certain situations.</p>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0035","usgsCitation":"Sullivan, C., Isermann, D.A., Whitlock, K.E., and Hansen, J.F., 2020, Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 3, p. 520-533, https://doi.org/10.1139/cjfas-2019-0035.","productDescription":"14 p.","startPage":"520","endPage":"533","ipdsId":"IP-094995","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconain","otherGeospatial":"Big Arbor Vitae Lake, Big Sissabagama Lake, Jungle 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disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitlock, Kaitlin E.","contributorId":273695,"corporation":false,"usgs":false,"family":"Whitlock","given":"Kaitlin","email":"","middleInitial":"E.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":832750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Jonathan F.","contributorId":171519,"corporation":false,"usgs":false,"family":"Hansen","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":832602,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70220183,"text":"70220183 - 2020 - A primer of fishery studies in Grand Canyon: The nonnative fish removal story","interactions":[],"lastModifiedDate":"2025-03-14T15:13:40.124095","indexId":"70220183","displayToPublicDate":"2020-03-01T11:16:00","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":8569,"text":"Boatman's Quarterly Review","active":true,"publicationSubtype":{"id":30}},"title":"A primer of fishery studies in Grand Canyon: The nonnative fish removal story","docAbstract":"Globally, rivers have become the most altered of ecosystems, chiefly due to pollution, water withdrawals, and dams that have modified their former function, and led to large and unforeseen impacts, particularly for fish populations. Extensive research is directed at studying impacts of dams because they sever migration routes and change the physical template (flow, temperature, and sediment and organic loads), and by extension, influence vital rates of fish populations such as growth, survival, movement and recruitment. Prior to introduction of nonnative fishes and network of dams, the humpback chub (Gila cypha, chub) was broadly distributed throughout the Colorado River (mainstem). Since then, chub have declined over their entire historical range and are now restricted to six populations, a factor that led to it being Federally listed as an endangered species. The largest of these chub populations is found in Grand Canyon and is isolated from other upstream populations by Glen Canyon Dam (Dam). Over 90% of this population resides within the Little Colorado River (LCR) and mainstem in regions adjacent to the confluence. The remainder is broadly distributed in small aggregations throughout the ecosystem. Cold water temperatures from the Dam has largely impeded the growth and spawning of chub in the mainstem. Luckily, chub spawn and rear young successfully in the seasonally warm and saline waters of the LCR, though survival of some juveniles (< 200 mm total length) that disperse into the mainstem varies among years.","language":"English","publisher":"Grand Canyon River Guides","usgsCitation":"Yard, M.D., 2020, A primer of fishery studies in Grand Canyon: The nonnative fish removal story: Boatman's Quarterly Review, v. 33, no. 1, p. 8-10.","productDescription":"3 p.","startPage":"8","endPage":"10","ipdsId":"IP-114117","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":399159,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcrg.org/bqr"},{"id":399160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.04907226562499,\n              35.49198366469642\n            ],\n            [\n              -111.412353515625,\n              35.49198366469642\n            ],\n            [\n              -111.412353515625,\n              36.97183825093165\n            ],\n            [\n              -114.04907226562499,\n              36.97183825093165\n            ],\n            [\n              -114.04907226562499,\n              35.49198366469642\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"33","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":814658,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70227680,"text":"70227680 - 2020 - Testing prediction accuracy in short-term ecological studies","interactions":[],"lastModifiedDate":"2022-01-26T17:27:52.033911","indexId":"70227680","displayToPublicDate":"2020-03-01T11:13:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":970,"text":"Basic and Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Testing prediction accuracy in short-term ecological studies","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0005\" class=\"abstract author\"><div id=\"abst0005\"><p id=\"spar0035\"><span>Applied&nbsp;ecology&nbsp;is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via&nbsp;</span><i>k</i><span>-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored&nbsp;small mammal&nbsp;abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (</span><i>n</i>&nbsp;=&nbsp;28 sampling locations across 125&nbsp;km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.baae.2020.01.003","usgsCitation":"Wood, C.M., Loman, Z., McKinney, S.T., and Loftin, C., 2020, Testing prediction accuracy in short-term ecological studies: Basic and Applied Ecology, v. 43, p. 77-85, https://doi.org/10.1016/j.baae.2020.01.003.","productDescription":"9 p.","startPage":"77","endPage":"85","ipdsId":"IP-073394","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.baae.2020.01.003","text":"Publisher Index Page"},{"id":394885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, New Hampshire","otherGeospatial":"Appalachian Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -72.08129882812499,\n              44.190082025040525\n            ],\n            [\n              -72.00439453125,\n              43.739352079154706\n            ],\n            [\n              -71.52099609375,\n              43.58834891179792\n            ],\n            [\n              -69.66430664062499,\n              45.127804527473224\n            ],\n            [\n              -70.125732421875,\n              45.598665689820635\n            ],\n            [\n              -70.86181640625,\n              45.22848059584359\n            ],\n            [\n              -71.817626953125,\n              44.72332018895825\n            ],\n            [\n              -72.08129882812499,\n              44.190082025040525\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Connor M.","contributorId":167785,"corporation":false,"usgs":false,"family":"Wood","given":"Connor","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":831705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loman, Zachary G.","contributorId":145932,"corporation":false,"usgs":false,"family":"Loman","given":"Zachary G.","affiliations":[],"preferred":false,"id":831788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinney, Shawn T. smckinney@usgs.gov","contributorId":5175,"corporation":false,"usgs":true,"family":"McKinney","given":"Shawn","email":"smckinney@usgs.gov","middleInitial":"T.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":831707,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70249358,"text":"70249358 - 2020 - Transitioning from change detection to monitoring with remote sensing: A paradigm shift","interactions":[],"lastModifiedDate":"2023-10-04T23:41:21.337275","indexId":"70249358","displayToPublicDate":"2020-03-01T09:55:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Transitioning from change detection to monitoring with remote sensing: A paradigm shift","docAbstract":"The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111558","usgsCitation":"Woodcock, C.E., Loveland, T., Herold, M., and Bauer, M.E., 2020, Transitioning from change detection to monitoring with remote sensing: A paradigm shift: Remote Sensing of Environment, v. 238, 111558, 5 p., https://doi.org/10.1016/j.rse.2019.111558.","productDescription":"111558, 5 p.","ipdsId":"IP-113612","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457553,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111558","text":"Publisher Index Page"},{"id":421598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodcock, Curtis E.","contributorId":294423,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","email":"","middleInitial":"E.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":885300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herold, Martin","contributorId":330558,"corporation":false,"usgs":false,"family":"Herold","given":"Martin","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":885302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Marvin E.","contributorId":330559,"corporation":false,"usgs":false,"family":"Bauer","given":"Marvin","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":885303,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215305,"text":"70215305 - 2020 - Planetary sensor models interoperability using the community sensor model specification","interactions":[],"lastModifiedDate":"2020-10-15T14:38:30.665861","indexId":"70215305","displayToPublicDate":"2020-03-01T09:35:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Planetary sensor models interoperability using the community sensor model specification","docAbstract":"<p><span>This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation (</span><a class=\"linkBehavior\" href=\"https://github.com/USGS-Astrogeology/usgscsm\" data-mce-href=\"https://github.com/USGS-Astrogeology/usgscsm\">https://github.com/USGS-Astrogeology/usgscsm</a><span>). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.</span></p>","language":"English","publisher":"Wiley","doi":"10.1029/2019EA000713","usgsCitation":"Laura, J., Mapel, J., and Hare, T.M., 2020, Planetary sensor models interoperability using the community sensor model specification: Earth and Space Science, v. 7, no. 6, e2019EA000713, 17 p., https://doi.org/10.1029/2019EA000713.","productDescription":"e2019EA000713, 17 p.","ipdsId":"IP-108414","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":457556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019ea000713","text":"Publisher Index Page"},{"id":379404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mapel, Jesse 0000-0001-5756-0373","orcid":"https://orcid.org/0000-0001-5756-0373","contributorId":206344,"corporation":false,"usgs":true,"family":"Mapel","given":"Jesse","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227142,"text":"70227142 - 2020 - Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","interactions":[],"lastModifiedDate":"2022-01-03T16:02:02.227914","indexId":"70227142","displayToPublicDate":"2020-03-01T08:28:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","docAbstract":"<p><span>Eurasian zebra and quagga mussels were likely introduced to the Laurentian Great Lakes via ballast water release in the 1980s, and their range has since expanded across the US, including some of their southernmost occurrences in Texas. Their spread into the state has resulted in a need to revise previous delimitations of suitable dreissenid habitat. We therefore assessed invasion risk in Texas by 1) predicting distribution of suitable habitat of zebra and quagga mussels using Maxent species distribution models based upon global occurrence and climate data; and 2) refining lake-specific predictions via collection and analysis of physicochemical data. Maxent models predicted a lack of suitable habitat for quagga mussels within Texas. However, models did predict the presence of suitable zebra mussel habitat, with hotspots of suitable habitat occurring along the Red and Sabine Rivers of north and east Texas, as well as patches of suitable habitat in central Texas between the Colorado and Brazos Rivers and extending inland along the Gulf Coast. Although predicted suitable habitat extended further west than in previous models, most of the Texas panhandle, west Texas extending toward El Paso, and the Rio Grande valley were predicted to provide poor zebra mussel habitat suitability. Collection of physicochemical data (i.e., dissolved oxygen, pH, specific conductance, and temperature on-site as well as laboratory analysis for Ca, N, and P) from zebra mussel-invaded lakes and a subset of uninvaded but high-risk lakes of North and Central Texas, did not refine model predictions because there was no apparent distinction between invaded and uninvaded lakes. Overall, we demonstrated that while quagga mussels do not appear to represent an invasive threat in Texas, abundant suitable habitat for continuing zebra mussel invasion exists within the state. The threat of continued expansion of this poster-child for negative invasive species impacts warrants further prevention efforts, management, and research.</span></p>","language":"English","publisher":"REABIC","usgsCitation":"Barnes, M., and Patino, R., 2020, Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics: Management of Biological Invasions, v. 11, no. 1, p. 63-79.","productDescription":"17 p.","startPage":"63","endPage":"79","ipdsId":"IP-107295","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":393733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393746,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.reabic.net/journals/mbi/2020/Issue1.aspx"}],"country":"United States","state":"Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -99.30541992187499,\n              29.334298230315675\n            ],\n            [\n              -95.361328125,\n              29.334298230315675\n            ],\n            [\n              -95.361328125,\n              33.925129700072\n            ],\n            [\n              -99.30541992187499,\n              33.925129700072\n            ],\n            [\n              -99.30541992187499,\n              29.334298230315675\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, M. 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,{"id":70209032,"text":"70209032 - 2020 - The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes","interactions":[],"lastModifiedDate":"2020-06-04T16:59:39.703625","indexId":"70209032","displayToPublicDate":"2020-03-01T07:42:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes","docAbstract":"(Munson) The challenges of restoration in dryland ecosystems are growing due to a rise in anthropogenic disturbance and increasing aridity. Plant functional traits are often used to predict plant performance and can offer a window into the potential outcomes of restoration efforts across environmental gradients. We tracked 15 years of seeding outcomes across 150 sites on the Colorado Plateau, a cold desert ecoregion in the western United States, and analyzed the independent and interactive effects of functional traits (seed mass, height, and specific leaf area) and local biologically relevant climate variables on seeding success. We predicted that the best models would include an interaction between plant traits and climate, indicating a need to match the right trait value to the right climate conditions in order to maximize seeding success. Indeed, we found that both plant height and seed size significantly interacted with temperature seasonality, with larger seeds and taller plants performing better in more seasonal environments. We also determined that these trait-environment patterns are not driven by the use of native vs. non-native species. Our results lend insight to using plant traits to inform the selection of seed mixes for restoring areas with specific climatic conditions, while also demonstrating the strong influence of temperature seasonality on seeding success in the Colorado Plateau region.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2110","usgsCitation":"Balazs, K.R., Kramer, A.T., Munson, S.M., Talkington, N., Still, S., and Butterfield, B.J., 2020, The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes: Ecological Applications, v. 30, no. 4, e02110, 7 p., https://doi.org/10.1002/eap.2110.","productDescription":"e02110, 7 p.","ipdsId":"IP-104892","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":457560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2110","text":"Publisher Index 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,{"id":70208943,"text":"70208943 - 2020 - Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach","interactions":[],"lastModifiedDate":"2024-05-17T15:47:05.397919","indexId":"70208943","displayToPublicDate":"2020-03-01T06:33:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach","docAbstract":"<p><span>Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the&nbsp;</span>U.S.<span>&nbsp;Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111356","usgsCitation":"Brown, J.F., Tollerud, H.J., Barber, C., Zhou, Q., Dwyer, J.L., Vogelmann, J., Loveland, T., Woodcock, C., Stehman, S.V., Zhu, Z., Pengra, B., Smith, K., Horton, J., Xian, G.Z., Auch, R.F., Sohl, T.L., Sayler, K., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J.R., 2020, Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, 111356, 18 p.; 3 Data Releases, https://doi.org/10.1016/j.rse.2019.111356.","productDescription":"111356, 18 p.; 3 Data Releases","ipdsId":"IP-102378","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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,{"id":70211874,"text":"70211874 - 2020 - Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis","interactions":[],"lastModifiedDate":"2020-08-12T15:03:49.11224","indexId":"70211874","displayToPublicDate":"2020-02-29T10:53:30","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis","docAbstract":"We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis.  Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Faulds, J., Brown, S.C., Coolbaugh, M.F., Queen, J.H., Treitel, S., Fehler, M., Mlawsky, E., Glen, J.M., Lindsey, C., Burns, E., Smith, C.M., Gu, C., and Ayling, B.F., 2020, Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis, <i>in</i> Proceedings: 45th workshop on geothermal reservoir engineering, Stanford, CA, February 10-12, 2020, p. 229-234.","productDescription":"6 p.","startPage":"229","endPage":"234","ipdsId":"IP-115745","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377336,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.proceedings.com/53283.html"}],"country":"United 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F.","contributorId":193870,"corporation":false,"usgs":false,"family":"Coolbaugh","given":"Mark","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":795497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Queen, John H.","contributorId":237883,"corporation":false,"usgs":false,"family":"Queen","given":"John","email":"","middleInitial":"H.","affiliations":[{"id":47634,"text":"Hi-Q Geophysical, Inc.","active":true,"usgs":false}],"preferred":false,"id":795499,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Treitel, Sven","contributorId":237884,"corporation":false,"usgs":false,"family":"Treitel","given":"Sven","email":"","affiliations":[{"id":47634,"text":"Hi-Q Geophysical, Inc.","active":true,"usgs":false}],"preferred":false,"id":795500,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fehler, Michael","contributorId":237888,"corporation":false,"usgs":false,"family":"Fehler","given":"Michael","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":795501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mlawsky, Eli","contributorId":237889,"corporation":false,"usgs":false,"family":"Mlawsky","given":"Eli","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":795502,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795503,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lindsey, Cary","contributorId":237893,"corporation":false,"usgs":false,"family":"Lindsey","given":"Cary","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":795504,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795505,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Smith, Connor M.","contributorId":237894,"corporation":false,"usgs":false,"family":"Smith","given":"Connor","email":"","middleInitial":"M.","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":795506,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Gu, Chen","contributorId":237896,"corporation":false,"usgs":false,"family":"Gu","given":"Chen","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":795507,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ayling, Bridget F.","contributorId":237899,"corporation":false,"usgs":false,"family":"Ayling","given":"Bridget","email":"","middleInitial":"F.","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":795508,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70211876,"text":"70211876 - 2020 - Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","interactions":[],"lastModifiedDate":"2020-08-12T15:04:28.21349","indexId":"70211876","displayToPublicDate":"2020-02-29T10:39:46","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","docAbstract":"The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that integrates data at the regional or basin scale to define favorable plays for exploration in a systematic fashion. The success of play fairway analysis in geothermal exploration depends critically on defining a systematic methodology that is grounded in theory and adapted to the geologic and hydrologic framework of real geothermal systems. \nThis study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the Snake River Plain: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for Heat were heat flow, the distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Permeability was derived from stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetic and gravity data. Data for Seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment (CRS) maps for Heat, Permeability and Seal, which were combined to create a Composite Common Risk Segment (CCRS) map for all of southern Idaho that reflects the risk associated with geothermal resource exploration and helps to identify favorable resource tracks. \nOur data suggests that important undiscovered geothermal resources may be located in several areas of the SRP, including the western SRP (associated with buried lineaments capped by lacustrine sediment), at lineament intersections in the central SRP, and along the margins of the eastern SRP. These blind resources are associated with temperatures sufficient to support electricity production, and may be exploitable with existing deep drilling technology. We are testing our methodology by drilling a geothermal test well in Camas Prairie, ID, confirm our predictions of permeability and reservoir temperature.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Shervais, J., Glen, J.M., Siler, D.L., Liberty, L., Nielson, D., Garg, S., Dobson, P., Gasperikova, E., Sonnenthal, E., Newell, D., Evans, J.E., DeAngelo, J., Peacock, J., Earney, T.E., Schermerhorn, W.D., and Neupane, G., 2020, Play fairway analysis in geothermal exploration: The Snake River plain volcanic province, <i>in</i> Proceedings: 45th workshop on geothermal reservoir engineering, Stanford, CA, February 10-12, 2020, p. 186-194.","productDescription":"9 p.","startPage":"186","endPage":"194","ipdsId":"IP-115891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377334,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.proceedings.com/53283.html"}],"country":"United States","state":"Idaho","otherGeospatial":"Snake River Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.5936279296875,\n              43.36512572875844\n            ],\n            [\n              -111.544189453125,\n              44.15462243076731\n            ],\n            [\n              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Center","active":true,"usgs":true}],"preferred":true,"id":795548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liberty, Lee","contributorId":189113,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","affiliations":[],"preferred":false,"id":795550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielson, Dennis","contributorId":237918,"corporation":false,"usgs":false,"family":"Nielson","given":"Dennis","affiliations":[{"id":47642,"text":"DOSECC Exploration 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Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":795555,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Newell, Dennis","contributorId":237921,"corporation":false,"usgs":false,"family":"Newell","given":"Dennis","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795556,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":795557,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795558,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795559,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795560,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795561,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Neupane, Ghanashyam","contributorId":237924,"corporation":false,"usgs":false,"family":"Neupane","given":"Ghanashyam","email":"","affiliations":[{"id":27243,"text":"Idaho National Laboratory","active":true,"usgs":false}],"preferred":false,"id":795562,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70209184,"text":"70209184 - 2020 - A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states","interactions":[],"lastModifiedDate":"2020-07-09T14:43:19.756855","indexId":"70209184","displayToPublicDate":"2020-02-29T07:07:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states","docAbstract":"Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: 1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples, and 2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack-Jolly-Seber model. Next, we apply marginalization to multistate capture-recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R2. Then, we focus on recovery of discrete latent states, defining different types of conditional probabilities and showing how quantities such as population abundance or species richness can be estimated in marginalized code. Lastly, we show that occupancy and site abundance models with auto-covariates can be fit with marginalized code with minimal impact on parameter estimates.\n\nMarginalized code was anywhere from five to >1000 times faster than discrete code. Differences in inferences were minimal using marginalized code. Discrete latent states and fully conditional approaches provide the best estimates of conditional probabilities for a given site or individual. However, estimates for parameters and derived quantities such as species richness and abundance were minimally affected by marginalization and use of imperfect estimates of conditional probabilities. The results applied even when auto-covariates based on imperfect estimates of conditional probabilities were used. Understanding how marginalization works shrinks the divide between Bayesian and maximum likelihood approaches to population models. Some models that have only been presented in a Bayesian framework can easily be fit in maximum likelihood. On the other hand, factors such as informative priors, random effects, or pseudo-R2 values may motivate a Bayesian approach in some applications. An understanding of marginalization allows users to minimize the speed that is sacrificed when switching from a maximum likelihood approach. Widespread application of marginalization in Bayesian population models will facilitate more thorough simulation studies, comparisons of alternative model structures, and faster learning.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2112","usgsCitation":"Yackulic, C.B., Dodrill, M.J., Dzul, M.C., Sanderlin, J.S., and Reid, J.A., 2020, A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states: Ecological Applications, v. 30, no. 5, e02112, 19 p., https://doi.org/10.1002/eap.2112.","productDescription":"e02112, 19 p.","ipdsId":"IP-108648","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437083,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JN5C0L","text":"USGS data release","linkHelpText":"Marginalizing Bayesian population models - data for examples in the Grand Canyon region, southeastern Arizona, western Oregon USA - 1990-2015"},{"id":373429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dodrill, Michael J. 0000-0002-7038-7170 mdodrill@usgs.gov","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":5468,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","email":"mdodrill@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanderlin, Jamie S.","contributorId":223514,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":40727,"text":"USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ 86001 USA","active":true,"usgs":false}],"preferred":false,"id":785283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reid, Janice A.","contributorId":223515,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","email":"","middleInitial":"A.","affiliations":[{"id":40726,"text":"USDA Forest Service, Pacific Northwest Research Station, Roseburg Field Station, Roseburg, OR USA","active":true,"usgs":false}],"preferred":false,"id":785284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208312,"text":"ofr20201011 - 2020 - Development of a process-based littoral sediment transport model for Dauphin Island, Alabama","interactions":[],"lastModifiedDate":"2022-04-21T20:39:46.098727","indexId":"ofr20201011","displayToPublicDate":"2020-02-28T14:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1011","displayTitle":"Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama","title":"Development of a process-based littoral sediment transport model for Dauphin Island, Alabama","docAbstract":"<p>Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic resources. The dynamic island shoreline of Dauphin Island evolved across time scales while constantly acted upon by waves and currents during both storms and calm periods. Reductions in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore sand placement, breach closure, berm construction, and other means—have been used to protect the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the long-term evolution of the barrier island system and the dominant, temporally varying processes that influence it, including littoral alongshore sediment transport under typical wave conditions, beach and dune erosion, the island overwash and breaching that occur rapidly during storm events, and the recovery of primary sand dunes through Aeolian transport over decadal time scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, and sediment transport are incorporated into the model. Model skill in the prediction of waves, water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery to predict the evolution of Dauphin Island on decadal time scales.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201011","usgsCitation":"Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011.","productDescription":"vii, 43 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109477","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399455,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109731.htm"},{"id":372743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1011/ofr20201011.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1011"},{"id":372742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1011/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.36715698242186,\n              30.210421455819937\n            ],\n            [\n              -88.06640625,\n              30.210421455819937\n            ],\n            [\n              -88.06640625,\n              30.26974231529823\n            ],\n            [\n              -88.36715698242186,\n              30.26974231529823\n            ],\n            [\n              -88.36715698242186,\n              30.210421455819937\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/spcmsc\" data-mce-href=\"https://www.usgs.gov/centers/spcmsc\">St. Petersburg Coastal and Marine Science Center</a><br>U.S. Geological Survey<br>600 4th Street South<br>St. Petersburg, FL 33701</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Model Setup</li><li>Results</li><li>Conclusions</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-02-28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Robert L. 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,{"id":70208827,"text":"70208827 - 2020 - Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA","interactions":[],"lastModifiedDate":"2020-03-03T09:05:19","indexId":"70208827","displayToPublicDate":"2020-02-28T09:04:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA","docAbstract":"Recreational activities can negatively affect protected area landscapes and resources and soil erosion is frequently cited as the most significant long-term impact to recreational trails. Comprehensive modeling of soil loss on trails can identify influential factors that managers can manipulate to design and manage more sustainable trails.  Field measurements assessed soil loss as the mean vertical depth along 135 trail transects across the Appalachian Trail sampled along three 5km trail segments in the White Mountains National Forest of New Hampshire. Using LiDAR data to accurately measure terrain characteristics that influence trail erosion can improve predictive models of trail system soil loss. Borrowing from geomorphic and agricultural soil erosion models, this study evaluated a variety of terrain and hydrology characteristics to model trail soil loss at three spatial scales: transect, trail corridor, and watershed. The model for each spatial scale and a combined model are presented. The adjusted R2 explaining variation in soil loss is 0.57 using variables from all spatial scales, a substantial improvement on previous trail erosion models. Environmental and trail design factors such as slope and watershed flow length were found to be significantly correlated to soil loss and have implications for sustainable trail design and management.","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2020.103765","usgsCitation":"Eagleston, H., and Marion, J.L., 2020, Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA: Landscape and Urban Planning, v. 198, 103765, 9 p., https://doi.org/10.1016/j.landurbplan.2020.103765.","productDescription":"103765, 9 p.","ipdsId":"IP-088107","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457565,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/98678","text":"External 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,{"id":70221103,"text":"70221103 - 2020 - Towards reproducible environmental modeling for decision support: A worked example","interactions":[],"lastModifiedDate":"2021-06-03T12:05:09.55066","indexId":"70221103","displayToPublicDate":"2020-02-28T07:20:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8916,"text":"Frontiers in Earth Science, Hydrosphere","active":true,"publicationSubtype":{"id":10}},"title":"Towards reproducible environmental modeling for decision support: A worked example","docAbstract":"<p><span>A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to corresponding state observations of spring flow and groundwater level. Then a hindcast of a historical drought is made. Using available state observations recorded during drought conditions, the combined UQ and PE analyses are shown to yield an ensemble of model results that bracket the observed hydrologic responses. All files and scripts used for the analyses are placed in the public domain to serve as a template for other practitioners who are interested in undertaking these types of analyses.</span></p>","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00050","usgsCitation":"White, J.T., Foster, L.K., Fienen, M., Knowling, M.J., Hemmings, B., and Winterle, J.R., 2020, Towards reproducible environmental modeling for decision support: A worked example: Frontiers in Earth Science, Hydrosphere, v. 28, 50, 11 p., https://doi.org/10.3389/feart.2020.00050.","productDescription":"50, 11 p.","ipdsId":"IP-115342","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":457567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00050","text":"Publisher Index Page"},{"id":437084,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AUZMI7","text":"USGS data release","linkHelpText":"Towards reproducible environmental modeling for decision support: a worked example"},{"id":386114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Texas","otherGeospatial":"southern-central Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -100.96435546875,\n              28.76765910569123\n            ],\n            [\n              -96.83349609375,\n              28.76765910569123\n            ],\n            [\n              -96.83349609375,\n              30.14512718337613\n            ],\n            [\n              -100.96435546875,\n              30.14512718337613\n            ],\n            [\n              -100.96435546875,\n              28.76765910569123\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hemmings, Brioch","contributorId":259187,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winterle, James R.","contributorId":259189,"corporation":false,"usgs":false,"family":"Winterle","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":52328,"text":"Edwards Aquifer Authority","active":true,"usgs":false}],"preferred":false,"id":816777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208792,"text":"70208792 - 2020 - Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine","interactions":[],"lastModifiedDate":"2020-03-02T06:42:23","indexId":"70208792","displayToPublicDate":"2020-02-28T06:41:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine","docAbstract":"Pesticides are important for agriculture in the United States, and atrazine is one of the most widely used and widely detected pesticides in surface water. A better understanding of the mechanisms by which atrazine and its degradation product, deethylatrazine, increase and decrease in surface waters can help inform future decisions for water-quality improvement. This study considers causal factors for trends in pesticide concentration in streams in the United States and models the causal factors, other than use, in structural equation models. The structural equation models use a concomitant trend in corn and a latent variable model indicating moisture supply and management. The moisture supply and management latent variable incorporates long-term moisture conditions in the individual watersheds by using the Palmer Hydrologic Drought Index; human influence on the hydrologic cycle through the percent of the watershed drained by tile drains in 2012; and the base-flow contribution to streamflow, using the base-flow index. The structural equation models explain 77% and 38% of the variability in atrazine and deethylatrazine trends, respectively, across the conterminous United States. The models highlight future water-quality challenges, particularly in tile-drained settings where fall precipitation and heavy precipitation are increasing.","language":"English","publisher":"ACSESS","doi":"10.1002/jeq2.20045","usgsCitation":"Ryberg, K.R., Stone, W.W., and Baker, N.T., 2020, Causal factors for pesticide trends in streams of the United States: Atrazine and deethylatrazine: Journal of Environmental Quality, v. 49, no. 1, p. 152-162, https://doi.org/10.1002/jeq2.20045.","productDescription":"11 p.","startPage":"152","endPage":"162","ipdsId":"IP-102928","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457571,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jeq2.20045","text":"Publisher Index Page"},{"id":372755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -126.73828125,\n              24.686952411999155\n            ],\n            [\n              -66.181640625,\n              24.686952411999155\n            ],\n            [\n              -66.181640625,\n              49.095452162534826\n            ],\n            [\n              -126.73828125,\n              49.095452162534826\n            ],\n            [\n              -126.73828125,\n              24.686952411999155\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"49","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Wesley W. 0000-0003-0239-2063 wwstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0239-2063","contributorId":1496,"corporation":false,"usgs":true,"family":"Stone","given":"Wesley","email":"wwstone@usgs.gov","middleInitial":"W.","affiliations":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":783395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, Nancy T. 0000-0002-7979-5744 ntbaker@usgs.gov","orcid":"https://orcid.org/0000-0002-7979-5744","contributorId":1955,"corporation":false,"usgs":true,"family":"Baker","given":"Nancy","email":"ntbaker@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":783396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70266785,"text":"70266785 - 2020 - Co-producing knowledge: The Integrated Ecosystem Model for resource management in Arctic Alaska","interactions":[],"lastModifiedDate":"2025-05-13T16:30:41.584275","indexId":"70266785","displayToPublicDate":"2020-02-27T11:17:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Co-producing knowledge: The Integrated Ecosystem Model for resource management in Arctic Alaska","docAbstract":"<p><span>Assessments of climate-change effects on ecosystem processes and services in high-latitude regions are hindered by a lack of decision-support tools capable of forecasting possible future landscapes. We describe a collaborative effort to develop and apply the Integrated Ecosystem Model (IEM) for Alaska and northwestern Canada to explore how climate change influences interactions among disturbance regimes, permafrost integrity, hydrology, and vegetation, and how these dynamics in turn influence resource management decisions. This process emphasizes co-production of knowledge among decision makers, scientists, major funders, partners, and stakeholders. We highlight research findings based on IEM applications in Arctic Alaska, as well as successes and challenges of the co-production process. The overall framework and lessons from our work with the IEM are relevant to other collaborative efforts outside the Arctic that aim to develop a decision-support tool or an undertaking of equivalent scope.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2176","usgsCitation":"Euskirchen, E.S., Timm, K., Breen, A.L., Gray, S., Rupp, T., Martin, P., Reynolds, J.H., Sesser, A., Murphy, K., Littell, J., Bennett, A., Bolton, W.R., Carman, T., Genet, H., Griffith, B., Kurkowski, T., Lara, M.J., Marchenko, S., Nicolsky, D., Santosh, P., Romanovsky, V., Rutter, R., Tucker, C., and McGuire, A.D., 2020, Co-producing knowledge: The Integrated Ecosystem Model for resource management in Arctic Alaska: Frontiers in Ecology and the Environment, v. 18, no. 8, p. 447-455, https://doi.org/10.1002/fee.2176.","productDescription":"9 p.","startPage":"447","endPage":"455","ipdsId":"IP-094448","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":485836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -133.03771862405338,\n              54.449282262495586\n            ],\n            [\n              -124.23849104872949,\n              54.963179322366955\n            ],\n            [\n              -127.37957259839732,\n              62.539236061020546\n            ],\n            [\n              -134.5159081711619,\n              69.49951413324942\n            ],\n            [\n              -160.0873123544818,\n              71.48715481528723\n            ],\n            [\n              -168.2315176008282,\n              68.43184600082128\n            ],\n            [\n              -167.50613138738822,\n              64.4563020433356\n            ],\n            [\n              -167.8878826181007,\n              59.901416725449195\n            ],\n            [\n              -158.89957316923457,\n              57.913366649388536\n            ],\n            [\n              -169.2280660138636,\n              53.60184015623466\n            ],\n            [\n              -170.94410510519103,\n              53.21196387103788\n            ],\n            [\n              -169.6494550660574,\n              52.08508422023331\n            ],\n            [\n              -152.2209018203102,\n              56.88215554508227\n            ],\n            [\n              -146.25556902863565,\n              59.80908327331997\n            ],\n            [\n              -141.68648886181515,\n              59.433840122176434\n            ],\n            [\n              -133.03771862405338,\n              54.449282262495586\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Euskirchen, Eugenie S.","contributorId":207139,"corporation":false,"usgs":false,"family":"Euskirchen","given":"Eugenie","email":"","middleInitial":"S.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Timm, Kristin","contributorId":139461,"corporation":false,"usgs":false,"family":"Timm","given":"Kristin","email":"","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breen, Amy L.","contributorId":81396,"corporation":false,"usgs":true,"family":"Breen","given":"Amy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":936974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gray, Stephen T. 0000-0002-0959-3418 sgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-3418","contributorId":209851,"corporation":false,"usgs":true,"family":"Gray","given":"Stephen","email":"sgray@usgs.gov","middleInitial":"T.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":936776,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rupp, T. Scott","contributorId":21395,"corporation":false,"usgs":true,"family":"Rupp","given":"T. 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,{"id":70210990,"text":"70210990 - 2020 - Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources","interactions":[],"lastModifiedDate":"2020-07-10T13:54:41.871444","indexId":"70210990","displayToPublicDate":"2020-02-27T08:48:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of soil zone processes and a novel radiocarbon correction approach for groundwater with mixed sources","docAbstract":"Estimates of groundwater age based on 14C is often limited by the uncertainty in geochemical processes that alter the 14C concentration measured in water and the composition (δ13C and 14C) of carbon sources needed to appropriately parametrize 14C adjustment models. Estimated ages for samples that contain a mixture of young and old groundwater will be particularly sensitive to model parametrization as relatively small additions of modern 14C from recent recharge can mask the presence and amount of old groundwater. A novel multi-model approach based on inverse geochemical modeling and lumped parameter modeling of age tracers (3H, 3Hetrit, and SF6) was used to better constrain 14C dilution caused by dissolution of carbonates in the unsaturated zone or shallow parts of the Glacial aquifer, which extends over 2000 miles across the northern contiguous United States. Calibration of 14C inverse geochemical models to LPM computed 14C concentrations in modern water indicated that 14C of soil zone and shallow aquifer carbonates were not 14C-dead (0 pmC), as is typically assumed for 14C correction models. 14C of such carbonates was on average about 53 pmC (ranged 0-110 pmC, n = 72). This information was used to correct 14C concentrations for water recharged entirely before 1950 and water that is a mixture of pre- and post-1950 water. The multi-model approach developed here was compared to an analytical 14C-adjustment model (Revised Fontes and Garnier) that assumed solid carbonates were 14C-dead. 14C corrections using the analytical adjustment model tended to over-correct final 14C concentrations by 21 pmC and underestimates mean ages by 40% for groundwater mixtures.  In fact, 14C corrections based on analytical model yielded negative ages (14C > 120 pmC) in nearly 36% of mixed samples. This work presents a new approach to constraining 14C corrections and age estimates of mixtures of young and old groundwater. 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0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurgens, Bryant 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792357,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209424,"text":"70209424 - 2020 - Geology of the Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) in Pendleton County, West Virginia (USA), and implications regarding the origin of maze caves","interactions":[],"lastModifiedDate":"2020-04-09T17:51:25.200543","indexId":"70209424","displayToPublicDate":"2020-02-26T12:26:03","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geology of the Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) in Pendleton County, West Virginia (USA), and implications regarding the origin of maze caves","docAbstract":"<p>The Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) are located in a hill named Cave Knob that overlooks the South Branch of the Potomac River in Pendleton County, West Virginia (U.S.A). The geologic structure of this hill is a northeasttrending anticline, and the caves are located at different elevations primarily along the contact between the Devonian New Creek Limestone (Helderberg Group) and the overlying Devonian Corriganville Limestone (Helderberg Group). The entrance to New Trout Cave (Stop 1) is located on the east flank of Cave Knob anticline at an elevation of 585 m (1,920 ft) relative to sea level, or 39 m (128 ft) above the modern river. Much of the cave consists of passages that extend to the northeast along strike, and many of these passages have developed along primary joints that trend N40E or secondary joints that trend N40W. Sediments in New Trout Cave include mud and sand (some of which was mined for nitrate during the American Civil War), as well as large boulders in the front part of the cave. Gypsum crusts are present in a maze section of the cave ~213 to 305 m (700 to 1,000 ft) from the cave entrance. Excavations in New Trout Cave have produced vertebrate fossils of Rancholabrean age, ~300,000 to 10,000 years Before Present (BP). The entrance to Trout Cave (Stop 2) is located on the east flank of Cave Knob anticline ~100 m (328 ft) northwest of the New Trout Cave entrance at an elevation of 622 m (2,040 ft) relative to sea level, or 76 m (249 ft) above the modern river. Much of the cave consists of passages that extend to the northeast along strike, although a small area of network maze passages is present in the western portion of Trout Cave that is closest to Hamilton Cave. Many of the passages of Trout Cave have developed along primary joints that trend N40E or secondary joints that trend N40W. Sediments in Trout Cave include mud (also mined for nitrate during the American Civil War), as well as large boulders in the front part of the cave. Excavations in the upper levels of Trout Cave have produced vertebrate fossils of Rancholabrean age (~300,000 to 10,000 years BP), whereas excavations in the lower levels of the cave have produced vertebrate fossils of Irvingtonian age (~1,810,000 to 300,000 years BP). The entrance to Hamilton Cave (Stop 3) is located along the axis of Cave Knob anticline ~165 m (540 ft) northwest of the Trout Cave entrance at an elevation of 640 m (2,100 ft) relative to sea level, or 94 m (308 ft) above the modern river. The front (upper) part of Hamilton Cave has a classic network maze pattern that is an angular grid of relatively horizontal passages, most of which follow vertical or near-vertical primary joints that trend N40W and N50W and secondary joints that trend N60W and N80E. This part of the cave lies along the axis of Cave Knob anticline. In contrast, the passages in the back (lower) part of Hamilton Cave lie along the west flank of Cave Knob anticline at ~58 to 85 m (190 to 279 ft) above the modern river. These passages do not display a classic maze pattern, and instead they may be divided into the following two categories: (1) longer northeast-trending passages that are relatively horizontal and follow the strike of the beds; and (2) shorter northwest-trending passages that descend steeply to the west and follow the dip of the beds. Sediments in Hamilton Cave include mud (which was apparently not mined for nitrate during the American Civil War), as well as large boulders in the front part of the cave. Gypsum crusts are present along passage walls of the New Creek Limestone from the Slab Room to the Airblower. Excavations in the front part of Hamilton Cave (maze section) have produced vertebrate fossils of Irvingtonian age (~1,810,000 to 300,000 years BP). The network maze portions of Hamilton Cave are interpreted as having developed at or near the water table where water did not have a free surface in contact with air and where the following conditions were present: (1) Location on or near the axis of an anticline (the location of the greatest amount of flexure); (2) Abundant vertical or near vertical joints, which are favored by location in the area of greatest flexure and by a lithologic unit (chert-rich limestone) that is more likely to experience brittle rather than ductile deformation; (3) Widening of joints to enhance ease of water infiltration, favored by location in area of greatest amount of flexure; and (4) Dissolution along nearly all major joints to produce cave passages of approximately the same size (which would most likely occur via water without a free surface in contact with air). The cave passages that are located along anticline axes and along strike at the New Creek-Corriganville contact are interpreted as having formed initially during times of base level stillstand at or near the water table where water did not have a free surface in contact with air and where the water flowed along the hydraulic gradient at gentle slopes. Under such conditions, dissolution occurred in all directions to produce cave passages with relatively linear wall morphologies. In the lower portions of some of the along-strike passages, the cave walls have a more sinuous (meandering) morphology, which is interpreted as having formed during subsequent initial base level fall as cave development continued under vadose conditions where the water had a free surface in contact with air, and where water flow was governed primarily by gravitational processes. Steeply inclined cave passages that are located along dip at the New Creek-Corriganville contact are interpreted as having formed during subsequent true vadose conditions (after base level fall). This chronology of base level stasis (with cave development in the phreatic zone a short distance below top of water table) followed by base level fall (with cave development in the vadose or epiphreatic zone) has repeated multiple times at Cave Knob during the past ~4 to 3 million years, resulting in multiple cave passages at different elevations, with different passage morphologies, and at different passage locations with respect to strike and dip.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society of America Field Guide","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2020.0057(03)","collaboration":"","usgsCitation":"Swezey, C.S., and Brent, E.L., 2020, Geology of the Trout Rock caves (Hamilton Cave, Trout Cave, New Trout Cave) in Pendleton County, West Virginia (USA), and implications regarding the origin of maze caves, chap. <i>of</i> Geological Society of America Field Guide, v. 57, p. 43-77, https://doi.org/10.1130/2020.0057(03).","productDescription":"35 p.","startPage":"43","endPage":"77","ipdsId":"IP-113405","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":457583,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2020.0057(03)","text":"Publisher Index Page"},{"id":373863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","county":"Pendleton County","otherGeospatial":"Trout Rock Caves","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -79.06036376953125,\n              38.768004230175954\n            ],\n            [\n              -79.37759399414062,\n              38.975424875431436\n            ],\n            [\n              -79.4586181640625,\n              38.932707274379595\n            ],\n            [\n              -79.53826904296875,\n              38.839707613545144\n            ],\n            [\n              -79.66323852539062,\n              38.59970036588819\n            ],\n            [\n              -79.53414916992186,\n              38.543869175876154\n            ],\n            [\n              -79.47509765625,\n              38.460041065720446\n            ],\n            [\n              -79.33364868164062,\n              38.415938460513274\n            ],\n            [\n              -79.27322387695312,\n              38.41486245064945\n            ],\n            [\n              -79.20867919921875,\n              38.50304202775689\n            ],\n            [\n              -79.21005249023438,\n              38.515937313413474\n            ],\n            [\n              -79.12216186523438,\n              38.66299474019031\n            ],\n            [\n              -79.1015625,\n              38.659777730712534\n            ],\n            [\n              -79.08233642578124,\n              38.6897975322717\n            ],\n            [\n              -79.06036376953125,\n              38.768004230175954\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"57","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":786454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brent, Emily L","contributorId":223860,"corporation":false,"usgs":false,"family":"Brent","given":"Emily","email":"","middleInitial":"L","affiliations":[],"preferred":false,"id":786455,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209422,"text":"70209422 - 2020 - Foreward: Geology Field Trips in and around the U.S. Capital","interactions":[],"lastModifiedDate":"2020-04-28T20:30:35.335374","indexId":"70209422","displayToPublicDate":"2020-02-26T12:11:17","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Foreward: Geology Field Trips in and around the U.S. Capital","docAbstract":"The first annual meeting of the Geological Society of America (GSA) was held in 1888 in Ithaca, New York (Fairchild, 1932), but official Sections of GSA formed much later.  During the spring of 1949, a symposium in Knoxville, Tennessee, on mineral resources of the southeastern United States became the catalyst for the creation of the Southeastern Section of the Geological Society of America (King, 1964), and the first annual meeting of the Southeastern Section was held in 1952 in Roanoke, Virginia (Wilson, 1954).  The Northeastern Section formed much later, and its first annual meeting was held in 1966 in Philadelphia, Pennsylvania (Socolow, 1968).  At all of these section meetings, field trips have been important venues for geologists and especially students to gather together, examine rocks in the field, and discuss ideas.  These field trips have been especially important at combined section meetings because they provide settings for geologists who are experienced in one geographic region to examine and compare the geology of other regions.  The first combined meeting of the Southeastern and Northeastern sections occurred in 1976 in Arlington, Virginia.  Since then, the Southeastern and Northeastern sections have met together on numerous occasions, including 1982 in Washington, DC; 1991 in Baltimore, Maryland; 2004 in Tysons Corner, Virginia; and 2010 in Baltimore, Maryland.  \n Since the first combined section meeting in 1976, there has been a gradual increase in the role of technology in geology field studies.  In fact, during the past several decades there has been an increase in emphasis in our society on the instrumental component of science, the goal of which is operational techniques to do or control things, and a corresponding decrease in emphasis on the natural philosophy component of science, the goal of which is a greater understanding of the natural world (Dear, 2006).  The modern education acronym STEM (Science, Technology, Engineering, and Mathematics), for example, is often used as a catch-all term that implies that science and technology are relatively synonymous, and implies that greater technology leads automatically to greater understanding of the natural world.  This assumption, however, is not always valid (Dear, 2006), and technology should not be promoted as a substitute for field experiences.  Technology can be a tool that leads to greater understanding of the natural world, but not all Science uses technology as a means of providing greater understanding.  The benefits of new technologies include: (1) data of greater resolution; and (2) greater efficiency of capturing, storing, and visualizing data.  The risks of new technologies include: (1) an overabundance of data, some of which may be of little value; (2) less time available for analysis of data, if geologists become occupied primarily with capturing and storing data; and (3) errors that arise from complacency and the perception that field-checking may not be necessary.  In other words, there is a risk that a glut of data and vast amounts of time devoted to the capturing and storing of data may result in a reduced interest and (or) willingness to field-check data.  \nIn the spirit of the early GSA section meetings, we feel that there are still enormous advantages to conducting geology field trips in conjunction with traditional meeting presentations and posters.  In 2020, with this current combined Southeastern and Northeastern section meeting in Reston, Virginia, we have assembled eight different field trips that cover a wide range of territory in and around the Nation’s capital.  These field trip localities include the immediate vicinity of Washington, DC, as well as various locations in nearby areas of Virginia, Maryland, and West Virginia.  The physiographic provinces include Mesozoic Rift Basins, the Piedmont, the Blue Ridge, the Valley and Ridge, and the Allegheny Plateau of the Appalachian Basin. The field trip sites exhibit a wide range of igneous, metamorphic, and sedimentary rocks, as well as rocks with a wide range of geologic ages from the Mesoproterozoic to the Holocene.  We hope that this guidebook provides new motivation for geologists to examine rocks in the field, to discuss ideas with colleagues in the field, and to avoid becoming complacent.  \n The editors of this volume would like to thank the authors of the different field trip guides, the field trip leaders, and all of the reviewers who made suggestions for improving the field trip manuscripts.  The editors would also like to thank Elle Derwent of GSA for her logistical help and guidance regarding the field trips, and April Leo and the staff of the GSA Publications Department for seeing this book through to publication.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society of America Field Guide","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2020.0057(00)","collaboration":"","usgsCitation":"Swezey, C.S., and Carter, M.W., 2020, Foreward: Geology Field Trips in and around the U.S. Capital, chap. <i>of</i> Geological Society of America Field Guide, v. 57, p. v-vi, https://doi.org/10.1130/2020.0057(00).","productDescription":"2 p.","startPage":"v","endPage":"vi","ipdsId":"IP-113973","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":373862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia, West Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.870361328125,\n              36.84006462037767\n            ],\n            [\n              -75.8056640625,\n              36.84006462037767\n            ],\n            [\n              -75.8056640625,\n              39.65222681530652\n            ],\n            [\n              -80.870361328125,\n              39.65222681530652\n            ],\n            [\n              -80.870361328125,\n              36.84006462037767\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"57","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":786450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":786451,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70211287,"text":"70211287 - 2020 - The role of Northeast Pacific meltwater events in deglacial climate change","interactions":[],"lastModifiedDate":"2020-07-22T15:13:57.928397","indexId":"70211287","displayToPublicDate":"2020-02-26T10:11:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"The role of Northeast Pacific meltwater events in deglacial climate change","docAbstract":"Columbia River megafloods occurred repeatedly during the last deglaciation, but the impacts of this fresh water on Pacific hydrography are largely unknown. To reconstruct changes in ocean circulation during this period, we used a numerical model to simulate the flow trajectory of Columbia River megafloods and compiled records of sea surface temperature, paleo-salinity, and deep-water radiocarbon from marine sediment cores in the Northeast Pacific. The North Pacific sea surface cooled and freshened during the early deglacial (19.0-16.5 ka) and Younger Dryas (12.9-11.7 ka) intervals, coincident with the appearance of subsurface water masses depleted in radiocarbon relative to the sea surface. We infer that Pacific meltwater fluxes contributed to net Northern Hemisphere cooling prior to North Atlantic Heinrich Events, and again during the Younger Dryas stadial. Abrupt warming in the Northeast Pacific similarly contributed to hemispheric warming during the Bølling and Holocene transitions. These findings underscore the importance of changes in North Pacific freshwater fluxes and circulation in deglacial climate events.","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.aay2915","usgsCitation":"Praetorius, S.K., Condron, A., Mix, A., Walczak, M., McKay, J., and Du, J., 2020, The role of Northeast Pacific meltwater events in deglacial climate change: Science Advances, v. 6, no. 9, eaay2915, 18 p., https://doi.org/10.1126/sciadv.aay2915.","productDescription":"eaay2915, 18 p.","ipdsId":"IP-093675","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457590,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aay2915","text":"Publisher Index Page"},{"id":376636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Praetorius, Summer K. 0000-0003-2683-3652","orcid":"https://orcid.org/0000-0003-2683-3652","contributorId":206966,"corporation":false,"usgs":true,"family":"Praetorius","given":"Summer","email":"","middleInitial":"K.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":793519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Condron, Alan 0000-0002-7337-1713","orcid":"https://orcid.org/0000-0002-7337-1713","contributorId":229547,"corporation":false,"usgs":false,"family":"Condron","given":"Alan","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":793520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mix, Alan","contributorId":184163,"corporation":false,"usgs":false,"family":"Mix","given":"Alan","affiliations":[],"preferred":false,"id":793521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walczak, Maureen 0000-0002-4123-6998","orcid":"https://orcid.org/0000-0002-4123-6998","contributorId":206972,"corporation":false,"usgs":false,"family":"Walczak","given":"Maureen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":793522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKay, Jennifer","contributorId":229548,"corporation":false,"usgs":false,"family":"McKay","given":"Jennifer","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":793523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Du, Jianghui 0000-0002-3386-9314","orcid":"https://orcid.org/0000-0002-3386-9314","contributorId":206970,"corporation":false,"usgs":false,"family":"Du","given":"Jianghui","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":793524,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70263647,"text":"70263647 - 2020 - Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence","interactions":[],"lastModifiedDate":"2025-02-19T14:20:57.140542","indexId":"70263647","displayToPublicDate":"2020-02-26T09:53:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence","docAbstract":"<p><span>The 2019 Ridgecrest, California, earthquake sequence, including an&nbsp;</span><span class=\"inline-formula no-formula-id\">Mw</span><span>&nbsp;6.4 event on 4 July and an&nbsp;</span><span class=\"inline-formula no-formula-id\">Mw</span><span>&nbsp;7.1 approximately 34&nbsp;hr later, was recorded by 15 instruments within 55&nbsp;km nearest‐fault distance. To characterize and explore near‐field ground motions from the&nbsp;</span><span class=\"inline-formula no-formula-id\">Mw</span><span>&nbsp;6.4 foreshock and&nbsp;</span><span class=\"inline-formula no-formula-id\">Mw</span><span>&nbsp;7.1 mainshock, we augment these records with available macroseismic information, including conventional intensities and displaced rocks. We conclude that near‐field shaking intensities were generally below modified Mercalli intensity 9, with concentrations of locally high values toward the northern and southern termini of the mainshock rupture. We further show that, relative to near‐field ground motions at hard‐rock sites, instrumental ground motions at alluvial near‐field sites for both the&nbsp;</span><span class=\"inline-formula no-formula-id\">Mw</span><span>&nbsp;6.4 foreshock and&nbsp;</span><span class=\"inline-formula no-formula-id\">Mw</span><span>&nbsp;7.1 mainshock were depleted in energy at frequencies higher than 2–3&nbsp;Hz, as expected from ground‐motion models. Both the macroseismic and instrumental observations suggest that sediments in the Indian Wells Valley experienced a pervasively nonlinear response, which helps explain why shaking intensities and damage in the closest population center, Ridgecrest, were relatively modest given its proximity to the earthquakes.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190279","usgsCitation":"Hough, S.E., Thompson, E.M., Parker, G., Graves, R., Hudnut, K.W., Patton, J., Dawson, T., Ladinsky, T.C., Oskin, M., Sirorattanakul, K., Blake, K., Baltay Sundstrom, A.S., and Cochran, E.S., 2020, Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence: Seismological Research Letters, v. 91, no. 3, p. 1542-1555, https://doi.org/10.1785/0220190279.","productDescription":"14 p.","startPage":"1542","endPage":"1555","ipdsId":"IP-112076","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482164,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -119.50916574226079,\n              36.688337109123\n            ],\n            [\n              -119.50916574226079,\n              33.93284050953977\n            ],\n            [\n              -115.58563079978865,\n              33.93284050953977\n            ],\n            [\n              -115.58563079978865,\n              36.688337109123\n            ],\n            [\n              -119.50916574226079,\n              36.688337109123\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  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A.","contributorId":350992,"corporation":false,"usgs":false,"family":"Parker","given":"Grace  A.","affiliations":[],"preferred":false,"id":927655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927656,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hudnut, Kenneth W. 0000-0002-3168-4797 hudnut@usgs.gov","orcid":"https://orcid.org/0000-0002-3168-4797","contributorId":2550,"corporation":false,"usgs":true,"family":"Hudnut","given":"Kenneth","email":"hudnut@usgs.gov","middleInitial":"W.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD 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abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927664,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927665,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70211485,"text":"70211485 - 2020 - Multi‐species occupancy models: Review, roadmap, and recommendations","interactions":[],"lastModifiedDate":"2020-11-13T15:46:27.710581","indexId":"70211485","displayToPublicDate":"2020-02-25T19:40:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Multi‐species occupancy models: Review, roadmap, and recommendations","docAbstract":"<p><span>Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/ecog.04957","usgsCitation":"Devarajan, K., Tenan, S., and Morelli, T.L., 2020, Multi‐species occupancy models: Review, roadmap, and recommendations: Ecography, v. 43, no. 11, p. 1612-1624, https://doi.org/10.1111/ecog.04957.","productDescription":"14 p.","startPage":"1612","endPage":"1624","ipdsId":"IP-114395","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":457599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.04957","text":"Publisher Index Page"},{"id":376821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Devarajan, Kadambari","contributorId":236828,"corporation":false,"usgs":false,"family":"Devarajan","given":"Kadambari","email":"","affiliations":[],"preferred":false,"id":794271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tenan, Simone","contributorId":177519,"corporation":false,"usgs":false,"family":"Tenan","given":"Simone","email":"","affiliations":[],"preferred":false,"id":794272,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794273,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70221837,"text":"70221837 - 2020 - Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre","interactions":[],"lastModifiedDate":"2021-07-09T19:30:10.52931","indexId":"70221837","displayToPublicDate":"2020-02-25T14:24:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre","docAbstract":"<p><span>Phytoplankton play key roles in the oceans by regulating global biogeochemical cycles and production in marine food webs. Global warming is thought to affect phytoplankton production both directly, by impacting their photosynthetic metabolism, and indirectly by modifying the physical environment in which they grow. In this respect, the Bermuda Atlantic Time-series Study (BATS) in the Sargasso Sea (North Atlantic gyre) provides a unique opportunity to explore effects of warming on phytoplankton production across the vast oligotrophic ocean regions because it is one of the few multidecadal records of measured net primary productivity (NPP). We analysed the time series of phytoplankton primary productivity at BATS site using machine learning techniques (ML) to show that increased water temperature over a 27-year period (1990–2016), and the consequent weakening of vertical mixing in the upper ocean, induced a negative feedback on phytoplankton productivity by reducing the availability of essential resources, nitrogen and light. The unbalanced availability of these resources with warming, coupled with ecological changes at the community level, is expected to intensify the oligotrophic state of open-ocean regions that are far from land-based nutrient sources.</span></p>","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-020-59989-y","usgsCitation":"D’Alelio, D., Rampone, S., Cusano, L.M., Morfino, V., Russo, L., Sanseverino, N., Cloern, J.E., and Lomas, M.W., 2020, Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre: Scientific Reports, v. 10, 3287, 12 p., https://doi.org/10.1038/s41598-020-59989-y.","productDescription":"3287, 12 p.","ipdsId":"IP-111898","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-59989-y","text":"Publisher Index Page"},{"id":387061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North Atlantic Gyre","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -64.3359375,\n              23.885837699862005\n            ],\n            [\n              -38.84765625,\n              28.613459424004414\n            ],\n            [\n              -19.51171875,\n              34.016241889667015\n            ],\n            [\n              -17.75390625,\n              41.11246878918088\n            ],\n            [\n              -26.3671875,\n              47.754097979680026\n            ],\n            [\n              -41.66015625,\n              46.6795944656402\n            ],\n            [\n              -61.17187499999999,\n              39.639537564366684\n            ],\n            [\n              -69.78515625,\n              35.31736632923788\n            ],\n            [\n              -76.9921875,\n              31.203404950917395\n            ],\n            [\n              -75.41015624999999,\n              26.902476886279832\n            ],\n            [\n              -71.54296874999999,\n              23.563987128451217\n            ],\n            [\n              -64.3359375,\n              23.885837699862005\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Alelio, Domenico","contributorId":260813,"corporation":false,"usgs":false,"family":"D’Alelio","given":"Domenico","email":"","affiliations":[{"id":27945,"text":"Stazione Zoologica Anton Dohrn","active":true,"usgs":false}],"preferred":false,"id":818883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rampone, Salvatore","contributorId":260814,"corporation":false,"usgs":false,"family":"Rampone","given":"Salvatore","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cusano, Luigi Maria","contributorId":260815,"corporation":false,"usgs":false,"family":"Cusano","given":"Luigi","email":"","middleInitial":"Maria","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morfino, Valerio","contributorId":260816,"corporation":false,"usgs":false,"family":"Morfino","given":"Valerio","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818886,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russo, Luca","contributorId":260817,"corporation":false,"usgs":false,"family":"Russo","given":"Luca","email":"","affiliations":[{"id":27945,"text":"Stazione Zoologica Anton Dohrn","active":true,"usgs":false}],"preferred":false,"id":818887,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sanseverino, Nadia","contributorId":260818,"corporation":false,"usgs":false,"family":"Sanseverino","given":"Nadia","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818888,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - 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,{"id":70218295,"text":"70218295 - 2020 - A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","interactions":[],"lastModifiedDate":"2021-02-23T13:39:06.774641","indexId":"70218295","displayToPublicDate":"2020-02-25T07:36:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104657","usgsCitation":"White, J., Knowling, M., Fienen, M., Feinstein, D.T., McDonald, G.W., and Moore, C.R., 2020, A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support: Environmental Modelling and Software, v. 126, 104657, 11 p., https://doi.org/10.1016/j.envsoft.2020.104657.","productDescription":"104657, 11 p.","ipdsId":"IP-106798","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowling, Matthew 0000-0002-7273-3495","orcid":"https://orcid.org/0000-0002-7273-3495","contributorId":251904,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Garry W. 0000-0003-3746-4346","orcid":"https://orcid.org/0000-0003-3746-4346","contributorId":251906,"corporation":false,"usgs":false,"family":"McDonald","given":"Garry","email":"","middleInitial":"W.","affiliations":[{"id":50421,"text":"Market Economics","active":true,"usgs":false}],"preferred":false,"id":810894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moore, Catherine R.","contributorId":251908,"corporation":false,"usgs":false,"family":"Moore","given":"Catherine","email":"","middleInitial":"R.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810895,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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