{"pageNumber":"292","pageRowStart":"7275","pageSize":"25","recordCount":165309,"records":[{"id":70240146,"text":"sir20225109 - 2023 - Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18","interactions":[],"lastModifiedDate":"2026-02-23T19:37:25.988863","indexId":"sir20225109","displayToPublicDate":"2023-01-31T10:00:00","publicationYear":"2023","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":"2022-5109","displayTitle":"Comparison of Surrogate Models To Estimate Pesticide Concentrations at Six U.S. Geological Survey National Water Quality Network Sites During Water Years 2013–18","title":"Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18","docAbstract":"<p>During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality.</p><p>The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized <i>R</i><sup>2</sup>), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit.</p><p>In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized <i>R</i><sup>2</sup> values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized <i>R</i><sup>2</sup> values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225109","programNote":"National Water Quality Program","usgsCitation":"Covert, S.A., Bunch, A.R., Crawford, C.G., and Oelsner, G.P., 2023, Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18: U.S. Geological Survey Scientific Investigations Report 2022–5109, 17 p., https://doi.org/10.3133/sir20225109.","productDescription":"Report: v, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-132946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/oki-water\" data-mce-href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>5957 Lakeside Blvd.<br>Indianapolis, IN 46278-1996</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Study Design and Methods</li><li>Results</li><li>Summary and Conclusions</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2023-01-31","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Covert, S. 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,{"id":70239338,"text":"mcs2023 - 2023 - Mineral commodity summaries 2023","interactions":[],"lastModifiedDate":"2026-02-09T18:08:28.062813","indexId":"mcs2023","displayToPublicDate":"2023-01-31T08:25:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":323,"text":"Mineral Commodity Summaries","code":"MCS","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023","displayTitle":"Mineral Commodity Summaries 2023","title":"Mineral commodity summaries 2023","docAbstract":"<p>Each mineral commodity chapter of the 2023 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production, reserves, and resources. The MCS is the earliest comprehensive source of 2022 mineral production data for the world. More than 90 individual minerals and materials are covered by 2-page synopses.</p><p>For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the 2-page synopsis.</p><p>Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.</p><p>The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2023 are welcomed.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2023","usgsCitation":"U.S. Geological Survey, 2023, Mineral commodity summaries 2023: U.S. Geological Survey, 210 p., https://doi.org/10.3133/mcs2023.","productDescription":"Report: 210 p.; Data Release","numberOfPages":"210","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147940","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":499701,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114301.htm","linkFileType":{"id":5,"text":"html"}},{"id":412083,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WCYUI6","text":"USGS data release","linkHelpText":"Data release for mineral commodity summaries 2023"},{"id":412082,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/commodity-statistics-and-information","text":"Commodity Statistics and Information"},{"id":412081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2023"},{"id":412080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2023/mcs2023.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2023"},{"id":412079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2023/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/nmic\" data-mce-href=\"https://www.usgs.gov/centers/nmic\">National Minerals Information Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>988 National Center<br>Reston, VA 20192<br>Email: <a href=\"mailto:nmicrecordsmgt@usgs.gov\" data-mce-href=\"mailto:nmicrecordsmgt@usgs.gov\">nmicrecordsmgt@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Figure 1—The Role of Nonfuel Mineral Commodities in the U.S. Economy</li><li>Significant Events, Trends, and Issues</li><li>Figure 2—2021 U.S. Net Import Reliance</li><li>Figure 3—Major Import Sources of Nonfuel Mineral Commodities in 2022</li><li>Table 1—U.S. Mineral Industry Trends</li><li>Table 2—U.S. Mineral-Related Economic Trends</li><li>Table 3—Value of Nonfuel Mineral Production in the United States in 2022</li><li>Figures 4–8—Value of Nonfuel Minerals Produced in 2022</li><li>Table 4—The 2022 U.S. List of Critical Minerals</li><li>U.S. Critical Minerals Update</li><li>Table 5—Salient Critical Minerals Statistics in 2022</li><li>Figure 9—20-Year Trend of U.S. Net Import Reliance for Critical Minerals</li><li>Figure 10—1-Year Percent Change and 5-Year Compound Annual Growth Rate in Prices of Critical Minerals</li><li>Figures 11–13—Changes in U.S. Consumption of Nonfuel Mineral Commodities</li><li>Figure 14—Relation Between Byproduct Elements and Host Metals</li><li>Mineral Commodities</li><li>Appendix A—Abbreviations and Units of Measure</li><li>Appendix B—Definitions of Selected Terms Used in This Report</li><li>Appendix C—Reserves and Resources</li><li>Appendix D—Country Specialists Directory</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-01-31","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":152492,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":861908,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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24-25, 2023.","productDescription":"164 p.","startPage":"1-164","ipdsId":"IP-147835","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":413615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":413603,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/uc/progact/amp/twg/2023-01-26-twg-meeting/20230126-AnnualReportingMeeting-ProceedingsFY2022AnnualReportingMeeting-508-UCRO.pdf"}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.49843272529527,\n              36.94476574454417\n            ],\n            [\n              -111.49843272529527,\n              36.92625011372043\n            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nvoichick@usgs.gov","orcid":"https://orcid.org/0000-0002-9716-5906","contributorId":203632,"corporation":false,"usgs":true,"family":"Voichick","given":"Nicholas","email":"nvoichick@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":865427,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":865428,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Tennant, Laura A. 0000-0003-0062-7287 ltennant@usgs.gov","orcid":"https://orcid.org/0000-0003-0062-7287","contributorId":5984,"corporation":false,"usgs":true,"family":"Tennant","given":"Laura","email":"ltennant@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":865429,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Dibble, Kimberly","contributorId":302767,"corporation":false,"usgs":false,"family":"Dibble","given":"Kimberly","affiliations":[{"id":65552,"text":"NPS (formerly Southwest Biological Science Center)","active":true,"usgs":false}],"preferred":false,"id":865430,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":865431,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}}
,{"id":70243090,"text":"70243090 - 2023 - Appendix 1: Lake Powell water quality monitoring","interactions":[],"lastModifiedDate":"2023-04-28T12:08:05.429933","indexId":"70243090","displayToPublicDate":"2023-01-31T07:04:01","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Appendix 1: Lake Powell water quality monitoring","docAbstract":"<p>No abstract available.&nbsp;</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Fiscal Year 2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program","conferenceDate":"January 24-25, 2023","conferenceLocation":"Flagstaff, AZ","language":"English","collaboration":"Bureau of Reclamation","usgsCitation":"Deemer, B., Voichick, N., Sabol, T.A., Andrews, C.M., and Mihalevich, B.A., 2023, Appendix 1: Lake Powell water quality monitoring, <i>in</i> Proceedings of the Fiscal Year 2022 Annual Reporting Meeting to the Glen Canyon Dam Adaptive Management Program, Flagstaff, AZ, January 24-25, 2023, p. 134-142.","productDescription":"9 p.","startPage":"134","endPage":"142","ipdsId":"IP-147768","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":416490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416481,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/uc/progact/amp/twg/2023-01-26-twg-meeting/20230126-AnnualReportingMeeting-ProceedingsFY2022AnnualReportingMeeting-508-UCRO.pdf"}],"country":"United States","state":"Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.98991581400394,\n              36.92878972693423\n            ],\n            [\n              -109.82511758909659,\n              36.92878972693423\n            ],\n            [\n              -109.82511758909659,\n              38.054642642406435\n            ],\n            [\n              -111.98991581400394,\n              38.054642642406435\n            ],\n            [\n              -111.98991581400394,\n              36.92878972693423\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voichick, Nicholas 0000-0002-9716-5906 nvoichick@usgs.gov","orcid":"https://orcid.org/0000-0002-9716-5906","contributorId":203632,"corporation":false,"usgs":true,"family":"Voichick","given":"Nicholas","email":"nvoichick@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mihalevich, Bryce Anthony 0000-0001-5492-221X","orcid":"https://orcid.org/0000-0001-5492-221X","contributorId":304586,"corporation":false,"usgs":true,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"Anthony","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":871019,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70240201,"text":"70240201 - 2023 - Joint spatiotemporal models to predict seabird densities at sea","interactions":[],"lastModifiedDate":"2023-02-01T13:11:05.524963","indexId":"70240201","displayToPublicDate":"2023-01-31T07:01:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Joint spatiotemporal models to predict seabird densities at sea","docAbstract":"<p><strong>Introduction:</strong><span>&nbsp;</span>Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type.</p><p><strong>Methods:</strong><span>&nbsp;</span>Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions.</p><p><strong>Results:</strong><span>&nbsp;</span>The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters).</p><p><strong>Discussion:</strong><span>&nbsp;</span>Our results indicated that pelagic shearwaters (<i>Ardenna</i><span>&nbsp;</span>spp.) and tufted puffin (<i>Fratercula cirrhata</i>) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable energy.</p>","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2023.1078042","usgsCitation":"Arimitsu, M.L., Piatt, J., Thorson, J., Kuletz, K., Drew, G., Schoen, S.K., Cushing, D., Kroeger, C., and Sydeman, W., 2023, Joint spatiotemporal models to predict seabird densities at sea: Frontiers in Marine Science, v. 10, 1078042, 11 p., https://doi.org/10.3389/fmars.2023.1078042.","productDescription":"1078042, 11 p.","ipdsId":"IP-145204","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":444664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2023.1078042","text":"Publisher Index 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sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cushing, Dan","contributorId":301895,"corporation":false,"usgs":false,"family":"Cushing","given":"Dan","email":"","affiliations":[{"id":65202,"text":"Pole Star Ecological LLC","active":true,"usgs":false}],"preferred":false,"id":862951,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kroeger, Caitlin","contributorId":301897,"corporation":false,"usgs":false,"family":"Kroeger","given":"Caitlin","email":"","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862952,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sydeman, 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,{"id":70241025,"text":"70241025 - 2023 - Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","interactions":[],"lastModifiedDate":"2023-03-07T12:47:56.075162","indexId":"70241025","displayToPublicDate":"2023-01-31T06:40:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","docAbstract":"<div class=\"article-section__content en main\"><p>Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best-performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR032064","usgsCitation":"Mei, Y., Mai, J., Do, H.X., Gronewold, A., Reeves, H.W., Eberts, S.M., Niswonger, R.G., Regan, R.S., and Hunt, R., 2023, Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?: Water Resources Research, v. 59, no. 2, e2022WR032064, 19 p., https://doi.org/10.1029/2022WR032064.","productDescription":"e2022WR032064, 19 p.","ipdsId":"IP-137447","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":444667,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022wr032064","text":"External Repository"},{"id":435477,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DOVISZ","text":"USGS data release","linkHelpText":"PRMS Model Archive for Selected Catchments in the Lake Michigan Basin Used in Examination of Multi-Objective Model Calibration"},{"id":413758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -85.15070336490274,\n              46.27321789689552\n            ],\n            [\n              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,{"id":70240488,"text":"70240488 - 2023 - Variation in leaf reflectance spectra across the California flora partitioned by evolutionary history, geographic origin, and deep time","interactions":[],"lastModifiedDate":"2023-02-14T14:55:21.389929","indexId":"70240488","displayToPublicDate":"2023-01-31T06:35:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Variation in leaf reflectance spectra across the California flora partitioned by evolutionary history, geographic origin, and deep time","docAbstract":"<div class=\"article-section__content en main\"><p>Evolutionary relatedness underlies patterns of functional diversity in the natural world. Hyperspectral remote sensing has the potential to detect these patterns in plants through inherited patterns of leaf reflectance spectra. We collected leaf reflectance data from across the California flora from plants grown in a common garden. Regions of the reflectance spectra vary in the depth and strength of phylogenetic signal. We also show that these differences are much greater than variation due to the geographic origin of the plant. At the phylogenetic extent of the California flora, spectral variation explained by the combination of ecotypic variation (divergent evolution) and convergent evolution of disparate lineages was minimal (3 to 7 %) but statistically significant. Interestingly, at the extent of a single genus (<i>Arctostaphylos</i>) no unique variation could be attributed to geographic origin. However, up to 18% of the spectral variation among<span>&nbsp;</span><i>Arctostaphylos</i><span>&nbsp;</span>individuals was shared between phylogeny and intraspecific variation stemming from ecotypic differences (i.e., geographic origin). Future studies could conduct more structured experiments (e.g., transplants or observations along environmental gradients) to disentangle these sources of variation and include other intraspecific variation (e.g., plasticity). We constrain broad scale spectral variability due to ecotypic sources, providing further support for the idea that phylogenetic clusters of species might be detectable through remote sensing. Phylogenetic clusters could represent a valuable dimension of biodiversity monitoring and detection.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG007160","usgsCitation":"Griffith, D., Byrd, K.B., Taylor, N.C., Allan, E., Bittner, L., O’Brien, B., Parker, V.T., Vasey, M.C., Pavlick, R., and Nemani, R.R., 2023, Variation in leaf reflectance spectra across the California flora partitioned by evolutionary history, geographic origin, and deep time: Journal of Geophysical Research: Biogeosciences, v. 128, no. 2, e2022JG007160, https://doi.org/10.1029/2022JG007160.","productDescription":"e2022JG007160","ipdsId":"IP-149076","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444669,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jg007160","text":"Publisher Index Page"},{"id":412903,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70240145,"text":"sir20225130 - 2023 - Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021","interactions":[],"lastModifiedDate":"2026-02-24T17:59:35.435101","indexId":"sir20225130","displayToPublicDate":"2023-01-30T12:58:46","publicationYear":"2023","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":"2022-5130","displayTitle":"Linear Regression Model Documentation for Computing Water-Quality Constituent Concentrations or Densities Using Continuous Real-Time Water-Quality Data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021","title":"Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021","docAbstract":"<p>The Kansas River and its associated alluvial aquifer provide drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical processes to treat and remove contaminants before public distribution. An early-notification system of changing water-quality conditions allows water suppliers to proactively make decisions that affect water treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, began collecting water-quality data at the Kansas River above Topeka Weir at Topeka, Kansas (USGS site 06888990, hereafter referred to as the “Topeka site”), during November 2018 to develop linear regression models that relate continuous in situ water-quality sensor measurements to discretely sampled water-quality constituent concentrations or densities. The addition of the Topeka site expanded an existing water-quality monitoring network, which included the upstream Kansas River at Wamego, Kans., and downstream Kansas River at De Soto, Kans., sites. Linear regression analysis was used to develop models that compute real-time concentrations or densities for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll <i>a</i>, total suspended solids, suspended sediment, and <i>Escherichia coli</i> at the Topeka site using data collected during November 2018 through June 2021. Water-quality constituent concentrations or densities computed from the models documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov), are useful to the public for cultural and recreational purposes, and can be used to guide water-treatment processes, compare conditions with Federal and State water-quality criteria, and characterize changes in Kansas River water-quality conditions through time.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225130","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., 2023, Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021: U.S. Geological Survey Scientific Investigations Report 2022–5130, 14 p., https://doi.org/10.3133/sir20225130.","productDescription":"Report: vii, 14 p.; 14 Appendixes; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141414","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":412468,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225130/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412446,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412445,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5130/downloads","text":"Appendixes 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The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223016","usgsCitation":"Dahm, K.G., Jones, D.K., Anderson, P.J., Dick, M.C., Hawbaker, T.J., and Horton, R.J., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST): U.S. Geological Survey Fact Sheet 2022–3016, 4 p., https://doi.org/10.3133/fs20223016.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-133121","costCenters":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":416352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223016/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 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Office","active":true,"usgs":true}],"preferred":true,"id":861609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Daniel K. 0000-0003-0724-8001 dkjones@usgs.gov","orcid":"https://orcid.org/0000-0003-0724-8001","contributorId":4959,"corporation":false,"usgs":true,"family":"Jones","given":"Daniel","email":"dkjones@usgs.gov","middleInitial":"K.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":861611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dick, Meghan C. 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,{"id":70239513,"text":"cir1502 - 2023 - Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science strategy","interactions":[],"lastModifiedDate":"2023-05-04T17:18:07.377372","indexId":"cir1502","displayToPublicDate":"2023-01-30T11:45:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1502","displayTitle":"Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science Strategy","title":"Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science strategy","docAbstract":"<p>The U.S. Geological Survey (USGS) conducts a wide variety of science that improves understanding of droughts and their effects on ecosystems and society. This work includes data collection and monitoring of aquatic and terrestrial systems; assessment and analysis of patterns, trends, drivers, and impacts of drought; development and application of predictive models; and delivery of information and decision-making tools to stakeholders. Stakeholders, which include Federal, Tribal, State, and local agencies, nongovernmental organizations, and others, use this information to anticipate, assess, react to, and mitigate drought conditions and impacts. There is no obvious near-term solution to reduce the frequency and severity of droughts or to mitigate drought impacts. Multidecadal drought is a “grand challenge” that benefits from integration of existing technolo­gies, data, knowledge, and models across related and dispa­rate disciplines, facilitated by new science and technology. In response, the USGS initiated a new integrated-science approach in the Colorado River Basin in 2020. The Colorado River Basin was specifically selected because of concerns about future drought and its consequences for the region. This document explains how the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) project extends and enhances the science supported by USGS Mission Areas and Programs and articulates scientific gaps and stakeholder needs to identify and reduce drought risks. The approach seeks to answer complex Earth science questions developed in partner­ship with stakeholders about severe long-term drought. An inte­grated approach is required to tackle these complex questions, which any single science discipline cannot answer on its own.</p><p>In addition to increased understanding of drought and drought effects in complex systems, the Colorado River Basin ASIST project was designed to improve efficiencies through rapid location of a broad array of data sources, assembly of model-ready multidisciplinary data, and delivery of actionable science to stakeholders at the speeds and scales needed for deci­sion making. The project team identified the following actions needed for USGS to implement and advance an integrated science approach in the Colorado River Basin: (1) engage with stakeholders to document their needs and iteratively co-produce science and science delivery tools to address these needs, (2) integrate monitoring and observation systems developed by USGS and other agencies that track droughts and their effects, (3) collect and provide analysis-ready data to support integrated applications, (4) integrate data and model connections to predict multiple drought impacts, (5) conduct multidisciplinary coor­dination to improve interpretations, (6) leverage the knowledge base across USGS to enhance decision making, and (7) support the development of new integrated science approaches and technologies that provide analysis and management tools that can be used to adapt to the effects of drought in the Colorado River Basin. Proposals for initial short-term use-case projects were solicited, a subset of which was selected for funding to test implementation of these actions. Additionally, the project organized and convened a series of science and technology collaboration workshops in the USGS focused on challenges that were identified and prioritized by the short-term use-case projects and during the initial stakeholder analysis. These work­shops were designed to bring together diverse perspectives to discuss science and technology challenges, stakeholder needs, capabilities, and knowledge gaps, with the goal of determin­ing how the USGS can address challenges, identify future opportunities for continued engagement between participants, and inform the next steps for the Colorado River Basin ASIST project. Continuing to collaboratively engage with a wide range of stakeholders using an integrated approach will provide a suitable foundation of data and tools to formulate actionable intelligence for predicting droughts and informing adaptation to the effects of long-term drought in a holistic, timely, and effective manner.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1502","usgsCitation":"Dahm, K., Hawbaker, T., Frus, R., Monroe, A., Bradford, J., Andrews, W., Torregrosa, A., Anderson, E., Dean, D., and Qi, S., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science strategy: U.S. Geological Survey Circular 1502, 57 p., https://doi.org/10.3133/cir1502.","productDescription":"vi, 53 p.","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-130999","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science 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Drought-Related Topics Relevant to Stakeholder Needs and Integrated Science Priorities</li><li>Appendix 2. Colorado River Basin ASIST Project Use Cases</li><li>Appendix 3. Science and Collaboration Workshops</li></ul>","publishedDate":"2023-01-30","noUsgsAuthors":false,"publicationDate":"2023-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Dahm, Katharine G. 0000-0002-4024-8110","orcid":"https://orcid.org/0000-0002-4024-8110","contributorId":299422,"corporation":false,"usgs":true,"family":"Dahm","given":"Katharine","email":"","middleInitial":"G.","affiliations":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"preferred":true,"id":861599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":861600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frus, Rebecca J. 0000-0002-2435-7202","orcid":"https://orcid.org/0000-0002-2435-7202","contributorId":206261,"corporation":false,"usgs":true,"family":"Frus","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861601,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":861602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":861603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrews, William J. 0000-0003-4780-8835","orcid":"https://orcid.org/0000-0003-4780-8835","contributorId":216006,"corporation":false,"usgs":true,"family":"Andrews","given":"William","email":"","middleInitial":"J.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861604,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861605,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Eric D. 0000-0002-0138-6166","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":202072,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric D.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":861606,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":861607,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":861608,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70237756,"text":"70237756 - 2023 - Chapter 5: Health and diseases","interactions":[],"lastModifiedDate":"2024-01-26T18:10:33.975527","indexId":"70237756","displayToPublicDate":"2023-01-30T10:31:31","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Chapter 5: Health and diseases","docAbstract":"<p><span>Health and diseases are integral parts of the life of seabirds that merit attention if we expect to truly understand, protect, and conserve them. Diseases such as avian influenza, avian pox, pasteurellosis, and paralytic shellfish poisoning have a proven history of decreasing the survival or breeding success of seabirds. However, each host-pathogen-environment system is unique, and our current knowledge about seabird health is limited and subject to biases. Thus, an exploratory mindset should be maintained, always considering that new or previously undiagnosed diseases could have substantial effects on a given seabird population. Therefore, incorporating a health monitoring component in seabird population monitoring programs, wherein data and biological samples are routinely collected for long-term pathogen surveillance and physiological analyses, would help us understand factors that limit seabird populations. Finally, the implementation of biosecurity best practices at seabird aggregations is imperative to avoid the accidental introduction or spread of pathogens.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Conservation of marine birds","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","doi":"10.1016/B978-0-323-88539-3.00003-0","usgsCitation":"Vanstreels, R., Uhart, M., and Work, T.M., 2023, Chapter 5: Health and diseases, chap. <i>of</i> Conservation of marine birds, p. 131-176, https://doi.org/10.1016/B978-0-323-88539-3.00003-0.","productDescription":"46 p.","startPage":"131","endPage":"176","ipdsId":"IP-134281","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":408613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vanstreels, Ralph","contributorId":298368,"corporation":false,"usgs":false,"family":"Vanstreels","given":"Ralph","email":"","affiliations":[{"id":64540,"text":"Institute of Research and Rehabilitation of Marine Animals","active":true,"usgs":false}],"preferred":false,"id":855456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uhart, Marcella","contributorId":298369,"corporation":false,"usgs":false,"family":"Uhart","given":"Marcella","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":855458,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247111,"text":"70247111 - 2023 - Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States","interactions":[],"lastModifiedDate":"2023-07-25T15:10:34.1453","indexId":"70247111","displayToPublicDate":"2023-01-30T10:06:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States","docAbstract":"<p><span>Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;=&nbsp;0.836,&nbsp;</span><i>p</i><span>-value &lt; 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (</span><i>p</i><span>-value&nbsp;=&nbsp;0.013) but was comparable to that at the sampling sites in the western rivers (</span><i>p</i><span>-value&nbsp;=&nbsp;0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2023.119679","usgsCitation":"Zhang, C., McIntosh, K.D., Sienkiewicz, N., Stelzer, E., Graham, J.L., and Lu, J., 2023, Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States: Water Research, v. 235, 119679, 16 p., https://doi.org/10.1016/j.watres.2023.119679.","productDescription":"119679, 16 p.","ipdsId":"IP-147638","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":444674,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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,{"id":70240042,"text":"sir20225124 - 2023 - Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota","interactions":[],"lastModifiedDate":"2026-02-23T20:52:37.679793","indexId":"sir20225124","displayToPublicDate":"2023-01-30T09:30:21","publicationYear":"2023","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":"2022-5124","displayTitle":"Hydrologic Change in the St. Louis River Basin from Iron Mining on the Mesabi Iron Range, Northeastern Minnesota","title":"Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota","docAbstract":"<p>This study compares the results of two regional steady-state U.S. Geological Survey Modular Three-Dimensional Finite-Difference Ground-Water Flow (MODFLOW) models constructed to quantify the hydrologic changes in the St. Louis River Basin from iron mining on the Mesabi Iron Range in northeastern Minnesota. The U.S. Geological Survey collaborated in this study with bands of the Minnesota Chippewa Tribe, and the Minnesota Pollution Control Agency to inform management decisions about aquatic resources in the St. Louis River Basin. A model constructed and calibrated to represent average 1995–2015 mining conditions produced regional groundwater heads and flows. A pre-mining scenario model was constructed from this mining model but had the land and bedrock surfaces restored to pre-mining topographies and had modeled mining features (mine pits, tailings basins, waste-rock piles, and mining-disturbed areas) eliminated to represent general pre-mining stratigraphy and hydrogeology. Many of the features important to the hydrology of this mining area (like individual mine pits) are difficult to represent in groundwater models and required the use of modeling tools to indirectly account for their effects. The difference between the results of these two models represents mining’s effects on the hydrology in the Mesabi Iron Range area of the St Louis River Basin. The mining and pre-mining regional models also can provide boundary conditions and initial properties for future local or site-specific groundwater-flow models in the area.</p><p>Total groundwater flow through the mining model is 171 million cubic feet per day. Areal recharge is the largest source of groundwater (78 and 81 percent of total groundwater flow in the mining and pre-mining scenario models, respectively). Seepage from streams and lakes provides another 17 percent of the total groundwater flow through both models. Water leaves aquifers through seepage to streams (discharge as base flow, 43 percent in both models) and areal seepage to the land surface (surface seepage), for example to wetlands (45 and 49 percent, mining and pre-mining scenario models respectively).</p><p>Comparison of the results from the mining and pre-mining scenario models shows that iron mining has produced measurable hydrologic changes in the St. Louis River Basin, but that most of those changes and the highest magnitude changes occur near the mining features. Flow changes to and from surface-water bodies like streams and wetlands were analyzed in detail because of their importance in sustaining surface waters and aquatic life. Overall, groundwater flow in the mining model was 3.62 million cubic feet per day (2.2 percent) greater than total pre-mining model groundwater flow. This was caused by an increase in recharge from tailings basins and a decrease in discharge from surface seepage. Groundwater discharge to mine pits was the largest change in groundwater flows between the models (a change representing 2.8 percent of total pre-mining model groundwater flow). Net recharge to groundwater from tailings basins (2.4 percent), net decrease in surface seepage from groundwater (2.7 percent), and net increase in seepage to streams (1.0 percent) were all in this same range of total pre-mining model groundwater flow. Groundwater lost through mine-pit withdrawals was nearly offset by groundwater gained through recharge from tailings basins. However, because losses and gains occurred in different areas, the effect of mining can have more substantial effects on local areas than the model-wide averages represent.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225124","collaboration":"Prepared in cooperation with bands of the Minnesota Chippewa Tribe, the Great Lakes Indian Fish & Wildlife Commission, and the Minnesota Pollution Control Agency","usgsCitation":"Cowdery, T.K., Baker, A.C., Haserodt, M.J., Feinstein, D.T., and Hunt, R.J., 2023, Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota: U.S. Geological Survey Scientific Investigations Report 2022–5124, 59 p., https://doi.org/10.3133/sir20225124.","productDescription":"Report: viii, 59 p.; 2 Data Releases","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-122102","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":412380,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412376,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5124/images"},{"id":412373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5124/coverthb.jpg"},{"id":412374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5124"},{"id":500466,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114303.htm","linkFileType":{"id":5,"text":"html"}},{"id":412504,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225124/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412378,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","linkHelpText":"Soil-water-balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995–2010"},{"id":412377,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6KSBJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT simulations of regional groundwater flow under mining and pre-mining scenarios near the Mesabi Iron Range within the St. Louis River Basin, northeastern Minnesota"}],"country":"United States","state":"Minnesota","otherGeospatial":"Mesabi Iron Range, St Louis River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -93.333,\n              48\n            ],\n            [\n              -93.3333,\n              47\n            ],\n            [\n              -91.666,\n              47\n            ],\n            [\n              -91.666,\n              48\n            ],\n            [\n              -93.333,\n              48\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/upper-midwest-water-science-center%20\" data-mce-href=\"https://www.usgs.gov/centers/upper-midwest-water-science-center%20\">Upper Midwest Water Science Center</a> <br>U.S. Geological Survey<br>1 Gifford Pinchot Drive <br>Madison, WI 53726</p><p><a href=\"https://pubs.er.usgs.gov/contactt\" data-mce-href=\"../contactt\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Geology, Groundwater Flow, and Interaction with Surface Waters</li><li>Mining Groundwater-Flow Model</li><li>Pre-Mining Scenario Model</li><li>Differences Between the Mining and Pre-Mining Model Results</li><li>Hydrologic Changes from Iron Mining</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-01-31","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Cowdery, Timothy K. 0000-0001-9402-6575 cowdery@usgs.gov","orcid":"https://orcid.org/0000-0001-9402-6575","contributorId":456,"corporation":false,"usgs":true,"family":"Cowdery","given":"Timothy","email":"cowdery@usgs.gov","middleInitial":"K.","affiliations":[],"preferred":true,"id":862567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Anna C. 0000-0001-8194-7535 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0000-0003-1151-2530","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":214256,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":862570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862571,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70241044,"text":"70241044 - 2023 - National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product","interactions":[],"lastModifiedDate":"2023-03-08T14:51:21.272891","indexId":"70241044","displayToPublicDate":"2023-01-30T08:40:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5571,"text":"Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product","docAbstract":"<p><span>The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at&nbsp;</span><a href=\"http://www.mrlc.gov/nlcd-2019-science-research-products\" data-mce-href=\"http://www.mrlc.gov/nlcd-2019-science-research-products\">https://www.mrlc.gov/nlcd-2019-science-research-products</a><span>.</span></p>","language":"English","publisher":"AAAS","doi":"10.34133/remotesensing.0021","usgsCitation":"Jin, S., Dewitz, J., Li, C., Sorenson, D.G., Zhu, Z., Shogib, R., Danielson, P., Granneman, B., Costello, C., Case, A., and Gass, L., 2023, National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product: Journal of Remote Sensing, v. 3, 0021, 14 p., https://doi.org/10.34133/remotesensing.0021.","productDescription":"0021, 14 p.","ipdsId":"IP-147293","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":444676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":302919,"corporation":false,"usgs":false,"family":"Jin","given":"Suming","affiliations":[{"id":65581,"text":"AFDS, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":215192,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":865830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":865831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sorenson, Daniel G. 0000-0003-0365-9444 dsorenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0365-9444","contributorId":2898,"corporation":false,"usgs":true,"family":"Sorenson","given":"Daniel","email":"dsorenson@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhe","contributorId":260473,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shogib, Rakibul 0000-0001-6524-7838","orcid":"https://orcid.org/0000-0001-6524-7838","contributorId":302920,"corporation":false,"usgs":false,"family":"Shogib","given":"Rakibul","email":"","affiliations":[{"id":65582,"text":"KBR, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Danielson, Patrick 0000-0002-2990-2783","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":302921,"corporation":false,"usgs":false,"family":"Danielson","given":"Patrick","affiliations":[{"id":65582,"text":"KBR, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865835,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Granneman, Brian 0000-0002-1910-0955","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":302922,"corporation":false,"usgs":false,"family":"Granneman","given":"Brian","affiliations":[{"id":65582,"text":"KBR, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865836,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Costello, Catherine 0000-0001-7158-2675","orcid":"https://orcid.org/0000-0001-7158-2675","contributorId":223238,"corporation":false,"usgs":true,"family":"Costello","given":"Catherine","email":"","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865837,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Case, Adam 0000-0002-6342-5853","orcid":"https://orcid.org/0000-0002-6342-5853","contributorId":302923,"corporation":false,"usgs":false,"family":"Case","given":"Adam","affiliations":[{"id":65583,"text":"Innovate! Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865838,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865839,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70240258,"text":"70240258 - 2023 - Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau","interactions":[],"lastModifiedDate":"2023-03-01T17:23:31.199955","indexId":"70240258","displayToPublicDate":"2023-01-30T07:12:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau","docAbstract":"<div class=\"article-text wd-jnl-art-abstract cf\"><p>Livestock removal is increasingly used as a management option to mitigate the negative impacts of grazing-related disturbances on rangelands. Removal generally increases plant cover, but it is unclear when, where, and by how much plant and soil cover changes can be expected. On the Colorado Plateau, complex geology, topography, soils, and climate all interact to mediate the relationship between land cover, climate, and disturbance. In this study we used new developments in land cover mapping and analysis to assess landscape plant and bare soil cover up to 30 years after livestock removal from two grazing allotments in Capitol Reef National Park, Utah, USA. Results indicate that livestock removal increases plant cover 0.17-0.32% per year and reduces bare soil cover 0.34-0.41% per year, although these rates may be suppressed by warming temperatures. Soils, assessed through Soil Geomorphic Units, played a strong but complex role in mediating land cover changes through time. These results suggest that livestock removal is an effective strategy for increasing plant cover and reducing bare soil on the Colorado Plateau, but including soil information in decision making will enhance efficiency by improving manager's ability to prioritize management actions effectively across space and through time.</p></div>","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/acb728","usgsCitation":"McNellis, B.E., Knight, A.C., Nauman, T.W., Chambers, S., Brungard, C., Fick, S., Livensperger, C., Borthwick, C., and Duniway, M.C., 2023, Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau: Environmental Research Letters, v. 18, 034034, 18 p., https://doi.org/10.1088/1748-9326/acb728.","productDescription":"034034, 18 p.","ipdsId":"IP-145563","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444677,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1088/1748-9326/acb728","text":"Publisher Index Page"},{"id":435478,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK2PHY","text":"USGS data release","linkHelpText":"Plant cover, climate, grazing disturbance, and soil class data from 1991-2020 compiled from remotely sensed data on two retired grazing allotments in Capitol Reef National Park, Utah, USA"},{"id":412611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2023-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McNellis, Brandon E 0000-0001-9604-8727","orcid":"https://orcid.org/0000-0001-9604-8727","contributorId":271065,"corporation":false,"usgs":true,"family":"McNellis","given":"Brandon","email":"","middleInitial":"E","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chambers, Samuel Norton 0000-0002-4734-2855","orcid":"https://orcid.org/0000-0002-4734-2855","contributorId":297994,"corporation":false,"usgs":true,"family":"Chambers","given":"Samuel Norton","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":863116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brungard, C.W.","contributorId":301936,"corporation":false,"usgs":false,"family":"Brungard","given":"C.W.","email":"","affiliations":[{"id":65369,"text":"Department of Plant and Environmental Sciences. New Mexico State University. Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":863117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fick, S.E.","contributorId":301937,"corporation":false,"usgs":false,"family":"Fick","given":"S.E.","email":"","affiliations":[{"id":65370,"text":"formerly: US Geological Survey, Southwest Biological Science Center, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":863118,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Livensperger, C.G.","contributorId":301938,"corporation":false,"usgs":false,"family":"Livensperger","given":"C.G.","affiliations":[{"id":65371,"text":"National Park Service, Capitol Reef National Park, Fruita, UT; National Park Service, Northern Colorado Plateau Network, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":863119,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borthwick, C.G.","contributorId":301939,"corporation":false,"usgs":false,"family":"Borthwick","given":"C.G.","email":"","affiliations":[{"id":65372,"text":"National Park Service, Capitol Reef National Park, Fruita, UT","active":true,"usgs":false}],"preferred":false,"id":863120,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863121,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70241176,"text":"70241176 - 2023 - Changes in suspended-sediment yields under divergent land-cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA","interactions":[],"lastModifiedDate":"2023-06-27T16:41:52.717587","indexId":"70241176","displayToPublicDate":"2023-01-30T07:02:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Changes in suspended-sediment yields under divergent land-cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Improvements in timber harvest practices and reductions in harvest volumes over the past half&nbsp;century are commonly presumed to have reduced sediment loads in many western US rivers. However, direct assessments in larger watersheds are relatively sparse. Here, we compare 2019–21 sediment concentrations against those of the late 1970s in the Bogachiel and Calawah &nbsp;River watersheds, adjacent and similarly sized (~300 km<sup>2</sup>) basins in the western Olympic Mountains of Washington State. The Calawah River&nbsp;watershed has experienced significant land-cover disturbance, including a large 1951 fire, extensive post-fire salvage logging, and relatively high rates of timber harvest through the 1990s. In contrast, the Bogachiel&nbsp;River watershed did not burn, and experienced only modest timber harvest that largely post-dated 1970s sediment monitoring. Channel-width trends suggest the Calawah River was still recovering from 1950s disturbances in the late 1970s. We found that 2019–21 suspended-sediment loads in the Calawah River were 2.3–2.6 times lower than would have been expected based on 1970s sediment rating curves, while recent loads in the Bogachiel River were a factor of 1.4 ± 1.0 lower. We consider the plausibility and possible explanations of declining concentrations in the less-disturbed Bogachiel River. Suspended-sediment yields in the Bogachiel River were two times higher than yields in the Calawah River, which is attributed to a combination of modestly higher precipitation, more efficient runoff generation, and more extensive and erodible Quaternary valley fills in the Bogachiel River. Regional shifts in flood hydrology have also influenced suspended-sediment loads in both watersheds. Our results then document a significant decline in suspended-sediment concentrations in the Calawah River over the past half&nbsp;century. Reduced land-cover disturbance provides the simplest and most likely explanation for this decline, though the wide range of possible concentration changes in the Bogachiel River leaves open possibilities that other processes (human, natural, or methodologic) could be a factor.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/esp.5556","usgsCitation":"Jaeger, K.L., Anderson, S.W., and Dunn, S., 2023, Changes in suspended-sediment yields under divergent land-cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA: Earth Surface Processes and Landforms, v. 48, no. 7, p. 1398-1413, https://doi.org/10.1002/esp.5556.","productDescription":"16 p.","startPage":"1398","endPage":"1413","ipdsId":"IP-144931","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":444679,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5556","text":"Publisher Index Page"},{"id":435479,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95L5ADD","text":"USGS data release","linkHelpText":"Supporting Spatial Data for Sediment Studies in the Bogachiel and Calawah River Watersheds, Washington"},{"id":414086,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -124.35640085378517,\n              48.1377522152041\n            ],\n            [\n              -124.35640085378517,\n              47.1761943193718\n            ],\n            [\n              -122.80798616934896,\n              47.1761943193718\n            ],\n            [\n              -122.80798616934896,\n              48.1377522152041\n            ],\n            [\n              -124.35640085378517,\n              48.1377522152041\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"48","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Sarah B. 0000-0003-4463-0074","orcid":"https://orcid.org/0000-0003-4463-0074","contributorId":291768,"corporation":false,"usgs":false,"family":"Dunn","given":"Sarah B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":866350,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70242920,"text":"70242920 - 2023 - Peat decomposition and erosion contribute to pond deepening in a temperate salt marsh","interactions":[],"lastModifiedDate":"2023-04-24T11:45:32.606708","indexId":"70242920","displayToPublicDate":"2023-01-30T06:41:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Peat decomposition and erosion contribute to pond deepening in a temperate salt marsh","docAbstract":"<div class=\"article-section__content en main\"><p>Salt marsh ponds expand and deepen over time, potentially reducing ecosystem carbon storage and resilience. The water&nbsp;filled volumes of ponds represent missing carbon due to prevented soil accumulation and removal by erosion and decomposition. Removal mechanisms have different implications as eroded carbon can be redistributed while decomposition results in loss. We constrained ponding effects on carbon dynamics in a New England marsh and determined whether expansion and deepening impact nearby soils by conducting geochemical characterizations of cores from three ponds and surrounding high marshes and models of wind-driven erosion. Radioisotope profiles demonstrate that ponds are not depositional environments and that contemporaneous marsh accretion represents prevented accumulation accounting for 32%–42% of the missing carbon. Erosion accounted for 0%–38% and was bracketed using radioisotope inventories and wind-driven resuspension models. Decomposition, calculated by difference, removes 22%–68%, and when normalized over pond lifespans, produces rates that agree with previous metabolism measurements. Pond surface soils contain new contributions from submerged primary producers and evidence of microbial alteration of underlying peat, as higher levels of detrital biomarkers and thermal stability indices, compared to the marsh. Below pond surface horizons, soil properties and organic matter composition were similar to the marsh, indicating that ponding effects are shallow. Soil bulk density, elemental content, and accretion rates were similar between marsh sites but different from ponds, suggesting that lateral effects are spatially confined. Consequently, ponds negatively impact ecosystem carbon storage but at current densities are not causing pervasive degradation of marshes in this system.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG007063","usgsCitation":"Luk, S., Eagle, M.J., Mariotti, G., Gosselin, K., Sanderman, J., and Spivak, A.C., 2023, Peat decomposition and erosion contribute to pond deepening in a temperate salt marsh: Biogeosciences, v. 128, no. 2, e2022JG007063, 19 p., https://doi.org/10.1029/2022JG007063.","productDescription":"e2022JG007063, 19 p.","ipdsId":"IP-144333","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444682,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jg007063","text":"Publisher Index Page"},{"id":416169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -70.89116181462337,\n              42.8226718850903\n            ],\n            [\n              -70.89116181462337,\n              42.6915958038642\n            ],\n            [\n              -70.70631330997368,\n              42.6915958038642\n            ],\n            [\n              -70.70631330997368,\n              42.8226718850903\n            ],\n            [\n              -70.89116181462337,\n              42.8226718850903\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"128","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Luk, Sheron","contributorId":247610,"corporation":false,"usgs":false,"family":"Luk","given":"Sheron","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":870201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mariotti, Giulio","contributorId":207541,"corporation":false,"usgs":false,"family":"Mariotti","given":"Giulio","email":"","affiliations":[{"id":37557,"text":"Louisiana State University, Baton Rouge LA","active":true,"usgs":false}],"preferred":false,"id":870203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gosselin, Kelsey","contributorId":247614,"corporation":false,"usgs":false,"family":"Gosselin","given":"Kelsey","email":"","affiliations":[{"id":49592,"text":"Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":870204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sanderman, Jonathan","contributorId":187477,"corporation":false,"usgs":false,"family":"Sanderman","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":870205,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivak, Amanda C.","contributorId":191376,"corporation":false,"usgs":false,"family":"Spivak","given":"Amanda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":870206,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70240150,"text":"70240150 - 2023 - iBluff: An open-source R package for geomorphic analysis of coastal bluffs/cliffs","interactions":[],"lastModifiedDate":"2023-01-31T12:35:05.584499","indexId":"70240150","displayToPublicDate":"2023-01-30T06:32:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5923,"text":"SoftwareX","active":true,"publicationSubtype":{"id":10}},"title":"iBluff: An open-source R package for geomorphic analysis of coastal bluffs/cliffs","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"d1e104\" class=\"abstract author\"><div id=\"d1e107\"><p id=\"d1e108\">The R package<span>&nbsp;</span><strong><i>iBluff</i></strong><span>&nbsp;</span>is designed for coastal bluffs/bluffs morphological analysis and offers an automatic and reproducible alternative to identify bluff edges using a bare earth digital elevation model (DEM) instead of hand digitizing. This package extracts elevation profiles along automatically identified transects on the bluff-face, bluff top, toe, secondary inflections, relative concavity/convexity of bluff-face, and beach dunes (crests and troughs). The package requires at a minimum a bare earth DEM as a raster and a generalized line shapefile (shoreline) approximately parallel with the bluff-face. Both files should be in the same projected coordinate system. The<span>&nbsp;</span><strong><i>iBluff</i></strong><span>&nbsp;</span>package was developed to expand and generalize studies of high-relief coastal areas, investigate erosion and seasonality, and could be extended to use three-dimensional (3D) point-cloud data instead of a DEM.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.softx.2023.101325","usgsCitation":"Palaseanu-Lovejoy, M., 2023, iBluff: An open-source R package for geomorphic analysis of coastal bluffs/cliffs: SoftwareX, v. 21, 101325, 8 p., https://doi.org/10.1016/j.softx.2023.101325.","productDescription":"101325, 8 p.","ipdsId":"IP-147257","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":444684,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.softx.2023.101325","text":"Publisher Index Page"},{"id":412490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862773,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70264031,"text":"70264031 - 2023 - Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses","interactions":[],"lastModifiedDate":"2025-03-05T15:18:51.590688","indexId":"70264031","displayToPublicDate":"2023-01-30T00:00:00","publicationYear":"2023","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":"Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses","docAbstract":"<p>1. Integrating ecological theory with empirical methods is ubiquitous in ecology using hierarchical Bayesian models. However, there has been little development focused on integration of ecological theory into models for survival analysis. Survival is a fundamental process, linking individual fitness with population dynamics, but incorporating life history strategies to inform survival estimation can be challenging because mortality processes occur at multiple scales.</p><p>2. We develop an approach to survival analysis, incorporating model constraints based on a species' life history strategy using functional analytical tools. Specifically, we structurally separate intrinsic patterns of mortality that arise from age-specific processes (e.g. increasing survival during early life stages due to growth or maturation, versus senescence) from extrinsic mortality patterns that arise over different periods of time (e.g. seasonal temporal shifts). We use shape constrained generalized additive models (CGAMs) to obtain age-specific hazard functions that incorporate theoretical information based on classical survivorship curves into the age component of the model and capture extrinsic factors in the time component.</p><p>3. We compare the performance of our modelling approach to standard survival modelling tools that do not explicitly incorporate species life history strategy in the model structure, using metrics of predictive power, accuracy, efficiency and computation time. We applied these models to two case studies that reflect different functional shapes for the underlying survivorship curves, examining age-period survival for white-tailed deer <i>Odocoileus virginianus</i> in Wisconsin, USA and Columbian sharp-tailed grouse T<i>ympanuchus phasianellus columbianus</i> in Colorado, USA.</p>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210x.14057","usgsCitation":"Ketz, A., Storm, D., Barker, R., Apa, A.D., Oliva-Aviles, C., and Walsh, D.P., 2023, Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses: Methods in Ecology and Evolution, v. 14, no. 3, p. 952-967, https://doi.org/10.1111/2041-210x.14057.","productDescription":"16 p.","startPage":"952","endPage":"967","ipdsId":"IP-141205","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487398,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70256548,"text":"70256548 - 2023 - The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass","interactions":[],"lastModifiedDate":"2024-08-23T13:48:44.826956","indexId":"70256548","displayToPublicDate":"2023-01-29T10:12:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The first documented interaction between a long-tailed weasel (<i>Mustela frenata</i>) and a plains spotted skunk (<i>Spilogale interrupta</i>) carcass","title":"The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass","docAbstract":"<p><span>A novel interaction between a long-tailed weasel (</span><i>Mustela frenata</i><span>) and a plains spotted skunk (</span><i>Spilogale interrupta</i><span>) carcass is detailed. In November 2020, a farmer in Edmunds County in north-central South Dakota sent in a video recording of a long-tailed weasel with a spotted skunk carcass. Location of the event, carcass condition, and recorded behavior of the long-tailed weasel offer probable, but unconfirmed, evidence that the spotted skunk was killed by the long-tailed weasel.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9758","usgsCitation":"White, K., Stafford, J.D., and Lonsinger, R.C., 2023, The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass: Ecology and Evolution, v. 13, no. 1, e9758, 5 p., https://doi.org/10.1002/ece3.9758.","productDescription":"e9758, 5 p.","ipdsId":"IP-135025","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":444688,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9758","text":"Publisher Index Page"},{"id":433064,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","county":"Edmunds County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.7096,45.5953],[-99.3464,45.5941],[-98.73,45.5911],[-98.7242,45.5905],[-98.7254,45.4963],[-98.7246,45.33],[-98.7249,45.2459],[-99.2054,45.2454],[-99.2177,45.2465],[-99.3414,45.2462],[-99.4515,45.2453],[-99.4735,45.2464],[-99.5751,45.2458],[-99.6962,45.2465],[-99.7111,45.2462],[-99.7096,45.5953]]]},\"properties\":{\"name\":\"Edmunds\",\"state\":\"SD\"}}]}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"White, K.M.","contributorId":341090,"corporation":false,"usgs":false,"family":"White","given":"K.M.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stafford, Joshua D. 0000-0001-7590-8708 jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907930,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70250078,"text":"70250078 - 2023 - Lessons learned in knowledge co-production for climate-smart decision-making","interactions":[],"lastModifiedDate":"2023-11-16T12:39:27.946106","indexId":"70250078","displayToPublicDate":"2023-01-28T06:37:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1563,"text":"Environmental Science and Policy","active":true,"publicationSubtype":{"id":10}},"title":"Lessons learned in knowledge co-production for climate-smart decision-making","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"ab0010\" class=\"abstract author\"><div id=\"abs0010\"><p id=\"sp0015\">Knowledge co-production, a process that involves both creators and users of information in knowledge generation, is growing in popularity in the conservation and ecology fields. While examples of successful co-production are becoming more common, many barriers and challenges remain in this work. Here, we reflect on our experiences in knowledge co-production from three recent case studies, using a prominent framework to understand and improve our efforts at each phase of the co-production process. Our reflections yield insights that may help other scientists seeking to support decision-making. We found that paying particular attention to the composition of the team and connecting with agency representatives early and often are key to success. Long-term commitment to the project and the people involved are also key. We conclude with suggestions for refining the framework to incorporate our primary lessons learned and include the valuation of a plurality of knowledge systems and empowerment as an ultimate impact of knowledge co-production.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsci.2023.01.010","usgsCitation":"Rosemartin, A., Crimmins, T., Gerst, K.L., Posthumus, E.E., Ramirez, A.R., Wallace, C.S., and Morelli, T.L., 2023, Lessons learned in knowledge co-production for climate-smart decision-making: Environmental Science and Policy, v. 141, p. 178-187, https://doi.org/10.1016/j.envsci.2023.01.010.","productDescription":"10 p.","startPage":"178","endPage":"187","ipdsId":"IP-133517","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":444690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsci.2023.01.010","text":"Publisher Index Page"},{"id":422651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosemartin, Alyssa","contributorId":175226,"corporation":false,"usgs":false,"family":"Rosemartin","given":"Alyssa","affiliations":[],"preferred":false,"id":888242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, Theresa 0000-0001-9592-625X","orcid":"https://orcid.org/0000-0001-9592-625X","contributorId":222414,"corporation":false,"usgs":false,"family":"Crimmins","given":"Theresa","email":"","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":888243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerst, Katherine L.","contributorId":196324,"corporation":false,"usgs":false,"family":"Gerst","given":"Katherine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":888244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Posthumus, Erin E. 0000-0003-3855-2380","orcid":"https://orcid.org/0000-0003-3855-2380","contributorId":204418,"corporation":false,"usgs":false,"family":"Posthumus","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":888245,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramirez, Aaron R.","contributorId":149780,"corporation":false,"usgs":false,"family":"Ramirez","given":"Aaron","email":"","middleInitial":"R.","affiliations":[{"id":17824,"text":"UC Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":888246,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wallace, Cynthia S. 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":331632,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","middleInitial":"S.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":888247,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":888248,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70247102,"text":"70247102 - 2023 - Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs","interactions":[],"lastModifiedDate":"2023-07-25T15:05:03.23572","indexId":"70247102","displayToPublicDate":"2023-01-27T09:54:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs","docAbstract":"<p><span>Parasite infrapopulation size - the population of parasites affecting a single host - is a central metric in parasitology. However, parasites are small and elusive such that imperfect detection is expected. Repeated sampling of parasites during primary sampling occasions (e.g., each host capture) informs the detection process. Here, we estimate flea (Siphonaptera) infrapopulation size on black-tailed&nbsp;prairie dogs&nbsp;(</span><i>Cynomys ludovicianus</i><span>, BTPDs) as a proof-of-concept for estimating parasite infrapopulations given imperfect detection. From Jun–Aug 2011, we live-trapped 299 BTPDs for a total of 573 captures on 20 plots distributed among 13 colonies at the Vermejo Park Ranch, New Mexico, USA. During each capture, an anesthetized BTPD was combed 3 times consecutively, 15&nbsp;s each, to remove and count fleas. Each flea (</span><i>n</i><span>&nbsp;=&nbsp;4846) was linked to the BTPD from which it was collected and assigned an encounter history (’100’, ‘010’, ‘001’). We analyzed the encounter histories using Huggins closed captures models, setting recapture probabilities to 0, thereby accounting for flea removal from hosts. The probability of detecting an individual flea (</span><i>p</i><span>) increased with Julian date; field personnel may have become more efficient at combing fleas as the field season progressed. Combined&nbsp;</span><i>p</i><span>&nbsp;across 3 combings equaled 0.99. Estimates of flea infrapopulation size were reasonable and followed the negative&nbsp;binomial distribution. Our general approach may be broadly applicable to estimating infrapopulation sizes for parasites. The utility of this approach increases as&nbsp;</span><i>p</i><span>&nbsp;declines but, if&nbsp;</span><i>p</i><span>&nbsp;is very low, inference is likely limited.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijppaw.2023.01.002","usgsCitation":"Eads, D.A., Huyvaert, K.P., and Biggins, D.E., 2023, Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs: International Journal for Parasitology: Parasites and Wildlife, v. 20, p. 117-121, https://doi.org/10.1016/j.ijppaw.2023.01.002.","productDescription":"5 p.","startPage":"117","endPage":"121","ipdsId":"IP-146545","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444692,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijppaw.2023.01.002","text":"Publisher Index Page"},{"id":419306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Vermejo Park Ranch","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -104.8,\n              36.6\n            ],\n            [\n              -104.8,\n              36.5\n            ],\n            [\n              -104.7,\n              36.5\n            ],\n            [\n              -104.7,\n              36.6\n            ],\n            [\n              -104.8,\n              36.6\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":878902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huyvaert, Kathryn P.","contributorId":202514,"corporation":false,"usgs":false,"family":"Huyvaert","given":"Kathryn","email":"","middleInitial":"P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":878903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":878904,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256545,"text":"70256545 - 2023 - Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs","interactions":[],"lastModifiedDate":"2024-08-22T14:59:53.775057","indexId":"70256545","displayToPublicDate":"2023-01-27T09:42:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs","docAbstract":"<p><span>Recreational-grade side-scan sonar (SSS) has become an invaluable tool for&nbsp;inland fisheries, particularly when characterizing underwater habitat, but it is being increasingly used for enumerating large-bodied (&gt; 1&nbsp;m total length [TL]) aquatic fauna. We used SSS in river and reservoir environments to evaluate methods for identifying and counting&nbsp;Paddlefish&nbsp;</span><span><i>Polyodon spathula</i></span><span>, a large pelagic&nbsp;planktivore&nbsp;of recreational and economic importance that can exceed 2&nbsp;m in length and weigh over 70&nbsp;kg. We assessed accuracy and precision among readers to identify Paddlefish by assigning confidence scores (1–3; with 3 being more confident) to sonar images of a ballistics-gel filled fiberglass replica Paddlefish. Readers varied in their confidence scores for the replica Paddlefish and no reader could identify the target beyond 25&nbsp;m from the transducer. Afterwards, we used SSS to survey several kilometers of a reservoir during summer residency and a large river during springtime spawning migrations. Two readers counted Paddlefish images in the SSS recordings and we estimated&nbsp;population size&nbsp;in the surveyed area with distance sampling. In the reservoir, the number of Paddlefish counted ranged from 172 to 184. In the river, the number of Paddlefish counted ranged from 165 to 617. The exponential model of distance was most-supported for detection in both environments, except there was support for a half-norm distribution for one reader in the river. In the reservoir, abundance estimates were statistically similar between readers at approximately 1500 (7/ha) in the total scanned area. In the river, similar abundance estimates were obtained with the half-norm model from one reader compared to the exponential model of the other reader, resulting in approximately 1500 individuals (30/ha) in the surveyed area. The application of SSS to count Paddlefish has some clear advantages to traditional methods, such as gill netting, and can be done at multiple times of the year. Distance sampling methods compensated for differences in counts among readers, indicating distance sampling can produce similar abundance estimates even when variation in counts exists among readers.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2023.106639","usgsCitation":"Wolfenkoehler, W., Long, J.M., Gary, R., Snow, R., Schooley, J.D., Bruckerhoff, L.A., and Lonsinger, R.C., 2023, Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs: Fisheries Research, v. 261, 106639, 9 p., https://doi.org/10.1016/j.fishres.2023.106639.","productDescription":"106639, 9 p.","ipdsId":"IP-142414","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Keystone Lake, Lake Carl Blackwell, Verdigris River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -97.32079029272471,\n              36.18635094806682\n            ],\n            [\n              -97.32079029272471,\n              36.089827328506544\n            ],\n            [\n              -97.1696333849386,\n              36.089827328506544\n            ],\n            [\n              -97.1696333849386,\n              36.18635094806682\n            ],\n            [\n              -97.32079029272471,\n              36.18635094806682\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -96.2644501788143,\n              36.125328208556084\n         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\"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.52086370103724,\n              36.84221290357827\n            ],\n            [\n              -95.63789552781877,\n              36.84221290357827\n            ],\n            [\n              -95.63789552781877,\n              36.6881372389295\n            ],\n            [\n              -95.52086370103724,\n              36.6881372389295\n            ],\n            [\n              -95.52086370103724,\n              36.84221290357827\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"261","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfenkoehler, Wyatt","contributorId":341077,"corporation":false,"usgs":false,"family":"Wolfenkoehler","given":"Wyatt","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gary, Ryan","contributorId":341078,"corporation":false,"usgs":false,"family":"Gary","given":"Ryan","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snow, Richard A.","contributorId":341079,"corporation":false,"usgs":false,"family":"Snow","given":"Richard A.","affiliations":[{"id":81697,"text":"Oklahoma Fishery Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":907911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schooley, Jason D.","contributorId":341080,"corporation":false,"usgs":false,"family":"Schooley","given":"Jason","email":"","middleInitial":"D.","affiliations":[{"id":81698,"text":"Paddlefish Research Center","active":true,"usgs":false}],"preferred":false,"id":907912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bruckerhoff, Lindsey Ann 0000-0002-9523-4808","orcid":"https://orcid.org/0000-0002-9523-4808","contributorId":292594,"corporation":false,"usgs":true,"family":"Bruckerhoff","given":"Lindsey","email":"","middleInitial":"Ann","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70247865,"text":"70247865 - 2023 - Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001","interactions":[],"lastModifiedDate":"2023-08-22T13:39:04.295519","indexId":"70247865","displayToPublicDate":"2023-01-27T08:32:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001","docAbstract":"<p><span>In 1999–2001, Guagua Pichincha volcano, Ecuador, produced a series of cyclic explosive and effusive eruptions. Rock samples, including dense blocks and pumiceous clasts collected during the eruption sequence, and ballistic bombs later collected from the crater floor, provide information about magma storage, ascent, decompression, degassing, repressurization, and regassing prior to eruption. Pairs of Fe-Ti oxides indicate equilibrium within 1.2–1.5 log units above the NNO oxidation buffer and equilibrium temperatures from 805 to 905 °C. Melt inclusions record H</span><sub>2</sub><span>O contents of 2.7–4.6 wt% and CO</span><sub>2</sub><span>&nbsp;contents (uncorrected for CO</span><sub>2</sub><span>&nbsp;segregation into bubbles) from 19 to 310 ppm. Minimum melt inclusion saturation pressures fall between 69 and 168 MPa, or equilibration depths of 2.8 and 6.8 km, the lower end of which is coincident with the maximum inferred equilibration depths for the most vesicular breadcrust bombs sampled. Amphibole phenocrysts lack breakdown rims (except for one sample) and plagioclase phenocrysts have abundant oscillatory compositional zones. Plagioclase areal microlite number densities (</span><i>N</i><sub><i>a</i></sub><span>) range over less than one order of magnitude (8.9×10</span><sup>3</sup><span>–8.7×10</span><sup>4</sup><span>&nbsp;mm</span><sup>-2</sup><span>) among all samples, with the exception of a dense, low crystallinity sample (</span><i>N</i><sub><i>a</i></sub><span>&nbsp;= 3.0×10</span><sup>3</sup><span>&nbsp;mm</span><sup>−2</sup><span>) and a pumiceous sample erupted on 17 December 1999 (</span><i>N</i><sub><i>a</i></sub><span>&nbsp;= 1.7×10</span><sup>3</sup><span>&nbsp;mm</span><sup>−2</sup><span>). Plagioclase microlite shapes include tabular, hopper, and swallowtail forms. Taken together, the relatively high plagioclase microlite number densities, the high number of oscillatory zones in plagioclase phenocrysts, the presence of CO</span><sub>2</sub><span>&nbsp;in groundmass glass, seismicity, and time-varying tilt cycles provide a picture of sudden evacuation of magma residing at different levels in the shallow conduit. Explosive eruptions punctuate inter-eruptive repose periods marked by time-varying rates of degassing (volatile fluxing) and re-pressurization. Shallow residence time in the conduit was sufficient to allow precipitation of silica-phase in the groundmass, but insufficient to allow breakdown of hornblende phenocrysts, with the one exception of the final dome sample from 2000, which has the longest preceding repose time. These results support a model of cyclic pressure cycling, volatile exsolution and regassing, and magma decompression decoupled from ascent.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00445-023-01626-3","usgsCitation":"Wright, H.M., Cioni, R., Cashman, K.V., Mothes, P., and Rosi, M., 2023, Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001: Bulletin of Volcanology, v. 85, 12, 24 p., https://doi.org/10.1007/s00445-023-01626-3.","productDescription":"12, 24 p.","ipdsId":"IP-143035","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444695,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-023-01626-3","text":"Publisher Index Page"},{"id":420012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Guagua Pichincha Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -78.64295621873246,\n              -0.14110488224878281\n            ],\n            [\n              -78.64295621873246,\n              -0.201181738857926\n            ],\n            [\n              -78.58224335456136,\n              -0.201181738857926\n            ],\n            [\n              -78.58224335456136,\n              -0.14110488224878281\n            ],\n            [\n              -78.64295621873246,\n              -0.14110488224878281\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"85","noUsgsAuthors":false,"publicationDate":"2023-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Heather M. 0000-0001-9013-507X hwright@usgs.gov","orcid":"https://orcid.org/0000-0001-9013-507X","contributorId":3949,"corporation":false,"usgs":true,"family":"Wright","given":"Heather","email":"hwright@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":880787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cioni, Raffaello 0000-0002-2526-9095","orcid":"https://orcid.org/0000-0002-2526-9095","contributorId":328622,"corporation":false,"usgs":false,"family":"Cioni","given":"Raffaello","email":"","affiliations":[{"id":78424,"text":"Universita degli Studi di Firenzi","active":true,"usgs":false}],"preferred":false,"id":880788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cashman, Katharine V.","contributorId":199542,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine","email":"","middleInitial":"V.","affiliations":[{"id":13025,"text":"Department of Geological Sciences, University of Oregon","active":true,"usgs":false}],"preferred":false,"id":880789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mothes, Patricia","contributorId":178532,"corporation":false,"usgs":false,"family":"Mothes","given":"Patricia","affiliations":[],"preferred":false,"id":880790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosi, Mauro","contributorId":206499,"corporation":false,"usgs":false,"family":"Rosi","given":"Mauro","email":"","affiliations":[],"preferred":false,"id":880791,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70240770,"text":"70240770 - 2023 - Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions","interactions":[],"lastModifiedDate":"2023-03-01T17:27:19.786874","indexId":"70240770","displayToPublicDate":"2023-01-27T07:27:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section c-article-content-visibility\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>The spatial patterns and context of invasions are increasingly recognized as important for successful and efficient management actions. Beyond mapping occurrence or percent cover in pixels, spatial summary information that describes the size and arrangement of patches in the context of a larger landscape (e.g., infested regions, connected patch networks) can add a depth of information for managing invasive grasses that threaten native ecosystems. Few invasive annual grass analyses have explored the use of landscape and circuit-based connectivity metrics to characterize and compare spatial patterns of invasion. To assess the transferability and applicability of these landscape ecology analyses, we calculated landscape metrics (4 area-based, 3 configuration) and a connectivity metric (circuit-based centrality), using a weighted-average map of invasive annual grass cover in the Great Basin, USA. We calculated metrics at local and regional scales, allowing invasion statistics to be compared across the landscape and illustrating varying patterns of invasion extent and connectedness. We found the metrics provided additional, complementary information at the sampled local and regional scales beyond abundance measures alone. We also illustrated how key metrics could be used to categorize and map areas needing different management strategies, for example, where strategies could proactively protect uninvaded cores, disconnect fine fuel patches, or contain established invasions. The landscape and connectivity metric approach can be applied across scales to spatially target patches locally, provide broader context within a single region, as well as to compare metrics and spatial variation in patterns among different regions.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s10530-022-02945-w","usgsCitation":"Buchholtz, E.K., Heinrichs, J., and Crist, M., 2023, Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions: Biological Invasions, v. 25, p. 637-644, https://doi.org/10.1007/s10530-022-02945-w.","productDescription":"8 p.","startPage":"637","endPage":"644","ipdsId":"IP-139058","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444697,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-022-02945-w","text":"Publisher Index Page"},{"id":435480,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B4H00Q","text":"USGS data release","linkHelpText":"Landscape and connectivity metrics based on invasive annual grass cover from 2016-2018 summarized at 15 kilometer grid cells in the Great Basin, USA"},{"id":413280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -115.10368059965884,\n              44.852271924466294\n            ],\n            [\n              -119.58419968653757,\n              44.131579771142185\n            ],\n            [\n              -120.55058615625657,\n              40.160272981776586\n            ],\n            [\n              -119.93561294825372,\n              37.908635991022905\n            ],\n            [\n              -116.28970035795007,\n              35.442907909031135\n            ],\n            [\n              -112.38022782135972,\n              38.11628851194823\n            ],\n            [\n              -110.71101482820848,\n              42.953528205336\n            ],\n            [\n              -111.63347464021301,\n              44.50870422504519\n            ],\n            [\n              -113.03912768707703,\n              45.00776554269126\n            ],\n            [\n              -115.10368059965884,\n              44.852271924466294\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2023-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchholtz, Erin K. 0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":864777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":864778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crist, Michele R.","contributorId":178453,"corporation":false,"usgs":false,"family":"Crist","given":"Michele R.","affiliations":[],"preferred":false,"id":864779,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
]}