Species range sizes and realized niche breadths vary tremendously. Understanding the source of this variation has been a long-term aim in evolutionary ecology and is a major tool in efforts to ameliorate the impacts of changing climates on species distributions. Species ranges that span a large climatic envelope can be achieved by a collection of specialized genotypes locally adapted to a small range of conditions, by genotypes with stable fitness across variable environments, or a combination of these factors. We asked whether fitness expressed along a key niche axis, water availability, could explain a species' realized niche breadth, its geographic range and climate breadth, in 11 species from a clade of jewelflowers whose range sizes vary by two orders of magnitude. Specifically, we explored whether the range size of a species was related to the ability of genotypes (maternal families) to maintain fitness across a range of experimental water availabilities based on 30-year historical field precipitation regimes. We operationally characterized fitness homeostasis through the coefficient of variation in fitness of a genotype (family) across the experimental water gradient. We found that species with genotypes that had high fitness homeostasis, low variation in fitness over our treatments, had larger climatic niche breadth and geographic range in their field distributions. The result was robust to alternate measures of fitness homeostasis. Our results show that the fitness homeostasis of genotypes can be a major factor contributing to niche breadth and range size in this clade. Fitness homeostasis can buffer species from loss of genetic diversity and under changing climates, provides time for adaptation to future conditions.
Overstory basal area, ericaceous shrub cover (Kalmia latifolia L. and Rhododendron maximum L.), and fuels (i.e., woody fuel loads and depths and O Horizon thickness) were assessed within Great Smoky Mountains National Park, USA, in 2003 − 2004. Due to recent wildfire activity within the southern Appalachian Mountain region (including Great Smoky Mountains National Park), the potential spread and expansion of ericaceous shrubs, and the impacts of the hemlock woolly adelgid (Adelges tsugae Annand) on eastern hemlock (Tsuga canadensis (L.) Carrière), these same ecosystem components were again assessed in 2019. Elevation and moisture regime (xeric, intermediate, and mesic) were included in this assessment as potential influential factors. An evaluation of repeated measurements from 40 plots suggested that O Horizon thickness did not change significantly over the 16-year period, but increased as elevation increased, and moisture regime (xeric O Horizon thickness > mesic O Horizon thickness) was a significant, related factor. The sum of 1-, 10-, and 100-h fuel loads (fuels less < 7.6 cm diameter) increased, whereas woody fuel depth decreased over the 16-year period. No significant changes in 1000-h fuel loads (> 7.6 cm diameter), total woody fuel loads, ericaceous shrub cover, total basal area, or live T. canadensis basal area were observed. Live T. canadensis basal area decreased with increasing elevation. Dead, standing T. canadensis basal area increased from 2003–2019, and that increase was most pronounced as elevation increased on xeric and intermediate sites. Overall, we found that: 1. hypothesized increases in total woody fuel loads and ericaceous shrub cover were not present; and 2. elevation and moisture regime were most related to observed changes in vegetation and fuel condition.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11676-022-01515-z","usgsCitation":"Coates, T., and Ford, W., 2022, Fuels and vegetation changes in southwestern, unburned portions of Great Smoky Mountains National Park, USA, 2003-2019: Journal of Forestry Research, v. 33, p. 1459-1470, https://doi.org/10.1007/s11676-022-01515-z.","productDescription":"12 p.","startPage":"1459","endPage":"1470","ipdsId":"IP-135457","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481079,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11676-022-01515-z","text":"Publisher Index Page"},{"id":480745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.9220143101735,\n 35.74602329026733\n ],\n [\n -83.9220143101735,\n 35.49903011724885\n ],\n [\n -83.02028585855729,\n 35.49903011724885\n ],\n [\n -83.02028585855729,\n 35.74602329026733\n ],\n [\n -83.9220143101735,\n 35.74602329026733\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, T. Adam","contributorId":348790,"corporation":false,"usgs":false,"family":"Coates","given":"T. Adam","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":923770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":923769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70233191,"text":"ofr20221056 - 2022 - Relative contributions of suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017","interactions":[],"lastModifiedDate":"2026-03-27T20:27:07.048789","indexId":"ofr20221056","displayToPublicDate":"2022-07-19T12:01:14","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1056","displayTitle":"Relative Contributions of Suspended Sediment between the Upper Suiattle River Basin and a Non-Glacial Tributary, Washington, May 2016–September 2017","title":"Relative contributions of suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017","docAbstract":"Concentrations of suspended sediment were measured in discrete samples and turbidity was continuously monitored at four U.S. Geological Survey streamgages in western Washington State, including one gage on the Sauk River; two gages on the Suiattle River, a tributary to the Sauk River; and one gage on Downey Creek, a tributary to the Suiattle River. The Suiattle River is a sediment-rich stream with headwaters on Glacier Peak, a glaciated volcano in the northern Cascade Range.
Contributions of suspended sediment to the Suiattle River from unglaciated tributaries, represented by Downey Creek, were compared to the contributions from Glacier Peak in the upper Suiattle River watershed. During summer 2017, a period for which complete records of discharge and sediment data were available for all three streamgages in the Suiattle River Basin, the suspended-sediment load from Downey Creek (drainage area [DA] 93 square kilometers [km2]) was 1,400 metric tons, which is equivalent to a sediment yield of about 15 metric tons per km2. During the same period, the suspended-sediment load from the upper Suiattle River (DA 176 km2) was 142,000 metric tons, or a sediment yield of about 800 metric tons per km2; and the suspended-sediment load from the lower Suiattle River (DA 733 km2) was 230,000 metric tons, or a sediment yield of about 300 metric tons per km2. The Downey Creek Basin accounts for 13 percent of the drainage area of the Suiattle River watershed but contributed only 0.6 percent of the suspended-sediment load over the summer of 2017 and water year 2017 (October 1, 2016–September 30, 2017). In contrast, the upper Suiattle River Basin, which accounts for 24 percent of the entire Suiattle River watershed, contributed 62 percent of the suspended-sediment load during the summer of 2017.
Given the short period for which data were collected, it cannot be known with certainty whether the above values are representative of long-term means. The relatively minor contribution of suspended sediment from Downey Creek, however, is consistent with the expectation that the upper Suiattle River, which drains Glacier Peak, is the dominant contemporary source of suspended sediment to the Sauk River. During summer 2016, the suspended-sediment load in the upper Siuattle River (180,000 metric tons) was more than double the estimated load in the lower Sauk River (80,000 metric tons), even though the upper Suiattle River represents only 10 percent of the total contributing area to the lower Sauk River Basin. This ratio of relative contribution is interpreted as an indication of transient storage of sediment along the Suiattle and Sauk Rivers between the two streamgaging stations. In the glaciated upper Suiattle River Basin, sediment is transported by annual glacial-melt processes in spring and summer months, deposited during the summer base-flow period, and then remobilized by fall and winter floods for delivery to the lower Sauk River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221056","collaboration":"Prepared in cooperation with the Sauk-Suiattle Indian Tribe","usgsCitation":"Jaeger, K.L., Anderson, S.W., Senter, C.A., Curran, C.A., and Morris, S., 2022, Relative contributions of suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017: U.S. Geological Survey Open-File Report 2022–1056, 18 p., https://doi.org/10.3133/ofr20221056.","productDescription":"v, 18 p.","onlineOnly":"Y","ipdsId":"IP-132545","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":403971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1056"},{"id":403970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1056/coverthb.jpg"},{"id":501781,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113307.htm","linkFileType":{"id":5,"text":"html"}},{"id":403972,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1056"},{"id":404178,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W74K0K","text":"USGS data release","description":"USGS data release","linkHelpText":"Suspended sediment and water temperature data in the Suiattle River and the Downey Creek Tributary, Washington for select time periods over 2013 - 2017 (ver. 2.0, October 2021)"},{"id":403974,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.XML"},{"id":403973,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1056/images"}],"country":"United States","state":"Washington","otherGeospatial":"Upper Suiattle River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.4,\n 48.0\n ],\n [\n -121.0,\n 48.0\n ],\n [\n -121.0,\n 48.4\n ],\n [\n -121.4,\n 48.4\n ],\n [\n -121.4,\n 48.0\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The 2018 eruption of Kīlauea Volcano was a dynamic event involving explosions, collapses, and fountaining at multiple vents spread over tens of kilometers. The permanent infrasound network operated by the USGS Hawaiian Volcano Observatory (HVO) was well prepared to observe the collapse of the summit, and additional deployments permitted infrasound observations during fissuring in the lower East Rift Zone (LERZ). We provide a summary of infrasound observations, including lava lake spattering, collapses, explosions, rockfall, and lava fountaining, using seismicity and tilt at times to help constrain our interpretations. At the summit of Kīlauea Volcano, we document the process of partial caldera collapse and examine a set of “proto-collapse” events that precede the widely observed events but share many of the same qualities as the larger collapses. For the initial twelve collapse events, we compare the timing of collapse onset to other observations and illustrate the repeatable characteristics of the recorded waveforms and infrasound characteristics associated with each episode of caldera collapse. In the LERZ, we match the acoustic signals with visual observations, including fissure migration, explosions near fissures, and littoral explosions. Lastly, we document and discuss the performance of infrasound alarms during the 2018 Kīlauea eruption. In general, alarming became successful in detecting collapse events at the summit of the volcano after tuning and became a key discriminant in the initial determination of collapse events, especially when visual observations were not available.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01583-3","usgsCitation":"Thelen, W., Waite, G.P., Lyons, J.J., and David Fee, 2022, Infrasound observations and constraints on the 2018 eruption of Kīlauea Volcano, Hawaii: Bulletin of Volcanology, v. 84, 76, 24 p., https://doi.org/10.1007/s00445-022-01583-3.","productDescription":"76, 24 p.","ipdsId":"IP-131617","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.53549432982868,\n 19.57384518533665\n ],\n [\n -155.53549432982868,\n 19.32020786420226\n ],\n [\n -154.80441193441976,\n 19.32020786420226\n ],\n [\n -154.80441193441976,\n 19.57384518533665\n ],\n [\n -155.53549432982868,\n 19.57384518533665\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"84","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waite, Gregory P.","contributorId":146613,"corporation":false,"usgs":false,"family":"Waite","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":917133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":917134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"David Fee","contributorId":345625,"corporation":false,"usgs":false,"family":"David Fee","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":917135,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237620,"text":"70237620 - 2022 - Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations","interactions":[],"lastModifiedDate":"2022-10-14T14:46:49.261277","indexId":"70237620","displayToPublicDate":"2022-07-19T09:45:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations","docAbstract":"Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021RG000749","usgsCitation":"Ringler, A.T., Anthony, R.E., Aster, R., Ammon, C., Arrowsmith, S., Benz, H.M., Ebeling, C., Frassetto, A., Kim, W.Y., Koelemeijer, P., Lau, H.C., Lekic, V., Montagner, J.P., Richards, P., Schaff, D., Vallee, M., and Yeck, W.L., 2022, Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations: Reviews of Geophysics, v. 60, no. 3, e2021RG000749, 98 p., https://doi.org/10.1029/2021RG000749.","productDescription":"e2021RG000749, 98 p.","ipdsId":"IP-132866","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447073,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021rg000749","text":"External Repository"},{"id":408320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert E. 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aster, R. C.","contributorId":215408,"corporation":false,"usgs":false,"family":"Aster","given":"R. C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":854670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ammon, C. J.","contributorId":297931,"corporation":false,"usgs":false,"family":"Ammon","given":"C. J.","affiliations":[{"id":64457,"text":"The Pennsylvania State University, University Park","active":true,"usgs":false}],"preferred":false,"id":854671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arrowsmith, S.","contributorId":297932,"corporation":false,"usgs":false,"family":"Arrowsmith","given":"S.","email":"","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":854672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854673,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebeling, C.","contributorId":297933,"corporation":false,"usgs":false,"family":"Ebeling","given":"C.","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":854674,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frassetto, A.","contributorId":297942,"corporation":false,"usgs":false,"family":"Frassetto","given":"A.","email":"","affiliations":[],"preferred":false,"id":854695,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kim, W. Y.","contributorId":297934,"corporation":false,"usgs":false,"family":"Kim","given":"W.","email":"","middleInitial":"Y.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854675,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koelemeijer, Paula","contributorId":236644,"corporation":false,"usgs":false,"family":"Koelemeijer","given":"Paula","email":"","affiliations":[{"id":47486,"text":"Department of Earth Sciences, Royal Holloway University of London, Egham, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":854696,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lau, H. C. P.","contributorId":297935,"corporation":false,"usgs":false,"family":"Lau","given":"H.","email":"","middleInitial":"C. P.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":854676,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lekic, V.","contributorId":251944,"corporation":false,"usgs":false,"family":"Lekic","given":"V.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":854677,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Montagner, J. P.","contributorId":297943,"corporation":false,"usgs":false,"family":"Montagner","given":"J.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":854697,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Richards, P. G.","contributorId":297937,"corporation":false,"usgs":false,"family":"Richards","given":"P. 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P.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854678,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Vallee, M.","contributorId":297938,"corporation":false,"usgs":false,"family":"Vallee","given":"M.","affiliations":[{"id":64458,"text":"Universite de Paris, CNRS","active":true,"usgs":false}],"preferred":false,"id":854680,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854681,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70233274,"text":"fs20223054 - 2022 - New Jersey and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:02:16.04284","indexId":"fs20223054","displayToPublicDate":"2022-07-19T09:20:57","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":"2022-3054","displayTitle":"New Jersey and Landsat","title":"New Jersey and Landsat","docAbstract":"New Jersey ranks among the smallest of States but packs a lot within its borders. Of course, that includes the more than 9 million people who make it the most densely populated State, but it also includes diverse landscapes. Ranging from Atlantic Ocean barrier islands and beaches to the Appalachian Mountains, and Pine Barrens forests to swampland, the “Garden State” retains remnants of an agricultural past with produce, horse, and dairy farms and plant nurseries.
The third State to join the Union has had a strong geographic presence in U.S. history. More than 200 American Revolution battles and skirmishes were fought in New Jersey—more than in any other State. Manufacturing, tourism, and fishing have each had a significant effect on New Jersey’s industrial history. Today, many residents commute from this strategic location to work in New York City, just across the Hudson River to the northeast, or in Philadelphia, just across the Delaware River to the west.
A dense population and climate change can increase risks for residents and the natural resources around them. Landsat helps officials monitor and plan for resilient cities and landscapes. Here are a few specific ways Landsat benefits New Jersey.
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Jersey\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Clear correlations between human and bird visual assessments of color have been documented, and are often assumed, despite fundamental differences in human and avian visual physiology and morphology. Analyses of plumage colors with avian perceptual models have shown widespread hidden inter-sexual and inter-specific color variation among passerines perceived as monochromatic to humans, highlighting the uncertainty of human vision to predict potentially relevant variation in color. Herein, we use reflectance data from 13 Larus gull species as an exemplar data set to study concordance between human vision and avian visual modeling of feather colors near, or below, the human threshold for discrimination. We found little evidence among gulls for sexual dichromatism hidden from human vision, but did find inter-specific color variation among gulls that is not seen by humans. Neither of these results were predictable a priori, and we reassert that reflectance measurements of actual feather colors, analyzed with avian relevant visual models, represent best practice when studying bird coloration.
","language":"English","publisher":"Finnish Zoological and Botanical Publishing Board","doi":"10.5735/086.059.0116","usgsCitation":"Eaton, M.D., Benites, P., Campillo, L., Wilson, R.E., and Sonsthagen, S.A., 2022, Gull plumages are, and are not, what they appear to human vision: Annales Zoologici Fennici, v. 59, no. 1, p. 187-203, https://doi.org/10.5735/086.059.0116.","productDescription":"7 p.","startPage":"187","endPage":"203","ipdsId":"IP-118461","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":404018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Muir D","contributorId":293231,"corporation":false,"usgs":false,"family":"Eaton","given":"Muir","email":"","middleInitial":"D","affiliations":[{"id":63252,"text":"Drake University","active":true,"usgs":false}],"preferred":false,"id":846813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benites, Pilar","contributorId":293232,"corporation":false,"usgs":false,"family":"Benites","given":"Pilar","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":846814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campillo, Luke","contributorId":293233,"corporation":false,"usgs":false,"family":"Campillo","given":"Luke","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":846815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Robert E.","contributorId":293234,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert","email":"","middleInitial":"E.","affiliations":[{"id":63255,"text":"Nebraska State Museum","active":true,"usgs":false}],"preferred":false,"id":846816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":846817,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233201,"text":"70233201 - 2022 - Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability","interactions":[],"lastModifiedDate":"2022-07-19T14:12:33.66365","indexId":"70233201","displayToPublicDate":"2022-07-19T09:08:07","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":"Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability","docAbstract":"Quantifying the petrophysical and geomechanical properties of gas hydrate reservoirs is essential for understanding the natural hydrate system and predicting gas production behavior for future resource development. Pressure-core analysis tools were used to characterize methane hydrate–bearing sediments recovered from the Gulf of Mexico Green Canyon Block 955, under an international collaboration with The University of Texas and the National Institute of Advanced Industrial Science and Technology. Pressure-core samples were successfully transferred from Austin, Texas to Sapporo, Japan. Index property measurements (grain size, grain density, hydration number, gas composition, thermal conductivity), along with triaxial compression, consolidation, and permeability tests with a nuclear magnetic resonance (NMR) analyzer were conducted. Compression tests at different strain rates confirmed a strain rate dependence for hydrate-bearing sediment, and an equation for predicting strength as a function of hydrate saturation and strain rate is proposed. Compression and swelling indices were obtained from high-effective stress consolidation tests. Furthermore, secondary compression coefficients for hydrate-bearing sediments were obtained, suggesting that hydrate exhibits creeping behavior on timescales of minutes to hours. A relatively high initial permeability of a few millidarcys was confirmed. In addition, the first NMR signal measurement was performed on a hydrate-bearing pressure core to acquire the NMR transverse or spin-spin (T2) distribution. Results confirm that the Schlumberger Doll Research model and Timur-Coates model predictions underestimate permeability measured directly via fluid flow. Permeability estimated using specific surface values derived from NMR T2 distributions is in good agreement with flow test results. Finally, an extended Timur-Coates model was proposed and predicts intrinsic permeability with high accuracy.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/04272120204","usgsCitation":"Yoneda, J., Jin, Y., Muraoka, M., Oshima, M., Suzuki, K., Waite, W., and Flemings, P., 2022, Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability: AAPG Bulletin, v. 106, no. 5, p. 1143-1177, https://doi.org/10.1306/04272120204.","productDescription":"35 p.","startPage":"1143","endPage":"1177","ipdsId":"IP-124941","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":404017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Green Canyon, Green Canyon Block 955, 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 -90.76904296874999,\n 26.194876675795218\n ],\n [\n -89.23095703125,\n 26.194876675795218\n ],\n [\n -89.23095703125,\n 27.6251403350933\n ],\n [\n -90.76904296874999,\n 27.6251403350933\n ],\n [\n -90.76904296874999,\n 26.194876675795218\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yoneda, Jun","contributorId":240073,"corporation":false,"usgs":false,"family":"Yoneda","given":"Jun","affiliations":[{"id":40273,"text":"National Institute of Advanced Industrial Science and Technology","active":true,"usgs":false}],"preferred":false,"id":846772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Yusuke","contributorId":220832,"corporation":false,"usgs":false,"family":"Jin","given":"Yusuke","email":"","affiliations":[],"preferred":false,"id":846773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muraoka, Michihiro","contributorId":248423,"corporation":false,"usgs":false,"family":"Muraoka","given":"Michihiro","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oshima, Motoi","contributorId":248424,"corporation":false,"usgs":false,"family":"Oshima","given":"Motoi","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suzuki, Kiyofumi","contributorId":248425,"corporation":false,"usgs":false,"family":"Suzuki","given":"Kiyofumi","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":846777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flemings, Peter","contributorId":198205,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter","affiliations":[{"id":13127,"text":"Jackson School of Geosciences, University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":846778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233209,"text":"70233209 - 2022 - Evidence of increased mussel abundance related to the Pacific marine heatwave and sea star wasting","interactions":[],"lastModifiedDate":"2022-09-01T14:44:10.995654","indexId":"70233209","displayToPublicDate":"2022-07-19T09:01:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5377,"text":"Marine Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of increased mussel abundance related to the Pacific marine heatwave and sea star wasting","docAbstract":"Mussels occupy a key middle trophic position in nearshore food webs linking primary producers to predators. Climate-related environmental changes may synergistically combine with changes in predator abundance to affect intertidal ecosystems. We examined the influence of two major events on mussel (Mytilus trossulus) abundance in the northern Gulf of Alaska: the recent Pacific marine heatwave (PMH, 2014–2016) and an outbreak of sea star wasting (SSW). We investigated how mussel abundance changed since the onset of SSW and whether the density of predatory sea stars or PMH-related temperature metrics explain variation in mussel abundance. Sea stars and mussels were surveyed since 2005 approximately annually in four regions of the northern Gulf of Alaska: Katmai (KATM), Kachemak Bay (KBAY), Kenai Fjords (KEFJ) and western Prince William Sound (WPWS). Mussel percent cover in the mid-intertidal increased 1–3 years after declines in sea stars at all regions and in the low-intertidal at KATM, KBAY, and KEFJ, but not at WPWS. After the onset of SSW, large (≥20 mm length) mussel density and mussel bed width increased at KATM but not the other regions. Total mussel densities, including recruits, did not differ before and after the onset of SSW. The total number of sea stars significantly explained variation in mussel metrics, but the proportions of the three sea star species examined did not. We did not find strong evidence for direct effects of temperature on mussels. The effects of the PMH and the SSW outbreak appear to have combined, with increased temperatures indirectly benefiting mussels in concert with relaxed top-down pressure from sea stars, allowing for increased mussel abundance. Changing mussel abundance may affect intertidal local productivity and the abundance or performance of other nearshore consumers of mussels.
","language":"English","publisher":"Wiley","doi":"10.1111/maec.12715","usgsCitation":"Traiger, S.B., Bodkin, J., Coletti, H., Ballachey, B., Thomas, D., Esler, D., Iken, K., Konar, B., Lindeberg, M., Monson, D., Robinson, B.H., Suryan, R.M., and Weitzman, B., 2022, Evidence of increased mussel abundance related to the Pacific marine heatwave and sea star wasting: Marine Ecology, v. 43, no. 4, e12715, 16 p., https://doi.org/10.1111/maec.12715.","productDescription":"e12715, 16 p.","ipdsId":"IP-133085","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":447078,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/maec.12715","text":"External Repository"},{"id":435767,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EDM6NL","text":"USGS data release","linkHelpText":"Sea Otter Spraint Data from Kachemak Bay, Katmai National Park and Preserve, Kenai Fjords National Park and Prince William Sound"},{"id":435766,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WS8RD4","text":"USGS data release","linkHelpText":"SUPERSEDED: Gulf Watch Alaska Nearshore Component: Intertidal Mussel Site Data from Prince William Sound, Katmai National Park and Preserve, and Kenai Fjords National Park, 2016"},{"id":435765,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WH2N3T","text":"USGS data release","linkHelpText":"Intertidal Temperature Data from Kachemak Bay, Prince William Sound, Katmai National Park and Preserve, and Kenai Fjords National Park"},{"id":435764,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FN1498","text":"USGS data release","linkHelpText":"Intertidal Mussel (Mytilus) Data from Prince William Sound, Katmai National Park and Preserve, and Kenai Fjords National Park"},{"id":404016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kachemak Bay, Katmai National Park and Preserve, Kenai Fjords National Park, Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.09375,\n 58.17070248348609\n ],\n [\n -147.216796875,\n 58.17070248348609\n ],\n [\n -147.216796875,\n 61.36514522233485\n ],\n [\n -156.09375,\n 61.36514522233485\n ],\n [\n -156.09375,\n 58.17070248348609\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Traiger, Sarah Beth 0000-0002-6222-1445","orcid":"https://orcid.org/0000-0002-6222-1445","contributorId":293218,"corporation":false,"usgs":true,"family":"Traiger","given":"Sarah","email":"","middleInitial":"Beth","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":846790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodkin, James L. 0000-0003-1641-4438","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":264733,"corporation":false,"usgs":false,"family":"Bodkin","given":"James L.","affiliations":[{"id":40616,"text":"former USGS PI","active":true,"usgs":false}],"preferred":false,"id":846791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coletti, 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desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":true,"id":846795,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Iken, Katrin","contributorId":199008,"corporation":false,"usgs":false,"family":"Iken","given":"Katrin","email":"","affiliations":[],"preferred":false,"id":846796,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Konar, Brenda","contributorId":131034,"corporation":false,"usgs":false,"family":"Konar","given":"Brenda","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":846797,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lindeberg, Mandy","contributorId":195895,"corporation":false,"usgs":false,"family":"Lindeberg","given":"Mandy","email":"","affiliations":[],"preferred":false,"id":846798,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":846799,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robinson, Brian H. 0000-0001-8588-7162 brobinson@usgs.gov","orcid":"https://orcid.org/0000-0001-8588-7162","contributorId":191406,"corporation":false,"usgs":true,"family":"Robinson","given":"Brian","email":"brobinson@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":846800,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Suryan, Robert M. 0000-0003-0755-8317","orcid":"https://orcid.org/0000-0003-0755-8317","contributorId":221852,"corporation":false,"usgs":false,"family":"Suryan","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":40443,"text":"Oregon State University, NOAA","active":true,"usgs":false}],"preferred":false,"id":846801,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weitzman, Ben","contributorId":252838,"corporation":false,"usgs":false,"family":"Weitzman","given":"Ben","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":846802,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70233469,"text":"70233469 - 2022 - Relocated beaver can increase water storage and decrease stream temperature in headwater streams","interactions":[],"lastModifiedDate":"2022-07-21T14:06:28.869203","indexId":"70233469","displayToPublicDate":"2022-07-19T09:01:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Relocated beaver can increase water storage and decrease stream temperature in headwater streams","docAbstract":"Many areas are experiencing increasing stream temperatures due to climate change, and some are experiencing reduced summer stream flows and water availability. Because dam building and pond formation by beaver can increase water storage, stream cooling, and riparian ecosystem resilience, beaver have been proposed as a potential climate adaption tool. Despite the large number of studies that have evaluated how beaver activity may affect hydrology and water temperature, few experimental studies have quantified these outcomes following beaver relocation. We evaluated changes in temperature and water storage following the relocation of 69 beaver into 13 headwater stream reaches of the Skykomish River watershed within the Snohomish River basin, Washington, USA. We evaluated how beaver dams affected surface and groundwater storage and stream temperature. Successful relocations created 243 m3 of surface water storage per 100 m of stream in the first year following relocation. Dams raised water table elevations by up to 0.33 m and stored approximately 2.4 times as much groundwater as surface water per relocation reach. Stream reaches downstream of dams exhibited an average decrease of 2.3°C during summer base-flow conditions. We also assessed how dam age, condition, maintenance frequency, and pond morphology influenced stream temperature at naturally colonized wetland complexes. Our findings demonstrate that dam building can increase water storage and reduce stream temperatures in the first year following successful beaver relocation. Fluvial and floodplain morphology of candidate reaches for relocation is an important consideration because it determines the type and magnitude of response. Relocation to reaches with existing small, abandoned ponds may address thermal criteria by conversion from warming to cooling reaches, whereas relocation within large, abandoned complexes or vacant habitat may result in greater water storage. Although beaver relocation can be an effective climate adaptation strategy to retain more stable hydrologic regimes and water quality in our study area, there appear to be regionally specific environmental and geomorphic factors that influence how beaver affect water storage and temperature. More research is needed to investigate how and why these regional differences affect water storage and stream temperature response in beaver-influenced systems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4168","usgsCitation":"Dittbrenner, B.J., Schilling, J.W., Torgersen, C.E., and Lawler, J.J., 2022, Relocated beaver can increase water storage and decrease stream temperature in headwater streams: Ecosphere, v. 13, no. 7, e4168, 17 p., https://doi.org/10.1002/ecs2.4168.","productDescription":"e4168, 17 p.","ipdsId":"IP-134665","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4168","text":"Publisher Index Page"},{"id":404214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skykomish River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23800659179686,\n 47.6737103919566\n ],\n [\n -121.15,\n 47.6737103919566\n ],\n [\n -121.15,\n 48.026672195436014\n ],\n [\n -122.23800659179686,\n 48.026672195436014\n ],\n [\n -122.23800659179686,\n 47.6737103919566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dittbrenner, Benjamin J.","contributorId":202890,"corporation":false,"usgs":false,"family":"Dittbrenner","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":847172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schilling, Jason W.","contributorId":202892,"corporation":false,"usgs":false,"family":"Schilling","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":36547,"text":"Tulalip Tribes Natural Resources","active":true,"usgs":false}],"preferred":false,"id":847173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","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":847174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":847175,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233230,"text":"70233230 - 2022 - A phylogeny based on cytochrome-c oxidase gene sequences identifies sympatric Ichthyophonus genotypes in the NE Pacific Ocean","interactions":[],"lastModifiedDate":"2022-07-19T14:00:36.255795","indexId":"70233230","displayToPublicDate":"2022-07-19T08:57:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A phylogeny based on cytochrome-c oxidase gene sequences identifies sympatric Ichthyophonus genotypes in the NE Pacific Ocean","title":"A phylogeny based on cytochrome-c oxidase gene sequences identifies sympatric Ichthyophonus genotypes in the NE Pacific Ocean","docAbstract":"ABSTRACT: In recent decades, evidence has accumulated to suggest that the widespread and highly variable parasite Ichthyophonus hoferi is actually a species complex. Highly plastic morphology and a general lack of defining structures has contributed to the likely underestimate of biodiversity within this group. Molecular methods are a logical next step in the description of these parasites, but markers used to date have been too conserved to resolve species boundaries. Here we use mitochondrial encoded cytochrome-c oxidase (MTCO1) gene sequences and phylogenic analysis to compare Ichthyophonus spp. isolates from several marine and anadromous fish hosts. The resulting phylogeny displays lineage separation among isolates and possible host/niche segregation not previously described. The parasite type that infects Pacific herring Clupea pallasii, Atlantic herring C. harengus, Atlantic salmon Salmo salar, and Pacific staghorn sculpin Oligocottus maculosus (Clade A) is different from that which infects Chinook salmon Oncorhynchus tshawytscha, walleye pollock Gadus chalcogrammus, Greenland halibut Reinhardtius hippoglossoides, and Pacific halibut Hippoglossus stenolepsis (Clade B). MTCO1 sequences confirmed the presence of a more divergent Ichthyophonus sp. isolated from American shad Alosa sapidissima in rivers of eastern North America (Clade C), while American shad introduced to the Pacific Ocean are infected with the same parasite that infects Pacific herring (Clade A). Currently there are no consensus criteria for delimiting species within Ichthyophonidae, but MTCO1 sequences hold promise as a potential species identifying marker and useful epizootiological tool.
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2022 - Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems","interactions":[],"lastModifiedDate":"2022-07-21T13:59:42.32542","indexId":"70233462","displayToPublicDate":"2022-07-19T08:55:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems","docAbstract":"Sagebrush ecosystems of western North America are threatened by invasive annual grasses and wildfires that can remove fire-intolerant shrubs for decades. Fuel reduction treatments are used ostensibly to aid in fire suppression, conserve wildlife habitat, and restore historical fire regimes, but long-term ecological impacts of these treatments are not clear. In 2006, we initiated fuel reduction treatments (prescribed fire, mowing, and herbicide applications [tebuthiuron and imazapic]) in six Artemisia tridentata ssp. wyomingensis communities. We evaluated long-term effects of these fuel treatments on: (1) magnitude and longevity of fuel reduction; (2) Greater Sage-grouse habitat characteristics; and (3) ecological resilience and resistance to invasive annual grasses. Responses were analyzed using repeated-measures linear mixed models. Response variables included plant biomass, cover, density and height, distances between perennial plants, and exposed soil cover. Prescribed fire produced the greatest reduction in woody fuel over time. Mowing initially reduced woody biomass, which recovered by year 10. Tebuthiuron did not significantly reduce woody biomass compared to controls. All woody fuel treatments reduced sagebrush cover to below 15% (recommended minimum for Greater Sage-grouse habitat), but only prescribed fire reduced cover to below controls. Median mowed sagebrush height remained above the recommended 30 cm. Cheatgrass (Bromus tectorum) cover increased to above the recommended maximum of 10% across all treatments and controls. Ecological resilience to woody fuel treatments was lowest with fire and greatest with mowing. Low resilience over the 10 posttreatment years was identified by: (1) poor perennial plant recovery posttreatment with sustained reductions in cover and density of some perennial plant species; (2) sustained reductions in lichen and moss cover; and (3) increases in cheatgrass cover. Although 10 years is insufficient to conclusively describe final ecological responses to fuel treatments, mowing woody fuels has the greatest potential to reduce woody fuel, minimize shrub mortality and soil disturbance, maintain lichens and mosses, and minimize long-term negative impacts on Greater Sage-grouse habitat. However, maintaining ecological resilience and resistance to invasion may be threatened by increases in cheatgrass cover, which are occurring regionally.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4176","usgsCitation":"Pyke, D.A., Shaff, S.E., Chambers, J., Schupp, E.W., Newingham, B.A., Gray, M.L., and Ellsworth, L.M., 2022, Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems: Ecosphere, v. 13, no. 7, e4176, 21 p., https://doi.org/10.1002/ecs2.4176.","productDescription":"e4176, 21 p.","ipdsId":"IP-128463","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4176","text":"Publisher Index Page"},{"id":404212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nevada, Oregon, Utah, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.4541015625,\n 38.37611542403604\n ],\n [\n -110.8740234375,\n 38.37611542403604\n ],\n [\n -110.8740234375,\n 48.1367666796927\n ],\n [\n -120.4541015625,\n 48.1367666796927\n ],\n [\n -120.4541015625,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaff, Scott E. 0000-0001-8978-9260","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":219813,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chambers, Jeanne C.","contributorId":75889,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[],"preferred":false,"id":847156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schupp, Eugene W.","contributorId":178262,"corporation":false,"usgs":false,"family":"Schupp","given":"Eugene","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":847157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newingham, Beth A.","contributorId":195932,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":847158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, Margaret L 0000-0002-4810-8876","orcid":"https://orcid.org/0000-0002-4810-8876","contributorId":221166,"corporation":false,"usgs":false,"family":"Gray","given":"Margaret","email":"","middleInitial":"L","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":847159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellsworth, Lisa M.","contributorId":255109,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":51436,"text":"Fisheries and Wildlife Department, Oregon State University, Corvallis, Oregon 97331 USA","active":true,"usgs":false}],"preferred":false,"id":847160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236158,"text":"70236158 - 2022 - Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques","interactions":[],"lastModifiedDate":"2022-08-30T13:41:54.415555","indexId":"70236158","displayToPublicDate":"2022-07-19T08:37:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing pharmaceutical contaminants in Great Lakes tributaries using risk-based screening techniques","docAbstract":"In a study of 44 diverse sampling sites across 16 Great Lakes tributaries, 110 pharmaceuticals were detected of 257 monitored. The present study evaluated the ecological relevance of detected chemicals and identified heavily impacted areas to help inform resource managers and guide future investigations. Ten pharmaceuticals (caffeine, nicotine, albuterol, sulfamethoxazole, venlafaxine, acetaminophen, carbamazepine, gemfibrozil, metoprolol, and thiabendazole) were distinguished as having the greatest potential for biological effects based on comparison to screening-level benchmarks derived using information from two biological effects databases, the ECOTOX Knowledgebase and the ToxCast database. Available evidence did not suggest substantial concern for 75% of the monitored pharmaceuticals, including 147 undetected pharmaceuticals and 49 pharmaceuticals with screening-level alternative benchmarks. However, because of a lack of biological effects information, screening values were not available for 51 detected pharmaceuticals. Samples containing the greatest pharmaceutical concentrations and having the highest detection frequencies were from Lake Erie, southern Lake Michigan, and Lake Huron tributaries. Samples collected during low-flow periods had higher pharmaceutical concentrations than those collected during increased-flow periods. The wastewater-treatment plant effluent content in streams correlated positively with pharmaceutical concentrations. However, deviation from this correlation demonstrated that secondary factors, such as multiple pharmaceutical sources, were likely present at some sites. Further research could investigate high-priority pharmaceuticals as well as those for which alternative benchmarks could not be developed.
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Center","active":true,"usgs":true}],"preferred":true,"id":850281,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nott, Michelle A. 0000-0003-3968-7586","orcid":"https://orcid.org/0000-0003-3968-7586","contributorId":221766,"corporation":false,"usgs":true,"family":"Nott","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850282,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70246646,"text":"70246646 - 2022 - Divergent successional trajectories of soil seed bank and post-fire vegetation in a semiarid oak forest: Implications for post-fire ecological restoration","interactions":[],"lastModifiedDate":"2023-07-12T12:22:46.173093","indexId":"70246646","displayToPublicDate":"2022-07-19T07:21:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Divergent successional trajectories of soil seed bank and post-fire vegetation in a semiarid oak forest: Implications for post-fire ecological restoration","docAbstract":"Wildfires are a major disturbance in forest ecosystems around the world and may lead to changes in vegetation succession trajectories. This study examined the impact of time since wildfires on the successional gradients of the degraded Zagros semi-arid oak forest in Iran. Here, we investigated the role of soil seed bank in postfire understory vegetation successional trajectories after wildfires and how time-since-fire influenced plant recovery of this disturbed site. Three adjacent high severity burned areas with different fire histories and the same physiographic conditions were considered. In sampling, we surveyed both aboveground understory vegetation and soil seed bank in all the 96 plots taken along the transects of each area. Soil samples were also collected from each plot and physicochemical properties were analysed in the laboratory. Species composition in the seed bank showed divergent successional trajectories compared to the aboveground vegetation after wildfire. The diversity of soil seed banks followed a gradual decrease, while aboveground understory plants revealed an increasing trend of diversity over time. In addition, the physical and chemical composition of soils was significantly altered by fire. This study presents important insights into soil seed bank dynamics compared to the corresponding aboveground vegetation during postfire succession. The observed changes in diversity and vegetation composition after wildfire can give important insights to management strategies involving prescribed fire in the restoration efforts of highly disturbed semiarid oak forest.
The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-15-5481-2022","usgsCitation":"Hodson, T.O., 2022, Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not: Geoscientific Model Development, v. 15, p. 5481-5487, https://doi.org/10.5194/gmd-15-5481-2022.","productDescription":"7 p.","startPage":"5481","endPage":"5487","ipdsId":"IP-136463","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-15-5481-2022","text":"Publisher Index Page"},{"id":406059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodson, Timothy O. 0000-0003-0962-5130","orcid":"https://orcid.org/0000-0003-0962-5130","contributorId":78634,"corporation":false,"usgs":true,"family":"Hodson","given":"Timothy","email":"","middleInitial":"O.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850544,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239310,"text":"70239310 - 2022 - Thermophysical and compositional properties of paleobedforms on Mars","interactions":[],"lastModifiedDate":"2023-01-09T13:00:41.226167","indexId":"70239310","displayToPublicDate":"2022-07-19T06:59:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7353,"text":"Journal of Geophysical Research - Planets","active":true,"publicationSubtype":{"id":10}},"title":"Thermophysical and compositional properties of paleobedforms on Mars","docAbstract":"Bedforms on Earth and Mars are often preserved in the rock record in the form of sedimentary rock with distinct cross-bedding. On rare occasions, the full-surface geometry of a bedform can be preserved through burial and lithification. These features, known as paleobedforms, are found in a variety of geographic locations on Mars. Evidence in the morphology of paleobedforms, such as the retention of impact craters and steep erosional scarps, suggests that these features are well-lithified and capable of withstanding prolonged weathering and erosion. Here, we present results from thermophysical and compositional analyses on a subset of the best preserved paleobedform candidate fields on Mars. Thermophysical modeling elucidates the changes these bedforms underwent from their unconsolidated, particulate nature to their currently observed properties. Certain paleobedforms have elevated thermal inertias (e.g., ∼300–500 J·m−2·s−1/2·K−1) when compared with modern bedforms (∼250 J·m−2·s−1/2·K−1), and modeling indicates that they have cement volumes of 0.8%–1.5% even as high as 30%. However, most paleobedform candidates have unexpectedly low thermal inertia when compared with modern dunes. Additionally, compositional analyses reveal a range of spectral characteristics within paleobedforms (e.g., primary and secondary alteration products). These features add to the already existing class of Martian surfaces in which thermal inertia does not seem to correspond to erodibility, cohesion, or mechanical strength. Studying paleobedforms with both raised and nonraised thermal inertia has provided new insights into lithification on Mars and constrained the environmental conditions leading to the formation of these enigmatic features.
The State of North Dakota once did not figure prominently in the Nation’s economy. The sparsely populated State supported food production, and hunters and anglers were drawn to its lakes, rivers, and wide-open spaces, but its economy was overshadowed by that of other States. However, the State and its prairie expanses recently rocketed from an economic afterthought to a national energy leader with the soaring production of oil and natural gas in the Bakken oil patch.
The Bakken development has been transformative for North Dakota’s landscapes in myriad ways. It has boosted economic output, drawn thousands of new residents to cities like Williston and Watford City, and led to a proliferation of oil and gas pads.
In the past two decades, North Dakota experienced other major changes, such as the expansion of the depressional wetlands of the Prairie Pothole Region on the eastern side of the State. These critical breeding areas for waterfowl, which stretch across Minnesota, South Dakota, North Dakota, and Canada, are home to more than 50 percent of North America’s migratory birds.
Changes from oil and gas production, urban development, and wetland resurgence can all be tracked over time using the unparalleled Earth observation record of the U.S. Geological Survey Landsat data archive. Its 50-year record of repeat imagery also aids in the monitoring, cataloging, and management of cropland, invasive insect species, and natural or human-made disaster recovery. Here are just a few examples of the benefits offered to North Dakota by the Landsat Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223053","usgsCitation":"U.S. Geological Survey, 2022, North Dakota and Landsat: U.S. Geological Survey Fact Sheet 2022–3053, 2 p., https://doi.org/10.3133/fs20223053.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-142198","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":406516,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223053/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":404519,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3053/images"},{"id":404518,"rank":3,"type":{"id":31,"text":"Publication 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Dakota\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status.
2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval.
3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan.
4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures.
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Agriculture adds more than $30 billion to the economy in Indiana, which placed eighth in the country for agricultural exports at $4.6 billion in 2017. Indiana ranks in the top five States nationally for the production of corn and soybeans. The “Hoosier State” also grows sizable crops of popcorn, spearmint, peppermint, pumpkins, tomatoes, and watermelon. Additionally, hogs, cattle, dairy, and poultry contribute to the agricultural economy. Other industries important to Indiana include manufacturing, medicine, energy, and mining. Mineral sources vary from coal, building stone, and gypsum to sand, gravel, and shale.
Landsat can help monitor the condition of natural resources and the effects of extreme weather events. Here are several ways Landsat has benefited Indiana.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Acajutla debris-avalanche deposit is dated to about 40,000 cal BP. The dating is based on two 14C dates on pieces of wood from the debris-avalanche deposit recovered from a core at the Santa Águeda School Center. The debris-avalanche deposit overlies a 1.2-m-thick paleosol and four ash layers. One of these ash layers is geochemically correlated to the Los Chocoyos ash from Atitlán Caldera, while the others are possibly from eruptions of Coatepeque and Ilopango Calderas. The new data, collected in well reports from the years 1966 to 2019, include deposit thicknesses identified in 25 wells for water monitoring, bathymetric data from nautical charts, GEBCO’s grids, updated topographic data from LIDAR, and a reassessment of the deposit’s lateral limits. This new data will allow us to better constrain the volume of the Acajutla debris-avalanche deposit.
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Here we combine tephrochronology and biostratigraphy in order to provide numerical age control for eight sedimentary sequences of the Monterey and Modelo Formations from Monterey, California to Orange County, California. We correlate 38 tuffs and tephra beds in the Monterey and Modelo Formations to 26 different dated tuffs found mainly in non-marine sequences in Nevada, Idaho and New Mexico. We also include geochemical data for an additional 19 tuffs in the Monterey and Modelo Formations for which there are no known correlative tuffs and geochemical data for 11 additional tuffs in other units that will add to the Miocene tephrostratigraphy. The identified tuffs range in age from 16 to 7 Ma with 31 tuffs erupted from volcanic centers of the Snake River Plain of northern Nevada to eastern Idaho. Twelve other tuffs erupted from the Southern Nevada Volcanic Field, one from the Sonoma Volcanic Field, north of San Francisco, and the eruptive source of 12 other tuffs is uncertain. These tuffs provide useful correlations of marine sequences deposited at varying depths along offshore Miocene California and possible insight into the distribution of air-fall tephra from so-called super eruptions","language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.2556(08)","usgsCitation":"Knott, J.R., Sarna-Wojcicki, A., Barron, J.A., Wan, E., Heizler, N., and Martinez, P., 2022, Tephrochronology of the Miocene Monterey and Modelo Formations, California: GSA Special Papers, https://doi.org/10.1130/2022.2556(08).","ipdsId":"IP-122368","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.2556(08)","text":"Publisher Index Page"},{"id":406061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knott, Jeffrey R. 0000-0002-4600-5961","orcid":"https://orcid.org/0000-0002-4600-5961","contributorId":218427,"corporation":false,"usgs":false,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":39844,"text":"CSU Fullerton, Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":850545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sarna-Wojcicki, Andrei M. 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":296073,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":850547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":296074,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":850548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heizler, Nancy","contributorId":296075,"corporation":false,"usgs":false,"family":"Heizler","given":"Nancy","email":"","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":850549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martinez, Priscilla","contributorId":296076,"corporation":false,"usgs":false,"family":"Martinez","given":"Priscilla","email":"","affiliations":[{"id":63349,"text":"California State University Fullerton","active":true,"usgs":false}],"preferred":false,"id":850550,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263335,"text":"70263335 - 2022 - The effects of earthquake experience on intentions to respond to Earthquake Early Warnings","interactions":[],"lastModifiedDate":"2025-02-06T17:05:30.263398","indexId":"70263335","displayToPublicDate":"2022-07-17T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20070,"text":"Frontiers in Communications","active":true,"publicationSubtype":{"id":10}},"title":"The effects of earthquake experience on intentions to respond to Earthquake Early Warnings","docAbstract":"Warning systems are essential for providing people with information so they can take protective action in response to perils. Systems need to be human-centered, which requires an understanding of the context within which humans operate. Therefore, our research sought to understand the human context for Earthquake Early Warning (EEW) in Aotearoa New Zealand, a location where no comprehensive EEW system existed in 2019 when we did this study. We undertook a survey of people's previous experiences of earthquakes, their perceptions of the usefulness of a hypothetical EEW system, and their intended responses to a potential warning (for example, Drop, Cover, Hold (DCH), staying still, performing safety actions). Results showed little difference in perceived usefulness of an EEW system between those with and without earthquake experience, except for a weak relationship between perceived usefulness and if a respondent's family or friends had previously experienced injury, damage or loss from an earthquake. Previous earthquake experience was, however, associated with various intended responses to a warning. The more direct, or personally relevant a person's experiences were, the more likely they were to intend to take a useful action on receipt of an EEW. Again, the type of experience which showed the largest difference was having had a family member or friend experience injury, damage or loss. Experience of participation in training, exercises or drills did not seem to prompt the correct intended actions for earthquake warnings; however, given the hypothetical nature of the study, it is possible people did not associate their participation in drills, for example, with a potential action that could be taken on receipt of an EEW. Our analysis of regional differences highlighted that intentions to mentally prepare on receipt of a warning were significantly higher for Canterbury region participants, most likely related to strong shaking and subsequent impacts experienced during the 2010–11 Canterbury Earthquake Sequence. Our research reinforces that previous experience can influence earthquake-related perceptions and behaviors, but in different ways depending on the context. Public communication and interventions for EEW could take into consideration different levels and types of experiences of the audience for greater success in response.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcomm.2022.857004","usgsCitation":"Becker, J., Vinnell, L., McBride, S., Nakayachi, K., Doyle, E., Potter, S., and Bostrom, A., 2022, The effects of earthquake experience on intentions to respond to Earthquake Early Warnings: Frontiers in Communications, v. 7, 857004, 14 p., https://doi.org/10.3389/fcomm.2022.857004.","productDescription":"857004, 14 p.","ipdsId":"IP-133466","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcomm.2022.857004","text":"Publisher Index Page"},{"id":481762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[173.02037,-40.91905],[173.24723,-41.332],[173.95841,-40.9267],[174.24759,-41.34916],[174.24852,-41.77001],[173.87645,-42.23318],[173.22274,-42.97004],[172.71125,-43.37229],[173.08011,-43.85334],[172.30858,-43.86569],[171.45293,-44.24252],[171.18514,-44.8971],[170.6167,-45.90893],[169.83142,-46.35577],[169.33233,-46.64124],[168.41135,-46.61994],[167.76374,-46.2902],[166.67689,-46.21992],[166.50914,-45.8527],[167.04642,-45.11094],[168.30376,-44.12397],[168.94941,-43.93582],[169.66781,-43.55533],[170.52492,-43.03169],[171.12509,-42.51275],[171.56971,-41.76742],[171.94871,-41.51442],[172.09723,-40.9561],[172.79858,-40.49396],[173.02037,-40.91905]]],[[[174.61201,-36.1564],[175.33662,-37.2091],[175.3576,-36.52619],[175.80889,-36.79894],[175.95849,-37.55538],[176.7632,-37.88125],[177.43881,-37.96125],[178.01035,-37.57982],[178.51709,-37.69537],[178.27473,-38.58281],[177.97046,-39.16634],[177.20699,-39.14578],[176.93998,-39.44974],[177.03295,-39.87994],[176.88582,-40.06598],[176.50802,-40.60481],[176.01244,-41.28962],[175.23957,-41.68831],[175.0679,-41.42589],[174.65097,-41.28182],[175.22763,-40.45924],[174.90016,-39.90893],[173.82405,-39.50885],[173.85226,-39.1466],[174.5748,-38.79768],[174.74347,-38.02781],[174.69702,-37.38113],[174.29203,-36.71109],[174.319,-36.53482],[173.841,-36.12198],[173.05417,-35.23713],[172.63601,-34.52911],[173.00704,-34.45066],[173.5513,-35.00618],[174.32939,-35.2655],[174.61201,-36.1564]]]]},\"properties\":{\"name\":\"New Zealand\"}}]}","volume":"7","noUsgsAuthors":false,"publicationDate":"2022-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":926487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vinnell, Lauren","contributorId":292282,"corporation":false,"usgs":false,"family":"Vinnell","given":"Lauren","email":"","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":926488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nakayachi, K.","contributorId":350629,"corporation":false,"usgs":false,"family":"Nakayachi","given":"K.","affiliations":[{"id":48933,"text":"Doshisha University","active":true,"usgs":false}],"preferred":false,"id":926490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doyle, Emma","contributorId":118709,"corporation":false,"usgs":true,"family":"Doyle","given":"Emma","affiliations":[],"preferred":false,"id":926585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Potter, Sally H.","contributorId":217521,"corporation":false,"usgs":false,"family":"Potter","given":"Sally H.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":926586,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bostrom, Ann 0000-0002-6399-3404","orcid":"https://orcid.org/0000-0002-6399-3404","contributorId":239575,"corporation":false,"usgs":false,"family":"Bostrom","given":"Ann","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":926587,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.