Secondary forests regenerating from human disturbance are increasingly becoming a predominant forest type in many regions, and they play a significant role in forest community dynamics. Understanding the factors that underlie the variation in species responses during secondary succession is important for understanding community assembly and biodiversity monitoring and management. Because species vary in ecology and behaviour, responses to ecosystem change should vary among species. Here, we show that habitat type (riparian, upland), phylogeny, and species traits mediate anuran and lizard probability of occurrence and species richness in pasture and secondary forest.
Sarapiquí and Osa Peninsula, Costa Rica.
We used phylogenetic occupancy models to estimate assemblage-level and species-specific responses to forest succession in 30 chronosequence sites that include pasture, secondary forest regenerating from pasture, and mature forest sites.
For the majority of species, we found increasing probability of occurrence in upland habitats as forest regenerated from pasture to secondary forest and similar probability of occurrence in riparian habitats across pasture, secondary forest, and mature forest sites. Species' responses to forest stage were phylogenetically correlated, and the trend was especially strong for anuran response to pasture sites. Anurans with lentic larval habitat had a positive occupancy response to pasture upland habitat, and anurans with lotic larval habitat had a variable response to different forest stages compared to mature forest.
Our study, which focuses on sites that are minimally isolated from mature forest reference sites, indicated that anuran and lizard occupancy rapidly recovered to a level similar to mature forest in a relatively short time span (approximately 20 years). Riparian habitats are key ecosystem features in our system and provide refugia for organisms in early successional stages. Maintenance of vegetation along streams shows that we can mitigate forest conversion by maintaining riparian buffers.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13601","usgsCitation":"Thompson, M.E., Halstead, B., and Donnelly, M., 2022, Riparian buffers provide refugia during secondary forest succession: Diversity and Distributions, v. 28, no. 9, p. 2008-2019, https://doi.org/10.1111/ddi.13601.","productDescription":"12 p.","startPage":"2008","endPage":"2019","ipdsId":"IP-132963","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13601","text":"Publisher Index Page"},{"id":404477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Osa Peninsula, Sarapiquí","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9,\n 10.3\n ],\n [\n -83.9,\n 10.5\n ],\n [\n -84.1,\n 10.5\n ],\n [\n -84.1,\n 10.3\n ],\n [\n -83.9,\n 10.3\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.4,\n 8.32\n ],\n [\n -83.3,\n 8.32\n ],\n [\n -83.3,\n 8.52\n ],\n [\n -83.4,\n 8.52\n ],\n [\n -83.4,\n 8.32\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Michelle E.","contributorId":210341,"corporation":false,"usgs":false,"family":"Thompson","given":"Michelle","email":"","middleInitial":"E.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":847546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":847547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Maureen A.","contributorId":293649,"corporation":false,"usgs":false,"family":"Donnelly","given":"Maureen A.","affiliations":[{"id":63353,"text":"Department of Biological Sciences, Florida International University, Miami, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":847548,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70233184,"text":"70233184 - 2022 - Evapotranspiration covers at uranium mill tailings sites","interactions":[],"lastModifiedDate":"2023-03-24T16:50:15.597022","indexId":"70233184","displayToPublicDate":"2022-07-15T09:09:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Evapotranspiration covers at uranium mill tailings sites","docAbstract":"Waste isolation is a key strategy for mitigating risk from municipal solid waste (MSW) and hazardous waste streams. Conventional covers at MSW facilities are designed for a 30-yr post-closure period where compacted soils and geosynthetics are used to minimize percolation into buried waste. Recently, evapotranspiration (ET) covers have shown beneficial use for MSW management. Evapotranspiration covers encourage infiltration, storage, and transpiration of precipitation to minimize percolation. Such covers may also have beneficial use for long-term waste issues, such as at Uranium Mill Tailings Radiation Control Act (UMTRCA) sites. These sites were covered by a clay radon barrier to create tortuous flow paths that allow radioactive decay and attenuation of short-lived, radon-222 gas. For long-term waste isolation, an ET-radon cover may provide greater resilience by exploiting natural processes instead of resisting them. This update presents a review of the current state-of-the-science regarding ET covers and considerations for long-term applications.
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Sarah","contributorId":293202,"corporation":false,"usgs":false,"family":"Tabatabai","given":"Sarah","email":"","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":846712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Jena 0000-0002-9291-1404","orcid":"https://orcid.org/0000-0002-9291-1404","contributorId":204033,"corporation":false,"usgs":true,"family":"Huntington","given":"Jena","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davies, Gwendolyn Elizabeth 0000-0003-1538-8610","orcid":"https://orcid.org/0000-0003-1538-8610","contributorId":293203,"corporation":false,"usgs":true,"family":"Davies","given":"Gwendolyn","email":"","middleInitial":"Elizabeth","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuhrmann, Mark","contributorId":293204,"corporation":false,"usgs":false,"family":"Fuhrmann","given":"Mark","email":"","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":846715,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233546,"text":"70233546 - 2022 - Scanning the horizon for invasive plant threats using a data-driven approach","interactions":[],"lastModifiedDate":"2022-07-25T12:25:01.412207","indexId":"70233546","displayToPublicDate":"2022-07-15T07:21:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"Scanning the horizon for invasive plant threats using a data-driven approach","docAbstract":"Early detection and eradication of invasive plants are more cost-effective than managing well-established invasive plant populations and their impacts. However, there is high uncertainty around which taxa are likely to become invasive in a given area. Horizon scanning that combines a data-driven approach with rapid risk assessment and consensus building among experts can help identify invasion threats. We performed a horizon scan of potential invasive plant threats to Florida, USA—a state with a high influx of introduced species, conditions that are generally favorable for plant establishment, and a history of negative impacts from invasive plants. We began with an initial list of 2128 non-native plant taxa that are known invaders or crop pests. We built on previous invasive species horizon scans by developing data-based criteria to prioritize 100 taxa for rapid risk assessment. The semi-automated prioritization process included selecting taxa “on the horizon” (i.e., not yet in the target location and not on a noxious weed list) with climate matching, naturalization history, “weediness” record, and global commonness. We derived overall invasion risk scores with rapid risk assessment by evaluating the likelihood of each of the taxa arriving, establishing, and having an impact in Florida. Then, following a consensus-building discussion, we identified six plant taxa as high risk, with overall risk scores ranging from 75 to 100 out of a possible 125. The six taxa are globally distributed, easily transported to new areas, found in regions with climates similar to Florida’s, and can impact native plant communities, human health, or agriculture. Finally, we evaluated our initial and final lists for potential biases. Assessors tended to assign higher risk scores to taxa that had more available information. In addition, we identified biases towards four plant families and certain geographical regions of origin. Our horizon scan approach identified taxa conforming to metrics of high invasion risk and used a methodology refined for plants that can be applied to other locations.
Numerous porphyry copper-molybdenum-gold and epithermal deposits define a belt that extends from Eastern Alaska to western Yukon, Canada. An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221046","usgsCitation":"Kelley, K.D., Pfaff, K., and Graham, G.E., 2022, Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska: U.S. Geological Survey Open-File Report 2022–1046, 12 p., https://doi.org/10.3133/ofr20221046.","productDescription":"Report: vi, 12 p.; Table; Data Release","onlineOnly":"Y","ipdsId":"IP-132987","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":403682,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1046/table1_1.csv","text":"Table 1.1","size":"12.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2022-1046 Table 1.1"},{"id":403617,"rank":3,"type":{"id":30,"text":"Data 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Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 973
Denver, CO 80225
Minerals occurring in marine environments span the globe and encompass a broad range of mineral categories, forming within varied geologic and oceanographic settings. They occur in coastal regions, either from the continuation or mechanical reworking of terrestrial mineralization, as well as in the deep ocean, from diagenetic, hydrogenetic, and hydrothermal processes. The oceans cover most of the Earth’s surface and as a result, any inventory of global resources is incomplete without the inclusion of marine minerals. This study by the U.S. Geological Survey reviews current knowledge regarding deep-ocean and coastal marine minerals within the marine areas surrounding Alaska, including the Alaska Outer Continental Shelf (OCS). For the purposes of this study, we have divided these areas in to eight regions: (1) Gulf of Alaska seamounts, (2) Chukchi Borderland, (3) Canada Basin, (4) Aleutian Arc, (5) Seward Peninsula, (6) Goodnews Bay, (7) Bristol Bay and Alaska Peninsula, and (8) southern and southeastern Alaska. The Alaska OCS encompasses several areas broadly conducive to marine mineral formation, including extensional basins resulting from an active subduction zone where massive sulfide deposits may form, deep abyssal plains with conditions that may lead to manganese nodule formation, seamounts that can provide substrate for the growth of ferromanganese crusts, and erosional settings and submerged continental crust where placer deposits are found. For deep-ocean hydrothermal minerals and manganese nodules, the Alaska OCS contains prospective regions, including the Canada Basin and the Aleutian Arc; however, no such minerals have yet been identified. We explore the probability that these minerals occur based on reviews of existing geologic and oceanographic data within the relevant sections. In regions far from shore data are limited. Deep-ocean ferromanganese crusts are known to occur in two regions: (1) the Gulf of Alaska seamounts and (2) the Chukchi Borderland in the Arctic Ocean. Limited sampling has occurred in both regions, and along the Chukchi Borderland the sampling was outside of the OCS and the U.S. Exclusive Economic Zone. Data relevant to coastal minerals is more extensive, and in some places fairly systematic sampling was conducted. Several nearshore placer deposits have been exploited for decades; however, the potential for nearshore extension of terrestrial ore deposits is less well considered. This contribution considers the state of knowledge regarding marine mineral occurrences within the Alaska regions and identifies the data gaps in order to help inform future marine mineral related research efforts around Alaska.
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Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02222","usgsCitation":"Stevens, B., Conway, C.J., Luke, K., Weldon, A., Hand, C., Schwarzer, A., Smith, F., Watson, C., and Watts, B.D., 2022, Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems: Global Ecology and Conservation, v. 38, e02222, 12 p., https://doi.org/10.1016/j.gecco.2022.e02222.","productDescription":"e02222, 12 p.","ipdsId":"IP-136723","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02222","text":"Publisher Index Page"},{"id":430022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":903426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luke, Kirsten","contributorId":338653,"corporation":false,"usgs":false,"family":"Luke","given":"Kirsten","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weldon, Aimee","contributorId":338654,"corporation":false,"usgs":false,"family":"Weldon","given":"Aimee","email":"","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hand, Christy","contributorId":338655,"corporation":false,"usgs":false,"family":"Hand","given":"Christy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarzer, Amy","contributorId":338656,"corporation":false,"usgs":false,"family":"Schwarzer","given":"Amy","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":903431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Fletcher","contributorId":338657,"corporation":false,"usgs":false,"family":"Smith","given":"Fletcher","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Watson, Craig","contributorId":338659,"corporation":false,"usgs":false,"family":"Watson","given":"Craig","email":"","affiliations":[{"id":81184,"text":"Atlanti Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watts, Bryan D.","contributorId":338660,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":37406,"text":"College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":903434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274308,"text":"70274308 - 2022 - Fluid transport and storage in the Cascadia forearc influenced by overriding plate lithology","interactions":[],"lastModifiedDate":"2026-03-26T16:14:27.133091","indexId":"70274308","displayToPublicDate":"2022-07-14T10:55:18","publicationYear":"2022","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":"Fluid transport and storage in the Cascadia forearc influenced by overriding plate lithology","docAbstract":"Subduction of hydrated oceanic lithosphere can carry water deep into the Earth, with consequences for a range of tectonic and magmatic processes. Most of the fluid is released in the forearc where it plays a critical role in controlling the mechanical properties and seismic behaviour of the subduction megathrust. Here we present results from three-dimensional inversions of data from nearly 400 long-period magnetotelluric sites, including 64 offshore, to provide insights into the distribution of fluids in the forearc of the Cascadia subduction zone. We constrain the geometry of the electrically resistive Siletz terrane, a thickened section of oceanic crust accreted to North America in the Eocene, and the conductive accretionary complex underthrust along the margin. We find that fluids accumulate over timescales exceeding 1 My above the plate in metasedimentary units, while the mafic rocks of Siletzia remain dry. Fluid concentrations tend to peak at slab depths of 17.5 and 30 km, suggesting control by metamorphic processes, but also concentrate around the edges of Siletzia, suggesting that this mafic block is impermeable, with dehydration fluids escaping up-dip along the megathrust. Our results demonstrate that the lithology of the overriding crust can play a critical role in controlling fluid transport in a subduction zone.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-022-00981-8","usgsCitation":"Egbert, G.D., Yang, B., Bedrosian, P.A., Key, K., Livelybrooks, D., Schultz, A., Kelbert, A., and Parris, B., 2022, Fluid transport and storage in the Cascadia forearc influenced by overriding plate lithology: Nature Geoscience, v. 15, p. 677-682, https://doi.org/10.1038/s41561-022-00981-8.","productDescription":"6 p.","startPage":"677","endPage":"682","ipdsId":"IP-130443","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"Cascadia forearc","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.49903582325707,\n 50.200003229484764\n ],\n [\n -132,\n 50.200003229484764\n ],\n [\n -132,\n 40\n ],\n [\n -121.49903582325707,\n 40\n ],\n [\n -121.49903582325707,\n 50.200003229484764\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2022-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":957814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Bo","contributorId":149369,"corporation":false,"usgs":false,"family":"Yang","given":"Bo","email":"","affiliations":[{"id":13653,"text":"University South Florida","active":true,"usgs":false}],"preferred":false,"id":957815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":957816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Key, Kerry","contributorId":367853,"corporation":false,"usgs":false,"family":"Key","given":"Kerry","affiliations":[{"id":87628,"text":"Lamont-Doherty Earth Observatory, Columbia Univ.","active":true,"usgs":false}],"preferred":false,"id":957817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Livelybrooks, Dean","contributorId":367854,"corporation":false,"usgs":false,"family":"Livelybrooks","given":"Dean","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":957818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schultz, Adam","contributorId":197380,"corporation":false,"usgs":false,"family":"Schultz","given":"Adam","affiliations":[],"preferred":false,"id":957819,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":957820,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Parris, Blake","contributorId":367855,"corporation":false,"usgs":false,"family":"Parris","given":"Blake","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":957821,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232943,"text":"ofr20191023E - 2022 - Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium","interactions":[],"lastModifiedDate":"2026-03-25T16:59:26.525114","indexId":"ofr20191023E","displayToPublicDate":"2022-07-14T10:34:00","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":"2019-1023","chapter":"E","displayTitle":"Alaska Focus Area Definition for Data Acquisition for Potential Domestic Sources of Critical Minerals in Alaska for Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Magnesium, Manganese, Uranium, Vanadium, and Zirconium","title":"Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium","docAbstract":"Phase 3 of the Earth Mapping Resources Initiative (Earth MRI) focuses on geologic belts that are favorable for hosting mineral systems that could contain the critical minerals antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium. Prior phases of the Earth MRI program in Alaska focused only on rare earth elements, aluminum, cobalt, graphite, lithium, niobium, platinum-group metals, tantalum, tin, titanium, and tungsten. An additional 11 critical minerals planed for future phases of Earth MRI (As, Bi, Cs, Ga, Ge, In, Re, Rb, Sc, Sr, Te) are considered prospective in these focus areas. Together, Alaska focus areas address 22 of the 35 minerals or mineral material groups presently deemed critical. This report describes the methodology and techniques utilized to define focus areas for future data acquisition in Alaska; the conterminous United States are covered in a separate report.
Focus areas are identified using a mineral systems framework, which accounts for all the possible tectonic and geologic settings where co-genetic mineral deposits may form. These deposits contain many commodities, including byproduct and critical minerals. Large system-scale processes may be evaluated using such a framework to determine the influence they play on critical mineral endowment within the deposits. Analyzing larger mineral systems provides an integrated and broad context to determine how and where critical minerals are sourced, transported, and deposited in geologic systems.
Statewide geological, geochemical, geophysical, and mineral occurrence datasets informed the delineation of focus areas in Alaska. For some mineral systems, previously published data-driven prospectivity analyses for critical mineral-bearing deposit types provided the basis for focus areas. We report a total of 22 new focus areas that are prospective for phase 3 critical minerals. These new focus areas represent four different mineral systems that are known or suspected to occur in Alaska. An additional 55 focus areas that were previously identified for phase 1 and phase 2 commodities were also identified as being prospective for phase 3 critical minerals. Collectively, 102 focus areas in Alaska have known or suspected potential for hosting phase 1, phase 2, and (or) phase 3 critical minerals. These focus areas represent 17 different mineral systems also containing critical minerals that are planned for consideration in future Earth MRI phases. Thus, the focus areas delineated herein, and in previous reports for Alaska, are comprehensive for all critical minerals as presently defined and may be used to guide the collection of new geologic, geochemical, and geophysical data in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023E","collaboration":"Prepared in cooperation with the Alaska Division of Geology & Geophysics","usgsCitation":"Kreiner, D.C., Jones, J.V., III, and Case, G. N., 2022, Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium, chap. 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Niobium, Platinum Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":501525,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113261.htm","linkFileType":{"id":5,"text":"html"}},{"id":403736,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023B","text":"Open-File Report 2019-1023-B","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in the Conterminous United States, Hawaii, and Puerto Rico—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum-Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":403735,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023A","text":"Open-File Report 2019-1023-A","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Sources of Critical Minerals—Rare Earth 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\"}}]}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The Earth Mapping Resources Initiative (Earth MRI) is conducted in phases to identify areas for acquiring new geologic framework data to identify potential domestic resources of the 35 mineral materials designated as critical minerals for the United States. This report describes the data sources and summary results for 13 critical minerals evaluated in the conterminous United States and Puerto Rico during phase 3 of the study (antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium). Phases 1 and 2 of the Earth MRI addressed aluminum, cobalt, graphite, lithium, niobium, platinum-group elements (PGEs), rare earth elements (REEs), tantalum, tin, titanium, and tungsten. Critical minerals in Alaska are covered in a separate report. No focus areas for phase 3 critical minerals are delineated for Hawaii.
The geologic, geochemical, topographic, and geophysical mapping provided by the Earth MRI documents geologic features that reflect the extent of individual mineral systems and provides information about critical mineral deposits that may not have been previously considered. The mineral-systems approach links critical mineral commodities to deposit types that represent the manifestations of large mineral systems.
Each of the 13 critical mineral commodities for phase 3 of the Earth MRI is discussed in terms of its importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types, and is accompanied by maps and tables listing examples of focus areas in the conterminous United States and Puerto Rico. Examples of important mineral systems for this group of 13 critical minerals include basin brine path systems for barite and fluorspar, Carlin-type systems and Coeur d’Alene systems for antimony, chemical weathering and volcanogenic seafloor systems for manganese, Climax-type systems for beryllium, mafic magmatic systems for chromium, marine evaporite systems for potash and magnesium, meteoric recharge systems for uranium, petroleum systems for helium, and placer systems for zirconium and hafnium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023D","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Dicken, C.L., Woodruff, L.G., Andersen, A.K., Brennan, S., Day, W.C., Drenth, B.J., Foley, N.K., Hall, S., Hofstra, A.H., McCafferty, A.E., Shah, A.K., and Ponce, D.A., 2022, Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico—Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium, chap. 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Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
Drought and nutrient pollution can affect the dynamics of stream ecosystems in diverse ways. While the individual effects of both stressors are broadly examined in the literature, we still know relatively little about if and how these stressors interact. Here, we performed a mesocosm experiment that explores the compounded effects of seasonal drought via water withdrawals and nutrient pollution (1.0 mg/L of N and 0.1 mg/L of P) on a subset of Ozark stream community fauna and ecosystem processes. We observed biological responses to individual stressors as well as both synergistic and antagonistic stressor interactions. We found that drying negatively affected periphyton assemblages, macroinvertebrate colonization, and leaf litter decomposition in shallow habitats. However, in deep habitats, drought-based increases in fish density caused trophic cascades that released algal communities from grazing pressures; while nutrient enrichment caused bottom-up cascades that influenced periphyton variables and crayfish growth rates. Finally, the combined effects of drought and nutrient enrichment interacted antagonistically to increase survival in longear sunfish; and stressors acted synergistically on grazers causing a trophic cascade that increased periphyton variables. Because stressors can directly and indirectly impact biota—and that the same stressor pairing can act differentially on various portions of the community simultaneously—our broad understanding of individual stressors might not adequately inform our knowledge of multi-stressor systems.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0269222","usgsCitation":"Fournier, R., and Magoulick, D.D., 2022, Drought and nutrient pollution produce multiple interactive effects in stream ecosystems: PLoS ONE, v. 17, no. 7, e0269222, 16 p., https://doi.org/10.1371/journal.pone.0269222.","productDescription":"e0269222, 16 p.","ipdsId":"IP-111266","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0269222","text":"Publisher Index Page"},{"id":433446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Fournier, R.J.","contributorId":341731,"corporation":false,"usgs":false,"family":"Fournier","given":"R.J.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908838,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232969,"text":"70232969 - 2022 - Warming temperatures affect meadow-wide nectar resources, with implications for plant-pollinator communities","interactions":[],"lastModifiedDate":"2022-07-14T13:57:25.353308","indexId":"70232969","displayToPublicDate":"2022-07-14T08:39:38","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":"Warming temperatures affect meadow-wide nectar resources, with implications for plant-pollinator communities","docAbstract":"Nectar production may be a point of sensitivity that can help link primary and secondary trophic responses to climate shifts, and is therefore important to our understanding of ecosystem responses. We evaluated the nectar response of two widespread native forbs, Balsamorhiza sagittata and Eriogonum umbellatum, to experimental warming in a high-elevation sagebrush meadow in the Teton Range, WY, USA, over two years, 2015 and 2016. Warming treatments reduced the occurrence of nighttime freezing and nectar volume but increased sugar concentration in nectar in both species in both years. Warming effects were also evident in a consistent increase in the number of flowers produced by B. sagittata. Our research suggests that warming associated with climate change has the potential to induce shifts in the nectar-feeding community by changing nectar characteristics such as volume and sugar concentration to which nectar feeders are adapted.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4162","usgsCitation":"McCombs, A.L., Debinski, D., Reinhardt, K., Germino, M., and Caragea, P., 2022, Warming temperatures affect meadow-wide nectar resources, with implications for plant-pollinator communities: Ecosphere, v. 13, no. 7, e4162, 9 p., https://doi.org/10.1002/ecs2.4162.","productDescription":"e4162, 9 p.","ipdsId":"IP-132652","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447116,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecs2.4162","text":"External Repository"},{"id":403740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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C.","contributorId":36903,"corporation":false,"usgs":false,"family":"Peters","given":"Debra P. C.","affiliations":[{"id":25579,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":846596,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"McCombs, Audrey L.","contributorId":293175,"corporation":false,"usgs":false,"family":"McCombs","given":"Audrey","email":"","middleInitial":"L.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":846584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Debinski, Diane","contributorId":293176,"corporation":false,"usgs":false,"family":"Debinski","given":"Diane","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":846585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reinhardt, Keith","contributorId":178543,"corporation":false,"usgs":false,"family":"Reinhardt","given":"Keith","email":"","affiliations":[],"preferred":false,"id":846586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":846587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caragea, Petrutza","contributorId":293177,"corporation":false,"usgs":false,"family":"Caragea","given":"Petrutza","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":846588,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232960,"text":"70232960 - 2022 - Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions","interactions":[],"lastModifiedDate":"2022-07-14T13:39:00.417801","indexId":"70232960","displayToPublicDate":"2022-07-14T08:29:28","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":11125,"text":"Special Papers of the Geological Society of London","active":true,"publicationSubtype":{"id":24}},"title":"Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions","docAbstract":"Subaerial volcaniclastic deposits are produced principally by volcanic debris avalanches, pyroclastic density currents, lahars, and tephra falls. Those deposits have widely ranging geomorphic and sedimentologic characteristics; they can mantle, modify, or create new topography, and their emplacement and subsequent reworking can have an outsized impact on the geomorphic and sedimentologic responses of watersheds surrounding, and channels draining, volcanoes. Volcaniclastic deposits provide a wealth of information about eruptive histories, volcanic processes, and landscape responses to eruptions. The volcanic processes that produce these deposits, and consequently the character and sedimentary structures of the deposits themselves, are influenced by initiation mechanism. Deposit preservation is affected by deposit magnitude, texture, and composition, depositional environment, and climate regime. Innovative analyses of deposits from several modern eruptions and advancements in physical and numerical modelling have vastly improved our understanding of volcanic processes, interpretations of eruptive histories, and recognition of the hazards posed by volcanic eruptions. This contribution highlights and summarizes major advances that have occurred in the past few\ndecades in understanding of volcaniclastic deposits and linkages with volcanic processes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanic processes in the sedimentary record: When volcanoes meet the environment","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of London","doi":"10.1144/SP520-2021-142","usgsCitation":"Major, J.J., 2022, Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions, chap. of Volcanic processes in the sedimentary record: When volcanoes meet the environment: Special Papers of the Geological Society of London, v. 520, 72 p., https://doi.org/10.1144/SP520-2021-142.","productDescription":"72 p.","ipdsId":"IP-138407","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/sp520-2021-142","text":"Publisher Index Page"},{"id":403724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Major, Jon J. 0000-0003-2449-4466 jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846570,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70232972,"text":"70232972 - 2022 - Gill-net selectivity for fifteen fish species of the upper San Francisco Estuary","interactions":[],"lastModifiedDate":"2022-07-14T13:27:46.712019","indexId":"70232972","displayToPublicDate":"2022-07-14T08:19:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Gill-net selectivity for fifteen fish species of the upper San Francisco Estuary","docAbstract":"Gill-net size selectivity for 15 fish species occurring in the upper San Francisco Estuary was estimated from a data set compiled from multiple studies which together contained 7,096 individual fish observations from 882 gill net sets. The gill nets considered in this study closely resembled the American Fisheries Society’s recommended standardized experimental gill nets for sampling inland waters. Relationships between gill-net mesh sizes and the sizes for each fish species retained in them were estimated indirectly using generalized linear modeling and maximum likelihood. Selectivity curves are provided for each species to inform researchers about population characteristics of fishes sampled with similar gill nets.","language":"English","publisher":"University of California","doi":"10.15447/sfews.2022v20iss2art4","usgsCitation":"Wulff, M.L., Feyrer, F.V., and Young, M.J., 2022, Gill-net selectivity for fifteen fish species of the upper San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 20, no. 2, 4, 10 p., https://doi.org/10.15447/sfews.2022v20iss2art4.","productDescription":"4, 10 p.","ipdsId":"IP-101973","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":447121,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2022v20iss2art4","text":"Publisher Index Page"},{"id":403721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Napa River, Petaluma River, San Francisco Estuary, San Pablo Bay, Suisun Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14187622070311,\n 38.052416771864834\n ],\n [\n -122.17758178710939,\n 38.07620357665235\n ],\n [\n -122.21603393554688,\n 38.08052761936274\n ],\n [\n -122.25036621093749,\n 38.11619121500379\n ],\n [\n -122.25723266601562,\n 38.156156969924915\n ],\n [\n -122.26684570312499,\n 38.19286295796692\n ],\n [\n -122.26409912109375,\n 38.285624966683756\n ],\n [\n -122.2723388671875,\n 38.34704152882893\n ],\n [\n -122.61291503906249,\n 38.24788726821097\n ],\n [\n -122.6348876953125,\n 38.23278669950994\n ],\n [\n -122.57858276367186,\n 38.17235306715556\n ],\n [\n -122.53463745117186,\n 38.13779704369437\n ],\n [\n -122.49618530273438,\n 38.09674050228651\n ],\n [\n -122.49893188476561,\n 38.06647354576796\n ],\n [\n -122.29980468749999,\n 38.002655740556705\n ],\n [\n -122.24624633789062,\n 38.0513353697269\n ],\n [\n -122.19955444335938,\n 38.04160203158016\n ],\n [\n -122.18032836914062,\n 38.025376925938865\n ],\n [\n -122.15011596679688,\n 38.01239425385966\n ],\n [\n -122.11303710937499,\n 38.025376925938865\n ],\n [\n -122.07733154296875,\n 38.039438891821746\n ],\n [\n -122.00729370117188,\n 38.048091067457236\n ],\n [\n -121.81365966796874,\n 38.01239425385966\n ],\n [\n -121.84936523437499,\n 38.08701320402273\n ],\n [\n -121.91253662109376,\n 38.059986139487975\n ],\n [\n -121.90017700195312,\n 38.07512252602562\n ],\n [\n -121.92214965820311,\n 38.098901948321256\n ],\n [\n -121.97021484374999,\n 38.08701320402273\n ],\n [\n -122.00180053710936,\n 38.095659755295614\n ],\n [\n -121.97708129882812,\n 38.11727165830543\n ],\n [\n -121.9976806640625,\n 38.15075747130226\n ],\n [\n -122.07595825195312,\n 38.13779704369437\n ],\n [\n -122.0745849609375,\n 38.11078875872392\n ],\n [\n -122.14187622070311,\n 38.052416771864834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Wulff, Marissa L. 0000-0003-0121-9066","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":229534,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846591,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232967,"text":"70232967 - 2022 - Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin","interactions":[],"lastModifiedDate":"2022-07-14T13:18:11.726912","indexId":"70232967","displayToPublicDate":"2022-07-14T08:08:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11122,"text":"BMC Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin","docAbstract":"Background
Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae.
Results
A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi. No evidence was found for genetic diversification across depth in G. childressi, likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments.
Conclusions
These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
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2022 - Impact of climate change on mollusks and other invertebrate resources at the Dominican University of California archaeological site (CA-MRN-254), Marin County, California","interactions":[],"lastModifiedDate":"2022-07-26T11:39:17.783231","indexId":"70233568","displayToPublicDate":"2022-07-14T06:33:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3217,"text":"Quaternary International","active":true,"publicationSubtype":{"id":10}},"title":"Impact of climate change on mollusks and other invertebrate resources at the Dominican University of California archaeological site (CA-MRN-254), Marin County, California","docAbstract":"We have identified and provided ecological interpretations of 30 taxa recovered at two shellmounds at the Dominican University of California archaeology site in Marin County, California (CA-MRN-254). A Q-mode cluster analysis was used to group the samples according to their faunal similarity. The clusters ranged from a diverse grouping of 100 samples with 27 taxa (Cluster A) to those with a more restricted assemblage (4–9 taxa in Clusters B to E). The Q-mode clusters were then used to interpret the variability in food resources utilized through the 1800 years of site occupation. During the Intermediate Middle Period (A.D.100-300), the inhabitants appeared to be selective in the marine taxa they used, evident by the presence of Cluster B and E assemblages. A diverse (Cluster A) assemblage was then utilized at the site at one or both of the shellmounds through the remainder of the occupancy period, including the Middle/Late Period Transition (A.D. 700–900) and Late Period Phase 1C (A.D. 900–1300), coincident with the extensive drought conditions of the Medieval Climatic Anomaly (MCA) in the San Francisco Bay area. These findings suggest the marine invertebrate resources utilized by the site occupants were not significantly affected by the persistent aridity associated with the MCA.
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2150 Centre Ave., Bldg. C
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Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2088651","usgsCitation":"Gumma, M., Thenkabail, P., Panjala, P., Teluguntla, P., Yamano, T., and Mohammad, I., 2022, Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security: GIScience & Remote Sensing, v. 59, no. 1, p. 1048-1077, https://doi.org/10.1080/15481603.2022.2088651.","productDescription":"30 p.","startPage":"1048","endPage":"1077","ipdsId":"IP-135578","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2088651","text":"Publisher Index 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Murali Krishna","contributorId":294754,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali Krishna","affiliations":[{"id":39044,"text":"The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":848826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panjala, Pranay","contributorId":294756,"corporation":false,"usgs":false,"family":"Panjala","given":"Pranay","email":"","affiliations":[{"id":39044,"text":"The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi","contributorId":294758,"corporation":false,"usgs":false,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":63639,"text":"Bay Area Environmental Research Institute (BAERI) @ USGS","active":true,"usgs":false}],"preferred":false,"id":848828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yamano, Takashi","contributorId":294759,"corporation":false,"usgs":false,"family":"Yamano","given":"Takashi","email":"","affiliations":[{"id":63641,"text":"Asian Development Bank (ADB)","active":true,"usgs":false}],"preferred":false,"id":848829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mohammad, Ismail","contributorId":294760,"corporation":false,"usgs":false,"family":"Mohammad","given":"Ismail","email":"","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233233,"text":"70233233 - 2022 - Martian gully activity and the gully sediment transport system","interactions":[],"lastModifiedDate":"2022-07-19T12:01:29.241941","indexId":"70233233","displayToPublicDate":"2022-07-13T06:55:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Martian gully activity and the gully sediment transport system","docAbstract":"The formation process for Martian gullies is a critical unknown for understanding recent climate conditions. Leading hypotheses include formation by snowmelt in a past climate, or formation via currently active CO2 frost processes. This paper presents an expanded catalog of >300 recent flows in gullies. The results indicate that sediment transport in current gully flows moves the full range of materials needed for gully formation. New flows are more likely to transport boulders in gullies that have pre-existing boulder-covered aprons, indicating that current flows are transporting the same materials required for gully formation overall. The distribution of gully activity frequencies can be described by a power law and indicates that the recurrence intervals for flows in individual gullies are commonly tens to hundreds of Mars years. Over the last ~300 kyr, climate variations have been modest but individual gullies have had tens to thousands of flow events. This could be sufficient to account for the entirety of gully formation in some cases, although the same processes are likely to have occurred further in the past. For any gullies that may have initiated under higher-obliquity conditions, this level of recent activity indicates that the observable morphology has been shaped by CO2-driven flows. These observations of sediment transport and the tempo of gully activity are consistent with gully formation entirely by CO2 frost processes, likely with spatial and temporal variability, but with no role required for liquid water.
Sediment budgets are fundamentally important for planetary science. However, only one primary method, based on remote sensing, is currently available for determining extraterrestrial sediment budgets. For determining sediment budgets on Earth, both in-situ and remote sensing methods are available. Despite the widespread use of the two methods, there has been surprisingly little research on how well the sediment budgets produced by these two approaches reconcile with one another, which highlights the lack of quantitative understanding of errors for sediment budgets measured with remote sensing in planetary research. Therefore, there is a general need to expand our knowledge of sediment budgets. Here we use a background review and analog case study of an aeolian dunefield in Grand Canyon, Earth to frame a path forward for addressing shortcomings of remote sensing sediment budgets on Mars. We estimate a 53% percent difference in the sediment budget determined with remote sensing relative to in-situ methods for a simple endmember scenario of a dunefield within a unimodal wind directional regime and no external sediment supply. However, when we incorporated key sources of uncertainty in remote sensing change detection following methods commonly used by geomorphologists on Earth, the estimates of sediment budget differences relative to the in-situ method spanned a much larger range, from 3% to 138%. Our case study also suggests that sediment budget errors could be much larger under more complex wind direction, sediment supply, and physiographic settings, and that variability in those landscape characteristics might be used to better estimate errors for dunefield sediment budgets. We conclude that by comparing sediment budgets derived from in-situ measurements of sediment fluxes and from remote sensing measurements at many more analog sites on Earth, the aeolian research community, and the geomorphology discipline, could gain an understanding of the errors of the remote sensing method, which is used by investigators on other planetary bodies such as Mars. This could improve the ability to quantify sediment budgets on Mars – and, in the future, other planetary environments where high-resolution topographic data are available – as well as directly improve our ability to interpret extraterrestrial landscape evolution related to climate, weather, and geologic history.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2022.117682","usgsCitation":"Sankey, J., Kasprak, A., Chojnacki, M., Titus, T.N., Caster, J., and DeBenedetto, G., 2022, Can we accurately estimate sediment budgets on Mars?: Earth and Planetary Science Letters, v. 593, 117682, 11 p., https://doi.org/10.1016/j.epsl.2022.117682.","productDescription":"117682, 11 p.","ipdsId":"IP-137953","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447133,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2022.117682","text":"Publisher Index Page"},{"id":435775,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971IOAI","text":"USGS data release","linkHelpText":"Sediment budget data for Lees Ferry dune field, February-May 2019"},{"id":407376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"593","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":852909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chojnacki, Matthew 0000-0001-8497-8994","orcid":"https://orcid.org/0000-0001-8497-8994","contributorId":296931,"corporation":false,"usgs":false,"family":"Chojnacki","given":"Matthew","email":"","affiliations":[{"id":64240,"text":"Planetary Science Institute, Lakewood, CO, USA","active":true,"usgs":false}],"preferred":false,"id":852910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":852911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeBenedetto, Geoffrey 0000-0003-0696-4567 gdebened@usgs.gov","orcid":"https://orcid.org/0000-0003-0696-4567","contributorId":220988,"corporation":false,"usgs":true,"family":"DeBenedetto","given":"Geoffrey","email":"gdebened@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852913,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232445,"text":"ofr20211125 - 2022 - Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19","interactions":[],"lastModifiedDate":"2026-03-25T17:54:53.444918","indexId":"ofr20211125","displayToPublicDate":"2022-07-12T12:35:00","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":"2021-1125","displayTitle":"Characterization of the Bathymetry, Hydrodynamics, Water Quality, Infrastructure, and Channel Condition of the Old Erie Canal from DeWitt to its Junction with the Current Erie Canal in Verona, near Rome, New York, 2018–19","title":"Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19","docAbstract":"The Old Erie Canal has undergone sedimentation and aquatic growth that have restricted flow and diminished the aesthetic quality of the canal during the nearly 200 years since its construction. During 2018–2019, the U.S. Geological Survey (USGS) in cooperation with the Madison County Planning Department and the New York State Canal Corporation conducted a study of the Old Erie Canal between the Town of DeWitt, New York, and its junction with the current Erie Canal of the New York State Canal System near Rome, N.Y. The study comprised bathymetric, velocity, and water-quality surveys and documentation of the canal infrastructure. The USGS established benchmarks and staff gages along the 30.8 miles of the canal study area to reference the water-surface level in the canal to the North American Vertical Datum of 1988 (NAVD 88). Bathymetric survey results indicated that during the time of the survey, the canal depths ranged from 1.26 feet (ft) to 7.33 ft between the Butternut and Durhamville aqueducts (with a mean depth of 3.52 ft). Shallow depths are located throughout the canal, but the section north of the Durhamville aqueduct was the shallowest, with depths ranging from 0.68 ft to 2.44 ft (and a mean depth of 1.36 ft). The reach-averaged water velocity was 0.28 feet per second. The system generally flows west to east from the Butternut aqueduct to the entrance to the Erie Canal.
Water-quality data (dissolved oxygen, water temperature, specific conductance, pH, and turbidity) were collected concurrently with the bathymetric survey (spring 2018) to characterize changes in water quality along the length of the canal. Specific-conductance values measured upstream from the hamlet of Kirkville, Manlius, N.Y. may reflect road salts being flushed into the canal through the Butternut and Limestone feeder system (designed to divert water from nearby creeks to supply water for the Old Erie Canal) from recent stormwater runoff. Increases in pH in the downstream direction are possibly caused by increasing amounts of aquatic vegetation. During the time of the survey, turbidity was highest near inflows from the canal feeder system and tributary inputs which were elevated by stormwater runoff that transported sediment into the canal.
The canal infrastructure was documented to provide a baseline assessment. The feeder system, designed to bring water into the canal, does not deliver flow when the creeks supplying water to those feeders are at base flow, but does bring water into the system when flows in the feeder creeks are elevated. A recent report provides an example of repair work completed on the Chittenango feeder to improve flow through the feeder into the canal (Welch and Madison County Planning Department, 1996). Two non-regulated tributaries, Meadow Brook and Pools Brook, consistently delivered flow to the canal. Outfalls where canal water discharges into nearby creeks were sealed in the Butternut and Limestone aqueducts. Outfalls in the Chittenango, Cowaselon, and Durhamville aqueducts were found with flashboards installed at an elevation that allows water to be discharged from the canal. These structures are designed to accept additional flashboards to raise the canal water surface with the potential to convey flow farther down the system. The general condition of the channel was open and navigable between Butternut aqueduct and Chittenago aqueduct. On the segment of the canal east of the Chittenango aqueduct, an increasing number of downed trees and tangled wads of vegetation affected flow and made navigation by boat difficult to the Durhamville aqueduct. North of the Durhamville aqueduct, numerous downed trees and an increased density of aquatic vegetation limited navigation by boat and reduced the flow rate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211125","usgsCitation":"Wernly, J.F., 2022, Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19: U.S. Geological Survey Open-File Report 2021–1125, 75 p., https://doi.org/10.3133/ofr20211125.","productDescription":"Report: viii, 75 p.; Data Release","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118164","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":402850,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1125/ofr20211125.XML"},{"id":402848,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1125/ofr20211125.pdf","text":"Report","size":"94.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1125"},{"id":402847,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1125/coverthb.jpg"},{"id":402849,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QRL294","text":"USGS data release","linkHelpText":"Geospatial dataset of the characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to Rome, New York 2018–2019"},{"id":402851,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1125/images/"},{"id":403542,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20211125/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2021-1125"},{"id":501536,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113265.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Old Erie Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.13250732421875,\n 42.974511174899156\n ],\n [\n -76.11328125,\n 42.968984647488014\n ],\n [\n -75.9375,\n 42.96044267380142\n ],\n [\n -75.74798583984375,\n 42.96446257387128\n ],\n [\n -75.57769775390625,\n 43.002638523957906\n ],\n [\n -75.41839599609375,\n 43.1270477646888\n ],\n [\n -75.35522460937499,\n 43.207177786666655\n ],\n [\n -75.42388916015625,\n 43.271206115959785\n ],\n [\n -75.52001953125,\n 43.25920592943639\n ],\n [\n -75.65460205078125,\n 43.23920036180898\n ],\n [\n -75.92926025390625,\n 43.219188223481325\n ],\n [\n -76.09405517578125,\n 43.13105676219153\n ],\n [\n -76.18194580078124,\n 43.07891929985966\n ],\n [\n -76.18743896484375,\n 43.022721607058344\n ],\n [\n -76.13250732421875,\n 42.974511174899156\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Upper Geyser Basin at Yellowstone National Park (Wyoming, USA) harbors the greatest concentration of geysers worldwide. Research suggests that individual geysers are not isolated but rather are hydraulically connected in the subsurface with other geysers and thermal springs. To quantify such connections, we combined techniques from machine learning, causal inference, and dynamical systems to characterize the collective eruptive behavior of a set of 10 geysers over 18 months (April 2007 – September 2008) focusing on geyser-geyser interactions. Model predictions were up to 15 times more accurate when we sought to predict a geyser's eruption time series based on outflow channel temperatures from the network than based on its own time series alone, suggesting the existence of a complex interconnected subsurface groundwater system. On average, cone-type geysers had larger impacts on other geysers than did fountain-type geysers. Similarly, cone-type geysers were on average more insulated from other geysers. However, substantial unexplained variation remained after considering the cone versus fountain dichotomy. Distance between geysers also affected interactions: nearby geysers had stronger effects on focal geysers than did geysers located farther away. Collectively, results support the hypothesis of geyser interdependence at timescales of 5 min–10 days. Our analyses highlight the existence of quantifiable geyser-to-geyser interactions that can be resolved through pairwise and system-level analyses. These findings emphasize the subsurface interconnectedness of thermal features, provide information relevant to visitor experiences in Yellowstone National Park, and suggest strategies for exploring patterns of interdependence that may exist among other episodic geological phenomena.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB023749","usgsCitation":"Fagan, W., Swain, A., Banerjee, A., Ranade, H., Thompson, P., Staniczenko, P.P., Flynn, B., Hungerford, J., and Hurwitz, S., 2022, Quantifying interdependencies in geyser eruptions at the Upper Geyser Basin, Yellowstone National Park: Journal of Geophysical Research, v. 127, no. 8, e2021JB023749, 23 p., https://doi.org/10.1029/2021JB023749.","productDescription":"e2021JB023749, 23 p.","ipdsId":"IP-137582","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":405590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Upper Geyser Basin, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.839,\n 44.459\n ],\n [\n -110.823,\n 44.459\n ],\n [\n -110.823,\n 44.467\n ],\n [\n -110.839,\n 44.467\n ],\n [\n -110.839,\n 44.459\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Fagan, William F.","contributorId":108239,"corporation":false,"usgs":true,"family":"Fagan","given":"William F.","affiliations":[],"preferred":false,"id":849649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swain, Anshuman","contributorId":295531,"corporation":false,"usgs":false,"family":"Swain","given":"Anshuman","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banerjee, Amitava","contributorId":295532,"corporation":false,"usgs":false,"family":"Banerjee","given":"Amitava","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ranade, Hamir","contributorId":295533,"corporation":false,"usgs":false,"family":"Ranade","given":"Hamir","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Peter","contributorId":295535,"corporation":false,"usgs":false,"family":"Thompson","given":"Peter","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staniczenko, Phillip P. A.","contributorId":295537,"corporation":false,"usgs":false,"family":"Staniczenko","given":"Phillip","email":"","middleInitial":"P. A.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flynn, Barrett","contributorId":295539,"corporation":false,"usgs":false,"family":"Flynn","given":"Barrett","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":849655,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hungerford, Jefferson 0000-0003-2651-2285","orcid":"https://orcid.org/0000-0003-2651-2285","contributorId":229552,"corporation":false,"usgs":false,"family":"Hungerford","given":"Jefferson","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":849656,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849657,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256670,"text":"70256670 - 2022 - Trout responses to stocking rates and river discharge within a southeast U.S. hydropeaking tailwater","interactions":[],"lastModifiedDate":"2024-08-30T14:09:12.548651","indexId":"70256670","displayToPublicDate":"2022-07-12T09:02:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Trout responses to stocking rates and river discharge within a southeast U.S. hydropeaking tailwater","docAbstract":"Freshwater fish populations often exist in systems characterized by novel ecological processes resulting from human alteration. Salmonid populations embedded within coldwater sections of warmwater rivers are spatially constrained by habitat availability. Tailwater fish contend with fluctuating river discharges and density-dependent processes associated with fish stocking and exploitation. Salmonid populations sustained through stocking versus natural reproduction may respond differently to changes in hydrologic patterns (e.g., hydropeaking) as well as declines in fish abundance. We assessed differences between stocked (Rainbow Trout Oncorhynchus mykiss) and naturalized (Brown Trout Salmo trutta) trout populations in Greers Ferry tailwater, Arkansas, regarding (1) spatial and temporal patterns of mean length, electrofishing catch rates, and relative condition following reduced number of stocked Rainbow Trout and (2) evidence that hydrologic characteristics and fish stocking intensity influenced relative condition. A 56% reduction in Rainbow Trout stocking did not result in systemwide change in mean length or relative abundance for Rainbow Trout or Brown Trout over the 16-year study period. Hydrologic variability, where river discharge spanned both reduced and elevated levels, positively influenced condition of both Rainbow Trout and Brown Trout. Assessment of survival of stocked Rainbow Trout may aid in further refining the timing and amount of stocking needed to sustain the population at a desired abundance. Further, assessing the influence of stocking fewer but perhaps larger (in terms of mean length) fish to meet management goals may be warranted. The persistent differences in relative abundance among river sections can inform management actions directed at Brown Trout, including harvest regulations. Such regulations may aid in reaching desired management goals, including abundance and mean length targets not observed after reduced stocking.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10779","usgsCitation":"Spurgeon, J.J., Kaiser, J., Graham, C., and Lochmann, S., 2022, Trout responses to stocking rates and river discharge within a southeast U.S. hydropeaking tailwater: North American Journal of Fisheries Management, v. 42, no. 4, p. 926-938, https://doi.org/10.1002/nafm.10779.","productDescription":"13 p.","startPage":"926","endPage":"938","ipdsId":"IP-135390","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10779","text":"Publisher Index Page"},{"id":433360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Greers Ferry tailwater","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.12227720560406,\n 35.56794921986193\n ],\n [\n -92.12227720560406,\n 35.37950970740478\n ],\n [\n -91.72539494699616,\n 35.37950970740478\n ],\n [\n -91.72539494699616,\n 35.56794921986193\n ],\n [\n -92.12227720560406,\n 35.56794921986193\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":304259,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Joseph","contributorId":341541,"corporation":false,"usgs":false,"family":"Kaiser","given":"Joseph","email":"","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Christy","contributorId":341542,"corporation":false,"usgs":false,"family":"Graham","given":"Christy","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lochmann, Steve","contributorId":341543,"corporation":false,"usgs":false,"family":"Lochmann","given":"Steve","affiliations":[{"id":81661,"text":"University of Arkansas at Pine Bluff","active":true,"usgs":false}],"preferred":false,"id":908586,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237720,"text":"70237720 - 2022 - Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA","interactions":[],"lastModifiedDate":"2022-10-21T13:37:46.04698","indexId":"70237720","displayToPublicDate":"2022-07-12T08:30:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA","docAbstract":"The Kodiak Islands lie near the southern terminus of the 1964 Great Alaska earthquake rupture area and within the Kodiak subduction zone segment. Both local and trans-Pacific tsunamis were generated during this devastating megathrust event, but the local tsunami source region and the causative faults are poorly understood. We provide an updated view of the tsunami and earthquake hazard for the Kodiak Islands region through tsunami modeling and geophysical data analysis. Using seismic and bathymetric data, we characterize a regionally extensive seafloor lineament related to the Kodiak shelf fault zone, with focused uplift along a 50-km-long portion of the newly named Ugak fault as the most likely source of the local Kodiak Islands tsunami in 1964. We present evidence of Holocene motion along the Albatross Banks fault zone, but we suggest that this fault did not produce a tsunami in 1964. We relate major structural boundaries to active forearc splay faults, where tectonic uplift is collocated with gravity lineations. Differences in interseismic locking, seismicity rates, and potential field signatures argue for different stress conditions at depth near presumed segment boundaries. We find that the Kodiak segment boundaries have a clear geophysical expression and are linked to upper-plate structure and splay faulting. The tsunamigenic fault hazard is higher for the Kodiak shelf fault zone when compared to the nearby Albatross Banks fault zone, suggesting short wave travel paths and little tsunami warning time for nearby communities.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02486.1","usgsCitation":"Ramos, M.D., Liberty, L.M., Haeussler, P., and Humphreys, R.J., 2022, Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA: Geosphere, v. 18, no. 5, p. 1474-1491, https://doi.org/10.1130/GES02486.1.","productDescription":"18 p.","startPage":"1474","endPage":"1491","ipdsId":"IP-135286","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02486.1","text":"Publisher Index Page"},{"id":408601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.4037941171657,\n 60.562811262269065\n ],\n [\n -151.83516963619837,\n 61.55980516417185\n ],\n [\n -156.47488362730599,\n 57.79635099382884\n ],\n [\n -154.29366188512998,\n 55.202746146556194\n ],\n [\n -147.4037941171657,\n 60.562811262269065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos, Marlon D. 0000-0003-4449-8624","orcid":"https://orcid.org/0000-0003-4449-8624","contributorId":293255,"corporation":false,"usgs":false,"family":"Ramos","given":"Marlon","email":"","middleInitial":"D.","affiliations":[{"id":63266,"text":"Air Force Research Lab","active":true,"usgs":false}],"preferred":false,"id":855359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liberty, Lee M","contributorId":194078,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":855360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphreys, Robert John 0000-0002-6733-6399","orcid":"https://orcid.org/0000-0002-6733-6399","contributorId":298308,"corporation":false,"usgs":true,"family":"Humphreys","given":"Robert","email":"","middleInitial":"John","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855362,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251319,"text":"70251319 - 2022 - Virtual special issue of recent advances on gas hydrates scientific drilling in Alaska","interactions":[],"lastModifiedDate":"2024-02-03T14:21:43.534432","indexId":"70251319","displayToPublicDate":"2022-07-12T08:19:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17150,"text":"Journal of Energy & Fuels","active":true,"publicationSubtype":{"id":10}},"title":"Virtual special issue of recent advances on gas hydrates scientific drilling in Alaska","docAbstract":"