{"pageNumber":"159","pageRowStart":"3950","pageSize":"25","recordCount":40783,"records":[{"id":70239848,"text":"70239848 - 2022 - Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i","interactions":[],"lastModifiedDate":"2023-01-23T14:59:19.873812","indexId":"70239848","displayToPublicDate":"2022-09-02T08:46:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5898,"text":"Data in Brief","onlineIssn":"2352-3409","active":true,"publicationSubtype":{"id":10}},"title":"Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i","docAbstract":"<p><span>Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (T</span><sub>min</sub><span>, T</span><sub>mean</sub><span>, T</span><sub>max</sub><span>) and mean precipitation (P</span><sub>mean</sub><span>) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.dib.2022.108572","usgsCitation":"Fortini, L., Kaiser, L.R., Xue, L., and Wang, Y., 2022, Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i: Data in Brief, v. 45, 108572, 11 p., https://doi.org/10.1016/j.dib.2022.108572.","productDescription":"108572, 11 p.","ipdsId":"IP-138113","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":446553,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dib.2022.108572","text":"Publisher Index Page"},{"id":435702,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MF7SG","text":"USGS data release","linkHelpText":"Hawaiian Islands bioclimatic variables for baseline and future climate scenarios"},{"id":412214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":862133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Lauren R.","contributorId":200422,"corporation":false,"usgs":false,"family":"Kaiser","given":"Lauren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":862134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xue, Lulin","contributorId":301129,"corporation":false,"usgs":false,"family":"Xue","given":"Lulin","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":862135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Yaping","contributorId":191943,"corporation":false,"usgs":false,"family":"Wang","given":"Yaping","email":"","affiliations":[],"preferred":false,"id":862136,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70245397,"text":"70245397 - 2022 - Modelling the transport and deposition of ash following a magnitude 7 eruption: The distal Mazama tephra","interactions":[],"lastModifiedDate":"2023-06-22T12:05:22.567726","indexId":"70245397","displayToPublicDate":"2022-09-02T06:59:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Modelling the transport and deposition of ash following a magnitude 7 eruption: The distal Mazama tephra","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Volcanic ash transport and dispersion models (VATDMs) are necessary for forecasting tephra dispersal during volcanic eruptions and are a useful tool for estimating the eruption source parameters (ESPs) of prehistoric eruptions. Here we use Ash3D, an Eulerian VATDM, to simulate the tephra deposition from the ~ 7.7&nbsp;ka climactic eruption of Mount Mazama. We investigate how best to apply a VATDM using the ESPs characteristic of a large magnitude eruption (M ≥ 7). We simplify the approach to focus on the distal deposit as if it were formed by a single phase of Plinian activity. Our results demonstrate that it is possible to use modern wind profiles to simulate the tephra dispersal from a prehistoric eruption; however, this introduces an inherent uncertainty to the subsequent simulations where we explore different ESPs. We show, using the well-documented distal Mazama tephra, that lateral umbrella cloud spreading, rather than advection–diffusion alone, must be included in the VATDM to reproduce the width of the isopachs. In addition, the Ash3D particle size distribution must be modified to simulate the transport and deposition of distal fine-grained (&lt; 125&nbsp;µm) Mazama ash. With these modifications, the Ash3D simulations reproduce the thickness and grain size of the Mazama tephra deposit. Based on our simulations,&nbsp;however, we conclude that the exact relationship between mass eruption rate and the scale of umbrella cloud spreading remains unresolved. Furthermore, for ground-based grain size distributions to be input directly into Ash3D, further research is required into the atmospheric and particle processes that control the settling behaviour of fine volcanic ash.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01593-1","usgsCitation":"Buckland, H.M., Mastin, L.G., Engwell, S., and Cashman, K.V., 2022, Modelling the transport and deposition of ash following a magnitude 7 eruption: The distal Mazama tephra: Bulletin of Volcanology, v. 84, 87, 24 p., https://doi.org/10.1007/s00445-022-01593-1.","productDescription":"87, 24 p.","ipdsId":"IP-142605","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446561,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-022-01593-1","text":"Publisher Index Page"},{"id":418352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -131.58198981834062,\n              57.28643967809876\n            ],\n            [\n              -131.58198981834062,\n              37.25148601937889\n            ],\n            [\n              -108.38881591448018,\n              37.25148601937889\n            ],\n            [\n              -108.38881591448018,\n              57.28643967809876\n            ],\n            [\n              -131.58198981834062,\n              57.28643967809876\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"84","noUsgsAuthors":false,"publicationDate":"2022-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Buckland, Hannah Maeve 0000-0002-9954-4993","orcid":"https://orcid.org/0000-0002-9954-4993","contributorId":311130,"corporation":false,"usgs":false,"family":"Buckland","given":"Hannah","email":"","middleInitial":"Maeve","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":875959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":875960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engwell, Samantha 0000-0001-7719-6257","orcid":"https://orcid.org/0000-0001-7719-6257","contributorId":251719,"corporation":false,"usgs":false,"family":"Engwell","given":"Samantha","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":875961,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cashman, Katharine V.","contributorId":199542,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine","email":"","middleInitial":"V.","affiliations":[{"id":13025,"text":"Department of Geological Sciences, University of Oregon","active":true,"usgs":false}],"preferred":false,"id":875962,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70262472,"text":"70262472 - 2022 - Movement of white‐tailed deer in contrasting landscapes influences management of chronic wasting disease","interactions":[],"lastModifiedDate":"2025-01-22T17:54:13.753634","indexId":"70262472","displayToPublicDate":"2022-09-02T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Movement of white‐tailed deer in contrasting landscapes influences management of chronic wasting disease","docAbstract":"<p><span>Chronic wasting disease (CWD) continues to expand in distribution and prevalence across North America. Upon detection, either for the first time in a novel area or in a region with an existing outbreak, wildlife management agencies are tasked with responding to mitigate the disease. This response often entails creation or modification of a management zone with modified rules and regulations that support an agency's disease management plan. To guide the process of creating an appropriately sized CWD management zone, assuming that wild deer movements are a major risk factor for disease spread, we used data from global positioning system (GPS)-collared white-tailed deer (</span><i>Odocoileus virginianus</i><span>) in southeastern Minnesota and southcentral Pennsylvania, USA, between 2018 and 2021 to estimate long-distance movements associated with dispersal and migratory behaviors. These contrasting study areas with active CWD outbreaks permitted an evaluation of deer movement dynamics in different parts of their range. We quantified the proportion, distribution, timing, and orientation of dispersing and migratory deer. We observed 21% of female and 58% of male yearlings disperse from their apparent natal home range in Minnesota, while in Pennsylvania 4% of female and 68% of male yearlings dispersed. We also documented 20% of females and 6% of males migrated between seasonal home ranges in Minnesota, while in Pennsylvania no females and 5% of males migrated. The average distance deer dispersed or migrated in Minnesota was 20 km and 11 km, respectively, while in Pennsylvania male deer dispersed only about 4 km. Both sexes in Minnesota tended to disperse in a consistent, westerly direction; however, there was no directional preference observed for migratory deer or for dispersing deer in Pennsylvania. We found differences between natal and adult home range size for both sexes in Minnesota but not for males in Pennsylvania. Our results identify the considerable variability in dispersal and migration dynamics of white-tailed deer in disparate landscapes, which is important to agencies managing CWD. We summarize the distribution of these movements and suggest agencies use this information to help make decisions about optimal management zone size. We suggest the development of formalized assessments of the tradeoffs associated with optimizing decisions about creation of CWD management zones across white-tailed deer populations that exhibit variation in dispersal behavior and suggest careful evaluation to avoid using an arbitrary size or shape to create disease management zones.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22306","usgsCitation":"Jennelle, C., Walter, W., Crawford, J., Rosenberry, C., and Wallingford, B., 2022, Movement of white‐tailed deer in contrasting landscapes influences management of chronic wasting disease: Journal of Wildlife Management, v. 86, no. 8, e22306, 21 p., https://doi.org/10.1002/jwmg.22306.","productDescription":"e22306, 21 p.","ipdsId":"IP-136125","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, 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David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Joanne","contributorId":349406,"corporation":false,"usgs":false,"family":"Crawford","given":"Joanne","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenberry, Christopher S.","contributorId":349408,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":83357,"text":"Bureau of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":924295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallingford, Bret D.","contributorId":349410,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret D.","affiliations":[{"id":83357,"text":"Bureau of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":924296,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70238578,"text":"70238578 - 2022 - Advancing geophysical techniques to image a stratigraphic hydrothermal resource","interactions":[],"lastModifiedDate":"2022-11-30T17:25:35.358802","indexId":"70238578","displayToPublicDate":"2022-09-01T11:18:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Advancing geophysical techniques to image a stratigraphic hydrothermal resource","docAbstract":"Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi-disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this and analogous resources. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data, in conjunction with new and preceding geoscientific measurements and observations, are being integrated and evaluated in this study for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings (i.e., whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs) will also be evaluated. This paper provides details and current updates on the course of this study in-progress.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Schwering, P., Winn, C., Jaysaval, P., Knox, H., Siler, D.L., Hardwick, C., Ayling, B., Faulds, J., Mlawsky, E., McConville, E., Norbeck, J., Hinz, N., Matson, G., and Queen, J., 2022, Advancing geophysical techniques to image a stratigraphic hydrothermal resource: Geothermal Resources Council Transactions, v. 46, p. 976-991.","productDescription":"16 p.","startPage":"976","endPage":"991","ipdsId":"IP-141659","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":409862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409843,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034650","linkFileType":{"id":5,"text":"html"}}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schwering, Paul","contributorId":299507,"corporation":false,"usgs":false,"family":"Schwering","given":"Paul","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":857964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winn, Carmen","contributorId":299508,"corporation":false,"usgs":false,"family":"Winn","given":"Carmen","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":857965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaysaval, Piyoosh","contributorId":299509,"corporation":false,"usgs":false,"family":"Jaysaval","given":"Piyoosh","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":857966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knox, Hunter","contributorId":299510,"corporation":false,"usgs":false,"family":"Knox","given":"Hunter","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":857967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":857968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hardwick, Christian","contributorId":299511,"corporation":false,"usgs":false,"family":"Hardwick","given":"Christian","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":857969,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayling, Bridget","contributorId":299512,"corporation":false,"usgs":false,"family":"Ayling","given":"Bridget","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857970,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Faulds, James","contributorId":299513,"corporation":false,"usgs":false,"family":"Faulds","given":"James","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857971,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mlawsky, Elijah","contributorId":299515,"corporation":false,"usgs":false,"family":"Mlawsky","given":"Elijah","email":"","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857972,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McConville, Emma","contributorId":299518,"corporation":false,"usgs":false,"family":"McConville","given":"Emma","email":"","affiliations":[{"id":51825,"text":"Fervo Energy","active":true,"usgs":false}],"preferred":false,"id":857973,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Norbeck, Jack","contributorId":299519,"corporation":false,"usgs":false,"family":"Norbeck","given":"Jack","affiliations":[{"id":51825,"text":"Fervo Energy","active":true,"usgs":false}],"preferred":false,"id":857974,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hinz, Nicholas","contributorId":299524,"corporation":false,"usgs":false,"family":"Hinz","given":"Nicholas","affiliations":[{"id":64866,"text":"Geologica Geothermal Group, Inc","active":true,"usgs":false}],"preferred":false,"id":857975,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Matson, Gabe","contributorId":299527,"corporation":false,"usgs":false,"family":"Matson","given":"Gabe","email":"","affiliations":[{"id":64866,"text":"Geologica Geothermal Group, Inc","active":true,"usgs":false}],"preferred":false,"id":857976,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Queen, John","contributorId":299529,"corporation":false,"usgs":false,"family":"Queen","given":"John","affiliations":[{"id":47634,"text":"Hi-Q Geophysical, Inc.","active":true,"usgs":false}],"preferred":false,"id":857977,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70245163,"text":"70245163 - 2022 - The centenary of IAVCEI 1919–2019 and beyond: The people, places, and things of volcano geodesy","interactions":[],"lastModifiedDate":"2023-06-19T16:06:57.613709","indexId":"70245163","displayToPublicDate":"2022-09-01T10:56:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"The centenary of IAVCEI 1919–2019 and beyond: The people, places, and things of volcano geodesy","docAbstract":"<p><span>Over the first century of the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI), volcano geodesy grew from roots as an accidental and incidental system of measurements to an important method for monitoring volcanic activity and forecasting eruptions. The first practitioners in volcano geodesy were experts in other disciplines, and it was not until the latter half of the twentieth century that specialists in the field emerged—scientists who developed new methods, measured geodetic change at volcanoes, and quantitatively interpreted the results in terms of magmatic processes. Much of the early work in the field was restricted to a few volcanoes and involved techniques that had been adapted from other applications; relatively few methods were developed specifically for use on volcanoes. These volcanoes, however, provided the natural laboratories needed to advance the field. By the start of the twenty-first century, geodetic studies, especially using space-based techniques, contributed to the recognition of deformation and gravity change at hundreds of volcanoes on Earth. In coming years, IAVCEI researchers will focus on comprehensive exploitation of the growing volumes of geodetic data to better model, forecast, and track activity at volcanoes worldwide. Meanwhile, the field needs to become more diverse, better representing people who live in the shadows of volcanoes around the globe.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01598-w","usgsCitation":"Poland, M.P., and de Zeeuw-van Dalfsen, E., 2022, The centenary of IAVCEI 1919–2019 and beyond: The people, places, and things of volcano geodesy: Bulletin of Volcanology, v. 84, 90, 23 p., https://doi.org/10.1007/s00445-022-01598-w.","productDescription":"90, 23 p.","ipdsId":"IP-138073","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":418215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","noUsgsAuthors":false,"publicationDate":"2022-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":875725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Zeeuw-van Dalfsen, Elske 0000-0003-2527-4932","orcid":"https://orcid.org/0000-0003-2527-4932","contributorId":217967,"corporation":false,"usgs":false,"family":"de Zeeuw-van Dalfsen","given":"Elske","email":"","affiliations":[{"id":39727,"text":"KNMI","active":true,"usgs":false}],"preferred":false,"id":875726,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70256750,"text":"70256750 - 2022 - Seasonal context of bristly cave crayfish Cambarus setosus habitat use and life history","interactions":[],"lastModifiedDate":"2024-09-04T15:39:26.083626","indexId":"70256750","displayToPublicDate":"2022-09-01T10:33:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2201,"text":"Journal of Cave and Karst Studies","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal context of bristly cave crayfish Cambarus setosus habitat use and life history","docAbstract":"<p>Cave crayfishes are important members of groundwater communities, but many cave crayfishes are threatened or endangered. Unfortunately, we lack basic life history and ecological data that are needed for developing conservation plans for most cave crayfishes, especially the role of seasonal and annual fluctuations in structuring populations. Therefore, we determined the seasonal life history and habitat use of <i>Cambarus setosus</i> in Smallin Civil War Cave, Christian County, Missouri, United States. We conducted visual crayfish surveys over a 400 m section of the cave from 2006 to 2019. We used multinomial logit, multiple linear regression, and logistic regression models to estimate crayfish substrate, water depth, and water velocity use, respectively. All models included sex, carapace length, season, distance into the cave, and interactions between all variables and sex as predictor terms. We also used t-tests to assess morphometric differences between male and female crayfish. Six mark-recapture events (2010 to 2019) were used to estimate population sizes using a nil-recapture model. We attempted to age eight individuals using gastric mill bands, but annual bands were not discernable. We found reproductively active males during all seasons. We captured one ovigerous female during the spring, though ovigerous females were observed during show cave tours during spring, summer, and autumn. Male <i>C. setosus</i> were more likely to use homogenous and heterogeneous rock substrates and shallower and calmer water when compared to females; however, these relationships varied based on distance into the cave and season. Females sampled were significantly larger than males, and males regenerated chelae more often. Minimum population size estimates ranged from 9 to 159 individuals and indicated the population was relatively stable. Our data provide both a baseline population estimate for comparison with future studies and valuable trait information that is often lacking but useful for developing conservation efforts. </p>","language":"English","publisher":"National Speleological Society","doi":"10.4311/2021LSC0110","usgsCitation":"Mouser, J., Ashley, D., Zenter, D., and Brewer, S.K., 2022, Seasonal context of bristly cave crayfish Cambarus setosus habitat use and life history: Journal of Cave and Karst Studies, v. 84, no. 3, p. 85-95, https://doi.org/10.4311/2021LSC0110.","productDescription":"11 p.","startPage":"85","endPage":"95","ipdsId":"IP-127872","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446569,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.4311/2021lsc0110","text":"Publisher Index Page"},{"id":433451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Christian County","otherGeospatial":"Smallin Civil War Cave","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -93.18894102326013,\n              37.05222350256189\n            ],\n            [\n              -93.18894102326013,\n              37.04918995811687\n            ],\n            [\n              -93.18586955215672,\n              37.04918995811687\n            ],\n            [\n              -93.18586955215672,\n              37.05222350256189\n            ],\n            [\n              -93.18894102326013,\n              37.05222350256189\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"84","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mouser, J.B.","contributorId":244447,"corporation":false,"usgs":false,"family":"Mouser","given":"J.B.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908855,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashley, D.C.","contributorId":244487,"corporation":false,"usgs":false,"family":"Ashley","given":"D.C.","email":"","affiliations":[{"id":48915,"text":"Missouri Western State University","active":true,"usgs":false}],"preferred":false,"id":908856,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zenter, D.L.","contributorId":341751,"corporation":false,"usgs":false,"family":"Zenter","given":"D.L.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908857,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":908858,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70233185,"text":"70233185 - 2022 - Basis for technical guidance to evaluate evapotranspiration covers","interactions":[],"lastModifiedDate":"2022-12-12T15:58:40.79318","indexId":"70233185","displayToPublicDate":"2022-09-01T09:55:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"NUREG/CR-7297","title":"Basis for technical guidance to evaluate evapotranspiration covers","docAbstract":"This report provides technical guidance to evaluate evapotranspiration (ET) cover design criteria with emphasis on applications to long-term disposal sites such as Uranium Mill Tailings Radiation Control Act of 1978 (UMTRCA) sites. Water balance covers, also known as ET covers, reduce percolation by storing precipitation then allowing vegetation to cycle it back to the atmosphere. For long-term (over 200 years) waste isolation, ET covers may provide significant benefits over conventional, resistive covers that rely on engineered components, such as compacted clay barriers and geomembranes, to divert precipitation. UMTRCA covers were designed to impede and attenuate radioactive radon-222 gas flux from the underlying tailings, while minimizing percolation of any contaminants to groundwater. Such covers have implicit regulatory compliance post-construction. Alternative cover systems, such as ET covers, must explicitly meet some anticipated performance, and demonstrate beneficial use. While all engineered structures will change over time, an ET cover evolves with nature rather than resisting it, which may perpetuate a more reliable waste isolation system. For example, UMTRCA sites must provide safe and environmentally sound disposal, long-term stabilization, and control of uranium mill tailings and remain effective for up to 1,000 years, to the extent reasonably achievable, and, in any case, for at least 200 years. UMTRCA covers rely on the engineered properties to meet regulatory requirements during and immediately after construction. Subsequent compliance is implicit in the design. The design of an ET cover is far more dependent on mesoscale meteorology, native vegetation, and edaphic soil properties which are site-specific. Therefore, the design and anticipated performance of an ET cover must be demonstrated through a combination of modeling, natural analogues and pilot studies, and then verified with monitoring data. There is no single ET cover design that can likely meet performance standards across different climates, available soils, and vegetation. The technical information presented in this report reviews guidelines and performance criteria commonly used for ET covers at municipal waste facilities and the consideration factors of such covers to meet the regulatory requirements at long-term disposal sites.","language":"English","publisher":"U.S. Nuclear Regulatory Commission","usgsCitation":"Caldwell, T., Huntington, J., Davies, G.E., Tabatabai, S., and Fuhrmann, M., 2022, Basis for technical guidance to evaluate evapotranspiration covers, 127 p.","productDescription":"127 p.","ipdsId":"IP-120445","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":410286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":403886,"type":{"id":15,"text":"Index Page"},"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/contract/cr7297/index.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, Todd 0000-0003-4068-0648","orcid":"https://orcid.org/0000-0003-4068-0648","contributorId":217924,"corporation":false,"usgs":true,"family":"Caldwell","given":"Todd","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Jena 0000-0002-9291-1404","orcid":"https://orcid.org/0000-0002-9291-1404","contributorId":204033,"corporation":false,"usgs":true,"family":"Huntington","given":"Jena","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davies, Gwendolyn Elizabeth 0000-0003-1538-8610","orcid":"https://orcid.org/0000-0003-1538-8610","contributorId":293203,"corporation":false,"usgs":true,"family":"Davies","given":"Gwendolyn","email":"","middleInitial":"Elizabeth","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tabatabai, S.","contributorId":293205,"corporation":false,"usgs":false,"family":"Tabatabai","given":"S.","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":846719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuhrmann, M.","contributorId":138800,"corporation":false,"usgs":false,"family":"Fuhrmann","given":"M.","affiliations":[{"id":12528,"text":"US Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":846720,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70236641,"text":"70236641 - 2022 - A process-model perspective on recent changes in the carbon cycle of North America","interactions":[],"lastModifiedDate":"2022-09-14T14:51:20.282288","indexId":"70236641","displayToPublicDate":"2022-09-01T09:43:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"A process-model perspective on recent changes in the carbon cycle of North America","docAbstract":"<p><span>Continental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state-of-the-art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37&nbsp;±&nbsp;0.38 (mean and one standard deviation) PgC year</span><sup>−1</sup><span>&nbsp;for the period 2000–2019 (0.31 and 0.44 PgC year</span><sup>−1</sup><span>&nbsp;in each decade); split into 0.18&nbsp;±&nbsp;0.12 PgC year</span><sup>−1</sup><span>&nbsp;in Canada (0.15 and 0.20), 0.16&nbsp;±&nbsp;0.17 in the United States (0.14 and 0.17), 0.02&nbsp;±&nbsp;0.05 PgC year</span><sup>−1</sup><span>&nbsp;in Mexico (0.02 and 0.02) and 0.01&nbsp;±&nbsp;0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02&nbsp;±&nbsp;0.46 PgC year</span><sup>−1</sup><span>). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental-scale land C cycle along with the spatial distribution of local sink-source dynamics.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG006904","usgsCitation":"Murray-Tortarolo, G., Poulter, B., Vargas, R., Hayes, D., Michalak, A., Williams , C., Windham-Myers, L., Wang, J., Wickland, K., Butman, D., Tian, H., Sitch, S., Friedlingstein, P., O’Sullivan, M., Briggs, P., Arora, V., Lombardozzi, D., Jain, A., Yuan, W., Seferian, R., Nabel, J., Wiltshire, A., Arneth, A., Lienerte, S., Zaehle, S., Bastrikov, V., Goll, D., Vuichard, N., Walker, A.P., Kato, E., Xu, Y., Zhang, Z., Chaterjee, A., and Kurz, W., 2022, A process-model perspective on recent changes in the carbon cycle of North America: Journal of Geophysical Research Biogeosciences, v. 127, no. 9, e2022JG006904, 19 p., https://doi.org/10.1029/2022JG006904.","productDescription":"e2022JG006904, 19 p.","ipdsId":"IP-144503","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":446574,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jg006904","text":"Publisher Index Page"},{"id":406674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -42.1875,\n              60.413852350464914\n            ],\n            [\n              -20.7421875,\n              70.61261423801925\n            ],\n            [\n              -12.3046875,\n              82.02137801950887\n            ],\n            [\n              -28.828124999999996,\n              83.63810565804015\n            ],\n            [\n              -83.671875,\n              83.31873282163234\n            ],\n            [\n              -130.78125,\n              75.58493740869223\n            ],\n            [\n              -131.1328125,\n              70.72897946208789\n            ],\n            [\n              -164.1796875,\n              71.74643171904148\n            ],\n            [\n              -172.96875,\n              62.91523303947614\n            ],\n            [\n              -159.9609375,\n              52.696361078274485\n            ],\n            [\n              -142.03125,\n              58.07787626787517\n            ],\n            [\n              -128.671875,\n              47.989921667414194\n            ],\n            [\n              -120.9375,\n              24.84656534821976\n            ],\n            [\n              -83.3203125,\n              4.915832801313164\n            ],\n            [\n              -76.640625,\n              11.178401873711785\n            ],\n            [\n              -67.8515625,\n              17.97873309555617\n            ],\n            [\n              -42.1875,\n              60.413852350464914\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Murray-Tortarolo, Guillermo","contributorId":296446,"corporation":false,"usgs":false,"family":"Murray-Tortarolo","given":"Guillermo","email":"","affiliations":[{"id":64038,"text":"Instituto de Investigaciones en Ecosistemas y Sustentabilidad. Universidad Nacional Autónoma de México, Morelia, México","active":true,"usgs":false}],"preferred":false,"id":851583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poulter, Benjamin 0000-0002-9493-8600","orcid":"https://orcid.org/0000-0002-9493-8600","contributorId":200477,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":851584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vargas, Rodrigo 0000-0001-6829-5333","orcid":"https://orcid.org/0000-0001-6829-5333","contributorId":224770,"corporation":false,"usgs":false,"family":"Vargas","given":"Rodrigo","email":"","affiliations":[{"id":39556,"text":"U. 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,{"id":70236341,"text":"70236341 - 2022 - Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range","interactions":[],"lastModifiedDate":"2022-09-02T14:17:32.925032","indexId":"70236341","displayToPublicDate":"2022-09-01T09:13:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12565,"text":"GeoHazards","active":true,"publicationSubtype":{"id":10}},"title":"Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range","docAbstract":"<p><span>Although accurate root cohesion model estimates are essential to quantify the effect of vegetation roots on shallow slope stability, few means exist to independently validate such model outputs. One validation approach for cohesion estimates is back-calculation of apparent root cohesion at a landslide site with well-documented failure conditions. The catchment named CB1, near Coos Bay, Oregon, USA, which experienced a shallow landslide in 1996, is a prime locality for cohesion model validation, as an abundance of data and observations from the site generated broad insights related to hillslope hydrology and slope stability. However, previously published root cohesion values at CB1 used the Wu and Waldron model (WWM), which assumes simultaneous root failure and therefore likely overestimates root cohesion. Reassessing published cohesion estimates from this site is warranted, as more recently developed models include the fiber bundle model (FBM), which simulates progressive failure with load redistribution, and the root bundle model-Weibull (RBMw), which accounts for differential strain loading. We applied the WWM, FBM, and RBMw at CB1 using post-failure root data from five vegetation species. At CB1, the FBM and RBMw predict values that are less than 30% of the WWM-estimated values. All three models show that root cohesion has substantial spatial heterogeneity. Most parts of the landslide scarp have little root cohesion, with areas of high cohesion concentrated near plant roots. These findings underscore the importance of using physically realistic models and considering lateral and vertical spatial heterogeneity of root cohesion in shallow landslide initiation and provide a necessary step towards independently assessing root cohesion model validity.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/geohazards3030022","usgsCitation":"Cronkite-Ratcliff, C., Schmidt, K.M., and Wirion, C., 2022, Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range: GeoHazards, v. 3, no. 3, p. 428-451, https://doi.org/10.3390/geohazards3030022.","productDescription":"24 p.","startPage":"428","endPage":"451","ipdsId":"IP-133079","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geohazards3030022","text":"Publisher Index Page"},{"id":406136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","city":"Coos Bay","otherGeospatial":"Coast Range","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124.56298828125001,\n              43.14909399920127\n            ],\n            [\n              -123.50830078125,\n              43.14909399920127\n            ],\n            [\n              -123.50830078125,\n              43.75522505306928\n            ],\n            [\n              -124.56298828125001,\n              43.75522505306928\n            ],\n            [\n              -124.56298828125001,\n              43.14909399920127\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Kevin M. 0000-0003-2365-8035 kschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-2365-8035","contributorId":1985,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kevin","email":"kschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wirion, Charlotte 0000-0003-0721-3036","orcid":"https://orcid.org/0000-0003-0721-3036","contributorId":296101,"corporation":false,"usgs":false,"family":"Wirion","given":"Charlotte","email":"","affiliations":[{"id":63984,"text":"ETH Zurich, Switzerland (now at WEO, Luxembourg)","active":true,"usgs":false}],"preferred":false,"id":850666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256644,"text":"70256644 - 2022 - Seabird vulnerability to oil: Exposure potential, sensitivity, and uncertainty in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2024-08-29T14:09:34.922004","indexId":"70256644","displayToPublicDate":"2022-09-01T09:02:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Seabird vulnerability to oil: Exposure potential, sensitivity, and uncertainty in the northern Gulf of Mexico","docAbstract":"<p><span>The northern Gulf of Mexico (nGoM) is a globally important region for oil extraction and supports a diverse assemblage of marine birds. Due to their frequent contact with surface waters, diverse foraging strategies, and the ease with which oil adheres to feathers, seabirds are particularly susceptible to hydrocarbon contamination. Given the chronic and acute exposure of seabirds to oiling and a lack of studies that focus on the exposure of seabirds to oiling in sub-tropical and tropical regions, a greater understanding of the vulnerability of seabirds to oil in the nGoM appears warranted. We present an oil vulnerability index for seabirds in the nGoM tailored to the current state of knowledge using new, spatiotemporally expensive vessel-based seabird observations. We use information on the exposure and sensitivity of seabirds to oil to rank seabird vulnerability. Exposure variables characterized the potential to encounter oil and gas (O&amp;G). Sensitivity variables characterized the potential impact of seabirds interacting with O&amp;G and are related to life history and productivity. We also incorporated uncertainty in each variable, identifying data gaps. We found that the percent of seabirds’ habitat defined as highly suitable within 10&nbsp;km of an O&amp;G platform ranged from 0%-65% among 24 species. Though O&amp;G platforms only overlap with 15% of highly suitable seabird habitat, overlap occurs in areas of moderate to high vulnerability of seabirds, particularly along the shelf-slope. Productivity-associated sensitivity variables were primarily responsible for creating the gradient in vulnerability scores and had greater uncertainty than exposure variables. Highly vulnerable species (e.g., Northern gannet (</span><i>Morus bassanus</i><span>)) tended to have high exposure to the water surface&nbsp;</span><i>via</i><span>&nbsp;foraging behaviors (e.g., plunge-diving), older age at first breeding, and an extended incubating and fledging period compared to less vulnerable species (e.g., Pomarine jaeger (</span><i>Stercorarius pomarinus</i><span>)). Uncertainty related to productivity could be reduced through at-colony monitoring. Strategic seabird satellite tagging could help target monitoring efforts to colonies known to use the nGoM, and continued vessel-based observations could improve habitat characterization. As offshore energy development in the nGoM continues, managers and researchers could use these vulnerability ranks to identify information gaps to prioritize research and focal species.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.880750","usgsCitation":"Michael, P.E., Hixson, K.M., Haney, J., Satge, Y., Gleason, J., and Jodice, P.G., 2022, Seabird vulnerability to oil: Exposure potential, sensitivity, and uncertainty in the northern Gulf of Mexico: Frontiers in Marine Science, v. 9, 880750, 20 p., https://doi.org/10.3389/fmars.2022.880750.","productDescription":"880750, 20 p.","ipdsId":"IP-136731","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446580,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.880750","text":"Publisher Index Page"},{"id":433301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83.65064732144825,\n              24.16865530700734\n            ],\n            [\n              -80.74028091647656,\n              24.49674089923498\n            ],\n            [\n              -80.6110475132637,\n              25.375716312633244\n            ],\n            [\n              -82.48124501487644,\n              27.87595132514076\n            ],\n            [\n              -82.50401354053463,\n              28.975470074471573\n            ],\n            [\n              -84.01377542803263,\n              30.278116834370664\n            ],\n            [\n              -85.21105352771927,\n              29.813919882762193\n            ],\n            [\n              -86.41030861654544,\n              30.53771106924384\n            ],\n            [\n              -88.05949534476994,\n              30.614388650912403\n            ],\n            [\n              -89.250977846713,\n              30.128789114669402\n            ],\n            [\n              -88.84045762268858,\n              28.94436305824601\n            ],\n            [\n              -90.45644421555457,\n              29.213975074748845\n            ],\n            [\n              -91.6391457751865,\n              29.567867845383958\n            ],\n            [\n              -92.61153919455367,\n              29.52398569897167\n            ],\n            [\n              -94.15861129214014,\n              29.615060750195013\n            ],\n            [\n              -95.85111266718562,\n              28.497269906948034\n            ],\n            [\n              -97.05244440317941,\n              27.930797727982196\n            ],\n            [\n              -97.4997935110664,\n              26.841188159759383\n            ],\n            [\n              -97.23172322974906,\n              26.093166420472286\n            ],\n            [\n              -86.07549373074224,\n              26.17610757333493\n            ],\n            [\n              -85.31650372657558,\n              24.614391708025977\n            ],\n            [\n              -83.65064732144825,\n              24.16865530700734\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Michael, Pamela E.","contributorId":341457,"corporation":false,"usgs":false,"family":"Michael","given":"Pamela","email":"","middleInitial":"E.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hixson, K. 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,{"id":70249920,"text":"70249920 - 2022 - Puerto Rico’s state of the climate 2014-2021","interactions":[],"lastModifiedDate":"2023-11-07T14:32:49.34824","indexId":"70249920","displayToPublicDate":"2022-09-01T08:28:32","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"Puerto Rico’s State of the Climate 2014-2021","title":"Puerto Rico’s state of the climate 2014-2021","docAbstract":"The climate of Puerto Rico is influenced by the changing global climate. The following chapters present the current knowledge of the geophysical and chemical drivers and signals of global climate change as they affect the climate of Puerto Rico and influence the climate-dependent services, risks, and vulnerabilities that govern human well-being. These include sustainable economic development, delivery of ecosystem services, the conservation of natural and cultural resources, resiliency in built and natural systems, and food security.  The chapters draw on global expertise of land, atmosphere, and ocean geophysical interactions associated with increasing greenhouse gases that drive global warming and on local scientific expertise, data, observations, and modeled projections. They present the global warming scenario (section 1), the contribution of Puerto Rico to global climate change as GHG emissions and aerosols (section 2), the context of natural climate variability (section 3), observed and projected trends in temperature (section 4), rainfall (section 5), sea level rise (section 6), ocean acidification and sea surface warming (section 7), and the expected implications of warming climate on tropical cyclones affecting Puerto Rico (section 8).","language":"English","publisher":"Puerto Rico Climate Change Council","usgsCitation":"Gould, W.A., Dias, E., Terando, A., Jury, M., Bowden, J., Chardon, P., Melendez Oyola, M., and Morell, J., 2022, Puerto Rico’s state of the climate 2014-2021, 260 p.","productDescription":"260 p.","ipdsId":"IP-133876","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science 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,{"id":70236514,"text":"70236514 - 2022 - Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements","interactions":[],"lastModifiedDate":"2022-09-09T13:32:07.905743","indexId":"70236514","displayToPublicDate":"2022-09-01T08:22:11","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements","docAbstract":"<p><span>The Eocene Green River Formation contains the largest oil shale deposits in the world and is a welldocumented example of a lacustrine depositional system. In addition, mineral resources associated with oil shale in the Piceance Basin nahcolite [NaHCO3] and dawsonite [NaAl(CO3)(OH)2)] are of current and potential economic value, respectively. Detailed geochemical analysis across the basin can aid in the understanding of the depositional environment, sedimentary processes, and water-chemistry evolution in this system. Quantitative geochemical data for Green River oil shale from the Piceance Basin of Colorado were collected by inductively coupled plasma optical emission spectroscopy and mass spectrometry as part of this study. The basin margin is represented by samples from exposures at Douglas Pass (Garfield County) and the basin center area is characterized by core samples from two drilled wells: the Shell 23X-2 and John Savage 24-1 (Rio Blanco County). Major elements and groups of elements are used as proxies for clastic influx (Si, Al, K, Ti), carbonate deposition (Ca, Mg), salinity (Na), paleo-productivity (P), and redox state (Fe, S), respectively. Minor and trace elements reinforce observations based on major elements, including Rb, Zr, Nb for clastic influx and Mn, Sr for carbonate. Trace elements are used to characterize redox conditions (As, Mo, U, V, Co, Ni, Cu, Zn) and salinity (Rb/K, B/Ga). Chemical distinctions between the basin margin and the basin center, in terms of these components and total organic carbon concentrations, support the model of a permanently stratified lake through most of the depositional interval. A primary purpose of the study was to conduct more extensive sampling to confirm conclusions of a previous reconnaissance study. Geochemical data from this study indicates elevated Na around the basin margin occurring earlier than in the deeper basin. Early in the history of Lake Uinta, the salinity may have been elevated first in the shallower marginal waters, due to increased evaporation, which then led to elevated salinity in the basin center through transport of saline density currents. Other indicators of salinity (Rb/K, B/Ga) do not track Na content in intervals where clay minerals are absent due to diagenetic alteration under hypersaline conditions but may be used to indicate the salinities at which authigenic Na-bearing minerals begin to form. Most Na-rich samples show high proportions of clastic constituents (Si, Al, K, Ti) compared to conventional carbonate constituents (Ca, Mg). Redox-sensitive period IV transition metal elements (V, Co, Ni, Cu, Zn) show only local occurrence of significant enrichment relative to average shale abundances. Analysis of Fe/Al ratios for this dataset suggests that the depletion of these elements may be related to source rocks depleted in mafic constituents, with apparent redox-related enrichments subdued by this effect. The basin margin samples reflect generally oxic bottom waters, with some intervals deposited under more reducing, possibly dysoxic to anoxic conditions. The basin center results indicate more reducing conditions, with Mo and U enrichment factors suggesting operation of a particulate shuttle mechanism that scavenged Mo on Fe/Mn-oxyhydroxides that redissolved at depth, with Mo precipitating along with sulfides and/or organic matter at or near the sediment/water interface.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Utah Geological Association","doi":"10.31711/ugap.v50i.114","usgsCitation":"Boak, J., Wu, T., and Birdwell, J.E., 2022, Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements, chap. <i>of</i> The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 266-297, https://doi.org/10.31711/ugap.v50i.114.","productDescription":"32 p.","startPage":"266","endPage":"297","ipdsId":"IP-127516","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446590,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.114","text":"Publisher Index Page"},{"id":435705,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q5VOQB","text":"USGS data release","linkHelpText":"Geochemical data for the Green River Formation in the Piceance Basin, Colorado: Major and trace element concentrations and total organic carbon content"},{"id":406448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Green River Formation, Piceance Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -108.1439208984375,\n              39.48284540453334\n            ],\n            [\n              -107.8692626953125,\n              39.64799732373418\n            ],\n            [\n              -107.91320800781249,\n              40.027614437486655\n            ],\n            [\n              -108.2647705078125,\n              40.17467622056341\n            ],\n            [\n              -108.6492919921875,\n              40.069664523297774\n            ],\n            [\n              -108.7811279296875,\n              39.88023492849342\n            ],\n            [\n              -108.5394287109375,\n              39.6437675734185\n            ],\n            [\n              -108.1439208984375,\n              39.48284540453334\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"50","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"editors":[{"text":"Hurst, C. J.","contributorId":206942,"corporation":false,"usgs":false,"family":"Hurst","given":"C.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":851360,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Boak, Jeremy 0000-0003-0251-434X","orcid":"https://orcid.org/0000-0003-0251-434X","contributorId":296328,"corporation":false,"usgs":false,"family":"Boak","given":"Jeremy","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":851288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Tengfei 0000-0003-2804-5537","orcid":"https://orcid.org/0000-0003-2804-5537","contributorId":296330,"corporation":false,"usgs":false,"family":"Wu","given":"Tengfei","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":851289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":851290,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70238985,"text":"70238985 - 2022 - Potential cheatgrass abundance within lightly invaded areas of the Great Basin","interactions":[],"lastModifiedDate":"2022-12-20T14:13:06.35989","indexId":"70238985","displayToPublicDate":"2022-09-01T08:06:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Potential cheatgrass abundance within lightly invaded areas of the Great Basin","docAbstract":"<p><strong>Context</strong><br>Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high.</p><p><strong>Objectives</strong><br>We aimed to support management by developing predictive maps of potential cover for cheatgrass (<i>Bromus tectorum</i>), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas.</p><p><strong>Methods</strong><br>We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data.</p><p><strong>Results</strong><br>Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts.</p><p><strong>Conclusions</strong><br>We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species’ current and potential abundance can yield spatial predictions to guide resource allocation and management action.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01487-9","usgsCitation":"Sofaer, H., Jarnevich, C.S., Buchholtz, E.K., Cade, B.S., Abatzoglou, J.T., Aldridge, C.L., Comer, P., Manier, D., Parker, L.E., and Heinrichs, J., 2022, Potential cheatgrass abundance within lightly invaded areas of the Great Basin: Landscape Ecology, v. 37, p. 2607-2618, https://doi.org/10.1007/s10980-022-01487-9.","productDescription":"12 p.","startPage":"2607","endPage":"2618","ipdsId":"IP-137660","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467165,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/2t8682dh","text":"External Repository"},{"id":435706,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEY7X5","text":"USGS data release","linkHelpText":"Great Basin predicted potential cheatgrass abundance, with model estimation and validation data from 2011-2019"},{"id":410796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114.06350715079611,\n              37.05045686188801\n            ],\n            [\n              -113.83957046100997,\n              37.2497689044458\n            ],\n            [\n              -112.36153847089972,\n              38.33739320449902\n            ],\n            [\n              -111.5774391837922,\n              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0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":859539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":859540,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abatzoglou, John T.","contributorId":191729,"corporation":false,"usgs":false,"family":"Abatzoglou","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":33345,"text":" University of Idaho","active":true,"usgs":false}],"preferred":false,"id":859541,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":859542,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Comer, Patrick","contributorId":191654,"corporation":false,"usgs":false,"family":"Comer","given":"Patrick","affiliations":[],"preferred":false,"id":859543,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Manier, Daniel 0000-0002-1105-1327","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":244206,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":859544,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Parker, Lauren E.","contributorId":225389,"corporation":false,"usgs":false,"family":"Parker","given":"Lauren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":859545,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":859546,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70236442,"text":"70236442 - 2022 - Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA","interactions":[],"lastModifiedDate":"2022-09-07T12:10:54.664669","indexId":"70236442","displayToPublicDate":"2022-09-01T07:07:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA","docAbstract":"<div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\"><span>Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic&nbsp;macroinvertebrate&nbsp;index of biotic integrity&nbsp;for small, non-tidal streams (upstream area ≤200&nbsp;km</span><sup>2</sup><span>) in the Chesapeake Bay&nbsp;watershed&nbsp;(CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and&nbsp;catchment area&nbsp;predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.</span></p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.116068","usgsCitation":"Maloney, K.O., Buchanan, C., Jepsen, R., Krause, K.P., Cashman, M.J., Gressler, B.P., Young, J.A., and Schmid, M., 2022, Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA: Journal of Environmental Management, v. 322, 116068, 12 p., https://doi.org/10.1016/j.jenvman.2022.116068.","productDescription":"116068, 12 p.","ipdsId":"IP-139303","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science 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,{"id":70262281,"text":"70262281 - 2022 - Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan","interactions":[],"lastModifiedDate":"2025-01-21T15:17:43.018461","indexId":"70262281","displayToPublicDate":"2022-09-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan","docAbstract":"<p><span>Fish behavior after passage or transfer around dams is a critical component in determining whether the goals of these efforts are achieved, but these behaviors are often poorly understood. An elevator was constructed in the lowermost hydroelectric dam on the Menominee River, Wisconsin–Michigan; it is the first elevator specifically designed to capture Lake Sturgeon&nbsp;</span><i>Acipenser fulvescens</i><span>&nbsp;for upstream transfer above two dams, providing access to high-quality spawning and early life habitat. Our objectives were to determine whether (1) Lake Sturgeon transferred upstream remained upstream for at least one spawning opportunity; (2) spawning opportunity, time to reach the next dam upstream, and residency in different segments of the river were related to sex, capture method (elevator versus electrofishing), and season of transfer; and (3) the probability of fish transitioning back downstream of the two dams varied among months. We evaluated posttransfer behaviors of 139 Lake Sturgeon that were captured in the elevator or by electrofishing, implanted with acoustic transmitters, transferred upstream (in spring or fall) from fall 2014 to spring 2017, and monitored until fall 2018 using 20–23 stationary acoustic receivers deployed throughout the river. Most Lake Sturgeon (91%) remained upstream for at least one spawning opportunity. The probability of remaining for one spawning opportunity was not related to sex, fish capture method, or season of transfer. Residency times within the two impoundments and time to reach the next dam upstream varied among individual fish. A multistate model indicated that monthly survival after upstream transfer was high and that Lake Sturgeon typically remained above both dams in late fall to early spring, with most downstream movements occurring in April and May. Our results indicate that Lake Sturgeon transferred upstream have the potential to contribute offspring that may help to bolster the Lake Sturgeon population in Lake Michigan, but additional research may help in determining whether these contributions occur.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10379","usgsCitation":"Isermann, D.A., Raabe, J., Easterly, E., Schulze, J., Porter, N., Dembkowski, D., Donofrio, M., Kramer, D., and Elliott, R., 2022, Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan: Transactions of the American Fisheries Society, v. 151, no. 5, p. 611-629, https://doi.org/10.1002/tafs.10379.","productDescription":"19 p.","startPage":"611","endPage":"629","ipdsId":"IP-137127","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Menominee River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -87.52923848272457,\n              45.083121335445355\n            ],\n            [\n              -87.52923848272457,\n              45.431368318822194\n            ],\n            [\n              -87.97927795298747,\n              45.431368318822194\n            ],\n            [\n              -87.97927795298747,\n              45.083121335445355\n            ],\n            [\n              -87.52923848272457,\n              45.083121335445355\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"151","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raabe, Joshua K.","contributorId":348735,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterly, Emma G.","contributorId":348736,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma G.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulze, Joshua C.","contributorId":348738,"corporation":false,"usgs":false,"family":"Schulze","given":"Joshua C.","affiliations":[{"id":83404,"text":"USDA Forest Service Region 1","active":true,"usgs":false}],"preferred":false,"id":923729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Nicholas J.","contributorId":348741,"corporation":false,"usgs":false,"family":"Porter","given":"Nicholas J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dembkowski, Daniel J.","contributorId":348743,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":923731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael C.","contributorId":348744,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923732,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kramer, Darren R.","contributorId":348745,"corporation":false,"usgs":false,"family":"Kramer","given":"Darren R.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923733,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elliott, Robert F.","contributorId":348746,"corporation":false,"usgs":false,"family":"Elliott","given":"Robert F.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923734,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70237577,"text":"70237577 - 2022 - Causality guided machine learning model on wetland CH4 emissions across global wetlands","interactions":[],"lastModifiedDate":"2022-10-14T13:48:15.777176","indexId":"70237577","displayToPublicDate":"2022-08-31T16:42:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Causality guided machine learning model on wetland CH<sub>4</sub> emissions across global wetlands","title":"Causality guided machine learning model on wetland CH4 emissions across global wetlands","docAbstract":"<p><span>Wetland CH</span><sub>4</sub><span>&nbsp;emissions are among the most uncertain components of the global CH</span><sub>4</sub><span>&nbsp;budget. The complex nature of wetland CH</span><sub>4</sub><span>&nbsp;processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH</span><sub>4</sub><span>&nbsp;emissions. In this study, we used the flux measurements of CH</span><sub>4</sub><span>&nbsp;from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH</span><sub>4</sub><span>&nbsp;emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH</span><sub>4</sub><span>&nbsp;emissions in all studied wetland types. Ecosystem respiration (CO</span><sub>2</sub><span>) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH</span><sub>4</sub><span>&nbsp;emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH</span><sub>4</sub><span>&nbsp;emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH</span><sub>4</sub><span>&nbsp;emissions within earth system land models.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2022.109115","usgsCitation":"Yuan, K., Zhu, Q., Li, F., Riley, W.J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K.B., Wu, H., Baldocchi, D., Ma, H., Desai, A.R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., Tuittila, E., Jurasinski, G., Koebsch, F., Campbell, D.I., Schmid, H.P., Lohila, A., Goeckede, M., Nilsson, M.B., Friborg, T., Jansen, J., Zona, D., Euskirchen, E.S., Ward, E., Bohrer, G., Jin, Z., Liu, L., Iwata, H., Goodrich, J.P., and Jackson, R.B., 2022, Causality guided machine learning model on wetland CH4 emissions across global wetlands: Agricultural and Forest Meteorology, v. 324, 109115, 10 p., https://doi.org/10.1016/j.agrformet.2022.109115.","productDescription":"109115, 10 p.","ipdsId":"IP-140199","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2022.109115","text":"Publisher Index Page"},{"id":408305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"324","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yuan, Kunxiaojia","contributorId":297856,"corporation":false,"usgs":false,"family":"Yuan","given":"Kunxiaojia","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Qing","contributorId":260547,"corporation":false,"usgs":false,"family":"Zhu","given":"Qing","affiliations":[],"preferred":false,"id":854488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Fa","contributorId":297906,"corporation":false,"usgs":false,"family":"Li","given":"Fa","email":"","affiliations":[],"preferred":false,"id":854608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riley, William J. 0000-0002-4615-2304","orcid":"https://orcid.org/0000-0002-4615-2304","contributorId":194645,"corporation":false,"usgs":false,"family":"Riley","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":854490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torn, Margaret","contributorId":240709,"corporation":false,"usgs":false,"family":"Torn","given":"Margaret","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chu, Housen","contributorId":217396,"corporation":false,"usgs":false,"family":"Chu","given":"Housen","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":854492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McNicol, Gavin 0000-0002-6655-8045","orcid":"https://orcid.org/0000-0002-6655-8045","contributorId":260536,"corporation":false,"usgs":false,"family":"McNicol","given":"Gavin","email":"","affiliations":[],"preferred":false,"id":854493,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chen, Mingshu","contributorId":220088,"corporation":false,"usgs":false,"family":"Chen","given":"Mingshu","email":"","affiliations":[{"id":37968,"text":"Sun Yat-Sen 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Most such studies are limited by the number of dimensions of the streamflow regime investigated. This study, in contrast, provides a comprehensive evaluation of the streamflow regime based on three widely available machine learning approaches (support vector regression, random forest, and cubist regression) and on multiple linear regression to predict 106 natural streamflow metrics at ungaged locations. This evaluation is done for 545 streamgages across the northwest United States for recurrence-interval flood metrics and for 1,851 sites in the conterminous United States for non-flood metrics. The results indicate that for flood metrics, predictions by cubist regression and support vector regressions have substantially less error than the other approaches. 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         -119.081,\n                34.078\n              ],\n              [\n                -119.43884,\n                34.34848\n              ],\n              [\n                -120.36778,\n                34.44711\n              ],\n              [\n                -120.62286,\n                34.60855\n              ],\n              [\n                -120.74433,\n                35.15686\n              ],\n              [\n                -121.71457,\n                36.16153\n              ],\n              [\n                -122.54747,\n                37.55176\n              ],\n              [\n                -122.51201,\n                37.78339\n              ],\n              [\n                -122.95319,\n                38.11371\n              ],\n              [\n                -123.7272,\n                38.95166\n              ],\n              [\n                -123.86517,\n                39.76699\n              ],\n              [\n                -124.39807,\n                40.3132\n              ],\n              [\n                -124.17886,\n                41.14202\n              ],\n              [\n                -124.2137,\n                41.99964\n              ],\n              [\n                -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/programs/water-availability-and-use-science-program\" data-mce-href=\"https://www.usgs.gov/programs/water-availability-and-use-science-program\">Water Availability and Use Science Program</a><br>U.S. Geological Survey <br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Area and Basin Attributes</li><li>Methods</li><li>Performance Evaluation</li><li>Discussion on Performance of Approaches</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. 176 Basin Attributes and Corresponding Descriptions</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-08-31","noUsgsAuthors":false,"publicationDate":"2022-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850082,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70236602,"text":"70236602 - 2022 - What did they just say? Building a Rosetta stone for geoscience and machine learning","interactions":[],"lastModifiedDate":"2022-09-14T13:16:23.671752","indexId":"70236602","displayToPublicDate":"2022-08-31T09:17:57","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"What did they just say? Building a Rosetta stone for geoscience and machine learning","docAbstract":"Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(s) to represent very specific, precise, but, perhaps, different concepts. Under the best circumstances, misunderstanding key terms will lead toward a breakdown of efficiency. Under less optimal conditions, miscommunication will sow frustration, lead to errors, and inhibit scientific breakthroughs. Here, our research group of geoscientists and machine learning experts presents a process to help geoscientists understand the fundamentals of supervised learning by describing the general workflow (i.e., a conceptual pipeline) for supervised learning that must be understood by all the parties involved in a geoscience-machine learning endeavor. Terms critical for machine learning are introduced, defined, and used within the context of an overly simplified mock hydrological study to illustrate their appropriate usage, and then used again in the context of a published geothermal-machine learning study. These key terms are divided into two groups, which are 1) essential to the field of machine learning but are predominantly absent in geoscience or 2) homonyms (i.e., words with the same spelling or pronunciation but with different meanings) between the fields. Lastly, we discuss a few other important homonyms that were not introduced in the general workflow but arise regularly in machine learning applications","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the earth to save the earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 Geothermal Rising Conference","conferenceDate":"Aug 28-31, 2022","conferenceLocation":"Reno, NV","language":"English","publisher":"Geothermal Rising","usgsCitation":"Mordensky, S.P., Lipor, J., Burns, E., and Lindsey, C.R., 2022, What did they just say? Building a Rosetta stone for geoscience and machine learning, <i>in</i> Using the earth to save the earth, v. 46, Reno, NV, Aug 28-31, 2022, p. 1347-1374.","productDescription":"28 p.","startPage":"1347","endPage":"1374","ipdsId":"IP-140223","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":406592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":406573,"type":{"id":15,"text":"Index Page"},"url":"https://grc2022.mygeoenergynow.org/"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":851489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70236950,"text":"70236950 - 2022 - Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","interactions":[],"lastModifiedDate":"2024-05-16T15:46:58.587708","indexId":"70236950","displayToPublicDate":"2022-08-31T06:58:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12603,"text":"Journal of the American Society of Agricultural and Biological Engineers","active":true,"publicationSubtype":{"id":10}},"title":"Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","docAbstract":"<p>The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.</p>","language":"English","publisher":"American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org","doi":"10.13031/ja.14964","usgsCitation":"Douglas-Mankin, K., Rumsey, C., Sexstone, G., Ivahnenko, T.I., Houston, N., Chavarria, S., Senay, G.B., Foster, L.K., Thomas, J., Flickinger, A.K., Galanter, A.E., Moeser, C.D., Welborn, T.L., Pedraza, D.E., Lambert, P., and Johnson, M.S., 2022, Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis: Journal of the American Society of Agricultural and Biological Engineers, v. 65, no. 4, p. 881-901, https://doi.org/10.13031/ja.14964.","productDescription":"21 p.","startPage":"881","endPage":"901","ipdsId":"IP-133533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":446605,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.13031/ja.14964","text":"Publisher Index Page"},{"id":407213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.7314453125,\n              30.410781790845864\n            ],\n            [\n              -102.21679687500001,\n              30.410781790845864\n            ],\n            [\n              -102.21679687500001,\n              38.30718056188316\n            ],\n            [\n              -109.7314453125,\n              38.30718056188316\n            ],\n            [\n              -109.7314453125,\n              30.410781790845864\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"65","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas-Mankin, Kyle  R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":223378,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle  R.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":852780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rumsey, Christine 0000-0001-7536-750X crumsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7536-750X","contributorId":146240,"corporation":false,"usgs":true,"family":"Rumsey","given":"Christine","email":"crumsey@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivahnenko, Tamara I. 0000-0002-1124-7688 ivahnenk@usgs.gov","orcid":"https://orcid.org/0000-0002-1124-7688","contributorId":2050,"corporation":false,"usgs":true,"family":"Ivahnenko","given":"Tamara","email":"ivahnenk@usgs.gov","middleInitial":"I.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":false,"id":852783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Houston, Natalie 0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chavarria, Shaleene 0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852785,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":852786,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852787,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thomas, Jonathan V. 0000-0003-0903-9713","orcid":"https://orcid.org/0000-0003-0903-9713","contributorId":217874,"corporation":false,"usgs":true,"family":"Thomas","given":"Jonathan V.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852788,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flickinger, Allison K. 0000-0002-8638-2569","orcid":"https://orcid.org/0000-0002-8638-2569","contributorId":223702,"corporation":false,"usgs":true,"family":"Flickinger","given":"Allison","email":"","middleInitial":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852789,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":205393,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852790,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852791,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852793,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pedraza, Diana E. 0000-0003-4483-8094","orcid":"https://orcid.org/0000-0003-4483-8094","contributorId":207782,"corporation":false,"usgs":true,"family":"Pedraza","given":"Diana","email":"","middleInitial":"E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852796,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lambert, Patrick M. 0000-0001-6808-2303","orcid":"https://orcid.org/0000-0001-6808-2303","contributorId":296913,"corporation":false,"usgs":false,"family":"Lambert","given":"Patrick M.","affiliations":[{"id":32931,"text":"USGS - Retired","active":true,"usgs":false}],"preferred":false,"id":852794,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Michael Scott 0000-0003-2378-7144 johnsonm@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-7144","contributorId":296914,"corporation":false,"usgs":true,"family":"Johnson","given":"Michael","email":"johnsonm@usgs.gov","middleInitial":"Scott","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852795,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70236124,"text":"ofr20221044 - 2022 - Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19","interactions":[],"lastModifiedDate":"2022-09-27T13:30:35.071237","indexId":"ofr20221044","displayToPublicDate":"2022-08-30T13:38:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1044","displayTitle":"Distribution and Demography of Coastal Cactus Wrens in Southern California, 2015–19","title":"Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19","docAbstract":"<p>Surveys and monitoring for the coastal Cactus Wren (<i>Campylorhynchus brunneicapillus</i>) were completed in San Diego County between March 2015 and July 2019. A total of 383 plots were surveyed across 3 genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto). From 2015 to 2019, 317 plots were surveyed 8 times (twice per year in 2015, 2017–19). Additional plots were added in later years as wrens were discovered in new locations. We found differences in the proportion of plots occupied in the genetic clusters, with a lower proportion of plots occupied in the Otay cluster than in the Lake Jennings and Sweetwater/Encanto clusters in all years. Plot occupancy increased each year in the Otay and Sweetwater/Encanto clusters but not in the Lake Jennings cluster. The number of Cactus Wren territories increased from 2015 through 2018, and then decreased in 2019 in all three genetic clusters.</p><p>We monitored nesting activities for two populations of Cactus Wrens in southern San Diego County. The Otay population consisted of two sites within the Otay genetic cluster, and the San Diego population consisted of two sites within the Sweetwater/Encanto and Lake Jennings genetic clusters. Nest monitoring occurred at 10–13 territories per year in the Otay population and 14–18 territories in the San Diego population from 2015 through 2019. All territories were occupied by pairs except two territories in 2015, five in 2016, and two in 2019. Between 46 and 74 Cactus Wren nests were monitored each year, which totaled 295 monitored nests from 2015 to 2019. To evaluate the direct influence of precipitation on breeding success, bio-year precipitation (“precipitation”) was calculated from July 1 of the prior year through June 30 of the breeding season year. Overall apparent nest success was positively influenced by precipitation with the lowest apparent nest success of 50 percent in 2015 and the highest apparent nest success of 72 percent in 2017, corresponding to the second lowest and the highest precipitation years, respectively. Apparent nest success also was higher in the Otay population than in the San Diego population. The number of brood nests initiated per pair and the number of renesting attempts per pair also were higher in years with more precipitation. Other metrics of Cactus Wren nesting success and productivity were positively influenced by the amount of precipitation, including clutch size and egg hatching success. The percent of hatchlings that fledged was greater in the Otay population than in the San Diego population but was not influenced by precipitation. The number of fledglings per pair was higher in years with more precipitation and was greater in the Otay population than in the San Diego population. Predation was the predominant cause of nest failure in both populations.</p><p>Analysis of Cactus Wren daily nest survival rate indicated that there was a population, and possibly a precipitation effect on nest survival, with the daily survival rate for the Otay population significantly higher than for the San Diego population and weak increase in the daily survival rate with more precipitation.</p><p>A total of 629 Cactus Wrens were banded during the course of the study, 360 in the San Diego population and 269 in the Otay population. Between 2015 and 2019, we resighted 301 color-banded adult birds that ranged between 1 and 8 years old. One additional color-banded bird was resighted in San Pasqual Valley (as part of a separate study); this bird originated in the San Diego population and was excluded from our analyses.</p><p>Annual survival was higher for adult Cactus Wrens (ranging from 60 to 70 percent) than for first-year wrens (ranging from 20 to 28 percent) and varied by year. Annual survival was also weakly but positively correlated with precipitation. Annual survival was higher for first year and adult Cactus Wrens following years with increased precipitation. We found no evidence that survival differed by population.</p><p>Banding also allowed us to examine whether there were differences in movement of adult and first-year Cactus Wrens by year or by population. We found that average dispersal distance for first-year Cactus Wrens was 1.9 kilometers in the Otay population and 1.6 kilometers in the San Diego population and did not differ by population or year. Dispersal between populations was not common. We detected five instances of movement of first-year wrens between the San Diego and Otay populations. All movements into and out of the San Diego population were from or into territories in the Sweetwater area. We detected no movement between the Lake Jennings site and either of the Sweetwater or Otay sites; however, we did detect one wren that dispersed from Lake Jennings to the San Pasqual Valley population in 2019, which was a distance of 26.4 kilometers. Adult Cactus Wrens were site-faithful, with 87 percent of adults remaining on the same territory between breeding seasons. Precipitation may be a weak driver of movement for adult Cactus Wrens, with adults more likely to remain on the same territory following years of increased precipitation. There was no difference in adult movement between populations.</p><p>Arthropods were collected in pitfall traps and by vacuum in 23 Cactus Wren territories during 3 sampling periods in 2016 (early nesting, peak nesting, and late nesting). Arthropods of 19 orders and at least 128 families were collected. Analysis of 43 Cactus Wren fecal samples identified 10 arthropod orders that were present in more than 10 percent of fecal samples. The most abundant arthropod order collected was Hymenoptera; however, Cactus Wrens consumed arthropods in the order Hymenoptera significantly less than their availability, suggesting that this order was avoided. No other orders were significantly selected or avoided; however, selection indices of arthropod families identified that two families of arthropods (Isopoda Porcellionidae [woodlice] and Hymenoptera Formicidae [ants]) were avoided. After excluding the taxa that were avoided or not represented in fecal samples, 95 percent of Cactus Wren prey items were collected in pitfall traps and 5 percent were collected by vacuum. The most abundant prey orders captured were Diptera, Coleoptera, Hemiptera, Hymenoptera, and Aranea.</p><p>Analysis of the abundance of Cactus Wren prey items by vegetation type and sampling period indicated that vegetation type by itself was not a significant predictor of arthropod abundance but interacted with sampling period. Seasonal availability of arthropods was highest in the peak nesting period, followed by early and late nesting periods for California sagebrush (<i>Artemisia californica</i>), lemonadeberry (<i>Rhus integrifolia</i>), non-native grass, and bare ground, whereas availability increased from early to late nesting periods for blue elderberry (<i>Sambucus mexicana</i> spp. <i>caerulea</i>), cactus (<i>Opuntia</i> spp. and <i>Cylindropuntia</i> spp.), California buckwheat (<i>Eriogonum fasciculatum</i>), native bunch grasses, and black mustard (<i>Brassica nigra</i>). During the early nesting period, arthropods were most abundant in native bunch grasses and least abundant in lemonadeberry. During the peak nesting period, arthropods were most abundant in native bunch grasses and in areas of bare ground and were least abundant in cactus and blue elderberry. During late nesting, arthropods were most abundant in blue elderberry and non-native grass and least abundant in lemonadeberry and mustard.</p><p>Each year from 2015 to 2019, vegetation data were collected at the same 23 territories where arthropods were sampled: 9 territories in the Otay population and 14 territories in the San Diego population. Cactus, California buckwheat, and non-native grasses were detected within at least 60 percent of sampling points in the Otay population. Cactus, California sagebrush, California buckwheat, non-native grass, and black mustard each were detected within an average of 40 percent of sampling points in the San Diego population. No native bunch grass or lemonadeberry were recorded at the Lake Jennings site within the San Diego population. The cover of shrub species was relatively stable throughout the 5 years. Cover of herbaceous species and bare ground had greater annual variation than shrub species.</p><p>We found that vegetation cover varied widely among territories, with territory accounting for 69 percent of the variation in vegetation cover. Redundancy analysis allowed us to identify the vegetation types that accounted for the most variation. We used the top scores from the redundancy analysis to identify six vegetation types to be used in generalized linear mixed models analyzing the relationships between vegetation type, precipitation, and Cactus Wren breeding productivity. Three vegetation variables influenced the number of fledglings produced per pair. California sagebrush had a positive effect on the number of fledglings per pair whereas non-native grass and black mustard had a negative effect.</p><p>Breeding productivity, survival, and movements of adult and first-year Cactus Wrens indicated that the Otay population behaved similarly to, if not out-performed, the San Diego population during the span of our project, suggesting that the driving forces behind low numbers of Cactus Wrens in the Otay population before 2015 were no longer in effect. The Cactus Wren populations in Otay and San Diego reached a peak in 2018, which followed a year of high productivity and survivorship, both of which were correlated with high precipitation. This peak in population size was consistent with reproductive timing and productivity in other bird populations in semi-arid ecosystems that were linked to precipitation and arthropod abundance. We did not find a strong link among arthropod abundance, vegetation composition, and Cactus Wren breeding productivity, likely in part because arthropod abundance varied by vegetation type and sampling period, suggesting that different vegetation types provided important sources of prey at different periods of the breeding season. Arthropod abundance also may not represent arthropod availability when vegetation structure discourages the ground foraging behavior of species such as Cactus Wrens. Cover of non-native grass negatively influenced breeding productivity, although arthropods were abundant in non-native grass. Other factors that could have influenced differential breeding productivity between the Otay and San Diego populations were habitat restoration, control of annual herbaceous vegetation, human disturbance, lingering effects of wildfire, and nest predation. Overall, precipitation appeared to be a driver of Cactus Wren breeding productivity and possibly survival, potentially obscuring proximate effects of arthropod or vegetation composition.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221044","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., Houston, A., and Kus, B.E., 2022, Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19: U.S. Geological Survey Open-File Report 2022-1044, 44 p., https://doi.org/10.3133/ofr20221044.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-136839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435711,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143ZTB2","text":"USGS data release","linkHelpText":"Cactus Wren Invertebrate Diet Derived from Sequencing of Nestling Fecal Samples in San Diego County, California"},{"id":405951,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1044"},{"id":405841,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1044/images"},{"id":405840,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.xml"},{"id":405839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.pdf","text":"Report","size":"4 MB"},{"id":405838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1044/covrthb.jpg"},{"id":405842,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Diego County: U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5."}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.3065185546875,\n              32.52365781569917\n            ],\n            [\n              -116.630859375,\n              32.52365781569917\n            ],\n            [\n              -116.630859375,\n              32.983324091837474\n            ],\n            [\n              -117.3065185546875,\n              32.983324091837474\n            ],\n            [\n              -117.3065185546875,\n              32.52365781569917\n            ]\n          ]\n        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suellen_lynn@usgs.gov","orcid":"https://orcid.org/0000-0003-1543-0209","contributorId":3843,"corporation":false,"usgs":true,"family":"Lynn","given":"Suellen","email":"suellen_lynn@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":850162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houston, Alexandra 0000-0002-8599-8265 ahouston@usgs.gov","orcid":"https://orcid.org/0000-0002-8599-8265","contributorId":139460,"corporation":false,"usgs":true,"family":"Houston","given":"Alexandra","email":"ahouston@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":850163,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":850164,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70236156,"text":"70236156 - 2022 - Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads","interactions":[],"lastModifiedDate":"2022-08-30T14:07:18.106436","indexId":"70236156","displayToPublicDate":"2022-08-30T09:01:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads","docAbstract":"Coastal ecosystems are increasingly threatened by anthropogenic stressors such as harmful algal blooms and hypoxia projected to intensify through the combined effects of eutrophication and warming.  As a major terrestrial nitrogen (N) source to the ocean, rivers play a critical role in shaping both coastal and global biogeochemical cycling.  Combining an enhanced-resolution (1/4°), global ocean physical-biogeochemical model with dynamic river inputs, we estimate that elevated river nitrogen loads alone resulted in an increase of 16.6 Tg (+5.8%) in the global coastal nitrogen inventory (CNI) over the half century between 1961 and 2010.  This change was accompanied by increases in coastal net primary productivity (NPP, +4.6%) and benthic detrital flux (BDF, +7.3%), the latter of which is indicative of an overall higher oxygen demand in coastal sediments.  After normalization by area, the ecosystems most sensitive to added river nitrogen (g N m-2 yr-1) were those with long residence times and strong nitrogen limitation.  While even enhanced-resolution global models remain limited in their capacity to resolve near-shore responses, these basic sensitivity factors provide two relevant axes for frameworks assessing the comparative susceptibility of globally distributed coastal ecosystems to enhanced nitrogen loading, and the effectiveness of mitigation strategies.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL094367","usgsCitation":"Liu, X., Stock, C., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., and Milly, P.C., 2022, Simulated global coastal ecosystem responses to a half-century increase in river nitrogen loads: Geophysical Research Letters, v. 48, no. 17, e2021GL094367, 14 p., https://doi.org/10.1029/2021GL094367.","productDescription":"e2021GL094367, 14 p.","ipdsId":"IP-114921","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":446613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gl094367","text":"Publisher Index Page"},{"id":405903,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"17","noUsgsAuthors":false,"publicationDate":"2021-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Xiao","contributorId":295963,"corporation":false,"usgs":false,"family":"Liu","given":"Xiao","email":"","affiliations":[],"preferred":false,"id":850285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Charles A.","contributorId":217586,"corporation":false,"usgs":false,"family":"Stock","given":"Charles A.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":850268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunne, John P.","contributorId":88995,"corporation":false,"usgs":true,"family":"Dunne","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":850269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Minjin","contributorId":177261,"corporation":false,"usgs":false,"family":"Lee","given":"Minjin","email":"","affiliations":[],"preferred":false,"id":850270,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shevliakova, Elena","contributorId":201589,"corporation":false,"usgs":false,"family":"Shevliakova","given":"Elena","email":"","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":850271,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Malyshev, Sergey","contributorId":189177,"corporation":false,"usgs":false,"family":"Malyshev","given":"Sergey","affiliations":[],"preferred":false,"id":850272,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":850273,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70236623,"text":"70236623 - 2022 - Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2023-03-24T16:46:52.398893","indexId":"70236623","displayToPublicDate":"2022-08-30T06:44:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (<i>Rana pipiens</i>) in the Prairie Pothole Region","title":"Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Context</h3><p>Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Objectives</h3><p>This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (<i>Rana pipiens</i>) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Methods</h3><p>Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusion</h3><p>While the<span>&nbsp;</span><i>R. pipiens</i><span>&nbsp;</span>populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01515-8","usgsCitation":"Waraniak, J.M., Mushet, D., and Stockwell, C.A., 2022, Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region: Landscape Ecology, v. 37, p. 2877-2893, https://doi.org/10.1007/s10980-022-01515-8.","productDescription":"17 p.","startPage":"2877","endPage":"2893","ipdsId":"IP-137156","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446618,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01515-8","text":"Publisher Index Page"},{"id":406585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -102.39257812499999,\n              45.920587344733654\n            ],\n            [\n              -96.85546875,\n              45.920587344733654\n            ],\n            [\n              -96.85546875,\n              48.574789910928864\n            ],\n            [\n              -102.39257812499999,\n              48.574789910928864\n            ],\n            [\n              -102.39257812499999,\n              45.920587344733654\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":851527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":851528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":851529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70236251,"text":"70236251 - 2022 - Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century","interactions":[],"lastModifiedDate":"2022-08-31T11:35:53.919041","indexId":"70236251","displayToPublicDate":"2022-08-30T06:32:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century","docAbstract":"<div class=\"article-section__content en main\"><p>Streamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long-term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period-of-record flows. We then analyzed drought trends using the Mann-Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR031930","usgsCitation":"Hammond, J., Simeone, C.E., Hecht, J.S., Hodgkins, G.A., Lombard, M.A., McCabe, G.J., Wolock, D.M., Wieczorek, M., Olson, C., Caldwell, T., Dudley, R., and Price, A.N., 2022, Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century: Water Resources Research, v. 58, no. 9, e2022WR031930, 20 p., https://doi.org/10.1029/2022WR031930.","productDescription":"e2022WR031930, 20 p.","ipdsId":"IP-134958","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":435712,"rank":0,"type":{"id":30,"text":"Data 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,{"id":70235726,"text":"tm2A19 - 2022 - Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data","interactions":[],"lastModifiedDate":"2022-08-30T10:50:17.58358","indexId":"tm2A19","displayToPublicDate":"2022-08-29T14:10:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2-A19","displayTitle":"Methods for Evaluating Gap Analysis Project Habitat Distribution Maps with Species Occurrence Data","title":"Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data","docAbstract":"<p>The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random.</p><p>In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, <i>Desmognathus quadramaculatus</i> (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters.&nbsp;We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm2A19","collaboration":"Prepared in cooperation with North Carolina State University, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology","usgsCitation":"Rubino, M.J., McKerrow, A.J., Tarr, N.M., and Williams, S.G., 2022, Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data: U.S. Geological Survey Techniques and Methods 2-A19, 13 p., 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