{"pageNumber":"379","pageRowStart":"9450","pageSize":"25","recordCount":184776,"records":[{"id":70232106,"text":"ofr20221017 - 2022 - Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon","interactions":[],"lastModifiedDate":"2026-03-27T19:55:47.696889","indexId":"ofr20221017","displayToPublicDate":"2022-06-06T12:07:08","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1017","displayTitle":"Updates to Models of Streamflow and Water Temperature for 2011, 2015, and 2016 in Rivers of the Willamette River Basin, Oregon","title":"Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon","docAbstract":"<p class=\"p1\">Mechanistic river models capable of simulating hydrodynamics and stream temperature are valuable tools for investigating thermal conditions and their relation to streamflow in river basins where upstream water storage and management decisions have an important influence on river reaches with threatened fish populations. In the Willamette River Basin in northwestern Oregon, a two-dimensional, hydrodynamic water-quality model (CE<span class=\"s1\">‑</span>QUAL<span class=\"s1\">‑</span>W2) has been used to investigate the downstream effects of dam operations and other anthropogenic influences on stream temperature. By simulating the managed releases of water and various temperatures from the large Willamette Valley Project dams upstream of the modeling domain, these models can be used to investigate riverine temperature conditions and their relation to streamflow to determine where and when conditions are most challenging for threatened fish populations and how dam operations and flow management can affect and optimize thermal conditions in the river.</p><p class=\"p1\">The original models were initially developed to simulate conditions in spring–autumn of 2001 and 2002. This report documents (1) the upgrade of the river models to CE‑QUAL‑W2 version 4.2 and (2) the update of those models to simulate conditions that occurred from March through October of 2011, 2015, and 2016. These years were selected to represent a range of climatic and hydrologic conditions in the Willamette River Basin, including a “cool, wet” year (2011), a “hot, dry” year (2015), and a “normal” year (2016). Six submodels comprise the modeling system updated in this report; each submodel can be run independently or run with the others as a system. These models include the Coast Fork and Middle Fork Willamette River submodel, which includes the Coast Fork and Middle Fork Willamette Rivers, the Row River, and Fall Creek; the McKenzie River submodel, which includes the South Fork McKenzie River downstream of Cougar Dam and the McKenzie River from its confluence with the South Fork McKenzie River to its mouth; the South Santiam River submodel, which comprises the South Santiam River from Foster Dam to the Santiam River; the North Santiam and Santiam River submodel, which includes the Santiam River and the North Santiam River downstream of Big Cliff Dam; the Upper Willamette River submodel, which includes the Willamette River from Eugene to Salem; and the Middle Willamette River submodel, which includes the Willamette River from Salem to Willamette Falls near Oregon City.</p><p class=\"p2\">The models included in this report were originally developed, calibrated, and documented by other researchers. As part of the model updates described here, some model parameters were adjusted to improve stability and decrease runtime. Boundary conditions including meteorological, hydrologic, and thermal parameters were developed and updated for model years 2011, 2015, and 2016. In many cases, the data sources used to drive the 2001 and 2002 models were no longer available, which required the use of new data sources, the determination of a proxy record, or the development of appropriate estimation techniques. Goodness-of-fit statistics for the updated models show a good model fit, with the models simulating subdaily water temperatures at most comparable locations with a mean absolute error of generally less than 1 °C and often nearing 0.5 °C, depending on the individual submodel, and a reasonably low bias. The subdaily mean error for the South Santiam River submodel produced the highest bias of any of the submodels. Goodness-of-fit statistics indicate that the results may be biased cool (ranging from -0.43 °C in 2016 to -0.80 °C in 2011 for subdaily results), but the only water temperature data available for comparison on the South Santiam River is itself estimated, and those estimates are known to be too high in summer. Depending on future modeling needs, that submodel may warrant further refinement, along with additional data collection to properly define and minimize any model bias.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221017","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon: U.S. Geological Survey Open-File Report 2022–1017, 73 p., https://doi.org/10.3133/ofr20221017.","productDescription":"Report: x, 73 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119723","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401872,"rank":8,"type":{"id":31,"text":"Publication 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Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113157.htm","linkFileType":{"id":5,"text":"html"}},{"id":401754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1017/coverthb.jpg"},{"id":401755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.pdf","text":"Report","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1017"},{"id":401756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P908DXKH","text":"USGS data release","description":"USGS data release","linkHelpText":"CE-QUAL-W2 models for the Willamette River and major tributaries below U.S. Army Corps of Engineers dams—2011, 2015, and 2016"},{"id":401813,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225006","text":"SIR 2022-5006 —","linkHelpText":"Tracking heat in the Willamette River system, Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.134765625,\n              42.779275360241904\n            ],\n            [\n              -120.673828125,\n              42.779275360241904\n            ],\n            [\n              -120.673828125,\n              45.9511496866914\n            ],\n            [\n              -123.134765625,\n              45.9511496866914\n            ],\n            [\n              -123.134765625,\n              42.779275360241904\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/or-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/or-water\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>2130 SW 5th Avenue<br>Portland, Oregon 97201</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods and Data</li><li>Model Updates</li><li>Summary and Possible Future Research</li><li>Supplementary Material</li><li>References Cited</li></ul>","publishedDate":"2022-06-06","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Stratton Garvin, Laurel E. 0000-0001-8567-8619 lstratton@usgs.gov","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":270182,"corporation":false,"usgs":true,"family":"Stratton Garvin","given":"Laurel","email":"lstratton@usgs.gov","middleInitial":"E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buccola, Norman L. 0000-0002-9590-2458 nbuccola@usgs.gov","orcid":"https://orcid.org/0000-0002-9590-2458","contributorId":139096,"corporation":false,"usgs":true,"family":"Buccola","given":"Norman","email":"nbuccola@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70232147,"text":"70232147 - 2022 - Geologic map of the Stibnite mining area, Valley County, Idaho","interactions":[],"lastModifiedDate":"2022-06-14T15:30:34.521482","indexId":"70232147","displayToPublicDate":"2022-06-06T10:30:05","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":138,"text":"Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"T-22-03","title":"Geologic map of the Stibnite mining area, Valley County, Idaho","docAbstract":"<p>The Stibnite mining area, as used herein, is bounded by the map extent that includes the Yellow Pine, West End, and Hangar Flats ore bodies. Other ore bodies are nearby, but the purpose of this map is to offer a detailed (1:8,000 scale) geologic map with new cross sections in the immediate area of Stibnite, Idaho. This geologic map is very similar to the Stibnite quadrangle map (Stewart and others, 2016) particularly the units and structure descriptions, because of the overlap of map extent. The new work by the author includes: (1) the topographic lines generated from the LiDAR base (courtesy of Midas Gold Corporation); (2) additional structural measurements; (3) revision of geologic unit contact placements particular around West End and Stibnite pits among other locations; and (4) seven new cross sections. New structural measurements from field work account for 20 percent of measurements shown with the remaining from Midas Gold Corp., Smitherman (1985), and the Stibnite quadrangle map (Stewart and others, 2016). Locations of many shallow features in the cross sections are controlled by core logs of 48 drillholes provided by Midas Gold Corp. The logs include dike placement, dike to plutonic bodies relationships, metasedimentary body localities, and dips of stratigraphic units. The law of sines was used to calculate dip of contacts between metasedimentary units for each cross section. Other features at depth in the cross sections are schematic based on nearby surface features and overall geologic interpretation. </p><p>The map area contains metamorphosed sediments of Neoproterozoic and Paleozoic age within the Stibnite roof pendant. This rock package is open to tightly folded and reached lower amphibolite facies metamorphism during the Cretaceous Period. Most of the metasedimentary rocks are nearly vertical to overturned and young to the southwest, except on the southwestern flank of the Garnet Creek syncline. Pulses of the Idaho batholith granitoids intruded the metasedimentary units found in the Stibnite roof pendant. Faulting with apparent reverse, normal, and/or strike-slip offset are all present within the map area. Mineralization is largely fault controlled with some stratigraphic control. Volumetrically minor dikes, sills, and small intrusions are of Eocene age, and these intrusions are mostly depicted on the cross sections. Quaternary surficial deposits occur in stream beds and glaciated areas. </p><p>Field work was conducted during the summers of 2013, 2015, and 2016. For consistency with recent research, most of the Stibnite quadrangle geologic map units (Stewart and others, 2016) are used for this geologic map. Intrusive units Kqd and Tba are new. The additional geologic mapping by the authors and compilation of detailed geologic maps from Midas Gold Corp. enhanced resolution. Cross sections incorporated drill core data including rock type, unit thickness, and oriented structural measurements offering detailed subsurface control. Data access was courtesy of Midas Gold Corp. Reed S. Lewis, Russell V. Di Fiori, and Claudio Berti provided constructive reviews that significantly improved this maps and cross sections. Previous studies that focus on mineralization include Schrader and Ross (1925), Currier (1935), White (1940), Cooper (1951), Cookro and others (1988), and more recently Gillerman and others (2019). Digital map files are available online (Wintzer, 2022).</p>","language":"English","publisher":"Idaho Geological Survey","usgsCitation":"Wintzer, N.E., 2022, Geologic map of the Stibnite mining area, Valley County, Idaho: Technical Report T-22-03, 2 Plates: 45.48 x 30.06 inches and 49.17 x 36.00 inches.","productDescription":"2 Plates: 45.48 x 30.06 inches and 49.17 x 36.00 inches","ipdsId":"IP-135563","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":402158,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401908,"type":{"id":15,"text":"Index Page"},"url":"https://idahogeology.org/product/T-22-03","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Stibnite mining area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.3,\n              44.9\n            ],\n            [\n              -115.341667,\n              44.9\n            ],\n            [\n              -115.341667,\n              44.936111\n            ],\n            [\n              -115.3,\n              44.936111\n            ],\n            [\n              -115.3,\n              44.9\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wintzer, Niki E. 0000-0003-3085-435X nwintzer@usgs.gov","orcid":"https://orcid.org/0000-0003-3085-435X","contributorId":5297,"corporation":false,"usgs":true,"family":"Wintzer","given":"Niki","email":"nwintzer@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844339,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70232175,"text":"70232175 - 2022 - Managing macropods without poisoning ecosystems","interactions":[],"lastModifiedDate":"2022-09-27T16:45:35.265923","indexId":"70232175","displayToPublicDate":"2022-06-06T08:45:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10931,"text":"Ecological Management & Restoration","active":true,"publicationSubtype":{"id":10}},"title":"Managing macropods without poisoning ecosystems","docAbstract":"<p><span>A recent review of the management of hyperabundant macropods in Australia proposed that expanded professional shooting is likely to lead to better biodiversity and animal welfare outcomes. While the tenets of this general argument are sound, it overlooks one important issue for biodiversity and animal health and welfare: reliance on toxic lead-based ammunition. Lead poisoning poses a major threat to Australia's wildlife scavengers. Current proposals to expand professional macropod shooting would see tonnes of an extremely toxic and persistent heavy metal continue to be introduced into Australian environments. This contrasts with trends in many other countries, where lead ammunition is, through legislation or voluntary programs, being phased out. Fortunately, there are alternatives to lead ammunition that could be investigated and adopted for improved macropod management. A transition to lead-free ammunition would allow the broad environmental and animal welfare goals desired from macropod management to be pursued without secondarily and unintentionally poisoning scavengers. Through this article, we hope to increase awareness of this issue and encourage discussion of this potential change.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/emr.12555","usgsCitation":"Hampton, J.O., Pay, J.M., Katzner, T., Arnemo, J.M., Pokras, M.A., Buenz, E., Kanstrup, N., Thomas, V.G., Uhart, M., Lambertucci, S.A., Krone, O., Singh, N., Naidoo, V., Ishizuka, M., Saito, K., Helander, B., and Green, R.E., 2022, Managing macropods without poisoning ecosystems: Ecological Management & Restoration, v. 23, no. 2, p. 153-157, https://doi.org/10.1111/emr.12555.","productDescription":"5 p.","startPage":"153","endPage":"157","ipdsId":"IP-135596","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447521,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/emr.12555","text":"External 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],[139.10854,-17.06268],[139.26057,-17.3716],[140.21525,-17.7108],[140.87546,-17.36907],[141.07111,-16.83205],[141.2741,-16.38887],[141.39822,-15.84053],[141.70218,-15.04492],[141.56338,-14.56133],[141.63552,-14.27039],[141.51987,-13.69808],[141.65092,-12.94469],[141.84269,-12.74155],[141.68699,-12.40761],[141.92863,-11.87747],[142.11849,-11.32804],[142.14371,-11.04274],[142.51526,-10.66819],[142.79731,-11.15735],[142.86676,-11.78471],[143.11595,-11.90563],[143.15863,-12.32566],[143.52212,-12.83436],[143.59716,-13.40042],[143.56181,-13.76366]]]]},\"properties\":{\"name\":\"Australia\"}}]}","volume":"23","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hampton, Jordan O","contributorId":292391,"corporation":false,"usgs":false,"family":"Hampton","given":"Jordan","email":"","middleInitial":"O","affiliations":[{"id":13336,"text":"University of Melbourne","active":true,"usgs":false}],"preferred":false,"id":844442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pay, James M.","contributorId":245078,"corporation":false,"usgs":false,"family":"Pay","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arnemo, Jon M","contributorId":292393,"corporation":false,"usgs":false,"family":"Arnemo","given":"Jon","email":"","middleInitial":"M","affiliations":[{"id":62892,"text":"Inland Norway University of Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":844445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pokras, Mark A","contributorId":292394,"corporation":false,"usgs":false,"family":"Pokras","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":844446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buenz, Eric","contributorId":292395,"corporation":false,"usgs":false,"family":"Buenz","given":"Eric","email":"","affiliations":[{"id":62894,"text":"Nelson Marlborough Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":844447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kanstrup, Niels","contributorId":292396,"corporation":false,"usgs":false,"family":"Kanstrup","given":"Niels","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":844448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thomas, Vernon G","contributorId":292397,"corporation":false,"usgs":false,"family":"Thomas","given":"Vernon","email":"","middleInitial":"G","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":844449,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Uhart, Marcela","contributorId":292398,"corporation":false,"usgs":false,"family":"Uhart","given":"Marcela","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":844450,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lambertucci, Sergio A","contributorId":292399,"corporation":false,"usgs":false,"family":"Lambertucci","given":"Sergio","email":"","middleInitial":"A","affiliations":[{"id":62895,"text":"National Scientific and Technical Research Council","active":true,"usgs":false}],"preferred":false,"id":844451,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Krone, Oliver","contributorId":292400,"corporation":false,"usgs":false,"family":"Krone","given":"Oliver","email":"","affiliations":[{"id":39836,"text":"Leibniz Institute for Zoo and Wildlife Research","active":true,"usgs":false}],"preferred":false,"id":844452,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Singh, Navinder J","contributorId":292401,"corporation":false,"usgs":false,"family":"Singh","given":"Navinder J","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":844453,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Naidoo, Vinny","contributorId":292402,"corporation":false,"usgs":false,"family":"Naidoo","given":"Vinny","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":844454,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ishizuka, Mayumi","contributorId":292403,"corporation":false,"usgs":false,"family":"Ishizuka","given":"Mayumi","email":"","affiliations":[{"id":16855,"text":"Hokkaido University","active":true,"usgs":false}],"preferred":false,"id":844455,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Saito, Keisuke","contributorId":292404,"corporation":false,"usgs":false,"family":"Saito","given":"Keisuke","email":"","affiliations":[{"id":62896,"text":"Institute for Raptor Biomedicine Japan","active":true,"usgs":false}],"preferred":false,"id":844456,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Helander, Bjorn","contributorId":218367,"corporation":false,"usgs":false,"family":"Helander","given":"Bjorn","email":"","affiliations":[{"id":39823,"text":"Environmental Research & Monitoring, Swedish Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":844457,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Green, Rhys E.","contributorId":174406,"corporation":false,"usgs":false,"family":"Green","given":"Rhys","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":844458,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}}
,{"id":70232530,"text":"70232530 - 2022 - Subspecies differentiation and range-wide genetic structure are driven by climate in the California gnatcatcher, a flagship species for coastal sage scrub conservation","interactions":[],"lastModifiedDate":"2022-08-02T15:07:24.562089","indexId":"70232530","displayToPublicDate":"2022-06-06T07:19:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1601,"text":"Evolutionary Applications","active":true,"publicationSubtype":{"id":10}},"title":"Subspecies differentiation and range-wide genetic structure are driven by climate in the California gnatcatcher, a flagship species for coastal sage scrub conservation","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Understanding genetic structure and diversity within species can uncover associations with environmental and geographic attributes that highlight adaptive potential and inform conservation and management. The California gnatcatcher,<span>&nbsp;</span><i>Polioptila californica</i>, is a small songbird found in desert and coastal scrub habitats from the southern end of Baja California Sur to Ventura County, California. Lack of congruence among morphological subspecies hypotheses and lack of measurable genetic structure found in a few genetic markers led to questions about the validity of subspecies within<span>&nbsp;</span><i>P.&nbsp;californica</i><span>&nbsp;</span>and the listing status of the coastal California gnatcatcher,<span>&nbsp;</span><i>P.&nbsp;c.&nbsp;californica</i>. As a U.S. federally threatened subspecies,<span>&nbsp;</span><i>P.&nbsp;c. californica</i><span>&nbsp;</span>is recognized as a flagship for coastal sage scrub conservation throughout southern California. We used restriction site-associated DNA sequencing to develop a genomic dataset for the California gnatcatcher. We sampled throughout the species' range, examined genetic structure, gene–environment associations, and demographic history, and tested for concordance between genetic structure and morphological subspecies groups. Our data support two distinct genetic groups with evidence of restricted movement and gene flow near the U.S.- Mexico international border. We found that climate-associated outlier loci were more strongly differentiated than climate neutral loci, suggesting that local climate adaptation may have helped to drive differentiation after Holocene range expansions. Patterns of habitat loss and fragmentation are also concordant with genetic substructure throughout the southern California portion of the range. Finally, our genetic data supported the morphologically defined<span>&nbsp;</span><i>P.&nbsp;c.&nbsp;californica</i><span>&nbsp;</span>as a distinct group, but there was little evidence of genetic differentiation among other previously hypothesized subspecies in Baja California. Our data suggest that retaining and restoring connectivity, and protecting populations, particularly at the northern range edge, could help preserve existing adaptive potential to allow for future range expansion and long-term persistence of the California gnatcatcher.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/eva.13429","usgsCitation":"Vandergast, A.G., Kus, B., Wood, D.A., Milano, E.R., and Preston, K.L., 2022, Subspecies differentiation and range-wide genetic structure are driven by climate in the California gnatcatcher, a flagship species for coastal sage scrub conservation: Evolutionary Applications, v. 15, no. 7, p. 1201-1217, https://doi.org/10.1111/eva.13429.","productDescription":"17 p.","startPage":"1201","endPage":"1217","ipdsId":"IP-139764","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447523,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.13429","text":"Publisher Index Page"},{"id":435814,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MB2YE2","text":"USGS data release","linkHelpText":"Polioptila californica Genotype Data from California, USA and Baja California, Mexico"},{"id":403055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"California","otherGeospatial":"Baja Peninsula","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.09277343749999,\n              32.62087018318113\n            ],\n            [\n              -115.57617187499999,\n              34.05265942137599\n            ],\n            [\n              -116.71874999999999,\n              34.52466147177172\n            ],\n            [\n              -118.125,\n              34.92197103616377\n            ],\n            [\n              -119.4873046875,\n              34.92197103616377\n            ],\n            [\n              -119.66308593749999,\n              34.05265942137599\n            ],\n            [\n              -118.16894531249999,\n              33.50475906922609\n            ],\n            [\n              -117.6416015625,\n              32.62087018318113\n            ],\n            [\n              -116.89453125,\n              29.53522956294847\n            ],\n            [\n              -115.13671875,\n              27.410785702577023\n            ],\n            [\n              -112.9833984375,\n              25.045792240303445\n            ],\n            [\n              -111.09374999999999,\n              23.241346102386135\n            ],\n            [\n              -109.64355468749999,\n              22.755920681486405\n            ],\n            [\n              -108.9404296875,\n              23.079731762449878\n            ],\n            [\n              -111.62109375,\n              27.254629577800063\n            ],\n            [\n              -114.08203125,\n              30.031055426540206\n            ],\n            [\n              -115.04882812499999,\n              31.12819929911196\n            ],\n            [\n              -115.09277343749999,\n              32.62087018318113\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milano, Elizabeth R. 0000-0003-4143-9303","orcid":"https://orcid.org/0000-0003-4143-9303","contributorId":292788,"corporation":false,"usgs":false,"family":"Milano","given":"Elizabeth","email":"","middleInitial":"R.","affiliations":[{"id":63006,"text":"USFS; formerly USGS","active":true,"usgs":false}],"preferred":false,"id":845805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Preston, Kristine L. 0000-0002-6958-1128 kpreston@usgs.gov","orcid":"https://orcid.org/0000-0002-6958-1128","contributorId":207765,"corporation":false,"usgs":true,"family":"Preston","given":"Kristine","email":"kpreston@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":845806,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70232013,"text":"ofr20221053 - 2022 - Sample size estimation for savanna monitoring protocol development","interactions":[],"lastModifiedDate":"2022-06-06T13:22:10.424969","indexId":"ofr20221053","displayToPublicDate":"2022-06-06T07:14:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1053","displayTitle":"Sample Size Estimation for Savanna Monitoring Protocol Development","title":"Sample size estimation for savanna monitoring protocol development","docAbstract":"When designing data collection protocols for a new research project, it is important to have a large enough sample size to detect a desired effect, but not so large to be wasting time collecting more data than needed. Power analysis methods can be used to estimate this sample size. In this report, power analyses used to estimate sample sizes needed for a savanna monitoring study, for which the U.S. Fish and Wildlife Service are developing protocols, are described. Power analyses were run to estimate the sample sizes needed to detect a specified difference (that is, effect size) between means from two savanna areas or between yearly means for a savanna area. Sample sizes were estimated for nine different vegetation metrics that will be measured in savanna areas. Analyses were run for each metric using a range of means and variances, effect sizes, and correlation among repeated measures. Sample size estimates varied among vegetation metrics. Within each vegetation metric, estimated sample sizes varied with means, variances, effect size, and correlation. Many of the sample size estimates were too large to be feasible when sampling; therefore, the tables of estimated sample sizes may be first used as a guide to determine an adequate and feasible sample size that will detect differences in some vegetation metrics. Then, using this sample size, the tables can be used to estimate the effect sizes for each vegetation metric that may be detectable for a given mean, variance, and correlation.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221053","collaboration":"U.S. Fish and Wildlife Service","usgsCitation":"Buhl, D.A., 2022, Sample size estimation for savanna monitoring protocol development: U.S. Geological Survey Open-File Report 2022–1053, 49 p., https://doi.org/10.3133/ofr20221053.","productDescription":"vi, 49 p.","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-135111","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":401747,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1053/ofr20221053.pdf","text":"Report","size":"1.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1053"},{"id":401746,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1053/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/npwrc\" data-mce-href=\"https://www.usgs.gov/centers/npwrc\">Northern Prairie Wildlife Research Center</a> <br>U.S. Geological Survey<br>8711 37th Street Southeast <br>Jamestown, ND 58401</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Savanna Monitoring Study Design and Questions</li><li>Power Analysis</li><li>Results and Discussion</li><li>Summary</li><li>References Cited</li><li>Appendix 1. SAS Programs for Running Power Analyses</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-06-06","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Buhl, Deborah A. 0000-0002-8563-5990 dbuhl@usgs.gov","orcid":"https://orcid.org/0000-0002-8563-5990","contributorId":146226,"corporation":false,"usgs":true,"family":"Buhl","given":"Deborah","email":"dbuhl@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844163,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70232138,"text":"70232138 - 2022 - Regional walrus abundance estimate in the United States Chukchi Sea in autumn","interactions":[],"lastModifiedDate":"2022-08-02T14:29:29.483926","indexId":"70232138","displayToPublicDate":"2022-06-06T06:57:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Regional walrus abundance estimate in the United States Chukchi Sea in autumn","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Human activities (e.g., shipping, tourism, oil, gas development) have increased in the Chukchi Sea because of declining sea ice. The declining sea ice itself and these activities may affect Pacific walrus (<i>Odobenus rosmarus divergens</i>) abundance; however, previous walrus abundance estimates have been notably imprecise. When sea ice is absent from the eastern Chukchi Sea, walruses in waters of the United States usually rest together onshore at a single Alaska coastal haulout, where they can be surveyed more easily than when they rest on dispersed offshore ice floes. We estimated the number of walruses on land (herd size) at this haulout from 13 unoccupied aircraft system (UAS) surveys flown within a 10-day period in each of 2018 and 2019. We estimated population size of walruses using the haulout over the course of the surveys by combining herd size data with data from satellite-linked transmitters that indicated whether tagged walruses were in or out of water during each survey. Our estimates of the population size of walruses using the haulout during each year's survey period were similar to each other and more precise than historical walrus abundance estimates: posterior means (95% credibility intervals) were 166,000 (133,000–201,000) for 2018 and 189,000 (135,000–251,000) for 2019. Auxiliary observations support using these estimates to represent the size of the population using the eastern Chukchi Sea in autumn during the surveyed years. Our study site was the only substantial Chukchi Sea coastal haulout in the United States during the survey periods and study-specific tracking data (consistent with known distribution and movement patterns) indicated tagged walruses remained in eastern Chukchi waters during the survey periods. In addition, the imagery, telemetry, and analytical methods developed for this study advance the prospect for precise range-wide walrus population size estimates.</p></div></div>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22256","usgsCitation":"Fischbach, A.S., Taylor, R.L., and Jay, C.V., 2022, Regional walrus abundance estimate in the United States Chukchi Sea in autumn: Journal of Wildlife Management, v. 86, no. 6, e22256, 18 p., https://doi.org/10.1002/jwmg.22256.","productDescription":"e22256, 18 p.","ipdsId":"IP-128374","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":447525,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22256","text":"Publisher Index Page"},{"id":435820,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X1C0WX","text":"USGS data release","linkHelpText":"Walrus Haulout Aerial Survey Data Near Point Lay Alaska, Autumn 2018 and 2019"},{"id":435819,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DB7ZWP","text":"USGS data release","linkHelpText":"Behavior of Pacific Walruses (Odobenus rosmarus divergens) Hauled Out on Sea Ice During UAS Overflights, Eastern Chukchi Sea, 2015 "},{"id":435818,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FQ9TP6","text":"USGS data release","linkHelpText":"Tracking Data for Pacific Walrus (Odobenus rosmarus divergens)"},{"id":401915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -163.3447265625,\n              67.15289820820026\n            ],\n            [\n              -152.490234375,\n              70.65633017009853\n            ],\n            [\n              -153.896484375,\n              71.6498329432346\n            ],\n            [\n              -158.994140625,\n              71.76019138754775\n            ],\n            [\n              -165.58593749999997,\n              70.58341752317065\n            ],\n            [\n              -167.431640625,\n              69.28725695167886\n            ],\n            [\n              -167.431640625,\n              67.60922060496382\n            ],\n            [\n              -164.7509765625,\n              66.93006025862448\n            ],\n            [\n              -163.3447265625,\n              67.15289820820026\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"86","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":2865,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony","email":"afischbach@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":844322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":844323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":844324,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70232183,"text":"70232183 - 2022 - Damage assessment for the 2018 lower East Rift Zone lava flows of Kīlauea volcano, Hawaiʻi","interactions":[],"lastModifiedDate":"2022-06-10T11:53:01.951056","indexId":"70232183","displayToPublicDate":"2022-06-06T06:50:48","publicationYear":"2022","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":"Damage assessment for the 2018 lower East Rift Zone lava flows of Kīlauea volcano, Hawaiʻi","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Cataloguing damage and its correlation with hazard intensity is one of the key components needed to robustly assess future risk and plan for mitigation as it provides important empirical data. Damage assessments following volcanic eruptions have been conducted for buildings and other structures following hazards such as tephra fall, pyroclastic density currents, and lahars. However, there are relatively limited quantitative descriptions of the damage caused by lava flows, despite the number of communities that have been devastated by lava flows in recent decades (e.g., Cumbre Vieja, La Palma, 2021; Nyiragongo, Democratic Republic of Congo, 2002 and 2021; Fogo, Cape Verde, 2014–2015). The 2018 lower East Rift Zone (LERZ) lava flows of Kīlauea volcano, Hawaiʻi, inundated 32.4 km<sup>2</sup><span>&nbsp;</span>of land in the Puna District, including residential properties, infrastructure, and farmland. During and after the eruption, US Geological Survey scientists and collaborators took over 8000 aerial and ground photographs and videos of the eruption processes, deposits, and impacts. This reconnaissance created one of the largest available impact datasets documenting an effusive eruption and provided a unique opportunity to conduct a comprehensive damage assessment. Drawing on this georeferenced dataset, satellite imagery, and 2019 ground-based damage surveys, we assessed the pre-event typology and post-event condition of structures within and adjacent to the area inundated by lava flows during the 2018 LERZ eruption. We created a database of damage: each structure was assigned a newly developed damage state and data quality category value. We assessed 3165 structures within the Puna District and classified 1839 structures (58%) as destroyed, 90 structures (3%) as damaged, and 1236 (39%) as unaffected. We observed a range of damage states, affected by the structural typology and hazard characteristics. Our study reveals that structures may be damaged or destroyed beyond the lava flow margin, due to thermal effects from the lava flow, fire spread, or from exposure to a range of hazards associated with fissure eruptions, such as steam, volcanic gases, or tephra fall. This study provides a major contribution to the currently limited evidence base required to forecast future lava flow impacts and assess risk.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01568-2","usgsCitation":"Meredith, E.S., Jenkins, S.F., Hayes, J.L., Deligne, N.I., Lallemant, D., Patrick, M.R., and Neal, C.A., 2022, Damage assessment for the 2018 lower East Rift Zone lava flows of Kīlauea volcano, Hawaiʻi: Bulletin of Volcanology, v. 84, 65, 23 p., https://doi.org/10.1007/s00445-022-01568-2.","productDescription":"65, 23 p.","ipdsId":"IP-130595","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447528,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-022-01568-2","text":"Publisher Index Page"},{"id":402057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.3521728515625,\n              19.32280716454424\n            ],\n            [\n              -155.126953125,\n              19.32280716454424\n            ],\n            [\n              -155.126953125,\n              19.480834276134903\n            ],\n            [\n              -155.3521728515625,\n              19.480834276134903\n            ],\n            [\n              -155.3521728515625,\n              19.32280716454424\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"84","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Meredith, Elinor S. 0000-0002-3869-1180","orcid":"https://orcid.org/0000-0002-3869-1180","contributorId":270269,"corporation":false,"usgs":false,"family":"Meredith","given":"Elinor","email":"","middleInitial":"S.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":844485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Susanna F. 0000-0002-7523-1423","orcid":"https://orcid.org/0000-0002-7523-1423","contributorId":270268,"corporation":false,"usgs":false,"family":"Jenkins","given":"Susanna","email":"","middleInitial":"F.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":844486,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Josh L. 0000-0001-7099-1063","orcid":"https://orcid.org/0000-0001-7099-1063","contributorId":270275,"corporation":false,"usgs":false,"family":"Hayes","given":"Josh","email":"","middleInitial":"L.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":844487,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":844488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lallemant, David 0000-0001-5759-9972","orcid":"https://orcid.org/0000-0001-5759-9972","contributorId":290680,"corporation":false,"usgs":false,"family":"Lallemant","given":"David","email":"","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":844489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":844490,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neal, Christina A. 0000-0002-7697-7825 tneal@usgs.gov","orcid":"https://orcid.org/0000-0002-7697-7825","contributorId":131135,"corporation":false,"usgs":true,"family":"Neal","given":"Christina","email":"tneal@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":844491,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70232187,"text":"70232187 - 2022 - Maize yield forecasts for Sub-Saharan Africa using Earth Observation data and machine learning","interactions":[],"lastModifiedDate":"2022-06-10T11:47:56.19594","indexId":"70232187","displayToPublicDate":"2022-06-06T06:44:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5055,"text":"Global Food Security","active":true,"publicationSubtype":{"id":10}},"title":"Maize yield forecasts for Sub-Saharan Africa using Earth Observation data and machine learning","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"d1e582\" class=\"abstract author\"><div id=\"d1e585\"><p id=\"d1e586\"><span>Food insecurity continues to grow in Sub-Saharan Africa (SSA). In 2019, chronically malnourished people numbered nearly 240 million, or 20% of the population in SSA. Globally, numerous efforts have been made to anticipate potential droughts, crop conditions, and&nbsp;food shortages&nbsp;in order to improve early warning and risk management for food insecurity. To support this goal, we develop an Earth Observation (EO) and machine-learning-based operational, subnational maize yield forecast system and evaluate its out-of-sample forecast skills during the growing seasons for Kenya, Somalia, Malawi, and&nbsp;Burkina&nbsp;Faso. In general, forecast skills improve substantially during the&nbsp;vegetative growth&nbsp;period (VP) and gradually during the reproductive development period (RP). Thus, mid-season assessment can provide effective early warning months before harvest. Skillful forecasts (Nash Sutcliffe Efficiency (NSE)&nbsp;</span><span class=\"math\">&gt;</span><span>&nbsp;</span>0.6 and Mean Absolute Percentage Error (MAPE)<span>&nbsp;</span><span class=\"math\">&lt;</span><span>&nbsp;20%) appear approximately two dekads after the VP; for example, skillful forecasts appear in May in Kenya and Somalia, January in Malawi, and July in Burkina Faso. During model development, effective EO features are also identified, such as precipitation and available water during VP, and dry days and extreme temperatures in early VP. Compared to monthly standard EO features, sub-monthly (dekadal), non-standard, and serial EO features significantly improve forecast skills by ＋ 0.3 NSE and -10% of MAPE, demonstrating the ability to precisely and effectively capture favorable or detrimental crop development conditions. Finally, skillful forecasts and practical utility are demonstrated in the recent normal and dry years in each region. Overall, the developed yield&nbsp;forecasting&nbsp;system can provide skillful predictions during the growing season, supporting regional and international agricultural decision-making processes, including informing food-security planning and management, thereby helping to mitigate food shortages caused by unfavorable climate conditions.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gfs.2022.100643","usgsCitation":"Lee, D., Davenport, F., Shukla, S., Husak, G., Funk, W., Harrison, L., McNally, A., Budde, M., Rowland, J., and Verdin, J., 2022, Maize yield forecasts for Sub-Saharan Africa using Earth Observation data and machine learning: Global Food Security, v. 33, 100643, 30 p., https://doi.org/10.1016/j.gfs.2022.100643.","productDescription":"100643, 30 p.","ipdsId":"IP-136632","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447531,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gfs.2022.100643","text":"Publisher Index Page"},{"id":402055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Burkina Faso, Kenya, Malawi, Somalia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[41.58513,-1.68325],[40.88477,-2.08255],[40.63785,-2.49979],[40.26304,-2.57309],[40.12119,-3.27768],[39.80006,-3.68116],[39.60489,-4.34653],[39.20222,-4.67677],[37.7669,-3.67712],[37.69869,-3.09699],[34.07262,-1.05982],[33.90371,-0.95],[33.89357,0.10981],[34.18,0.515],[34.6721,1.17694],[35.03599,1.90584],[34.59607,3.05374],[34.47913,3.5556],[34.005,4.24988],[34.6202,4.84712],[35.29801,5.506],[35.81745,5.33823],[35.81745,4.77697],[36.15908,4.44786],[36.85509,4.44786],[38.12091,3.59861],[38.43697,3.58851],[38.67114,3.61607],[38.89251,3.50074],[39.55938,3.42206],[39.85494,3.83879],[40.76848,4.25702],[41.1718,3.91909],[41.85508,3.91891],[42.12861,4.23413],[42.76967,4.25259],[43.66087,4.95755],[44.9636,5.00162],[47.78942,8.003],[48.48674,8.83763],[48.93813,9.45175],[48.93823,9.9735],[48.93849,10.98233],[48.94201,11.39427],[48.9482,11.41062],[49.26776,11.43033],[49.72862,11.5789],[50.25878,11.67957],[50.73202,12.0219],[51.1112,12.02464],[51.13387,11.74815],[51.04153,11.16651],[51.04531,10.6409],[50.83418,10.27972],[50.55239,9.19874],[50.07092,8.08173],[49.4527,6.80466],[48.59455,5.33911],[47.74079,4.2194],[46.56476,2.85529],[45.56399,2.04576],[44.06815,1.05283],[43.13597,0.2922],[42.04157,-0.91916],[41.81095,-1.44647],[41.58513,-1.68325]]],[[[-2.8275,9.64246],[-3.5119,9.90033],[-3.98045,9.86234],[-4.33025,9.61083],[-4.77988,9.82198],[-4.95465,10.15271],[-5.40434,10.37074],[-5.47056,10.95127],[-5.19784,11.37515],[-5.22094,11.71386],[-4.42717,12.54265],[-4.28041,13.22844],[-4.00639,13.47249],[-3.5228,13.33766],[-3.10371,13.54127],[-2.96769,13.79815],[-2.19182,14.24642],[-2.00104,14.55901],[-1.06636,14.97382],[-0.51585,15.11616],[-0.26626,14.92431],[0.37489,14.92891],[0.29565,14.44423],[0.42993,13.98873],[0.99305,13.33575],[1.0241,12.85183],[2.17711,12.62502],[2.15447,11.94015],[1.93599,11.64115],[1.44718,11.54772],[1.24347,11.11051],[0.89956,10.99734],[0.0238,11.01868],[-0.4387,11.09834],[-0.76158,10.93693],[-1.20336,11.00982],[-2.94041,10.96269],[-2.9639,10.39533],[-2.8275,9.64246]]],[[[34.55999,-11.52002],[34.28001,-12.28003],[34.55999,-13.58],[34.90715,-13.56542],[35.26796,-13.88783],[35.68685,-14.61105],[35.7719,-15.89686],[35.33906,-16.10744],[35.03381,-16.8013],[34.38129,-16.18356],[34.30729,-15.47864],[34.51767,-15.01371],[34.45963,-14.61301],[34.06483,-14.35995],[33.7897,-14.45183],[33.21402,-13.97186],[32.68817,-13.71286],[32.99176,-12.78387],[33.30642,-12.43578],[33.11429,-11.6072],[33.31531,-10.79655],[33.48569,-10.52556],[33.23139,-9.67672],[32.75938,-9.2306],[33.73973,-9.41715],[33.94084,-9.69367],[34.28001,-10.16],[34.55999,-11.52002]]]]},\"properties\":{\"name\":\"Kenya\"}}]}","volume":"33","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Donghoon 0000-0001-5438-903X","orcid":"https://orcid.org/0000-0001-5438-903X","contributorId":292417,"corporation":false,"usgs":false,"family":"Lee","given":"Donghoon","email":"","affiliations":[{"id":62899,"text":"Climate Hazards Center, University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":844504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":844505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shukla, Shraddhanand","contributorId":140735,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","email":"","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":844506,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":844507,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":189580,"corporation":false,"usgs":false,"family":"Funk","given":"W. Chris","affiliations":[],"preferred":false,"id":844508,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":844509,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McNally, Amy","contributorId":254957,"corporation":false,"usgs":false,"family":"McNally","given":"Amy","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":844510,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":844511,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":844512,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Verdin, James 0000-0003-0238-9657","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":198697,"corporation":false,"usgs":false,"family":"Verdin","given":"James","affiliations":[],"preferred":false,"id":844513,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70232131,"text":"70232131 - 2022 - Maintenance and dissemination of avian-origin influenza A virus within the northern Atlantic Flyway of North America","interactions":[],"lastModifiedDate":"2023-01-06T13:11:09.711099","indexId":"70232131","displayToPublicDate":"2022-06-06T06:40:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2981,"text":"PLoS Pathogens","active":true,"publicationSubtype":{"id":10}},"title":"Maintenance and dissemination of avian-origin influenza A virus within the northern Atlantic Flyway of North America","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>Wild waterbirds, the natural reservoirs for avian influenza viruses, undergo migratory movements each year, connecting breeding and wintering grounds within broad corridors known as flyways. In a continental or global view, the study of virus movements within and across flyways is important to understanding virus diversity, evolution, and movement. From 2015 to 2017, we sampled waterfowl from breeding (Maine) and wintering (Maryland) areas within the Atlantic Flyway (AF) along the east coast of North America to investigate the spatio-temporal trends in persistence and spread of influenza A viruses (IAV). We isolated 109 IAVs from 1,821 cloacal / oropharyngeal samples targeting mallards<span>&nbsp;</span><i>(Anas platyrhynchos)</i><span>&nbsp;</span>and American black ducks<span>&nbsp;</span><i>(Anas rubripes)</i>, two species having ecological and conservation importance in the flyway that are also host reservoirs of IAV. Isolates with &gt;99% nucleotide similarity at all gene segments were found between eight pairs of birds in the northern site across years, indicating some degree of stability among genome constellations and the possibility of environmental persistence. No movement of whole genome constellations were identified between the two parts of the flyway, however, virus gene flow between the northern and southern study locations was evident. Examination of banding records indicate direct migratory waterfowl movements between the two locations within an annual season, providing a mechanism for the inferred viral gene flow. Bayesian phylogenetic analyses provided evidence for virus dissemination from other North American wild birds to AF dabbling ducks (Anatinae), shorebirds (Charidriformes), and poultry (Galliformes). Evidence was found for virus dissemination from shorebirds to gulls (Laridae), and dabbling ducks to shorebirds and poultry. The findings from this study contribute to the understanding of IAV ecology in waterfowl within the AF.</p></div></div>","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.ppat.1010605","usgsCitation":"Prosser, D., Chen, J., Ahlstrom, C., Reeves, A.B., Poulson, R., Sullivan, J.D., McAuley, D., Callahan, C.R., McGowan, P.C., Bahl, J., Stallknecht, D., and Ramey, A.M., 2022, Maintenance and dissemination of avian-origin influenza A virus within the northern Atlantic Flyway of North America: PLoS Pathogens, v. 18, no. 6, e1010605, 25 p., https://doi.org/10.1371/journal.ppat.1010605.","productDescription":"e1010605, 25 p.","ipdsId":"IP-134661","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447535,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.ppat.1010605","text":"Publisher Index Page"},{"id":435822,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UF27FI","text":"USGS data release","linkHelpText":"Data concerning maintenance and dissemination of avian-origin influenza A virus within the Northern Atlantic Flyway of North America"},{"id":401911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.87109375,\n              36.491973470593685\n            ],\n            [\n              -75.322265625,\n              36.491973470593685\n            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,{"id":70262278,"text":"70262278 - 2022 - Movement of Canada geese in urban and rural areas of Iowa, USA","interactions":[],"lastModifiedDate":"2025-01-22T15:47:54.652422","indexId":"70262278","displayToPublicDate":"2022-06-06T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Movement of Canada geese in urban and rural areas of Iowa, USA","docAbstract":"<p><span>Temperate-breeding Canada Goose (</span><i>Branta canadensis maxima</i><span>) abundance has increased to previously unrecorded levels, providing social, ecological, and economic value. However, there are also costs associated with abundant Canada Geese. Although hunter harvest is a valued, sustainable use of Canada Geese, the adaptability of geese to urban areas may result in lower susceptibility of geese to hunters, potentially reducing the contribution of hunter harvest to conflict reduction. Our goal was to compare movement of geese marked in urban and rural areas to assess efficacy of hunter harvest in managing urban goose populations. We marked 71 adult female Canada Geese during brood-rearing with GPS-GSM transmitters in urban (n = 45) and rural (n = 26) locations in Iowa, USA to monitor movement during the 2018-19 and 2019-20 Mississippi Flyway goose hunting season frameworks. We estimated the mean proportion of locations each group was available for hunter harvest and examined factors affecting home range areas using generalized linear mixed models. Additionally, we estimated habitat selection of urban- and rural-marked geese using a step-selection analysis. Urban geese had a lower proportion of locations in areas available to hunters (0.07 [95% CI: 0.04-0.13]) than rural geese (0.56 [95% CI: 0.34-0.76]), but median home range area was similar for each group and decreased in size from autumn to late winter. Canada Geese marked in urban areas were more likely to select developed areas and less likely to select wetlands than rural geese, and they had high selection of agricultural fields within city limits during goose hunting seasons. Although Canada Geese breeding in urban areas may be less available for hunter harvest, movement data show when and where opportunity exists to increase harvest susceptibility. Canada Goose management could require actions in addition to hunter harvest to achieve goals in urban areas.</span></p>","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ace-02128-170127","usgsCitation":"Luukkonen, B., Klaver, R.W., and Jones III, O., 2022, Movement of Canada geese in urban and rural areas of Iowa, USA: Avian Conservation and Ecology, v. 17, no. 1, 27, 17 p., https://doi.org/10.5751/ace-02128-170127.","productDescription":"27, 17 p.","ipdsId":"IP-137532","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481084,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02128-170127","text":"Publisher Index Page"},{"id":480925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Luukkonen, Benjamin Z.","contributorId":348730,"corporation":false,"usgs":false,"family":"Luukkonen","given":"Benjamin Z.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones III, Orrin E.","contributorId":348731,"corporation":false,"usgs":false,"family":"Jones III","given":"Orrin E.","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923725,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70236537,"text":"70236537 - 2022 - Evidence of alternative trophic pathways for fish consumers in a large river system in the face of invasion","interactions":[],"lastModifiedDate":"2022-09-09T12:08:10.245228","indexId":"70236537","displayToPublicDate":"2022-06-05T07:06:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of alternative trophic pathways for fish consumers in a large river system in the face of invasion","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Large rivers are susceptible to anthropogenic alteration, which can result in drastic changes to their functional ecology. We evaluated spatial–temporal changes in the functional fish communities of the Upper Mississippi River System (UMRS) using data from six study reaches. Species were classified into one of 14 feeding guilds and mass per unit effort (MPUE) was then calculated for each feeding guild annually per gear type. MPUE was standardized using the multigear mean standardization method (MGMS) and log-transformed. Both ANOSIM and Chi-square tests were used to determine differences in MPUE among reaches. We then estimated functional diversity by calculating the number of functional groups (<i>N</i>), Margalef's<span>&nbsp;</span><i>d</i>, Pielou's J′, Shannon's Diversity, and Simpson's Diversity Index. An AR(1) time series model was used to investigate proportional changes in each guild over 25 years. To evaluate the effect of invasive Carp species in invaded reaches, a Chow test was applied to observations between 2000 and 2005. Analyses revealed differences in the functional fish community among reaches. We found differences in functional diversity metrics among study reaches, but there was little evidence that this differed between invaded and non-invaded reaches. Results determined that invertivore/detritivores have been consistently declining system-wide, with few groups showing a net change. There was also little evidence that invasion altered the proportion of any functional guild. Evaluating the spatial–temporal patterns of functional communities is beneficial to understanding the resilience of a system and can provide further insight into its trophic needs when considering future restoration initiatives.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/rra.3992","usgsCitation":"Gatto, J.V., Ickes, B., and Chick, J.H., 2022, Evidence of alternative trophic pathways for fish consumers in a large river system in the face of invasion: River Research and Applications, v. 38, no. 7, p. 1321-1332, https://doi.org/10.1002/rra.3992.","productDescription":"12 p.","startPage":"1321","endPage":"1332","ipdsId":"IP-133926","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":447539,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3992","text":"Publisher Index Page"},{"id":406441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Gatto, John V. 0000-0002-9793-0997","orcid":"https://orcid.org/0000-0002-9793-0997","contributorId":296376,"corporation":false,"usgs":false,"family":"Gatto","given":"John","email":"","middleInitial":"V.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":851341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ickes, Brian 0000-0001-5622-3842 bickes@usgs.gov","orcid":"https://orcid.org/0000-0001-5622-3842","contributorId":2925,"corporation":false,"usgs":true,"family":"Ickes","given":"Brian","email":"bickes@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":851342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chick, John H.","contributorId":229508,"corporation":false,"usgs":false,"family":"Chick","given":"John","email":"","middleInitial":"H.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":851343,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70232117,"text":"70232117 - 2022 - Streamflow reconstructions from tree rings and variability in drought and surface water supply for the Milk and St. Mary River basins","interactions":[],"lastModifiedDate":"2022-06-07T11:57:15.561989","indexId":"70232117","displayToPublicDate":"2022-06-05T06:55:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow reconstructions from tree rings and variability in drought and surface water supply for the Milk and St. Mary River basins","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">The Milk and St. Mary Rivers are international waterways straddling the United States and Canada and traversing four Tribal Nations before draining into the Missouri and South Saskatchewan Rivers respectively. Management of water resources in the region is challenged by the complexity of stakeholder interests, the limitations of existing management infrastructure, and by a limited characterization of the long-term streamflow and hydroclimatic variability across the area. We used existing records of natural streamflow to investigate the relationships between seasonal climate variability and differences in the timing and volume of flow from the headwaters to the prairie tributaries. Then, using a network of tree-ring chronologies to reconstruct records of past streamflow, we assessed whether drought risk relates to these sub-basin specific differences and if drought events experienced during the observational period are representative of those that have occurred over the long-term. Observed climate-flow relationships suggest that outside of the mountain headwaters, where precipitation dominates the hydrograph, streamflow variability on lower reaches of the Milk River is particularly sensitive to winter temperatures. This sensitivity was reflected by severe drought conditions over the prairies during the 2000s, implying potentially large future flow reductions with warming. The streamflow reconstructions show sub-basin specific drought risks that also imply greater temperature driven drought severities across the prairie tributaries. Within the mountain and foothill sub-basins numerous past drought episodes exceed the magnitude and duration of observational period events, which implies the potential for future water supply management challenges stemming from severe, long-duration droughts coupled with the negative hydrologic effects of warmer temperatures.</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.quascirev.2022.107574","usgsCitation":"Martin, J.T., and Pederson, G.T., 2022, Streamflow reconstructions from tree rings and variability in drought and surface water supply for the Milk and St. Mary River basins: Quaternary Science Reviews, v. 288, 107574, 13 p., https://doi.org/10.1016/j.quascirev.2022.107574.","productDescription":"107574, 13 p.","ipdsId":"IP-134446","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":447542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107574","text":"Publisher Index Page"},{"id":435823,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95HTCJM","text":"USGS data release","linkHelpText":"A network of eight naturalized streamflow reconstructions for the Milk and St Mary Rivers spanning years 1017 - 1998 CE"},{"id":401847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Montana","otherGeospatial":"Milk and St. Mary River basins","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.99414062499999,\n              47.635783590864854\n            ],\n            [\n              -105.90820312499999,\n              47.635783590864854\n            ],\n            [\n              -105.90820312499999,\n              50.00773901463685\n            ],\n            [\n              -113.99414062499999,\n              50.00773901463685\n            ],\n            [\n              -113.99414062499999,\n              47.635783590864854\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"288","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":844252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":844253,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70232114,"text":"70232114 - 2022 - Global tellurium supply potential from electrolytic copper refining","interactions":[],"lastModifiedDate":"2022-06-07T11:42:35.843929","indexId":"70232114","displayToPublicDate":"2022-06-05T06:40:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10927,"text":"Resources, Conservation & Recycling","active":true,"publicationSubtype":{"id":10}},"title":"Global tellurium supply potential from electrolytic copper refining","docAbstract":"<div id=\"abs0001\" class=\"abstract author\"><div id=\"abss0001\"><p id=\"spara010\"><span>The transition towards renewable energy requires increasing quantities of nonfuel mineral commodities, including&nbsp;tellurium&nbsp;used in certain&nbsp;</span>photovoltaics. While demand for tellurium may increase markedly, the potential to increase tellurium supply is not well-understood. In this analysis, we estimate the quantity of tellurium contained in anode slimes generated by electrolytic copper refining by country between 1986 and 2018, including uncertainties. For 2018, the results indicate that 1930 (1500-2700, 95% confidence interval) metric tons of tellurium were contained in anode slimes globally. This is nearly quadruple the reported tellurium production for that year. China has the greatest potential to increase tellurium supplies. However, most of the tellurium potentially recoverable by Chinese refineries appears to come from copper mined elsewhere. Further research into the business decisions associated with tellurium recovery may help translate the physical availability of tellurium into economic availability. The methodology presented here can be applied to other byproduct elements.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.resconrec.2022.106434","usgsCitation":"Nassar, N.T., Kim, H., Frenzel, M., Moats, M.S., and Hayes, S.M., 2022, Global tellurium supply potential from electrolytic copper refining: Resources, Conservation & Recycling, v. 184, 106434, 11 p., https://doi.org/10.1016/j.resconrec.2022.106434.","productDescription":"106434, 11 p.","ipdsId":"IP-136410","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":467181,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resconrec.2022.106434","text":"Publisher Index Page"},{"id":401844,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"184","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":844246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Haeyeon 0000-0003-0028-4237","orcid":"https://orcid.org/0000-0003-0028-4237","contributorId":292297,"corporation":false,"usgs":true,"family":"Kim","given":"Haeyeon","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":844247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frenzel, Max 0000-0001-6625-559X","orcid":"https://orcid.org/0000-0001-6625-559X","contributorId":292298,"corporation":false,"usgs":false,"family":"Frenzel","given":"Max","email":"","affiliations":[{"id":62860,"text":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology (HIF), Freiberg, Germany","active":true,"usgs":false}],"preferred":false,"id":844248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moats, Michael S. 0000-0001-9288-076X","orcid":"https://orcid.org/0000-0001-9288-076X","contributorId":292299,"corporation":false,"usgs":false,"family":"Moats","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":62861,"text":"Materials Research Center, Missouri University of Science & Technology, Rolla, MO, USA","active":true,"usgs":false}],"preferred":false,"id":844249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Sarah M. 0000-0001-5887-6492","orcid":"https://orcid.org/0000-0001-5887-6492","contributorId":208569,"corporation":false,"usgs":true,"family":"Hayes","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":844250,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70232207,"text":"70232207 - 2022 - Interpreting long-distance movements of non-migratory golden eagles: Prospecting and nomadism?","interactions":[],"lastModifiedDate":"2022-06-13T16:35:54.384199","indexId":"70232207","displayToPublicDate":"2022-06-04T11:30:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Interpreting long-distance movements of non-migratory golden eagles: Prospecting and nomadism?","docAbstract":"<p><span>Movements by animals can serve different functions and occur over a variety of spatial and temporal scales. Routine movement types, such as residency (localized movements) and migration, have been well studied. However, nonroutine movement types, such as dispersal, prospecting, and nomadism, are less well understood. Documenting these rarely detected events requires tracking large numbers of individuals across all age classes. We studied &gt;500 golden eagles (</span><i>Aquila chrysaetos</i><span>) tracked by telemetry over a 10-year period in western North America, of which 160 engaged in nonroutine, long-distance (&gt;300 km) movements. We identified spatial and temporal correlates of those movements at both small and large scales, and we quantified movement timing and direction. We further tested which age and sex classes of eagles were more likely to engage in these movements. Our analysis of 88,093 daily tracks suggested that distances traveled by eagles were responsive to the updraft potential of the spatial and temporal landscape they encountered. Tracks covered longer distances at locations and times of higher updraft potential, and older birds traveled farther than younger birds. By contrast, after decomposing daily tracks into 563 nonroutine, long-distance movements measured at a multiday scale, only the duration of travel was responsive to environmental conditions encountered by eagles. Multiday trips that were longer were those initiated in open and warm landscapes and those that ended in mountainous regions. Finally, long-distance movements were more frequently made in seasons other than winter, in north–south directions, and by young birds. We documented clear correlates of nonroutine, long-distance movements by golden eagles at small, local scales but found little evidence of such correlates at larger, regional scales. Most long-distance movements we documented fit patterns associated with traditional definitions of prospecting and nomadism but not migration. Our study is the first to describe these movement types by golden eagles, and as such provides a foundation for subsequent study into the movement ecology of other species.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4072","usgsCitation":"Poessel, S.A., Woodbridge, B., Smith, B.W., Murphy, R.K., Bedrosian, B.E., Bell, D., Bittner, D., Bloom, P., Crandall, R.H., Domenech, R., Fisher, R.N., Haggerty, P., Slater, S.J., Tracey, J.A., Watson, J.W., and Katzner, T., 2022, Interpreting long-distance movements of non-migratory golden eagles: Prospecting and nomadism?: Ecosphere, v. 13, no. 6, e4072, 17 p., https://doi.org/10.1002/ecs2.4072.","productDescription":"e4072, 17 p.","ipdsId":"IP-124395","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488383,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4072","text":"Publisher Index Page"},{"id":435824,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91T2VMO","text":"USGS data release","linkHelpText":"Long-distance movements of non-migratory golden eagles in western North America, 2007-2017"},{"id":402104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -108.984375,\n              21.94304553343818\n            ],\n            [\n              -98.7890625,\n              24.686952411999155\n            ],\n            [\n              -93.515625,\n              35.460669951495305\n            ],\n            [\n              -101.6015625,\n              52.16045455774706\n            ],\n            [\n              -114.60937499999999,\n              59.88893689676585\n            ],\n            [\n              -126.91406249999999,\n              59.085738569819505\n            ],\n            [\n              -128.671875,\n              54.97761367069628\n            ],\n            [\n              -124.98046874999999,\n              48.10743118848039\n            ],\n            [\n              -124.8046875,\n              40.713955826286046\n            ],\n            [\n              -121.46484375,\n              34.30714385628804\n            ],\n            [\n              -118.65234374999999,\n              32.54681317351514\n            ],\n            [\n              -113.90625,\n              25.958044673317843\n            ],\n            [\n              -108.984375,\n              21.94304553343818\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodbridge, Brian","contributorId":198923,"corporation":false,"usgs":false,"family":"Woodbridge","given":"Brian","email":"","affiliations":[{"id":17821,"text":"U.S. Fish and Wildlife Service, Division of Migratory Birds","active":true,"usgs":false}],"preferred":false,"id":844611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Brian W.","contributorId":199748,"corporation":false,"usgs":false,"family":"Smith","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":17821,"text":"U.S. Fish and Wildlife Service, Division of Migratory Birds","active":true,"usgs":false}],"preferred":false,"id":844612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Robert K.","contributorId":67643,"corporation":false,"usgs":false,"family":"Murphy","given":"Robert","email":"","middleInitial":"K.","affiliations":[{"id":56253,"text":"Eagle Environmental, Inc","active":true,"usgs":false}],"preferred":false,"id":844613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Bryan E","contributorId":292459,"corporation":false,"usgs":false,"family":"Bedrosian","given":"Bryan","email":"","middleInitial":"E","affiliations":[{"id":35591,"text":"Teton Raptor Center","active":true,"usgs":false}],"preferred":false,"id":844614,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bell, Douglas A.","contributorId":279590,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas A.","affiliations":[{"id":57302,"text":"East Bay Regional Park District, 2950 Peralta Oaks Court, Oakland, CA","active":true,"usgs":false}],"preferred":false,"id":844615,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bittner, David","contributorId":292460,"corporation":false,"usgs":false,"family":"Bittner","given":"David","email":"","affiliations":[{"id":62911,"text":"Wildlife Research Institute, Inc.","active":true,"usgs":false}],"preferred":false,"id":844616,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bloom, Peter H.","contributorId":289557,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter H.","affiliations":[{"id":38830,"text":"Bloom Research Inc.","active":true,"usgs":false}],"preferred":false,"id":844617,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crandall, Ross H.","contributorId":198926,"corporation":false,"usgs":false,"family":"Crandall","given":"Ross","email":"","middleInitial":"H.","affiliations":[{"id":6657,"text":"Craighead Beringia South","active":true,"usgs":false}],"preferred":false,"id":844618,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Domenech, Robert","contributorId":199743,"corporation":false,"usgs":false,"family":"Domenech","given":"Robert","email":"","affiliations":[{"id":35594,"text":"Raptor View Research Institute","active":true,"usgs":false}],"preferred":false,"id":844619,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":844620,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Haggerty, Patricia 0000-0003-0834-8143","orcid":"https://orcid.org/0000-0003-0834-8143","contributorId":202970,"corporation":false,"usgs":true,"family":"Haggerty","given":"Patricia","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844621,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Slater, Steven J.","contributorId":199746,"corporation":false,"usgs":false,"family":"Slater","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":35596,"text":"HawkWatch International","active":true,"usgs":false}],"preferred":false,"id":844622,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":844623,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Watson, James W.","contributorId":198921,"corporation":false,"usgs":false,"family":"Watson","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":844624,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844625,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70237904,"text":"70237904 - 2022 - A three-dimensional Lagrangian particle tracking model for predicting transport of eggs of rheophilic-spawning carps in turbulent rivers","interactions":[],"lastModifiedDate":"2022-10-31T12:20:46.232586","indexId":"70237904","displayToPublicDate":"2022-06-04T07:19:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"A three-dimensional Lagrangian particle tracking model for predicting transport of eggs of rheophilic-spawning carps in turbulent rivers","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"d1e806\" class=\"abstract author\"><div id=\"d1e809\"><p id=\"d1e810\"><span>Grass carp,&nbsp;bighead carp, and silver carp spawn in flowing water. Their eggs, and then larvae, develop while drifting. Hydraulic conditions and water temperature control spawning locations, egg survival, and the downstream distance traveled before the hatched larvae can swim for low velocity nursery habitats. Existing egg drift models simulate the fluvial transport of carp eggs but have limitations in capturing the effect of localized turbulence on egg transport due to inadequate dimensions of hydrodynamics and/or empirical parameterization of river dispersion. We present a three-dimensional Lagrangian particle tracking model that uses fully resolved river hydrodynamics and a continuous random walk algorithm driven by local turbulent kinetic energy and its dissipation rate. We incorporate a new set of equations to compute evolving egg characteristics with fully resolved 3-D hydrodynamics. To demonstrate the performance of the model, we conducted a case study in an eight-kilometer reach of the Missouri River at the discharge of approximately 25% daily flow exceedance. Three-dimensional river hydrodynamics was modeled, calibrated, and evaluated with measurement data. Egg drift was modeled and compared using fully three-dimensional, depth-averaged two-dimensional, and zone-averaged one-dimensional hydrodynamics. The comparison shows a generally good agreement among models of downstream egg transport due to&nbsp;</span>advection<span>&nbsp;but a different dispersion pattern of eggs in the river, as a result of&nbsp;turbulent diffusion&nbsp;and shear induced dispersion.</span></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.ecolmodel.2022.110035","usgsCitation":"Li, G., Wang, B., Elliott, C.M., Call, B., Chapman, D., and Jacobson, R., 2022, A three-dimensional Lagrangian particle tracking model for predicting transport of eggs of rheophilic-spawning carps in turbulent rivers: Ecological Modelling, v. 470, https://doi.org/10.1016/j.ecolmodel.2022.110035.","productDescription":"110035, 16 p.","startPage":"110035","ipdsId":"IP-136512","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":447545,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2022.110035","text":"Publisher Index Page"},{"id":435825,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X5M3WH","text":"USGS data release","linkHelpText":"Field Data and Models of the Missouri River at Sheepnose Bend, near Lexington, Missouri, 2019-2021"},{"id":408882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"470","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Geng","contributorId":298636,"corporation":false,"usgs":false,"family":"Li","given":"Geng","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":856141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Bin","contributorId":298637,"corporation":false,"usgs":false,"family":"Wang","given":"Bin","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":856142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elliott, Caroline M. 0000-0002-9190-7462 celliott@usgs.gov","orcid":"https://orcid.org/0000-0002-9190-7462","contributorId":2380,"corporation":false,"usgs":true,"family":"Elliott","given":"Caroline","email":"celliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":856143,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Call, Bruce 0000-0001-9064-2231","orcid":"https://orcid.org/0000-0001-9064-2231","contributorId":217707,"corporation":false,"usgs":true,"family":"Call","given":"Bruce","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":856144,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapman, Duane","contributorId":298640,"corporation":false,"usgs":false,"family":"Chapman","given":"Duane","affiliations":[{"id":64637,"text":"Former USGS Columbia Environmental Research Center Employee","active":true,"usgs":false}],"preferred":false,"id":856145,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":856140,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70232136,"text":"70232136 - 2022 - Multi-stage soil-hydraulic recovery and limited ravel accumulations following the 2017 Nuns and Tubbs wildfires in Northern California","interactions":[],"lastModifiedDate":"2022-07-08T13:45:20.841908","indexId":"70232136","displayToPublicDate":"2022-06-04T06:51:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Multi-stage soil-hydraulic recovery and limited ravel accumulations following the 2017 Nuns and Tubbs wildfires in Northern California","docAbstract":"<div class=\"article-section__content en main\"><p>Wildfire can impact soil-hydraulic properties by reducing saturated hydraulic conductivity and sorptivity, making recently burned landscapes prone to debris flows and flash floods. The post-fire hazard window can range from years to decades. In Northern California, where wildfire frequency is steadily increasing, the impact and soil-hydraulic recovery from wildfires is unknown. Following the October 2017 Nuns and Tubbs fires in the Northern Bay Area of California, we established 41 monitoring sites for repeat tension-disc infiltrometer measurements of field-saturated hydraulic conductivity (<i>K</i><sub><i>fs</i></sub>) over 3.5 years. Our site arrays, which encompass grasslands, chaparral, and oak and conifer forests across a range in lithology, show a marked decrease in<span>&nbsp;</span><i>K</i><sub><i>fs</i></sub><span>&nbsp;</span>following the wildfires and a swift partial recovery following the initial post-fire rainy season. Our time series reveals a complex path to soil-hydraulic recovery marked by distinct seasonal stages. Analysis of changing<span>&nbsp;</span><i>K</i><sub><i>fs</i></sub>, sorptivity, and infiltration model residuals collectively suggests that these stages are related to transitions between soil-hydraulic processes like structural soil sealing from rainsplash, thermal cracking of bare soil, and vegetation regrowth. While soil infiltration rates were strongly impacted by the 2017 fires, dry ravel estimates are an order of magnitude less for similar slopes than the 2009 Station fire in the San Gabriel mountains of Southern California, suggesting that limited ravel flux may insufficiently load channels for debris flows that initiate from within-channel failure. Our analysis suggests that burned landscapes in the Northern Bay Area of California may experience rapid soil-hydraulic recovery and limited pathways toward post-fire debris flow initiation.</p></div>","language":"English","publisher":"Wiley","doi":"10.1029/2022JF006591","usgsCitation":"Perkins, J.P., Diaz, C., Corbett, S.C., Cerovski-Darriau, C., Stock, J.D., Prancevic, J.P., Micheli, L., and Jasperse, J., 2022, Multi-stage soil-hydraulic recovery and limited ravel accumulations following the 2017 Nuns and Tubbs wildfires in Northern California: Journal of Geophysical Research: Earth Surface, v. 127, no. 6, e2022JF006591, 19 p., https://doi.org/10.1029/2022JF006591.","productDescription":"e2022JF006591, 19 p.","ipdsId":"IP-136584","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":447547,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006591","text":"Publisher Index Page"},{"id":435827,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J5BTSN","text":"USGS data release","linkHelpText":"Field-saturated hydraulic conductivity time series and sediment accumulations following the 2017 Nuns and Tubbs wildfires, Napa and Sonoma Counties, CA, USA"},{"id":401914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pepperwood Preserve, Sugarloaf  Ridge  State  Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.61016845703124,\n              38.37773029803778\n            ],\n            [\n              -122.47901916503906,\n              38.37773029803778\n            ],\n            [\n              -122.47901916503906,\n              38.501967316378895\n            ],\n            [\n              -122.61016845703124,\n              38.501967316378895\n            ],\n            [\n              -122.61016845703124,\n              38.37773029803778\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.7777099609375,\n              38.49954915714596\n            ],\n            [\n              -122.65033721923827,\n              38.49954915714596\n            ],\n            [\n              -122.65033721923827,\n              38.61123697842133\n            ],\n            [\n              -122.7777099609375,\n              38.61123697842133\n            ],\n            [\n              -122.7777099609375,\n              38.49954915714596\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"127","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diaz, Carlos","contributorId":292327,"corporation":false,"usgs":false,"family":"Diaz","given":"Carlos","email":"","affiliations":[{"id":17863,"text":"Sonoma County Water Agency","active":true,"usgs":false}],"preferred":false,"id":844315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Corbett, Skye C. 0000-0003-3277-1021 scorbett@usgs.gov","orcid":"https://orcid.org/0000-0003-3277-1021","contributorId":200617,"corporation":false,"usgs":true,"family":"Corbett","given":"Skye","email":"scorbett@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":844320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stock, Jonathan D. 0000-0001-8565-3577 jstock@usgs.gov","orcid":"https://orcid.org/0000-0001-8565-3577","contributorId":3648,"corporation":false,"usgs":true,"family":"Stock","given":"Jonathan","email":"jstock@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844317,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Prancevic, Jeffrey Paul 0000-0003-1890-7551","orcid":"https://orcid.org/0000-0003-1890-7551","contributorId":292330,"corporation":false,"usgs":true,"family":"Prancevic","given":"Jeffrey","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":844321,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Micheli, Lisa","contributorId":292329,"corporation":false,"usgs":false,"family":"Micheli","given":"Lisa","email":"","affiliations":[{"id":37798,"text":"Pepperwood Preserve","active":true,"usgs":false}],"preferred":false,"id":844319,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jasperse, Jay","contributorId":168661,"corporation":false,"usgs":false,"family":"Jasperse","given":"Jay","affiliations":[{"id":17863,"text":"Sonoma County Water Agency","active":true,"usgs":false}],"preferred":false,"id":844318,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70232132,"text":"70232132 - 2022 - Petrogenesis and rare earth element mineralization of the Elk Creek carbonatite, Nebraska, USA","interactions":[],"lastModifiedDate":"2022-06-08T11:50:52.591025","indexId":"70232132","displayToPublicDate":"2022-06-04T06:48:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Petrogenesis and rare earth element mineralization of the Elk Creek carbonatite, Nebraska, USA","docAbstract":"<div id=\"ab015\" class=\"abstract author\"><div id=\"as015\"><p id=\"sp0015\">Although carbonatites are the primary source of the world’s rare earth elements (REEs), the processes responsible for ore-grade REE enrichment in carbonatites are still poorly understood. In this study, we present a petrologic, geochemical, and isotopic evaluation of the Elk Creek carbonatite in southeast Nebraska to constrain the origin of REE mineralization. The Elk Creek carbonatite is a multilithologic carbonatite comprised of an early apatite-dolomite carbonatite, a middle/heavy REE-enriched magnetite-dolomite carbonatite, and a late-stage light REE-enriched, barite-dolomite carbonatite, as well as a suite of breccias. Neodymium, strontium, and carbon isotopic data from the early apatite-dolomite carbonatite, ε<sub>Nd</sub>(T)&nbsp;=&nbsp;2.3 to 3.4,<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr<sub>(i)</sub>&nbsp;=&nbsp;0.702704 to 0.702857, and δ<sup>13</sup>C&nbsp;=&nbsp;−3.3 to −3.4, indicate that the parental magma and REEs were derived from the mantle, and textural and chemical data suggest that hydrothermal processes played an important role in reaching ore-grade enrichment. Higher initial<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr values (∼0.7041) of REE-mineralized lithologies are evidence that these fluids were derived, in part, from meteoric water that interacted with the country rock. Modeling of the C-O isotopic data reveals that some of the isotopic variation results from closed-system Rayleigh fractionation of an evolving carbonatitic magma between 300 and 500&nbsp;°C, but an excursion to heavier δ<sup>18</sup>O is likely the result of interaction with H<sub>2</sub>O-CO<sub>2</sub>-fluids at temperatures from 400 to 100&nbsp;°C. Hydrothermal dolomite has higher<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr values than early-formed magmatic dolomite, consistent with metasomatism by fluids derived, in part, from a more radiogenic source such as the Precambrian-age wall rock. Rare earth element mineralization occurs primarily in fine-grained, cavity filling minerals including monazite, bastnäsite, parisite, and synchysite along with barite, dolomite, quartz, and iron oxides. We interpret the LREE enrichment at Elk Creek to be the product of hydrothermal fluids derived from the evolving carbonatite magma and fluids from the wall rock. The REEs likely became enriched in late-stage fluids from the evolving magma as well as being remobilization by the dissolution of earlier formed minerals. Middle/heavy REE-enrichment in the magnetite-dolomite carbonatite is hosted in hydrothermal dolomite and is attributed to variations in the composition of hydrothermal fluids.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2022.104953","usgsCitation":"Verplanck, P., Farmer, G.L., Kettler, R.M., Lowers, H.A., Johnson, C.A., Koenig, A.E., and Blessington, M.J., 2022, Petrogenesis and rare earth element mineralization of the Elk Creek carbonatite, Nebraska, USA: Ore Geology Reviews, v. 146, 104953, 19 p., https://doi.org/10.1016/j.oregeorev.2022.104953.","productDescription":"104953, 19 p.","ipdsId":"IP-137353","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":447551,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Lang","contributorId":15075,"corporation":false,"usgs":false,"family":"Farmer","given":"G.","email":"","middleInitial":"Lang","affiliations":[],"preferred":false,"id":844305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kettler, Richard M. 0000-0001-5016-5602","orcid":"https://orcid.org/0000-0001-5016-5602","contributorId":291503,"corporation":false,"usgs":false,"family":"Kettler","given":"Richard","email":"","middleInitial":"M.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":844306,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":844307,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":844308,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koenig, Alan E. 0000-0002-5230-0924","orcid":"https://orcid.org/0000-0002-5230-0924","contributorId":223400,"corporation":false,"usgs":false,"family":"Koenig","given":"Alan","email":"","middleInitial":"E.","affiliations":[{"id":40710,"text":"Applied Spectra","active":true,"usgs":false}],"preferred":false,"id":844309,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blessington, Michael J.","contributorId":292325,"corporation":false,"usgs":false,"family":"Blessington","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":844310,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70231901,"text":"ofr20211111 - 2022 - Methods for computing 7Q2 and 7Q20 low-streamflow statistics to account for possible trends","interactions":[],"lastModifiedDate":"2022-06-03T16:53:01.091072","indexId":"ofr20211111","displayToPublicDate":"2022-06-03T11:49:35","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1111","displayTitle":"Methods for Computing 7Q2 and 7Q20 Low-Streamflow Statistics to Account for Possible Trends","title":"Methods for computing 7Q2 and 7Q20 low-streamflow statistics to account for possible trends","docAbstract":"<p>Low-streamflow statistics, such as the annual minimum 7-day streamflow (which is the 7-day streamflow likely to be exceeded in 9 out of 10 years on average [7Q10]), that are computed by using the full historical streamflow record may not accurately represent current conditions at sites with statistically significant trends in low streamflow over time. Recent research suggests that using a contemporary subset of the historical streamflow record (specifically, the most recent 30 years) to compute an estimate of 7Q10 more accurately represents current streamflow conditions when a statistically significant trend in the streamflow record is present. This report presents the results of a Monte Carlo simulation experiment on artificial low-streamflow records, derived from the characteristics of streamflows at 174 U.S. Geological Survey streamgages, to test whether a similar approach is appropriate for the computation of the annual minimum 7-day streamflow exceeded in 1 out of 2 years on average (7Q2) and the annual minimum 7-day streamflow exceeded in 19 out of 20 years on average (7Q20). The results indicate that using the most recent 30-year subset of the low-streamflow record also may be the best approach when computing 7Q2 and 7Q20 at sites where a statistically significant trend in low streamflows is detected.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211111","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Schalk, L., Dudley, R.W., and Blum, A.G., 2022, Methods for computing 7Q2 and 7Q20 low-streamflow statistics to account for possible trends: U.S. Geological Survey Open-File Report 2021–1111, 15 p., https://doi.org/10.3133/ofr20211111.","productDescription":"iv, 15 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119807","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":401572,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1111/images/"},{"id":401570,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1111/ofr20211111.pdf","text":"Report","size":"996 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1111"},{"id":401569,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1111/coverthb.jpg"}],"contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data</li><li>Methods</li><li>Results</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Tabulation of Highest Improvement Factor by Bin</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2022-06-03","noUsgsAuthors":false,"publicationDate":"2022-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Schalk, Luther 0000-0003-3957-1794 lschalk@usgs.gov","orcid":"https://orcid.org/0000-0003-3957-1794","contributorId":4366,"corporation":false,"usgs":true,"family":"Schalk","given":"Luther","email":"lschalk@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844056,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blum, Annalise G. 0000-0003-4618-6181","orcid":"https://orcid.org/0000-0003-4618-6181","contributorId":245883,"corporation":false,"usgs":false,"family":"Blum","given":"Annalise","email":"","middleInitial":"G.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":844057,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256664,"text":"70256664 - 2022 - Breeding dynamics of gopher frog metapopulations over 10 years","interactions":[],"lastModifiedDate":"2024-08-29T16:09:10.614626","indexId":"70256664","displayToPublicDate":"2022-06-03T11:02:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Breeding dynamics of gopher frog metapopulations over 10 years","docAbstract":"<p><span>Populations of amphibians that breed in isolated, ephemeral wetlands may be particularly sensitive to breeding and recruitment rates, which can be influenced by dynamic and difficult-to-predict extrinsic factors. The gopher frog&nbsp;</span><i>Rana capito</i><span>&nbsp;is a declining species currently proposed for listing under the U.S. Endangered Species Act, as well as one of many pond-breeding amphibians of conservation concern in the southeastern United States. To represent gopher frog breeding dynamics, we applied an occupancy modeling framework that integrated multiple data sets collected across the species' range to 1) estimate the influence of climate, habitat, and other factors on wetland-specific seasonal breeding probabilities; and 2) use those estimates to characterize seasonal, annual, and regional breeding patterns over a 10-y period. Breeding probability at a wetland was positively influenced by seasonal precipitation (Standardized Precipitation Index) and negatively influenced by fish presence. We found some evidence that the amount of suitable habitat surrounding a wetland was positively correlated with breeding probability during drought conditions. The percentage of sampled wetlands (</span><i>N</i><span>&nbsp;= 192) predicted to have breeding varied seasonally, annually, and regionally across the study. Within-year temporal patterns of breeding differed across the range: in most locations north of Florida, peaks of breeding occurred in winter and spring months; whereas breeding was more dispersed throughout the year in Florida. Peaks of breeding across the 10-y period often occurred during or in the season following high rainfall events (e.g., hurricanes). These results have direct applications for site-level management that aims to increase successful breeding opportunities of gopher frogs and other associated pond-breeding amphibians, including monitoring protocol and intensity, removal of fish, and improving terrestrial habitat conditions surrounding wetlands (e.g., via tree or shrub removal and prescribed fire). The results also have implications for better-informed management through the closer alignment of breeding activity monitoring with predicted seasonal peaks. Furthermore, estimates of breeding frequency can be incorporated into population viability analyses to inform forthcoming assessments of extinction risk and designation of the species' conservation status by the U.S. Fish and Wildlife Service.</span></p>","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-21-076","usgsCitation":"Crawford, B., Farmer, A.L., Enge, K.M., Greene, A.H., Diaz, L., Maerz, J., and Moore, C.T., 2022, Breeding dynamics of gopher frog metapopulations over 10 years: Journal of Fish and Wildlife Management, v. 13, no. 2, p. 422-436, https://doi.org/10.3996/JFWM-21-076.","productDescription":"15 p.","startPage":"422","endPage":"436","ipdsId":"IP-132970","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447552,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-076","text":"Publisher Index Page"},{"id":433319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, North Carolina, South Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.37623620146309,\n              35.01905610973961\n            ],\n            [\n              -79.58387829875939,\n              35.765474175904856\n            ],\n            [\n              -84.05790907036402,\n              32.71553184084284\n            ],\n            [\n              -86.95743456481551,\n              31.313371252353505\n            ],\n            [\n              -87.97753689244945,\n              31.255714516567366\n            ],\n            [\n              -87.90062766475464,\n              30.410921928153\n    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Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":908553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, Anna L.","contributorId":341520,"corporation":false,"usgs":false,"family":"Farmer","given":"Anna","email":"","middleInitial":"L.","affiliations":[{"id":36335,"text":"Fish and Wildlife Research Institute","active":true,"usgs":false}],"preferred":false,"id":908554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Enge, Kevin M","contributorId":177669,"corporation":false,"usgs":false,"family":"Enge","given":"Kevin","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":908555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greene, Aubrey Heupel","contributorId":341522,"corporation":false,"usgs":false,"family":"Greene","given":"Aubrey","email":"","middleInitial":"Heupel","affiliations":[{"id":36335,"text":"Fish and Wildlife Research Institute","active":true,"usgs":false}],"preferred":false,"id":908556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diaz, Lauren","contributorId":341523,"corporation":false,"usgs":false,"family":"Diaz","given":"Lauren","email":"","affiliations":[{"id":36335,"text":"Fish and Wildlife Research Institute","active":true,"usgs":false}],"preferred":false,"id":908557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maerz, John C.","contributorId":341524,"corporation":false,"usgs":false,"family":"Maerz","given":"John C.","affiliations":[{"id":81749,"text":"Warnell School of Forestry and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":908558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908559,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70231909,"text":"70231909 - 2022 - Biogeography of freshwater mussels (Bivalvia: Unionida) in Texas and implications on conservation biology","interactions":[],"lastModifiedDate":"2022-07-08T13:38:13.520477","indexId":"70231909","displayToPublicDate":"2022-06-03T08:43:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Biogeography of freshwater mussels (Bivalvia: Unionida) in Texas and implications on conservation biology","docAbstract":"<p><strong>Aim</strong></p><p>Biogeography seeks to identify and explain the spatial distributions of species and has become an important tool used by conservationists to protect and manage aquatic organisms. Texas, located in the southwestern United States, is home to 52 species of freshwater mussels, 9 of which are endemic to Texas and 7 that are endemic to Texas and neighboring states or countries. There have been two major attempts to classify this fauna into biogeographical provinces; however, both efforts relied on limited distribution information and outdated taxonomy. To address both issues, we set out to delineate biogeographic provinces for freshwater mussels in Texas by using a comprehensive distributional dataset of &gt;28,000 records and molecular information.</p><p><strong>Location</strong></p><p>Southwestern United States.</p><p><strong>Methods</strong></p><p>We compiled community and molecular data for 48 of the 52 freshwater mussel species that occur in Texas. We performed algorithmic hierarchal cluster analysis (HCA) and nonmetric multidimensional scaling (NMDS) based on Euclidean distance to identify biogeographic groupings. We conducted a similar analysis using molecular sequence data for our target species.</p><p><strong>Results</strong></p><p>Based on the results from community and molecular data, we identified seven biogeographic provinces for freshwater mussels in Texas: Great Plains, Mississippi Embayment, Sabine-Neches, Trinity-San Jacinto, Central Texas, Rio Grande and Coastal. However, the Coastal and Great Plains provinces were not included in our analysis and were recognized based on previous work.</p><p><strong>Main conclusions</strong></p><p>Our approach integrating community and molecular datasets provides a comprehensive assessment of the biogeography of freshwater mussels in Texas, which serves as a model for future biogeographic studies. Our findings also shed light on the ecological, evolutionary and geologic processes shaping freshwater mussel communities in Texas, which is important for the conservation of remaining biodiversity in the state.</p>","language":"English","publisher":"John Wiley & Sons, Inc.","doi":"10.1111/ddi.13555","usgsCitation":"de Moulpied, M., Smith, C.H., Robertson, C.R., Johnson, N., Lopez, R., and Randklev, C.R., 2022, Biogeography of freshwater mussels (Bivalvia: Unionida) in Texas and implications on conservation biology: Diversity and Distributions, v. 28, no. 7, p. 1458-1474, https://doi.org/10.1111/ddi.13555.","productDescription":"17 p.","startPage":"1458","endPage":"1474","ipdsId":"IP-132849","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447553,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13555","text":"Publisher Index 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,{"id":70231907,"text":"70231907 - 2022 - Structured decision making to rank North American Wetland Conservation Act proposals within joint venture regions","interactions":[],"lastModifiedDate":"2023-01-18T15:51:07.526101","indexId":"70231907","displayToPublicDate":"2022-06-03T08:32:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Structured decision making to rank North American Wetland Conservation Act proposals within joint venture regions","docAbstract":"<p>The North American Wetlands Conservation Act (16 U.S.C. 4401-4412) provided funding and administration for wetland management and conservation projects. The North American Wetland Conservation Fund, enabled in 1989 with the Act, provides financial resources. Resource allocation decisions are based, in part, on regional experts, particularly migratory bird Joint Ventures (JVs) (i.e., partnerships for cooperative planning and coordinated management of the continent’s waterfowl populations and habitats). The JVs evaluate funding proposals submitted with their respective regions each year and make funding recommendations to decision makers. Proposal evaluation procedures differ among JVs, however, it could be helpful to consider a transparent, repeatable, and data-driven framework for prioritization within regions. We used structured decision making and linear additive value models for ranking proposals within JV regions. We used two JVs as case studies and constructed two different value models using JV-specific objectives and weights. The framework was developed through a collaborative process with JV staff and stakeholders. Models were written in Microsoft Excel. To test these models, we used six NAWCA proposals submitted to the Upper Mississippi / Great Lakes Joint Venture in 2016 and seven proposals submitted to the Gulf Coast Joint Venture in 2017. We compared proposal ranks assigned by the value model to ranks assigned by each JV’s management board. Ranks assigned by the value model differed from ranks assigned by the board for the Upper Mississippi / Great Lakes Joint Venture, but not for the Gulf Coast Joint Venture. However, ranks from the value model could change markedly with different objective weights and value functions. The weighted linear value model was beneficial for ranking NAWCA proposals because it allows JVs to treat the ranking as a multiple objective problem and tailor the ranking to their specific regional concerns. We believe a structured decision making approach could be adapted by JV staff to facilitate a systematic and transparent process for proposal ranking by their management boards.</p>","language":"English","publisher":"U. S. 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,{"id":70231934,"text":"70231934 - 2022 - Can coastal habitats rise to the challenge? Resilience of estuarine habitats, carbon accumulation, and economic value to sea-level rise in a Puget Sound estuary","interactions":[],"lastModifiedDate":"2022-10-31T14:24:53.776047","indexId":"70231934","displayToPublicDate":"2022-06-03T08:09:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Can coastal habitats rise to the challenge? Resilience of estuarine habitats, carbon accumulation, and economic value to sea-level rise in a Puget Sound estuary","docAbstract":"<p>Sea-level rise (SLR) and obstructions to sediment delivery pose challenges to the persistence of estuarine habitats and the ecosystem services they provide. Restoration actions and sediment management strategies may help mitigate such challenges by encouraging the vertical accretion of sediment in and horizontal migration of tidal forests and marshes. We used a process-based soil accretion model (Coastal Wetland Equilibrium Model) combined with a habitat classification model (MOSAICS) to estimate the effects of SLR, suspended sediment, and inland habitat migration on estuarine habitats, soil carbon accumulation, and economic value of climate change mitigation of carbon accumulation (social cost of carbon dioxide) in a macrotidal estuary in the northwest USA over 100 years (2011 to 2110). Under present-day sediment levels, we projected that after 100 years, most high salt marsh would remain with &lt; 100 cm SLR, but substantial area converted to transitional (low) salt marsh and mudflat with ≥ 100 cm SLR. Increasing sediment availability increased the projected resilience of transitional salt marsh to SLR but did not prevent declines in high marsh area. Projected total carbon accumulation plateaued or declined with ≥ 100 cm SLR, yet the economic value of carbon accumulation continued to rise over time, suggesting that the value of this ecosystem service was resilient to SLR. Doubling or tripling sediment availability increased projected carbon accumulation up to 7.69 and 14.2 kg m<sup>−2</sup><span>&nbsp;</span>and increased total economic value up to $373,000 and $710,000, respectively. Allowing marsh migration supported conversion of upland to freshwater marsh, with slight increases in carbon accumulation. These results inform climate adaptation planning for wetland managers seeking to understand the resilience of estuarine habitats and ecosystem services to SLR under multiple management strategies.</p>","language":"English","publisher":"Springer","doi":"10.1007/s12237-022-01087-5","usgsCitation":"Moritsch, M.M., Byrd, K.B., Davis, M.J., Good, A.J., Drexler, J.Z., Morris, J.T., Woo, I., Windham-Myers, L., Grossman, E.E., Nakai, G., Poppe, K.L., and Rybczyk, J.M., 2022, Can coastal habitats rise to the challenge? Resilience of estuarine habitats, carbon accumulation, and economic value to sea-level rise in a Puget Sound estuary: Estuaries and Coasts, v. 45, p. 2293-2309, https://doi.org/10.1007/s12237-022-01087-5.","productDescription":"17 p.","startPage":"2293","endPage":"2309","ipdsId":"IP-134918","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447559,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-022-01087-5","text":"Publisher Index Page"},{"id":435828,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ONIUCK","text":"USGS data release","linkHelpText":"Projected future habitat, elevation change, and carbon accumulation of coastal wetlands in the Nisqually River Delta, Washington"},{"id":401676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.72981643676756,\n              47.05269803965603\n            ],\n            [\n              -122.67969131469727,\n              47.05515408550348\n            ],\n            [\n              -122.68999099731445,\n              47.06521099790879\n            ],\n            [\n              -122.6905059814453,\n              47.07526601334617\n            ],\n            [\n              -122.67539978027342,\n              47.089760603235646\n            ],\n            [\n              -122.67127990722655,\n              47.1067049202684\n            ],\n            [\n              -122.7366828918457,\n              47.10647124222482\n            ],\n            [\n              -122.7385711669922,\n              47.10647124222482\n            ],\n            [\n              -122.7389144897461,\n              47.07175866526645\n            ],\n            [\n              -122.7344512939453,\n              47.06684799015446\n            ],\n            [\n              -122.72981643676756,\n              47.05269803965603\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Moritsch, Monica Mei Jeen 0000-0002-3890-1264","orcid":"https://orcid.org/0000-0002-3890-1264","contributorId":225210,"corporation":false,"usgs":true,"family":"Moritsch","given":"Monica","email":"","middleInitial":"Mei Jeen","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":844144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":844145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":844146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Good, Anthony J. 0000-0002-0276-136X","orcid":"https://orcid.org/0000-0002-0276-136X","contributorId":203553,"corporation":false,"usgs":true,"family":"Good","given":"Anthony","email":"","middleInitial":"J.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":844147,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844148,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morris, James T.","contributorId":29118,"corporation":false,"usgs":true,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":844149,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":844150,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844151,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":844152,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nakai, Glynnis","contributorId":172123,"corporation":false,"usgs":false,"family":"Nakai","given":"Glynnis","email":"","affiliations":[{"id":26986,"text":"US Fish and Wildlife Service, Nisqually Nat'l Wildlife Refuge, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":844153,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Poppe, Katrina L. 0000-0003-2830-4578","orcid":"https://orcid.org/0000-0003-2830-4578","contributorId":292257,"corporation":false,"usgs":false,"family":"Poppe","given":"Katrina","email":"","middleInitial":"L.","affiliations":[{"id":62851,"text":"Western Washington University, Department of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":844154,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rybczyk, John M.","contributorId":89794,"corporation":false,"usgs":true,"family":"Rybczyk","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844155,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70231846,"text":"sir20215113 - 2022 - Long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i","interactions":[],"lastModifiedDate":"2026-04-02T19:46:57.445783","indexId":"sir20215113","displayToPublicDate":"2022-06-03T08:07:20","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5113","displayTitle":"Long-Term Groundwater Availability in the Waihe‘e, ‘Īao, and Waikapū Aquifer Systems, Maui, Hawai‘i","title":"Long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i","docAbstract":"<p>Groundwater levels have declined since the 1940s in the Wailuku area of central Maui, Hawai‘i, on the eastern flank of West Maui volcano, mainly in response to increased groundwater withdrawals. Available data since the 1980s also indicate a thinning of the freshwater lens and an increase in chloride concentrations of pumped water from production wells. These trends, combined with projected increases in demand for groundwater in central Maui, have led to concerns over groundwater availability and have highlighted a need to improve general understanding of the hydrologic effects of proposed groundwater withdrawals in the Waihe‘e, ‘Īao, and Waikapū areas of central Maui.</p><p>A numerical groundwater model was constructed to simulate the flow and salinity of groundwater in central Maui. The model simulates the effects of changes in groundwater withdrawals and recharge on water levels, freshwater-lens thicknesses, and chloride concentrations of pumped water from production wells. The model incorporates updated water-budget estimates of groundwater recharge from infiltration and direct recharge, seepage in stream channels, and inflow from inland areas. Mean annual groundwater recharge from infiltration and direct recharge was estimated using a daily water-budget model and the most current data, including the distributions of monthly rainfall and potential evapotranspiration, for the study area for nine historical periods from 1926 through 2012: 1926–69, 1970–79, 1980–84, 1985–89, 1990–94, 1995–99, 2000–04, 2005–09, and 2010–12. The water-budget model also estimated groundwater recharge based on one hypothetical scenario that used 1980–2010 rainfall and 2017 land cover. For the nine historical periods, estimated recharge from infiltration and direct recharge within the area of the groundwater model ranged from 30.4 million gallons per day (Mgal/d) during 2010–12 to 98.7 Mgal/d during 1926–69. Variability in recharge during these periods mainly reflects changes in rainfall and irrigation over time. Between 2010 and 2014, streamflow restoration in previously diverted streams resulted in an estimated increase in recharge from seepage in stream channels of about 12.5 Mgal/d. Average groundwater inflow of about 39.6 Mgal/d from inland, dike-intruded areas to the main area of interest was estimated from an existing island-wide numerical groundwater-flow model, which is at a larger scale and incorporates a greater number of simplifying assumptions.</p><p>The numerical groundwater model developed for this study was calibrated to 1926–2012 transient water levels, vertical salinity profiles, and chloride concentrations of water pumped by production wells in the study area. The model was then used to evaluate one future recharge and six selected withdrawal scenarios, developed in consultation with the Maui Department of Water Supply, in terms of long-term changes in water level and 50-percent ocean-water salinity surface. The groundwater model was also used to simulate the future salinity of water withdrawn by existing and proposed production wells. The simulations were run to steady-state conditions, providing an estimate of the long-term effects of changes in withdrawal and recharge on the groundwater resource. Results of the simulated future withdrawal scenarios indicate that, relative to 2017–18 rates, the scenarios’ long-term effect of increased withdrawals ultimately leads to lower water levels and a higher 50-percent ocean-water salinity surface indicating a thinning of the freshwater lens. Results also indicate that the increased withdrawals produce some groundwater with chloride concentration below 250 milligrams per liter and some groundwater with higher chloride concentration. The amount of drawdown near production wells and the quality of water withdrawn from production wells is dependent on the rate and spatial distribution of the withdrawals.</p><p>The model was also used to evaluate how groundwater availability may be affected for a drier recharge scenario based on a published study of future climate. Model results of the future recharge scenario indicate that the rate of groundwater recharge is a controlling factor for (1) water levels, (2) the 50-percent ocean-water salinity surface, and (3) the quality of water withdrawn from production wells in the Wailuku area. Coupled with reduced groundwater recharge (with all other factors remaining equal), the modeled future withdrawals in the scenario would tend to cause lower water levels, a higher 50-percent ocean-water salinity surface, and increased salinity of water withdrawn from production wells.</p><p>The three-dimensional numerical groundwater model developed for this study utilizes the latest available hydrologic and geologic information and is a useful tool for understanding the long-term hydrologic effects of additional groundwater withdrawals in central Maui. The model has several limitations, including its non-uniqueness and inability to account for local-scale heterogeneities. Short-term effects of changes in recharge and withdrawals—and optimization of pumping rates to meet increased demand for water with acceptable salinity—are possible conditions for future simulation analyses.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215113","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply","usgsCitation":"Rotzoll, K., Oki, D.S., Johnson, A.G., and Souza, W.R., 2022, Long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2021–5113, 80 p., https://doi.org/10.3133/sir20215113.","productDescription":"Report: ix, 80 p., Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-119307","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":502120,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113156.htm","linkFileType":{"id":5,"text":"html"}},{"id":401475,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P959B45F","text":"SUTRA model used to evaluate long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i","description":"Rotzoll, K., 2022, SUTRA model used to evaluate long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i: U.S. Geological Survey data release, https://doi.org/10.5066/P959B45F."},{"id":401474,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5113/sir20215113.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":401473,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5113/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.95916748046875,\n              20.720153096892663\n            ],\n            [\n              -156.0003662109375,\n              20.792066100825636\n            ],\n            [\n              -156.26953125,\n              20.958874775031518\n            ],\n            [\n              -156.47003173828125,\n              20.912700155617568\n            ],\n            [\n              -156.60736083984375,\n              21.056307701901847\n            ],\n            [\n              -156.7254638671875,\n              20.93578924489374\n            ],\n            [\n              -156.65679931640625,\n              20.802336592979056\n            ],\n            [\n              -156.533203125,\n              20.756113874762082\n            ],\n            [\n              -156.4947509765625,\n              20.781794909576234\n            ],\n            [\n              -156.4617919921875,\n              20.750977144077833\n            ],\n            [\n              -156.4398193359375,\n              20.601936194281016\n            ],\n            [\n              -156.3629150390625,\n              20.56593890346526\n            ],\n            [\n              -156.2860107421875,\n              20.571081893508193\n            ],\n            [\n              -156.02783203124997,\n              20.6379249854131\n            ],\n            [\n              -155.9564208984375,\n              20.7098770198879\n            ],\n            [\n              -155.95916748046875,\n              20.720153096892663\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_hi@usgs.gov\" data-mce-href=\"mailto:dc_hi@usgs.gov\">Director</a>,<br><a href=\"https://www.usgs.gov/piwsc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/piwsc\">Pacific Islands Water Science Center</a><br><a href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov\">U.S. Geological Survey</a><br>Inouye Regional Center<br>1845 Wasp Blvd., B176<br>Honolulu, HI 96818</p>","tableOfContents":"<ul><li>Acknowledgements&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Purpose and Scope&nbsp;&nbsp;</li><li>Setting&nbsp;&nbsp;</li><li>Hydrogeologic Framework and Rock Properties&nbsp;&nbsp;</li><li>Groundwater-Flow System&nbsp;&nbsp;</li><li>Simulation of Groundwater Flow&nbsp;&nbsp;</li><li>Limitations&nbsp;&nbsp;</li><li>Summary&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendix 1. Estimation of Recharge with a Water Budget&nbsp;</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2022-06-03","noUsgsAuthors":false,"publicationDate":"2022-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rotzoll, Kolja 0000-0002-5910-888X kolja@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":3325,"corporation":false,"usgs":true,"family":"Rotzoll","given":"Kolja","email":"kolja@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":false,"id":843965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oki, Delwyn S. 0000-0002-6913-8804 dsoki@usgs.gov","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":1901,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn","email":"dsoki@usgs.gov","middleInitial":"S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Adam G. 0000-0003-2448-5746 ajohnson@usgs.gov","orcid":"https://orcid.org/0000-0003-2448-5746","contributorId":4752,"corporation":false,"usgs":true,"family":"Johnson","given":"Adam","email":"ajohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Souza, William R.","contributorId":90295,"corporation":false,"usgs":true,"family":"Souza","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":843968,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70231902,"text":"70231902 - 2022 - Quantifying the effects of tides, river flow, and barriers on movements of Chinook Salmon smolts at junctions in the Sacramento–San Joaquin River Delta using multistate models","interactions":[],"lastModifiedDate":"2022-12-15T14:52:42.877287","indexId":"70231902","displayToPublicDate":"2022-06-02T09:54:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the effects of tides, river flow, and barriers on movements of Chinook Salmon smolts at junctions in the Sacramento–San Joaquin River Delta using multistate models","docAbstract":"<p>Successful migration of Chinook Salmon (<i>Oncorhynchus tshawytscha</i>) smolts seaward in the Sacramento – San Joaquin River Delta (hereafter, Delta) requires navigating a network of numerous branching channels. Within the Delta, several key junctions route smolts either towards more direct paths to the ocean or towards the interior Delta, an area associated with decreased survival. Movements within these junctions that determine route choice can be influenced by numerous behavioral and environmental factors, including the complex interplay between tidal and riverine hydraulics. Here, we apply continuous time multistate Markov models to examine the influence of tidal and riverine hydraulics, behavioral factors, and management actions on smolt movements. These models incorporate more information from acoustic telemetry data compared with previous approaches to modeling smolt movements in the Delta. By decomposing modeled flows into tidal and net flow signals we elucidate how each component influences movements into and out of distributary channels. Increasing net flows generally increased movement rates, while flood tides decreased seaward movement rates. Similarly, ebb tides increased downstream movements as fish go with the flow. We found less support for diel movement behaviors compared to flow metrics. Additionally, we quantify the effects of a large management action, the placement of a physical barrier, which was effective at decreasing entrainment into the interior Delta. Together, these results help inform management of Chinook Salmon and increase our understanding of the major factors driving smolt movements within these key junctions.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10641-022-01273-1","usgsCitation":"Dodrill, M., Perry, R., Pope, A., and Wang, X., 2022, Quantifying the effects of tides, river flow, and barriers on movements of Chinook Salmon smolts at junctions in the Sacramento–San Joaquin River Delta using multistate models: Environmental Biology of Fishes, v. 105, p. 2065-2082, https://doi.org/10.1007/s10641-022-01273-1.","productDescription":"18 p.","startPage":"2065","endPage":"2082","ipdsId":"IP-133966","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San-Joaquin River Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.6513671875,\n              37.86618078529668\n            ],\n            [\n              -122.56347656249999,\n              37.779398571318765\n            ],\n            [\n              -122.55249023437501,\n              37.54022177661216\n            ],\n            [\n              -122.0745849609375,\n              37.21720611325497\n            ],\n            [\n              -121.541748046875,\n              37.020098201368114\n            ],\n            [\n              -120.9210205078125,\n              37.24782120155428\n            ],\n            [\n              -120.5804443359375,\n              37.4356124041315\n            ],\n            [\n              -120.62988281249999,\n              37.63163475580643\n            ],\n            [\n              -121.1846923828125,\n              37.896530447543\n            ],\n            [\n              -121.31103515625,\n              38.052416771864834\n            ],\n            [\n              -121.38244628906251,\n              38.268375880204744\n            ],\n            [\n              -121.42639160156249,\n              38.45789034424927\n            ],\n            [\n              -121.56921386718751,\n              38.70265930723801\n            ],\n            [\n              -122.01416015625,\n              38.31149091244452\n            ],\n            [\n              -122.61291503906249,\n              38.238180119798635\n            ],\n            [\n              -122.6513671875,\n              37.86618078529668\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"105","noUsgsAuthors":false,"publicationDate":"2022-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":844059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":844060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Xiaochun","contributorId":225264,"corporation":false,"usgs":false,"family":"Wang","given":"Xiaochun","email":"","affiliations":[{"id":41085,"text":"California Department of Water Resources, Sacramento, CA, 95819","active":true,"usgs":false}],"preferred":false,"id":844061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70231905,"text":"70231905 - 2022 - Dynamic sensitivity to resource availability influences population responses to mismatches in a shorebird","interactions":[],"lastModifiedDate":"2022-09-01T14:36:44.26282","indexId":"70231905","displayToPublicDate":"2022-06-02T09:46:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic sensitivity to resource availability influences population responses to mismatches in a shorebird","docAbstract":"<p>Climate change has caused shifts in seasonally recurring biological events leading to the temporal decoupling of consumer-resource pairs – i.e., phenological mismatching. Although mismatches often affect individual fitness, they do not invariably scale up to affect populations, making it difficult to assess the risk they pose. Individual variation may contribute to this inconsistency, with changes in resource availability and consumer needs leading mismatches to have different outcomes over time. Nevertheless, most models estimate a consumer’s match from a single timepoint, potentially obscuring when mismatches matter to populations. We analyzed how the effects of mismatches varied over time by studying precocial Hudsonian godwit (<i>Limosa haemastica)</i><span>&nbsp;</span>chicks and their invertebrate prey from 2009 to 2019. We developed individual and population level models to determine how age-specific variation affect the relationship between godwits and resource availability. We found that periods with abundant resources led to higher growth and survival of godwit chicks, but also that chick survival was increasingly related to the availability of larger prey as chicks aged. At the population level, estimates of mismatches using age-structured consumer demand explained more variation in annual godwit fledging rates than more commonly used alternatives. Our study suggests that modeling the effects of mismatches as the disrupted interaction between dynamic consumer needs and resource availability clarifies when mismatches matter to both individuals and populations.</p>","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3743","usgsCitation":"Wilde, L.R., Simmons, J.E., Swift, R.J., and Senner, N.R., 2022, Dynamic sensitivity to resource availability influences population responses to mismatches in a shorebird: Ecology, v. 103, no. 9, e3743, 14 p., https://doi.org/10.1002/ecy.3743.","productDescription":"e3743, 14 p.","ipdsId":"IP-125364","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":447562,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.3743","text":"External Repository"},{"id":401639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beluga River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -151.11968994140625,\n              61.18992667218288\n            ],\n            [\n              -150.91506958007812,\n              61.18992667218288\n            ],\n            [\n              -150.91506958007812,\n              61.264621333832146\n            ],\n            [\n              -151.11968994140625,\n              61.264621333832146\n            ],\n            [\n              -151.11968994140625,\n              61.18992667218288\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"103","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilde, Luke R.","contributorId":291481,"corporation":false,"usgs":false,"family":"Wilde","given":"Luke","email":"","middleInitial":"R.","affiliations":[{"id":62717,"text":"Dept. of Biological Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":844068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simmons, Josiah E.","contributorId":292206,"corporation":false,"usgs":false,"family":"Simmons","given":"Josiah","email":"","middleInitial":"E.","affiliations":[{"id":62841,"text":"Division of Biological Sciences, University of Montana","active":true,"usgs":false}],"preferred":false,"id":844069,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swift, Rose J. 0000-0001-7044-6196","orcid":"https://orcid.org/0000-0001-7044-6196","contributorId":212082,"corporation":false,"usgs":true,"family":"Swift","given":"Rose","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senner, Nathan R.","contributorId":140465,"corporation":false,"usgs":false,"family":"Senner","given":"Nathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":844071,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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