{"pageNumber":"410","pageRowStart":"10225","pageSize":"25","recordCount":184617,"records":[{"id":70234356,"text":"70234356 - 2022 - Activity patterns of Hawaiian forest birds in a fragmented and continuous landscape","interactions":[],"lastModifiedDate":"2022-08-09T12:13:46.931348","indexId":"70234356","displayToPublicDate":"2022-02-02T07:11:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2190,"text":"Journal of Avian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Activity patterns of Hawaiian forest birds in a fragmented and continuous landscape","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Activity patterns are a key component of avian life history and behavior, and decisions about how activity periods are structured can have important fitness implications. Despite the importance of activity patterns, individual variability in activity is poorly studied in small birds. We used automated radio telemetry to track Hawaiian forest birds from three dietary guilds in a continuous forest, and in a similar forest that was naturally fragmented in the 1800s by lava flows but supports stable populations of native birds. We generated kernel density profiles of activity (KDEs) over the full tracking period of each bird and used the KDEs to derive seven activity metrics that characterized daily activity patterns. We identified three distinct activity strategies using a cluster analysis of the activity metrics and assessed day-to-day variability in activity strategy to understand intraindividual consistency in activity behavior. We also evaluated the influence of landscape structure, dietary guild and sex on the suite of activity metrics using constrained ordination. Most birds (61%) exhibited high levels of sustained activity throughout the day, while 23% showed irregular activity most of the day followed by a peak in the late afternoon, and 16% were mainly active in the morning. Though activity patterns varied by sex and dietary guild, landscape structure had the strongest influence on activity patterns. Birds sustained high levels of activity throughout the day in the fragmented landscape regardless of dietary guild, suggesting they needed to be more active to obtain necessary resources. However, activity patterns of birds in the fragmented landscape varied more day-to-day, indicating they retained a method for adjusting behavior to match internal and external conditions. This study reveals how a diverse suite of tropical birds has adapted to long-term fragmentation in Hawaiʻi and demonstrates the utility of automated radio telemetry for studying the activity strategies of birds.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/jav.02905","usgsCitation":"Smetzer, J.R., Paxton, K.L., Hart, P.J., and Paxton, E.H., 2022, Activity patterns of Hawaiian forest birds in a fragmented and continuous landscape: Journal of Avian Biology, v. 2022, no. 4, e02905, 15 p., https://doi.org/10.1111/jav.02905.","productDescription":"e02905, 15 p.","ipdsId":"IP-130717","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":448924,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jav.02905","text":"Publisher Index Page"},{"id":435983,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OI0E5P","text":"USGS data release","linkHelpText":"Hawaii Island bird activity from 2014 through 2019"},{"id":404991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"2022","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Smetzer, Jennifer R","contributorId":255352,"corporation":false,"usgs":false,"family":"Smetzer","given":"Jennifer","email":"","middleInitial":"R","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":848630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false},{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":848631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":848632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":848633,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70241787,"text":"70241787 - 2022 - Context-dependent variation in persistence of host populations in the face of disease","interactions":[],"lastModifiedDate":"2023-03-27T12:07:35.971729","indexId":"70241787","displayToPublicDate":"2022-02-02T07:06:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Context-dependent variation in persistence of host populations in the face of disease","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p><strong>Research Highlight</strong>: Valenzuela-Sánchez, A., Azat, C., Cunningham, A. A., Delgado, S., Bacigalupe, L. D., Beltrand, J., Serrano, J. M., Sentenac, H., Haddow, N., Toledo, V., Schmidt, B. R., &amp; Cayuela, H. (2022). Interpopulation differences in male reproductive effort drive the population dynamics of a host exposed to an emerging fungal pathogen.<span>&nbsp;</span><i>Journal of Animal Ecology</i>,<span>&nbsp;</span><i>00</i>, 1– 12.<span>&nbsp;</span><a class=\"linkBehavior\" href=\"https://doi.org/10.1111/1365-2656.13603\" data-mce-href=\"https://doi.org/10.1111/1365-2656.13603\">https://doi.org/10.1111/1365-2656.13603</a>. Understanding the nuances of population persistence in the face of a stressor can help predict extinction risk and guide conservation actions. However, the exact mechanisms driving population stability may not always be known. In this paper, Valenzuela-Sánchez et al. (2022) integrate long-term mark–recapture data, focal measurements of reproductive effort, a population matrix model and inferences on life-history variation to reveal differences in demographic response to disease in a susceptible frog species (<i>Rhinoderma darwinii</i>). Valenzuela-Sánchez et al. found that demographic compensation via recruitment explained the positive population growth rate in their high disease prevalence population whereas the low disease prevalence population did not compensate and thus had decreasing population growth. Compensatory recruitment was likely due to the high probability of males brooding, and the high number of brooded larvae in the high prevalence population compared to low prevalence and disease-free populations. Valenzuela-Sánchez et al. also document faster generation times in the high prevalence population, which may indicate a faster life history that may be contributing to the population's ability to compensate for reduced survival. Lastly, the authors find a positive relationship between disease prevalence and the proportion of juveniles in a given population that suggest that there may be a threshold for disease prevalence that triggers increased reproductive effort. Altogether, their study provides novel support for increased reproductive effort as the pathway for compensatory recruitment leading to increasing population growth despite strong negative effects of disease on adult survival. Their results also caution the overgeneralization of the effects of stressors (e.g. disease) on population dynamics, where context-dependent responses may differ among host populations of a given species.</p></div></div>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13654","usgsCitation":"Hardy, B., Muths, E.L., and Koons, D.N., 2022, Context-dependent variation in persistence of host populations in the face of disease: Journal of Animal Ecology, v. 91, no. 2, p. 282-286, https://doi.org/10.1111/1365-2656.13654.","productDescription":"5 p.","startPage":"282","endPage":"286","ipdsId":"IP-135071","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":414767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Hardy, Bennett","contributorId":303568,"corporation":false,"usgs":false,"family":"Hardy","given":"Bennett","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":867564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":867565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koons, David N.","contributorId":28137,"corporation":false,"usgs":false,"family":"Koons","given":"David","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":867566,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70241895,"text":"70241895 - 2022 - Quantifying streamflow depletion from groundwater pumping: A practical review of past and emerging approaches for water management","interactions":[],"lastModifiedDate":"2023-03-30T11:53:25.809423","indexId":"70241895","displayToPublicDate":"2022-02-02T06:51:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying streamflow depletion from groundwater pumping: A practical review of past and emerging approaches for water management","docAbstract":"<div class=\"abstract-group \"><div class=\"article-section__content en main\"><p>Groundwater pumping can cause reductions in streamflow (“streamflow depletion”) that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management-focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion-related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12998","usgsCitation":"Zipper, S., Farmer, W., Brookfield, A.E., Ajami, H., Reeves, H.W., Wardropper, C., Hammond, J., Gleeson, T., and Deines, J.M., 2022, Quantifying streamflow depletion from groundwater pumping: A practical review of past and emerging approaches for water management: Journal of the American Water Resources Association, v. 58, no. 2, p. 289-312, https://doi.org/10.1111/1752-1688.12998.","productDescription":"24 p.","startPage":"289","endPage":"312","ipdsId":"IP-126304","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448929,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/1752-1688.12998","text":"External Repository"},{"id":414952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Zipper, Samuel 0000-0002-8735-5757","orcid":"https://orcid.org/0000-0002-8735-5757","contributorId":225160,"corporation":false,"usgs":false,"family":"Zipper","given":"Samuel","email":"","affiliations":[{"id":41056,"text":"Kansas Geological Survey, University of Kansas, Lawrence KS 66047, USA","active":true,"usgs":false}],"preferred":false,"id":868129,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":868130,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brookfield, Andrea E.","contributorId":202677,"corporation":false,"usgs":false,"family":"Brookfield","given":"Andrea","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":868131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ajami, Hoori 0000-0001-6883-7630","orcid":"https://orcid.org/0000-0001-6883-7630","contributorId":303806,"corporation":false,"usgs":false,"family":"Ajami","given":"Hoori","email":"","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":868132,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reeves, Howard W. 0000-0001-8057-2081 hwreeves@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-2081","contributorId":2307,"corporation":false,"usgs":true,"family":"Reeves","given":"Howard","email":"hwreeves@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868133,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wardropper, Chloe 0000-0002-0652-2315","orcid":"https://orcid.org/0000-0002-0652-2315","contributorId":303807,"corporation":false,"usgs":false,"family":"Wardropper","given":"Chloe","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":868134,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868135,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gleeson, Tom","contributorId":42694,"corporation":false,"usgs":false,"family":"Gleeson","given":"Tom","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":868136,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Deines, Jillian M. 0000-0002-4279-8765","orcid":"https://orcid.org/0000-0002-4279-8765","contributorId":303808,"corporation":false,"usgs":false,"family":"Deines","given":"Jillian","email":"","middleInitial":"M.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":868137,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70262534,"text":"70262534 - 2022 - Mark-recapture models accurately predict growth trajectories of known-age Muskellunge in Green Bay, Lake Michigan","interactions":[],"lastModifiedDate":"2025-01-21T18:57:53.395514","indexId":"70262534","displayToPublicDate":"2022-02-01T12:32:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Mark-recapture models accurately predict growth trajectories of known-age Muskellunge in Green Bay, Lake Michigan","docAbstract":"<p><span>Length-at-age data are commonly used to describe growth of fish, and obtaining these data typically involves estimating ages from calcified structures (e.g., fin spines or rays, otoliths, or cleithra). Verifying the accuracy of age and growth estimates for long-lived fish is often difficult because known-age fish are not available for all ages in a population. Mark–recapture methods offer nonlethal alternatives for estimating growth of fish that do not require age data. However, few studies have compared growth trajectories estimated from mark–recapture data with trajectories estimated using the standard von Bertalanffy growth function (VBGF) incorporating length-at-age data from known-age fish. We used a robust data set of Muskellunge&nbsp;</span><i>Esox masquinongy</i><span>&nbsp;sampled from Green Bay, Lake Michigan, during 1990–2018 to compare growth trajectories estimated from three mark–recapture models and a VBGF fitted to length-at-age data from known-age individuals. Growth trajectories estimated with mark–recapture models were similar to trajectories estimated with a VBGF using known-age fish. Our results suggest that using recapture of tagged fish provides a viable alternative for describing Muskellunge growth trajectories compared with using ages estimated from calcified structures, where incorrect age estimates represent an additional source of error.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10757","usgsCitation":"Sheffer, R., Hogler, S., and Isermann, D.A., 2022, Mark-recapture models accurately predict growth trajectories of known-age Muskellunge in Green Bay, Lake Michigan: North American Journal of Fisheries Management, v. 42, no. 2, p. 410-424, https://doi.org/10.1002/nafm.10757.","productDescription":"15 p.","startPage":"410","endPage":"424","ipdsId":"IP-129450","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay, Lake Michigan, Sturgeon Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.27706007733632,\n              44.35374357415128\n            ],\n            [\n              -86.88196993224543,\n              44.35374357415128\n            ],\n            [\n              -86.88196993224543,\n              45.190198563184424\n            ],\n            [\n              -88.27706007733632,\n              45.190198563184424\n            ],\n            [\n              -88.27706007733632,\n              44.35374357415128\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Sheffer, Robert J.","contributorId":349585,"corporation":false,"usgs":false,"family":"Sheffer","given":"Robert J.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogler, Steven R.","contributorId":349586,"corporation":false,"usgs":false,"family":"Hogler","given":"Steven R.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":924494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":924492,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227605,"text":"cir1489 - 2022 - National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results","interactions":[],"lastModifiedDate":"2026-03-16T19:44:20.869218","indexId":"cir1489","displayToPublicDate":"2022-02-01T12:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1489","displayTitle":"National Assessment of Carbon Dioxide Enhanced Oil Recovery and Associated Carbon Dioxide Retention Resources—Results","title":"National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Results","docAbstract":"<p>In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources available if current carbon dioxide enhanced oil recovery (CO<sub>2</sub>-EOR) technologies were applied to amenable oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO<sub>2</sub> that could be stored (retained) as a result of CO<sub>2</sub>-EOR activities. The USGS assessment team evaluated more than 3,500 oil reservoirs that were miscible to injected CO<sub>2</sub>. The assessed reservoirs are in 185 previously defined USGS plays in 33 petroleum provinces of 7 national regions. The assessment team estimated that the technically recoverable oil associated with CO<sub>2</sub>-EOR ranges from approximately 25,000 million barrels (MMbbl) at the P<sub>5</sub> percentile to as much as 32,000 MMbbl at the P<sub>95</sub> percentile, with a mean of 29,000 MMbbl. The associated CO<sub>2</sub> retention ranges from approximately 7,400 million metric tons (Mt) at the P<sub>5</sub> percentile to as much as 9,500 Mt at the P<sub>95</sub> percentile, with a mean of 8,400 Mt. The West Texas and Eastern New Mexico region and the Gulf Coast region together contain 60 percent of the mean assessed CO<sub>2</sub>-EOR oil potential and 61 percent of the mean assessed CO<sub>2</sub> retention. Other regions with significant resource potential include the Midcontinent region and Rocky Mountains and Northern Great Plains region.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1489","programNote":"Energy Resources Program","usgsCitation":"Warwick, P.D., Attanasi, E.D., Blondes, M.S., Brennan, S.T., Buursink, M.L., Cahan, S.M., Doolan, C.A., Freeman, P.A., Karacan, C.Ö., Lohr, C.D., Merrill, M.D., Olea, R.A., Shelton, J.L., Slucher, E.R., and Varela, B.A., 2022, National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources—Results: U.S. Geological Survey Circular 1489, 39 p., https://doi.org/10.3133/cir1489.","productDescription":"Report: viii, 39 p.; Data release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122375","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":501193,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112323.htm","linkFileType":{"id":5,"text":"html"}},{"id":395079,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20213057","text":"U.S. Geological Survey Fact Sheet 2021–3057","linkHelpText":"- National Assessment of Carbon Dioxide Enhanced Oil Recovery and Associated Carbon Dioxide Retention Resources—Summary"},{"id":395078,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AG37KI","text":"USGS data release","linkHelpText":"National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources—Data"},{"id":395077,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1489/cir1489.pdf","text":"Report","size":"6.05 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href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/connect\" data-mce-href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/connect\">Energy Resources Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 20192<br>Telephone: 703–648–6470</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>National Subdivisions</li><li>Data Sources</li><li>Assessment Process</li><li>Results of the Assessment of Carbon Dioxide Enhanced Oil Recovery and Associated Carbon Dioxide Retention Resources</li><li>Discussion of Results</li><li>Comparison of Results With Findings From Previous Assessments</li><li>Conclusions</li><li>References Cited</li><li>Glossary</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-02-01","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brennan, Sean T. 0000-0002-7102-9359 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buursink, Marc L. 0000-0001-6491-386X mbuursink@usgs.gov","orcid":"https://orcid.org/0000-0001-6491-386X","contributorId":3362,"corporation":false,"usgs":true,"family":"Buursink","given":"Marc","email":"mbuursink@usgs.gov","middleInitial":"L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831336,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cahan, Steven M. 0000-0002-4776-3668 scahan@usgs.gov","orcid":"https://orcid.org/0000-0002-4776-3668","contributorId":4529,"corporation":false,"usgs":true,"family":"Cahan","given":"Steven","email":"scahan@usgs.gov","middleInitial":"M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831337,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doolan, Colin A. 0000-0002-7595-7566 cdoolan@usgs.gov","orcid":"https://orcid.org/0000-0002-7595-7566","contributorId":3046,"corporation":false,"usgs":true,"family":"Doolan","given":"Colin","email":"cdoolan@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":831338,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":831339,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Karacan, C. 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,{"id":70227601,"text":"fs20213057 - 2022 - National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Summary","interactions":[],"lastModifiedDate":"2026-03-25T14:46:01.13385","indexId":"fs20213057","displayToPublicDate":"2022-02-01T12:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3057","displayTitle":"National Assessment of Carbon Dioxide Enhanced Oil Recovery and Associated Carbon Dioxide Retention Resources—Summary","title":"National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources — Summary","docAbstract":"<h1>Introduction</h1><p>In 2020, the U.S. Geological Survey (USGS) completed a probabilistic assessment of the volume of technically recoverable oil resources that might be produced by using current carbon dioxide enhanced oil recovery (CO<sub>2</sub>-EOR) technologies in amenable conventional oil reservoirs underlying the onshore and State waters areas of the conterminous United States. The assessment also includes estimates of the mass of CO<sub>2</sub> that could be stored (retained) in the assessed oil reservoirs following the application of the CO<sub>2</sub>-EOR process. The USGS assessment team evaluated more than 3,500 oil reservoirs that were miscible to injected CO<sub>2</sub>. The assessed reservoirs are in 185 previously defined USGS plays in 33 petroleum provinces of 7 national regions. The team estimated that the total technically recoverable oil resulting from the application of the CO<sub>2</sub>-EOR process ranges from approximately 25,000 million barrels (MMbbl) at the P<sub>5</sub> percentile to as much as 32,000 MMbbl at the P<sub>95</sub> percentile, with a mean of 29,000 MMbbl. The associated CO<sub>2</sub> retention ranges from approximately 7,400 million metric tons (Mt) at the P<sub>5</sub> percentile to as much as 9,500 Mt at the P<sub>95</sub> percentile, with a mean of 8,400 Mt. The results are summarized in this fact sheet and are provided in more detail in the companion data release and circular.</p><p>The West Texas and Eastern New Mexico region (primarily its Permian Basin) and the Gulf Coast region together contain 60 percent of the mean assessed CO<sub>2</sub>-EOR oil potential and 61 percent of the mean assessed CO<sub>2</sub> retention. Other regions with significant resource potential include the Midcontinent region and the Rocky Mountains and Northern Great Plains region.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213057","programNote":"Energy Resources Program","usgsCitation":"Warwick, P.D., Attanasi, E.D., Blondes, M.S., Brennan, S.T., Buursink, M.L., Cahan, S.M., Doolan, C.A., Freeman, P.A., Karacan, C.Ö., Lohr, C.D., Merrill, M.D., Olea, R.A., Shelton, J.L., Slucher, E.R., and Varela, B.A., 2022, National assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources—Summary: U.S. Geological Survey Fact Sheet 2021–3057, 6 p., https://doi.org/10.3133/fs20213057.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122387","costCenters":[{"id":241,"text":"Eastern Energy Resources Science 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href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/connect\" data-mce-href=\"https://www.usgs.gov/energy-and-minerals/energy-resources-program/connect\">Energy Resources Program</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 20192<br>Telephone: 703–648–6470</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-02-01","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science 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,{"id":70243263,"text":"70243263 - 2022 - Spreading like wildfire: The rising threat of extraordinary landscape fires - A rapid response assessment","interactions":[],"lastModifiedDate":"2023-05-05T16:34:26.725858","indexId":"70243263","displayToPublicDate":"2022-02-01T11:33:54","publicationYear":"2022","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Spreading like wildfire: The rising threat of extraordinary landscape fires - A rapid response assessment","docAbstract":"Free-burning landscape fire is an important natural phenomenon critical to the healthy functioning of many ecosystems. It is an important land management tool, culturally, economically, and ecologically. Therefore, not all vegetation fires are unwanted. This Rapid Response Assessment (RRA) focuses on the apparent increase in the occurrence, extent, duration, and consequences of wildfires – unusual or extraordinary free-burning vegetation fires. The RRA considers the changing patterns of wildfires around the world, their impact on people and the environment, and options for mitigating risk and improving our response.","language":"English","publisher":"United Nations Environment Program (UNEP)","usgsCitation":"Popescu, A., Paulson, A., Christianson, A.C., Sullivan, A.S., Tulloch, A., Bilbao, B., Mathison, C., Robinson, C., Ganz, D., Nangoma, D., Saah, D., Armenteras, D., Driscoll, D.A., Hankins, D.L., Kelley, D.I., Langer, E.R., Baker, E., Reisen, F., Robinne, F., Galudra, G., Humphrey, G., Safford, H., Baird, I.G., Oliveras, I., Littell, J., Kieft, J., Chew, J., Maclean, K., Wittenberg, L., Anderson, L.O., Gillson, L., Plucinski, M., Moritz, M., Brown, M., Castillo Soto, M., Flannigan, M., Costello, O., Silva, P.S., Fernandes, P., Moore, P., Jandt, R., Blanchi, R., Libonati, R., Archibald, S., Dunlop, S., McCaffrey, S., Page, S., Gonzalez Delgado, T.M., Kurvits, T., Sokchea, T., and Charlton, V., 2022, Spreading like wildfire: 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,{"id":70256686,"text":"70256686 - 2022 - Five years of monitoring a bio-engineered living shoreline: Comparison of oyster population development by reef technology.","interactions":[],"lastModifiedDate":"2024-08-30T16:28:58.233888","indexId":"70256686","displayToPublicDate":"2022-02-01T11:19:29","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"139-2022","title":"Five years of monitoring a bio-engineered living shoreline: Comparison of oyster population development by reef technology.","docAbstract":"<p><span>The Living Shoreline Demonstration Project (PO-148) used five bio-engineered reef technologies (Reef Balls in two configurations; Figure 1) acting as breakwaters to protect vulnerable shorelines. While the primary goal is to attenuate wave energy, the sustainability and success of these products as “living” shorelines are based on their ability to enhance oyster habitat, enabling the reef to maintain elevation within the rapidly changing environment (i.e., sea level rise, subsidence). This report documents the recruitment, survival, and growth of the living components of the reef – oysters and other encrusting organisms (e.g. mussels, barnacles). This final technical report provides data from five years of monitoring (November 2017 – December 2021) of reefs located along the western side of Eloi Bay in Pontchartrain Basin (Figure 2). Monitoring goals included assessment of (1) annual oyster densities and population dynamics on the reefs, (2) annual density and diversity of other encrusting organisms, and (3) comparisons of outcomes by reef technology, exposure, and water quality. Detailed information on technologies used, construction design, as-built elevations are available in Coast &amp; Harbor Engineering (2016) Design Memorandum dated March 25, 2016, submitted to Louisiana Coastal Protection and Restoration Authority.</span></p>","language":"English","publisher":"U.S. Fish & Wildlife Service","doi":"10.3996/css70529922","usgsCitation":"Swam, L.M., Marshall, D.A., and La Peyre, M., 2022, Five years of monitoring a bio-engineered living shoreline: Comparison of oyster population development by reef technology.: Cooperator Science Series 139-2022, ii, 19 p., https://doi.org/10.3996/css70529922.","productDescription":"ii, 19 p.","ipdsId":"IP-137095","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -89.38780223527601,\n              29.790840321829222\n            ],\n            [\n              -89.42069143656458,\n              29.790840321829222\n            ],\n            [\n              -89.42069143656458,\n              29.750906524072846\n            ],\n            [\n              -89.38780223527601,\n              29.750906524072846\n            ],\n            [\n              -89.38780223527601,\n              29.790840321829222\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Swam, Lauren M.","contributorId":341585,"corporation":false,"usgs":false,"family":"Swam","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Danielle Aguilar","contributorId":341509,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle","email":"","middleInitial":"Aguilar","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":908645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908646,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70230821,"text":"70230821 - 2022 - The Denver Well Logging Society February 2022 Newsletter: From the VP - Technology","interactions":[],"lastModifiedDate":"2022-04-26T16:10:46.350732","indexId":"70230821","displayToPublicDate":"2022-02-01T11:08:07","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":9980,"text":"Denver Well Drilling Society Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"The Denver Well Logging Society February 2022 Newsletter: From the VP - Technology","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"The Denver Well Logging Society","usgsCitation":"Lagesse, J.H., 2022, The Denver Well Logging Society February 2022 Newsletter: From the VP - Technology: Denver Well Drilling Society Newsletter, no. February 2022, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-137345","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":399681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399680,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dwls.spwla.org/2022-02-Newsletter.html"}],"issue":"February 2022","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lagesse, Jenny Heather 0000-0002-3541-4751","orcid":"https://orcid.org/0000-0002-3541-4751","contributorId":290621,"corporation":false,"usgs":true,"family":"Lagesse","given":"Jenny","email":"","middleInitial":"Heather","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":841417,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70249400,"text":"70249400 - 2022 - Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. Lambotte","interactions":[],"lastModifiedDate":"2023-10-05T15:52:18.805799","indexId":"70249400","displayToPublicDate":"2022-02-01T10:40:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. Lambotte","docAbstract":"<p><span>Based on numerous studies of the relevant geodetic data, a low-angle thrusting mechanism has been assigned to the 1960 Chile earthquake. Kanamori, Rivera and Lambotte recently suggested that a component of dextral slip comparable to the thrusting be included in the mechanism to satisfy long-period, teleseismic observations. The absence of geodetic evidence for that huge strike-slip component is the subject of this comment. The geodetic data are largely measurements of coseismic uplift associated with the earthquake but include eight measurements of the coseismic change in shear strain. Because strike-slip produces relatively little uplift except near the end points of the rupture, identification of that strike-slip component in the geodetic data depends upon the measured, shear-strain change. I consider elastic, half-space models of oblique slip on the plate interface possibly supplemented by simultaneous dextral slip on the nearby, intra-arc Liquiñe-Ofqui Fault Zone. Slip is assumed to be uniform along strike. The best fits to the geodetic data for these models furnish little evidence for strike-slip on those structures. To satisfy the long-period, teleseismic data, Kanamori&nbsp;</span><i>et&nbsp;al</i><span>. proposed six examples, each of which requires a large amount of dextral slip. Because the long-period, teleseismic data do not define the slip distributions, I have used the best fits of those examples to the geodetic data to define those distributions. The large thrusting near the deformation front required by those slip distributions implies large uplift there, contrary to the uplift inferred from the inversion of tsunami data. However, an acceptable fit to the geodetic data and the tsunami data for the six examples suggested by Kanamori&nbsp;</span><i>et&nbsp;al</i><span>. can be obtained if the seismic moments specified by them are reduced by a factor ∼1.8, a factor within the uncertainties in estimating seismic moments of the 1960 Chile earthquake. The presence of strike-slip in those reduced-moment examples despite the lack of geodetic evidence for strike-slip is due to a remarkable coincidence that requires careful balancing of contributions from the shallower (depths &lt;&nbsp;70&nbsp;km) coseismic sources against those from the deeper coseismic sources to nullify the geodetic evidence for strike-slip. Such balancing is possible, but it is remarkable that the balancing is so nearly perfect that it nullifies the geodetic evidence for strike-slip and thereby confounds the interpretation of the geodetic data.</span></p>","language":"English","publisher":"Oxford University Press","doi":"10.1093/gji/ggab364","usgsCitation":"Savage, J.C., 2022, Comment on ‘Evidence for a large strike-slip component during the 1960 Chilean earthquake’ by H. Kanamori, L. Rivera, and S. 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,{"id":70227892,"text":"70227892 - 2022 - Health surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants","interactions":[],"lastModifiedDate":"2022-05-13T14:39:58.812289","indexId":"70227892","displayToPublicDate":"2022-02-01T10:17:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Health surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants","docAbstract":"<p>Augmentation of wild populations with captive-bred individuals presents an inherent risk of co-introducing novel pathogens to naïve species, but it can be an important tool for supplementing small or declining populations. Game species used for human enterprise and recreation such as the ring-necked pheasant (<i>Phasianus colchicus</i>) are commonly raised in captivity and released onto public and private wildlands as a method of augmenting naturalized pheasant populations. This study presents findings on pathogen exposure from three sources of serological data collected in California during 2014–2017 including (a) 71 pen-reared pheasants sampled across seven game bird breeding farms, (b) six previously released pen-reared pheasants captured at two study sites where wild pheasants occurred and (c) 79 wild pheasants captured across six study sites. In both pen-reared and wild pheasants, antibodies were detected against haemorrhagic enteritis virus (HEV), infectious laryngotracheitis (ILT), infectious bursal disease virus (IBDV), paramyxovirus type 1 (PMV-1) and<span>&nbsp;</span><i>Pasteurella multocida</i><span>&nbsp;</span>(PM). Previously released pen-reared pheasants were seropositive for HEV, ILT, and PM. Generalized linear mixed models accounting for intraclass correlation within groups indicated that pen-reared pheasants were more than twice as likely to test positive for HEV antibodies. Necropsy and ancillary diagnostics were performed in addition to serological testing on 40 pen-reared pheasants sampled from five of the seven farms. Pheasants from three of these farms tested positive by PCR for Siadenovirus, the causative agent of both haemorrhagic enteritis in turkeys and marble spleen disease of pheasants, which are serologically indistinguishable. Following necropsy, owners from the five farms were surveyed regarding husbandry and biosecurity practices. Farms ranged in size from 10,000 to more than 100,000 birds, two farms raised other game bird species on premises, and two farms used some form of vaccination. Biosecurity practices varied by farm, but the largest farm implemented the strictest practices.</p>","language":"English","publisher":"Wiley","doi":"10.1111/tbed.14068","usgsCitation":"Dwight, I., Coates, P.S., Stoute, S.T., and Pitesky, M.E., 2022, Health surveillance of a potential bridge host: Pathogen exposure risks posed to avian populations augmented with captive-bred pheasants: Transboundary and Emerging Diseases, v. 69, no. 3, p. 1095-1107, https://doi.org/10.1111/tbed.14068.","productDescription":"13 p.","startPage":"1095","endPage":"1107","ipdsId":"IP-119580","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/tbed.14068","text":"Publisher Index Page"},{"id":395209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento and San Joaquin Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.33349609375,\n              34.88593094075317\n            ],\n            [\n              -118.71826171875,\n              35.191766965947394\n            ],\n            [\n              -118.41064453125,\n              35.871246850027966\n            ],\n            [\n              -119.44335937499999,\n              37.3002752813443\n            ],\n            [\n              -120.80566406250001,\n              39.14710270770074\n            ],\n            [\n              -121.53076171875,\n              40.51379915504413\n            ],\n            [\n              -120.43212890625,\n              41.244772343082076\n            ],\n            [\n              -120.4541015625,\n              42.01665183556825\n            ],\n            [\n              -122.32177734375,\n              42.032974332441405\n            ],\n            [\n              -122.9150390625,\n              41.07935114946899\n            ],\n            [\n              -122.87109375,\n              39.26628442213066\n            ],\n            [\n              -121.88232421875,\n              37.75334401310656\n            ],\n            [\n              -120.73974609374999,\n              36.155617833818525\n            ],\n            [\n              -119.33349609375,\n              34.88593094075317\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"69","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Dwight, Ian 0000-0002-8393-5391 idwight@usgs.gov","orcid":"https://orcid.org/0000-0002-8393-5391","contributorId":192077,"corporation":false,"usgs":true,"family":"Dwight","given":"Ian","email":"idwight@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":832482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":832483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoute, Simone T.","contributorId":202770,"corporation":false,"usgs":false,"family":"Stoute","given":"Simone","email":"","middleInitial":"T.","affiliations":[{"id":36526,"text":"California Animal Health and Food Safety Laboratory","active":true,"usgs":false}],"preferred":false,"id":832484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pitesky, Maurice E.","contributorId":176920,"corporation":false,"usgs":false,"family":"Pitesky","given":"Maurice","email":"","middleInitial":"E.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":832485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227893,"text":"70227893 - 2022 - Management foundations for navigating ecological transformation by resisting, accepting, or directing social-ecological change","interactions":[],"lastModifiedDate":"2022-02-02T14:24:42.435166","indexId":"70227893","displayToPublicDate":"2022-02-01T09:58:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Management foundations for navigating ecological transformation by resisting, accepting, or directing social-ecological change","docAbstract":"Despite striking global change, management to ensure healthy landscapes and sustained natural resources has tended to set objectives on the basis of the historical range of variability in stationary ecosystems. Many social–ecological systems are moving into novel conditions that can result in ecological transformation. We present four foundations to enable a transition to future-oriented conservation and management that increases capacity to manage change. The foundations are to identify plausible social–ecological trajectories, to apply upstream and deliberate engagement and decision-making with stakeholders, to formulate management pathways to desired futures, and to consider a portfolio approach to manage risk and account for multiple preferences across space and time. We use the Kenai National Wildlife Refuge in Alaska as a case study to illustrate how the four foundations address common land management challenges for navigating transformation and deciding when, where, and how to resist, accept, or direct social–ecological change.","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biab083","usgsCitation":"Magness, D., Hoang, L., Belote, T., Brennan, J., Carr, W., Chapin, F.S., Clifford, K.R., Morrison, W., Morton, J., and Sofaer, H., 2022, Management foundations for navigating ecological transformation by resisting, accepting, or directing social-ecological change: BioScience, v. 72, no. 1, p. 30-44, https://doi.org/10.1093/biosci/biab083.","productDescription":"15 p.","startPage":"30","endPage":"44","ipdsId":"IP-130226","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":448939,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70228376,"text":"70228376 - 2022 - Integrating urban planning and water management through green infrastructure in the United States-Mexico border","interactions":[],"lastModifiedDate":"2022-02-09T15:53:52.635912","indexId":"70228376","displayToPublicDate":"2022-02-01T09:49:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Integrating urban planning and water management through green infrastructure in the United States-Mexico border","docAbstract":"<p><span>Creating sustainable, resilient, and livable cities calls for integrative approaches and collaborative practices across temporal and spatial scales. However, practicability is challenged by institutional, social, and technical complexities and the need to build collective understanding of integrated approaches. Rapid urbanization along the United States-Mexico border, fueled by industrialization, trade, and migration, has resulted in cities confronted with recurrent flooding risk, extended drought, water pollution, habitat destruction and systemic vulnerabilities. The international border, which separates natural and built ecosystems, is both a challenge and an opportunity, making a unique social and institutional setting ideal for testing the integration of urban planning and water management. Our research focuses on fusing multi-functional and multi-scalar green infrastructure to restore ecosystem services through a strategic binational planning process. This paper describes this planning process, including the development and application of both a land suitability analysis and a hydrological model to optimally site green infrastructure in the Nogales, Arizona, United States—Nogales, Sonora, Mexico, cross border region. We draw lessons from this process and stakeholder feedback focused on the potential for urban green infrastructure, to allow for adaptation and even transformation in the face of current and future challenges such as limited resources, underdeveloped governance, bordering, and climate change. In sum, a cross border network of green infrastructure can provide a backbone to connect this transboundary watershed while providing both hydrological and social benefits.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2022.782922","usgsCitation":"Lara-Valencia, F., Garcia, M., Norman, L., Anides Morales, A., and Castellanos-Rubio, E.E., 2022, Integrating urban planning and water management through green infrastructure in the United States-Mexico border: Frontiers in Water, v. 4, 782922, 17 p., https://doi.org/10.3389/frwa.2022.782922.","productDescription":"782922, 17 p.","ipdsId":"IP-132393","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":448941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2022.782922","text":"Publisher Index Page"},{"id":395668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.0772705078125,\n              31.23159167205059\n            ],\n            [\n              -110.8685302734375,\n              31.23159167205059\n            ],\n            [\n              -110.8685302734375,\n              31.423975737976697\n            ],\n            [\n              -111.0772705078125,\n              31.423975737976697\n            ],\n            [\n              -111.0772705078125,\n              31.23159167205059\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2022-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lara-Valencia, Francisco","contributorId":275344,"corporation":false,"usgs":false,"family":"Lara-Valencia","given":"Francisco","affiliations":[{"id":56763,"text":"Arizona State University, Phoenix, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":834019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Margaret","contributorId":275345,"corporation":false,"usgs":false,"family":"Garcia","given":"Margaret","email":"","affiliations":[{"id":56763,"text":"Arizona State University, Phoenix, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":834020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":834021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anides Morales, Alma","contributorId":275346,"corporation":false,"usgs":false,"family":"Anides Morales","given":"Alma","email":"","affiliations":[{"id":50057,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":834022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Castellanos-Rubio, Edgar E.","contributorId":275347,"corporation":false,"usgs":false,"family":"Castellanos-Rubio","given":"Edgar","email":"","middleInitial":"E.","affiliations":[{"id":56764,"text":"Instituto Municipal de Investigación y Planeación de Nogales Sonora","active":true,"usgs":false}],"preferred":false,"id":834023,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70231295,"text":"70231295 - 2022 - Thermodynamic insights into the production of methane hydrate reservoirs from depressurization of pressure cores","interactions":[],"lastModifiedDate":"2022-05-06T13:59:17.797111","indexId":"70231295","displayToPublicDate":"2022-02-01T09:46:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Thermodynamic insights into the production of methane hydrate reservoirs from depressurization of pressure cores","docAbstract":"<p><span>We present results of slow (multiple day) depressurization experiments of pressure cores recovered from Green Canyon Block 955 in the northern Gulf of Mexico during The University of Texas at Austin Hydrate Pressure Coring Expedition (UT-GOM2-1). These stepwise depressurization experiments monitored the pressure and temperature within the core storage chamber during each pressure step, or “shut-in” period to better understand dissociation behavior and to provide insight on the thermodynamic state of gas hydrate reservoirs during production. The pressure rebound that occurs in response to a depressurization step occurs more slowly during later dissociation steps, likely reflecting a slower heat transfer rate, decreasing salinity gradient, and increased compressibility of the pore and surrounding fluids with progressive dissociation. We demonstrate that displacement of water by gas within the core storage chamber during successive dissociations both insulates the core and increases the compressibility of the pore and chamber fluid. The increased compressibility requires that a larger hydrate volume dissociates per unit of pressure recovery. Pressures observed during progressive dissociation steps are lower than predicted by the sample’s average salinity, with pressures approaching the freshwater phase boundary during frequent dissociation steps, suggesting that local pore-water freshening strongly influences dissociation behavior. To avoid underestimating the magnitude of pressure drawdown required to sustain dissociation in the reservoir, we suggest that hydrate production models use the freshwater phase boundary rather than a phase boundary determined from bulk salinity.</span></p>","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08182120216","usgsCitation":"Phillips, S.C., Flemings, P., You, K., and Waite, W., 2022, Thermodynamic insights into the production of methane hydrate reservoirs from depressurization of pressure cores: AAPG Bulletin, v. 106, no. 5, p. 1025-1049, https://doi.org/10.1306/08182120216.","productDescription":"25 p.","startPage":"1025","endPage":"1049","ipdsId":"IP-125570","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":400207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Green Canyon Block 955 (GC 955) study area, northern Gulf of Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96,\n              25\n            ],\n            [\n              -88,\n              25\n            ],\n            [\n              -88,\n              30\n            ],\n            [\n              -96,\n              30\n            ],\n            [\n              -96,\n              25\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"106","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Stephen C. 0000-0003-0858-4701","orcid":"https://orcid.org/0000-0003-0858-4701","contributorId":268177,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":842257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flemings, Peter  B.","contributorId":242641,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter  B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":842258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"You, Kehua","contributorId":239915,"corporation":false,"usgs":false,"family":"You","given":"Kehua","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":842259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":842260,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70229539,"text":"70229539 - 2022 - Guiding principles for using satellite-derived maps in rangeland management","interactions":[],"lastModifiedDate":"2024-05-17T16:00:34.965217","indexId":"70229539","displayToPublicDate":"2022-02-01T09:27:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Guiding principles for using satellite-derived maps in rangeland management","docAbstract":"<p>On the Ground</p><ul><li>Rangeland management has entered a new era with the accessibility and advancement of satellite-derived maps.</li><li>Maps provide a comprehensive view of rangelands in space and time, and challenge us to think critically about natural variability.</li><li>Here, we advance the practice of using satellite-derived maps with four guiding principles designed to increase end user confidence and thereby accessibility of these data for decision-making.</li></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.09.004","usgsCitation":"Allred, B.W., Creutzburg, M., Carlson, J.C., Cole, C.C., Dovichin, C., Duniway, M.C., Jones, M.O., Maestas, J.D., Naugle, D., Nauman, T.W., Okin, G.S., Reeves, M.C., Rigge, M.B., Savage, S., Twidwell, D., Uden, D.R., and Zhou, B., 2022, Guiding principles for using satellite-derived maps in rangeland management: Rangelands, v. 44, no. 1, p. 78-86, https://doi.org/10.1016/j.rala.2021.09.004.","productDescription":"9 p.","startPage":"78","endPage":"86","ipdsId":"IP-123606","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448947,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rala.2021.09.004","text":"Publisher Index Page"},{"id":396993,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.96044921875,\n              42.032974332441405\n            ],\n            [\n              -111.11572265625,\n              42.032974332441405\n            ],\n            [\n              -111.11572265625,\n              45.1433047394883\n            ],\n            [\n              -116.96044921875,\n              45.1433047394883\n            ],\n            [\n              -116.96044921875,\n              42.032974332441405\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"44","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Brady W","contributorId":216378,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W","affiliations":[{"id":39397,"text":"W.A. Franke College of Forestry and Conservation University of Montana, Missoula","active":true,"usgs":false}],"preferred":false,"id":837784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creutzburg, Megan K","contributorId":288365,"corporation":false,"usgs":false,"family":"Creutzburg","given":"Megan K","affiliations":[{"id":61733,"text":"Institute for Natural Resources, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":837785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, John C","contributorId":288366,"corporation":false,"usgs":false,"family":"Carlson","given":"John","email":"","middleInitial":"C","affiliations":[{"id":61734,"text":"Bureau of Land Management, Montana/Dakotas State Office","active":true,"usgs":false}],"preferred":false,"id":837786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Christopher C","contributorId":288367,"corporation":false,"usgs":false,"family":"Cole","given":"Christopher","email":"","middleInitial":"C","affiliations":[{"id":61735,"text":"Bureau of Land Management, National Operations Center","active":true,"usgs":false}],"preferred":false,"id":837787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dovichin, Colin M. 0000-0002-9325-5779","orcid":"https://orcid.org/0000-0002-9325-5779","contributorId":244127,"corporation":false,"usgs":false,"family":"Dovichin","given":"Colin M.","affiliations":[{"id":33701,"text":"former USGS-NPWRC","active":true,"usgs":false}],"preferred":false,"id":837788,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":837789,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Matthew O.","contributorId":169805,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"O.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":837790,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maestas, Jeremy D","contributorId":191086,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D","affiliations":[],"preferred":false,"id":837791,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Naugle, David E.","contributorId":255114,"corporation":false,"usgs":false,"family":"Naugle","given":"David E.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":837792,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":837793,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Okin, Gregory S","contributorId":193068,"corporation":false,"usgs":false,"family":"Okin","given":"Gregory","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":837794,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Reeves, Matthew C","contributorId":288368,"corporation":false,"usgs":false,"family":"Reeves","given":"Matthew","email":"","middleInitial":"C","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":837795,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837796,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Savage, Shannon L","contributorId":288369,"corporation":false,"usgs":false,"family":"Savage","given":"Shannon L","affiliations":[{"id":61735,"text":"Bureau of Land Management, National Operations Center","active":true,"usgs":false}],"preferred":false,"id":837797,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":837798,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Uden, Daniel R.","contributorId":219904,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":40095,"text":"Nebraska Cooperative Fish and Wildlife Unit, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":837799,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zhou, Bo","contributorId":288370,"corporation":false,"usgs":false,"family":"Zhou","given":"Bo","email":"","affiliations":[{"id":61738,"text":"Department of Geography, University of California","active":true,"usgs":false}],"preferred":false,"id":837800,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}}
,{"id":70236587,"text":"70236587 - 2022 - Predicting flood damage probability across the conterminous United States","interactions":[],"lastModifiedDate":"2022-09-12T14:44:05.940487","indexId":"70236587","displayToPublicDate":"2022-02-01T09:26:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Predicting flood damage probability across the conterminous United States","docAbstract":"<p>Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of flood damage probability for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events (<i>n</i> = 71,434) and a suite of geospatial predictors (e.g., flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a flood damage probability for each 100-m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g., forest) resulting in higher error rates than others (e.g., wetlands). Our results identified flood damage probability hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating flood damage probability estimates across the nation.</p>","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac4f0f","usgsCitation":"Collins, E., Sanchez, G., Terando, A., Stillwell, C.C., Mitasova, H., Sebastian, A., and Meentemeyer, R.K., 2022, Predicting flood damage probability across the conterminous United States: Environmental Research Letters, v. 17, 034006, 15 p., https://doi.org/10.1088/1748-9326/ac4f0f.","productDescription":"034006, 15 p.","ipdsId":"IP-133941","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":448948,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac4f0f","text":"Publisher Index Page"},{"id":435984,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954TTQN","text":"USGS data release","linkHelpText":"Data and Code for Predicting Flood Damage Probability Across the Conterminous United States"},{"id":406535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                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,{"id":70229242,"text":"70229242 - 2022 - Action plan for restoration of coral reef coastal protection services: Case study example and workbook","interactions":[],"lastModifiedDate":"2022-03-03T14:52:52.979222","indexId":"70229242","displayToPublicDate":"2022-02-01T08:44:16","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":8915,"text":"EPA Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"EPA/600/R-21/306","title":"Action plan for restoration of coral reef coastal protection services: Case study example and workbook","docAbstract":"<p>This report was prepared by the U.S. Environmental Protection Agency (USEPA), Office of Research and Development, as part of the Air, Climate and Energy (ACE) research program, with support from Tetra Tech, Inc., and in collaboration with the National Oceanic and Atmospheric Administration, the U.S. Geological Survey, and The Nature Conservancy. The ACE research program provides scientific information and tools to support USEPA’s commitment to clean air, clean water and sustainable natural resources, even as environmental conditions change. A key component of this is the development of sound science to support adaptation. Adaptation involves preparing for and adjusting to the effects of climate change and its interactions with other global and local stressors. Because these effects are diverse, interactive, and difficult to predict, adapting management of natural resources in this context can be very challenging. </p><p>Coral reefs—which provide valued ecosystem services such as fisheries, coastal protection, and tourism—are threatened by the effects of increased sea surface temperatures, sea level rise, and intensifying storms. These large-scale stressors are interacting with local stressors such as pollution, overfishing, and recreational misuse to drive ongoing and accelerating declines in coral reef ecosystems. Thus, there is a rising urgency to design and implement climate change adaptation measures that will enable reef resilience in the face of these changes. This includes accounting for, and adjusting to, the combined effects of climate change and local stressors in coral reef protection and restoration efforts. </p><p>The action plan, example case study, and workbook found in this report demonstrate a structured process for integrating climate-smart design considerations into restoration planning using A Manager’s Guide to Coral Reef Restoration Planning and Design. The focus is a hypothetical coral reef restoration project that has a goal of recovering nature-based coastal protection services using restoration interventions. The intent is to provide readers with a completed example of how to use the Guide workbook to inform a draft action plan, centering on the topic of coastal protection as a burgeoning area of interest in coral reef science and management communities. The information in this hypothetical case study is not intended for direct use; rather, it provides a starting point for more detailed planning that would occur in specific places. And while a full review of the current literature on reef restoration methods is outside the scope of this report, readers are encouraged to use the examples herein as well as in the Guide as a jumping-off point for exploring the rapidly growing body of information on methods, techniques, successes, failures, monitoring challenges and future directions of coral reef restoration in a changing world. The workbook, together with the action plan, can serve as a valuable record of the planning thought process as well as a living document for adaptive management, to be updated through time as improved information becomes available.</p>","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Courtney, C.A., West, J.M., Storlazzi, C.D., Viehman, T.S., Czaplinski, R., Hague, E., and Shaver, E.C., 2022, Action plan for restoration of coral reef coastal protection services: Case study example and workbook: EPA Report EPA/600/R-21/306, 73 p.","productDescription":"73 p.","ipdsId":"IP-130997","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396695,"type":{"id":11,"text":"Document"},"url":"https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=544353&Lab=CPHEA"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Courtney, Catherine A.","contributorId":287687,"corporation":false,"usgs":false,"family":"Courtney","given":"Catherine","email":"","middleInitial":"A.","affiliations":[{"id":61627,"text":"TetraTech, Inc.","active":true,"usgs":false}],"preferred":false,"id":837024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"West, Jordon M.","contributorId":287688,"corporation":false,"usgs":false,"family":"West","given":"Jordon","email":"","middleInitial":"M.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":837025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viehman, T. Shay","contributorId":259297,"corporation":false,"usgs":false,"family":"Viehman","given":"T.","email":"","middleInitial":"Shay","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":true,"id":837027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Czaplinski, Richard","contributorId":287689,"corporation":false,"usgs":false,"family":"Czaplinski","given":"Richard","email":"","affiliations":[{"id":39152,"text":"TetraTech","active":true,"usgs":false}],"preferred":false,"id":837028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hague, Erin","contributorId":287690,"corporation":false,"usgs":false,"family":"Hague","given":"Erin","email":"","affiliations":[{"id":39152,"text":"TetraTech","active":true,"usgs":false}],"preferred":false,"id":837029,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaver, Elizabeth C.","contributorId":287691,"corporation":false,"usgs":false,"family":"Shaver","given":"Elizabeth","email":"","middleInitial":"C.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":837030,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70231875,"text":"70231875 - 2022 - From the president","interactions":[],"lastModifiedDate":"2022-06-01T13:44:01.459403","indexId":"70231875","displayToPublicDate":"2022-02-01T08:43:24","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10773,"text":"EMD Newsletter","active":true,"publicationSubtype":{"id":30}},"title":"From the president","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"American Association of Petroleum Geologists","usgsCitation":"Birdwell, J.E., 2022, From the president: EMD Newsletter, no. February 2022, p. 1-4.","productDescription":"4 p.","startPage":"1","endPage":"4","ipdsId":"IP-136789","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":401535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401509,"type":{"id":15,"text":"Index Page"},"url":"https://www.aapg.org/divisions/emd/newsletter/currentpage/1"}],"issue":"February 2022","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":844018,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70227733,"text":"sir20215139 - 2022 - Simulation of groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California","interactions":[],"lastModifiedDate":"2026-04-08T16:30:38.872221","indexId":"sir20215139","displayToPublicDate":"2022-02-01T08:15:45","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5139","displayTitle":"Simulation of Groundwater and Surface-Water Resources of the San Antonio Creek Valley Watershed, Santa Barbara County, California","title":"Simulation of groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California","docAbstract":"<p>In the San Antonio Creek Valley watershed (SACVW), western Santa Barbara County, California, groundwater is the primary source of water for agricultural irrigation, the town of Los Alamos, and supplemental water to Vandenberg Space Force Base (VSFB). Groundwater pumpage has increased since the 1970s as non-irrigated agricultural land has been converted to irrigated land and as local pumping for municipal use has increased. This increase in groundwater use has resulted in declining groundwater levels, adjustments in surface-water flows and species habitats, and changes in water quality. Water managers are addressing the challenges of meeting this increased demand while maintaining sustainable groundwater supplies. To address these challenges, Santa Barbara County Water Agency, Vandenberg Space Force Base (VSFB), and the U.S. Geological Survey (USGS) undertook a cooperative study to characterize the integrated hydrologic system of the SACVW and develop tools to better understand and manage the groundwater system. The objectives of this study were to improve the understanding of the integrated hydrologic system and incorporate the understanding into an integrated groundwater and surface-water flow model that can be used to help manage the water resources in the SACVW.</p><p>The San Antonio Creek integrated model (SACIM) was developed using the USGS coupled groundwater and surface-water flow model to simulate the hydrologic system of the SACVW and provide annual and average water budgets for 1948–2018 water years. Results from the SACIM indicated that between 1948 and 2018, total groundwater from storage (storage depletion) for the period was 453,300 acre-feet (acre-ft). Agricultural pumpage was the largest discharge and accounted for a total of 1,020,000 acre-ft of groundwater. Increased pumpage since the mid-1980s (of which agricultural pumpage is the primary component) is tied to an increased rate of storage depletion and reduced rates of groundwater evapotranspiration and surface leakage (groundwater discharge to the surface and soil zone). The increased pumpage also reduced subsurface inflow to Barka Slough, resulting in a decline in upward flow through the underlying hydrogeologic units and surface leakage. In addition to quantifying historical changes in the integrated hydrologic system, the SACIM is a tool than can be used by water managers to evaluate the effects of different climatic and hydrologic conditions and management strategies.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215139","collaboration":"Prepared in cooperation with Santa Barbara County Water Agency and Vandenberg Space  Force Base","programNote":"Groundwater Availability and Use Assessments","usgsCitation":"Woolfenden, L.R., Engott, J.A., Larsen, J.D., and Cromwell, G., 2022, Simulation of groundwater and surface-water resources of the San Antonio Creek Valley Watershed, Santa Barbara County, California: U.S. Geological Survey Scientific Investigations Report 2021–5139, 76 p., https://doi.org/10.3133/sir20215139.","productDescription":"Report: xii, 76 p.; Data Release","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-108916","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":502284,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112321.htm","linkFileType":{"id":5,"text":"html"}},{"id":394985,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5139/covrthb.png"},{"id":394986,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5139/sir20215139.pdf","text":"Report","size":"11 Mb","linkFileType":{"id":1,"text":"pdf"}},{"id":394989,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225001","text":"Scientific Investigations Report 2022–5001","linkHelpText":"- Hydrogeologic characterization of the San Antonio Creek Valley watershed, Santa Barbara County, California"},{"id":394984,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P960EOK8","linkHelpText":"GSFLOW model used to evaluate the groundwater and surface-water resources of the San Antonio Creek Valley watershed, Santa Barbara County, California"}],"country":"United States","state":"California","county":"Santa Barbara County","otherGeospatial":"San Antonio Creek Valley watershed,","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.61889648437501,\n              34.37517887533528\n            ],\n            [\n              -119.75,\n              34.37517887533528\n            ],\n            [\n              -119.75,\n              35\n            ],\n            [\n              -120.61889648437501,\n              35\n            ],\n            [\n              -120.61889648437501,\n              34.37517887533528\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ca@usgs.gov\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,<br><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>6000 J Street, Placer Hall<br>Sacramento, California 95819</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Conceptual Model&nbsp;&nbsp;</li><li>Simulation of Groundwater and Surface-Water Resources&nbsp;&nbsp;</li><li>PRMS-Only Model Calibration and Model Fit&nbsp;&nbsp;</li><li>Integrated Model Calibration&nbsp;&nbsp;</li><li>Assessment of Integrated Model Fit&nbsp;&nbsp;</li><li>Simulated Groundwater Budget&nbsp;&nbsp;</li><li>Model Limitations&nbsp;&nbsp;</li><li>Summary and Conclusions&nbsp;&nbsp;</li><li>References Cited&nbsp;</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2022-01-27","noUsgsAuthors":false,"publicationDate":"2022-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Woolfenden, Linda R. 0000-0003-3500-4709 lrwoolfe@usgs.gov","orcid":"https://orcid.org/0000-0003-3500-4709","contributorId":1476,"corporation":false,"usgs":true,"family":"Woolfenden","given":"Linda","email":"lrwoolfe@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engott, John A. 0000-0003-1889-4519 jaengott@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-4519","contributorId":1142,"corporation":false,"usgs":true,"family":"Engott","given":"John","email":"jaengott@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831983,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831984,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226588,"text":"70226588 - 2022 - Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling","interactions":[],"lastModifiedDate":"2024-05-17T16:56:00.80531","indexId":"70226588","displayToPublicDate":"2022-02-01T07:25:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0040\"><span>Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and&nbsp;aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance (</span><i>V</i><sub><i>Total</i></sub>) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance (<span class=\"math\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂Total</span></span></span>) and the variance component attributable to interpreters (<span class=\"math\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mn is=&quot;true&quot;>1</mn></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂1</span></span></span>) were obtained for the case of two reference class interpretations per repeated sample unit. To evaluate the effect of interpreter variability on variance estimation, we used land cover reference data interpreted by seven analysts who each interpreted the same 300 sample pixels from a region of the Pacific Northwest of the United States. From these data, we estimated the contribution of interpreter variance to the total variance (i.e.,<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mn is=&quot;true&quot;>1</mn></msub><mo is=&quot;true&quot;>/</mo><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂1/V̂Total</span></span></span>) and the relative bias of the standard simple random sampling variance estimator (<span class=\"math\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>stand</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂stand</span></span></span>) as an estimator of<span>&nbsp;</span><i>V</i><sub><i>Total</i></sub>, defined as 100%*(<span class=\"math\"><span id=\"MathJax-Element-5-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>stand</mi></msub><mo is=&quot;true&quot;>&amp;#x2212;</mo><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂stand−V̂Total</span></span></span>)/<span class=\"math\"><span id=\"MathJax-Element-6-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂Total</span></span></span>. For each of five land cover classes, we computed<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-7-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mn is=&quot;true&quot;>1</mn></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂1</span></span></span>,<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-8-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂Total</span></span></span>, and<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-9-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>stand</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂stand</span></span></span><span>&nbsp;</span>using the sample data from each of the 21 possible pairwise combinations of the seven interpreters, and then calculated the mean of<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-10-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mn is=&quot;true&quot;>1</mn></msub><mo is=&quot;true&quot;>/</mo><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂1/V̂Total</span></span></span><span>&nbsp;</span>and the mean of the estimated relative bias of<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-11-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>stand</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂stand</span></span></span><span>&nbsp;</span>over these 21 pairs. Based on the mean of<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-12-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mspace width=&quot;0.25em&quot; is=&quot;true&quot; /><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mn is=&quot;true&quot;>1</mn></msub><mo is=&quot;true&quot;>/</mo><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂1/V̂Total</span></span></span><span>&nbsp;</span>per class, interpreter variance contributed from 25% (cropland) to 76% (grass/shrub) of the total variance, indicating that interpreter variance was a non-negligible component of the total variance. Typically, the standard variance estimator,<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-13-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>stand</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂stand</span></span></span>, underestimated the total variance with the mean estimated relative bias ranging from −3% (cropland) to −33% (grass/shrub). Classes with greater inconsistency between pairs of interpreters had larger contributions of interpreter variance to the total variance (<span class=\"math\"><span id=\"MathJax-Element-14-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mn is=&quot;true&quot;>1</mn></msub><mo is=&quot;true&quot;>/</mo><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>Total</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂1/V̂Total</span></span></span>) and larger negative estimated relative bias of<span>&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-15-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub is=&quot;true&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><mi is=&quot;true&quot;>V</mi><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#x302;</mo></mover><mi mathvariant=&quot;italic&quot; is=&quot;true&quot;>stand</mi></msub></math>\"><span class=\"MJX_Assistive_MathML\">V̂stand</span></span></span>. Given that interpreter variance can contribute substantially to the total variance, the repeated measurements approach offers a practical way to incorporate this variability into an estimator of the total variance.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112806","usgsCitation":"Stehman, S.V., Mousoupetros, J., McRoberts, R.E., Naesset, E., Pengra, B., Xing, D., and Horton, J., 2022, Incorporating interpreter variability into estimation of the total variance of land cover area estimates under simple random sampling: Remote Sensing of Environment, v. 269, 112806, 10 p., https://doi.org/10.1016/j.rse.2021.112806.","productDescription":"112806, 10 p.","ipdsId":"IP-128389","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448953,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112806","text":"Publisher Index Page"},{"id":435985,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HEE6VK","text":"USGS data release","linkHelpText":"Land Cover Assignments of 300 locations in the Pacific Northwest in 2000"},{"id":392301,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.46435546875,\n              46.92025531537451\n            ],\n            [\n              -121.53076171875,\n              46.92025531537451\n            ],\n            [\n              -121.53076171875,\n              49.009050809382046\n            ],\n            [\n              -123.46435546875,\n              49.009050809382046\n            ],\n            [\n              -123.46435546875,\n              46.92025531537451\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"269","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":827413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mousoupetros, John","contributorId":269542,"corporation":false,"usgs":false,"family":"Mousoupetros","given":"John","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":827414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McRoberts, Ronald E.","contributorId":269543,"corporation":false,"usgs":false,"family":"McRoberts","given":"Ronald","email":"","middleInitial":"E.","affiliations":[{"id":55983,"text":"USFS Northern Research Station","active":true,"usgs":false}],"preferred":false,"id":827415,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naesset, Erik","contributorId":269544,"corporation":false,"usgs":false,"family":"Naesset","given":"Erik","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":827416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pengra, Bruce 0000-0003-2497-8284","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":264539,"corporation":false,"usgs":false,"family":"Pengra","given":"Bruce","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":827417,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xing, Dingfan 0000-0003-1233-7260","orcid":"https://orcid.org/0000-0003-1233-7260","contributorId":254318,"corporation":false,"usgs":false,"family":"Xing","given":"Dingfan","email":"","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":827418,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":191430,"corporation":false,"usgs":false,"family":"Horton","given":"Josephine","affiliations":[],"preferred":false,"id":827419,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70226715,"text":"70226715 - 2022 - Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data","interactions":[],"lastModifiedDate":"2024-05-17T16:58:05.876204","indexId":"70226715","displayToPublicDate":"2022-02-01T07:11:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0080\">Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Assessments of surface urban heat island (UHI) usually focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain period. However, the mechanisms and complex interactions in landscape dynamics and land surface thermal features are still being assessed. In this study, we developed and implemented a novel approach to characterize landscape thermal conditions by focusing on UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature and land cover change products. We analyzed land surface temperature changes in urban and surrounding non-urban lands to quantify the UHI intensity and landscape thermal conditions in the Atlanta and Minneapolis metropolitan areas of the United States. Our results revealed that UHI intensities had averages of 3.4&nbsp;°C and 3.3&nbsp;°C in the Atlanta and Minneapolis metropolitan areas, respectively. The dominant land cover type in rural areas and urban imperviousness cover determines the UHI intensity. Increasing trends of 0.04&nbsp;°C/year and 0.01&nbsp;°C/year in UHI intensity between 1985 and 2018 were found in Atlanta and Minneapolis, respectively. The UHI intensity variations in 1985 and 2018 suggest that the magnitudes and temporal variations of UHI intensity averaged from all urban land cover classes are close to the UHI intensity estimated from the low intensity urban area only while the UHI intensities are more than 2&nbsp;°C larger in medium to high and high intensity urban areas. The UHI intensities estimated from the maximum temperature that have statistically significant increasing trends suggest that the maximum temperature is a good element for measuring UHI effect. Urban land cover dynamics play an important role in controlling temporal variation of UHI and the UHI hotspots. Our findings support the scientific value of implementing the prototype approach as an objective framework to quantify and monitor UHI intensity at a large geographic extent.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112803","usgsCitation":"Xian, G.Z., Shi, H., Zhou, Q., Auch, R.F., Gallo, K., Wu, Z., and Kolian, M., 2022, Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data: Remote Sensing of Environment, v. 269, 112803, 16 p., https://doi.org/10.1016/j.rse.2021.112803.","productDescription":"112803, 16 p.","ipdsId":"IP-127385","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448956,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112803","text":"Publisher Index Page"},{"id":392569,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"269","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":827921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua","contributorId":269790,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[{"id":56030,"text":"ASRC Federal Data Solutions (AFDS), under contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":827922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":827923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":827924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallo, Kevin 0000-0001-9162-5011","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":257326,"corporation":false,"usgs":false,"family":"Gallo","given":"Kevin","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":827925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":827926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolian, Michael 0000-0002-7134-8317","orcid":"https://orcid.org/0000-0002-7134-8317","contributorId":257327,"corporation":false,"usgs":false,"family":"Kolian","given":"Michael","email":"","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":827927,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70230381,"text":"70230381 - 2022 - Pathways of productivity and influences on top consumers in forested streams","interactions":[],"lastModifiedDate":"2022-04-11T12:16:36.843874","indexId":"70230381","displayToPublicDate":"2022-02-01T07:09:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Pathways of productivity and influences on top consumers in forested streams","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab010\" class=\"abstract author\" lang=\"en\"><div id=\"as010\"><p id=\"sp0010\">Forested stream ecosystems involve complex physical and biotic pathways that can influence fish in numerous ways. Consequently, the responses of fish communities to disturbance can be difficult to understand. In this study, we employed a food web model that links biotic (e.g., physiology, predator–prey interactions) and abiotic (e.g., temperature, sunlight) attributes to address fish responses to changes in stream-riparian ecosystems. We modeled responses to food web dynamics in four streams, using scenarios that included responses to riparian disturbance, climate change, and shifts in top consumers. The two consumers we focused on were coastal cutthroat trout (<i>Oncorhynchus clarkii clarkii</i>) and sculpin (<i>Cottus</i><span>&nbsp;</span>spp., collectively treated as a functional group)<i>.</i><span>&nbsp;</span>We found the responses to environmental changes varied by fish species and among streams, and that responses were not independent due to exploitative interspecific competition. Simulations based on long-term data indicated that coastal cutthroat trout were responsive to changes in allochthonous resources including terrestrial detritus and invertebrates, whereas sculpin were more responsive to changes to autochthonous resources that included, periphyton and aquatic invertebrates. These results may be, in part, a consequence of species-specific foraging behavior. Trout have a higher propensity to drift feed and therefore receive a substantial subsidy from terrestrial invertebrates, whereas sculpin feed mostly on aquatic insects on the streambed. Simulations of changes in summer temperature and stream discharge suggest decreased biomass of both fish species because of physiological constraints on invertebrate prey which reduce fish foraging opportunities. Exploitative competition also may be important in fish responses: when one fish taxon was removed, the other showed increased biomass. Although the pattern of simulation results was consistent across the four streams, the magnitude of change varied among streams. Streams with food webs fueled by multiple energy sources may be more resilient to changes to riparian forests and climate. Through application of a systems model, we gained insights into pathways of productivity for fish in forested stream ecosystems that provide understanding of processes that influence fish and streams, as well as implications for management of both.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2022.120046","usgsCitation":"Benjamin, J.R., Dunham, J.B., Johnson, S.L., Ashkenas, L., Penaluna, B.E., Bilby, R., Bateman, D.S., Leer, D.W., and Bellmore, J.R., 2022, Pathways of productivity and influences on top consumers in forested streams: Forest Ecology and Management, v. 508, 120046, 11 p., https://doi.org/10.1016/j.foreco.2022.120046.","productDescription":"120046, 11 p.","ipdsId":"IP-134367","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2022.120046","text":"Publisher Index Page"},{"id":398460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"508","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":840126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Sherri L 0000-0002-4223-3465","orcid":"https://orcid.org/0000-0002-4223-3465","contributorId":192210,"corporation":false,"usgs":false,"family":"Johnson","given":"Sherri","email":"","middleInitial":"L","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":840128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ashkenas, Linda","contributorId":289996,"corporation":false,"usgs":false,"family":"Ashkenas","given":"Linda","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":840129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Penaluna, Brooke E","contributorId":192212,"corporation":false,"usgs":false,"family":"Penaluna","given":"Brooke","email":"","middleInitial":"E","affiliations":[],"preferred":false,"id":840130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bilby, Robert E","contributorId":146867,"corporation":false,"usgs":false,"family":"Bilby","given":"Robert E","affiliations":[{"id":16757,"text":"Oregon State Univ.","active":true,"usgs":false}],"preferred":false,"id":840131,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bateman, Douglas S. 0000-0002-5609-2085 doug_bateman@usgs.gov","orcid":"https://orcid.org/0000-0002-5609-2085","contributorId":207396,"corporation":false,"usgs":false,"family":"Bateman","given":"Douglas","email":"doug_bateman@usgs.gov","middleInitial":"S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":840132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leer, David W.","contributorId":207397,"corporation":false,"usgs":false,"family":"Leer","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":840133,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bellmore, James R 0000-0002-5140-6460","orcid":"https://orcid.org/0000-0002-5140-6460","contributorId":195609,"corporation":false,"usgs":false,"family":"Bellmore","given":"James","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":840134,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70236988,"text":"70236988 - 2022 - Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale","interactions":[],"lastModifiedDate":"2022-09-27T13:30:28.543738","indexId":"70236988","displayToPublicDate":"2022-02-01T07:00:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara010\">Predicting baseflow dynamics, protecting aquatic habitat, and managing legacy contaminants requires explicit characterization and prediction of groundwater discharge patterns throughout river networks. Using handheld thermal infrared (TIR) cameras, we surveyed 47&nbsp;km of stream length across the Farmington River watershed (1,570 km<sup>2</sup>; CT and MA, USA), mapping locations of bank and waterline groundwater discharges based on their thermal signature. Using the observed groundwater discharge locations and predicted groundwater discharge rates from 6 variations of a numerical groundwater-flow model (MODFLOW-NWT), we compared 1) predicted groundwater-discharge rates in areas with and without observed groundwater discharge, 2) spatial patterns of observed and predicted groundwater discharge locations, and 3) density of observed groundwater discharge locations with predicted discharge rates. Five of six models reasonably predicted the spatial patterns of discharge locations along the 5th order mainstem, but fewer models predicted groundwater discharge patterns in smaller streams. Our results highlight 1) the feasibility of using TIR observations to evaluate groundwater models, 2) model parameters that influence discharge prediction accuracy (riverbed sediment and bedrock hydraulic conductivity and river-aquifer connections), and 3) current strengths and future opportunities for improved modeling of groundwater-discharge patterns.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2021.104108","usgsCitation":"Barclay, J.R., Briggs, M., Moore, E., Starn, J., Hanson, A.E., and Helton, A., 2022, Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale: Advances in Water Resources, v. 160, 104108, 14 p., https://doi.org/10.1016/j.advwatres.2021.104108.","productDescription":"104108, 14 p.","ipdsId":"IP-111577","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448959,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.advwatres.2021.104108","text":"Publisher Index Page"},{"id":435986,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EIV8L5","text":"USGS data release","linkHelpText":"Thermal Infrared images and field data on areas of groundwater discharge in the Farmington River watershed"},{"id":407396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Massachusetts","otherGeospatial":"Farmington River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -72.9327392578125,\n              41.775408403663285\n            ],\n            [\n              -72.77755737304688,\n              41.6944496643259\n            ],\n            [\n              -72.75833129882812,\n              41.73852846935917\n            ],\n            [\n              -72.86819458007811,\n              41.792816561051815\n            ],\n            [\n              -72.88604736328125,\n              41.87671893034394\n            ],\n            [\n              -72.86407470703125,\n              42.173581898327754\n            ],\n            [\n              -73.1085205078125,\n              42.24071874922666\n            ],\n            [\n              -73.1195068359375,\n              41.88387623204765\n            ],\n            [\n              -72.9327392578125,\n              41.775408403663285\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"160","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":852936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Eric","contributorId":216658,"corporation":false,"usgs":false,"family":"Moore","given":"Eric","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":852937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starn, J. Jeffrey 0000-0001-5909-0010 jjstarn@usgs.gov","orcid":"https://orcid.org/0000-0001-5909-0010","contributorId":1916,"corporation":false,"usgs":true,"family":"Starn","given":"J. 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