{"pageNumber":"276","pageRowStart":"6875","pageSize":"25","recordCount":40783,"records":[{"id":70213352,"text":"70213352 - 2020 - Effects of climate change on plague exposure pathways and resulting disease dynamics","interactions":[],"lastModifiedDate":"2021-02-03T19:40:21.028748","indexId":"70213352","displayToPublicDate":"2020-05-12T12:24:55","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"16 RC01-012","title":"Effects of climate change on plague exposure pathways and resulting disease dynamics","docAbstract":"<p>Introduction and Objectives: Sylvatic plague, a zoonotic flea-borne disease, caused by the bacterium <i>Yersinia pestis</i>, is relevant to the Department of Defense (DOD), because prairie dogs and other susceptible rodents are present on military installations in several western states. Arthropod-borne diseases, like plague, are thought to be particularly sensitive to local climate conditions. Expected changes in temperature and humidity over the next several decades will likely increase the geographical expansion of plague outbreaks in wildlife. Through a combination of field and laboratory work, along with data-driven modeling, we evaluated the potential effects of climate change on plague exposure pathways in prairie dogs and associated rodents to provide guidance to DOD partners regarding the potential for future outbreaks. Briefly, our specific objectives were to determine the relation between local climate conditions and the prevalence of plague and other pathogens while assessing the ecological roles of specific rodent hosts and vector species in plague dynamics, evaluate flea intensity on rodent hosts and in burrows in relation to local climate conditions, and develop models to predict the effects of climate change on plague dynamics.</p><p><br>Technical Approach: Using data and samples collected during a large field study on the effectiveness of vaccination to manage plague in prairie dogs, we assessed rodent/flea assemblages, pathogen prevalence in fleas, and determined how local climate conditions influence flea development rates and relative abundance. Live animals (prairie dogs and some small rodents) were trapped to collect fleas and other samples on 46 prairie dog plots in 6 western states, many sites near DOD lands. At seven additional locations on a latitudinal gradient, fleas were collected from burrows several times per year to assess seasonality and effects of local climate conditions on flea abundance. These data were then used to develop predictive models that could be used to test specific hypotheses.</p><p><br>Results: We determined that flea developmental rates, on-host flea abundance, species composition of the flea community, and burrow temperatures varied across a latitudinal gradient. Rodent and flea community composition and abundance differed geographically and were highly specialized. Flea-switching between prairie dogs and short-lived rodents was rare. Flea development rates, on-host flea abundance, and burrow temperatures increased with increasing ambient temperature. Although relative humidity can affect flea development, burrow humidity was uniformly high (~85%) across sampling sites and seasons. A large increase in the number of fleas found on a prairie dog colony, coupled with a greater number of infested burrows, could have substantial effects on plague dynamics in the western United States as the climate warms. In addition to affecting flea load, climate change may also influence body condition of prairie dogs by reducing the amount of forage. This may result in animals being more tolerant of high flea loads (less engaged in grooming behavior) and more vulnerable to disease.</p>","language":"English","publisher":"Department of Defense","usgsCitation":"Rocke, T.E., Russell, R., Samuel, M., Abbott, R.C., and Poje, J., 2020, Effects of climate change on plague exposure pathways and resulting disease dynamics: Final Report 16 RC01-012, vii, 61 p.","productDescription":"vii, 61 p.","ipdsId":"IP-118526","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":378525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378498,"type":{"id":15,"text":"Index Page"},"url":"https://www.serdp-estcp.org/Program-Areas/Resource-Conservation-and-Resiliency/Natural-Resources/Species-Ecology-and-Management/RC-2634"}],"country":"United States","state":"Arizona, Montana, South Dakota, Texas, Utah, Wyoming","city":"Cedar City","otherGeospatial":"Buffalo Gap National Grassland, Charles M. Russell National Wildlife Refuge, Coyote Basin, Espee Ranch, Lower Brule Sioux tribal lands, Pitchfork Ranch, Rita Blanca National Grassland, Wind Cave National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.51074218749999,\n              34.92197103616377\n            ],\n            [\n              -102.26074218749999,\n              34.92197103616377\n            ],\n            [\n              -102.26074218749999,\n              48.86471476180277\n            ],\n            [\n              -113.51074218749999,\n              48.86471476180277\n            ],\n            [\n              -113.51074218749999,\n              34.92197103616377\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":799082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":799083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Samuel, Michael D.","contributorId":206351,"corporation":false,"usgs":false,"family":"Samuel","given":"Michael D.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":799084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abbott, Rachel C. 0000-0003-4820-9295 rabbott@usgs.gov","orcid":"https://orcid.org/0000-0003-4820-9295","contributorId":1183,"corporation":false,"usgs":true,"family":"Abbott","given":"Rachel","email":"rabbott@usgs.gov","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":799085,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poje, Julia","contributorId":248780,"corporation":false,"usgs":false,"family":"Poje","given":"Julia","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":799086,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70228550,"text":"70228550 - 2020 - Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication","interactions":[],"lastModifiedDate":"2022-02-14T18:12:27.741823","indexId":"70228550","displayToPublicDate":"2020-05-12T11:56:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication","docAbstract":"Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, we suggest analysts: 1) integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys); and 2) include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2020.00303","usgsCitation":"Winship, A.J., Thorson, J.T., Clarke, M., Coleman, H.M., Costa, B.M., Georgian, S., Gillett, D., Gruss, A., Henderson, M., Hourigan, T.F., Huff, D.D., Kreidler, N., Pirtle, J.L., Olson, J.V., Poti, M., Rooper, C.N., Sigler, M.F., Viehman, T.S., and Whitmire, C.E., 2020, Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication: Frontiers in Marine Science, v. 7, p. 1-7, https://doi.org/10.3389/fmars.2020.00303.","productDescription":"303, 15 p.","startPage":"1","endPage":"7","ipdsId":"IP-117999","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456794,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00303","text":"Publisher Index Page"},{"id":395904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"editors":[{"text":"Herrera, Santiago","contributorId":278597,"corporation":false,"usgs":false,"family":"Herrera","given":"Santiago","email":"","affiliations":[{"id":16160,"text":"Lehigh University","active":true,"usgs":false}],"preferred":false,"id":834833,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Winship, Arliss J","contributorId":275149,"corporation":false,"usgs":false,"family":"Winship","given":"Arliss","email":"","middleInitial":"J","affiliations":[{"id":56719,"text":"CSS, Inc., Fairfax, VA, USA","active":true,"usgs":false}],"preferred":false,"id":834550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thorson, James T.","contributorId":146580,"corporation":false,"usgs":false,"family":"Thorson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":834551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clarke, M. Elizabeth","contributorId":205699,"corporation":false,"usgs":false,"family":"Clarke","given":"M. Elizabeth","affiliations":[{"id":37147,"text":"Office of the Science Director, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration. Montlake Blvd E., Seattle, WA 98112, USA.","active":true,"usgs":false}],"preferred":false,"id":834552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coleman, Heather M.","contributorId":276106,"corporation":false,"usgs":false,"family":"Coleman","given":"Heather","email":"","middleInitial":"M.","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":false,"id":834553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Costa, Bryan M.","contributorId":146979,"corporation":false,"usgs":false,"family":"Costa","given":"Bryan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":834821,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Georgian, Samuel","contributorId":276107,"corporation":false,"usgs":false,"family":"Georgian","given":"Samuel","email":"","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":false,"id":834554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gillett, David","contributorId":276108,"corporation":false,"usgs":false,"family":"Gillett","given":"David","email":"","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":false,"id":834555,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gruss, Arnaud","contributorId":278591,"corporation":false,"usgs":false,"family":"Gruss","given":"Arnaud","email":"","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":false,"id":834822,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":834549,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hourigan, Thomas F.","contributorId":146754,"corporation":false,"usgs":false,"family":"Hourigan","given":"Thomas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834823,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Huff, David D.","contributorId":171694,"corporation":false,"usgs":false,"family":"Huff","given":"David","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":834824,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kreidler, Nissa","contributorId":278592,"corporation":false,"usgs":false,"family":"Kreidler","given":"Nissa","email":"","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":834825,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Pirtle, Jodi L.","contributorId":211305,"corporation":false,"usgs":false,"family":"Pirtle","given":"Jodi","email":"","middleInitial":"L.","affiliations":[{"id":38223,"text":"National Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":834826,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Olson, John V.","contributorId":278593,"corporation":false,"usgs":false,"family":"Olson","given":"John","email":"","middleInitial":"V.","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":false,"id":834827,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Poti, Matthew","contributorId":278594,"corporation":false,"usgs":false,"family":"Poti","given":"Matthew","email":"","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":false,"id":834828,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rooper, Christopher N.","contributorId":278595,"corporation":false,"usgs":false,"family":"Rooper","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":834829,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sigler, Michael F.","contributorId":278596,"corporation":false,"usgs":false,"family":"Sigler","given":"Michael","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834830,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Viehman, T. Shay","contributorId":259297,"corporation":false,"usgs":false,"family":"Viehman","given":"T.","email":"","middleInitial":"Shay","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":true,"id":834831,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Whitmire, Curt E.","contributorId":205702,"corporation":false,"usgs":false,"family":"Whitmire","given":"Curt","email":"","middleInitial":"E.","affiliations":[{"id":37149,"text":"Fishery Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, National Oceanic and Atmospheric Administration, 99 Pacific Street, Bldg. 255-A, Monterey, California, 97365,","active":true,"usgs":false}],"preferred":false,"id":834832,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70214028,"text":"70214028 - 2020 - Using multiple environmental proxies and hydrodynamic modeling to investigate Late Holocene climate and coastal change within a large Gulf of Mexico estuarine system (Mobile Bay, Alabama, USA)","interactions":[],"lastModifiedDate":"2025-05-13T16:09:23.411011","indexId":"70214028","displayToPublicDate":"2020-05-12T10:39:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Using multiple environmental proxies and hydrodynamic modeling to investigate Late Holocene climate and coastal change within a large Gulf of Mexico estuarine system (Mobile Bay, Alabama, USA)","docAbstract":"<p><span>A high degree of uncertainty exists for understanding and predicting coastal estuarine response to changing climate, land-use, and sea-level conditions, leaving geologic records as a best-proxy for constraining potential outcomes. With the majority of the world's population focused in coastal regions, understanding how local systems respond to global, regional, and even local pressures is key in developing mitigation, adaptation, and management plans. The geomorphology of Mobile Bay in southeast Alabama (USA) has evolved considerably (e.g., bayhead delta back-stepping) over the late Holocene in response to global and regional sea-level and climate change. Smaller-scale geomorphic changes (e.g., spit and beach ridge development) have also had a significant influence on the evolution of the estuary. Organic matter characteristics, inorganic sediment geochemistry, benthic microfossils, and pollen in a&nbsp;~&nbsp;3500&nbsp;cal&nbsp;yr BP sediment sequence recovered in a gravity core (20GC) from Bon Secour Bay, a small sub-bay in the southeast corner of Mobile Bay, record time-varying marine influence. Increases in marine influence during ~3500 to 2300&nbsp;cal&nbsp;yr BP and 1930 to 1160&nbsp;cal&nbsp;yr BP are defined as zones with high-density and pre-dominantly calcareous foraminiferal species, abundant sand (&gt;10%) and more marine-like geochemical signatures, which contrast the low-density and pre-dominantly agglutinated foraminiferal and more terrestrially influenced estuarine muds observed in other intervals of the sedimentary record (2300–1930 and 1160–400&nbsp;cal&nbsp;yr BP) and the modern bay. Hydrodynamic models constrained by geomorphic boundary conditions for the time&nbsp;~&nbsp;3500&nbsp;cal&nbsp;yr BP, consistent with the most prominent marine-influenced sediment, provide insight to potential coastal configuration that might have permitted such marine water intrusion into the bay. Of several scenarios evaluated, a breach in Morgan Peninsula produces tidal circulation within the basin supportive of persistent marine incursions in the bay between ~3500 to 2300&nbsp;cal&nbsp;yr BP. The findings show that slight variations in coastal configuration can have broad-scale effects on bays and estuaries with consequences that may relate to water quality, vertebrate and invertebrate habitat, and coastal vulnerability to episodic events like (extra)tropical storms.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2020.106218","usgsCitation":"Smith, C., Jones, M.C., Osterman, L., and Passeri, D., 2020, Using multiple environmental proxies and hydrodynamic modeling to investigate Late Holocene climate and coastal change within a large Gulf of Mexico estuarine system (Mobile Bay, Alabama, USA): Marine Geology, v. 427, 106218, 12 p., https://doi.org/10.1016/j.margeo.2020.106218.","productDescription":"106218, 12 p.","ipdsId":"IP-112885","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456795,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2020.106218","text":"Publisher Index Page"},{"id":378618,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":436990,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WGJO0S","text":"USGS data release","linkHelpText":"Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results"}],"country":"United States","state":"Alabama, Mississippi","otherGeospatial":"Mobile Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.14306640625,\n              30.107117887092357\n            ],\n            [\n              -87.60498046875,\n              30.107117887092357\n            ],\n            [\n              -87.60498046875,\n              30.95876857077987\n            ],\n            [\n              -89.14306640625,\n              30.95876857077987\n            ],\n            [\n              -89.14306640625,\n              30.107117887092357\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"427","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":799273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Osterman, Lisa 0000-0002-8603-5217 osterman@usgs.gov","orcid":"https://orcid.org/0000-0002-8603-5217","contributorId":218441,"corporation":false,"usgs":true,"family":"Osterman","given":"Lisa","email":"osterman@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799274,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70210075,"text":"70210075 - 2020 - Geometric and material variability influences stress states relevant to coastal permafrost bluff failure","interactions":[],"lastModifiedDate":"2020-05-13T14:17:55.552568","indexId":"70210075","displayToPublicDate":"2020-05-12T09:13:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Geometric and material variability influences stress states relevant to coastal permafrost bluff failure","docAbstract":"Scientific knowledge and engineering tools for predicting coastal erosion are largely confined to temperate climate zones that are dominated by non-cohesive sediments. The pattern of erosion exhibited by the ice-bonded permafrost bluffs in Arctic Alaska, however, is not well explained by these tools. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is needed. We conducted physics-based numerical simulations of mechanical response that focus on the impact of geometric and material variability on permafrost bluff stress states for a coastal setting in Arctic Alaska that is prone to toppling mode block failure. Our three-dimensional geomechanical boundary-value problems output static realizations of compressive and tensile stresses. We use these results to quantify variability in the loci of potential instability. We observe that niche dimension affects the location and magnitude of the simulated maximum tensile stress more strongly than the bluff height, ice wedge polygon size, ice wedge geometry, bulk density, Young’s Modulus, and Poisson’s Ratio. Our simulations indicate that variations in niche dimension can produce radically different potential failure areas and that even relatively shallow vertical cracks can concentrate displacement within ice-bonded permafrost bluffs. These findings suggest that stability assessment approaches, for which the geometry of the failure plane is delineated a priori, may not be ideal for coastlines similar to our study area and could hamper predictions of erosion rates and nearshore sediment/biogeochemical loading.","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00143","collaboration":"","usgsCitation":"Thomas, M.A., Mota, A., Jones, B., Choens, R.C., Frederick, J.M., and Bull, D.L., 2020, Geometric and material variability influences stress states relevant to coastal permafrost bluff failure: Frontiers in Earth Science, v. 143, no. 8, p. 1-13, https://doi.org/10.3389/feart.2020.00143.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-115840","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00143","text":"Publisher Index Page"},{"id":374750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":788998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mota, Alejandro","contributorId":208633,"corporation":false,"usgs":false,"family":"Mota","given":"Alejandro","email":"","affiliations":[{"id":37854,"text":"Sandia National Laboratories California, Livermore, California, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":788999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Benjamin M. 0000-0002-1517-4711","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":208625,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin M.","affiliations":[{"id":37848,"text":"Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":true,"id":789000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choens, R. Charles","contributorId":224660,"corporation":false,"usgs":false,"family":"Choens","given":"R.","email":"","middleInitial":"Charles","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":789001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frederick, Jennifer M. 0000-0003-2414-778X","orcid":"https://orcid.org/0000-0003-2414-778X","contributorId":208631,"corporation":false,"usgs":false,"family":"Frederick","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":789002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bull, Diana L.","contributorId":208628,"corporation":false,"usgs":false,"family":"Bull","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":789003,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70211059,"text":"70211059 - 2020 - Introduction to prediction and the value of information","interactions":[],"lastModifiedDate":"2020-07-13T14:16:24.071125","indexId":"70211059","displayToPublicDate":"2020-05-12T09:12:37","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"17","title":"Introduction to prediction and the value of information","docAbstract":"Predicting the consequences of alternative actions in terms of the objectives is central to decision making.  Modeling in the broadest sense, from simple to complex and based on data or expert judgment, comprises the essential toolkit for making decision-relevant predictions.  Gaps in knowledge and the resulting uncertainty can make predictive modeling challenging.  Gathering information to address knowledge gaps, thereby reducing uncertainty, can improve predictions.  However, within a decision analysis, the value of information gathering depends on the extent that reduced uncertainty will improve the decision’s outcome.  Decision makers commonly confront the choice to proceed directly to a decision in the face of uncertainty or to delay and attempt to reduce the uncertainty significantly before making the decision.  Value of information analysis can help make a smart choice. This chapter introduces the purpose, approaches, and tools for addressing knowledge gaps within decision analysis. The three case studies, which follow, illustrate some of the challenges and solutions encountered when addressing knowledge gaps within a decision analysis.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Smith, D.R., 2020, Introduction to prediction and the value of information, chap. 17 <i>of</i> Structured decision making: Case studies in natural resource management, p. 189-195.","productDescription":"7 p.","startPage":"189","endPage":"195","ipdsId":"IP-101561","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":376314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376312,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792631,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70211050,"text":"70211050 - 2020 - Introduction to resource allocation","interactions":[],"lastModifiedDate":"2020-08-06T19:09:51.866535","indexId":"70211050","displayToPublicDate":"2020-05-12T08:45:02","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"9","title":"Introduction to resource allocation","docAbstract":"With ongoing habitat loss and degradation, ever-increasing threats to biodiversity, and limited funding for conservation and management, nearly every natural resource manager routinely faces difficult resource allocation problems. Funding and capacity for natural resource management rarely meet the need, and informed resource allocations are increasingly important. These decision problems include not only habitat and species management but also a wide variety of administrative decisions. Ranking projects or plans by benefit-cost ratio is an intuitive, heuristic approach to resource allocation but may be inefficient. We present a general resource allocation framework in which these decision problems can be stated mathematically, making it relatively easy to find solutions using mathematical programming such as linear programming. amenable to Linear programming and other constrained optimization routines, which can be implemented in common software applications and used with a wide variety of decision problems, including project prioritization and portfolio decisions. Constrained optimization has advantages over intuitive benefit-cost ratios and can accommodate single and multiple objective problems. We also introduce the three case studies in this section illustrating a variety of resource allocation problems: the first case study shows how to select cost-effective management actions for discrete management units such as wetlands or grassland patches; the second, how to use a patch dynamics model to allocation allocate resources for a reserve network that protects habitat for multiple species of conservation concern; and the third, how to use stochastic simulation to determine allocation of resources in space and time for invasive species management.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Lyons, J., 2020, Introduction to resource allocation, chap. 9 <i>of</i> Structured decision making: Case studies in natural resource management, p. 99-107.","productDescription":"9 p.","startPage":"99","endPage":"107","ipdsId":"IP-107386","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376293,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":228916,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792602,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70213096,"text":"70213096 - 2020 - Aseismic transient slip on the Gofar transform fault, East Pacific Rise","interactions":[],"lastModifiedDate":"2020-09-09T13:43:43.218066","indexId":"70213096","displayToPublicDate":"2020-05-12T08:40:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Aseismic transient slip on the Gofar transform fault, East Pacific Rise","docAbstract":"<p><span>Oceanic transform faults display a unique combination of seismic and aseismic slip behavior, including a large globally averaged seismic deficit, and the local occurrence of repeating magnitude (M)&nbsp;</span><span id=\"inline-formula-1\" class=\"inline-formula\"><span class=\"mathjax mml-math\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mo>&amp;#x223C;</mo><mn>6</mn></math>\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mo\">∼</span><span id=\"MathJax-Span-4\" class=\"mn\">6</span></span></span></span><span class=\"MJX_Assistive_MathML\">∼6</span></span></span></span><span>&nbsp;earthquakes with abundant foreshocks and seismic swarms, as on the Gofar transform of the East Pacific Rise and the Blanco Ridge in the northeast Pacific Ocean. However, the underlying mechanisms that govern the partitioning between seismic and aseismic slip and their interaction remain unclear. Here we present a numerical modeling study of earthquake sequences and aseismic transient slip on oceanic transform faults. In the model, strong dilatancy strengthening, supported by seismic imaging that indicates enhanced fluid-filled porosity and possible hydrothermal circulation down to the brittle–ductile transition, effectively stabilizes along-strike seismic rupture propagation and results in rupture barriers where aseismic transients arise episodically. The modeled slow slip migrates along the barrier zones at speeds ∼10 to 600 m/h, spatiotemporally correlated with the observed migration of seismic swarms on the Gofar transform. Our model thus suggests the possible prevalence of episodic aseismic transients in M&nbsp;</span><span id=\"inline-formula-2\" class=\"inline-formula\"><span class=\"mathjax mml-math\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mo>&amp;#x223C;</mo><mn>6</mn></math>\"><span id=\"MathJax-Span-5\" class=\"math\"><span><span id=\"MathJax-Span-6\" class=\"mrow\"><span id=\"MathJax-Span-7\" class=\"mo\">∼</span><span id=\"MathJax-Span-8\" class=\"mn\">6</span></span></span></span><span class=\"MJX_Assistive_MathML\">∼6</span></span></span></span><span>&nbsp;rupture barrier zones that host active swarms on oceanic transform faults and provides candidates for future seafloor geodesy experiments to verify the relation between aseismic fault slip, earthquake swarms, and fault zone hydromechanical properties.</span></p>","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1913625117","usgsCitation":"Liu, Y., McGuire, J., and Behn, M., 2020, Aseismic transient slip on the Gofar transform fault, East Pacific Rise: Proceedings of the National Academy of Sciences, v. 117, no. 19, p. 10188-10194, https://doi.org/10.1073/pnas.1913625117.","productDescription":"7 p.","startPage":"10188","endPage":"10194","ipdsId":"IP-109525","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456801,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1913625117","text":"Publisher Index Page"},{"id":378259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"19","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Yajing","contributorId":202367,"corporation":false,"usgs":false,"family":"Liu","given":"Yajing","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":798241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":798242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Behn, Mark","contributorId":239965,"corporation":false,"usgs":false,"family":"Behn","given":"Mark","email":"","affiliations":[{"id":13422,"text":"Boston College","active":true,"usgs":false}],"preferred":false,"id":798243,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70218728,"text":"70218728 - 2020 - Keeping Hawai‘i's forest birds one step ahead of disease in a warming world","interactions":[],"lastModifiedDate":"2021-03-09T13:53:04.310438","indexId":"70218728","displayToPublicDate":"2020-05-12T07:50:18","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"4","title":"Keeping Hawai‘i's forest birds one step ahead of disease in a warming world","docAbstract":"Hawai‘i’s high-elevation forests provide a critical refuge from disease for native forest birds. However, global warming is facilitating the encroachment of mosquitoes and the diseases they transmit into increasingly higher elevations of remaining refugia, threatening the viability of the forest birds across the islands. Multiple management actions to address the threat of disease have been proposed, but there is an urgent need to identify which actions (or series of actions) should be prioritized as most effective, most cost-efficient, and most likely to produce results at a pace sufficient to stay ahead of climate change. A group of scientists, managers, and policy makers convened to evaluate a set of possible conservation strategies under a structured decision-making framework, focusing on management of Hakalau Forest National Wildlife Refuge, which was established to protect native Hawai‘ian forest birds. The biological models necessary to evaluate the set of conservation actions identified are not yet available, but the process of developing the framework for the decision analysis was immensely valuable for framing the issues and identifying information needs. Lessons learned from Hakalau Forest will\nbe applicable to many other areas in Hawai‘i facing the same threat to forest birds.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making- Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Paxton, E., and Kraus, J., 2020, Keeping Hawai‘i's forest birds one step ahead of disease in a warming world, chap. 4 <i>of</i> Structured decision making- Case studies in natural resource management, p. 36-47.","productDescription":"12 p.","startPage":"36","endPage":"47","ipdsId":"IP-088790","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":384243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384238,"type":{"id":15,"text":"Index 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,{"id":70211035,"text":"70211035 - 2020 - Resource allocation for coastal wetland management: Confronting uncertainty about sea level rise","interactions":[],"lastModifiedDate":"2020-07-13T12:49:43.365018","indexId":"70211035","displayToPublicDate":"2020-05-12T07:46:26","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"10","title":"Resource allocation for coastal wetland management: Confronting uncertainty about sea level rise","docAbstract":"Coastal wetlands are rich and diverse ecosystems with a wide variety of birdlife and other natural resources.  Decision making for coastal wetland management is difficult given the complex nature of these ecological systems and the frequent need to meet multiple objectives for varied resources.  Management challenges in the coastal zone are exacerbated by uncertainty about sea level rise and impacts on infrastructure, particularly the levees and structures which provide managers the ability to manipulate water levels in managed wetlands and create high quality habitat for birds and other wildlife.  The most challenging decisions in coastal wetland management involve resource allocation for habitat manipulations and longer-term investments to maintain management control in wetlands that are increasingly compromised by sea level rise and increasing storm frequency and intensity associated with a changing climate.\nWe used multi-criteria decision analysis to create a resource allocation framework for managed wetlands that identifies the most effective and efficient management strategies that are robust to uncertainty about sea level rise.  The prototype framework includes a small number of managed wetlands, for which subject matter experts articulated potential management and restoration actions.  The consequences of these actions were predicted using expert elicitation with the subject matter experts; furthermore, expert judgment was used to articulate expected outcomes with two hypotheses about the rate of sea level rise.  We used a constrained optimization (integer linear programming) to find optimal resource allocation strategies given a range of budget constraints; we also used a Pareto efficiency analysis for a graphical solution to the problem if the exact budget constraint is not known.  Finally, given the importance of preference weights in a multi-criteria decision analysis, we also evaluated sensitivity to objective weights.  With this resource allocation framework, we showed how to identify optimal combinations of management and restoration actions to maximize benefits in terms of stated objectives.  We show how multiple working hypotheses about sea level rise can be incorporated into decisions for coastal wetland management.  Our resource allocation approach can be modified for a wide variety of natural resource management settings.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins Press","usgsCitation":"Lyons, J., Kalasz, K., Breese, G., and Boal, C.W., 2020, Resource allocation for coastal wetland management: Confronting uncertainty about sea level rise, chap. 10 <i>of</i> Structured decision making: Case studies in natural resource management, p. 108-123.","productDescription":"16 p.","startPage":"108","endPage":"123","ipdsId":"IP-102721","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376270,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":228916,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalasz, Kevin S.","contributorId":228917,"corporation":false,"usgs":false,"family":"Kalasz","given":"Kevin S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":792526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breese, Gregory","contributorId":228918,"corporation":false,"usgs":false,"family":"Breese","given":"Gregory","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":792527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","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":792528,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226671,"text":"70226671 - 2020 - Feeding ecology of age-0 gar at Lake Texoma inferred from analysis of stable isotopes","interactions":[],"lastModifiedDate":"2021-12-03T13:14:39.832751","indexId":"70226671","displayToPublicDate":"2020-05-12T07:13:25","publicationYear":"2020","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":"Feeding ecology of age-0 gar at Lake Texoma inferred from analysis of stable isotopes","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Conservation and restoration of gar (Lepisosteidae) populations in North America are increasingly of interest to fisheries managers. Alligator Gar<span>&nbsp;</span><i>Atractosteus spatula</i><span>&nbsp;</span>are being stocked as age-0 fish in efforts to re-establish extirpated populations. However, gars are known to be highly cannibalistic in hatcheries, suggesting that age-0 Alligator Gar introduced into natural habitats may face predation pressures from other gar species, limiting the likelihood of released fish recruiting to the population. Furthermore, introduced age-0 gar may not have the proper prey resources for rapid growth that would facilitate recruitment to adulthood. Texoma Reservoir, located on the Oklahoma–Texas border, hosts four native gar species, including the Alligator Gar, whose population is supported by supplemental stocking of age-0 fingerlings. We investigated feeding by age-0 gar to obtain a baseline level of trophic ecology for this group of fishes, including the potential for poststocking cannibalism. Food webs were reconstructed via analysis of carbon (δ<sup>13</sup>C) and nitrogen (δ<sup>15</sup>N) isotopes using Bayesian mixing model approaches. Isotopic values for age-0 fish of all four gar species were similar. Invertebrates represented a large fraction of the diet for all gars, followed by Common Carp<span>&nbsp;</span><i>Cyprinus carpio</i><span>&nbsp;</span>and Grass Carp<span>&nbsp;</span><i>Ctenopharyngodon idella</i>. Competitive interactions among Bluegill<span>&nbsp;</span><i>Lepomis macrochirus</i>, White Bass<span>&nbsp;</span><i>Morone chrysops</i>, White Crappie<span>&nbsp;</span><i>Pomoxis annularis</i>, and young gar may occur for invertebrates, shads<span>&nbsp;</span><i>Dorosoma</i><span>&nbsp;</span>spp., and Western Mosquitofish<span>&nbsp;</span><i>Gambusia affinis</i><span>&nbsp;</span>but are likely short-lived due to the rapid growth of age-0 gar after their transition to piscivory. Trophic position of age-0 Alligator Gar, Longnose Gar<span>&nbsp;</span><i>Lepisosteus osseus</i>, and Spotted Gar<span>&nbsp;</span><i>Lepisosteus oculatus</i><span>&nbsp;</span>was positively related to TL, but this relationship was not observed for Shortnose Gar<span>&nbsp;</span><i>Lepisosteus platostomus</i>. In Texoma Reservoir, the four native gar species, including stocked Alligator Gar, appear to have adequate food resources for recruitment, with little indication of within-family or interspecific predation. This suggests that stocked Alligator Gar quickly acclimated to their new environment.</p></div></div>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10436","usgsCitation":"Snow, R.A., Stewart, D., Porta, M., and Long, J.M., 2020, Feeding ecology of age-0 gar at Lake Texoma inferred from analysis of stable isotopes: North American Journal of Fisheries Management, v. 40, no. 3, p. 638-650, https://doi.org/10.1002/nafm.10436.","productDescription":"12 p.","startPage":"638","endPage":"650","ipdsId":"IP-106049","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":392433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Snow, Richard A.","contributorId":264712,"corporation":false,"usgs":false,"family":"Snow","given":"Richard","middleInitial":"A.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":827623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, D.R.","contributorId":269640,"corporation":false,"usgs":false,"family":"Stewart","given":"D.R.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":827624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porta, M. J.","contributorId":264714,"corporation":false,"usgs":false,"family":"Porta","given":"M. J.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":827625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":827626,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70209985,"text":"sir20205035 - 2020 - Ecological status of aquatic communities in selected streams in the Milwaukee Metropolitan Sewerage District planning area of Wisconsin, 2004–13","interactions":[],"lastModifiedDate":"2020-05-12T11:44:31.472549","indexId":"sir20205035","displayToPublicDate":"2020-05-11T11:54:36","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5035","displayTitle":"Ecological Status of Aquatic Communities in Selected Streams in the Milwaukee Metropolitan Sewerage District Planning Area of Wisconsin, 2004–13","title":"Ecological status of aquatic communities in selected streams in the Milwaukee Metropolitan Sewerage District planning area of Wisconsin, 2004–13","docAbstract":"<p>A total of 14 wadable streams in urban or urbanizing watersheds near Milwaukee, Wisconsin, were sampled in 2004, 2007, 2010, and 2013 to assess the ecological status of aquatic communities (biota), including benthic algae and invertebrates, and fish. To assess temporal variation, additional community sampling was also done at a subset of three sites in 2011 and 2012. Relative abundances of each type of organism were used to calculate biological metrics, such as richness and diversity, percentages of intolerant and tolerant organisms, and indexes of biotic integrity for invertebrates and fish. Selected environmental (physical and chemical) data in the streams were collected to evaluate potential relations to the biota and the ecological health of the stream. Physical and chemical data included land use/land cover, stream discharge from U.S. Geological Survey (USGS) streamgages (except at 2 creeks that were not gaged), stream habitat, microhabitat at invertebrate collection locations, water quality (except at 2 creeks that were not gaged), field measurements of several water-quality constituents, measures of benthic algal biomass, and toxicity and chemical tests on extracts from passive samplers deployed at a subset of 6 sites. Relative abundances of organisms and biological metrics were compared among sampling years and with environmental metrics to evaluate the ecological status of these streams and determine primary stressors on the aquatic communities, with the aim of helping resource managers understand and work toward improving the ecological health of these and other urban and urbanizing rivers in the study area.</p><p>Biological metrics for most sites indicated some level of diminished ecological status when compared across all sampled sites and when compared with rating scales for selected metrics. The least degraded sites among all those sampled—indicated by aggregate bioassessments for algae, invertebrates, and fish metrics and in order starting with the best overall condition—were the Milwaukee River near Cedarburg, Menomonee River at Menomonee Falls, Jewel Creek, and Milwaukee River at Milwaukee. The most degraded sites were Menomonee River at Wauwatosa, Root River at Greenfield, Lincoln Creek, and the Kinnickinnic River. Differences in aggregate bioassessments indicate that aquatic communities at the Menomonee River at Wauwatosa site and the Root River at Greenfield site were worse in 2013 than in 2004; however, Oak Creek and Honey Creek sites were better. In 2013, several sites had less than 30-percent pollution-sensitive diatoms indicating degraded algal assemblages. Invertebrate metrics for most of the 14 sites in 2013 were lower than in 2004 and indicate that invertebrate assemblages at most sampled sites were more degraded in 2013. Tolerant fish taxa made up more than 40 percent of assemblages at most sites and nearly 100 percent of assemblages at four sites. At times, in some smaller streams, too few fish were captured to compute an Index of Biotic Integrity with confidence, and invertebrates provided a better means for assessing the ecological status and water quality. With these few exceptions, the use of all three groups of biota provided the most robust assessments at the 14 sites in 2004–13.</p><p>Physical and chemical stressors were correlated to adverse effects on aquatic biota at the sampled streams. Passive samplers were deployed at a subset of six sites in 2013. Microtox results indicated there was little or no toxicity at the Milwaukee River near Cedarburg site and at the Oak Creek site, slight toxicity at the Lincoln Creek and Honey Creek sites, and moderate toxicity at the Milwaukee River at Milwaukee site and the Little Menomonee River site; however, based on cytochrome-P450 reporter gene system toxicity tests, potential toxicity from hydrophobic organic contaminants was measured at all six sites. For all 14 sites, physical and chemical stressors related to urbanization correlated with biological metrics for algae, invertebrates, and fish. Most stressors for aquatic biota reflected an urban signature. Stressors related to ecological condition in our study were chemical and physical, such as developed land, impervious surface in the watershed, urban land in a buffer area around the stream (a 100-foot [30-meter]-wide area on each side of the stream, and maximum instantaneous discharge normalized by drainage area (a measure of flood and scour effects). Chemical stressors included low waterborne concentrations of dissolved oxygen and high concentrations of chloride, zinc and other metals, nutrients (nitrite and phosphorus), and fecal coliform bacteria.</p><p>Although algae, invertebrates, and fish did not always demonstrate a significant response to the same stressors, higher abundances of high total phosphorus-indicator diatoms, lower ratings for invertebrate biotic integrity indexes and percentages of mayflies-stoneflies-caddisflies, and lower values for fish biotic integrity indexes underscored possible adverse effects of even low levels of developed land. Developed land is typically associated with more rapid runoff, which washes chemicals from impervious surfaces into area waterways and degrades stream habitat for aquatic communities. However, with respect to at least chloride from road salt, diatoms tolerant to dissolved salts were significantly lower with as little as 1-percent mixed forest in the watershed. Lower percentages of urban land in the stream buffer correlated with healthier aquatic assemblages of algae, invertebrates, and fish. The assessment of algal, invertebrate, and fish assemblages coupled with physical and chemical data were highly useful in evaluating the ecological status of aquatic communities at the 14 sites and for determining environmental stressors that may be contributing to reduced stream condition. Some of the stressors could potentially be removed or lessened with stream rehabilitation or changes in watershed management.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205035","collaboration":"Prepared in cooperation with the Milwaukee Metropolitan Sewerage District","usgsCitation":"Scudder Eikenberry, B.C., Nott, M.A., Stewart, J.S., Sullivan, D.J., Alvarez, D.A., Bell, A.H., and Fitzpatrick, F.A., 2020, Ecological status of aquatic communities in selected streams in the Milwaukee Metropolitan Sewerage District planning area of Wisconsin, 2004–13: U.S. Geological Survey Scientific Investigations Report 2020–5035, 84 p., https://doi.org/10.3133/sir20205035.","productDescription":"Report: viii, 84 p.; Data Release; Dataset","numberOfPages":"96","onlineOnly":"Y","ipdsId":"IP-106552","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":374557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5035/coverthb.jpg"},{"id":374558,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5035/sir20205035.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5035"},{"id":374559,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FWMODL","text":"USGS data release","linkHelpText":"Aquatic community and environmental data for 14 rivers and streams in the Milwaukee Metropolitan Sewerage District Planning Area, 2004-13"},{"id":374560,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS water data for the Nation"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Milwaukee Metropolitan Sewerage District Planning Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -88.187255859375,\n              42.512601715736665\n            ],\n            [\n              -87.81372070312499,\n              42.512601715736665\n            ],\n            [\n              -87.81372070312499,\n              43.15710884095329\n            ],\n            [\n              -88.187255859375,\n              43.15710884095329\n            ],\n            [\n              -88.187255859375,\n              42.512601715736665\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umid-water\" href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a> <br>U.S. Geological Survey<br>8505 Research Way <br>Middleton, WI 53562</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Assessment of Aquatic Communities in Relation to Stream Condition</li><li>Summary and Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-05-11","noUsgsAuthors":false,"publicationDate":"2020-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Eikenberry, Barbara C. Scudder 0000-0001-8058-1201 beikenberry@usgs.gov","orcid":"https://orcid.org/0000-0001-8058-1201","contributorId":172148,"corporation":false,"usgs":true,"family":"Eikenberry","given":"Barbara C. Scudder","email":"beikenberry@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":788704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nott, Michelle A. 0000-0003-3968-7586","orcid":"https://orcid.org/0000-0003-3968-7586","contributorId":221766,"corporation":false,"usgs":true,"family":"Nott","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Jana S. 0000-0002-8121-1373 jsstewar@usgs.gov","orcid":"https://orcid.org/0000-0002-8121-1373","contributorId":539,"corporation":false,"usgs":true,"family":"Stewart","given":"Jana","email":"jsstewar@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Daniel J. 0000-0003-2705-3738","orcid":"https://orcid.org/0000-0003-2705-3738","contributorId":204322,"corporation":false,"usgs":true,"family":"Sullivan","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alvarez, David A. 0000-0002-6918-2709 dalvarez@usgs.gov","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":1369,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","email":"dalvarez@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788708,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bell, Amanda H. 0000-0002-7199-2145 ahbell@usgs.gov","orcid":"https://orcid.org/0000-0002-7199-2145","contributorId":1752,"corporation":false,"usgs":true,"family":"Bell","given":"Amanda","email":"ahbell@usgs.gov","middleInitial":"H.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788709,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788710,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70209987,"text":"ds1124 - 2020 - Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015","interactions":[],"lastModifiedDate":"2020-05-11T20:21:59.676539","indexId":"ds1124","displayToPublicDate":"2020-05-11T11:20:43","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1124","displayTitle":"Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2016, and Previously Unpublished Data from 2013 to 2015","title":"Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015","docAbstract":"<p>Environmental groundwater-quality data were collected from 648 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (514) were sampled from January through December 2016, and 60 of them were sampled in 2013 and 74 in 2014. The data were collected from seven types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public-water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Some data from environmental samples collected in 2013–14 and quality-control samples collected in 2012–15 also are included in the associated data release. Data from samples collected in 2016 are associated with networks described in this report and have not been published previously; data from samples collected between 2012 and 2015 are associated with networks described in previous reports in this data series.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1124","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124.  ","productDescription":"Report: ix, 135 p.; Data Release; Dataset","numberOfPages":"150","onlineOnly":"Y","ipdsId":"IP-111772","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":374561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1124/coverthb.jpg"},{"id":374562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1124/ds1124.pdf","text":"Report","size":"20.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1124"},{"id":374563,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W4RR74","text":"USGS data release","linkHelpText":"Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 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           37.55176\n              ],\n              [\n                -122.51201,\n                37.78339\n              ],\n              [\n                -122.95319,\n                38.11371\n              ],\n              [\n                -123.7272,\n                38.95166\n              ],\n              [\n                -123.86517,\n                39.76699\n              ],\n              [\n                -124.39807,\n                40.3132\n              ],\n              [\n                -124.17886,\n                41.14202\n              ],\n              [\n                -124.2137,\n                41.99964\n              ],\n              [\n                -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                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 -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_il@usgs.gov\" href=\"mailto:%20dc_il@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/cm-water\" href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a> <br>U.S. Geological Survey<br>405 North Goodwin <br>Urbana, IL 61801 <br></p>","tableOfContents":"<ul><li>Foreword</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Groundwater Study Design</li><li>Sample Collection and Analysis</li><li>Data Reporting</li><li>Quality-Assurance and Quality-Control Methods</li><li>Groundwater-Quality Data</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Information Contained in Previous Reports in This Series</li><li>Appendix 2. Well Depth and Open Interval by Study Network</li><li>Appendix 3. High-Frequency Data from Enhanced Trends Networks</li><li>Appendix 4. Quality-Control Samples and Data Analysis</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-05-11","noUsgsAuthors":false,"publicationDate":"2020-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Arnold, Terri 0000-0003-1406-6054 tlarnold@usgs.gov","orcid":"https://orcid.org/0000-0003-1406-6054","contributorId":1598,"corporation":false,"usgs":false,"family":"Arnold","given":"Terri","email":"tlarnold@usgs.gov","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":false,"id":788711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musgrove, MaryLynn 0000-0003-1607-3864 mmusgrov@usgs.gov","orcid":"https://orcid.org/0000-0003-1607-3864","contributorId":1316,"corporation":false,"usgs":true,"family":"Musgrove","given":"MaryLynn","email":"mmusgrov@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":788713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":788715,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Degnan, James R. 0000-0002-5665-9010 jrdegnan@usgs.gov","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":498,"corporation":false,"usgs":true,"family":"Degnan","given":"James","email":"jrdegnan@usgs.gov","middleInitial":"R.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788716,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788717,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788718,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":788719,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70214484,"text":"70214484 - 2020 - The influence of frequency and duration of seismic ground motion on the size of triggered landslides—A regional view","interactions":[],"lastModifiedDate":"2020-09-28T14:20:50.183031","indexId":"70214484","displayToPublicDate":"2020-05-11T09:18:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1517,"text":"Engineering Geology","active":true,"publicationSubtype":{"id":10}},"title":"The influence of frequency and duration of seismic ground motion on the size of triggered landslides—A regional view","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0065\">Observation, theory, and intuition all suggest that larger earthquakes should trigger larger landslides. Many factors could contribute to this, including depth-dependent shear strength or non-linearity of ground motion in soils and rock, but we hypothesize that the key characteristics of large earthquakes causing this phenomenon are (in addition to magnitude) the frequency and duration of the strong ground motion. Because of the paucity of site-specific data for detailed analysis, we take a regional approach to this question by analyzing strong-motion records and earthquake-induced landslide (EQIL) inventories from six well-documented earthquakes. Ground motion is characterized using earthquake magnitude and the median durations and frequencies (mean periods) of subsets of strong-motion records relevant to landslide triggering. EQIL inventories are characterized using the median landslide area of the entire inventory as well as the median areas of the largest 1% of the landslides and the largest 10 landslides. We then compare ground-motion characteristics with landslide size statistics to determine possible correlations. Comparisons of all earthquake- and landslide-size statistics show strong positive correlations between landslide size and (1) magnitude, (2) ground-motion duration, and (3) mean period. Although all the ground-motion measures yield highly correlated regressions, mean period appears to be the best overall predictor of landslide size. Landslide modeling using Newmark's sliding-block method also shows that longer mean periods and durations and larger magnitudes correlate strongly with increases in modeled displacements. These results support our hypothesis that increasing period and duration of seismic ground motion are the physical factors driving increased landslide sizes for larger earthquakes. Additional studies including data from a much larger set of earthquakes is needed to confirm the results of this initial study.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.enggeo.2020.105671","usgsCitation":"Jibson, R.W., and Tanyas, H., 2020, The influence of frequency and duration of seismic ground motion on the size of triggered landslides—A regional view: Engineering Geology, v. 273, 105671, 10 p., https://doi.org/10.1016/j.enggeo.2020.105671.","productDescription":"105671, 10 p.","ipdsId":"IP-119229","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":378806,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"273","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jibson, Randall W. 0000-0003-3399-0875 jibson@usgs.gov","orcid":"https://orcid.org/0000-0003-3399-0875","contributorId":2985,"corporation":false,"usgs":true,"family":"Jibson","given":"Randall","email":"jibson@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799701,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tanyas, Hakan","contributorId":215531,"corporation":false,"usgs":false,"family":"Tanyas","given":"Hakan","email":"","affiliations":[{"id":39272,"text":"University of Twente","active":true,"usgs":false}],"preferred":false,"id":799702,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210015,"text":"ofr20201049 - 2020 - 2018 U.S. Geological Survey–California Geological Survey fault-imaging surveys across the Hollywood and Santa Monica Faults, Los Angeles County, California","interactions":[],"lastModifiedDate":"2020-05-11T11:55:34.993752","indexId":"ofr20201049","displayToPublicDate":"2020-05-08T15:09:35","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1049","displayTitle":"2018 U.S. Geological Survey–California Geological Survey Fault-Imaging Surveys Across the Hollywood and Santa Monica Faults, Los Angeles County, California","title":"2018 U.S. Geological Survey–California Geological Survey fault-imaging surveys across the Hollywood and Santa Monica Faults, Los Angeles County, California","docAbstract":"<p>We acquired multiple types of seismic data across the Hollywood Fault in Hollywood, Calif., and the Santa Monica Fault in Beverly Hills, Calif., in May and June 2018. On the basis of our data, we infer near-surface locations of various traces of these faults.<br>From two separate profiles across the Hollywood Fault, we evaluated multiple seismic datasets and models, including guided-wave data, tomographic V<sub>P</sub> data, tomographic V<sub>S</sub> data, V<sub>P</sub>/V<sub>S</sub> and Poisson’s ratio models derived from tomographic V<sub>P</sub> and V<sub>S</sub> data, Rayleigh-wave–based V<sub>S</sub> models, Love-wave–based V<sub>S</sub> models, V<sub>P</sub>/V<sub>S</sub> and Poisson’s ratio models (derived from combinations of tomographic-based V<sub>P</sub> and surface-wave–based V<sub>S</sub> models), P-wave reflection images, and S-wave reflection images. All of these data and models can be used to delineate near-surface faulting, and the data consistently infer near-surface fault traces of the Hollywood Fault in the same locations. Importantly, the combined data indicate more than one near-surface fault trace of the Hollywood Fault. Between North Bronson and North Gower Avenues, evidence exists for a near-surface trace of the Hollywood Fault slightly south of Carlos Avenue. Farther west, along Argyle Avenue, our data contain high levels of cultural noise, but we interpret near-surface faulting slightly south of the intersection of Carlos and Argyle Avenues and between Carlos Avenue and Yucca Street.<br>For the Santa Monica Fault in Beverly Hills, we acquired guided-wave data only along Lasky Drive between Moreno Drive and South Santa Monica Boulevard, owing to limited access permissions. However, we used two separate source locations to generate the guided-wave data (SP1 and SP2). The data from more distant source location (relative to the recording array, SP1) were noisy, but on the basis of those data, we infer near-surface faulting at several locations along Lasky Drive, with concentrated near-surface faulting slightly south of the intersection of Lasky Drive and Charleville Boulevard. Guided-wave data generated at the closer source location (relative to recording array, SP2) more clearly show evidence for distributed near-surface faulting at several locations along Lasky Drive, with concentrated faulting near the intersection of Lasky Drive and Charleville Boulevard.<br>Although the seismic surveys across both faults provide strong evidence for the locations of near-surface fault traces, the seismic data provide little or no information about the rupture history of the fault traces.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201049","collaboration":"Prepared in cooperation with California Geological Survey","usgsCitation":"Catchings, R.D., Hernandez, J., Goldman, M.R., Chan, J.H., Sickler, R.R., Olson, B., and Criley, C.J., 2020, 2018 U.S. Geological Survey–California Geological Survey fault-imaging surveys across the Hollywood and Santa Monica Faults, Los Angeles County, California: U.S. Geological Survey Open-File Report 2020–1049, 42 p., https://doi.org/10.3133/ofr20201049.","productDescription":"Report: vi, 42 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-113953","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374593,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENA8D4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Release for the 2018 U.S. Geological Survey–California Geological Survey Fault-Imaging Surveys Across the Hollywood and Santa Monica Faults, Los Angeles County, California"},{"id":374591,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1049/coverthb.jpg"},{"id":374592,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1049/ofr20201049.pdf","text":"Report","size":"15.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1049"}],"country":"United States","state":"California ","county":"Los Angeles County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.49304199218749,\n              33.80653802509606\n            ],\n            [\n              -117.81875610351562,\n              33.529947711130646\n            ],\n            [\n              -117.476806640625,\n              33.742612777346864\n            ],\n            [\n              -117.52624511718749,\n              34.47712785074854\n            ],\n            [\n              -118.60290527343749,\n              34.45674800347809\n            ],\n            [\n              -118.83911132812499,\n              34.098159345215535\n            ],\n            [\n              -118.49304199218749,\n              33.80653802509606\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/natural-hazards/earthquake-hazards/connect\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/natural-hazards/earthquake-hazards/connect\">Contact Information, Menlo Park, Calif.</a><br><a href=\"https://earthquake.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://earthquake.usgs.gov/\">Office—Earthquake Science Center</a><br>U.S. Geological Survey<br>345 Middlefield Road, MS 977<br>Menlo Park, CA 94025</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Seismic Methodologies</li><li>Hollywood Fault Data Acquisition and Profiles</li><li>Guided-Waves Results for Profile HW1</li><li>Tomography, MASW, and Reflection Results for Profile HW1</li><li>Summary of Seismic Indicators of Faulting along Profile HW1</li><li>Guided-Wave Results for Profile HW2</li><li>Tomography, MASW, and Reflection Results for Profile HW2</li><li>Summary of Seismic Indicators of Faulting along Profile HW2</li><li>Summary of Observations, Hollywood Fault</li><li>Santa Monica Fault Data Acquisition (Beverly Hills)</li><li>Santa Monica Fault Data Analysis</li><li>Summary of Observations, Santa Monica Fault, Beverly Hills</li><li>References Cited</li></ul>","publishedDate":"2020-05-08","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hernandez, Janis","contributorId":216335,"corporation":false,"usgs":false,"family":"Hernandez","given":"Janis","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":788806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldman, Mark R. 0000-0002-0802-829X goldman@usgs.gov","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":1521,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","email":"goldman@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chan, Joanne H. 0000-0002-2065-2423 jchan@usgs.gov","orcid":"https://orcid.org/0000-0002-2065-2423","contributorId":178625,"corporation":false,"usgs":true,"family":"Chan","given":"Joanne","email":"jchan@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sickler, Robert R. 0000-0002-9141-625X rsickler@usgs.gov","orcid":"https://orcid.org/0000-0002-9141-625X","contributorId":3235,"corporation":false,"usgs":true,"family":"Sickler","given":"Robert","email":"rsickler@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olson, Brian","contributorId":217365,"corporation":false,"usgs":false,"family":"Olson","given":"Brian","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":788810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Criley, Coyn J. 0000-0002-0227-0165 ccriley@usgs.gov","orcid":"https://orcid.org/0000-0002-0227-0165","contributorId":3312,"corporation":false,"usgs":true,"family":"Criley","given":"Coyn","email":"ccriley@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788811,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70223419,"text":"70223419 - 2020 - Seasonal movements and tributary-specific fidelity of blue sucker Cycleptus elongatus in a Southern Plains riverscape","interactions":[],"lastModifiedDate":"2021-08-26T16:26:28.171603","indexId":"70223419","displayToPublicDate":"2020-05-08T11:21:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Seasonal movements and tributary-specific fidelity of blue sucker <i>Cycleptus elongatus</i> in a Southern Plains riverscape","title":"Seasonal movements and tributary-specific fidelity of blue sucker Cycleptus elongatus in a Southern Plains riverscape","docAbstract":"<p><span>This study used acoustic telemetry and a multistate Cormack–Jolly–Seber model to determine the seasonal movement patterns of blue sucker&nbsp;</span><i>Cycleptus elongatus</i><span>&nbsp;from 2015 to 2017. Several hypotheses were ranked using AIC</span><sub>c</sub><span>, and it was determined that the movement patterns of blue suckers in a mainstem reach below a hydropower dam (</span><i>i.e.</i><span>, tailwater) differed from those of blue suckers tagged in the major tributaries (perennial with stream order &gt;3). This study estimated a low probability (≤0.13) blue suckers would leave the tailwater reach at any time during the study. Conversely, blue suckers tagged in the major tributaries had a high probability (≥0.88) of leaving after the spawning season (February–May). Blue suckers tagged in the major tributaries displayed a high probability (0.83) of returning to the tributaries in the spawning season of 2016 when discharges were high. Blue suckers also had a higher probability of fidelity to the tributary where they were tagged (0.65) rather than straying to different tributaries (0.18). The majority of tagged blue suckers that strayed selected the only undammed tributary in the study area. In 2017, spring discharges were low, and the probability of blue suckers returning to any major tributary was low (0.19), with little difference in the probability of displaying site fidelity (0.10)&nbsp;</span><i>vs.</i><span>&nbsp;straying (0.09).</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/jfb.14374","usgsCitation":"Dyer, J.J., and Brewer, S.K., 2020, Seasonal movements and tributary-specific fidelity of blue sucker Cycleptus elongatus in a Southern Plains riverscape: Journal of Fish Biology, v. 97, no. 1, p. 279-292, https://doi.org/10.1111/jfb.14374.","productDescription":"14 p.","startPage":"279","endPage":"292","ipdsId":"IP-102994","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"lower Red River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -456.6522216796875,\n              33.687781758439364\n            ],\n            [\n              -455.1470947265625,\n              33.687781758439364\n            ],\n            [\n              -455.1470947265625,\n              34.334364487026306\n            ],\n            [\n              -456.6522216796875,\n              34.334364487026306\n            ],\n            [\n              -456.6522216796875,\n              33.687781758439364\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"97","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Dyer, J. J.","contributorId":264808,"corporation":false,"usgs":false,"family":"Dyer","given":"J.","email":"","middleInitial":"J.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":822007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":822008,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70210688,"text":"70210688 - 2020 - Projected impacts of climate change on the range and phenology of three culturally-important shrub species","interactions":[],"lastModifiedDate":"2020-06-17T13:34:51.108521","indexId":"70210688","displayToPublicDate":"2020-05-08T08:26:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Projected impacts of climate change on the range and phenology of three culturally-important shrub species","docAbstract":"<p><span>Climate change is shifting both the habitat suitability and the timing of critical biological events, such as flowering and fruiting, for plant species across the globe. Here, we ask how both the distribution and phenology of three food-producing shrubs native to northwestern North America might shift as the climate changes. To address this question, we compared gridded climate data with species location data to identify climate variables that best predicted the current bioclimatic niches of beaked hazelnut (</span><i>Corylus cornuta)</i><span>, Oregon grape (</span><i>Mahonia aquifolium</i><span>), and salal (</span><i>Gaultheria shallon</i><span>). We also developed thermal-sum models for the timing of flowering and fruit ripening for these species. We then used multi-model ensemble future climate projections to estimate how species range and phenology may change under future conditions. Modelling efforts showed extreme minimum temperature, climate moisture deficit, and mean summer precipitation were predictive of climatic suitability across all three species. Future bioclimatic niche models project substantial reductions in habitat suitability across the lower elevation and southern portions of the species’ current ranges by the end of the 21</span><sup>st</sup><span>&nbsp;century. Thermal-sum phenology models for these species indicate that flowering and the ripening of fruits and nuts will advance an average of 25 days by the mid-21</span><sup>st</sup><span>&nbsp;century, and 36 days by the late-21</span><sup>st</sup><span>&nbsp;century under a high emissions scenario (RCP 8.5). Future changes in the climatic niche and phenology of these important food-producing species may alter trophic relationships, with cascading impacts on regional ecosystems.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0232537","usgsCitation":"Prevey, J.S., Parker, L.E., and Harrington, C., 2020, Projected impacts of climate change on the range and phenology of three culturally-important shrub species: PLoS ONE, v. 15, no. 5, e0232537, 19 p., https://doi.org/10.1371/journal.pone.0232537.","productDescription":"e0232537, 19 p.","ipdsId":"IP-114286","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456827,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0232537","text":"Publisher Index Page"},{"id":436996,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G0UTKF","text":"USGS data release","linkHelpText":"Location and phenology observations for beaked hazelnut (Corylus cornuta), Oregon grape (Mahonia aquifolium), and salal (Gaultheria shallon) in western North America"},{"id":375664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British, Columbia, California, Idaho, Montana, Nevada, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.4560546875,\n              47.57652571374621\n            ],\n            [\n              -117.333984375,\n              50.56928286558243\n            ],\n            [\n              -121.5087890625,\n              52.32191088594773\n            ],\n            [\n              -133.2421875,\n              54.34214886448341\n            ],\n            [\n              -132.7587890625,\n              52.9883372533954\n            ],\n            [\n              -126.60644531250001,\n              48.922499263758255\n            ],\n            [\n              -124.541015625,\n              46.07323062540835\n            ],\n            [\n              -125.0244140625,\n              42.22851735620852\n            ],\n            [\n              -125.068359375,\n              39.80853604144591\n            ],\n            [\n              -120.4541015625,\n              33.797408767572485\n            ],\n            [\n              -117.333984375,\n              35.92464453144099\n            ],\n            [\n              -120.32226562500001,\n              40.01078714046552\n            ],\n            [\n              -119.00390625,\n              40.91351257612758\n            ],\n            [\n              -112.32421875,\n              42.52069952914966\n            ],\n            [\n              -111.357421875,\n              45.61403741135093\n            ],\n            [\n              -111.8408203125,\n              47.100044694025215\n            ],\n            [\n              -112.4560546875,\n              47.57652571374621\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Prevey, Janet S. 0000-0003-2879-6453","orcid":"https://orcid.org/0000-0003-2879-6453","contributorId":222702,"corporation":false,"usgs":true,"family":"Prevey","given":"Janet","email":"","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":790978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Lauren E.","contributorId":225389,"corporation":false,"usgs":false,"family":"Parker","given":"Lauren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":790979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrington, Constance A","contributorId":167297,"corporation":false,"usgs":false,"family":"Harrington","given":"Constance A","affiliations":[{"id":24677,"text":"USDA  Pacific Northwest Research Station, Olympia WA","active":true,"usgs":false}],"preferred":false,"id":790980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210040,"text":"70210040 - 2020 - Harnessing multiple models for outbreak management","interactions":[],"lastModifiedDate":"2020-05-12T12:22:55.482558","indexId":"70210040","displayToPublicDate":"2020-05-08T07:18:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Harnessing multiple models for outbreak management","docAbstract":"The coronavirus disease 2019 (COVID-19) pandemic has triggered efforts by multiple modeling groups to forecast disease trajectory, assess interventions, and improve understanding of the pathogen. Such models can often differ substantially in their projections and recommendations, reflecting different policy assumptions and objectives, as well as scientific, logistical, and other uncertainty about biological and management processes (1). Disparate predictions during any outbreak can hinder intervention planning and response by policy-makers (2, 3), who may instead choose to rely on single trusted sources of advice, or on consensus where it appears. Thus, valuable insights and information from other models may be overlooked, limiting the opportunity for decision-makers to account for risk and uncertainty and resulting in more lives lost or resources used than necessary. We advocate a more systematic approach, by merging two well-established research fields. The first element involves formal expert elicitation methods applied to multiple models to deliberately generate, retain, and synthesize valuable individual model ideas and share important insights during group discussions, while minimizing various cognitive biases. The second element uses a decision-theoretic framework to capture and account for within- and between-model uncertainty as we evaluate actions in a timely manner to achieve management objectives.","language":"English","publisher":"AAAS","doi":"10.1126/science.abb9934","collaboration":"","usgsCitation":"Shea, K., Runge, M.C., Pannell, D., Probert, W.J., Li, S., Tildesley, M.J., and Ferrari, M.J., 2020, Harnessing multiple models for outbreak management: Science, v. 368, no. 6491, p. 577-579, https://doi.org/10.1126/science.abb9934.","productDescription":"3 p.","startPage":"577","endPage":"579","ipdsId":"IP-118099","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456831,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://admin.research-repository.uwa.edu.au/en/publications/12b8d798-e5d2-4d7d-ac12-1493a2ed76d8","text":"Publisher Index Page"},{"id":374646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"368","issue":"6491","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":788901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":788902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pannell, David","contributorId":217709,"corporation":false,"usgs":false,"family":"Pannell","given":"David","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":788903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Probert, William J. M. 0000-0002-3437-759X","orcid":"https://orcid.org/0000-0002-3437-759X","contributorId":216183,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"J. M.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":788904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Shou-Li","contributorId":193644,"corporation":false,"usgs":false,"family":"Li","given":"Shou-Li","email":"","affiliations":[],"preferred":false,"id":788905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":788906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":788907,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70209328,"text":"ofr20201032 - 2020 - Simulation of the probabilistic plume extent for a potential replacement wastewater-infiltration lagoon, and probabilistic contributing areas for supply wells for the Town of Lac du Flambeau, Vilas County, Wisconsin","interactions":[],"lastModifiedDate":"2020-05-08T11:44:10.706967","indexId":"ofr20201032","displayToPublicDate":"2020-05-07T14:53:18","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1032","displayTitle":"Simulation of the Probabilistic Plume Extent for a Potential Replacement Wastewater-Infiltration Lagoon, and Probabilistic Contributing Areas for Supply Wells for the Town of Lac du Flambeau, Vilas County, Wisconsin","title":"Simulation of the probabilistic plume extent for a potential replacement wastewater-infiltration lagoon, and probabilistic contributing areas for supply wells for the Town of Lac du Flambeau, Vilas County, Wisconsin","docAbstract":"<p>An existing two-dimensional, steady-state groundwater-flow model of the shallow groundwater-flow system of the Lac du Flambeau Reservation in Vilas County, Wisconsin, originally developed by the U.S. Geological Survey, was used to simulate the potential for wastewater from a proposed relocation of a wastewater lagoon to contaminate the Lac du Flambeau Band of Lake Superior Chippewa’s drinking-water-supply wells. This simulation was completed by the U.S. Geological Survey in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa and Indian Health Service. The simulated scenarios consisted of removing wastewater infiltration from existing lagoons and re-applying that infiltration at the proposed location. Two analyses were performed for the scenarios. First, the probable extent of the plume discharging from the proposed infiltration lagoons was mapped with a Monte Carlo algorithm that used uncertainty identified during the calibration process to simulate thousands of possible outcomes. Second, the Monte Carlo method was again used to simulate a probabilistic contributing area for the Tribe’s nearby “Main Pumphouse” supply wells. The purpose of the simulations was to evaluate the potential for infiltrated wastewater to be captured by the public-supply wells.</p><p>Most features of the previously developed model remained unchanged, including calibrated parameters such as hydraulic conductivity and recharge. Thus, the same covariance distributions that were generated during calibration of the regional model (Juckem and others, 2014) remained unchanged and were used to inform the Monte Carlo simulations for the scenario simulations described in this report. The reader is encouraged to read the full report by Juckem and others (available at <a data-mce-href=\"https://doi.org/10.3133/sir20145020\" href=\"https://doi.org/10.3133/sir20145020\">https://doi.org/10.3133/sir20145020</a>) for a detailed description of the model design and calibration, as well as a description of the Monte Carlo method, its limitations, and the original results.</p><p>Results for these new scenarios indicate that the probabilistic plume extent for the proposed infiltration lagoons does not reach the Main Pumphouse wells using pumping rates and wastewater volumes estimated for 2010. Similarly, the contributing area for the Main Pumphouse wells does not capture water from within the proposed infiltration lagoon footprint. However, at higher pumping rates and wastewater volumes, as projected by the Tribe for about 2035, the contributing area for the Main Pumphouse wells do include particles that originated within the proposed lagoon footprint, albeit at low probabilities. That is, for a few of the thousands of simulations that represented a range of calibration-informed parameter covariances, some amount of infiltrated wastewater was captured by the Main Pumphouse wells under projected 2035 conditions.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201032","collaboration":"Prepared in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa and Indian Health Service","usgsCitation":"Juckem, P.F., and Fienen, M.N., 2020, Simulation of the probabilistic plume extent for a potential replacement wastewater-infiltration lagoon, and probabilistic contributing areas for supply wells for the Town of Lac du Flambeau, Vilas County, Wisconsin: U.S. Geological Survey Open-File Report 2020–1032, 10 p., https://doi.org/10.3133/ofr20201032.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-109305","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":374398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1032/coverthb.jpg"},{"id":374399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1032/ofr20201032.pdf","text":"Report","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1032"},{"id":374400,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z2YUW5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"GFLOW model files used to generate probabilistic waste-water plume extents and contributing areas to supply wells for a proposed waste-water infil-tration lagoon scenario, Lac du Flambeau, Wisconsin"}],"country":"United States","state":"Wisconsin ","county":"Vilas County","city":"Lac du Flambeau","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.92378234863281,\n              45.94386878224691\n            ],\n            [\n              -89.85013961791992,\n              45.94386878224691\n            ],\n            [\n              -89.85013961791992,\n              45.98408084285212\n            ],\n            [\n              -89.92378234863281,\n              45.98408084285212\n            ],\n            [\n              -89.92378234863281,\n              45.94386878224691\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umid-water\" href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a><br>U.S. Geological Survey<br>8505 Research Way <br>Middleton, WI 55562<br></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Simulation of the Wastewater Plume Extent from Proposed Infiltration Lagoons</li><li>Simulation of Areas Contributing Recharge to the Main Pumphouse Wells</li><li>Assumptions and Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-05-07","noUsgsAuthors":false,"publicationDate":"2020-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786107,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209795,"text":"sir20205043 - 2020 - Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015","interactions":[],"lastModifiedDate":"2020-05-07T19:58:50.668535","indexId":"sir20205043","displayToPublicDate":"2020-05-07T10:17:43","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5043","displayTitle":"Chemical Evaluation of Water and Gases Collected from Hydrothermal Systems Located in the Central Aleutian Arc, August 2015","title":"Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015","docAbstract":"<p>Five volcanic-hydrothermal systems in the central Aleutians Islands were sampled for water and gas geochemistry in 2015 to provide baseline data to help predict future volcanic unrest. Some areas had not been sampled in 20–30 years (Makushin volcano, Geyser Bight), and other areas had minimal to no prior sampling (Tana volcano and Fisher Caldera). The chemical and isotopic data of the waters show a wide variety of characteristics typical of hydrothermal settings. Stable isotopic analyses of the waters show no evidence for primary magmatic water, rather that waters have a meteoric origin that is variably influenced by boiling and evaporation processes. The carbon and helium isotopic analyses of gases suggest they contain a primary magmatic component typical of the upper mantle at most locations, and the CO<sub>2</sub>/S ratios show that these gases have been modified by interactions with groundwater along the flow paths. Some areas demonstrate stable compositions since the last sampling (for example, Akutan hydrothermal areas), with some being remarkably steady over very long periods (for example, Geyser Bight). Other areas show modifications because of either lower amounts of upwelling from hydrothermal sources or lower amounts of magmatic influence on the surface chemistry (for example, Upper Glacial valley of Makushin, an informally named valley leading south of the volcano toward Makushin Bay to the south). Finally, this report highlights that previously unsampled regions in the Aleutian Islands, such as Tana volcano and Fisher Caldera (the latter found to have one of the highest helium isotopic signatures ever measured in the Aleutian Islands), show evidence of ongoing subsurface magmatism that warrants continued investigation in terms of volcanic hazard.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205043","collaboration":"","usgsCitation":"Werner, C., Kern, C., and Kelly, P. K., 2020, Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015: U.S. Geological Survey Scientific Investigations Report 2020–5043, 35 p., https://doi.org/10.3133/sir20205043.","productDescription":"Report: viii, 35 p.; 2 Tables","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118716","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":374537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5043/coverthb.jpg"},{"id":374538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043.pdf","text":"Report","size":"21 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374539,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043_table1.pdf","text":"Table 1","size":"200 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":374540,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043_table2.pdf","text":"Table 2","size":"130 KB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alaska","county":"","city":"","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -169.2333984375,\n              52.81604319154934\n            ],\n            [\n              -168.96972656249997,\n              52.669720383688166\n            ],\n            [\n              -168.4423828125,\n              52.8691297276852\n            ],\n            [\n              -168.28857421875,\n              53.08082737207479\n            ],\n            [\n              -167.62939453124997,\n              53.1335898292448\n            ],\n            [\n              -166.75048828125,\n              53.30462107510271\n            ],\n            [\n              -166.13525390625,\n              53.657661020298\n            ],\n            [\n              -164.99267578125,\n              54.00776876193478\n            ],\n            [\n              -164.11376953125,\n              54.23955053156177\n            ],\n            [\n              -164.33349609375,\n              54.67383096593114\n            ],\n            [\n              -164.68505859375,\n              54.88924640307589\n            ],\n            [\n              -165.41015625,\n              54.648412502316695\n            ],\n            [\n              -166.57470703125,\n              54.316523240258256\n            ],\n            [\n              -168.77197265625,\n              53.605544099238\n            ],\n            [\n              -169.2333984375,\n              52.81604319154934\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:tlmurray@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:tlmurray@usgs.gov\">Director</a>,<br><a href=\"https://volcanoes.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://volcanoes.usgs.gov/\">Volcano Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>4210 University Drive<br>Anchorage, AK 99508</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Makushin Volcano</li><li>Akutan Volcano</li><li>Tana Volcano</li><li>Fisher Caldera</li><li>Geyser Bight Hydrothermal Area</li><li>Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-04-24","noUsgsAuthors":false,"publicationDate":"2020-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Werner, Cynthia A. cwerner@usgs.gov","contributorId":2540,"corporation":false,"usgs":true,"family":"Werner","given":"Cynthia","email":"cwerner@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":788058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":788059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":788060,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70211851,"text":"70211851 - 2020 - Geophysical characterization of the Northwest Geysers geothermal field, California","interactions":[],"lastModifiedDate":"2020-08-11T13:01:58.184934","indexId":"70211851","displayToPublicDate":"2020-05-06T09:26:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical characterization of the Northwest Geysers geothermal field, California","docAbstract":"The Clear Lake Volcanic Field in northern California is the youngest and northern-most part of a long-lived volcanic system that has produced recent (~10 ka) eruptions.  Adjacent to the Clear Lake Volcanic Field is the worlds largest energy producing geothermal field, The Geysers.  The hottest part of The Geysers geothermal field is in the northwest where temperatures reach ~400 C at 3 km depth. Low permeability, high thermal gradients, and low steam saturation prescribed development of an enhanced geothermal system (EGS) in the Northwest Geysers to increase energy producing capacity. Though the Northwest Geysers is known to be the hottest part of the field, geophysical methods have failed to adequately image any inferred heat source. This project aims to image the heat source of the Northwest Geysers using newly collected gravity and magnetotelluric (MT) measurements.  Gravity data were jointly modeled with existing magnetic data along a two-dimensional profile aligned with an existing geologic cross-section. The key feature of the potential field model is a low-density, low-susceptibility body at 5 km depth (bmsl) under the EGS.  MT data were modeled in three-dimensions to characterize subsurface resistivity structure, where the upper 3 km of the resistivity model agrees well with existing data.  Lithologic and steam saturation are estimated from modeled resistivity values using existing geophysical data.  Below 3 km depth (bmsl), the resistivity model images a possible young intrusion under the EGS. A possible zone of partial melt (<5%) below 7 km depth (bmsl) in the northwestern part of the field is also imaged which extends northeast towards the main part of the Clear Lake Volcanic Field.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106882","usgsCitation":"Peacock, J., Earney, T.E., Mangan, M.T., Schermerhorn, W.D., Glen, J.M., Walters, M., and Hartline, C., 2020, Geophysical characterization of the Northwest Geysers geothermal field, California: Journal of Volcanology and Geothermal Research, v. 339, 106882, 17 p., https://doi.org/10.1016/j.jvolgeores.2020.106882.","productDescription":"106882, 17 p.","ipdsId":"IP-117752","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Twin Lakes, Glenview","otherGeospatial":"Southern Clear Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.80105590820314,\n              38.85682013474361\n            ],\n            [\n              -122.53875732421875,\n              38.85682013474361\n            ],\n            [\n              -122.53875732421875,\n              38.94338908847991\n            ],\n            [\n              -122.80105590820314,\n              38.94338908847991\n            ],\n            [\n              -122.80105590820314,\n              38.85682013474361\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"339","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mangan, Margret T. 0000-0002-5273-8053","orcid":"https://orcid.org/0000-0002-5273-8053","contributorId":237813,"corporation":false,"usgs":false,"family":"Mangan","given":"Margret","email":"","middleInitial":"T.","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":795393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Mark 0000-0001-8458-4813","orcid":"https://orcid.org/0000-0001-8458-4813","contributorId":213428,"corporation":false,"usgs":false,"family":"Walters","given":"Mark","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":795397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hartline, Craig","contributorId":213429,"corporation":false,"usgs":false,"family":"Hartline","given":"Craig","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":795396,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70211702,"text":"70211702 - 2020 - The Mars Global Digital Dune Database (MGD3): Composition and stability","interactions":[],"lastModifiedDate":"2020-08-07T13:47:54.739118","indexId":"70211702","displayToPublicDate":"2020-05-06T08:45:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"The Mars Global Digital Dune Database (MGD3): Composition and stability","docAbstract":"We present an expansion to the Mars Global Digital Dune Database (MGD3) describing 1) bulk dune field composition determined by fitting a mineral spectral library to Thermal Emission Spectra (TES) data, and 2) a morphologic stability index that measures the degree of non-aeolian modification that has eroded and stabilized each dune field. This paper describes results for these two components, providing insight into global patterns of dune sand sources, postdepositional alteration, and mineral maturity. Consistent with the work of others, the main mineral components of each analyzed dune field are feldspar, pyroxene, and high-silica phases, with minor amounts of olivine and possibly sulfate minerals. Subtle global-scale spatial variations in olivine and feldspar abundances correspond with previously observed trends in surface mineralogy, suggesting that dune sand is reflective of its regional setting, and thus that aeolian sand has typically not traveled far (<~100s of km) from its source regions. Dune-field-scale stabilization features are found mainly south of 60S, and in a few areas north of 60N, consistent with observed dune and ripple migration rates in these areas. The presence of such stabilization features may be an indicator of where bedform migration rates are low. Abundances of high-silica phases are elevated in some dune fields located in the southern mid- to high-latitudes, particularly in those dune fields located on intercrater plains, where stabilization features tend to be less well developed and where ripples tend to be actively migrating. This correlation of high-silica phase abundance with bedform activity also occurs in the north polar sand seas. In the north polar sand seas, this spectral signature has been attributed to iron-bearing glass that has been weathered through acid leaching, leaving behind silica-enriched rinds, and kept free of any precipitated coatings through active saltation. We hypothesize that near and thermal infrared spectral signatures of acid leaching are indicators of aeolian activity, and thus potentially of mineral maturity, in dune sands abundant in iron-bearing glass.","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2019.04.025","usgsCitation":"Fenton, L.K., Gullikson, A.L., Hayward, R., Charles, H., and Titus, T.N., 2020, The Mars Global Digital Dune Database (MGD3): Composition and stability: Icarus, v. 330, p. 189-203, https://doi.org/10.1016/j.icarus.2019.04.025.","productDescription":"15 p.","startPage":"189","endPage":"203","ipdsId":"IP-104474","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":377170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"330","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fenton, Lori K.","contributorId":208682,"corporation":false,"usgs":false,"family":"Fenton","given":"Lori","email":"","middleInitial":"K.","affiliations":[{"id":37319,"text":"SETI Institute","active":true,"usgs":false}],"preferred":false,"id":795173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gullikson, Amber L. 0000-0002-1505-3151","orcid":"https://orcid.org/0000-0002-1505-3151","contributorId":208679,"corporation":false,"usgs":true,"family":"Gullikson","given":"Amber","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":795174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayward, Rosalyn 0000-0002-7428-0311 rhayward@usgs.gov","orcid":"https://orcid.org/0000-0002-7428-0311","contributorId":208680,"corporation":false,"usgs":true,"family":"Hayward","given":"Rosalyn","email":"rhayward@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":795175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Charles, Heather","contributorId":208681,"corporation":false,"usgs":false,"family":"Charles","given":"Heather","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":795176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":795177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70211980,"text":"70211980 - 2020 - Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.","interactions":[],"lastModifiedDate":"2020-08-12T23:12:31.627153","indexId":"70211980","displayToPublicDate":"2020-05-05T18:02:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.","docAbstract":"<p><span>Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km</span><sup>2</sup><span>&nbsp;year</span><sup>-1</sup><span>) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km</span><sup>2</sup><span>&nbsp;over the four-year period, and 186 km</span><sup>2</sup><span>, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs12091464","usgsCitation":"Vanderhoof, M.K., Christensen, J.R., Beal, Y.G., DeVries, B., Lang, M.W., Hwang, N., Mazzarella, C., and Jones, J., 2020, Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.: Remote Sensing, v. 12, no. 9, 1464, 29 p., https://doi.org/10.3390/rs12091464.","productDescription":"1464, 29 p.","ipdsId":"IP-116446","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":35993,"text":"Hydrologic Investigations and Research Section","active":true,"usgs":true}],"links":[{"id":456841,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12091464","text":"Publisher Index Page"},{"id":437000,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ODILGN","text":"USGS data release","linkHelpText":"Tracking disturbance and inundation to identify wetland loss"},{"id":377459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, MarylandPennsylvania, Virginia, West Virginia","otherGeospatial":"Mid-Atlantic Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.70703125,\n              41.44272637767212\n            ],\n            [\n              -75.05859375,\n              41.77131167976407\n            ],\n            [\n              -75.41015624999999,\n              42.09822241118974\n            ],\n            [\n              -79.5849609375,\n              42.06560675405716\n            ],\n            [\n              -79.9365234375,\n              42.293564192170095\n            ],\n            [\n              -80.6396484375,\n              41.672911819602085\n            ],\n            [\n              -80.6396484375,\n              40.1452892956766\n            ],\n            [\n              -81.474609375,\n              39.232253141714885\n            ],\n            [\n              -81.8701171875,\n              38.92522904714054\n            ],\n            [\n              -82.5732421875,\n              38.44498466889473\n            ],\n            [\n              -82.2216796875,\n              37.43997405227057\n            ],\n            [\n              -83.5400390625,\n              36.63316209558658\n            ],\n            [\n              -76.2451171875,\n              36.56260003738545\n            ],\n            [\n              -73.47656249999999,\n              34.30714385628804\n            ],\n            [\n              -70.6640625,\n              35.137879119634185\n            ],\n            [\n              -72.333984375,\n              40.212440718286466\n            ],\n            [\n              -73.8720703125,\n              40.48038142908172\n            ],\n            [\n              -74.6630859375,\n              39.027718840211605\n            ],\n            [\n              -75.6298828125,\n              39.470125122358176\n            ],\n            [\n              -75.5859375,\n              39.90973623453719\n            ],\n            [\n              -74.92675781249999,\n              40.1452892956766\n            ],\n            [\n              -75.234375,\n              40.48038142908172\n            ],\n            [\n              -74.70703125,\n              41.44272637767212\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Jay R.","contributorId":238115,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","middleInitial":"R.","affiliations":[],"preferred":false,"id":796081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beal, Yen-Ju G. 0000-0002-5538-5687 ygbeal@usgs.gov","orcid":"https://orcid.org/0000-0002-5538-5687","contributorId":5328,"corporation":false,"usgs":true,"family":"Beal","given":"Yen-Ju","email":"ygbeal@usgs.gov","middleInitial":"G.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeVries, Ben 0000-0003-2136-3401","orcid":"https://orcid.org/0000-0003-2136-3401","contributorId":198971,"corporation":false,"usgs":false,"family":"DeVries","given":"Ben","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":796083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hwang, Nora","contributorId":238116,"corporation":false,"usgs":false,"family":"Hwang","given":"Nora","email":"","affiliations":[],"preferred":false,"id":796085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mazzarella, Christine","contributorId":169818,"corporation":false,"usgs":false,"family":"Mazzarella","given":"Christine","email":"","affiliations":[],"preferred":false,"id":796086,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, John 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796087,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70209111,"text":"sir20205028 - 2020 - Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17","interactions":[],"lastModifiedDate":"2020-05-06T11:32:05.924687","indexId":"sir20205028","displayToPublicDate":"2020-05-05T14:18:55","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5028","displayTitle":"Simulation of Discharge, Water-Surface Elevations, and Water Temperatures for the St. Louis River Estuary, Minnesota-Wisconsin, 2016–17","title":"Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17","docAbstract":"<p>The St. Louis River estuary is a large freshwater estuary, next to Duluth, Minnesota, that encompasses the headwaters of Lake Superior. The St. Louis River estuary is one of the most complex and compromised near-shore systems in the upper Great Lakes with a long history of environmental contamination caused by logging, mining, paper mills, and other heavy industrial activities. Presently (2020), a widely available, science-based assessment tool capable of evaluating ecosystem-level responses to remediation and restoration projects has not existed for the estuary. To address this need, the U.S. Geological Survey (USGS) built a predictive, mechanistic, three-dimensional hydrodynamic model for the estuary using the Environmental Fluid Dynamics Code framework. In the current version, the model can simulate continuous discharge, water-surface elevations, water temperature, and flow velocity, although the modular framework allows for future additions of water-quality modeling.</p><p>The model was calibrated using data collected from April 2016 through November 2016 and validated with data collected from April 2017 through November 2017. The four types of data used to evaluate model performance were water-surface elevations, discharge, water temperature, and flow velocities. Streamflow and temperature boundary condition data included a mixture of USGS streamgage data, Minnesota Department of Natural Resources gage data, and estimates derived from the gage data.</p><p>The model was able to simulate the water-surface elevations with generally good agreement between the simulated and measured values for both years at the daily time step. Specifically, the model was able to demonstrate excellent<br>agreement with the measured data with Nash-Sutcliffe efficiency coefficients greater than 0.8 for all three locations; however, the model was unable to produce hourly water-surface elevations with such accuracy for 2016–17.</p><p>Discharge was more dynamic than the water-surface elevations, both for the measured and simulated data. Generally, most of the discharge ranged from −650 to 1,200 cubic meters per second, but the constantly changing flux exiting the estuary into Lake Superior (positive flows) and entering the estuary from Lake Superior (negative flows) occurred throughout the year. Even upstream at the St. Louis River at Oliver, Wisconsin, gage (USGS station 0402403250), the effect of flows into the estuary from Lake Superior did occur, demonstrating the strong effect of the Lake Superior seiche on flows for the estuary.</p><p>From a performance standpoint, the model was able to simulate discharge with generally good agreement in both years, although the 2017 validation was better than the 2016 calibration period. For the daily Nash-Sutcliffe efficiency coefficients, the simulated values were 0.98, 0.62, 0.49, and 0.71 for the Oliver gage; the Superior Bay entry channel at Superior, Wisc., (USGS station 464226092005600); the Superior Bay Duluth Ship Canal at Duluth, Minn., (USGS station 464646092052900); and total entries (combination of the Superior entry and Duluth entry), respectively. For the hourly evaluation criteria, the model performed poorly, with Nash-Sutcliffe efficiency coefficients less than 0 for the two entries into Lake Superior; therefore, as a predictor of discharge at the hourly scale, the model performed worse than using the measured data average. Similar to discharge, the model was a good predictor of flow velocity at the daily time scale but had difficulty matching the measured data at the hourly scale. For discharge and flow velocity, matching at subdaily time steps for a system as complicated as the St. Louis River estuary is considered difficult because the match is highly sensitive to coordinating the exact measurement location to the simulated value.</p><p>The final calibration target was water temperature, calibrated for the Oliver gage and the Duluth entry. For calibration purposes, the Duluth entry was the more important water temperature target because the Oliver gage was more of an internal check on the model. The Nash-Sutcliffe efficiency coefficients for the Duluth entry were high; hourly Nash-Sutcliffe efficiency coefficients at the Duluth entry were either at or greater than 0.7 for both years, and daily values were 0.84 and 0.82 for 2016 and 2017, respectively.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205028","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Smith, E.A., Kiesling, R.L., and Hayter, E.J., 2020, Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17: U.S. Geological Survey Scientific Investigations Report 2020–5028, 31 p., https://doi.org/10.3133/sir20205028.","productDescription":"Report: viii, 31 p.; Data Release; Dataset","onlineOnly":"Y","ipdsId":"IP-113167","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437002,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U1XXG0","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC model scenarios for velocity profiles around Munger Landing, selected years (2012-2019)"},{"id":374450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5028/coverthb.jpg"},{"id":374451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5028/sir20205028.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5028"},{"id":374452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990OUS6","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC hydrodynamic model for discharge and temperature simulations: 2016–17"},{"id":374455,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS Water Data for the Nation"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River estuary","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.548828125,\n              46.62869257083747\n            ],\n            [\n              -92.0050048828125,\n              46.62869257083747\n            ],\n            [\n              -92.0050048828125,\n              47.07199249565323\n            ],\n            [\n              -92.548828125,\n              47.07199249565323\n            ],\n            [\n              -92.548828125,\n              46.62869257083747\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umid-water\" href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a> <br>U.S. Geological Survey <br>2280 Woodale Drive <br>Mounds View, MN 55112</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Model Calibration and Results</li><li>Model Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-05-05","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayter, Earl J.","contributorId":223403,"corporation":false,"usgs":false,"family":"Hayter","given":"Earl","email":"","middleInitial":"J.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":784964,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70228787,"text":"70228787 - 2020 - Drivers and consequences of alternative landscape futures on wildlife distributions in New England, United States","interactions":[],"lastModifiedDate":"2022-02-21T15:46:27.686445","indexId":"70228787","displayToPublicDate":"2020-05-05T09:36:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Drivers and consequences of alternative landscape futures on wildlife distributions in New England, United States","docAbstract":"In an era of rapid climate and land transformation, it is increasingly important to understand how future changes impact natural systems. Scenario studies can offer the structure and perspective needed to understand the impacts of change and help inform management and conservation decisions. We implemented a scenario-based approach to assess how two high impact drivers of landscape change influence the distributions of managed wildlife species (n = 10) in the New England region of the northeastern United States. We used expert derived species distribution models (SDMs) and scenarios developed by the New England Landscape Futures Project (NELFP) to estimate how species distributions change under various trajectories (n = 5) of landscape change. The NELFP scenarios were built around two primary drivers – Socio-Economic Connectedness (SEC) and Natural Resource Planning and Innovation (NRPI) – and provide plausible alternatives for how the New England region may change over fifty years (2010 to 2060). Our models generally resulted in species occurrence and richness declines by 2060. The majority of species (7 of 10) experienced declines in regional occurrence for all NELFP scenarios, and one species experienced a projected increase in mean regional occurrence for all scenarios. Our results indicate that the NRPI and SEC drivers strongly influenced projected distribution changes compared to baseline projections. NRPI had a greater impact on distribution change for five species (coyote, moose, striped skunk, white-tailed deer, and wild turkey), while SEC had a greater impact on four species (American black bear, bobcat, raccoon, and red fox); one species (gray fox) was equally influenced by both NRPI and SEC. These results emphasize the importance of integrating both natural resource planning and socio-economic factors when addressing issues of distribution change and offer insights that can inform proactive management and conservation planning.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2020.00164","usgsCitation":"Pearman-Gillman, S., Duveneck, M.J., Murdoch, J.D., and Donovan, T.M., 2020, Drivers and consequences of alternative landscape futures on wildlife distributions in New England, United States: Frontiers in Ecology and Evolution, v. 8, p. 1-19, https://doi.org/10.3389/fevo.2020.00164.","productDescription":"164, 19 p.","startPage":"1","endPage":"19","ipdsId":"IP-114447","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":456846,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.00164","text":"Publisher Index 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,{"id":70220547,"text":"70220547 - 2020 - Combining genetic and demographic monitoring better informs conservation of an endangered urban snake","interactions":[],"lastModifiedDate":"2025-04-16T13:18:39.30065","indexId":"70220547","displayToPublicDate":"2020-05-05T08:10:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Combining genetic and demographic monitoring better informs conservation of an endangered urban snake","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>Conversion and fragmentation of wildlife habitat often leads to smaller and isolated populations and can reduce a species’ ability to disperse across the landscape. As a consequence, genetic drift can quickly lower genetic variation and increase vulnerability to extirpation. For species of conservation concern, quantification of population size and connectivity can clarify the influence of genetic drift in local populations and provides important information for conservation management and recovery strategies. Here, we used genome-wide single nucleotide polymorphism (SNP) data and capture-mark-recapture methods to evaluate the genetic diversity and demography within seven focal sites of the endangered San Francisco gartersnake (<i>Thamnophis sirtalis tetrataenia</i>), a species affected by alteration and isolation of wetland habitats throughout its distribution. The primary goals were to determine the population structure and degree of genetic isolation among<span>&nbsp;</span><i>T</i>.<span>&nbsp;</span><i>s</i>.<span>&nbsp;</span><i>tetrataenia</i><span>&nbsp;</span>populations and estimate effective size and population abundance within sites to better understand the present and future importance of genetic drift. We also used temporally sampled datasets to examine the magnitude of genetic change over time. We found moderate population genetic structure throughout the San Francisco Peninsula that partitions sites into northern and southern regional clusters. Point estimates of both effective size and population abundance were generally small (≤ 100) for a majority of the sites, and estimates were particularly low in the northern populations. Genetic analyses of temporal datasets indicated an increase in genetic differentiation, especially for the most geographically isolated sites, and decreased genetic diversity over time in at least one site (Pacifica). Our results suggest that drift-mediated processes as a function of small population size and reduced connectivity from neighboring populations may decrease diversity and increase differentiation. Improving genetic diversity and connectivity among<span>&nbsp;</span><i>T</i>.<span>&nbsp;</span><i>s</i>.<span>&nbsp;</span><i>tetrataenia</i><span>&nbsp;</span>populations could promote persistence of this endangered snake.</p></div></div><div id=\"figure-carousel-section\"><br></div>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0231744","usgsCitation":"Wood, D.A., Rose, J.P., Halstead, B., Stoelting, R.E., Swaim, K.E., and Vandergast, A.G., 2020, Combining genetic and demographic monitoring better informs conservation of an endangered urban snake: PLoS ONE, v. 15, no. 5, e0231744, 27 p.; Data Release, https://doi.org/10.1371/journal.pone.0231744.","productDescription":"e0231744, 27 p.; Data Release","ipdsId":"IP-114654","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458808,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0231744","text":"Publisher Index Page"},{"id":437231,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YKLBB5","text":"USGS data release","linkHelpText":"San Francisco Gartersnake (Thamnophis sirtalis tetrataenia) Genomic and Demographic Data from San Mateo County and Northeastern Santa Cruz County Collected Between 2016 - 2018"},{"id":385763,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.62939453125001,\n              37.243448378654115\n            ],\n            [\n              -122.05261230468751,\n              37.243448378654115\n            ],\n            [\n              -122.05261230468751,\n              37.81846319511331\n            ],\n            [\n              -122.62939453125001,\n              37.81846319511331\n            ],\n            [\n              -122.62939453125001,\n              37.243448378654115\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":815969,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoelting, Ricka E.","contributorId":171533,"corporation":false,"usgs":false,"family":"Stoelting","given":"Ricka","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":815970,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swaim, Karen E","contributorId":258210,"corporation":false,"usgs":false,"family":"Swaim","given":"Karen","email":"","middleInitial":"E","affiliations":[{"id":52239,"text":"Swaim Biological Incorporated","active":true,"usgs":false}],"preferred":false,"id":815971,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815972,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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