{"pageNumber":"220","pageRowStart":"5475","pageSize":"25","recordCount":41062,"records":[{"id":70223821,"text":"70223821 - 2021 - Advancing cave detection using terrain analysis and thermal imagery","interactions":[],"lastModifiedDate":"2021-09-09T12:48:45.771312","indexId":"70223821","displayToPublicDate":"2021-09-08T07:47:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Advancing cave detection using terrain analysis and thermal imagery","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.</div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13183578","usgsCitation":"Wynne, J.J., Jenness, J., Sonderegger, D., Titus, T.N., Jhabvala, M.D., and Cabrol, N.A., 2021, Advancing cave detection using terrain analysis and thermal imagery: Remote Sensing, v. 13, no. 8, 3578, 25 p., https://doi.org/10.3390/rs13183578.","productDescription":"3578, 25 p.","ipdsId":"IP-098740","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":450878,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183578","text":"Publisher Index Page"},{"id":436207,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NF0L2I","text":"USGS data release","linkHelpText":"Aircraft-Borne Thermal Imagery and Derived Terrain Analysis Layers, Pisgah Lava Field, California"},{"id":388995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wynne, J. Judson","contributorId":265476,"corporation":false,"usgs":false,"family":"Wynne","given":"J.","email":"","middleInitial":"Judson","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":822787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenness, Jeff","contributorId":265477,"corporation":false,"usgs":false,"family":"Jenness","given":"Jeff","affiliations":[{"id":54685,"text":"Jenness Enterprises","active":true,"usgs":false}],"preferred":false,"id":822788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonderegger, Derek","contributorId":265478,"corporation":false,"usgs":false,"family":"Sonderegger","given":"Derek","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":822789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":822790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jhabvala, Murzy D.","contributorId":265479,"corporation":false,"usgs":false,"family":"Jhabvala","given":"Murzy","email":"","middleInitial":"D.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":822791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cabrol, Nathalie A.","contributorId":51382,"corporation":false,"usgs":true,"family":"Cabrol","given":"Nathalie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":822861,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223828,"text":"70223828 - 2021 - Digital elevation models: Terminology and definitions","interactions":[],"lastModifiedDate":"2021-09-09T12:28:36.93368","indexId":"70223828","displayToPublicDate":"2021-09-08T07:27:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Digital elevation models: Terminology and definitions","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background.<span>&nbsp;</span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13183581","usgsCitation":"Guth, P.L., Van Niekerk, A., Grohmann, C., Muller, J., Hawker, L., Florinsky, I.V., Gesch, D.B., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., Lopez-Vazquez, C., Carabajal, C.C., Albinet, C., and Strobl, P., 2021, Digital elevation models: Terminology and definitions: Remote Sensing, v. 13, no. 18, 3581, 19 p., https://doi.org/10.3390/rs13183581.","productDescription":"3581, 19 p.","ipdsId":"IP-131782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450882,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183581","text":"Publisher Index Page"},{"id":388992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Guth, Peter L.","contributorId":265495,"corporation":false,"usgs":false,"family":"Guth","given":"Peter","email":"","middleInitial":"L.","affiliations":[{"id":54693,"text":"U.S. Naval Academy","active":true,"usgs":false}],"preferred":false,"id":822807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Niekerk, Adriaan","contributorId":265496,"corporation":false,"usgs":false,"family":"Van Niekerk","given":"Adriaan","email":"","affiliations":[{"id":39919,"text":"Stellenbosch University","active":true,"usgs":false}],"preferred":false,"id":822808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grohmann, Carlos H.","contributorId":265497,"corporation":false,"usgs":false,"family":"Grohmann","given":"Carlos H.","affiliations":[{"id":48623,"text":"University of Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":822809,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muller, Jan-Peter","contributorId":265498,"corporation":false,"usgs":false,"family":"Muller","given":"Jan-Peter","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":822810,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawker, Laurence","contributorId":265499,"corporation":false,"usgs":false,"family":"Hawker","given":"Laurence","email":"","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":822811,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Florinsky, Igor V.","contributorId":265500,"corporation":false,"usgs":false,"family":"Florinsky","given":"Igor","email":"","middleInitial":"V.","affiliations":[{"id":49898,"text":"Russian Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":822812,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":822813,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reuter, Hannes I.","contributorId":265501,"corporation":false,"usgs":false,"family":"Reuter","given":"Hannes","email":"","middleInitial":"I.","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":822814,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herrera-Cruz, Virginia","contributorId":265502,"corporation":false,"usgs":false,"family":"Herrera-Cruz","given":"Virginia","email":"","affiliations":[{"id":54696,"text":"Airbus Defence and Space","active":true,"usgs":false}],"preferred":false,"id":822815,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Riazanoff, Serge","contributorId":265503,"corporation":false,"usgs":false,"family":"Riazanoff","given":"Serge","email":"","affiliations":[{"id":54697,"text":"VisioTerra","active":true,"usgs":false}],"preferred":false,"id":822816,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lopez-Vazquez, Carlos","contributorId":265504,"corporation":false,"usgs":false,"family":"Lopez-Vazquez","given":"Carlos","email":"","affiliations":[{"id":54698,"text":"Universidad ORT Uruguay","active":true,"usgs":false}],"preferred":false,"id":822817,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Carabajal, Claudia C.","contributorId":265505,"corporation":false,"usgs":false,"family":"Carabajal","given":"Claudia","email":"","middleInitial":"C.","affiliations":[{"id":54699,"text":"SSAI Inc.","active":true,"usgs":false}],"preferred":false,"id":822818,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Albinet, Clement","contributorId":265506,"corporation":false,"usgs":false,"family":"Albinet","given":"Clement","email":"","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":822819,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Strobl, Peter","contributorId":265507,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":822820,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70223848,"text":"70223848 - 2021 - The application of metacommunity theory to the management of riverine ecosystems","interactions":[],"lastModifiedDate":"2021-10-18T15:04:59.504944","indexId":"70223848","displayToPublicDate":"2021-09-08T07:01:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"The application of metacommunity theory to the management of riverine ecosystems","docAbstract":"<p>River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure. However, this body of knowledge has only rarely been used to inform management practices for river ecosystems. In this article, we present a conceptual model outlining how the metacommunity processes of local niche sorting and dispersal can influence the outcomes of management interventions and provide a series of specific recommendations for applying these ideas as well as research needs. In all cases, we identify situations where traditional approaches to riverine management could be enhanced by incorporating an understanding of metacommunity dynamics. A common theme is developing guidelines for assessing the metacommunity context of a site or region, evaluating how that context may affect the desired outcome, and incorporating that understanding into the planning process and methods used. To maximize the effectiveness of management activities, scientists, and resource managers should update the toolbox of approaches to riverine management to reflect theoretical advances in metacommunity ecology.</p>","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1557","usgsCitation":"Patrick, C.J., Anderson, K.E., Brown, B.L., Hawkins, C.P., Metcalfe, A.N., Saffarinia, P., Siqueira, T., Swan, C.M., Tonkin, J.D., and Yuan, L.L., 2021, The application of metacommunity theory to the management of riverine ecosystems: WIREs Water, v. 8, no. 6, e1557, 21 p., https://doi.org/10.1002/wat2.1557.","productDescription":"e1557, 21 p.","ipdsId":"IP-119036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450888,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1557","text":"Publisher Index Page"},{"id":389050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Patrick, Christopher J.","contributorId":199778,"corporation":false,"usgs":false,"family":"Patrick","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":822932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kurt E.","contributorId":265545,"corporation":false,"usgs":false,"family":"Anderson","given":"Kurt","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":822933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Brown L.","contributorId":265546,"corporation":false,"usgs":false,"family":"Brown","given":"Brown","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":822934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawkins, Charles P.","contributorId":198331,"corporation":false,"usgs":false,"family":"Hawkins","given":"Charles","email":"","middleInitial":"P.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":822935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Metcalfe, Anya N. 0000-0002-6286-4889 ametcalfe@usgs.gov","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":5271,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","email":"ametcalfe@usgs.gov","middleInitial":"N.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saffarinia, Parsa","contributorId":265547,"corporation":false,"usgs":false,"family":"Saffarinia","given":"Parsa","email":"","affiliations":[],"preferred":false,"id":822937,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Siqueira, Tadeu","contributorId":265548,"corporation":false,"usgs":false,"family":"Siqueira","given":"Tadeu","email":"","affiliations":[],"preferred":false,"id":822938,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Swan, Christopher M.","contributorId":265549,"corporation":false,"usgs":false,"family":"Swan","given":"Christopher","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":822939,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tonkin, Jonathan D.","contributorId":260624,"corporation":false,"usgs":false,"family":"Tonkin","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":822940,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yuan, Lester L.","contributorId":198316,"corporation":false,"usgs":false,"family":"Yuan","given":"Lester","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":822941,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70223775,"text":"sir20215093 - 2021 - A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","interactions":[],"lastModifiedDate":"2021-09-08T11:52:20.913559","indexId":"sir20215093","displayToPublicDate":"2021-09-07T19:13:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5093","displayTitle":"A Machine Learning Approach to Modeling Streamflow with Sparse Data in Ungaged Watersheds on the Wyoming Range, Wyoming, 2012–17","title":"A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","docAbstract":"<p>Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse.</p><p>To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow.</p><p>The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [<i>R</i><sup>2</sup>]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period.</p><p>The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature.</p><p>The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance (<i>R</i><sup>2</sup>=0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance (<i>R</i><sup>2</sup>=0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent (<i>R</i><sup>2</sup>=0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations.</p><p>The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215093","usgsCitation":"McShane, R.R., and Eddy-Miller, C.A., 2021, A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17: U.S. Geological Survey Scientific Investigations Report 2021–5093, 29 p., https://doi.org/10.3133/sir20215093.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-117330","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388893,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCP1AE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012–17"},{"id":388895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.xml","text":"Report","size":"219 kB","linkFileType":{"id":8,"text":"xml"}},{"id":388896,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5093/images"},{"id":388894,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5093/coverthb.jpg"},{"id":388892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5093"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -110.90972900390625,\n              42.09618442380296\n            ],\n            [\n              -110.01708984374999,\n              42.09618442380296\n            ],\n            [\n              -110.01708984374999,\n              42.68041629144619\n            ],\n            [\n              -110.90972900390625,\n              42.68041629144619\n            ],\n            [\n              -110.90972900390625,\n              42.09618442380296\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_mt@usgs.gov\" href=\"mailto:%20dc_mt@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/wy-mt-water/\" href=\"https://www.usgs.gov/centers/wy-mt-water/\">Wyoming-Montana Water Science Center</a> <br>U.S. Geological Survey<br>3162 Bozeman Avenue <br>Helena, MT 59601</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods for Machine Learning Approach to Modeling Streamflow</li><li>Results of Machine Learning Approach to Modeling Streamflow</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-09-07","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"McShane, Ryan R. 0000-0002-3128-0039 rmcshane@usgs.gov","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":195581,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan","email":"rmcshane@usgs.gov","middleInitial":"R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eddy-Miller, Cheryl A. 0000-0002-4082-750X cemiller@usgs.gov","orcid":"https://orcid.org/0000-0002-4082-750X","contributorId":1824,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl A.","email":"cemiller@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":false,"id":822635,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70223768,"text":"70223768 - 2021 - Intraspecific variation mediates density dependence in a genetically diverse plant species","interactions":[],"lastModifiedDate":"2021-11-16T15:39:59.280124","indexId":"70223768","displayToPublicDate":"2021-09-07T11:08:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Intraspecific variation mediates density dependence in a genetically diverse plant species","docAbstract":"<p><span>Interactions between neighboring plants are critical for biodiversity maintenance in plant populations and communities. Intraspecific trait variation and genome duplication are common in plant species and can drive eco-evolutionary dynamics through genotype-mediated plant–plant interactions. However, few studies have examined how species-wide intraspecific variation may alter interactions between neighboring plants. We investigate how subspecies and ploidy variation in a genetically diverse species, big sagebrush (</span><i>Artemisia tridentata</i><span>), can alter the demographic outcomes of plant interactions. Using a replicated, long-term common garden experiment that represents range-wide diversity of&nbsp;</span><i>A.&nbsp;tridentata</i><span>, we ask how intraspecific variation, environment, and stand age mediate neighbor effects on plant growth and survival. Spatially explicit models revealed that ploidy variation and subspecies identity can mediate plant–plant interactions but that the effect size varied in time and across experimental sites. We found that demographic impacts of neighbor effects were strongest during early stages of stand development and in sites with greater growth rates. Within subspecies, tetraploid populations showed greater tolerance to neighbor crowding compared to their diploid variants. Our findings provide evidence that intraspecific variation related to genome size and subspecies identity impacts spatial demography in a genetically diverse plant species. Accounting for intraspecific variation in studies of conspecific density dependence will improve our understanding of how local populations will respond to novel genotypes and biotic interaction regimes. As introduction of novel genotypes into local populations becomes more common, quantifying demographic processes in genetically diverse populations will help predict long-term consequences of plant–plant interactions.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3502","usgsCitation":"Zaiats, A., Germino, M., Serpe, M.D., Richardson, B., and Caughlin, T., 2021, Intraspecific variation mediates density dependence in a genetically diverse plant species: Ecology, v. 102, no. 11, e03502, 11 p., https://doi.org/10.1002/ecy.3502.","productDescription":"e03502, 11 p.","ipdsId":"IP-122281","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":388887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Utah","city":"Ephraim, Majors Flat, Orchard","otherGeospatial":"Great Basin Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.861572265625,\n              43.46886761482925\n            ],\n            [\n              -114.04907226562499,\n              41.9921602333763\n            ],\n            [\n              -114.06005859375,\n              37.00255267215955\n            ],\n            [\n              -113.148193359375,\n              37.59682400108367\n            ],\n            [\n              -112.06054687499999,\n              39.39375459224348\n            ],\n            [\n              -111.64306640625,\n              40.10328591293439\n            ],\n            [\n              -111.90673828125,\n              40.83874913796459\n            ],\n            [\n              -112.071533203125,\n              42.00032514831621\n            ],\n            [\n              -115.8837890625,\n              43.492782808225\n            ],\n            [\n              -116.861572265625,\n              43.46886761482925\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"102","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii","contributorId":257073,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":822590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":822591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Serpe, Marcelo D.","contributorId":257074,"corporation":false,"usgs":false,"family":"Serpe","given":"Marcelo","email":"","middleInitial":"D.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":822592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richardson, Bryce 0000-0001-9521-4367","orcid":"https://orcid.org/0000-0001-9521-4367","contributorId":195702,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","affiliations":[],"preferred":false,"id":822593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":822594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70229777,"text":"70229777 - 2021 - Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","interactions":[],"lastModifiedDate":"2022-03-17T15:32:45.548565","indexId":"70229777","displayToPublicDate":"2021-09-07T10:17:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","docAbstract":"<ol class=\"\"><li>Translocations are essential for re-establishing wildlife populations. As they sometimes fail, it is critical to assess factors that influence their success pre-translocation.</li><li>Socioecological suitability models (SESMs) integrate social acceptance and ecological suitability to enable identification of areas where wildlife populations will expand, which makes it likely that SESMs will also be useful for predicting translocation success.</li><li>To inform site selection for potential elk<span>&nbsp;</span><i>Cervus canadensis</i><span>&nbsp;</span>reintroduction to north-eastern Minnesota, United States, we developed broadscale maps of social acceptance from surveys of local residents and landowners, animal use equivalence (AUE) from forage measured in the field and empirical conflict risk from geospatial data. Resulting SESMs integrated social acceptance favourability scores, AUE and conflict risk, and weighted SESMs showed the relative influences of acceptance and conflict.</li><li>Social acceptance was positive for local residents and landowners (mean ≥ 5.4; scale of 1–7). AUE (scaled to an elk home range) ranged between 1 and 9 elk/16&nbsp;km<sup>2</sup><span>&nbsp;</span>during winter, and from 14 to 83 elk/16 km<sup>2</sup><span>&nbsp;</span>during summer. Human–elk conflict risk was low (mean ≤ 0.10; scaled 0–1), increasing from north to south. Geographical distributions differed for social acceptance, AUE and conflict risk, and weighted SESMs revealed unsuitable areas that were otherwise obscured.</li><li><i>Synthesis and applications</i>. Integrating human–wildlife conflict risk into SESMs shows where social acceptance of translocated species is likely to erode, even where viewed favourably pre-translocation, to inform translocation planning by highlighting interactions between key factors. Such integrated models supplement existing reintroduction biology frameworks by supporting decision-making and knowledge development. In north-eastern Minnesota, natural resource managers who are considering elk reintroductions are using SESMs reported here to identify where human–elk conflict is unlikely to result in an isolated elk population and where addressing concerns for area residents about conflict risk is essential.</li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14021","usgsCitation":"McCann, N.P., Walberg, E.M., Forester, J., Schrage, M.W., Fulton, D.C., and Ditmer, M., 2021, Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate: Journal of Applied Ecology, v. 58, no. 12, p. 2810-2820, https://doi.org/10.1111/1365-2664.14021.","productDescription":"11 p.","startPage":"2810","endPage":"2820","ipdsId":"IP-127289","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502433,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":397248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Cloquet Valley Study Area, Fond du Lac Study Area, Nemadji Study Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.33984375,\n              46.195042108660154\n            ],\n            [\n              -92.10937499999999,\n              46.195042108660154\n            ],\n            [\n              -92.10937499999999,\n              47.338822694822\n            ],\n            [\n              -93.33984375,\n              47.338822694822\n            ],\n            [\n              -93.33984375,\n              46.195042108660154\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"58","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"McCann, Nicholas P.","contributorId":288723,"corporation":false,"usgs":false,"family":"McCann","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walberg, Eric M.","contributorId":288724,"corporation":false,"usgs":false,"family":"Walberg","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":838247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forester, James D.","contributorId":288725,"corporation":false,"usgs":false,"family":"Forester","given":"James D.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schrage, Michael W.","contributorId":288729,"corporation":false,"usgs":false,"family":"Schrage","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":61835,"text":"Fond du Lac Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":838249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838245,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ditmer, Mark A.","contributorId":288732,"corporation":false,"usgs":false,"family":"Ditmer","given":"Mark A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838250,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70263928,"text":"70263928 - 2021 - Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop","interactions":[],"lastModifiedDate":"2025-02-28T16:10:09.918844","indexId":"70263928","displayToPublicDate":"2021-09-07T10:05:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop","docAbstract":"<p><span>We develop a self‐consistent scaling model relating magnitude&nbsp;</span><span class=\"inline-formula no-formula-id\"><i>M</i><sub>w</sub></span><span>&nbsp;to surface rupture length (</span><span class=\"inline-formula no-formula-id\">⁠L<sub>E</sub>⁠</span><span>), surface displacement&nbsp;</span><span class=\"inline-formula no-formula-id\">D<sub>E</sub>⁠</span><span>, and rupture width&nbsp;</span><span class=\"inline-formula no-formula-id\">W<sub>E</sub>⁠</span><span>, for strike‐slip faults. Knowledge of the long‐term fault‐slip rate&nbsp;</span><span class=\"inline-formula no-formula-id\">S<sub>F</sub></span><span>&nbsp;improves magnitude estimates. Data are collected for 55 ground‐rupturing strike‐slip earthquakes that have geological estimates of&nbsp;</span><span class=\"inline-formula no-formula-id\">L<sub>E</sub>⁠</span><span>,&nbsp;</span><span class=\"inline-formula no-formula-id\">D<sub>E</sub>⁠</span><span>, and&nbsp;</span><span class=\"inline-formula no-formula-id\">S<sub>F⁠</sub></span><span>, and geophysical estimates of&nbsp;</span><span class=\"inline-formula no-formula-id\">W<sub>E</sub>⁠</span><span>. We begin with the model of&nbsp;</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf4\">Anderson<span>&nbsp;</span><i>et&nbsp;al.</i><span>&nbsp;</span>(2017)</a><span>, which uses a closed form equation for the seismic moment of a surface‐rupturing strike‐slip fault of arbitrary aspect ratio and given stress drop,&nbsp;</span><span class=\"inline-formula no-formula-id\">Δτ<sub>C</sub>⁠</span><span>. Using&nbsp;</span><span class=\"inline-formula no-formula-id\">W<sub>E</sub></span><span>&nbsp;estimates does not improve&nbsp;</span><span class=\"inline-formula no-formula-id\">M<sub>w</sub></span><span>&nbsp;estimates. However, measurements of&nbsp;</span><span class=\"inline-formula no-formula-id\">D<sub>E</sub></span><span>&nbsp;plus the relationship between&nbsp;</span><span class=\"inline-formula no-formula-id\">Δτ<sub>C</sub></span><span>&nbsp;and surface slip provide an alternate approach to study&nbsp;</span><span class=\"inline-formula no-formula-id\">W<sub>E</sub>⁠</span><span>. A grid of plausible stress drop and width pairs were used to predict displacement and earthquake magnitude. A likelihood function was computed from within the uncertainty ranges of the corresponding observed&nbsp;</span><span class=\"inline-formula no-formula-id\"><i>M</i><sub>w</sub></span><span>&nbsp;and&nbsp;</span><span class=\"inline-formula no-formula-id\">D<sub>E</sub></span><span>&nbsp;values. After maximizing likelihoods over earthquakes in length bins, we found the most likely values of&nbsp;</span><span class=\"inline-formula no-formula-id\">W<sub>E</sub></span><span>&nbsp;for constant stress drop; these depend on the rupture length. The best‐fitting model has the surprising form&nbsp;</span><span class=\"inline-formula no-formula-id\">W<sub>E</sub>∝logL<sub>E</sub></span><span>—a gentle increase in width with rupture length. Residuals from this model are convincingly correlated to the fault‐slip rate and also show a weak correlation with the crustal thickness. The resulting model thus supports a constant stress drop for ruptures of all lengths, consistent with teleseismic observation. The approach can be extended to test other observable factors that might improve the predictability of magnitude from a mapped fault for seismic hazard analyses.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210113","usgsCitation":"Anderson, J.G., Biasi, G., Angster, S.J., and Wesnousky, S., 2021, Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop: Bulletin of the Seismological Society of America, v. 111, no. 5, p. 2379-2392, https://doi.org/10.1785/0120210113.","productDescription":"14 p.","startPage":"2379","endPage":"2392","ipdsId":"IP-117223","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, John G.","contributorId":140379,"corporation":false,"usgs":false,"family":"Anderson","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":929142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angster, Stephen J. 0000-0001-9250-8415","orcid":"https://orcid.org/0000-0001-9250-8415","contributorId":225610,"corporation":false,"usgs":true,"family":"Angster","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wesnousky, Stephen G.","contributorId":351624,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Stephen G.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":929145,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70223753,"text":"70223753 - 2021 - A protocol for modelling generalised biological responses using latent variables in structural equation models","interactions":[],"lastModifiedDate":"2021-09-08T11:58:05.588979","indexId":"70223753","displayToPublicDate":"2021-09-07T09:21:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"A protocol for modelling generalised biological responses using latent variables in structural equation models","docAbstract":"In this paper we consider the problem of how to quantitatively characterize the degree to which a study object exhibits a generalized response. By generalized response, we mean a multivariate response where numerous individual properties change in concerted fashion due to some internal integration. In latent variable structural equation modeling (LVSEM), we would typically approach this situation using a latent variable to represent a general property of interest (e.g., performance) and multiple observed indicator variables that reflect the specific features associated with that general property. While ecologists have used LVSEM in a number of cases, there is substantial potential for its wider application. One obstacle is that LV models can be complex and easily over-specified, degrading their value as a means of generalization. It can also be challenging to diagnose causes of misspecification and understand which model modifications are sensible. In this paper we present a protocol, consisting of a series of questions, designed to guide the researchers through the evaluation process. These questions address (1) theoretical development, (2) data requirements, (3) whether responses to perturbation are general, (4) unique reactions by individual measures, and (5) how far generality can be extended. For this illustration, we reference a recent study considering the potential consequences of maintaining biodiversity as part of agricultural management on the overall quality of grapes used for wine making. We extend our presentation to include the complexities that occur when there are multiple species with unique reactions.","language":"English","publisher":"Pensoft Publishers","doi":"10.3897/oneeco.6.e67320","usgsCitation":"Grace, J., and Steiner, M., 2021, A protocol for modelling generalised biological responses using latent variables in structural equation models: One Ecosystem, v. 6, e67320, 20 p., https://doi.org/10.3897/oneeco.6.e67320.","productDescription":"e67320, 20 p.","ipdsId":"IP-128690","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450898,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.6.e67320","text":"Publisher Index Page"},{"id":388870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2021-07-08","publicationStatus":"PW","contributors":{"editors":[{"text":"Akomolafe, Gbenga","contributorId":265354,"corporation":false,"usgs":false,"family":"Akomolafe","given":"Gbenga","email":"","affiliations":[],"preferred":false,"id":822627,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":220095,"corporation":false,"usgs":true,"family":"Grace","given":"James B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":822551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steiner, Magdalena","contributorId":265327,"corporation":false,"usgs":false,"family":"Steiner","given":"Magdalena","email":"","affiliations":[{"id":54645,"text":"University of Fribourg, Ecology and Evolution, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":822552,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70223756,"text":"70223756 - 2021 - Instrumental variable methods in structural equation models","interactions":[],"lastModifiedDate":"2021-09-07T14:17:22.994951","indexId":"70223756","displayToPublicDate":"2021-09-07T09:12:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Instrumental variable methods in structural equation models","docAbstract":"<ol class=\"\"><li>Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present the motivations, requirements and basic procedures for using SEMIV.</li><li>I first consider causal inference and IVs from the perspective of a randomized experiment with partial control of the cause of interest. I consider common sources of bias, the role of randomization and limits to its capacity to exclude bias. Sources of bias include omitted confounders, reciprocal causation, reverse causation and measurement error, all of which can all be seen as a single problem—endogeneity. The approach to estimating IV models most commonly used in econometric practice, two-stage least squares regression (2SLS), is explained, followed by a brief exposition of the covariance modelling approach used in SEM. Using data from an ecological field experiment, I illustrate the use of the treatment variable as an IV and then illustrate procedures for evaluating candidate variables that might serve as additional IVs.</li><li>IV methods are shown to be useful for both detecting endogeneity and removing its influences. I illustrate some of the ways that bias can be generated, as well as diagnostic capabilities and means for remedy embedded within SEM. Procedures for screening and evaluating additional IVs reveal valuable lessons regarding the theoretical requirements and empirical standards for IVs.</li><li>SEMIV provides a useful way to detect and control for bias. I suggest that the use of IVs within the SEM framework can support the simultaneous pursuit of causal inference and explanatory modelling, a common pair of aspirations for ecologists. Moving forward, there is a need for a better understanding of the capabilities of SEMIV and requirements for successful application.</li></ol>","language":"English","publisher":"John Wiley & Sons","doi":"10.1111/2041-210X.13600","usgsCitation":"Grace, J., 2021, Instrumental variable methods in structural equation models: Methods in Ecology and Evolution, v. 12, no. 7, p. 1148-1157, https://doi.org/10.1111/2041-210X.13600.","productDescription":"10 p.","startPage":"1148","endPage":"1157","ipdsId":"IP-123523","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13600","text":"Publisher Index Page"},{"id":388864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-04-10","publicationStatus":"PW","contributors":{"editors":[{"text":"Morrissey, Michael","contributorId":202680,"corporation":false,"usgs":false,"family":"Morrissey","given":"Michael","email":"","affiliations":[],"preferred":false,"id":822559,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Grace, James 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":206247,"corporation":false,"usgs":true,"family":"Grace","given":"James","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":822553,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70224585,"text":"70224585 - 2021 - Conservation of northwestern and southwestern pond turtles: Threats, population size estimates, and population viability analysis","interactions":[],"lastModifiedDate":"2021-12-10T17:00:00.435086","indexId":"70224585","displayToPublicDate":"2021-09-07T07:57:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Conservation of northwestern and southwestern pond turtles: Threats, population size estimates, and population viability analysis","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p><span>Accurate status assessments of long-lived, widely distributed taxa depend on the availability of long-term monitoring data from multiple populations. However, monitoring populations across large temporal and spatial scales is often beyond the scope of any one researcher or research group. Consequently, wildlife managers may be tasked with utilizing limited information from different sources to detect range-wide evidence of population declines and their causes. When assessments need to be made under such constraints, the research and management communities must determine how to extrapolate from variable population data to species-level inferences. Here, using three different approaches, we integrate and analyze data from the peer-reviewed literature and government agency reports to inform conservation for northwestern pond turtles (NPT)&nbsp;</span><i>Actinemys marmorata</i><span>&nbsp;and southwestern pond turtles (SPT)&nbsp;</span><i>Actinemys pallida</i><span>. Both NPT and SPT are long-lived freshwater turtles distributed along the west coast of the United States and Mexico. Conservation concerns exist for both species; however, SPT may face more severe threats and are thought to exist at lower densities throughout their range than NPT. For each species, we ranked the impacts of 13 potential threats, estimated population sizes, and modeled population viability with and without long-term droughts. Our results suggest that predation of hatchlings by invasive predators, such as American bullfrogs&nbsp;</span><i>Lithobates catesbeianus</i><span>&nbsp;and Largemouth Bass&nbsp;</span><i>Micropterus salmoides,</i><span>&nbsp;is a high-ranking threat for NPT and SPT. Southwestern pond turtles may also face more severe impacts associated with natural disasters (droughts, wildfires, and floods) than do NPT. Population size estimates from trapping surveys indicate that SPT have smaller population sizes on average than do NPT (</span><i>P</i><span>&nbsp;= 0.0003), suggesting they may be at greater risk of local extirpation. Population viability analysis models revealed that long-term droughts are a key environmental parameter; as the frequency of severe droughts increases with climate change, the likelihood of population recovery decreases, especially when census sizes are low. Given current population trends and vulnerability to natural disasters throughout their range, we suggest that conservation and recovery actions first focus on SPT to prevent further population declines.</span></p></div>","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-094","usgsCitation":"Manzo, S., Nicholson, E.G., Devereux. Zachary, Fisher, R., Brown, C., Scott, P., and Shaffer, H.B., 2021, Conservation of northwestern and southwestern pond turtles: Threats, population size estimates, and population viability analysis: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 485-501, https://doi.org/10.3996/JFWM-20-094.","productDescription":"17 p.","startPage":"485","endPage":"501","ipdsId":"IP-130146","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450903,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-094","text":"Publisher Index Page"},{"id":389944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Manzo, Stephanie","contributorId":240852,"corporation":false,"usgs":false,"family":"Manzo","given":"Stephanie","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicholson, E. Griffin","contributorId":240850,"corporation":false,"usgs":false,"family":"Nicholson","given":"E.","email":"","middleInitial":"Griffin","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devereux. Zachary","contributorId":266038,"corporation":false,"usgs":false,"family":"Devereux. Zachary","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":824193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Christopher W. 0000-0002-2545-9171","orcid":"https://orcid.org/0000-0002-2545-9171","contributorId":240860,"corporation":false,"usgs":true,"family":"Brown","given":"Christopher W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Peter A","contributorId":240864,"corporation":false,"usgs":false,"family":"Scott","given":"Peter A","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaffer, H. Bradley","contributorId":202769,"corporation":false,"usgs":false,"family":"Shaffer","given":"H.","email":"","middleInitial":"Bradley","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824197,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70225492,"text":"70225492 - 2021 - The evolution of geospatial reasoning, analytics, and modeling","interactions":[],"lastModifiedDate":"2021-10-18T11:55:39.769997","indexId":"70225492","displayToPublicDate":"2021-09-07T06:54:45","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The evolution of geospatial reasoning, analytics, and modeling","docAbstract":"<div class=\"field field-name-body field-type-text-with-summary field-label-hidden\"><div class=\"field-items\"><div class=\"field-item even\"><p>The field of geospatial analytics and modeling has a long history coinciding with the physical and cultural evolution of humans. This history is analyzed relative to the four scientific paradigms: (1) empirical analysis through description, (2) theoretical explorations using models and generalizations, (3) simulating complex phenomena and (4) data exploration. Correlations among developments in general science and those of the geospatial sciences are explored. Trends identify areas ripe for growth and improvement in the fourth and current paradigm that has been spawned by the big data explosion, such as exposing the ‘black box’ of GeoAI training and generating big geospatial training datasets. Future research should focus on integrating both theory- and data-driven knowledge discovery.</p></div></div></div><div id=\"info\"><br></div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The Geographic Information Science & Technology Body of Knowledge","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University Consortium for Geographic Information Science","doi":"10.22224/gistbok/2021.3.4","usgsCitation":"Arundel, S., and Li, W., 2021, The evolution of geospatial reasoning, analytics, and modeling, chap. <i>of</i> The Geographic Information Science & Technology Body of Knowledge, https://doi.org/10.22224/gistbok/2021.3.4.","ipdsId":"IP-127804","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":450910,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22224/gistbok/2021.3.4","text":"Publisher Index Page"},{"id":390602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":825264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":825265,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70227662,"text":"70227662 - 2021 - Reinterpreting the Bruun Rule in the context of equilibrium shoreline models","interactions":[],"lastModifiedDate":"2022-01-25T12:47:37.130991","indexId":"70227662","displayToPublicDate":"2021-09-07T06:44:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Reinterpreting the Bruun Rule in the context of equilibrium shoreline models","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Long-term (&gt;decades) coastal recession due to sea-level rise (SLR) has been estimated using the Bruun Rule for nearly six decades. Equilibrium-based shoreline models have been shown to skillfully predict short-term wave-driven shoreline change on time scales of hours to decades. Both the Bruun Rule and equilibrium shoreline models rely on the equilibrium beach theory, which states that the beach profile shape equilibrates with its local wave and sea-level conditions. Integrating these two models into a unified framework can improve our understanding and predictive skill of future shoreline behavior. However, given that both models account for wave action, but over different time scales, a critical re-examination of the SLR-driven recession process is needed. We present a novel physical interpretation of the beach response to sea-level rise, identifying two main contributing processes: passive flooding and increased wave-driven erosion efficiency. Using this new concept, we analyze the integration of SLR-driven recession into equilibrium shoreline models and, with an idealized test case, show that the physical mechanisms underpinning the Bruun Rule are explicitly described within our integrated model. Finally, we discuss the possible advantages of integrating SLR-driven recession models within equilibrium-based models with dynamic feedbacks and the broader implications for coupling with hybrid shoreline models.<span>&nbsp;</span></div>","language":"English","publisher":"MDPI","doi":"10.3390/jmse9090974","usgsCitation":"D’Anna, M., Idier, D., Castelle, B., Vitousek, S., and Le Cozannet, G., 2021, Reinterpreting the Bruun Rule in the context of equilibrium shoreline models: Journal of Marine Science and Engineering, v. 9, no. 9, 974, 22 p., https://doi.org/10.3390/jmse9090974.","productDescription":"974, 22 p.","ipdsId":"IP-120485","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse9090974","text":"Publisher Index Page"},{"id":394810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Anna, Maurizio","contributorId":272161,"corporation":false,"usgs":false,"family":"D’Anna","given":"Maurizio","email":"","affiliations":[{"id":56362,"text":"University of Bordeaux; BGRM","active":true,"usgs":false}],"preferred":false,"id":831621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Idier, Deborah 0000-0003-1235-2348","orcid":"https://orcid.org/0000-0003-1235-2348","contributorId":272162,"corporation":false,"usgs":false,"family":"Idier","given":"Deborah","email":"","affiliations":[{"id":41640,"text":"BGRM","active":true,"usgs":false}],"preferred":false,"id":831622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castelle, Bruno 0000-0003-1740-7395","orcid":"https://orcid.org/0000-0003-1740-7395","contributorId":272163,"corporation":false,"usgs":false,"family":"Castelle","given":"Bruno","email":"","affiliations":[{"id":41639,"text":"University of Bordeaux","active":true,"usgs":false}],"preferred":false,"id":831623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":831624,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Le Cozannet, Goneri 0000-0003-2421-3003","orcid":"https://orcid.org/0000-0003-2421-3003","contributorId":272164,"corporation":false,"usgs":false,"family":"Le Cozannet","given":"Goneri","email":"","affiliations":[{"id":41640,"text":"BGRM","active":true,"usgs":false}],"preferred":false,"id":831625,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70227361,"text":"70227361 - 2021 - Post audit of simulated groundwater flow to a short-lived (2019-2020) crater lake at Kīlauea Volcano","interactions":[],"lastModifiedDate":"2022-01-11T12:52:24.354298","indexId":"70227361","displayToPublicDate":"2021-09-06T06:48:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Post audit of simulated groundwater flow to a short-lived (2019-2020) crater lake at Kīlauea Volcano","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>About 14.5&nbsp;months after the 2018 eruption and summit collapse of Kīlauea Volcano, Hawaiʻi, liquid water started accumulating in the deepened summit crater, forming a lake that attained 51 m depth before rapidly boiling off on December 20, 2020, when an eruption from the crater wall poured lava into the lake. Modeling the growth of the crater lake at Kīlauea summit is important for assessing the potential for explosive volcanism. Our current understanding of the past 2500 years of eruptive activity at Kīlauea suggests a slight dominance of explosive behavior over effusive. The deepened summit crater and presence of the crater lake in 2019 raised renewed concerns about explosive activity. Groundwater models using hydraulic-property data from a nearby drillhole successfully forecast the timing and rate of lake filling. Here we compare the groundwater-model predictions with observational data through the demise of the crater lake, examine the implications for local water-table configuration, consider the potential role of evaporation and recharge (neglected in previous models), and briefly discuss the energetics of the rapid boil-off. This post audit of groundwater-flow models of Kīlauea summit shows that simple models can sometimes be used effectively to simulate complex settings such as volcanoes.</p></div></div>","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwat.13133","usgsCitation":"Flinders, A.F., Kauahikaua, J.P., Hsieh, P.A., and Ingebritsen, S.E., 2021, Post audit of simulated groundwater flow to a short-lived (2019-2020) crater lake at Kīlauea Volcano: Groundwater, v. 60, no. 1, p. 64-70, https://doi.org/10.1111/gwat.13133.","productDescription":"7 p.","startPage":"64","endPage":"70","ipdsId":"IP-128614","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":394173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.41671752929688,\n              19.15165474470855\n            ],\n            [\n              -155.03082275390622,\n              19.15165474470855\n            ],\n            [\n              -155.03082275390622,\n              19.530024424775405\n            ],\n            [\n              -155.41671752929688,\n              19.530024424775405\n            ],\n            [\n              -155.41671752929688,\n              19.15165474470855\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"60","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Flinders, Ashton F. 0000-0003-2483-4635 aflinders@usgs.gov","orcid":"https://orcid.org/0000-0003-2483-4635","contributorId":196960,"corporation":false,"usgs":true,"family":"Flinders","given":"Ashton","email":"aflinders@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":153,"text":"California Volcano Observatory","active":false,"usgs":true}],"preferred":false,"id":830587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":830588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":830589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":830590,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70232157,"text":"70232157 - 2021 - Towards building a sustainable future: Positioning ecological modelling for impact in ecosystems management","interactions":[],"lastModifiedDate":"2022-06-09T13:46:21.236984","indexId":"70232157","displayToPublicDate":"2021-09-04T08:42:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1107,"text":"Bulletin of Mathematical Biology","active":true,"publicationSubtype":{"id":10}},"title":"Towards building a sustainable future: Positioning ecological modelling for impact in ecosystems management","docAbstract":"As many ecosystems worldwide are in peril, efforts to manage them sustainably require scientific advice. While numerous researchers around the world use a great variety of models to understand ecological dynamics and their responses to disturbances, only a small fraction of these models are ever used to inform ecosystem management. There seems to be a perception that ecological models are not useful for management, even though mathematical models are indispensable in many other fields. We were curious about this mismatch, its roots, and potential ways to overcome it. We searched the literature on recommendations and best practices for how to make ecological models useful to the management of ecosystems and we searched for ‘success stories’ from the past. We selected and examined several cases where models were instrumental in ecosystem management. We documented their success and asked whether and to what extent they followed recommended best practices. We found that there is not a unique way to conduct a research project that is useful in management decisions. While research is more likely to have impact when conducted with many stakeholders involved and specific to a situation for which data are available, there are great examples of small groups or individuals conducting highly influential research even in the absence of detailed data. We put the question of modelling for ecosystem management into a socio-economic and national context and give our perspectives on how the discipline could move forward.","language":"English","publisher":"Springer Nature","doi":"10.1007/s11538-021-00927-y","usgsCitation":"DeAngelis, D., Franco, D., Hastings, A., Hilker, F.M., Lenhart, S., Lutscher, F., Petrovskaya, N., Petrovskii, S., and Tyson, R.C., 2021, Towards building a sustainable future: Positioning ecological modelling for impact in ecosystems management: Bulletin of Mathematical Biology, v. 83, no. 10, 107, 28 p., https://doi.org/10.1007/s11538-021-00927-y.","productDescription":"107, 28 p.","ipdsId":"IP-126721","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"text":"Publisher Index Page"},{"id":401981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"83","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":844376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franco, Daniel","contributorId":292355,"corporation":false,"usgs":false,"family":"Franco","given":"Daniel","email":"","affiliations":[{"id":62878,"text":"Universidad Nacional de Educacion a Distancia (UNED)","active":true,"usgs":false}],"preferred":false,"id":844377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hastings, Alan","contributorId":175365,"corporation":false,"usgs":false,"family":"Hastings","given":"Alan","email":"","affiliations":[],"preferred":false,"id":844378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hilker, Frank M.","contributorId":292356,"corporation":false,"usgs":false,"family":"Hilker","given":"Frank","email":"","middleInitial":"M.","affiliations":[{"id":62879,"text":"Osnabrueck University","active":true,"usgs":false}],"preferred":false,"id":844379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lenhart, Suzanne","contributorId":292357,"corporation":false,"usgs":false,"family":"Lenhart","given":"Suzanne","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":844380,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lutscher, Frithjof","contributorId":195716,"corporation":false,"usgs":false,"family":"Lutscher","given":"Frithjof","email":"","affiliations":[],"preferred":false,"id":844381,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Petrovskaya, Natalia","contributorId":292358,"corporation":false,"usgs":false,"family":"Petrovskaya","given":"Natalia","email":"","affiliations":[{"id":7157,"text":"University of Birmingham","active":true,"usgs":false}],"preferred":false,"id":844382,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petrovskii, Sergei","contributorId":292359,"corporation":false,"usgs":false,"family":"Petrovskii","given":"Sergei","email":"","affiliations":[{"id":27194,"text":"University of Leicester","active":true,"usgs":false}],"preferred":false,"id":844383,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tyson, Rebecca C.","contributorId":292360,"corporation":false,"usgs":false,"family":"Tyson","given":"Rebecca","email":"","middleInitial":"C.","affiliations":[{"id":62881,"text":"University of British Columbia-Okanagan","active":true,"usgs":false}],"preferred":false,"id":844384,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70223890,"text":"70223890 - 2021 - Demographic modeling informs functional connectivity and management interventions in Graham’s beardtongue","interactions":[],"lastModifiedDate":"2021-10-18T15:06:55.286746","indexId":"70223890","displayToPublicDate":"2021-09-04T08:08:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Demographic modeling informs functional connectivity and management interventions in Graham’s beardtongue","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Functional connectivity (i.e., the movement of individuals across a landscape) is essential for the maintenance of genetic variation and persistence of rare species. However, illuminating the processes influencing functional connectivity and ultimately translating this knowledge into management practice remains a fundamental challenge. Here, we combine various population structure analyses with pairwise, population-specific demographic modeling to investigate historical functional connectivity in Graham’s beardtongue (<i>Penstemon grahamii</i>), a rare plant narrowly distributed across a dryland region of the western US. While principal component and population structure analyses indicated an isolation-by-distance pattern of differentiation across the species’ range, spatial inferences of effective migration exposed an abrupt shift in population ancestry near the range center. To understand these seemingly conflicting patterns, we tested various models of historical gene flow and found evidence for recent admixture (~ 3400 generations ago) between populations near the range center. This historical perspective reconciles population structure patterns and suggests management efforts should focus on maintaining connectivity between these previously isolated lineages to promote the ongoing transfer of genetic variation. Beyond providing species-specific knowledge to inform management options, our study highlights how understanding demographic history may be critical to guide conservation efforts when interpreting population genetic patterns and inferring functional connectivity.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s10592-021-01392-9","usgsCitation":"Jones, M.R., Winkler, D.E., and Massatti, R., 2021, Demographic modeling informs functional connectivity and management interventions in Graham’s beardtongue: Conservation Genetics, v. 22, p. 993-1003, https://doi.org/10.1007/s10592-021-01392-9.","productDescription":"11 p.","startPage":"993","endPage":"1003","ipdsId":"IP-129207","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450931,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-021-01392-9","text":"Publisher Index Page"},{"id":436210,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VRF7AR","text":"USGS data release","linkHelpText":"Penstemon grahamii genetic data from a dryland region of the western United States"},{"id":389140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationDate":"2021-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Matthew Richard 0000-0002-4822-157X","orcid":"https://orcid.org/0000-0002-4822-157X","contributorId":257921,"corporation":false,"usgs":true,"family":"Jones","given":"Matthew","email":"","middleInitial":"Richard","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":823142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70224307,"text":"70224307 - 2021 - Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA","interactions":[],"lastModifiedDate":"2021-09-21T12:49:42.842427","indexId":"70224307","displayToPublicDate":"2021-09-04T07:47:54","publicationYear":"2021","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":"Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab005\" class=\"abstract author\" lang=\"en\"><div id=\"as005\"><p id=\"sp0005\">The travel time or “age” of groundwater affects catchment responses to<span>&nbsp;</span>hydrologic changes<span>, geochemical reactions, and&nbsp;time lags&nbsp;between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE&nbsp;=&nbsp;0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE&nbsp;=&nbsp;0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4&nbsp;m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9&nbsp;years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and&nbsp;geochemistry&nbsp;flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2021.126908","usgsCitation":"Green, C., Ransom, K.M., Nolan, B.T., Liao, L., and Harter, T., 2021, Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA: Journal of Hydrology, v. 603, 126908, 16 p., https://doi.org/10.1016/j.jhydrol.2021.126908.","productDescription":"126908, 16 p.","ipdsId":"IP-108783","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":450933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2021.126908","text":"Publisher Index Page"},{"id":389537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.69140625,\n              40.58058466412761\n            ],\n            [\n              -75.498046875,\n              40.58058466412761\n            ],\n            [\n              -75.498046875,\n              49.439556958940855\n            ],\n            [\n              -93.69140625,\n              49.439556958940855\n            ],\n            [\n              -93.69140625,\n              40.58058466412761\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"603","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":823665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nolan, Bernard T. 0000-0002-6945-9659","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":265888,"corporation":false,"usgs":false,"family":"Nolan","given":"Bernard","email":"","middleInitial":"T.","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":823667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liao, Lixia 0000-0003-2513-0680","orcid":"https://orcid.org/0000-0003-2513-0680","contributorId":201643,"corporation":false,"usgs":true,"family":"Liao","given":"Lixia","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":823668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harter, Thomas","contributorId":178245,"corporation":false,"usgs":false,"family":"Harter","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":823669,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70225507,"text":"70225507 - 2021 - Evaluating stereo digital terrain model quality at Mars Rover Landing Sites with HRSC, CTX, and HiRISE Images","interactions":[],"lastModifiedDate":"2021-10-18T11:35:00.943596","indexId":"70225507","displayToPublicDate":"2021-09-04T06:33:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating stereo digital terrain model quality at Mars Rover Landing Sites with HRSC, CTX, and HiRISE Images","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">We have used high-resolution digital terrain models (DTMs) of two rover landing sites based on mosaicked images from the High-Resolution Imaging Science Experiment (HiRISE) camera as a reference to evaluate DTMs based on High-Resolution Stereo Camera (HRSC) and Context Camera (CTX) images. The Next-Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET and GXP® commercial photogrammetric systems produces DTMs with good (small) horizontal resolution but large vertical error. Somewhat surprisingly, results for NGATE are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give similar tradeoffs between resolution and error; a 5 × 5 lowpass filter is near optimal in terms of both combined resolution-error performance and local slope estimation. Smoothing with an area-based matcher, the standard processing for U.S. Geological Survey planetary DTMs, yields similar errors to the 5 × 5 filter at slightly worse resolution. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Considered individually, resolution and error each varied by approximately a factor of 2. Matching errors were 0.2–0.5 pixels but most results fell in the 0.2–0.3 pixel range that has been stated as a rule of thumb in multiple prior studies. Horizontal resolutions of 10–20 image pixels were found, consistently greater than the 3–5 pixel spacing generally used for stereo DTM production. Resolution and precision were inversely correlated; their product varied by ≤20% (4–5 pixels squared). Refinement of the stereo DTM by photoclinometry can yield quantitative improvement in resolution (more than a factor of 2), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe. We offer specific guidance for both producers and users of planetary stereo DTMs, based on our results.<span>&nbsp;</span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13173511","usgsCitation":"Kirk, R.L., Mayer, D., Fergason, R.L., Redding, B.L., Galuszka, D.M., Hare, T.M., and Gwinner, K., 2021, Evaluating stereo digital terrain model quality at Mars Rover Landing Sites with HRSC, CTX, and HiRISE Images: Remote Sensing, v. 13, no. 17, 3511, 40 p., https://doi.org/10.3390/rs13173511.","productDescription":"3511, 40 p.","ipdsId":"IP-131188","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":450938,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13173511","text":"Publisher Index Page"},{"id":390596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"17","noUsgsAuthors":false,"publicationDate":"2021-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirk, Randolph L. 0000-0003-0842-9226 rkirk@usgs.gov","orcid":"https://orcid.org/0000-0003-0842-9226","contributorId":2765,"corporation":false,"usgs":true,"family":"Kirk","given":"Randolph","email":"rkirk@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayer, David 0000-0001-8351-1807","orcid":"https://orcid.org/0000-0001-8351-1807","contributorId":215429,"corporation":false,"usgs":true,"family":"Mayer","given":"David","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fergason, Robin L. 0000-0002-2044-1714","orcid":"https://orcid.org/0000-0002-2044-1714","contributorId":206167,"corporation":false,"usgs":true,"family":"Fergason","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Redding, Bonnie L. 0000-0001-8178-1467 bredding@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-1467","contributorId":4798,"corporation":false,"usgs":true,"family":"Redding","given":"Bonnie","email":"bredding@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galuszka, Donna M. 0000-0003-1870-1182 dgaluszka@usgs.gov","orcid":"https://orcid.org/0000-0003-1870-1182","contributorId":3186,"corporation":false,"usgs":true,"family":"Galuszka","given":"Donna","email":"dgaluszka@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":825350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gwinner, Klaus","contributorId":211338,"corporation":false,"usgs":false,"family":"Gwinner","given":"Klaus","email":"","affiliations":[],"preferred":false,"id":825351,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70221823,"text":"sir20205104 - 2021 - Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia","interactions":[],"lastModifiedDate":"2021-09-03T15:08:46.12553","indexId":"sir20205104","displayToPublicDate":"2021-09-03T11:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5104","displayTitle":"Simulated Effects of Sea-Level Rise on the Shallow, Fresh Groundwater System of Assateague Island, Maryland and Virginia","title":"Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.</p><p>Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.</p><p>This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.42388916015625,\n              37.87376937332855\n            ],\n            [\n              -75.3826904296875,\n              37.83473402375478\n            ],\n            [\n              -75.30441284179688,\n              37.88027325525864\n            ],\n            [\n              -75.15335083007812,\n              38.11727165830543\n            ],\n            [\n              -75.12039184570312,\n              38.29101446582335\n            ],\n            [\n              -75.17120361328125,\n              38.22847167526397\n            ],\n            [\n              -75.28793334960938,\n              38.0513353697269\n            ],\n            [\n              -75.3826904296875,\n              37.93769926732864\n            ],\n            [\n              -75.42388916015625,\n              37.87376937332855\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_md@usgs.gov\" data-mce-href=\"mailto:dc_md@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/md-de-dc-water\" data-mce-href=\"https://www.usgs.gov/centers/md-de-dc-water\">Maryland-Delaware-D.C. Water Science Center</a><br>U.S. Geological Survey<br>5522 Research Park Drive<br>Catonsville, MD 21228</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Hydrogeologic Framework</li><li>Simulation of the Shallow Groundwater-Flow System</li><li>Long-term Monitoring to Assess Water Resources</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Water Level and Specific Conductance Data</li><li>Appendix 2. Model Development</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-07-16","noUsgsAuthors":false,"publicationDate":"2021-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raffensperger, Jeff P. 0000-0001-9275-6646 jpraffen@usgs.gov","orcid":"https://orcid.org/0000-0001-9275-6646","contributorId":199119,"corporation":false,"usgs":true,"family":"Raffensperger","given":"Jeff","email":"jpraffen@usgs.gov","middleInitial":"P.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Masterson, John P. 0000-0003-3202-4413 jpmaster@usgs.gov","orcid":"https://orcid.org/0000-0003-3202-4413","contributorId":1865,"corporation":false,"usgs":true,"family":"Masterson","given":"John P.","email":"jpmaster@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":818859,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70229740,"text":"70229740 - 2021 - Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA","interactions":[],"lastModifiedDate":"2022-03-16T15:46:50.776952","indexId":"70229740","displayToPublicDate":"2021-09-03T10:45:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA","docAbstract":"<p><span>Landscape features influence wildlife movements across spatial scales and have the potential to influence the spread of disease. Chronic wasting disease (CWD) is a fatal prion disease affecting members of the family Cervidae, particularly white-tailed deer (</span><i>Odocoileus virginianus</i><span>), and the first positive CWD case in a wild deer in Ohio, USA, was recorded in 2020. Landscape genetics approaches are increasingly used to better understand potential pathways for CWD spread in white-tailed deer, but little is known about genetic structure of white-tailed deer in Ohio. The objectives of our study were to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We collected genetic data from 619 individual deer from 24 counties across Ohio during 2007–2009. We used microsatellite genotypes from 619 individuals genotyped at 11 loci and haplotypes from a 547-base pair fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data. Non-spatial genetic clustering tests failed to detect spatial genetic structure, whereas spatial genetic clustering tests indicated subtle spatial genetic structure. The IBD model consistently outperformed IBR models that included land cover, traffic volume, and streams. Our results indicated widespread genetic connectivity of white-tailed deer across Ohio and negligible effects of landscape features. These patterns likely reflect some combination of minimal resistive effects of landscape features on white-tail deer movement in Ohio and the effects of regional recolonization or translocation. We encourage continued CWD surveillance in Ohio, particularly in the proximity of confirmed cases.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22120","usgsCitation":"Bauder, J., Anderson, C.S., Gibbs, H., Tonkovich, M., and Walter, W., 2021, Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA: Journal of Wildlife Management, v. 85, no. 8, p. 1669-1684, https://doi.org/10.1002/jwmg.22120.","productDescription":"16 p.","startPage":"1669","endPage":"1684","ipdsId":"IP-128673","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan M.","contributorId":288641,"corporation":false,"usgs":false,"family":"Bauder","given":"Javan M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":838159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christine S.","contributorId":288642,"corporation":false,"usgs":false,"family":"Anderson","given":"Christine","email":"","middleInitial":"S.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, H. Lisle","contributorId":288643,"corporation":false,"usgs":false,"family":"Gibbs","given":"H. Lisle","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tonkovich, Michael J.","contributorId":288644,"corporation":false,"usgs":false,"family":"Tonkovich","given":"Michael J.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":838162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838158,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70224927,"text":"70224927 - 2021 - Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement","interactions":[],"lastModifiedDate":"2021-10-05T12:14:42.13764","indexId":"70224927","displayToPublicDate":"2021-09-03T07:09:36","publicationYear":"2021","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":"Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement","docAbstract":"<div class=\"article-section__content en main\"><p>Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EA001896","usgsCitation":"Goldstein, E.B., Buscombe, D., Lazarus, E.D., Mohanty, S., Rafique, S.N., Anarde, K.A., Ashton, A.D., Beuzen, T., Castagno, K.A., Cohn, N., Conlin, M.P., Ellenson, A., Gillen, M., Hovenga, P.A., Over, J.R., Palermo, R., Ratlif, K., Reeves, I.R., Sanborn, L.H., Straub, J.A., Taylor, L.A., Wallace, E.J., Warrick, J.A., Wernette, P., and Williams, H.E., 2021, Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement: Earth and Space Science, v. 8, no. 9, e2021EA001896, 18 p., https://doi.org/10.1029/2021EA001896.","productDescription":"e2021EA001896, 18 p.","ipdsId":"IP-131036","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science 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,{"id":70225501,"text":"70225501 - 2021 - Individual variation in temporal dynamics of post-release habitat selection","interactions":[],"lastModifiedDate":"2021-10-18T11:28:27.245581","indexId":"70225501","displayToPublicDate":"2021-09-03T06:26:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"title":"Individual variation in temporal dynamics of post-release habitat selection","docAbstract":"<div class=\"JournalAbstract\"><p>Translocated animals undergo a phase of behavioral adjustment after being released in a novel environment, initially prioritizing exploration and gradually shifting toward resource exploitation. This transition has been termed post-release behavioral modification. Post-release behavioral modification may also manifest as changes in habitat selection through time, and these temporal dynamics may differ between individuals. We aimed to evaluate how post-release behavioral modification is reflected in temporal dynamics of habitat selection and its variability across individuals using a population of translocated female greater sage-grouse as a case study. Sage-grouse were translocated from Wyoming to North Dakota (USA) during the summers of 2018–2020. We analyzed individual habitat selection as a function of sagebrush cover, herbaceous cover, slope, and distance to roads. Herbaceous cover is a key foraging resource for sage-grouse during summer; thus, we expected a shift from exploration to exploitation to manifest as temporally-varying selection for herbaceous cover. For each individual sage-grouse (<i>N</i><span>&nbsp;</span>= 26), we tested two competing models: a null model with no time-dependence and a model with time-dependent selection for herbaceous cover. We performed model selection at the individual level using an information-theoretic approach. Time-dependence was supported for five individuals, unsupported for seven, and the two models were indistinguishable based on AIC<sub>c</sub><span>&nbsp;</span>for the remaining fourteen. We found no association between the top-ranked model and individual reproductive status (brood-rearing or not). We showed that temporal dynamics of post-release habitat selection may emerge in some individuals but not in others, and that failing to account for time-dependence may hinder the detection of steady-state habitat selection patterns. These findings demonstrate the need to consider both temporal dynamics and individual variability in habitat selection when conducting post-release monitoring to inform translocation protocols.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/fcosc.2021.703906","usgsCitation":"Picardi, S., Ranc, N., Smith, B., Coates, P.S., Mathews, S.R., and Dahlgren, D.K., 2021, Individual variation in temporal dynamics of post-release habitat selection: Frontiers in Conservation Science, v. 2, 703906, 8 p., https://doi.org/10.3389/fcosc.2021.703906.","productDescription":"703906, 8 p.","ipdsId":"IP-132923","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2021.703906","text":"Publisher Index Page"},{"id":390593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Picardi, Simona 0000-0002-2623-6623","orcid":"https://orcid.org/0000-0002-2623-6623","contributorId":237045,"corporation":false,"usgs":false,"family":"Picardi","given":"Simona","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":825309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ranc, Nathan","contributorId":267798,"corporation":false,"usgs":false,"family":"Ranc","given":"Nathan","email":"","affiliations":[{"id":55511,"text":"Center for Integrated Spatial Research, Environmental Studies Department, University of California, Santa Cruz, Santa Cruz, CA, United States","active":true,"usgs":false}],"preferred":false,"id":825310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":825311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":825312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mathews, Steven R. 0000-0002-3165-9460 smathews@usgs.gov","orcid":"https://orcid.org/0000-0002-3165-9460","contributorId":176922,"corporation":false,"usgs":true,"family":"Mathews","given":"Steven","email":"smathews@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":825313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dahlgren, David K.","contributorId":257565,"corporation":false,"usgs":false,"family":"Dahlgren","given":"David","email":"","middleInitial":"K.","affiliations":[{"id":52056,"text":"Department of Wildland Resources, Jack H. Berryman Institute, S. J. Quinney College of Natural Resources, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":825314,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70230525,"text":"70230525 - 2021 - Historical changes in plant water use and need in the continental United States","interactions":[],"lastModifiedDate":"2022-04-15T12:11:42.091767","indexId":"70230525","displayToPublicDate":"2021-09-02T07:07:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Historical changes in plant water use and need in the continental United States","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.</p></div></div>","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0256586","usgsCitation":"Terck, M.T., Thoma, D., Gross, J.E., Sherrill, K.R., Kagone, S., and Senay, G.B., 2021, Historical changes in plant water use and need in the continental United States: PLoS ONE, v. 16, no. 9, e0256586., 19 p., https://doi.org/10.1371/journal.pone.0256586.","productDescription":"e0256586., 19 p.","ipdsId":"IP-131683","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0256586","text":"Publisher Index 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]\n}","volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Terck, Michael T 0000-0002-8802-0158","orcid":"https://orcid.org/0000-0002-8802-0158","contributorId":290254,"corporation":false,"usgs":false,"family":"Terck","given":"Michael","email":"","middleInitial":"T","affiliations":[{"id":54820,"text":"Walking Shadow Ecology","active":true,"usgs":false}],"preferred":false,"id":840647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoma, David","contributorId":265911,"corporation":false,"usgs":false,"family":"Thoma","given":"David","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":840648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gross, John E.","contributorId":106777,"corporation":false,"usgs":false,"family":"Gross","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":840649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherrill, Kirk R.","contributorId":83017,"corporation":false,"usgs":true,"family":"Sherrill","given":"Kirk","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":840650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":210980,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":840698,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":840651,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70225724,"text":"70225724 - 2021 - Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise","interactions":[],"lastModifiedDate":"2021-11-05T11:58:08.77405","indexId":"70225724","displayToPublicDate":"2021-09-02T06:57:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise","docAbstract":"<p class=\"chapter-para\">Tilting of the ground due to loading by the variable atmosphere is known to corrupt very long period horizontal seismic records (below 10 mHz) even at the quietest stations. At BFO (Black Forest Observatory, SW-Germany), the opportunity arose to study these disturbances on a variety of simultaneously operated state-of-the-art broad-band sensors. A series of time windows with clear atmospherically caused effects was selected and attempts were made to model these ‘signals’ in a deterministic way. This was done by simultaneously least-squares fitting the locally recorded barometric pressure and its Hilbert transform to the ground accelerations in a bandpass between 100 and 3600&nbsp;s periods. Variance reductions of up to 97 per cent were obtained. We show our results by combining the ‘specific pressure induced accelerations’ for the two horizontal components of the same sensor as vectors on a horizontal plane, one for direct pressure and one for its Hilbert transform. It turned out that at BFO the direct pressure effects are large, strongly position dependent and largely independent of atmospheric events for instruments installed on piers, while three post-hole sensors are only slightly affected. The infamous ‘cavity effects’ are invoked to be responsible for these large effects on the pier sensors. On the other hand, in the majority of cases all sensors showed very similar magnitudes and directions for the vectors obtained for the regression with the Hilbert transform, but highly variable from event to event especially in direction. Therefore, this direction most certainly has to do with the gradient of the pressure field moving over the station which causes a larger scale deformation of the crust. The observations are very consistent with these two fundamental mechanisms of how fluctuations of atmospheric surface pressure causes tilt noise. The results provide a sound basis for further improvements of the models for these mechanisms. The methods used here can already help to reduce atmospherically induced noise in long-period horizontal seismic records.</p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab336","usgsCitation":"Zurn, W., Forbriger, T., Widmer-Schnidrig, R., Duffner, P., and Ringler, A.T., 2021, Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise: Geophysical Journal International, v. 228, no. 2, p. 927-943, https://doi.org/10.1093/gji/ggab336.","productDescription":"17 p.","startPage":"927","endPage":"943","ipdsId":"IP-131609","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":450964,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5445/ir/1000140172","text":"External Repository"},{"id":391423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zurn, W.","contributorId":268322,"corporation":false,"usgs":false,"family":"Zurn","given":"W.","affiliations":[{"id":55624,"text":"Black Forest Observatory (Schiltach)","active":true,"usgs":false}],"preferred":false,"id":826410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forbriger, T.","contributorId":268323,"corporation":false,"usgs":false,"family":"Forbriger","given":"T.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Widmer-Schnidrig, R.","contributorId":221153,"corporation":false,"usgs":false,"family":"Widmer-Schnidrig","given":"R.","email":"","affiliations":[{"id":40338,"text":"Black Forest Observatory, Institute of Geodesy, Stuttgart University, Wolfach, Germany","active":true,"usgs":false}],"preferred":false,"id":826412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duffner, P.","contributorId":268324,"corporation":false,"usgs":false,"family":"Duffner","given":"P.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826414,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70223694,"text":"sir20205150 - 2021 - Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013","interactions":[],"lastModifiedDate":"2021-09-02T11:51:45.887677","indexId":"sir20205150","displayToPublicDate":"2021-09-01T16:37:37","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5150","displayTitle":"Precipitation-Runoff Processes in the Merced River Basin, Central California, with Prospects for Streamflow Predictability, Water Years 1952–2013","title":"Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially detailed Precipitation-Runoff Modeling System (PRMS) model for the Merced River Basin, California, which is a tributary of the San Joaquin River in California. Operated through an Object User Interface (OUI) with Ensemble Streamflow Prediction (ESP) and daily climate distribution preprocessing functionality, the model is calibrated primarily to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season. The model is intended to become part of a suite of methods used by DWR for estimating daily streamflow from the Merced River Basin, especially during the snowmelt season. This study describes the results of the application of an analysis tool that simulates responses to climate and land-use variations at a higher spatial resolution than previously available to DWR.</p><p>A geographic information system was used to delineate the model domain, that is, areas draining to a single outlet at U.S. Geological Survey streamflow-gaging station 11270900, Merced River below Merced Falls Dam, near Snell, CA (also known as California Data Exchange Center station MRC), and subdrainage areas, including four draining to internal gages used as calibration targets. Using this delineation, three contiguous subbasins were recognized and, along with the model domain and nested calibration targets, are the simulation units evaluated in this report.</p><p>An auto-calibration tool, LUCA (Let Us CAlibrate), was used for each calibration node, from headwaters to basin outlet, and then parameters were manually adjusted to complete the calibration. The main objective was to match April–July snowmelt seasonal discharge values of simulated streamflow to observed (measured or reconstructed) discharge values. Calibration or validation periods used site-specific streamflows—mostly from October 1, 1988, through September 30, 2013—but differed according to the period-of-record available for the measurements collected at internal gages or reconstructed flows for the single outlet.</p><p>The accuracy of the Merced PRMS streamflow simulations varied seasonally, as compared to observed values. Based on statistical results, the Merced PRMS model satisfactorily simulated snowmelt seasonal streamflows. April–July calibrations for all areas had small negative bias (not greater than 7 percent) and low relative error (less than 8 percent). Less satisfactory performance for other seasons was attributed to several factors: (1) high uncertainty in low or zero flows in summer and fall, (2) lack of accounting for basin withdrawals and anthropogenic water use, (3) unavailability and (or) inaccuracy of observed (measured) meteorological input data, and (4) uncertainty in reconstructed streamflow data.</p><p>With some additional refinement, the Merced PRMS model may be used for forecasting seasonal and longer-term streamflow variations; evaluating forecasted and past climate and land cover changes; providing water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites within the basin; and aiding environmental studies, hydraulic design, water management, and water-quality projects in the Merced River Basin.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205150","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Koczot, K.M., Risley, J.C., Gronberg, J.M., Donovan, J.M., and McPherson, K.R., 2021, Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013: U.S. Geological Survey Scientific Investigations Report 2020–5150, 61 p., https://doi.org/10.3133/sir20205150.","productDescription":"Report: ix, 61 p.; 1 Figure: 16.0 x 10.0 inches; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-028665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":388739,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KFR","linkHelpText":"Archive of Merced River  Basin Precipitation-Runoff Modeling System, with forecasting, climate-file preparation, and data-visualization tools"},{"id":388738,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150_fig11_sheet.pdf","text":"Figure 11 (16\" x 10\" sheet)","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Physical architecture of the Merced River Basin Precipitation-Runoff Modeling System."},{"id":388737,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5150/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Merced River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              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95819</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Physical Characteristics of the Merced River Basin&nbsp;&nbsp;</li><li>Watershed Modeling&nbsp;&nbsp;</li><li>Streamflow Simulations: Results and Performance Assessment&nbsp;&nbsp;</li><li>Applications&nbsp;&nbsp;</li><li>Model Limitations and Future Enhancements&nbsp;&nbsp;</li><li>Summary and Conclusions&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendix&nbsp;</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-09-01","noUsgsAuthors":false,"publicationDate":"2021-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Koczot, Kathryn M. 0000-0001-5728-9798 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krmcpher@usgs.gov","orcid":"https://orcid.org/0000-0002-2340-4142","contributorId":1376,"corporation":false,"usgs":true,"family":"McPherson","given":"Kelly","email":"krmcpher@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822357,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70237357,"text":"70237357 - 2021 - LakeEnsemblR: An R package that facilitates ensemble modelling of lakes","interactions":[],"lastModifiedDate":"2022-10-11T15:49:16.703697","indexId":"70237357","displayToPublicDate":"2021-09-01T10:40:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"LakeEnsemblR: An R package that facilitates ensemble modelling of lakes","docAbstract":"Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105101","usgsCitation":"Moore, T.N., Mesman, J., Ladwig, R., Feldbauer, J., Olsson, F., Pilla, R.M., Shatwell, T., Venkiteswaran, J.J., Delany, A.D., Dugan, H., Rose, K.C., and Read, J., 2021, LakeEnsemblR: An R package that facilitates ensemble modelling of lakes: Environmental Modelling & Software, v. 143, 105101, 14 p., https://doi.org/10.1016/j.envsoft.2021.105101.","productDescription":"105101, 14 p.","ipdsId":"IP-122731","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":450973,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105101","text":"Publisher Index Page"},{"id":408161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Tadhg N.","contributorId":297476,"corporation":false,"usgs":false,"family":"Moore","given":"Tadhg","email":"","middleInitial":"N.","affiliations":[{"id":64406,"text":"Dundalk Institute of Technology, Centre for Freshwater and Environmental Studies, Dundalk, Co. 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