The Channeled Scabland of eastern Washington State, USA, brought megafloods to the scientific forefront. A 30,000-km2 landscape of coulees and cataracts carved into the region’s loess-covered basalt attests to overwhelming volumes of energetic water. The scarred landscape, garnished by huge boulder bars and far-travelled ice-rafted erratics, spurred J Harlen Bretz’s vigorously disputed flood hypothesis in the 1920s. First known as the Spokane flood, it was rebranded the Missoula flood once understood that the water came from glacial Lake Missoula, formed when the Purcell Trench lobe of the last-glacial Cordilleran ice sheet dammed the Clark Fork valley in northwestern Idaho with ice a kilometer thick. Bretz’s flood evidence in the once-remote Channeled Scabland, widely seen and elaborated by the 1950s, eventually swayed consensus for cataclysmic flooding. Missoula flood questions then turned to some that continue today: how many? when? how big? what routes? what processes?
The Missoula floods passed through eastern Washington by a multitude of valleys, coulees and scabland tracts, some contemporaneously, some sequentially. Which routings and their timing depended on the positions of various lobes of the multi-pronged Cordilleran ice sheet and the erosional development of the channels themselves. The first floods mostly followed the big bend of Columbia valley looping through north-central Washington. But the south-advancing Okanogan ice lobe soon blocked that path, forming long-lasting glacial Lake Columbia in the impounded Columbia valley. Missoula floods into this lake were diverted south out of the Columbia valley and into eastern Washington coulees and scabland tracts. At least four floods entered Moses Coulee, but then as the Okanogan lobe advanced over and blocked the head of that coulee, more eastern paths took the water, including Grand Coulee and the Telford-Crab-Creek and Cheney-Palouse scabland tracts. Flood routing also depended on the erosion of the coulees. At some point, headward erosion of upper Grand Coulee lowered the divide saddle between the west-running Columbia valley and the deep and wide Grand Coulee heading southwest. Still uncertain is when this happened and the consequences with respect to the stage and extent of glacial Lake Columbia and to flood access to the other, higher, flood routes. Downstream, all flood routes converged onto Pasco Basin, flowed through Wallula Gap and the Columbia River Gorge into the Pacific Ocean, following submarine canyons and depositing sediment layers on abyssal plains.
Stratigraphic studies indicate dozens—likely more than a hundred—separate Missoula floods during the last glacial period. Over the length of the flood route, backwater areas and depositional basins preserve multiple flood beds, many of which are separated by signs of time, including volcanic ash layers and soil development in subaerial environments; and varve-like beds and pelagic mud layers in lacustrine and marine settings. Evidence also comes from the glacial Lake Missoula basin, where stratigraphy indicates dozens of filling and emptying cycles. Varve counts in conjunction of radiocarbon dating and paleomagnetic secular variation show the repeated filling-and-release cycles of glacial Lake Missoula had intervals possibly as long as 100 years early in the lake’s history but diminished to just one or two years for the last few floods. This behavior accords with jökulhlaup-style floods released by subglacial drainage from a self-dumping ice-dammed lake. But not yet clear is whether such a mechanism applies to all the floods or if some emptied more cataclysmically as hypothesized by some.
Radiocarbon dating of sparse organic materials remains key to defining flood chronology but has been lately bolstered by analyses of terrestrial cosmogenic nuclides and optically stimulated luminescence. Varve counts and paleomagnetic secular variation studies help to define durations and intervals represented by sequences of flood beds. The ~16 ka Mount St. Helens Set S tephra is commonly interbedded within flood deposits, enabling correlation of deposits among sites. Tephra from the 13.7–13.4 ka eruption of Glacier Peak overlies all glacial Lake Missoula and Missoula flood deposits, defining an end time. Overall conclusions are that glacial Lake Missoula was extant and producing floods for at least 3–4 ky during 20–14 ka. At least ~75 floods preceded Mount St Helens Set S, followed by 30 or more after the tephra fall. Most floods entered glacial Lake Columbia, impounded by the Okanogan lobe, for 2–5 ky between about 18.5 and 15 ka. Glacial Lake Columbia outlived Lake Missoula by >200–400 yr but may have been born later since at least one flood came down the Columbia valley before the Okanogan ice lobe blocked the Columbia valley at 18.5–18 ka. The maximum extent of the Okanogan and Purcell Trench lobes, many Missoula floods, substantial erosion of upper Grand Coulee, and the widespread tephra falls from Mount St. Helens eruptions all happened about 17–15 ka. People, in the area since 16.6–15.3 ka, almost certainly witnessed the last of the Missoula floods and later large floods from other ice-dammed lakes in the Columbia River basin.
Quantitative flow analyses give peak discharge estimates and support understanding of erosional and depositional processes. The first flow assessments were simple cross-section calculations but recent assessments employ two-dimensional hydrodynamic models. The general finding is that emplacement of the maximum stage evidence requires about 20 million m3/s near the Lake Missoula outlet and about 5–15 million m3/s through Wallula Gap and downstream in the Columbia River Gorge. These hydraulic analyses raise still-unresolved questions regarding canyon erosion and possible additional water sources.
The large Pleistocene Bonneville flood entered the Columbia River system from the southeast from pluvial Lake Bonneville, the Pleistocene predecessor to Great Salt Lake in the eastern Great Basin. During the last glacial, the lake basin filled, covering >50,000 km2 with 10,400 km3 of water before reaching its maximum possible stage governed by Red Rock Pass, the lowest divide separating the basin from the Snake River basin to the north. The overtopping lake rapidly incised 108–125 m into the Red Rock Pass outlet, spilling half of its total lake volume. G.K. Gilbert described the essential sequence in the 1870s, but the flood was mostly forgotten until the late 1950s when Harold Malde linked the spectacular scabland topography and bouldery “melon gravel” on the Snake River Plain to the Lake Bonneville overflow. The Bonneville flood appears to have been a singular event at about 18 ka. No evidence of multiple or pre-last-glacial spillovers has yet been found. Its total volume was about twice that of a maximum Lake Missoula flood yet its peak discharge was ~1 million m3/s, less than a tenth of the largest Missoula floods. Its comparatively simple flow path and much steadier flow make the Bonneville flood ideal for new studies of erosional and depositional processes.
At least two floods seem to have passed down the Columbia valley after the last of the Missoula floods, including a large flood about ~14 ka likely from cataclysmic demise of the thinning Okanogan ice lobe dam impounding glacial Lake Columbia. Floods from earlier glacial ages left scant yet clear evidence in the Channeled Scabland and Columbia valley. But their source, timing, and magnitudes are little understood. Some deposits are paleomagnetically reversed, thus older than ~800 ka. Last-glacial floods and perhaps older ones affected the Snake River Plain, some likely sourced in lakes dammed by alpine glaciers in central Idaho.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2020.103181","usgsCitation":"O'Connor, J., Baker, V.R., Waitt, R.B., Smith, L.N., Cannon, C.M., George, D.L., and Denlinger, R.P., 2020, The Missoula and Bonneville floods—A review of ice-age megafloods in the Columbia River basin: Earth-Science Reviews, v. 208, 103181, 51 p., https://doi.org/10.1016/j.earscirev.2020.103181.","productDescription":"103181, 51 p.","ipdsId":"IP-117652","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":456992,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://archimer.ifremer.fr/doc/00624/73634/","text":"External Repository"},{"id":382128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.76171875,\n 45.73685954736049\n ],\n [\n -116.4111328125,\n 45.73685954736049\n ],\n [\n -116.4111328125,\n 48.31242790407178\n ],\n [\n -120.76171875,\n 48.31242790407178\n ],\n [\n -120.76171875,\n 45.73685954736049\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"208","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":808089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Victor R.","contributorId":201141,"corporation":false,"usgs":false,"family":"Baker","given":"Victor","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":808090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":808091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Larry N","contributorId":247679,"corporation":false,"usgs":false,"family":"Smith","given":"Larry","email":"","middleInitial":"N","affiliations":[{"id":49605,"text":"Montana Technological University","active":true,"usgs":false}],"preferred":false,"id":808092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cannon, Charles M. 0000-0003-4136-2350 ccannon@usgs.gov","orcid":"https://orcid.org/0000-0003-4136-2350","contributorId":247680,"corporation":false,"usgs":true,"family":"Cannon","given":"Charles","email":"ccannon@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":808093,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":808094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Denlinger, Roger P. 0000-0003-0930-0635 roger@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-0635","contributorId":2679,"corporation":false,"usgs":true,"family":"Denlinger","given":"Roger","email":"roger@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":808095,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209671,"text":"ofr20201023 - 2020 - Design and methods of the California stream quality assessment (CSQA), 2017","interactions":[],"lastModifiedDate":"2020-04-27T12:01:20.795249","indexId":"ofr20201023","displayToPublicDate":"2020-04-21T14:01:19","publicationYear":"2020","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":"2020-1023","displayTitle":"Design and Methods of the California Stream Quality Assessment (CSQA), 2017","title":"Design and methods of the California stream quality assessment (CSQA), 2017","docAbstract":"During 2017, as part of the National Water-Quality Assessment Project, the U.S. Geological Survey conducted the California Stream Quality Assessment to investigate the quality of streams in the Central California Foothills and Coastal Mountains ecoregion, United States. The goal of the California Stream Quality Assessment study was to assess the health of wadeable streams in the region by characterizing multiple water-quality factors that are stressors to aquatic biota and by evaluating the relation between these stressors and biological indicators of stream health. Urbanization, agriculture, and modifications to streamflow are anthropogenic changes that affect water quality in the region; consequently, the study design primarily targeted sites and specific stressors associated with these activities. For the study, 85 stream sites were selected to represent the types and intensity of land use in the watershed; categories of site types were undeveloped, urban (low, medium, high), agriculture (low, high), and mixed (urban and agriculture). Most sites (about 70 percent) represent a gradient of urbanization from undeveloped to 99-percent urbanized. At most of the sites, streamgages or pressure transducers were used to monitor stream discharge and stage, as well as temperature. Water-quality samples were collected routinely at all sites and were analyzed for major ions, organic contaminants, nutrients, and suspended sediment. Sampling frequency varied on the basis of site type and location. Discrete water samples were collected weekly and generally 6 times per site, except for 11 undeveloped sites that were sampled only 4 times (during the last 4 weeks). Water sampling began at sites in the southern part of the study on March 13, 2017, and at sites in the northern part of the study on April 3, 2017. Passive samplers were deployed at most sites for measurement of polar organic contaminants (pesticides and pharmaceuticals). In May 2017, coincident with completion of water-quality sampling, an ecological survey was conducted at each site to assess benthic algal and macroinvertebrate communities and instream habitat. During the ecological surveys, a single composite streambed-sediment sample was collected for chemical analysis and toxicity testing. In addition, a few focused studies were done at subsets of sites, namely, measuring pesticides using small-volume automated samplers, measuring pesticides in biofilms, and sampling suspended sediments using passive samplers. This report describes the various study components and methods of the California Stream Quality Assessment, including measurements of water quality, sediment chemistry, habitat assessments, and ecological surveys, as well as procedures for sample analysis, quality assurance and quality control, and data management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201023","collaboration":"National Water Quality Program","usgsCitation":"May, J.T., Nowell, L.H., Coles, J.F., Button, D.T., Bell, A.H., Qi, S.L., and Van Metre, P.C., 2020, Design and methods of the California stream quality assessment, 2017: U.S. Geological Survey Open-File Report 2020–1023, 88 p.,","productDescription":"Report: x, 88 p.; 1 Table","numberOfPages":"88","onlineOnly":"Y","ipdsId":"IP-105445","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":374128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1023/coverthb.jpg"},{"id":374197,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1023/ofr20201023_app_table_1.1.xlsx","text":"Table 1.1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":" - Sampling matrix for the 85 sites used in the U.S. Geological Survey California Stream Quality Assessment in 2017."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.34423828125,\n 34.50655662164561\n ],\n [\n -119.267578125,\n 34.95799531086792\n ],\n [\n -121.4208984375,\n 37.78808138412046\n ],\n [\n -122.10205078125,\n 39.14710270770074\n ],\n [\n -122.25585937500001,\n 39.41922073655956\n ],\n [\n -123.48632812499999,\n 38.85682013474361\n ],\n [\n -122.67333984374999,\n 37.87485339352928\n ],\n [\n -122.05810546875,\n 37.055177106660814\n ],\n [\n -121.79443359375,\n 36.65079252503471\n ],\n [\n -121.9482421875,\n 36.61552763134925\n ],\n [\n -121.83837890625,\n 36.155617833818525\n ],\n [\n -121.26708984374999,\n 35.585851593232356\n ],\n [\n -120.58593749999999,\n 35.04798673426734\n ],\n [\n -120.5419921875,\n 34.488447837809304\n ],\n [\n -120.34423828125,\n 34.50655662164561\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
1) Interannual variability of seed crops (CVp) has profound consequences for plant populations and food webs, where high CVp is termed ‘masting’. Here we ask: is global variation in CVp better predicted by plant or habitat differences consistent with adaptive economies of scale, in which flower and seed benefits increase disproportionately during mast years; or to passive mechanisms, in which seed production responds to variation in resource availability associated with climate variability?
2) To address this question, we compiled a dataset for phylogenetic comparative analysis of long-term fruit/seed production for plants comprising 920 time-series spanning 311 plant species.
3) Factors associated with both adaptive benefits of CVp (wind pollination and seed dispersal) and climatic variability (variability of summer precipitation) were among the best predictors of global variation in CVp. We observed a hump-shaped relationship between CVp and latitude and intermediate phylogenetic and geographic signals in CVp.
4) CVp is patterned non-randomly across the globe and over the plant tree of life, where high CVp is associated with species benefiting from economies of scale of seed or flower production and with species that experience variable rainfall over summer months when seeds usually mature.
","language":"English","publisher":"Wiley","doi":"10.1111/nph.16617","usgsCitation":"Pearse, I., LaMontagne, J., Lordon, M., Hipp, A., and Koenig, W.D., 2020, Biogeography and phylogeny of masting: Do global patterns fit functional hypotheses?: New Phytologist, v. 227, no. 5, p. 1557-1567, https://doi.org/10.1111/nph.16617.","productDescription":"11 p.","startPage":"1557","endPage":"1567","ipdsId":"IP-106437","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456994,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nph.16617","text":"Publisher Index Page"},{"id":437017,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U7278U","text":"USGS data release","linkHelpText":"Data on interannual seed set variation, weather, and reproductive traits for global plants"},{"id":377406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"227","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaMontagne, Jalene M.","contributorId":223096,"corporation":false,"usgs":false,"family":"LaMontagne","given":"Jalene","middleInitial":"M.","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":795875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lordon, Michael","contributorId":228860,"corporation":false,"usgs":false,"family":"Lordon","given":"Michael","email":"","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":795876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hipp, Andrew","contributorId":219598,"corporation":false,"usgs":false,"family":"Hipp","given":"Andrew","email":"","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":795877,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koenig, Walter D.","contributorId":46255,"corporation":false,"usgs":false,"family":"Koenig","given":"Walter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":795878,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216404,"text":"70216404 - 2020 - FiCli, the Fish and Climate Change Database, informs climate adaptation and management for freshwater fishes","interactions":[],"lastModifiedDate":"2021-06-04T15:34:12.008748","indexId":"70216404","displayToPublicDate":"2020-04-21T09:03:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"FiCli, the Fish and Climate Change Database, informs climate adaptation and management for freshwater fishes","docAbstract":"Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change. Fish respond to climate change in diverse and nuanced ways, which creates challenges for practitioners of fish conservation, climate change adaptation, and management. Although climate change is known to affect fish globally, a comprehensive online, public database of how climate change has impacted inland fishes worldwide and adaptation or management practices that may address these impacts does not exist. We conducted an extensive, systematic primary literature review to identify peer-reviewed journal publications describing projected and documented examples of climate change impacts on inland fishes. From this standardized Fish and Climate Change database, FiCli (pronounced fick-lee), researchers and managers can query fish families, species, response types, or geographic locations to obtain summary information on inland fish responses to climate change and recommended management actions. The FiCli database is updatable and provides access to comprehensive published information to inform inland fish conservation and adaptation planning in a changing climate.
Rangeland managers need tools to control invasive annual grasses, particularly following wildfire. We assessed responses of native and invasive/exotic grasses to the MB906 soil amendment containing live cultures of a purportedly weed-suppressive strain of the bacterium Pseudomonas fluorescens (“WSB”). MB906 was applied alone and in combination with the pre-emergent herbicide imazapic on >3000 ha across three sagebrush-steppe landscapes burned several months prior. Replicate plots of each treatment type were established and plant cover was measured in the following three years. Cover of invasive-annual grasses (“IAG”) was not responsive to MB906 when all IAG species were considered (“IAG-All”). However, MB906 led to a 54% reduction in the IAG's that were previously reported to be controlled by WSB (“IAG-Target”) in the second year following application (IAG-Target = cheatgrass, Bromus tectorum and medusahead, Taeniatherum caput-medusae; IAG-All also includes Vulpia myuros and Bromus arvensis). MB906 reduced the effectiveness of co-applied imazapic: Imazapic alone reduced IAG-All by 83% and 68% in years 1 and 2, respectively, while imazapic+MB906 reduced IAG-All by 48% and 38% in years 1 and 2, respectively, across all landscapes, and a similar response pattern was observed for IAG-Target. Perennial grass cover was unaffected by the treatments except where it increased 4-fold in response to imazapic applied at a high rate (0.140 kg a.i. ha−1) in one of the landscapes. Tank mixing MB906 and herbicide may have lessened the biological activity of the herbicide by altering the pH or mineral content of the spray solution or by direct metabolism of the herbicide by the bacteria. These results do not provide strong support for MB906 as a tool for annual grass control, though they suggest further investigation may be warranted.
Most state and provincial fish and wildlife agencies have access to important information about patterns in sportsperson participation through their license databases. Using transaction data from Nebraska Game and Parks Commission's electronic hunting and fishing license system, we tracked license purchases of Nebraska, USA, resident license holders in 2010 through 2017. We categorized sportspersons by gender and yearly purchases as hunting only (Hunter), fishing only (Angler), a combination of hunting and fishing (Hunter–Angler), or no purchases (Inactive). The probability of movement among active sportsperson groups was limited and varied little based on initial group participation. The Angler group had the greatest probability of an individual transitioning to the Inactive group (females = 0.39; males = 0.33). The Hunter–Angler group had the greatest probability of an individual remaining within the same group (females = 0.65; males = 0.76). There was a relatively low probability of an individual in the Hunter group moving to the Angler group and vice versa (≤0.02). The sportsperson population is dynamic and understanding patterns of sportsperson participation is important for the future of fish and wildlife management in North America. Using data readily available to most fish and wildlife agencies has the potential to significantly improve our understanding of hunter and angler participation and aid management agencies and conservation organizations in the development of more effective strategies for managing sportspersons. © 2020 The Wildlife Society.
Background
Biological controls with predators of larval mosquito vectors have historically focused almost exclusively on insectivorous animals, with few studies examining predatory plants as potential larvacidal agents. In this study, we experimentally evaluate a generalist plant predator of North America, Utricularia macrorhiza, the common bladderwort, and evaluate its larvacidal efficiency for the mosquito vectors Aedes aegypti and Aedes albopictus in no-choice, laboratory experiments. We sought to determine first, whether U. macrorhiza is a competent predator of container-breeding mosquitoes, and second, its predation efficiency for early and late instar larvae of each mosquito species.
Methods
Newly hatched, first instar Aedes albopictus and Aedes aegypti larvae were separately exposed in cohorts of 10 to field collected U. macrorhiza cuttings. Data on development time and larval survival were collected on a daily basis to ascertain the effectiveness of U. macrorhiza as a larval predator. Survival models were used to assess differences in larval survival between cohorts that were exposed to U. macrorhiza and those that were not. A permutation analysis was used to investigate whether storing U. macrorhiza in laboratory conditions for extended periods of time (1 month vs. 6 months) affected its predation efficiency.
Results
Our results indicated a 100% and 95% reduction of survival of Ae. aegypti and Ae. albopictus larvae respectively, in the presence of U. macrorhiza relative to controls within five days, with peak larvacidal efficiency in plant cuttings from ponds collected in August. Utricularia macrorhiza cuttings, which were prey-deprived, and maintained in laboratory conditions for 6 months were more effective larval predators than cuttings, which were maintained prey-free for 1 month.
Conclusions
Due to the combination of high predation efficiency and the unique biological feature of facultative predation, we suggest that U. macrorhiza warrants further development as a method for larval mosquito control.
No abstract available.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3455","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Ramos, F.A., and Santiago, A.A., 2020, Potentiometric surface and hydrologic conditions of the South Coast aquifer, Santa Isabel area, Puerto Rico, March–April, 2014: U.S. Geological Survey Scientific Investigations Map 3455, 4 p., 1 sheet, https://doi.org/10.3133/sim3455.","productDescription":"Pamphlet: vi, 4 p.; Sheet: 35.68 inches x 28.17 inches; Data Release","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-064406","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":374025,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NS0STQ","text":"USGS data release","linkHelpText":"Data and shapefiles for the potentiometric surface of the South Coast aquifer and hydrologic conditions in the Santa Isabel area, Puerto Rico, March–April 2014"},{"id":374019,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3455/coverthb.jpg"},{"id":374021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3455/sim3455.pdf","text":"Pamphlet","size":"350 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3455 Pamphlet"},{"id":374022,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3455/sim3455_sheet.pdf","text":"Sheet 1—","size":"4.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3455 Sheet","linkHelpText":"Potentiometric Surface and Hydrologic Conditions of the South Coast Aquifer, Santa Isabel Area, Puerto Rico, March–April, 2014"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Santa Isabel Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.4779281616211,\n 17.932682319509986\n ],\n [\n -66.29854202270508,\n 17.932682319509986\n ],\n [\n -66.29854202270508,\n 18.029995361346103\n ],\n [\n -66.4779281616211,\n 18.029995361346103\n ],\n [\n -66.4779281616211,\n 17.932682319509986\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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Constructed rock reefs have been used to remediate spawning habitat for Lake Sturgeon (Acipenser fulvescens) and other lithophilic spawning fishes in the St. Clair-Detroit River System, North America. Early projects used a cross-channel design and species-specific metrics (e.g., proximity to historical spawning locations) to guide reef placement. However, the Middle Channel Reefs and portions of other early projects were compromised by fine sediment accumulation. Therefore, geomorphological criteria were considered in siting reefs constructed after 2013 to avoid sediment sources and improve the likelihood of successful reef function. To evaluate the effectiveness of the revised placement process, we quantified physical maturation of constructed reefs using annual side-scan and down-looking sonar surveys beginning in 2014 and underwater video surveys beginning in 2015. Reef areas and hardness were measured from sonar surveys and underwater video was used to quantify surficial sediment composition. Size and hardness of reefs developed using geomorphological criteria decreased with time, but at rates slower than what was observed at the Middle Channel Reefs. Sediment composition of the reefs remained similar through 2017 and prevalence of reef rock was high, except at Hart's Light Reef, where dreissenid mussel shells composed 32% of the surficial substrate by age three. However, accumulation of fine sediments was documented at all reefs in 2018. Despite using geomorphic criteria to identify areas most suitable for reef construction, reef sediment composition has changed, and future reef restoration projects could benefit by incorporating methods for maintenance, in addition to using geomorphic criteria, to identify restoration sites.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2020.105837","usgsCitation":"Fischer, J., Roseman, E., Mayer, C., and Wills, T., 2020, If you build it and they come, will they stay? Maturation of constructed fish spawning reefs in the St. Clair-Detroit River System: Ecological Engineering, v. 150, 105837, 13 p., https://doi.org/10.1016/j.ecoleng.2020.105837.","productDescription":"105837, 13 p.","ipdsId":"IP-106280","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":377216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.5018310546875,\n 42.524748042348165\n ],\n [\n -82.3040771484375,\n 43.0287452513488\n ],\n [\n -82.584228515625,\n 43.03677585761058\n ],\n [\n -82.6611328125,\n 42.62587560259137\n ],\n [\n -82.69958496093749,\n 42.476148570254516\n ],\n [\n -82.5018310546875,\n 42.524748042348165\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.8424072265625,\n 42.313877566161864\n ],\n [\n -82.8753662109375,\n 42.407234661551875\n ],\n [\n -83.0950927734375,\n 42.370720143531976\n ],\n [\n -83.2049560546875,\n 42.224449701009725\n ],\n [\n -83.243408203125,\n 42.049292638686836\n ],\n [\n -83.2159423828125,\n 41.983994270935625\n ],\n [\n -83.0291748046875,\n 42.037054301883806\n ],\n [\n -83.03466796874999,\n 42.21224516288584\n ],\n [\n -83.023681640625,\n 42.293564192170095\n ],\n [\n -82.8424072265625,\n 42.313877566161864\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fischer, Jason 0000-0001-7226-6500 jfischer@usgs.gov","orcid":"https://orcid.org/0000-0001-7226-6500","contributorId":200339,"corporation":false,"usgs":true,"family":"Fischer","given":"Jason","email":"jfischer@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":795244,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":795245,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mayer, Christine","contributorId":237769,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","affiliations":[{"id":47604,"text":"University of Toledo, Lake Erie Center","active":true,"usgs":false}],"preferred":false,"id":795246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wills, Todd","contributorId":237770,"corporation":false,"usgs":false,"family":"Wills","given":"Todd","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":795247,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209640,"text":"fs20203022 - 2020 - Nature’s Notebook-A tool for recording the timing of seasonal activity of plants and animals","interactions":[],"lastModifiedDate":"2020-04-21T14:25:37.241199","indexId":"fs20203022","displayToPublicDate":"2020-04-20T13:15:00","publicationYear":"2020","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":"2020-3022","displayTitle":"Nature’s Notebook—A Tool for Recording the Timing of Seasonal Activity of Plants and Animals","title":"Nature’s Notebook-A tool for recording the timing of seasonal activity of plants and animals","docAbstract":"Nature's Notebook is a customizable program used by individual observers and Federal Government partners to document patterns in phenology—the timing of seasonal activity of plants and animals over the course of the calendar year. The USA National Phenology Network (USA-NPN) established Nature's Notebook in 2009 to create a standard approach for collecting phenology data on plants and animals across the country. Currently, about half of the data are submitted by independent backyard observers at individual sites and half are submitted by groups of participants as part of a Local Phenology Program. With support from the U.S. Geological Survey, the USA-NPN collects, organizes, and shares data on phenology. This information aids decision making, scientific discovery, and the development of a broader understanding of phenology.
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Mail Stop 300
12201 Sunrise Valley Drive
Reston, VA 20192
Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.
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Russell 0000-0002-5572-9064 rlotspei@usgs.gov","orcid":"https://orcid.org/0000-0002-5572-9064","contributorId":3388,"corporation":false,"usgs":true,"family":"Lotspeich","given":"R.","email":"rlotspei@usgs.gov","middleInitial":"Russell","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":791974,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Laveau, Christopher 0000-0002-4009-1889","orcid":"https://orcid.org/0000-0002-4009-1889","contributorId":206046,"corporation":false,"usgs":true,"family":"Laveau","given":"Christopher","email":"","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791975,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Moramarco, Tommaso 0000-0002-9870-1694","orcid":"https://orcid.org/0000-0002-9870-1694","contributorId":225686,"corporation":false,"usgs":false,"family":"Moramarco","given":"Tommaso","email":"","affiliations":[{"id":41180,"text":"IRPI-Consiglio Nazionale delle Ricerche","active":true,"usgs":false}],"preferred":false,"id":791976,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Jones, Mark E. 0000-0002-9242-1528","orcid":"https://orcid.org/0000-0002-9242-1528","contributorId":225687,"corporation":false,"usgs":true,"family":"Jones","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791977,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gourley, Jonathan J 0000-0001-7363-3755","orcid":"https://orcid.org/0000-0001-7363-3755","contributorId":225540,"corporation":false,"usgs":false,"family":"Gourley","given":"Jonathan","email":"","middleInitial":"J","affiliations":[{"id":41158,"text":"NOAA/OAR/National Severe Storms Laboratory, Norman, OK, USA 73072","active":true,"usgs":false}],"preferred":false,"id":791978,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wasielewski, Danny","contributorId":225688,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Danny","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":791979,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70210282,"text":"70210282 - 2020 - Biofilms provide new insight into pesticide occurrence in streams and links to aquatic ecological communities","interactions":[],"lastModifiedDate":"2020-05-29T15:28:16.294568","indexId":"70210282","displayToPublicDate":"2020-04-20T10:19:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Biofilms provide new insight into pesticide occurrence in streams and links to aquatic ecological communities","docAbstract":"Streambed sediment is commonly analyzed to assess occurrence of hydrophobic pesticides and risks to aquatic communities. However, stream biofilms also have the potential to accumulate pesticides and may be consumed by aquatic organisms. To better characterize risks to aquatic life, the U.S. Geological Survey Regional Stream Quality Assessment measured 93 current-use and 3 legacy pesticides in bed sediment and biofilm from 54 small streams in California across a range of land-use settings. On average, 4 times as many current-use pesticides were detected in biofilm at a site (median of 2) as in sediment (median of 0.5). Of 31 current-use pesticides detected, 20 were detected more frequently in biofilm than sediment and 10 equally frequently. Pyrethroids as a class were the most potentially toxic to benthic invertebrates, and of the nine pyrethroids detected, seven occurred more frequently in biofilm than sediment. We constructive General Additive Models to investigate relations between pesticides and six metrics of benthic community structure. Pesticides in biofilm improved fit in four of the six models, and pesticides in sediment improved fit in two. The results indicate that sampling of stream biofilms can complement bed-sediment sampling by identifying more current-use pesticides present and better estimating ecological risks.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b07430","usgsCitation":"Mahler, B., Schmidt, T., Nowell, L.H., Qi, S.L., Van Metre, P.C., Hladik, M.L., Carlisle, D.M., Munn, M., and May, J., 2020, Biofilms provide new insight into pesticide occurrence in streams and links to aquatic ecological communities: Environmental Science & Technology, v. 54, no. 9, p. 5509-5519, https://doi.org/10.1021/acs.est.9b07430.","productDescription":"11 p.","startPage":"5509","endPage":"5519","ipdsId":"IP-114380","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437021,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YYD8DI","text":"USGS data release","linkHelpText":"Concentrations of pesticides associated with streambed sediment and biofilm in California streams, 2017"},{"id":375146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.520263671875,\n 34.34343606848294\n ],\n [\n -118.5205078125,\n 34.813803317113155\n ],\n [\n -120.684814453125,\n 37.21283151445594\n ],\n [\n -121.56372070312499,\n 38.487994609214795\n ],\n [\n -122.025146484375,\n 39.027718840211605\n ],\n [\n -123.53027343749999,\n 38.75408327579141\n ],\n [\n -122.64038085937499,\n 37.32648861334206\n ],\n [\n -121.95922851562501,\n 36.29741818650811\n ],\n [\n -120.69580078125001,\n 34.89494244739732\n ],\n [\n -120.62988281249999,\n 34.50655662164561\n ],\n [\n -119.520263671875,\n 34.34343606848294\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - 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Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":789940,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Munn, Mark D. 0000-0002-7154-7252","orcid":"https://orcid.org/0000-0002-7154-7252","contributorId":205360,"corporation":false,"usgs":true,"family":"Munn","given":"Mark D.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789941,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"May, Jason 0000-0002-5699-2112","orcid":"https://orcid.org/0000-0002-5699-2112","contributorId":224991,"corporation":false,"usgs":false,"family":"May","given":"Jason","affiliations":[{"id":41015,"text":"Deceased (ex-USGS)","active":true,"usgs":false}],"preferred":false,"id":789937,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70210970,"text":"70210970 - 2020 - Subduction megathrust heterogeneity characterized from 3D seismic data","interactions":[],"lastModifiedDate":"2020-07-08T15:21:30.577495","indexId":"70210970","displayToPublicDate":"2020-04-20T10:13:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Subduction megathrust heterogeneity characterized from 3D seismic data","docAbstract":"Megathrust roughness and structural complexity are thought to be controls on earthquake slip at subduction zones because they result in heterogeneity in shear strength and resolved stress. However, because active megathrust faults are difficult to observe, the causes and scales of complexity are largely unknown. Here we measured the in situ properties of the megathrust of the Middle America subduction zone in a three-dimensional seismic reflection volume to determine how fault properties vary. We quantify spatial variability in the megathrust roughness, overburden and rock physical properties. Heterogeneity in the megathrust roughness exists at length scales of a few kilometres because the megathrust is dissected by active lower-plate normal faults, which offset the megathrust and renewed fault roughness. Spatial variations in the rock physical properties at the plate interface are characterized by correlation length scales of hundreds of metres. Frontal prism taper, historical seismicity and the variation in earthquake stress drop values local to the megathrust are all affected by the heterogeneity at these length scales. Both geometric and rheological complexities may therefore control the mechanical behaviour of the subduction plate interface, which includes earthquake rupture characteristics.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-020-0562-9","usgsCitation":"Kirkpatrick, J.D., Edwards, J.H., Verdecchia, A., Kluesner, J.W., Harrington, R.M., and Silver, E., 2020, Subduction megathrust heterogeneity characterized from 3D seismic data: Nature Geoscience, v. 13, p. 369-374, https://doi.org/10.1038/s41561-020-0562-9.","productDescription":"6 p.","startPage":"369","endPage":"374","ipdsId":"IP-106884","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467292,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.mcgill.ca/concern/articles/pc289q07k","text":"External Repository"},{"id":376202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Central America megathrust, Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.68212890625,\n 8.222363866437243\n ],\n [\n -83.77349853515625,\n 8.222363866437243\n ],\n [\n -83.77349853515625,\n 9.584500864717143\n ],\n [\n -86.68212890625,\n 9.584500864717143\n ],\n [\n -86.68212890625,\n 8.222363866437243\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirkpatrick, James D.","contributorId":202603,"corporation":false,"usgs":false,"family":"Kirkpatrick","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":792312,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, Joel H.","contributorId":202599,"corporation":false,"usgs":false,"family":"Edwards","given":"Joel","email":"","middleInitial":"H.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":792313,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Verdecchia, Alessandro","contributorId":228863,"corporation":false,"usgs":false,"family":"Verdecchia","given":"Alessandro","email":"","affiliations":[{"id":41521,"text":"Ruhr-Universität Bochum","active":true,"usgs":false}],"preferred":false,"id":792314,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792315,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harrington, Rebecca M.","contributorId":146633,"corporation":false,"usgs":false,"family":"Harrington","given":"Rebecca","email":"","middleInitial":"M.","affiliations":[{"id":16736,"text":"Dept. of Earth and Planetary Sci,.McGill Univ., Montreal, Quebec","active":true,"usgs":false}],"preferred":false,"id":792316,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silver, Eli","contributorId":173114,"corporation":false,"usgs":false,"family":"Silver","given":"Eli","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":792317,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210161,"text":"70210161 - 2020 - The Landsat Burned Area algorithm and products for the conterminous United States","interactions":[],"lastModifiedDate":"2022-04-14T19:23:11.991912","indexId":"70210161","displayToPublicDate":"2020-04-20T10:12:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"The Landsat Burned Area algorithm and products for the conterminous United States","docAbstract":"Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. Here, we present the BA algorithm and products, changes relative to the BAECV algorithm and products, and updated validation metrics. We also present spatial and temporal patterns of burned area across the conterminous U.S., how burned area varies in relation to the number of operational Landsat sensors, and a comparison with other burned area datasets, including the BAECV, Monitoring Trends in Burn Severity (MTBS), GeoMAC, and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1.006 data. The BA algorithm identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984 through 2018 using machine learning, thresholding, and image segmentation. Validation with reference data from high-resolution commercial satellite imagery resulted in omission and commission error rates averaging 19% and 41%, respectively. In comparison, validation with Landsat reference data had omission and commission error rates averaging 40% and 28%, respectively when burned areas in cultivated crops and pasture/hay land-cover types were excluded. Both validation tests documented lower commission error rates relative to the BAECV products. The amount of burned area detected varies not only in response to climate but also with the number of operational sensors and scenes collected. The combined amount of burned area detected by multiple sensors was larger than from any individual sensor, but there was no significant difference between individual sensors. Therefore, we used BA products from individual sensors to assess trends over time and all available sensors to compare with other existing BA products. From 1984 through 2018, annual burned area averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. Compared to existing burned area products, the new Landsat BA products identified 29% more burned area than the BAECV products (1984–2015), 183% more than the MTBS/GeoMAC products (1984–2018), and 56% more than the MCD64A1.006 products (2003–2018). The products had similar patterns of year-to-year variability; the R2 values of linear regressions between annual burned area were >0.70 with the BAECV products and the MTBS/GeoMAC products, but somewhat lower for the MCD64A1.006 product (R2 = 0.66). 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Reefs","active":true,"publicationSubtype":{"id":10}},"title":"Localized outbreaks of coral disease on Arabian reefs are linked to extreme temperatures and environmental stressors","docAbstract":"The Arabian Peninsula borders the hottest reefs in the world, and corals living in these extreme environments can provide insight into the effects of warming on coral health and disease. Here, we examined coral reef health at 17 sites across three regions along the northeastern Arabian Peninsula (Persian Gulf, Strait of Hormuz and Oman Sea) representing a gradient of environmental conditions. The Persian Gulf has extreme seasonal fluctuations in temperature and chronic hypersalinity, whereas the other two regions experience more moderate conditions. Field surveys identified 13 coral diseases including tissue loss diseases of unknown etiology (white syndromes) in Porites, Platygyra, Dipsastraea, Cyphastrea, Acropora and Goniopora; growth anomalies in Porites, Platygyra and Dipsastraea; black band disease in Platygyra, Dipsastraea, Acropora, Echinopora and Pavona; bleached patches in Porites and Goniopora and a disease unique to this region, yellow-banded tissue loss in Porites. The most widespread diseases were Platygyra growth anomalies (52.9% of all surveys), Acropora white syndrome (47.1%) and Porites bleached patches (35.3%). We found a number of diseases not yet reported in this region and found differential disease susceptibility among coral taxa. Disease prevalence was higher on reefs within the Persian Gulf (avg. 2.05%) as compared to reefs within the Strait of Hormuz (0.46%) or Oman Sea (0.25%). A high number of localized disease outbreaks (8 of 17 sites) were found, especially within the Persian Gulf (5 of 8 sites). Across all regions, the majority of variation in disease prevalence (82.2%) was associated with the extreme temperature range experienced by these corals combined with measures of organic pollution and proximity to shore. Thermal stress is known to drive a number of coral diseases, and thus, this region provides a platform to study disease at the edge of corals’ thermal range.
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2020 - Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers","interactions":[],"lastModifiedDate":"2020-06-18T14:36:43.859337","indexId":"70210710","displayToPublicDate":"2020-04-20T09:30:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers","docAbstract":"Quantifying suspended-sediment transport is critical for a variety of disciplines related to the management of water resources. However, the number of gauging stations and monitoring networks in most rivers around the world is insufficient to improve understanding of river dynamics and support water resource management decisions. This is mainly due to the high operational costs and intensive labor involved in traditional sediment measurement techniques, especially in sand bed rivers where coarse material varies spatially in the river cross section. Recently, the acoustic surrogate method has received attention as a potentially accurate surrogate technology for estimating suspended-sediment concentrations. In addition, the acoustic surrogate method, through use of acoustic Doppler current profilers (ADCPs), has the advantage of being able to simultaneously measure the flow velocity field and cross-sectional area when moving-boat measurements are performed. In spite of the important advances made in the implementation of this technique, there are no widely-available, free tools for processing the ADCP acoustic signal cross section measurements which include options to extrapolate velocity and sediment in unmeasured ADCP zones and develop calibrations with physical samples. This paper presents a new software called Acoustic Sediment Estimation Toolbox (ASET), which enables the user to develop a calibration between the acoustic signal collected with a down-looking Teledyne RD Instruments ADCP and sediment concentrations determined using traditional sediment sampling techniques. Moreover, ASET software uses dynamic ADCP measurements to estimate the total suspended-sediment transport through a river cross section. The theoretical framework and data processing routines applied by each module in ASET are presented. Finally, a comparison is made between the results obtained by ASET and by traditional methodologies for computing suspended-sediment transport in a large river system (Paraná River, Argentina).","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2020.104499","usgsCitation":"Dominguez Ruben, L.G., Szupiany, R., Latosinski, F., Lopez Weibel, C., Wood, M.S., and Boldt, J.A., 2020, Acoustic Sediment Estimation Toolbox (ASET): A software package for calibrating and processing TRDI ADCP data to compute suspended-sediment transport in sandy rivers: Computers & Geosciences, v. 140, https://doi.org/10.1016/j.cageo.2020.104499.","productDescription":"104499, 13 p.","startPage":"Article 104499","ipdsId":"IP-102073","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":375680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-65.5,-55.2],[-66.45,-55.25],[-66.95992,-54.89681],[-67.56244,-54.87001],[-68.63335,-54.8695],[-68.63401,-52.63637],[-68.25,-53.1],[-67.75,-53.85],[-66.45,-54.45],[-65.05,-54.7],[-65.5,-55.2]]],[[[-64.96489,-22.07586],[-64.37702,-22.79809],[-63.98684,-21.99364],[-62.84647,-22.03499],[-62.68506,-22.24903],[-60.84656,-23.88071],[-60.02897,-24.0328],[-58.80713,-24.77146],[-57.77722,-25.16234],[-57.63366,-25.60366],[-58.61817,-27.12372],[-57.60976,-27.3959],[-56.4867,-27.5485],[-55.69585,-27.38784],[-54.78879,-26.62179],[-54.62529,-25.73926],[-54.13005,-25.54764],[-53.62835,-26.12487],[-53.64874,-26.92347],[-54.49073,-27.47476],[-55.16229,-27.88192],[-56.2909,-28.85276],[-57.62513,-30.21629],[-57.87494,-31.01656],[-58.14244,-32.0445],[-58.13265,-33.04057],[-58.34961,-33.26319],[-58.42707,-33.90945],[-58.49544,-34.43149],[-57.22583,-35.28803],[-57.36236,-35.97739],[-56.73749,-36.41313],[-56.78829,-36.90157],[-57.74916,-38.18387],[-59.23186,-38.72022],[-61.23745,-38.92842],[-62.33596,-38.82771],[-62.12576,-39.4241],[-62.33053,-40.17259],[-62.14599,-40.6769],[-62.7458,-41.02876],[-63.77049,-41.16679],[-64.73209,-40.80268],[-65.11804,-41.06431],[-64.97856,-42.058],[-64.30341,-42.35902],[-63.75595,-42.04369],[-63.45806,-42.56314],[-64.3788,-42.87356],[-65.1818,-43.49538],[-65.32882,-44.50137],[-65.56527,-45.03679],[-66.50997,-45.03963],[-67.29379,-45.5519],[-67.58055,-46.30177],[-66.59707,-47.03392],[-65.64103,-47.23613],[-65.98509,-48.13329],[-67.16618,-48.69734],[-67.81609,-49.86967],[-68.72875,-50.26422],[-69.13854,-50.73251],[-68.81556,-51.7711],[-68.14999,-52.34998],[-68.57155,-52.29944],[-69.49836,-52.14276],[-71.9148,-52.00902],[-72.3294,-51.42596],[-72.30997,-50.67701],[-72.97575,-50.74145],[-73.32805,-50.37879],[-73.41544,-49.31844],[-72.64825,-48.87862],[-72.33116,-48.24424],[-72.44736,-47.73853],[-71.91726,-46.88484],[-71.55201,-45.56073],[-71.65932,-44.97369],[-71.22278,-44.78424],[-71.3298,-44.40752],[-71.79362,-44.20717],[-71.46406,-43.78761],[-71.91542,-43.40856],[-72.1489,-42.25489],[-71.7468,-42.05139],[-71.91573,-40.83234],[-71.68076,-39.80816],[-71.41352,-38.91602],[-70.81466,-38.553],[-71.11863,-37.57683],[-71.12188,-36.65812],[-70.36477,-36.00509],[-70.38805,-35.16969],[-69.81731,-34.19357],[-69.81478,-33.27389],[-70.0744,-33.09121],[-70.53507,-31.36501],[-69.91901,-30.33634],[-70.01355,-29.36792],[-69.65613,-28.45914],[-69.00123,-27.52121],[-68.29554,-26.89934],[-68.5948,-26.50691],[-68.386,-26.18502],[-68.41765,-24.51855],[-67.32844,-24.0253],[-66.98523,-22.98635],[-67.10667,-22.73592],[-66.27334,-21.83231],[-64.96489,-22.07586]]]]},\"properties\":{\"name\":\"Argentina\"}}]}","volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dominguez Ruben, Lucas Gerardo","contributorId":225404,"corporation":false,"usgs":false,"family":"Dominguez Ruben","given":"Lucas","email":"","middleInitial":"Gerardo","affiliations":[{"id":41098,"text":"Littoral National University, Argentina","active":true,"usgs":false}],"preferred":false,"id":791058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Szupiany, Ricardo","contributorId":225405,"corporation":false,"usgs":false,"family":"Szupiany","given":"Ricardo","affiliations":[{"id":41098,"text":"Littoral National University, Argentina","active":true,"usgs":false}],"preferred":false,"id":791059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latosinski, Francisco","contributorId":225406,"corporation":false,"usgs":false,"family":"Latosinski","given":"Francisco","affiliations":[{"id":41099,"text":"National Scientific and Technical Research Council, Argentina","active":true,"usgs":false}],"preferred":false,"id":791060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopez Weibel, Cecilia","contributorId":225407,"corporation":false,"usgs":false,"family":"Lopez Weibel","given":"Cecilia","affiliations":[{"id":32938,"text":"Littoral National University","active":true,"usgs":false}],"preferred":false,"id":791061,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":791062,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boldt, Justin A. 0000-0002-0771-3658","orcid":"https://orcid.org/0000-0002-0771-3658","contributorId":207849,"corporation":false,"usgs":true,"family":"Boldt","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":791063,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228074,"text":"70228074 - 2020 - Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes","interactions":[],"lastModifiedDate":"2022-02-16T14:39:39.755202","indexId":"70228074","displayToPublicDate":"2020-04-20T08:32:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes","docAbstract":"Efforts to quantify disturbances to aquatic systems often use landscape-level metrics, presumably linked to ecological integrity, but fewer studies have directly linked ecological integrity to instream habitat, and applied these results to unsampled stream reaches throughout a landscape. We developed a flexible, quantitative approach that characterizes stream impairment across a landscape and identifies least-disturbed stream reaches to serve as benchmarks for high quality physical habitat and ecological integrity. Fish and macroinvertebrate community characteristics, reach-level physical habitat and water quality metrics were summarized in 891 wadeable stream reaches across two ecoregions in Missouri, USA. The influence of reach and water-quality characteristics as well as landscape-level variables on 7 fish and 3 macroinvertebrate community biological indicator metrics was then modeled using boosted regression trees (BRTs). On average, reach-level models explained more variance (25 and 27% in the two ecoregions examined) in biotic metrics than landscape-level models (18% and 20%). Abiotic and biotic associations differed among ecoregions and stream sizes, however, reach-level habitat (e.g., bankfull width/depth ratio, channel incision height) and water quality (e.g., dissolved oxygen, total chlorophyll) were consistently top predictors. At the landscape scale, fish richness in the agriculturally dominated ecoregion increased with decreased fragmentation/flow modification, while invertebrate metrics responded to agricultural disturbances. Invertebrate metrics in the forested ecoregion showed community degradation apparent with crop coverage as low as 8-10% of the riparian zone, while urban impairment was best detected using invertebrate indicators of biotic integrity and measures of fish trophic ecology. Relationships among landscape-scale variables and reach characteristics identified as top predictors in BRTs also highlighted potential mechanistic relationships among landscape, habitat, and measures of ecological integrity. Using the results of the landscape-level models, estimates for overall ecological integrity were predicted for over 28,000 stream reaches throughout Missouri, and a total of 1,423 candidate reference reaches were identified. The objective approach to characterizing stream impairment developed in this study offers specific advantages, including a reach and landscape-level evaluation of human disturbance as well as an inductive, multi-metric determination of ecological integrity.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.105966","usgsCitation":"Paukert, C.P., Kleeklamp, E.R., and Tingley, R.W., 2020, Identifying candidate reference reaches to assess the physical and biological integrity of wadeable streams in different ecoregions and among stream sizes: Ecological Indicators, v. 111, 105966, 15 p., https://doi.org/10.1016/j.ecolind.2019.105966.","productDescription":"105966, 15 p.","ipdsId":"IP-097233","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"111","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleeklamp, Ethan R.","contributorId":274478,"corporation":false,"usgs":false,"family":"Kleeklamp","given":"Ethan","email":"","middleInitial":"R.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tingley, Ralph William 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":258043,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","email":"","middleInitial":"William","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":833062,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222531,"text":"70222531 - 2020 - Hydrologically induced deformation in Long Valley Caldera and adjacent Sierra Nevada","interactions":[],"lastModifiedDate":"2021-08-03T12:45:57.564571","indexId":"70222531","displayToPublicDate":"2020-04-20T07:43:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologically induced deformation in Long Valley Caldera and adjacent Sierra Nevada","docAbstract":"Vertical and horizontal components of GNSS displacements in the Long Valley Caldera and adjacent Sierra Nevada range show a clear correlation with hydrological trends at both multiyear and seasonal time scales. We observe a clear vertical and horizontal seasonal deformation pattern primarily attributable to the solid earth response to hydrological surface loading at large-to-regional (Sierra Nevada range) scales. Several GNSS sites, located at the eastern edge of the Sierra Nevada along the southwestern rim of Long Valley Caldera, also show significant horizontal deformation that cannot be explained by elastic deformation from surface loading. Due to the location of these sites and the strong correlation between their horizontal displacements and spring discharge, we hypothesize that this deformation reflects poroelastic processes related to snowmelt runoff water infiltrating into the Sierra Nevada slopes around Long Valley Caldera. Interestingly, this is also an area where water infiltrates to feed the local hydrothermal system, and where snowmelt-induced earthquake swarms have been recently detected.
The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement (
The distribution and relative abundance of Westslope Cutthroat Trout (WCT) Oncorhynchus clarkii lewisi in relation to habitat characteristics remain unknown across large portions of the species’ range. The goals of this research were to provide a foundational understanding of WCT distribution and relative abundance related to habitat characteristics in tributaries of the St. Maries River, Idaho—a highly altered watershed. The basin drains an area of approximately 1,863 km2 and has a longitudinal elevation difference of about 207 m. Backpack electrofishing and habitat assessments were conducted at 68 reaches in 35 different tributaries of the St. Maries River in 2017 and 2018. Habitat was measured at small (reach-level) and large (watershed-level) scales. A total of 652 WCT was sampled from 52 of 68 total reaches. Habitat characteristics varied by age-class, but most WCT were estimated to be age 0 and age 1. Logistic regression models indicated that the presence of age-0 WCT was positively related to stream gradient and elevation, but negatively related to water temperature, road density, fine substrate, stream depth, and the presence of Brook Trout (BKT) Salvelinus fontinalis. The relative abundance of age-0 WCT was positively associated with road density and inversely related to wetted width, canopy cover, and elevation. The presence of age-1 and older (age-1+) WCT was positively related to gradient, canopy cover, and elevation, but negatively associated with road density, temperature, stream depth, and the presence of BKT. Relative abundance of age-1+WCT was positively associated with gradient, large substrate, canopy cover, and road density. Conversely, the relative abundance of age-1+WCT was inversely related to wetted width and elevation. This research indicates that WCT populations can persist in response to altered landscapes when suitable habitat exists. However, unmitigated threats, such as nonnative species competition (e.g., BKT), hybridization with Rainbow Trout O. mykiss, habitat loss, and habitat fragmentation, pose persistent complications to WCT abundance in locations where populations appear robust but their actual abundance is unknown.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10450","usgsCitation":"Heckel, J.W., Quist, M.C., Watkins, C.J., and Dux, A.M., 2020, Distribution and abundance of Westslope Cutthroat Trout in relation to habitat characteristics at multiple spatial scales: North American Journal of Fisheries Management, v. 40, no. 4, p. 893-909, https://doi.org/10.1002/nafm.10450.","productDescription":"17 p,","startPage":"893","endPage":"909","ipdsId":"IP-107683","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":393744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"St. Maries River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.707763671875,\n 46.878968335076856\n ],\n [\n -115.631103515625,\n 46.878968335076856\n ],\n [\n -115.631103515625,\n 47.372314620566925\n ],\n [\n -116.707763671875,\n 47.372314620566925\n ],\n [\n -116.707763671875,\n 46.878968335076856\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Heckel, John W","contributorId":270716,"corporation":false,"usgs":false,"family":"Heckel","given":"John","email":"","middleInitial":"W","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":829822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":829821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watkins, Carson J.","contributorId":171708,"corporation":false,"usgs":false,"family":"Watkins","given":"Carson","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":829823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dux, Andrew M.","contributorId":212798,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":829824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}