Stoneflies are a globally threatened aquatic insect order. In Arkansas, a diverse group of winter stonefly (Capniidae: Allocapnia) have not been surveyed since the 1980s, likely because species-level identification requires the rarely-collected adult form. Allocapnia mohri, a regional endemic, was previously commonly found in mountainous, intermittent streams from the Ouachita Mountains ecoregion north to the Ozark Highlands, but no species distributional models including land use or climate variables exist to our knowledge. We collected adults from 71 stream reaches from the historic Arkansas range from November to April 2020 and 2021. We modeled distributions using random forest (RF) models populated with landscape, climate, and both data to determine which were most predictive of species presence. Correlations between landscape or climate variables and presence were examined using multiple logistic regression. The landscape RF models performed better than the climate or landscape + climate RF models. A. mohri presence sites tended to have a greater elevation, a lower mean July temperature, and a greater percentage of very slow infiltration soils in the watershed, compared to absence sites. A. mohri was absent at the Ouachita Mountains sites and may be experiencing a range contraction or migration northward.
","language":"English","publisher":"MDPI","doi":"10.3390/hydrobiology2010013","usgsCitation":"Annaratone, B., Larson, C., Prater, C., Dowling, A., Magoulick, D.D., and Evans-White, M.A., 2023, Predicting habitat and distribution of an interior highlands regional endemic winter stonefly (Allocapnia mohri) in Arkansas using random forest models: Hydrobiology, v. 2, no. 1, p. 196-211, https://doi.org/10.3390/hydrobiology2010013.","productDescription":"16 p.","startPage":"196","endPage":"211","ipdsId":"IP-148164","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444581,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrobiology2010013","text":"Publisher Index Page"},{"id":433011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.01694463644638,\n 36.49973760721201\n ],\n [\n -94.64880267254001,\n 36.47406350173745\n ],\n [\n -94.42836217709565,\n 35.38954127077116\n ],\n [\n -94.47263609475836,\n 33.690237372303145\n ],\n [\n -93.92329526083718,\n 33.50875350986783\n ],\n [\n -93.15882305498532,\n 33.95704519399207\n ],\n [\n -92.44070215460535,\n 34.644196725743356\n ],\n [\n -91.1183522293626,\n 35.76377161713451\n ],\n [\n -91.01694463644638,\n 36.49973760721201\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Annaratone, Brianna","contributorId":341024,"corporation":false,"usgs":false,"family":"Annaratone","given":"Brianna","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Camryn","contributorId":341025,"corporation":false,"usgs":false,"family":"Larson","given":"Camryn","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prater, Clay","contributorId":341026,"corporation":false,"usgs":false,"family":"Prater","given":"Clay","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dowling, Ashley","contributorId":341027,"corporation":false,"usgs":false,"family":"Dowling","given":"Ashley","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":907822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evans-White, Michelle A.","contributorId":341028,"corporation":false,"usgs":false,"family":"Evans-White","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907823,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266378,"text":"70266378 - 2023 - Evaluating the institutional and ecological effects of invasive species prevention policy: A case study from the U.S. Fish and Wildlife Service","interactions":[],"lastModifiedDate":"2025-05-06T13:53:23.902518","indexId":"70266378","displayToPublicDate":"2023-02-06T08:50:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the institutional and ecological effects of invasive species prevention policy: A case study from the U.S. Fish and Wildlife Service","docAbstract":"Wildlife and natural resource institutions play key roles in invasive species monitoring and management. Paradoxically, the extensive fieldwork undertaken by these institutions and their partners may result in the inadvertent movement and spread of invasive species within and between sensitive ecosystems. In this work, we consider the potential effects of internal guidelines and policies designed to prevent the spread of invasive species by the field activities of management institutions and their partners. Such policies could be high-leverage tools for invasive species prevention, however, as large, complex organizations seek to implement policies to limit the spread of invasive species, it may be challenging to accommodate the wide diversity of potential invasion pathways and mitigation efforts facilitated via the programs, activities, and ecosystems they manage. Prevention policies may also be met with resistance due to the costs of implementation unless concrete benefits can be demonstrated. Assessing and communicating the effects of prevention policies could motivate improved implementation and adherence by institutional units and partners and could help inform adaptive policy changes. However, assessing the effectiveness of invasive species prevention presents a unique set of challenges, including incomplete data on invasive species distribution and pathways, that make it difficult to measure the effects of prevention efforts. In this work, we present a conceptual framework for evaluating institutional policies for invasive species prevention. We describe a flexible, multifaceted approach that considers policy implementation and adherence as well as ecological outcomes. We discuss potential application of this framework using a policy recently implemented by the Pacific Region of the U.S. Fish and Wildlife Service to prevent the introduction and spread of invasive species by service personnel and partners during field activities as a case study.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2023.14.2.06","usgsCitation":"Couch, C., Peterson, J., and Heimowitz, P., 2023, Evaluating the institutional and ecological effects of invasive species prevention policy: A case study from the U.S. Fish and Wildlife Service: Management of Biological Invasions, v. 14, no. 2, p. 269-288, https://doi.org/10.3391/mbi.2023.14.2.06.","productDescription":"20 p.","startPage":"269","endPage":"288","ipdsId":"IP-136898","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.2.06","text":"Publisher Index Page"},{"id":485442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Couch, Claire E.","contributorId":354510,"corporation":false,"usgs":false,"family":"Couch","given":"Claire E.","affiliations":[{"id":84634,"text":"Oregon State University Department of Fisheries","active":true,"usgs":false}],"preferred":false,"id":935782,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935783,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heimowitz, Paul","contributorId":354511,"corporation":false,"usgs":false,"family":"Heimowitz","given":"Paul","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":935784,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241253,"text":"70241253 - 2023 - Ice and ocean constraints on early human migrations into North America along the Pacific Coast","interactions":[],"lastModifiedDate":"2023-03-16T13:29:11.876097","indexId":"70241253","displayToPublicDate":"2023-02-06T08:21:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Ice and ocean constraints on early human migrations into North America along the Pacific Coast","docAbstract":"Founding populations of the first Americans likely occupied parts of Beringia during the Last Glacial Maximum (LGM). The timing, pathways, and modes of their southward transit remain unknown, but blockage of the interior route by North American ice sheets between ~26 and 14 cal kyr BP (ka) favors a coastal route during this period. Using models and paleoceanographic data from the North Pacific, we identify climatically favorable intervals when humans could have plausibly traversed the Cordilleran coastal corridor during the terminal Pleistocene. Model simulations suggest that northward coastal currents strengthened during the LGM and at times of enhanced freshwater input, making southward transit by boat more difficult. Repeated Cordilleran glacial-calving events would have further challenged coastal transit on land and at sea. Following these events, ice-free coastal areas opened and seasonal sea ice was present along the Alaskan margin until at least 15 ka. Given evidence for humans south of the ice sheets by 16 ka and possibly earlier, we posit that early people may have taken advantage of winter sea ice that connected islands and coastal refugia. Marine ice-edge habitats offer a rich food supply and traversing coastal sea ice could have mitigated the difficulty of traveling southward in watercraft or on land over glaciers. We identify 24.5 to 22 ka and 16.4 to 14.8 ka as environmentally favorable time periods for coastal migration, when climate conditions provided both winter sea ice and ice-free summer conditions that facilitated year-round marine resource diversity and multiple modes of mobility along the North Pacific coast.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2208738120","usgsCitation":"Praetorius, S.K., Alder, J.R., Condron, A., Mix, A., Walczak, M., Caissie, B.E., and Erlandson, J., 2023, Ice and ocean constraints on early human migrations into North America along the Pacific Coast: Proceedings of the National Academy of Sciences, v. 120, no. 7, e2208738120, 11 p., https://doi.org/10.1073/pnas.2208738120.","productDescription":"e2208738120, 11 p.","ipdsId":"IP-133916","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":444585,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://archimer.ifremer.fr/doc/00820/93170/","text":"Publisher Index Page"},{"id":435467,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95V8DP2","text":"USGS data release","linkHelpText":"Data release for Ice and ocean constraints on early human migrations into North America along the Pacific coast"},{"id":414279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Pacific Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.72062762561171,\n 38.37875574725757\n ],\n [\n -119.99459846253501,\n 39.301388384686106\n ],\n [\n -122.4899431341521,\n 50.08880882681328\n ],\n [\n -133.2466928684764,\n 60.77522068257534\n ],\n [\n -146.986168078397,\n 63.47516560131629\n ],\n [\n -159.91403683656446,\n 70.55186684208343\n ],\n [\n -169.5393000094379,\n 69.74709289484804\n ],\n [\n -170.83530973680112,\n 63.391879804034915\n ],\n [\n -179.9,\n 50.520343621759565\n ],\n [\n -156.35796682332455,\n 54.28410261306894\n ],\n [\n -142.96073969458683,\n 58.298844624993336\n ],\n [\n -129.39963004812796,\n 49.10107755766387\n ],\n [\n -126.51878468736493,\n 43.42260588965587\n ],\n [\n -123.72062762561171,\n 38.37875574725757\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Praetorius, Summer K. 0000-0003-2683-3652","orcid":"https://orcid.org/0000-0003-2683-3652","contributorId":206966,"corporation":false,"usgs":true,"family":"Praetorius","given":"Summer","email":"","middleInitial":"K.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":866663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":866664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Condron, Alan","contributorId":303134,"corporation":false,"usgs":false,"family":"Condron","given":"Alan","affiliations":[{"id":34495,"text":"Woods Hole Oceanographic Inst.","active":true,"usgs":false}],"preferred":false,"id":866665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mix, Alan","contributorId":303135,"corporation":false,"usgs":false,"family":"Mix","given":"Alan","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":866666,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walczak, Maureen","contributorId":303136,"corporation":false,"usgs":false,"family":"Walczak","given":"Maureen","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":866667,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caissie, Beth Elaine 0000-0001-9587-1842","orcid":"https://orcid.org/0000-0001-9587-1842","contributorId":292500,"corporation":false,"usgs":true,"family":"Caissie","given":"Beth","email":"","middleInitial":"Elaine","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":866668,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erlandson, Jon","contributorId":303137,"corporation":false,"usgs":false,"family":"Erlandson","given":"Jon","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":866669,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70240729,"text":"70240729 - 2023 - A restructured Bayesian approach to estimate the abundance of a rare and invasive fish","interactions":[],"lastModifiedDate":"2023-05-25T15:45:17.03652","indexId":"70240729","displayToPublicDate":"2023-02-04T07:20:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"A restructured Bayesian approach to estimate the abundance of a rare and invasive fish","docAbstract":"Quantifying invasive species abundance informs management and control strategies. However, estimating abundance can be challenging, particularly when dealing with rare species early in the invasion process. Data generated from control strategies, such as removing invasive species, are usually not suited to conventional statistical modelling approaches. Hence, we developed a Bayesian model using data generated by a grass carp (Ctenopharyngodon idella) control program in the Sandusky River, Ohio (USA) for estimating the abundance of rare, invasive species. The model is a restructured N-mixture model modified to incorporate the data generating process (i.e., setting a trammel net to isolate a sampling area followed by boat-mounted electrofishing). Allowing the estimation of grass carp abundance from the species removal data, which had very few detections relative to the sampling effort. Our results indicated that the average number of grass carp present in the river at any one time did not change substantially from 2018 to 2020. The highest abundance estimates were in the lower and upper-middle segments of the river, suggesting possible recolonization from Lake Erie, and possibly other tributaries. Ultimately, the ability to use species-control data to estimate abundance provides important information for management, particularly for invasive ‘sleeper’ species in freshwater ecosystems.
The Boston Harbor Islands National Recreation Area (BOHA) is at high risk to the impacts of sea-level rise (SLR) and erosion from coastal storms. In June 2021, the National Trust for Historic Preservation listed the islands as one of America’s 11 Most Endangered Historic Places due to climate change. BOHA partners have been working to find climate adaptive solutions to protect and sustain critical ecological and cultural resources on the islands. A range of coastal adaptation efforts are currently under consideration including increased shoreline armoring and nature-based adaptation solutions. Any action taken in the coastal zone will require an assessment of environmental and ecological communities that could potentially be impacted by disturbance caused by restoration or adaptation projects. The primary goals of the initial phase of this project were to: 1) synthesize occurrence and distribution records of biodiversity living in and using mixed coarse substrate habitats of the intertidal zone of the Boston Harbor Islands; and 2) identify and compile potential methods to develop a standard and repeatable monitoring protocol to track changes (natural or anthropogenic) in intertidal biodiversity over time and across locations; and 3) conduct preliminary site scoping of target islands to identify locations for collecting new baseline data. A biodiversity inventory list was compiled, showing a total of 451 unique species were observed in BOHA between 1861-2020. Of this list, 55 species (invertebrates: 47; algae: 8) were considered nonindigenous species; a watchlist was also developed to help BOHA partners identify potential future invaders that could colonize and impact intertidal communities due to ongoing climate change or disturbance events. Native species observed in BOHA were evaluated using existing conservation frameworks and climate vulnerability information to prioritize species at greatest risk from anthropogenic and environmental stressors for future actions. Lastly, site scoping activities during 2021 identified three types of sites for future intertidal monitoring initiatives: (1) sites with relatively high biodiversity and foundational species, (2) erosional sites near cultural areas of importance to NPS, and (3) sites with generic (common across islands) biodiversity. Overall results are anticipated to help the NPS and BOHA partners identify a suite of species and sites for future monitoring given anticipated adaptation projects and ongoing changes due to SLR, coastal storms and other stressors.
","language":"English","publisher":"Northeast Climate Adaptation Science Center","usgsCitation":"Michelle Staudinger, Albert, M., Lockwood, L., Putnam, A., Taylor, J., and Endyke, S.C., 2023, Final report: A novel monitoring framework to assess intertidal biodiversity in mixed coarse substrate habitats across the Boston Harbor Islands: Final Report, 47 p.","productDescription":"47 p.","ipdsId":"IP-170131","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":493340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493339,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necasc.umass.edu/biblio/final-report-novel-monitoring-framework-assess-intertidal-biodiversity-mixed-coarse","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Massachusetts","otherGeospatial":"Boston Harbor Islands, Georges Island, Gallops Island, Long Island, Lovells Island, Peddocks Island, Rainsford Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.00767738335286,\n 42.28834449115362\n ],\n [\n -70.93663404369572,\n 42.28249762345229\n ],\n [\n -70.90828847299693,\n 42.31928823104346\n ],\n [\n -70.93110988668896,\n 42.34642791740188\n ],\n [\n -71.03579071906097,\n 42.31720948404873\n ],\n [\n -71.00767738335286,\n 42.28834449115362\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Michelle Staudinger","contributorId":358911,"corporation":false,"usgs":false,"family":"Michelle Staudinger","affiliations":[{"id":85708,"text":"School of Marine Sciences, Darling Marine Center, University of Maine","active":true,"usgs":false}],"preferred":false,"id":944458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albert, Marc","contributorId":335163,"corporation":false,"usgs":false,"family":"Albert","given":"Marc","email":"","affiliations":[],"preferred":false,"id":944459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockwood, Lucy A. D.","contributorId":356958,"corporation":false,"usgs":false,"family":"Lockwood","given":"Lucy A. D.","affiliations":[{"id":63571,"text":"University of Massachusetts Boston","active":true,"usgs":false}],"preferred":false,"id":944461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Putnam, Aly B. 0000-0003-3853-1416","orcid":"https://orcid.org/0000-0003-3853-1416","contributorId":352260,"corporation":false,"usgs":false,"family":"Putnam","given":"Aly B.","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":944462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Justin","contributorId":336859,"corporation":false,"usgs":false,"family":"Taylor","given":"Justin","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":944463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Endyke, Sarah C.","contributorId":335365,"corporation":false,"usgs":false,"family":"Endyke","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":944639,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240928,"text":"70240928 - 2023 - A Bayesian multi-stage modelling framework to evaluate impacts of energy development on wildlife populations: An application to Greater Sage-Grouse (Centrocercus urophasianus)","interactions":[],"lastModifiedDate":"2023-03-01T12:44:09.159474","indexId":"70240928","displayToPublicDate":"2023-02-03T06:41:47","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7479,"text":"MethodsX","active":true,"publicationSubtype":{"id":10}},"title":"A Bayesian multi-stage modelling framework to evaluate impacts of energy development on wildlife populations: An application to Greater Sage-Grouse (Centrocercus urophasianus)","docAbstract":"Increased demand for domestic production of renewable energy has led to expansion of energy infrastructure across western North America. Much of the western U.S. comprises remote landscapes that are home to a variety of vegetation communities and wildlife species, including the imperiled sagebrush ecosystem and indicator species such as greater sage-grouse (Centrocercus urophasianus). Geothermal sources in particular have potential for continued development across the western U.S. but impacts to greater sage-grouse and other species are unknown. To address this information gap, we describe a novel two-pronged methodology that analyzes impacts of geothermal energy production on pattern and process of greater sage-grouse populations using (a) before-after control-impact (BACI) measures of population growth and lek absence rates and (b) concurrent-to-operation evaluations of demographic rates. Growth and absence rate analyses utilized 14 years of lek survey data collected prior (2005–2011) and concurrent (2012–2018) to geothermal operations at two sites in Nevada, USA. Demographic analyses utilized relocation data, restricted inference to concurrent years, and incorporated 17 additional control sites. Demographic results were applied to >100 potential geothermal sites distributed across the study region to generate spatially explicit predictions of unrealized population-level impacts.
The 11 October 1918 devastating tsunami in northwest Puerto Rico had been used as an example for earthquake‐induced landslide tsunami hazard. Three pieces of evidence pointed to a landslide as the origin of the tsunami: the discovery of a large submarine landslide scar from bathymetry data collected by shipboard high‐resolution multibeam sonar, reported breaks of submarine cable within the scar, and the fit of tsunami models to flooding observations. Newly processed seafloor imagery collected by remotely operated vehicle (ROV) show, however, pervasive Fe–Mn crust (patina) on the landslide walls and floor, indicating that the landslide scar is at least several hundred years old.
Water samples were collected from July through December 2016 from 9 production wells and 13 domestic wells in the Mohawk River Basin, and from 17 production wells and 17 domestic wells in the western New York River Basins. The samples were collected and processed by using standard U.S. Geological Survey methods and were analyzed for 320 physicochemical properties and constituents, including dissolved gases, major ions, nutrients, trace elements, pesticides, volatile organic compounds, radionuclides, and indicator bacteria, to characterize groundwater quality in the basins. Analytical results are provided in the companion U.S. Geological Survey data release titled “Groundwater Quality Data From the Mohawk and Western New York River Basins, New York, 2016.”
The Mohawk River Basin study area covers 3,500 square miles in New York. Of the 22 wells sampled in the Mohawk River Basin, 8 are completed in sand and gravel, and 14 are completed in bedrock aquifers. Most constituents in the samples from the Mohawk River Basin were present in concentrations below the maximum contaminant levels used in public supply drinking-water regulations by the New York State Department of Health and the U.S. Environmental Protection Agency. Values for some of the properties and concentrations of some constituents—pH, color, iron, manganese, aluminum, sodium, chloride, dissolved solids, radon-222, and heterotrophic plate count—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards.
The western New York River Basins study area covers 5,340 square miles in western New York and includes parts of the Lake Erie and Niagara River Basins, the western Lake Ontario Basin (between the Niagara River and Genesee River Basins), and the Allegheny River Basin. Of the 34 wells sampled in the western New York River Basins, 16 are completed in sand and gravel, and 18 are completed in bedrock aquifers. Most constituents in the samples from the western New York River Basins were present in concentrations below the maximum contaminant levels used in public supply drinking-water regulations by the New York State Department of Health and the U.S. Environmental Protection Agency. Values for some of the properties and concentrations of some constituents—color, chloride, sodium, dissolved solids, iron, manganese, aluminum, arsenic, barium, radon-222, methane, total coliform bacteria, fecal coliform bacteria, and Escherichia coli bacteria—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221021","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Gaige, D.L., Scott, T.-M., Reddy, J.E., and Keefe, M.R., 2023, Groundwater quality in the Mohawk and western New York River Basins, New York, 2016: U.S. Geological Survey Open-File Report 2022–1021, 38 p., https://doi.org/10.3133/ofr20221021.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-115618","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":412503,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YNH96T","text":"USGS data release","linkHelpText":"Groundwater quality data from the Mohawk and western New York River Basins, New York, 2016"},{"id":412502,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1021/images/"},{"id":412500,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1021"},{"id":412499,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1021/ofr20221021.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1021"},{"id":412498,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1021/coverthb.jpg"},{"id":412501,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1021/ofr20221021.XML"},{"id":499717,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114305.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Mohawk and New York River basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.84977657608984,\n 43.556764188166994\n ],\n [\n -75.84977657608984,\n 41.81434325258104\n ],\n [\n -73.94567088326258,\n 41.81434325258104\n ],\n [\n -73.94567088326258,\n 43.556764188166994\n ],\n [\n -75.84977657608984,\n 43.556764188166994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm3/cm3). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.
The extrusion rate of a lava dome is a critical parameter for monitoring silicic eruptions and forecasting their development. Satellite radar backscatter can provide unique information about dome growth during a volcanic eruption when other datasets (e.g., optical, thermal, ground-based measurements, etc.) may be limited. Here, we present an approach for estimating volcanic topography from individual backscatter images. Using data from multiple SAR sensors we apply the method to the dome growth during the 2021 eruption at La Soufrière, St. Vincent. We measure an average extrusion rate of 1.8 m3s−1 between December 2020 and March 2021 before an acceleration in extrusion rate to 17.5 m3s−1 in the 2 days prior to the explosive eruption on 9 April 2021. We estimate a final dome volume of 19.4 million m3, extrapolated from the SAR sensors, with approximately 15% of the total extruded volume emplaced in the last 2 days. A possible explanation for the acceleration in extrusion rate could be the combined emptying of a conduit and reservoir of older material before the ascent of gas-rich magma in April 2021.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2022.117980","usgsCitation":"Dualeh, E., Ebmeier, S., Wright, T.J., Poland, M., Grandin, R., Stinton, A., Camejo-Harry, M., Esse, B., and Burton, M., 2023, Rapid pre-explosion increase in dome extrusion rate at La Soufrière, St. Vincent quantified from synthetic aperture radar backscatter: Earth and Planetary Science Letters, v. 603, 117980, 11 p., https://doi.org/10.1016/j.epsl.2022.117980.","productDescription":"117980, 11 p.","ipdsId":"IP-145595","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444630,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2022.117980","text":"Publisher Index Page"},{"id":418221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saint Vincent and the Grenadines","otherGeospatial":"La Soufrière, Saint Vincent","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -61.18433460946167,\n 13.317636486394136\n ],\n [\n -61.1758284849421,\n 13.31530183007591\n ],\n [\n -61.16590467300145,\n 13.318909925803595\n ],\n [\n -61.1526002218297,\n 13.33217452112659\n ],\n [\n -61.15270927470786,\n 13.339814597776197\n ],\n [\n -61.152927380465016,\n 13.350531520386895\n ],\n [\n -61.16056108195765,\n 13.355518441018035\n ],\n [\n -61.171902581317624,\n 13.359868649329982\n ],\n [\n -61.18357123931291,\n 13.357322195446258\n ],\n [\n -61.19349505125268,\n 13.348197181716188\n ],\n [\n -61.197093796241845,\n 13.333447884004542\n ],\n [\n -61.1966575847284,\n 13.32877885399833\n ],\n [\n -61.19316789261735,\n 13.321562903052666\n ],\n [\n -61.18433460946167,\n 13.317636486394136\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"603","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dualeh, Edna","contributorId":310448,"corporation":false,"usgs":false,"family":"Dualeh","given":"Edna","email":"","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":875709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebmeier, Susanna","contributorId":174225,"corporation":false,"usgs":false,"family":"Ebmeier","given":"Susanna","affiliations":[{"id":7172,"text":"University of Bristol, U.K. and University of Oregon, Eugene","active":true,"usgs":false}],"preferred":false,"id":875710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Tim J.","contributorId":244552,"corporation":false,"usgs":false,"family":"Wright","given":"Tim","email":"","middleInitial":"J.","affiliations":[{"id":48936,"text":"COMET, School of Earth and Environment, University of Leeds, UK","active":true,"usgs":false}],"preferred":false,"id":875711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poland, Michael 0000-0001-5240-6123","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":49920,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":875712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grandin, Raphael","contributorId":310449,"corporation":false,"usgs":false,"family":"Grandin","given":"Raphael","email":"","affiliations":[{"id":47633,"text":"IPGP","active":true,"usgs":false}],"preferred":false,"id":875713,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stinton, Adam","contributorId":310451,"corporation":false,"usgs":false,"family":"Stinton","given":"Adam","email":"","affiliations":[{"id":67195,"text":"University of the West Indies","active":true,"usgs":false}],"preferred":false,"id":875714,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Camejo-Harry, M.","contributorId":310452,"corporation":false,"usgs":false,"family":"Camejo-Harry","given":"M.","email":"","affiliations":[{"id":67195,"text":"University of the West Indies","active":true,"usgs":false}],"preferred":false,"id":875715,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Esse, B.","contributorId":310460,"corporation":false,"usgs":false,"family":"Esse","given":"B.","email":"","affiliations":[],"preferred":false,"id":875731,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burton, Mike","contributorId":255650,"corporation":false,"usgs":false,"family":"Burton","given":"Mike","email":"","affiliations":[{"id":37573,"text":"University of Manchester, UK","active":true,"usgs":false}],"preferred":false,"id":875717,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239888,"text":"70239888 - 2023 - Characterizing historic streamflow to support drought planning in the upper Missouri River basin","interactions":[],"lastModifiedDate":"2026-03-18T16:13:50.675788","indexId":"70239888","displayToPublicDate":"2023-02-01T11:07:46","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"title":"Characterizing historic streamflow to support drought planning in the upper Missouri River basin","docAbstract":"This project combined tree-ring based paleo and modern climate and hydrologic research aimed at understanding the primary influences on drought risk and water reliability in basins critical for western U.S. water resources. New paleohydrologic datasets and analyses were developed and applied to contextualize future streamflow projections and address specific water management questions. These questions centered around optimizing future water management protocols for numerous objectives ranging from improving agricultural water allocation during drought while maintaining instream flows for aquatic ecosystem health, to the testing of operations across large river systems with complex infrastructure critical for downstream flood control, navigation, and hydropower generation. USGS scientists worked closely with the Bureau of Reclamation to estimate both past and future drought risk at key management locations throughout the Missouri basin, the Milk and St. Mary River system, and across the major managed river systems in the western United States. These efforts provided a roadmap for future water management strategies under changing climate and water supply conditions, which are detailed in Reclamation’s newly completed Missouri Headwaters Basin Study, the 2021 SECURE Water Act Report, and the forthcoming update of the St. Mary and Milk Rivers Basin Study. Among the major scientific findings to emerge was a new understanding of the long-term (1200-year) history of drought variability for the Missouri River, which highlighted the unusual severity of the early 2000s drought across the Rocky Mountain headwaters and adjacent high plains. By combining the extended drought record with extensive modern and paleoclimate records, we document how warming exacerbates severities of naturally occurring droughts, with recent decades defined by “hot” droughts and the 2000s (2001-2010) drought ranking as the most severe event in 1200 years. Increasingly severe drought events such as this strain already over-allocated water resources that multiple sectors of society depend heavily upon.","language":"English","publisher":"North Central Climate Adaptation Science Center","usgsCitation":"Pederson, G.T., 2023, Characterizing historic streamflow to support drought planning in the upper Missouri River basin: Final Report, 33 p.","productDescription":"33 p.","ipdsId":"IP-148061","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":501261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501260,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/63d1958bd34e06fef1500594","linkFileType":{"id":5,"text":"html"}}],"country":"Canada, United States","otherGeospatial":"upper Missouri River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.05224962042496,\n 50.08090362730903\n ],\n [\n -117.05224962042496,\n 37.030824614225864\n ],\n [\n -89.46041753932424,\n 37.030824614225864\n ],\n [\n -89.46041753932424,\n 50.08090362730903\n ],\n [\n -117.05224962042496,\n 50.08090362730903\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":862279,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70243205,"text":"70243205 - 2023 - Buzzards Bay salt marshes: Vulnerability and adaptation potential","interactions":[],"lastModifiedDate":"2023-05-09T15:51:21.1177","indexId":"70243205","displayToPublicDate":"2023-02-01T10:46:17","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Buzzards Bay salt marshes: Vulnerability and adaptation potential","docAbstract":"Salt marshes with lush grass meadows teeming with shorebirds are iconic features of the Buzzards Bay coast and provide opportunities for recreation, aesthetic enjoyment, as well as important environmental benefits. These productive coastal wetlands are important because they protect properties from storm surges, remove nutrients from the water and carbon from the atmosphere, and provide critical habitats for fish, shellfish, and birds.
Found where the land meets the sea, salt marshes are naturally dynamic features that change with rising seas, waves, ice, and storms. In the past, humans purposely altered salt marshes by filling them to create buildable land or digging drainage ditches. These major alterations harmed marsh structure and health. In recent decades, however, marshes are degrading because of more diffuse and complex pressures such as nutrient pollution, sea level rise, major storms, and crab overgrazing. As a result, at many places along the East Coast, marshes have crumbling banks and large areas where the plants have died, leaving behind mudflats. The Buzzards Bay Coalition and the Buzzards Bay National Estuary Program began field monitoring of salt marshes around Buzzards Bay in 2019 to document changes (map below shows sites). We partnered with the U.S. Geological Survey and the Woodwell Climate Research Center to use aerial tools to investigate how different characteristics of the long-term marsh sites and their watersheds affect the marsh’s current health and likely future. This report brings together the results of on the ground monitoring with data from aerial imagery to look at marsh status at 12 long-term monitoring sites based on existing stressors, current marsh conditions, and potential for adaptation.
","language":"English","publisher":"Buzzards Bay Coalition","usgsCitation":"Jakuba, R.W., Besterman, A., Hoffart, L., Costa, J.E., Ganju, N., and Deegan, L., 2023, Buzzards Bay salt marshes: Vulnerability and adaptation potential, 32 p.","productDescription":"32 p.","ipdsId":"IP-136385","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416865,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.savebuzzardsbay.org/about-us/publications/special-reports/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Massachusetts","otherGeospatial":"Buzzards Bay salt marshes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.94176987700817,\n 41.41975469591827\n ],\n [\n -70.85599792880063,\n 41.42970016011691\n ],\n [\n -70.81532257191841,\n 41.450912058997574\n ],\n [\n -70.76934173370435,\n 41.472117024369965\n ],\n [\n -70.67738005727534,\n 41.509209028100685\n ],\n [\n -70.64554716928006,\n 41.53502841303356\n ],\n [\n -70.60045057795479,\n 41.73329455348431\n ],\n [\n -70.60045103343215,\n 41.77287411335024\n ],\n [\n -70.64466337786867,\n 41.76891720855332\n ],\n [\n -70.73043532607704,\n 41.76034307750683\n ],\n [\n -70.76757369540417,\n 41.73395394949611\n ],\n [\n -70.78525863317893,\n 41.66463088626202\n ],\n [\n -70.83742919961446,\n 41.66330972174504\n ],\n [\n -70.92231690093321,\n 41.6672731339705\n ],\n [\n -70.97448746736875,\n 41.6051516478891\n ],\n [\n -71.02931077447062,\n 41.53238027247775\n ],\n [\n -71.03638474958088,\n 41.50987071949933\n ],\n [\n -70.94176987700817,\n 41.41975469591827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jakuba, R. W","contributorId":304709,"corporation":false,"usgs":false,"family":"Jakuba","given":"R.","email":"","middleInitial":"W","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Besterman, A.","contributorId":304711,"corporation":false,"usgs":false,"family":"Besterman","given":"A.","email":"","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoffart, L.","contributorId":304710,"corporation":false,"usgs":false,"family":"Hoffart","given":"L.","email":"","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Costa, J. E.","contributorId":304712,"corporation":false,"usgs":false,"family":"Costa","given":"J.","email":"","middleInitial":"E.","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871469,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":871470,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deegan, L.","contributorId":304976,"corporation":false,"usgs":false,"family":"Deegan","given":"L.","email":"","affiliations":[],"preferred":false,"id":872135,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70245098,"text":"70245098 - 2023 - Equilibrated gas and carbonate standard-derived dual (Δ47 and Δ48) clumped isotope values","interactions":[],"lastModifiedDate":"2023-06-15T13:49:07.668","indexId":"70245098","displayToPublicDate":"2023-02-01T08:43:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Equilibrated gas and carbonate standard-derived dual (Δ47 and Δ48) clumped isotope values","title":"Equilibrated gas and carbonate standard-derived dual (Δ47 and Δ48) clumped isotope values","docAbstract":"Carbonate clumped isotope geochemistry has primarily focused on mass spectrometric determination of m/z 47 CO2 for geothermometry, but theoretical calculations and recent experiments indicate paired analysis of the m/z 47 (13C18O16O) and m/z 48 (12C18O18O) isotopologues (referred to as Δ47 and Δ48) can be used to study non-equilibrium isotope fractionations and refine temperature estimates. We utilize 5,448 Δ47 and 3,400 Δ48 replicate measurements of carbonate samples and standards, and 183 Δ47 and 195 Δ48 replicate measurements of gas standards from 2015 to 2021 from a multi-year and multi-instrument data set to constrain Δ47 and Δ48 values for 27 samples and standards, including Devils Hole cave calcite, and study equilibrium Δ47-Δ48, Δ47-temperature, and Δ48-temperature relationships. We compare results to previously published findings and calculate equilibrium regressions based on data from multiple laboratories. We report acid digestion fractionation factors, Δ*63-47 and Δ*64-48, and account for their dependence on the initial clumped isotope values of the mineral.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010458","usgsCitation":"Lucarelli, J.K., Carroll, H.M., Ulrich, R.N., Elliott, B.M., Coplen, T.B., Eagle, R.A., and Tripati, A.K., 2023, Equilibrated gas and carbonate standard-derived dual (Δ47 and Δ48) clumped isotope values: Geochemistry, Geophysics, Geosystems, v. 24, no. 2, e2022GC010458, 21 p., https://doi.org/10.1029/2022GC010458.","productDescription":"e2022GC010458, 21 p.","ipdsId":"IP-133724","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":444637,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010458","text":"Publisher Index Page"},{"id":418127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Lucarelli, Jamie K 0000-0002-9104-2518","orcid":"https://orcid.org/0000-0002-9104-2518","contributorId":310346,"corporation":false,"usgs":false,"family":"Lucarelli","given":"Jamie","email":"","middleInitial":"K","affiliations":[{"id":67149,"text":"Univ California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":875450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carroll, Hannah M. 0000-0003-3343-3358","orcid":"https://orcid.org/0000-0003-3343-3358","contributorId":310347,"corporation":false,"usgs":false,"family":"Carroll","given":"Hannah","email":"","middleInitial":"M.","affiliations":[{"id":67150,"text":"Univ. California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":875451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ulrich, Robert N.","contributorId":310413,"corporation":false,"usgs":false,"family":"Ulrich","given":"Robert","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":875552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elliott, Ben M.","contributorId":310348,"corporation":false,"usgs":false,"family":"Elliott","given":"Ben","email":"","middleInitial":"M.","affiliations":[{"id":67151,"text":"Univ. of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":875452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":875453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eagle, Robert A.","contributorId":190122,"corporation":false,"usgs":false,"family":"Eagle","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":875454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tripati, Aradhna K.","contributorId":190120,"corporation":false,"usgs":false,"family":"Tripati","given":"Aradhna","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":875455,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243560,"text":"70243560 - 2023 - Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers","interactions":[],"lastModifiedDate":"2023-05-12T12:20:15.008523","indexId":"70243560","displayToPublicDate":"2023-02-01T07:09:03","publicationYear":"2023","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}},"displayTitle":"Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers","title":"Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers","docAbstract":"Aim
Climate change is affecting the distribution of species and subsequent biotic interactions, including hybridization potential. The imperiled Golden-winged Warbler (GWWA) competes and hybridizes with the Blue-winged Warbler (BWWA), which may threaten the persistence of GWWA due to introgression. We examined how climate change is likely to alter the breeding distributions and potential for hybridization between GWWA and BWWA.
Location
North America.
Methods
We used GWWA and BWWA occurrence data to model climatically suitable conditions under historical and future climate scenarios. Models were parameterized with 13 bioclimatic variables and 3 topographic variables. Using ensemble modeling, we estimated historical and modern distributions, as well as a projected distribution under six future climate scenarios. We quantified breeding distribution area, the position of and amount of overlap between GWWA and BWWA distributions under each climate scenario. We summarized the top explanatory variables in our model to predict environmental parameters of the distributions under future climate scenarios relative to historical climate.
Results
GWWA and BWWA distributions are projected to substantially change under future climate scenarios. GWWA are projected to undergo the greatest change; the area of climatically suitable breeding season conditions is expected to shift north to northwest; and range contraction is predicted in five out of six future climate scenarios. Climatically suitable conditions for BWWA decreased in four of the six future climate scenarios, while the distribution is projected to shift east. A reduction in overlapping distributions for GWWA and BWWA is projected under all six future climate scenarios.
Main Conclusions
Climate change is expected to substantially alter the area of climatically suitable conditions for GWWA and BWWA, with the southern portion of the current breeding ranges likely to become climatically unsuitable. However, interactions between BWWA and GWWA are expected to decline with the decrease in overlapping habitat, which may reduce the risk of genetic introgression.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13659","usgsCitation":"Hightower, J.N., Crawford, D.L., Thogmartin, W.E., Aldinger, K.R., Barker Swarthout, S., Buehler, D.A., Confer, J., Friis, C., Larkin, J., Lowe, J.D., Piorkowski, M., Rohrbaugh, R., Rosenberg, K.V., Smalling, C.G., Wood, P.B., Vallender, R., and Roth, A.M., 2023, Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers: Diversity and Distributions, v. 29, no. 2, p. 254-271, https://doi.org/10.1111/ddi.13659.","productDescription":"18 p.","startPage":"254","endPage":"271","ipdsId":"IP-138007","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":444642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13659","text":"Publisher Index Page"},{"id":435475,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AS9YAC","text":"USGS data release","linkHelpText":"Blue-winged and Golden-winged Warbler Breeding Season Occurrences in North America, 1932-2021"},{"id":416983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hightower, Jessica N.","contributorId":204645,"corporation":false,"usgs":false,"family":"Hightower","given":"Jessica","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":872370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crawford, Dolly L.","contributorId":299588,"corporation":false,"usgs":false,"family":"Crawford","given":"Dolly","email":"","middleInitial":"L.","affiliations":[{"id":64892,"text":"Pennsylvania Western University","active":true,"usgs":false}],"preferred":false,"id":872371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":872372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldinger, Kyle R.","contributorId":171892,"corporation":false,"usgs":false,"family":"Aldinger","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false},{"id":34541,"text":"West Virginia Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":872373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barker Swarthout, Sara","contributorId":176239,"corporation":false,"usgs":false,"family":"Barker Swarthout","given":"Sara","email":"","affiliations":[{"id":34544,"text":"Cornell Lab of Ornithology, Cornell University","active":true,"usgs":false}],"preferred":false,"id":872374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buehler, David A.","contributorId":169746,"corporation":false,"usgs":false,"family":"Buehler","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":872375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Confer, John","contributorId":305334,"corporation":false,"usgs":false,"family":"Confer","given":"John","email":"","affiliations":[{"id":18877,"text":"Ithaca College","active":true,"usgs":false}],"preferred":false,"id":872376,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Friis, Christian","contributorId":194605,"corporation":false,"usgs":false,"family":"Friis","given":"Christian","email":"","affiliations":[],"preferred":false,"id":872377,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Larkin, Jeff","contributorId":199993,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeff","email":"","affiliations":[],"preferred":false,"id":872378,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lowe, James D.","contributorId":305336,"corporation":false,"usgs":false,"family":"Lowe","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":872379,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Piorkowski, Martin","contributorId":305338,"corporation":false,"usgs":false,"family":"Piorkowski","given":"Martin","email":"","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":872380,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rohrbaugh, Ronald W.","contributorId":305340,"corporation":false,"usgs":false,"family":"Rohrbaugh","given":"Ronald W.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":872381,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rosenberg, Kenneth V.","contributorId":171463,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Kenneth","email":"","middleInitial":"V.","affiliations":[{"id":27615,"text":"Cornell Lab of Ornithology, Conservation Science Program","active":true,"usgs":false}],"preferred":false,"id":872382,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Smalling, Curtis G.","contributorId":191724,"corporation":false,"usgs":false,"family":"Smalling","given":"Curtis","email":"","middleInitial":"G.","affiliations":[{"id":33352,"text":"Audubon North Carolina","active":true,"usgs":false}],"preferred":false,"id":872383,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wood, Petra B.","contributorId":305342,"corporation":false,"usgs":false,"family":"Wood","given":"Petra","email":"","middleInitial":"B.","affiliations":[{"id":66214,"text":"West Virginia Cooperative Fish and Wildlife Research Unit,","active":true,"usgs":false}],"preferred":false,"id":872384,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Vallender, Rachel","contributorId":194966,"corporation":false,"usgs":false,"family":"Vallender","given":"Rachel","email":"","affiliations":[{"id":27312,"text":"Canadian Wildlife Service, Environment and Climate Change Canada, 6 Bruce Street, Mount","active":true,"usgs":false},{"id":34540,"text":"Canadian Museum of Nature","active":true,"usgs":false}],"preferred":false,"id":872385,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Roth, Amber M.","contributorId":191723,"corporation":false,"usgs":false,"family":"Roth","given":"Amber","email":"","middleInitial":"M.","affiliations":[{"id":25614,"text":"School of Forest Resources, University of Maine","active":true,"usgs":false},{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false},{"id":27866,"text":"University of Maine, Department of Wildlife, Fisheries, and Conservation Biology, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":872386,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70239954,"text":"70239954 - 2023 - Toward consistent change detection across irregular remote sensing time series observations","interactions":[],"lastModifiedDate":"2024-05-20T13:49:30.008054","indexId":"70239954","displayToPublicDate":"2023-02-01T07:04:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Toward consistent change detection across irregular remote sensing time series observations","docAbstract":"The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.
Droughts that are hotter, more frequent, and last longer; pest outbreaks that are more extensive and more common; and fires that are more frequent, more extensive, and perhaps more severe have raised concern that forests in the western United States may not return once disturbed by one or more of these agents. Numerous field-based studies have been undertaken to better understand forest response to these changing disturbance regimes. Meta-analyses of these studies provide broad guidelines on the biotic and abiotic factors that hinder forest recovery, but study-to-study differences in methods and objectives do not support estimation of the total extent of potentially impaired forest succession. In this research, we provide an estimate of the area of potentially impaired forest succession. The estimate was derived from modeling of an 18-year land cover and Normalized Difference Vegetation Index (NDVI) time series supported by an extensive ancillary dataset. We estimate an upper bound of approximately 3470 km2 of disturbed forest that may not return or reattain prior composition and structure. Based on the data used, fire appears to be the main disturbance agent of impaired forest succession, although climatic factors cannot be discounted. The numerous field studies routinely cite distal seed sources as a factor that hinders forest recovery, and we estimate that 20 % of the upper bound estimate has no forest cover within a 4.4-ha neighborhood. Our upper bound estimate is about 0.5 % of the 2001 mapped extent of western United States forests. The estimate is cognizant of measurement and modeling uncertainties (i.e., upper bound) and uncertainties related to successional rates and trajectories (i.e., potential).
Reported here are geological, geophysical, mineralogical, and geochemical data on a previously unknown trachyte-hosted rare earth element (REE)-Nb-Zr occurrence at Pennington Mountain in northern Maine, USA. This occurrence was newly discovered by a regional multiparameter, airborne radiometric survey that revealed anomalously high equivalent Th (eTh) and U (eU), confirmed by a detailed ground radiometric survey and by portable X-Ray fluorescence (pXRF) and whole-rock analyses of representative rock samples. The mineralized area occurs within an elongate trachyte body (~1.2 km2) that intrudes Ordovician volcanic rocks. Geologic constraints suggest that the trachyte is also Ordovician in age. The eastern lobe (~900 × ~400 m) of the trachyte is pervasively brecciated with a matrix containing seams, lenses, and veinlets composed mainly of potassium feldspar, albite, and fine-grained zircon and monazite. Barite is locally abundant. Minor minerals within the matrix include columbite, bastnäsite, euxenite, chlorite, pyrite, sphalerite, and magnetite. The pXRF analyses of 22 samples (App. Table A1) collected from the eastern lobe demonstrate that this entire part of the trachyte is highly mineralized. Whole-rock geochemical analyses for samples from the eastern lobe document high average contents of Zr (1.17 wt %), Nb (1,656 ppm), Ba (3,132 ppm), Y (1,140 ppm), Hf (324 ppm), Ta (122 ppm), Th (124 ppm), U (36.5 ppm), Zn (689 ppm), and Sn (106 ppm). Among light REE, the highest average concentrations are shown by La (763 ppm) and Ce (1,479 ppm). For heavy REE (HREE), Dy and Er are the most abundant on average (167 and 114 ppm, respectively). No HREE-rich minerals such as xenotime have been identified; the HREE may reside chiefly in monazite and bastnäsite, and within the fine-grained zircon. Very strong positive correlations (R2) of 0.92 to 0.98 exist between Th and Zr, Nb, Y, Ce, Yb, and Sn, indicating that the radiometric data for eTh are valid proxies for concentrations of these metals in the mineralized rocks.
","language":"English","publisher":"Society for Economic Geologists","doi":"10.5382/econgeo.4993","usgsCitation":"Wang, C., Slack, J.F., Shah, A.K., Yates, M.G., Lentz, D.R., Whittaker, A.T., and Marvinney, R.G., 2023, A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA: Economic Geology, v. 118, no. 1, p. 1-13, https://doi.org/10.5382/econgeo.4993.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-143441","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":444655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.4993","text":"Publisher Index Page"},{"id":418496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.73204287455003,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 45.7741074745758\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"118","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Chunzeng 0000-0002-8362-5174","orcid":"https://orcid.org/0000-0002-8362-5174","contributorId":295415,"corporation":false,"usgs":false,"family":"Wang","given":"Chunzeng","email":"","affiliations":[{"id":63866,"text":"University of Maine at Presque-Isle","active":true,"usgs":false}],"preferred":false,"id":876284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":876285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","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}],"preferred":true,"id":876286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yates, Martin G.","contributorId":313571,"corporation":false,"usgs":false,"family":"Yates","given":"Martin","email":"","middleInitial":"G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":876287,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lentz, David R.","contributorId":313573,"corporation":false,"usgs":false,"family":"Lentz","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":876288,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whittaker, Amber T.H.","contributorId":313574,"corporation":false,"usgs":false,"family":"Whittaker","given":"Amber","email":"","middleInitial":"T.H.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marvinney, Robert G.","contributorId":131130,"corporation":false,"usgs":false,"family":"Marvinney","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876290,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70251801,"text":"70251801 - 2023 - Timing of rhyolite intrusion and Carlin-type gold mineralization at the Cortez Hills Carlin-type deposit, Nevada, USA","interactions":[],"lastModifiedDate":"2024-02-29T12:41:18.941661","indexId":"70251801","displayToPublicDate":"2023-02-01T06:37:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Timing of rhyolite intrusion and Carlin-type gold mineralization at the Cortez Hills Carlin-type deposit, Nevada, USA","docAbstract":"Carlin-type gold deposits (CTDs) of Nevada are the largest producers of gold in the United States, a leader in world gold production. Although much has been resolved about the characteristics and origin of CTDs in Nevada, major questions remain, especially about (1) the role of magmatism, whether only a source of heat or also metals, (2) whether CTDs only formed in the Eocene, and (3) whether pre-Eocene metal concentrations contributed to Eocene deposits. These issues are exemplified by the CTDs of the Cortez region, the second largest concentration of these deposits after the Carlin trend.
Carlin-type deposits are notoriously difficult to date because they rarely generate dateable minerals. An age can be inferred from crosscutting relationships with dated dikes and other intrusions, which we have done for the giant Cortez Hills CTD. What we term “Cortez rhyolites” consist of two petrographic-geochemical groups of siliceous dikes: (1) quartz-sanidine-plagioclase-biotite-phyric, high-SiO2 rhyolites emplaced at 35.7 Ma based on numerous 40Ar/39Ar dates and (2) plagioclase-biotite-quartz ± hornblende-phyric, low-SiO2 rhyolites, which probably were emplaced at the same time but possibly as early as ~36.2 Ma. The dikes form a NNW-trending belt that is ~6 to 10 km wide × 40 km long and centered on the Cortez Hills deposit, and they require an underlying felsic pluton that fed the dikes. Whether these dikes pre- or postdated mineralization has been long debated. We show that dike emplacement spanned the time of mineralization. Many of both high- and low-SiO2 dikes are altered and mineralized, although none constitute ore. In altered-mineralized dikes, plagioclase has been replaced by kaolinite and calcite, and biotite by smectite, calcite, and marcasite. Sanidine is unaltered except in a few samples that are completely altered to quartz and kaolinite. Sulfides present in mineralized dikes are marcasite, pyrite, arsenopyrite, and As-Sb–bearing pyrite. Mineralized dikes are moderately enriched in characteristic Carlin-type elements (Au, Hg, Sb, Tl, As, and S), as well as elements found in some CTDs (Ag, Bi, Cu, Mo), and variably depleted in MgO, CaO, Na2O, K2O, MnO, Rb, Sr, and Ba. In contrast, some high-SiO2 rhyolites are unaltered and cut high-grade ore, which shows that they are post-ore. Both mineralized and post-ore dikes have indistinguishable sanidine 40Ar/39Ar dates. These characteristics, along with published interpretations that other giant CTDs formed in a few tens of thousands of years, indicate the Cortez Hills CTD formed at 35.7 Ma. All Cortez-area CTDs are in or adjacent to the Cortez rhyolite dike swarm, which suggests that the felsic pluton that fed the dikes was the hydrothermal heat source. Minor differences in alteration and geochemistry between dikes and typical Paleozoic sedimentary rock-hosted ore probably reflect low permeability and low reactivity of the predominantly quartzofeldspathic dikes.
Despite widespread pre-35.7 Ma mineralization in the Cortez region, including deposits near several CTDs, we find no evidence that older deposits or Paleozoic basinal rocks contributed metals to Cortez-area CTDs. Combining our new information about the age of Cortez Hills with published and our dates on other CTDs demonstrates that CTD formation coincided with the southwestern migration of magmatism across Nevada, supporting a genetic relationship to Eocene magmatism. CTDs are best developed where deep-seated (~6–8 km), probably granitic plutons, expressed in deposits only as dikes, established large, convective hydrothermal systems.
Resource managers have rarely accounted for evolutionary dynamics in the design or implementation of climate change adaptation strategies. We brought the research and management communities together to identify challenges and opportunities for applying evidence from evolutionary science to support on-the-ground actions intended to enhance species' evolutionary potential. We amalgamated input from natural-resource practitioners and interdisciplinary scientists to identify information needs, current knowledge that can fill those needs, and future avenues for research. Three focal areas that can guide engagement include: (1) recognizing when to act, (2) understanding the feasibility of assessing evolutionary potential, and (3) identifying best management practices. Although researchers commonly propose using molecular methods to estimate genetic diversity and gene flow as key indicators of evolutionary potential, we offer guidance on several additional attributes (and their proxies) that may also guide decision-making, particularly in the absence of genetic data. Finally, we outline existing decision-making frameworks that can help managers compare alternative strategies for supporting evolutionary potential, with the goal of increasing the effective use of evolutionary information, particularly for species of conservation concern. We caution, however, that arguing over nuance can generate confusion; instead, dedicating increased focus on a decision-relevant evidence base may better lend itself to climate adaptation actions.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12855","usgsCitation":"Thompson, L., Thurman, L., Cook, C.N., Beever, E.A., Sgro, C., Battles, A., Botero, C., Gross, J.E., Hall, K., Hendry, A.P., Hoffmann, A., Hoving, C., LeDee, O.E., Mengelt, C., Nicotra, A., Niver, R., Pérez-Jvostov, F., Quiñones, R., Schuurman, G.W., Schwartz, M.K., Szymanski, J., and Whiteley, A., 2023, Connecting research and practice to enhance the evolutionary potential of species under climate change: Conservation Science and Practice, v. 5, no. 2, e12855, 18 p., https://doi.org/10.1111/csp2.12855.","productDescription":"e12855, 18 p.","ipdsId":"IP-134501","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":487930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12855","text":"Publisher Index Page"},{"id":485334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":212190,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":935461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thurman, Lindsey 0000-0003-3142-4909","orcid":"https://orcid.org/0000-0003-3142-4909","contributorId":269425,"corporation":false,"usgs":true,"family":"Thurman","given":"Lindsey","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cook, Carly N.","contributorId":204315,"corporation":false,"usgs":false,"family":"Cook","given":"Carly","email":"","middleInitial":"N.","affiliations":[{"id":36914,"text":"School of Biological Sciences, Monash University, Clayton, Victoria 3800, Australia","active":true,"usgs":false}],"preferred":false,"id":935463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":2934,"corporation":false,"usgs":true,"family":"Beever","given":"Erik","email":"ebeever@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sgro, Carla","contributorId":354351,"corporation":false,"usgs":false,"family":"Sgro","given":"Carla","affiliations":[],"preferred":false,"id":935465,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Battles, Andrew","contributorId":354352,"corporation":false,"usgs":false,"family":"Battles","given":"Andrew","affiliations":[],"preferred":false,"id":935466,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Botero, Carlos","contributorId":354353,"corporation":false,"usgs":false,"family":"Botero","given":"Carlos","affiliations":[],"preferred":false,"id":935467,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gross, John E.","contributorId":106777,"corporation":false,"usgs":false,"family":"Gross","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":935468,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hall, Kimberley","contributorId":354354,"corporation":false,"usgs":false,"family":"Hall","given":"Kimberley","affiliations":[],"preferred":false,"id":935469,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hendry, Andrew P.","contributorId":178839,"corporation":false,"usgs":false,"family":"Hendry","given":"Andrew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":935470,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hoffmann, Ary","contributorId":354355,"corporation":false,"usgs":false,"family":"Hoffmann","given":"Ary","affiliations":[],"preferred":false,"id":935471,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hoving, Christopher","contributorId":289379,"corporation":false,"usgs":false,"family":"Hoving","given":"Christopher","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":935472,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"LeDee, Olivia E. 0000-0002-7791-5829 oledee@usgs.gov","orcid":"https://orcid.org/0000-0002-7791-5829","contributorId":242820,"corporation":false,"usgs":true,"family":"LeDee","given":"Olivia","email":"oledee@usgs.gov","middleInitial":"E.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":935473,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mengelt, Claudia 0000-0001-7869-5170","orcid":"https://orcid.org/0000-0001-7869-5170","contributorId":304087,"corporation":false,"usgs":true,"family":"Mengelt","given":"Claudia","email":"","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":935474,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Nicotra, Adrienne","contributorId":147686,"corporation":false,"usgs":false,"family":"Nicotra","given":"Adrienne","affiliations":[{"id":16897,"text":"Division of Evolution, Ecology and Genetics, Research School of Biology, Australian National University, Canberra","active":true,"usgs":false}],"preferred":false,"id":935475,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Niver, Robin A.","contributorId":354356,"corporation":false,"usgs":false,"family":"Niver","given":"Robin A.","affiliations":[],"preferred":false,"id":935476,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Pérez-Jvostov, Felipe","contributorId":354357,"corporation":false,"usgs":false,"family":"Pérez-Jvostov","given":"Felipe","affiliations":[],"preferred":false,"id":935477,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Quiñones, Rebecca M.","contributorId":354358,"corporation":false,"usgs":false,"family":"Quiñones","given":"Rebecca M.","affiliations":[],"preferred":false,"id":935478,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Schuurman, Gregor W. 0000-0002-9304-7742","orcid":"https://orcid.org/0000-0002-9304-7742","contributorId":147698,"corporation":false,"usgs":false,"family":"Schuurman","given":"Gregor","email":"","middleInitial":"W.","affiliations":[{"id":16909,"text":"U.S. National Park Service, Natural Resource Stewardship and Science, Fort Collins, CO, 80525, USA","active":true,"usgs":false}],"preferred":false,"id":935479,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Schwartz, Michael K.","contributorId":199035,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":935480,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":935481,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Whiteley, Andrew R.","contributorId":286853,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":935482,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70239338,"text":"mcs2023 - 2023 - Mineral commodity summaries 2023","interactions":[],"lastModifiedDate":"2026-02-09T18:08:28.062813","indexId":"mcs2023","displayToPublicDate":"2023-01-31T08:25:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":323,"text":"Mineral Commodity Summaries","code":"MCS","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023","displayTitle":"Mineral Commodity Summaries 2023","title":"Mineral commodity summaries 2023","docAbstract":"Each mineral commodity chapter of the 2023 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production, reserves, and resources. The MCS is the earliest comprehensive source of 2022 mineral production data for the world. More than 90 individual minerals and materials are covered by 2-page synopses.
For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the 2-page synopsis.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2023 are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2023","usgsCitation":"U.S. Geological Survey, 2023, Mineral commodity summaries 2023: U.S. Geological Survey, 210 p., https://doi.org/10.3133/mcs2023.","productDescription":"Report: 210 p.; Data Release","numberOfPages":"210","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147940","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":499701,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114301.htm","linkFileType":{"id":5,"text":"html"}},{"id":412083,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WCYUI6","text":"USGS data release","linkHelpText":"Data release for mineral commodity summaries 2023"},{"id":412082,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/commodity-statistics-and-information","text":"Commodity Statistics and Information"},{"id":412081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2023"},{"id":412080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2023/mcs2023.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2023"},{"id":412079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2023/coverthb.jpg"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Introduction: Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type.
Methods: Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions.
Results: The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters).
Discussion: Our results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable energy.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2023.1078042","usgsCitation":"Arimitsu, M.L., Piatt, J., Thorson, J., Kuletz, K., Drew, G., Schoen, S.K., Cushing, D., Kroeger, C., and Sydeman, W., 2023, Joint spatiotemporal models to predict seabird densities at sea: Frontiers in Marine Science, v. 10, 1078042, 11 p., https://doi.org/10.3389/fmars.2023.1078042.","productDescription":"1078042, 11 p.","ipdsId":"IP-145204","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":444664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2023.1078042","text":"Publisher Index Page"},{"id":412531,"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 \"coordinates\": [\n [\n [\n -155.65747250220048,\n 58.811862749051784\n ],\n [\n -148.62920726788076,\n 58.811862749051784\n ],\n [\n -148.62920726788076,\n 61.49890808989605\n ],\n [\n -155.65747250220048,\n 61.49890808989605\n ],\n [\n -155.65747250220048,\n 58.811862749051784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatt, John F. 0000-0002-4417-5748","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":244053,"corporation":false,"usgs":true,"family":"Piatt","given":"John F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorson, James","contributorId":301893,"corporation":false,"usgs":false,"family":"Thorson","given":"James","affiliations":[{"id":65201,"text":"NOAA AFSC","active":true,"usgs":false}],"preferred":false,"id":862947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuletz, Kathy","contributorId":301894,"corporation":false,"usgs":false,"family":"Kuletz","given":"Kathy","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":862948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drew, Gary 0000-0002-6789-0891","orcid":"https://orcid.org/0000-0002-6789-0891","contributorId":300561,"corporation":false,"usgs":false,"family":"Drew","given":"Gary","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":862949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cushing, Dan","contributorId":301895,"corporation":false,"usgs":false,"family":"Cushing","given":"Dan","email":"","affiliations":[{"id":65202,"text":"Pole Star Ecological LLC","active":true,"usgs":false}],"preferred":false,"id":862951,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kroeger, Caitlin","contributorId":301897,"corporation":false,"usgs":false,"family":"Kroeger","given":"Caitlin","email":"","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862952,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sydeman, William","contributorId":301898,"corporation":false,"usgs":false,"family":"Sydeman","given":"William","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862953,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240145,"text":"sir20225130 - 2023 - Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021","interactions":[],"lastModifiedDate":"2026-02-24T17:59:35.435101","indexId":"sir20225130","displayToPublicDate":"2023-01-30T12:58:46","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5130","displayTitle":"Linear Regression Model Documentation for Computing Water-Quality Constituent Concentrations or Densities Using Continuous Real-Time Water-Quality Data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021","title":"Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021","docAbstract":"The Kansas River and its associated alluvial aquifer provide drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical processes to treat and remove contaminants before public distribution. An early-notification system of changing water-quality conditions allows water suppliers to proactively make decisions that affect water treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, began collecting water-quality data at the Kansas River above Topeka Weir at Topeka, Kansas (USGS site 06888990, hereafter referred to as the “Topeka site”), during November 2018 to develop linear regression models that relate continuous in situ water-quality sensor measurements to discretely sampled water-quality constituent concentrations or densities. The addition of the Topeka site expanded an existing water-quality monitoring network, which included the upstream Kansas River at Wamego, Kans., and downstream Kansas River at De Soto, Kans., sites. Linear regression analysis was used to develop models that compute real-time concentrations or densities for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and Escherichia coli at the Topeka site using data collected during November 2018 through June 2021. Water-quality constituent concentrations or densities computed from the models documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov), are useful to the public for cultural and recreational purposes, and can be used to guide water-treatment processes, compare conditions with Federal and State water-quality criteria, and characterize changes in Kansas River water-quality conditions through time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225130","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., 2023, Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021: U.S. Geological Survey Scientific Investigations Report 2022–5130, 14 p., https://doi.org/10.3133/sir20225130.","productDescription":"Report: vii, 14 p.; 14 Appendixes; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141414","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":412468,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225130/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412446,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412445,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5130/downloads","text":"Appendixes 1–14","linkFileType":{"id":1,"text":"pdf"}},{"id":412443,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500484,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114304.htm","linkFileType":{"id":5,"text":"html"}},{"id":412442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.pdf","text":"Report","size":"5.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5130"},{"id":412441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5130/coverthb.jpg"},{"id":412444,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5130/images"}],"country":"United States","state":"Kansas","city":"Topeka","otherGeospatial":"Kansas River, Topeka Weir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.66443090940162,\n 39.06286764035286\n ],\n [\n -95.67191642810837,\n 39.06117240426602\n ],\n [\n -95.64218228435746,\n 39.06262546626132\n ],\n [\n -95.64239021543237,\n 39.064239944513304\n ],\n [\n -95.63864745607941,\n 39.07925282377511\n ],\n [\n -95.63812762839112,\n 39.08401430643988\n ],\n [\n -95.63251348936144,\n 39.0836915042114\n ],\n [\n -95.63199366167355,\n 39.08054410506932\n ],\n [\n -95.61452745135817,\n 39.07780010407046\n ],\n [\n -95.61463141689583,\n 39.07465244209993\n ],\n [\n -95.6078736569523,\n 39.057620349038984\n ],\n [\n -95.60225951792262,\n 39.05794327053502\n ],\n [\n -95.59716520658046,\n 39.05826619055418\n ],\n [\n -95.58572899744581,\n 39.0606880436211\n ],\n [\n -95.58884796357322,\n 39.063432710002644\n ],\n [\n -95.59300658507682,\n 39.070778203846714\n ],\n [\n -95.59092727432522,\n 39.08958241237616\n ],\n [\n -95.60589831173833,\n 39.09127696606694\n ],\n [\n -95.60849745017785,\n 39.08732294411979\n ],\n [\n -95.61702262426043,\n 39.08691946002881\n ],\n [\n -95.62222090113984,\n 39.08611248492275\n ],\n [\n -95.63532055887627,\n 39.090712119361\n ],\n [\n -95.64166245666956,\n 39.08917894121052\n ],\n [\n -95.65101935505261,\n 39.08361080342297\n ],\n [\n -95.65663349408229,\n 39.076266645265974\n ],\n [\n -95.66058418451098,\n 39.067145912321024\n ],\n [\n -95.66443090940162,\n 39.06286764035286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The U.S. Geological Survey (USGS) is initiating a study approach focused on building cross-disciplinary connections to weave together the scientific knowledge related to drought conditions and effects in the Colorado River Basin. The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223016","usgsCitation":"Dahm, K.G., Jones, D.K., Anderson, P.J., Dick, M.C., Hawbaker, T.J., and Horton, R.J., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST): U.S. Geological Survey Fact Sheet 2022–3016, 4 p., https://doi.org/10.3133/fs20223016.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-133121","costCenters":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":416352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223016/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3016"},{"id":416351,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.xml"},{"id":411878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3016/coverthb.jpg"},{"id":411879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.pdf","text":"Report","size":"5.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3016"},{"id":416350,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3016/images"}],"country":"Mexico, United Statetes","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.7518896501698,\n 31.333179533551544\n ],\n [\n -108.1826779826668,\n 31.292530391528643\n ],\n [\n -108.22636216977008,\n 31.755580668004285\n ],\n [\n -107.83144654017931,\n 32.82766987491033\n ],\n [\n -107.89423052621623,\n 33.91908118889275\n ],\n [\n -107.60054235862646,\n 34.474546468527535\n ],\n [\n -107.70156965961073,\n 35.43530369556444\n ],\n [\n -106.96299416688146,\n 36.27632977347221\n ],\n [\n -106.49519707134272,\n 36.59537776353493\n ],\n [\n -106.5088572279181,\n 37.05777374754949\n ],\n [\n -106.96751070599771,\n 37.67745762309879\n ],\n [\n -106.2464517524759,\n 38.42849019741902\n ],\n [\n -106.3226667088301,\n 38.8688745253198\n ],\n [\n -105.84832248310391,\n 39.483565795806356\n ],\n [\n -105.6793295391808,\n 39.934832700838456\n ],\n [\n -105.7748145642044,\n 40.35785503337192\n ],\n [\n -106.21930010733132,\n 41.056311809879645\n ],\n [\n -106.42819382446118,\n 41.58215844907255\n ],\n [\n -107.34149070853772,\n 42.33476436232209\n ],\n [\n -108.89445962234083,\n 42.53851660809195\n ],\n [\n -109.80745236050603,\n 43.16088610307435\n ],\n [\n -110.49271378548221,\n 43.40850924462376\n ],\n [\n -111.6838924799572,\n 42.292277027250265\n ],\n [\n -111.99413836336885,\n 41.06269127724076\n ],\n [\n -111.87604170489686,\n 39.799065745689944\n ],\n [\n -112.57737199499377,\n 38.89296778795085\n ],\n [\n -112.78097622152214,\n 38.22122917305467\n ],\n [\n -114.0297938396215,\n 38.10468231708492\n ],\n [\n -114.62100982843458,\n 39.07842450911707\n ],\n [\n -115.69932199571724,\n 39.592082456027185\n ],\n [\n -115.77535041245778,\n 38.53884582465383\n ],\n [\n -115.96430598148137,\n 37.10476019901908\n ],\n [\n -116.19919117971875,\n 36.116449292573606\n ],\n [\n -116.47633007028378,\n 34.83686630451358\n ],\n [\n -116.51856809358253,\n 33.98410239909924\n ],\n [\n -116.31237481460064,\n 33.22592368319175\n ],\n [\n -115.86428179683787,\n 32.56152601033786\n ],\n [\n -115.88456371112946,\n 32.00683378175074\n ],\n [\n -115.57272525296867,\n 31.643556450442958\n ],\n [\n -114.66642714444782,\n 31.597816902553873\n ],\n [\n -113.1842586769861,\n 31.118083077039856\n ],\n [\n -113.25492854485302,\n 30.73760035553252\n ],\n [\n -112.93985982527391,\n 30.184363710503405\n ],\n [\n -110.51663294286107,\n 29.810620687264418\n ],\n [\n -109.58861740954063,\n 30.071266430362215\n ],\n [\n -109.61492936020454,\n 30.42734058413086\n ],\n [\n -109.35938589058884,\n 30.879775471921178\n ],\n [\n -108.4649251498791,\n 30.530080303802123\n ],\n [\n -108.7269534775038,\n 30.809783171064424\n ],\n [\n -108.57530723606152,\n 30.978771390834126\n ],\n [\n -108.7518896501698,\n 31.333179533551544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225
The U.S. Geological Survey (USGS) conducts a wide variety of science that improves understanding of droughts and their effects on ecosystems and society. This work includes data collection and monitoring of aquatic and terrestrial systems; assessment and analysis of patterns, trends, drivers, and impacts of drought; development and application of predictive models; and delivery of information and decision-making tools to stakeholders. Stakeholders, which include Federal, Tribal, State, and local agencies, nongovernmental organizations, and others, use this information to anticipate, assess, react to, and mitigate drought conditions and impacts. There is no obvious near-term solution to reduce the frequency and severity of droughts or to mitigate drought impacts. Multidecadal drought is a “grand challenge” that benefits from integration of existing technologies, data, knowledge, and models across related and disparate disciplines, facilitated by new science and technology. In response, the USGS initiated a new integrated-science approach in the Colorado River Basin in 2020. The Colorado River Basin was specifically selected because of concerns about future drought and its consequences for the region. This document explains how the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) project extends and enhances the science supported by USGS Mission Areas and Programs and articulates scientific gaps and stakeholder needs to identify and reduce drought risks. The approach seeks to answer complex Earth science questions developed in partnership with stakeholders about severe long-term drought. An integrated approach is required to tackle these complex questions, which any single science discipline cannot answer on its own.
In addition to increased understanding of drought and drought effects in complex systems, the Colorado River Basin ASIST project was designed to improve efficiencies through rapid location of a broad array of data sources, assembly of model-ready multidisciplinary data, and delivery of actionable science to stakeholders at the speeds and scales needed for decision making. The project team identified the following actions needed for USGS to implement and advance an integrated science approach in the Colorado River Basin: (1) engage with stakeholders to document their needs and iteratively co-produce science and science delivery tools to address these needs, (2) integrate monitoring and observation systems developed by USGS and other agencies that track droughts and their effects, (3) collect and provide analysis-ready data to support integrated applications, (4) integrate data and model connections to predict multiple drought impacts, (5) conduct multidisciplinary coordination to improve interpretations, (6) leverage the knowledge base across USGS to enhance decision making, and (7) support the development of new integrated science approaches and technologies that provide analysis and management tools that can be used to adapt to the effects of drought in the Colorado River Basin. Proposals for initial short-term use-case projects were solicited, a subset of which was selected for funding to test implementation of these actions. Additionally, the project organized and convened a series of science and technology collaboration workshops in the USGS focused on challenges that were identified and prioritized by the short-term use-case projects and during the initial stakeholder analysis. These workshops were designed to bring together diverse perspectives to discuss science and technology challenges, stakeholder needs, capabilities, and knowledge gaps, with the goal of determining how the USGS can address challenges, identify future opportunities for continued engagement between participants, and inform the next steps for the Colorado River Basin ASIST project. Continuing to collaboratively engage with a wide range of stakeholders using an integrated approach will provide a suitable foundation of data and tools to formulate actionable intelligence for predicting droughts and informing adaptation to the effects of long-term drought in a holistic, timely, and effective manner.
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U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225