Shiveluch volcano (Kamchatka, Russia) is an active andesitic volcano with a history of explosive activity, dome extrusion, and structural collapse during the Holocene. The most recent major (> 1 km3) dome collapse occurred in November 1964, producing a ~ 1.5 km3 debris avalanche that traveled over 15 km from the vent and triggered a phreatic explosion followed by a voluminous (~ 0.8 km3) eruption of juvenile pyroclastic material. Seismic records suggest that the collapse was likely triggered by a magnitude 5.1 earthquake associated with the ascent of magma into the edifice. The geomechanical properties of the pre-1964 dome are unknown; accordingly, the mechanics of the collapse are poorly understood. This project employs numerical slope stability modeling using the finite element method to constrain probable ranges of geomechanical properties for the materials involved in the collapse, considering earthquake loading as the most likely triggering mechanism. Model results show good agreement with the 1964 collapse geometry considering Geological Strength Index and horizontal pseudo-static seismic coefficient ranges of 30–60 and 0.05–0.15 g, respectively, representing variably fractured and altered dome rocks under moderate earthquake loading, confirming that ground acceleration alone could have triggered the dome collapse. Deep-seated rotational sliding is the dominant failure mode, but local extension within the dome during failure appears to play an important role in the development of the collapse. The findings of this work allow for better forward modeling of potential future collapses, the results of which can be incorporated into regional hazard and risk assessments.
Oyster reef chains grow in response to local hydrodynamics and can redirect flows, particularly when reef chains grow perpendicular to freshwater flow paths. Singularly, oyster reef chains can act as porous dams that may facilitate nearshore accumulation of fresh or low-salinity water, in turn creating intermediate salinities that support oyster growth and estuarine conditions. However, oyster-driven freshwater detention has only been confirmed by limited, point-scale observational data, and simplified models. Oyster reef-driven freshwater detention in real ecosystems at the estuary scale remains largely unexplored. In this study, we analyzed the visible bands in 30-m resolution remote sensing (RS) images recorded by the Operational Land Imager aboard Landsat-8 to characterize the freshwater detention effect of oyster reef chains across a set of hydrologic conditions. Our results support prior findings indicating that 30-m resolution RS images recorded by the Operational Land Imager aboard Landsat-8 are useful for analyzing coastal dynamics after atmospheric correction, despite having been originally designed for terrestrial studies. Statistical models of water-leaving reflectance revealed that freshwater detention by oyster reefs was evident across the estuary, with the greatest effect occurring in the region closest to shore. Additionally, statistical modeling results and spatial patterns apparent in the satellite images suggested that reef-driven freshwater detention occurred under high riverine discharge conditions, but was less evident when flow was low. Beyond offering insight on the potential role of oyster reefs as mediators of estuarine hydrology, this study presents a transferable methodological framework for exploring estuarine biophysical feedbacks in blackwater river estuaries using satellite remote sensing.
In a recent paper, Schoolmaster, Zirbel, and Cronin (SZC) (2020) claim “Formal causal analysis show[s] that biodiversity–ecosystem function (BEF) correlations are non-causal associations.” If this conclusion is accepted as true, it suggests a reconsideration of much of our current understanding of how biodiversity relates to the functioning of ecosystems. On the surface, it is easy to spot clear signs of something problematic with SZC’s presentation. They claim, for example, that (1) species richness is incapable of having a causal effect on ecosystem functioning on theoretical grounds, and (2) that trait diversity cannot be causally influenced by species diversity. These remarkable claims are counter to existing thought and evidence. We point to logical errors that lead them to a misapply causal analysis and produce erroneous conclusions.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/ecy.3378","usgsCitation":"Grace, J.B., Loreau, M., and Schmid, B., 2022, A graphical causal model for resolving species identity effects and biodiversity–ecosystem function correlations: comment: Ecology, v. 103, no. 2, e03378, https://doi.org/10.1002/ecy.3378.","productDescription":"e03378","ipdsId":"IP-118978","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488956,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":388863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-06-03","publicationStatus":"PW","contributors":{"editors":[{"text":"Inouye, Brian D.","contributorId":95409,"corporation":false,"usgs":true,"family":"Inouye","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":822558,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":822554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loreau, Michel","contributorId":17464,"corporation":false,"usgs":false,"family":"Loreau","given":"Michel","email":"","affiliations":[{"id":48706,"text":"Theoretical and Experimental Ecology Station (UMR 5371), National Centre for Scientific Research (CNRS), Paul Sabatier University (UPS), Moulis, France","active":true,"usgs":false}],"preferred":false,"id":822557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmid, Bernhard","contributorId":265329,"corporation":false,"usgs":false,"family":"Schmid","given":"Bernhard","affiliations":[{"id":54647,"text":"Department of Geography, Remote Sensing Laboratories, University of Zurich","active":true,"usgs":false}],"preferred":false,"id":822555,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228997,"text":"70228997 - 2022 - Cognitive and behavioral coping in response to wildlife disease: The case of hunters and chronic wasting disease","interactions":[],"lastModifiedDate":"2022-05-13T14:45:39.989544","indexId":"70228997","displayToPublicDate":"2021-04-30T09:45:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1909,"text":"Human Dimensions of Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Cognitive and behavioral coping in response to wildlife disease: The case of hunters and chronic wasting disease","docAbstract":"The transactional model of stress and coping (TMSC) provides a conceptual framework for understanding adaptations to stressors like chronic wasting disease (CWD). Understanding hunter response to stressors is important because decreased participation and satisfaction can affect individual well-being, cultural traditions, agency revenue, and local economies. Using TMSC, we explored how deer hunters coped with CWD. We also compared involvement, and impacts and emotions related to CWD, inside and outside a CWD management zone. Then we examined coping related to CWD presence, and if the disease affected human health. Most hunters would cope using product shift (i.e., eating meat after a negative test result) rather than displacement (i.e., hunting elsewhere) or dropout. Hunters who may be displaced reported lower involvement in deer hunting, and increased worry about CWD. Results suggest that CWD information and testing may increase hunter worry. Funding expanded testing without prompting displacement or dropout are important management considerations.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10871209.2021.1919340","usgsCitation":"Schroeder, S., Landon, A., Cornicelli, L., Fulton, D.C., and McInenly, L., 2022, Cognitive and behavioral coping in response to wildlife disease: The case of hunters and chronic wasting disease: Human Dimensions of Wildlife, v. 27, no. 3, p. 251-272, https://doi.org/10.1080/10871209.2021.1919340.","productDescription":"22 p.","startPage":"251","endPage":"272","ipdsId":"IP-119986","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":396488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.2030029296875,\n 43.52465500687185\n ],\n [\n -91.23046875,\n 43.84245116699039\n ],\n [\n -91.60400390625,\n 44.09547572946637\n ],\n [\n -92.0379638671875,\n 44.41808794374846\n ],\n [\n -92.3016357421875,\n 44.512176171071054\n ],\n [\n -92.7740478515625,\n 44.75453548416007\n ],\n [\n -92.8509521484375,\n 44.81691551782855\n ],\n [\n -93.1146240234375,\n 44.933696389694674\n ],\n [\n -93.1365966796875,\n 45.19752230305682\n ],\n [\n -93.636474609375,\n 45.16267407976458\n ],\n [\n -93.515625,\n 44.735027899515465\n ],\n [\n -93.0322265625,\n 44.69989765840318\n ],\n [\n -92.74658203125,\n 44.19795903948531\n ],\n [\n -92.3785400390625,\n 43.667871610117494\n ],\n [\n -92.318115234375,\n 43.50872101129684\n ],\n [\n -91.2030029296875,\n 43.52465500687185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Schroeder, Susan A.","contributorId":280190,"corporation":false,"usgs":false,"family":"Schroeder","given":"Susan A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":836094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landon, Adam","contributorId":280191,"corporation":false,"usgs":false,"family":"Landon","given":"Adam","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":836095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis J.","contributorId":280192,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis J.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":836096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836093,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McInenly, Leslie","contributorId":280193,"corporation":false,"usgs":false,"family":"McInenly","given":"Leslie","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":836097,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224968,"text":"70224968 - 2022 - Mechanisms controlling climate warming impact on the occurrence of hypoxia in Chesapeake Bay","interactions":[],"lastModifiedDate":"2023-01-18T15:38:30.872009","indexId":"70224968","displayToPublicDate":"2021-03-01T10:18:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms controlling climate warming impact on the occurrence of hypoxia in Chesapeake Bay","docAbstract":"AClimate change represents an increasing stressor on estuarine and coastal ecosystems. A series of simulations were run using the Integrated Compartment Water Quality Model to determine the magnitude of various mechanisms controlling the effect of climate warming on dissolved oxygen (DO) in the Chesapeake Bay. The results suggested that the average hypoxic volume in the summer would increase by 9% (410 Mm3) from 1995 to 2025 as air temperature increases by 1.06°C and water temperature by 0.9°C. The change in DO solubility contributes 55% of the total climate warming effect, biological rates 33%, and stratification 11%. The Rappahannock Shoal, a hydraulic control point, plays a major role in determining the effect of climate warming on DO in the Bay. Due to the abrupt change in bathymetry, the convergence between seaward-moving freshwater and landward-moving saltwater causes downwelling and enhanced vertical mixing which introduces surface water of higher temperature to the deep channel and accelerates organic matter remineralization and oxygen consumption in deep waters. Surface water DO concentrations will decrease under climate warming conditions due to lower DO solubility, reducing DO flux to the deep channel and contributing to hypoxia development. These findings provide critical information for future management decision making regarding the effects of climate warming on DO in Chesapeake Bay and other estuaries.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12907","usgsCitation":"Tian, R., Cerco, C., Bhatt, G., Linker, L.C., and Shenk, G.W., 2022, Mechanisms controlling climate warming impact on the occurrence of hypoxia in Chesapeake Bay: Journal of the American Water Resources Association, v. 58, no. 6, p. 855-875, https://doi.org/10.1111/1752-1688.12907.","productDescription":"21 p.","startPage":"855","endPage":"875","ipdsId":"IP-126223","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":390385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.662353515625,\n 36.88840804313823\n ],\n [\n -75.60791015625,\n 36.88840804313823\n ],\n [\n -75.60791015625,\n 39.54641191968671\n ],\n [\n -77.662353515625,\n 39.54641191968671\n ],\n [\n -77.662353515625,\n 36.88840804313823\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tian, Richard 0000-0002-9416-8669","orcid":"https://orcid.org/0000-0002-9416-8669","contributorId":261309,"corporation":false,"usgs":false,"family":"Tian","given":"Richard","email":"","affiliations":[{"id":52807,"text":"U.S. Environmental Protection Agency Chesapeake Bay Program","active":true,"usgs":false}],"preferred":false,"id":824914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerco, Carl 0000-0001-7855-3287","orcid":"https://orcid.org/0000-0001-7855-3287","contributorId":261306,"corporation":false,"usgs":false,"family":"Cerco","given":"Carl","email":"","affiliations":[{"id":52804,"text":"U.S. Army Corps of Eng.","active":true,"usgs":false}],"preferred":false,"id":824915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bhatt, Gopal 0000-0002-6627-793X","orcid":"https://orcid.org/0000-0002-6627-793X","contributorId":252963,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":824916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Linker, Lewis C. 0000-0002-3456-3659","orcid":"https://orcid.org/0000-0002-3456-3659","contributorId":252964,"corporation":false,"usgs":false,"family":"Linker","given":"Lewis","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":824917,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824918,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227954,"text":"70227954 - 2022 - River floodplain abandonment and channel deepening coincide with the onset of clear-cut logging in a coastal California redwood forest","interactions":[],"lastModifiedDate":"2022-03-28T16:47:00.826195","indexId":"70227954","displayToPublicDate":"2021-02-02T09:48:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"River floodplain abandonment and channel deepening coincide with the onset of clear-cut logging in a coastal California redwood forest","docAbstract":"Changes in both land use and climate can alter the balance of transport capacity and sediment supply in rivers. Hence, the primary driver of recent incision or aggradation in alluvial channels is often unclear. The San Lorenzo River on the central coast of California is one location where both climate and land use—specifically, clear-cut forestry of coastal redwoods—could explain recent vertical incision and floodplain abandonment. At our field site on the San Lorenzo, we estimate the magnitude of recent incision using both the ratio of bankfull to critical Shields numbers (
Tungsten (W) is used in a variety of industrial and technological applications and has been identified as a critical mineral for the United States, India, the European Union, and other countries. These countries rely on W imports mostly from China, which leaves them vulnerable to supply disruption. Consequently, the U.S. government has a current initiative to understand domestic resource potential. The eastern Alaska portion of the Yukon-Tanana Upland (YTU), is prospective for W skarn deposits, the major source of global W supply. The regional geology consists of juxtaposed Paleozoic lithotectonic packages that were reaccreted to North America in the Mesozoic. Multiple subsequent episodes of arc-related magmatism intruded the lithotectonic packages, accompanied by W skarn formation mostly associated with 100–90 Ma intrusions; major W skarn deposits in Canada are part of the same metallogenic event (e.g., Mactung, Cantung). In this paper, we present an assessment for undiscovered W skarn resources for parts of the lesser-explored western (Alaskan) portion of the YTU.
We used GIS proximity analysis to map the intersection of pluton and carbonate-bearing rocks to define three permissive tracts for W skarn deposits. The permissive tracts were qualitatively assessed by mineral potential mapping using region-wide sediment geochemistry and mineral concentrate datasets. This analysis showed that much of the western YTU has high potential for undiscovered W skarn deposits, whereas the eastern and southern YTU had only isolated areas of medium to high potential. Historical production and the quality of the geochemistry data of the western YTU tract (ca. 9200 km2) permitted a quantitative assessment of undiscovered W resources. Probabilistic estimates by a panel of 20 experts predicted a 70% chance of one to three undiscovered W skarn deposits in the western YTU tract. The rationale for favorability employed by the expert panel included favorable lithology, previous production, clustering of previously mined deposits, W placers in the area, lack of recent exploration, pan concentrates containing W minerals, and W geochemical anomalies. Estimates were combined with a global grade and tonnage model for W skarns in a Monte Carlo simulation and provided a median estimate of undiscovered resources of 94 kt WO3. If the undiscovered W skarn deposits are located close to infrastructure (e.g., near Fairbanks, or close to roads and/or power grid), application of an economic filter indicates that the median total economically recoverable WO3 is 63 kt with a net present value (NPV) of $330 million USD (2008 dollars). Whereas if deposits are far from infrastructure, median recoverable WO3 is only 30 kt and the NPV is $44 million.
Our models for contained WO3 resources and NPV estimates for the western YTU tract are considerably lower than the known resources in skarns in adjacent areas in Canada. Estimates for the western YTU are also lower than preliminary estimates for undiscovered W skarn deposits in areas of the western conterminous United States. We speculate that lower permeability and continuity of favorable carbonate rock horizons in the relatively higher-grade metamorphic country rocks in the Alaska portion of the YTU may explain some of the differences in prospectivity. More detailed geologic mapping, modern geochemistry, and geophysical surveys are needed to refine the resource potential of the whole YTU. Regardless, quantitative mineral resource assessment provides a useful tool for making first-order regional estimates of undiscovered resources, identifying target areas for new data acquisition, and guiding research on the fundamental controls of district-scale metallogenic endowments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2020.106700","usgsCitation":"Case, G.N., Graham, G.E., Marsh, E.E., Taylor, R., Green, C.J., Brown, P.J., and Labay, K.A., 2022, Tungsten skarn potential of the Yukon-Tanana Upland, eastern Alaska, USA—A mineral resource assessment: Journal of Geochemical Exploration, v. 232, 106700, 21 p., https://doi.org/10.1016/j.gexplo.2020.106700.","productDescription":"106700, 21 p.","ipdsId":"IP-119358","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":449855,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gexplo.2020.106700","text":"Publisher Index Page"},{"id":436068,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TDKQE4","text":"USGS data release","linkHelpText":"Qualitative Mineral Potential Map of Tungsten Skarn in the Yukon-Tanana Uplands, Eastern Alaska, USA, 2021"},{"id":390107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.3359375,\n 63.35212928507874\n ],\n [\n -141.240234375,\n 63.35212928507874\n ],\n [\n -141.240234375,\n 67.23806155909902\n ],\n [\n -154.3359375,\n 67.23806155909902\n ],\n [\n -154.3359375,\n 63.35212928507874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"232","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":824473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":824474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marsh, Erin E. 0000-0001-5245-9532 emarsh@usgs.gov","orcid":"https://orcid.org/0000-0001-5245-9532","contributorId":1250,"corporation":false,"usgs":true,"family":"Marsh","given":"Erin","email":"emarsh@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":201948,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Green, Carlin J. 0000-0002-6557-6268 cjgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6557-6268","contributorId":193013,"corporation":false,"usgs":true,"family":"Green","given":"Carlin","email":"cjgreen@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Philip J. 0000-0002-2415-7462 pbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-2415-7462","contributorId":759,"corporation":false,"usgs":true,"family":"Brown","given":"Philip","email":"pbrown@usgs.gov","middleInitial":"J.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":824477,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":217714,"corporation":false,"usgs":true,"family":"Labay","given":"Keith","email":"klabay@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":824478,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232999,"text":"70232999 - 2022 - Diagenesis of Vera Rubin ridge, Gale crater, Mars from Mastcam multispectral images","interactions":[],"lastModifiedDate":"2022-07-15T14:02:52.557747","indexId":"70232999","displayToPublicDate":"2020-11-01T08:44:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"Diagenesis of Vera Rubin ridge, Gale crater, Mars from Mastcam multispectral images","docAbstract":"Images from the Mars Science Laboratory (MSL) mission of lacustrine sedimentary rocks of Vera Rubin ridge on “Mt. Sharp” in Gale crater, Mars, have shown stark color variations from red to purple to gray. These color differences crosscut stratigraphy and are likely due to diagenetic alteration of the sediments after deposition. However, the chemistry and timing of these fluid interactions is unclear. Determining how diagenetic processes may have modified chemical and mineralogical signatures of ancient Martian environments is critical for understanding the past habitability of Mars and achieving the goals of the MSL mission. Here we use visible/near-infrared spectra from Mastcam and ChemCam to determine the mineralogical origins of color variations in the ridge. Color variations are consistent with changes in spectral properties related to the crystallinity, grain size, and texture of hematite. Coarse-grained gray hematite spectrally dominates in the gray patches and is present in the purple areas, while nanophase and fine-grained red crystalline hematite are present and spectrally dominate in the red and purple areas. We hypothesize that these differences were caused by grain-size coarsening of hematite by diagenetic fluids, as observed in terrestrial analogs. In this model, early primary reddening by oxidizing fluids near the surface was followed during or after burial by bleaching to form the gray patches, possibly with limited secondary reddening after exhumation. Diagenetic alteration may have diminished the preservation of biosignatures and changed the composition of the sediments, making it more difficult to interpret how conditions evolved in the paleolake over time.
","language":"English","publisher":"AGU","doi":"10.1029/2019JE006322","usgsCitation":"Horgan, B.H., Johnson, J., Fraeman, A.A., Rice, M., Seeger, C., Bell, J., Bennett, K.A., Cloutis, E., Edgar, L.A., Frydenvang, J., Grotzinger, J.P., L’Haridon, J., Jacob, S., Mangold, N., Rampe, E.B., Rivera-Hernandez, F., Sun, V.Z., Thompson, L., and Wellington, D., 2022, Diagenesis of Vera Rubin ridge, Gale crater, Mars from Mastcam multispectral images: JGR Planets, v. 125, no. 11, e2019JE006322, 33 p., https://doi.org/10.1029/2019JE006322.","productDescription":"e2019JE006322, 33 p.","ipdsId":"IP-122293","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":449866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019je006322","text":"Publisher Index Page"},{"id":403787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gale crater, Mars, Mt. Sharp, Vera Rubin ridge","volume":"125","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Horgan, Briony H. N. 0000-0001-6314-9724","orcid":"https://orcid.org/0000-0001-6314-9724","contributorId":258276,"corporation":false,"usgs":false,"family":"Horgan","given":"Briony","email":"","middleInitial":"H. N.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":846638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Jeffrey R.","contributorId":71688,"corporation":false,"usgs":true,"family":"Johnson","given":"Jeffrey R.","affiliations":[],"preferred":false,"id":846639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fraeman, Abigail A.","contributorId":200404,"corporation":false,"usgs":false,"family":"Fraeman","given":"Abigail","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":846640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Melissa","contributorId":172306,"corporation":false,"usgs":false,"family":"Rice","given":"Melissa","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":846641,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seeger, Christina","contributorId":293198,"corporation":false,"usgs":false,"family":"Seeger","given":"Christina","affiliations":[{"id":12723,"text":"Western Washington University","active":true,"usgs":false}],"preferred":false,"id":846642,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bell, James F.","contributorId":174126,"corporation":false,"usgs":false,"family":"Bell","given":"James F.","affiliations":[{"id":27362,"text":"ASU SESE","active":true,"usgs":false}],"preferred":false,"id":846643,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bennett, Kristen A. 0000-0001-8105-7129","orcid":"https://orcid.org/0000-0001-8105-7129","contributorId":237068,"corporation":false,"usgs":true,"family":"Bennett","given":"Kristen","email":"","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":846644,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cloutis, Edward A.","contributorId":147771,"corporation":false,"usgs":false,"family":"Cloutis","given":"Edward A.","affiliations":[{"id":16930,"text":"University of Winnipeg","active":true,"usgs":false}],"preferred":false,"id":846645,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":846649,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Frydenvang, Jens","contributorId":173225,"corporation":false,"usgs":false,"family":"Frydenvang","given":"Jens","email":"","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":846646,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Grotzinger, John P.","contributorId":181502,"corporation":false,"usgs":false,"family":"Grotzinger","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":846650,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"L’Haridon, Jonas","contributorId":229498,"corporation":false,"usgs":false,"family":"L’Haridon","given":"Jonas","email":"","affiliations":[{"id":41660,"text":"Université de Nantes","active":true,"usgs":false}],"preferred":false,"id":846647,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Jacob, Samantha","contributorId":293199,"corporation":false,"usgs":false,"family":"Jacob","given":"Samantha","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":846651,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mangold, Nicolas","contributorId":52903,"corporation":false,"usgs":false,"family":"Mangold","given":"Nicolas","email":"","affiliations":[],"preferred":false,"id":846648,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rampe, Elizabeth B.","contributorId":229501,"corporation":false,"usgs":false,"family":"Rampe","given":"Elizabeth","email":"","middleInitial":"B.","affiliations":[{"id":27209,"text":"NASA Johnson Space Center","active":true,"usgs":false}],"preferred":false,"id":846656,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rivera-Hernandez, Frances","contributorId":270378,"corporation":false,"usgs":false,"family":"Rivera-Hernandez","given":"Frances","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":846652,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sun, Vivian Z. 0000-0003-1480-7369","orcid":"https://orcid.org/0000-0003-1480-7369","contributorId":237064,"corporation":false,"usgs":false,"family":"Sun","given":"Vivian","email":"","middleInitial":"Z.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":846653,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Thompson, Lucy","contributorId":200401,"corporation":false,"usgs":false,"family":"Thompson","given":"Lucy","affiliations":[],"preferred":false,"id":846654,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":846655,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70215192,"text":"70215192 - 2022 - Why let the dogs out? Exploring variables associated with dog confinement and general characteristics of the free-ranging owned-dog population in a peri-urban area","interactions":[],"lastModifiedDate":"2022-08-01T16:44:05.974441","indexId":"70215192","displayToPublicDate":"2020-09-27T08:04:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7152,"text":"Journal of Applied Animal Welfare Science","active":true,"publicationSubtype":{"id":10}},"title":"Why let the dogs out? Exploring variables associated with dog confinement and general characteristics of the free-ranging owned-dog population in a peri-urban area","docAbstract":"Free-ranging dogs (FRDs), are a problem in several countries, with impacts on humans, domestic animals, and wildlife, although increasing evidence suggests that most FRDs are owned. Therefore, understanding dog ownership on a fine scale is critical. The main objectives of this study were to explore dog management in rural localities from central Chile focusing on modeling owner-related variables associated with dog confinement and characterize confined and FRDs populations. Interviews (170) were carried out in Paine municipality, reporting a human:dog ratio of 1.5:1, and dogs in most households (85.9%, 146/170). Thirty-seven percent (54/146) of those households did not confine their dog(s) to some degree, and 41% (196/472) of surveyed dogs were FRD. Based on multivariable logistic regression models, non-confinement was decreased by (i) negative opinion of owners toward roaming behavior of their dogs, (ii) negative opinion toward FRDs, among others. Dog confinement increased along with owners' concerns about the impacts of their dogs on others. Owned-FRDs tended to have poorer general care than confined dogs. Our findings represent a contribution to the understanding of the human dimensions behind FRDs and provide critical quantitative elements to consider when planning effective control strategies.
The Sulphide Queen carbonatite deposit at Mountain Pass in southeast California is a world class rare earth element (REE) resource. This study images electrical resistivity structure of the REE deposit and surrounding area to characterize resources under cover. An east-west elongated grid (35 × 15 km) of 65 wideband magnetotelluric stations spanning from eastern Shadow Valley to eastern Ivanpah Valley were collected and modeled in three-dimensions (3-D). Gravity, aeromagnetic, and geologic data are used to inform interpretation of structures in the resistivity model, including the following observations. Shadow Valley is filled with conductive sediment that locally dips southward to a depth of 1 km. The Kingston Range-Halloran Hills detachment fault dips westward at ∼15 degrees. The REE deposit is a moderate low resistivity zone dipping southwest to a possible depth of ∼1 km, and is bounded by the North and South faults and bisected by the Middle fault. Ivanpah Dry Lake is underlain by a north striking southward dipping sedimentary basin. Two possible zones of mineralization are observed in Ivanpah Valley, one along the western edge of Ivanpah Dry Lake and one on the western edge of valley along a new inferred fault. The brittle-ductile transition is imaged at ∼10 km below mean sea level. No deep electrically conductive structures are imaged to be related to the REE deposit likely due to the complex geologic history of the Mojave terrane. Future studies should regional target Proterozoic rocks and search within for geophysical signatures similar to Mountain Pass.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC010029","usgsCitation":"Peacock, J., Denton, K., and Ponce, D.A., 2021, Three-dimensional electrical resistivity characterization of Mountain Pass, California and surrounding region: Geochemistry, Geophysics, Geosystems, v. 22, no. 11, e2021GC010029, 16 p., https://doi.org/10.1029/2021GC010029.","productDescription":"e2021GC010029, 16 p.","ipdsId":"IP-132719","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":449891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gc010029","text":"Publisher Index Page"},{"id":424329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mountain Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115,\n 36\n ],\n [\n -116,\n 36\n ],\n [\n -116,\n 35\n ],\n [\n -115,\n 35\n ],\n [\n -115,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-08","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":891967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denton, Kevin 0000-0001-9604-4021","orcid":"https://orcid.org/0000-0001-9604-4021","contributorId":207718,"corporation":false,"usgs":true,"family":"Denton","given":"Kevin","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":891968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891969,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218463,"text":"70218463 - 2021 - Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085","interactions":[],"lastModifiedDate":"2021-06-10T13:56:04.581613","indexId":"70218463","displayToPublicDate":"2024-01-01T10:12:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085","docAbstract":"Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic influences are unknown. While many studies have projected the effects of climate change using several modeling approaches, none has evaluated the impacts to fractional component cover at a 30-m resolution across the full sagebrush (Artemisia spp.) biome. We used fractional component cover data for rangeland functional groups and weather data from the 1985 to 2018 reference period in conjunction with soils and topography data to develop empirical models describing the spatiotemporal variation in component cover. To investigate the ramifications of future change across the western United States, we extended models based on historical relationships over the reference period to model landscape effects based on future weather conditions from two emission scenarios and three time periods (2020s, 2050s, and 2080s). We tested both generalized additive models (GAMs) and regression tree models, finding that the former led to superior spatial and statistical results. Our results indicate more xeric vegetation across most of the study area, with an increasing dominance of non-sagebrush shrubs, annual herbaceous cover, and bare ground over herbaceous and sagebrush cover in both the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. In general, both scenarios yielded similar results, but RCP 8.5 tended to be more extreme, with greater change relative to the reference period. Results demonstrate that in cool sites some degree of warming to growing season maximum temperature or nongrowing season minimum temperature could be beneficial to sagebrush and shrub growth. However, warming nongrowing season maximum temperature was beneficial to shrub, but not to sagebrush growth. Our results inform rangeland managers of potential future vegetation composition, cover, and species distributions, which could improve prioritization of conservation and restoration efforts.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3538","usgsCitation":"Rigge, M.B., Shi, H., and Postma, K., 2021, Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085: Ecosphere, v. 12, no. 6, e03538, 25 p., https://doi.org/10.1002/ecs2.3538.","productDescription":"e03538, 25 p.","ipdsId":"IP-120420","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449892,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3538","text":"Publisher Index Page"},{"id":436071,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P134RA6V","text":"USGS data release","linkHelpText":"Projections of Rangeland Fractional Component Cover Across Western Northern American Rangelands for Representative Concentration Pathways (RCP) 4.5 and 8.5 Scenarios for the 2020s, 2050s, and 2080s Time-Periods"},{"id":383685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.765625,\n 42.45588764197166\n ],\n [\n -103.35937499999999,\n 44.02442151965934\n ],\n [\n -104.2822265625,\n 44.653024159812\n ],\n [\n -102.9638671875,\n 45.521743896993634\n ],\n [\n -102.48046875,\n 47.87214396888731\n ],\n [\n -104.32617187499999,\n 48.1367666796927\n ],\n [\n -105.99609375,\n 48.980216985374994\n ],\n [\n -111.357421875,\n 48.951366470947725\n ],\n [\n -114.521484375,\n 47.42808726171425\n ],\n [\n -115.1806640625,\n 46.10370875598026\n ],\n [\n -115.57617187499999,\n 45.30580259943578\n ],\n [\n -117.5537109375,\n 47.39834920035926\n ],\n [\n -119.00390625,\n 48.83579746243093\n ],\n [\n -122.03613281249999,\n 44.84029065139799\n ],\n [\n -121.5087890625,\n 42.74701217318067\n ],\n [\n -121.2451171875,\n 40.38002840251183\n ],\n [\n -119.17968749999999,\n 36.87962060502676\n ],\n [\n -117.1142578125,\n 36.13787471840729\n ],\n [\n -113.90625,\n 35.06597313798418\n ],\n [\n -110.1708984375,\n 36.94989178681327\n ],\n [\n -109.599609375,\n 35.782170703266075\n ],\n [\n -108.06152343749999,\n 34.34343606848294\n ],\n [\n -105.9521484375,\n 35.209721645221386\n ],\n [\n -104.765625,\n 42.45588764197166\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":192768,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":817368,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":252852,"corporation":false,"usgs":true,"family":"Postma","given":"Kory","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":817369,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243274,"text":"70243274 - 2021 - Developing a set of indicators to identify, monitor, and track impacts and change in forests of the United States","interactions":[],"lastModifiedDate":"2023-05-05T12:06:05.676027","indexId":"70243274","displayToPublicDate":"2023-03-10T07:04:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Developing a set of indicators to identify, monitor, and track impacts and change in forests of the United States","docAbstract":"United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment (NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.
In the northern Gulf of Mexico, island restoration and creation have been used to mitigate potential negative effects of anthropogenic and environmental stressors to breeding seabirds. The long-term success of such projects can be enhanced when data are available to elucidate how site-specific and larger-scale factors may contribute to reproductive success. Nest-specific daily survival rate (DSR) of Eastern Brown Pelicans (Pelecanus occidentalis carolinensis) during incubation (i.e., pre-hatch; n = 245) and brood-rearing (i.e., post-hatch; n = 185) were measured at two breeding islands in the northern Gulf of Mexico USA in 2017 and 2018 in relation to macro- and micro- scale habitat and environmental measurements. DSR of nests during incubation ranged from 91-99%, and the DSR during brood-rearing exceeded 99% each year. Regional weather variables occurred in top-performing models more often and with more significance compared to microhabitat variables. Results suggest that reproductive success of Brown Pelicans may respond at least in part to weather factors that occur outside of the scope of habitat structure as it is typically incorporated into the restoration or creation of breeding habitat, indicating that climate conditions are likely an important factor in the success of restoration efforts.
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Guidance for estuary habitat recovery (e.g., Clancy et al., 2009) rests on a paradigm of restoring historic habitats and connectivity by simply removing or lowering levees assuming sediment delivery and accumulation will occur. Outcomes of several restoration projects and studies show that restoring “opportunity” for sediment delivery may not be enough. Improved knowledge and predictive models of land-use and climate change effects on sediment budgets, sediment properties, and coastal change can refine restoration guidance to evaluate quantitative expectations for sediment flux, composition, accumulation, and timing critical to achieving more effective recovery, resilience, and community support.
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In response to a request from the EPA in 2019, the U.S. Geological Survey (USGS) prepared cross sections and maps to provide more information about the hydrogeologic framework at and near the site and assist in improving the conceptual site model using water level and contaminant data collected by the EPA in 2020. The cross sections present lithologic correlations from available geophysical logs collected in wells from 2002 to 2014; they show alternating intervals of relatively elevated and reduced natural gamma activity that correspond to changes in lithology, with water-bearing zones and well screens commonly located at lithologic contacts, sometimes near thin coal seams. Water-bearing zones commonly are associated with fractures at or near lithologic contacts but also may be associated with fractures at or near apparent faulting. Recent (March 2020) water-level data shown on cross sections and maps indicate large downward vertical gradients and apparent radial gradients laterally to the northeast, northwest, and southwest that generally following topography. Recent (February to March 2020) data for TCE groundwater concentration shown on cross sections and maps indicate the highest TCE concentrations (greater than 3,000 μg/L and as much as 75,000 μg/L) and combined PFOA and PFOS concentrations (greater than 1,000 ng/L and up to at least 2,350 ng/L) are from shallow (less than 60 feet [ft] below land surface [bls]) and intermediate depth (60 to 100 ft bls) wells near the center of the former Chromatex Plant. TCE and PFAS (as combined PFOA and PFOS) contamination is present at greater depths, as much as 304 ft bls, as evidenced by samples collected from one well (a reconstructed former production well) near the plant, that contained concentrations of about 240 μg/L and 508 ng/L, respectively. The 2020 data also indicate that TCE and PFAS concentrations which exceed drinking-water MCL or HA levels are present in groundwater depths of less than 200 ft in an area that extends predominantly in a northeast direction from the former Chromatex Plant, and is apparently influenced by hydraulic gradients, lithology, and geologic structure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Fiore, A.R., and Bird, P.H., 2021, Hydrogeologic framework, water levels, and selected contaminant concentrations at Valmont TCE Superfund Site, Luzerne County, Pennsylvania, 2020 (ver. 1.1, August 2022): U.S. Geological Survey Open-File Report 2021–1093, 80 p., https://doi.org/10.3133/ofr20211093.","productDescription":"Report: xii, 80 p.; 17 Plates: 17.00 x 11.00 inches or smaller","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-128502","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":501527,"rank":21,"type":{"id":36,"text":"NGMDB Index 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215 Limekiln Road
New Cumberland, PA 17070-2424
Diminishing sea ice is impacting the wave field across the Arctic region. Recent observation- and model-based studies highlight the spatiotemporal influence of sea ice on offshore wave climatologies, but effects within the nearshore region are still poorly described. This study characterizes the wave climate in the central Beaufort Sea coast from 1979 to 2019 by utilizing a wave hindcast model that uses ERA5 winds, waves, and ice concentrations as input. The spectral wave model SWAN (Simulating Waves Nearshore) is calibrated and validated based on more than 10 000 in situ time point measurements collected over a 13-year time period across the region, with friction variations and empirical coefficients for newly implemented empirical ice formulations for the open-water and shoulder seasons. Model results and trends are analyzed over the 41-year time period using the non-parametric Mann–Kendall test, including an estimate of Sen's slope. The model results show that the reduction in sea ice concentration correlates strongly with increases in average and extreme wave conditions. In particular, the open-water season extended by ∼96 d over the 41-year time period (∼2.4 d yr−1), resulting in a 5-fold increase in the yearly cumulative wave power. Moreover, the open-water season extends later into the year, resulting in relatively more open-water conditions during fall storms with high wind speeds. The later freeze-up results in an increase in the annual offshore median wave heights of 1 % yr−1 and an increase in the average number of rough wave days (defined as days when maximum wave heights exceed 2.5 m) from 1.5 in 1979 to 13.1 d in 2019. Trends in the nearshore areas deviate from the patterns offshore. Model results indicate a saturation limit for high wave heights in the shallow areas of Foggy Island Bay. Similar patterns are found for yearly cumulative wave power.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/tc-16-1609-2022","usgsCitation":"Nederhoff, C.M., Erikson, L.H., Engelstad, A.C., Bieniek, P.A., and Kasper, J., 2021, The effect of changing sea ice on wave climate trends along Alaska's central Beaufort Sea coast: The Cryosphere, v. 16, p. 1609-1629, https://doi.org/10.5194/tc-16-1609-2022.","productDescription":"21 p.","startPage":"1609","endPage":"1629","ipdsId":"IP-130100","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-16-1609-2022","text":"Publisher Index Page"},{"id":400377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.4130859375,\n 68.92681148621786\n ],\n [\n -141.1083984375,\n 68.92681148621786\n ],\n [\n -141.1083984375,\n 71.10254274232307\n ],\n [\n -153.4130859375,\n 71.10254274232307\n ],\n [\n -153.4130859375,\n 68.92681148621786\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":842511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":842512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Anita C 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":268303,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita","email":"","middleInitial":"C","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":842513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bieniek, Peter A.","contributorId":210907,"corporation":false,"usgs":false,"family":"Bieniek","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":842514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kasper, Jeremy L. 0000-0003-0975-6114","orcid":"https://orcid.org/0000-0003-0975-6114","contributorId":208630,"corporation":false,"usgs":false,"family":"Kasper","given":"Jeremy L.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":842515,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230006,"text":"70230006 - 2021 - Towards improving an Area of Concern: Main-channel habitat rehabilitation priorities for the Maumee River","interactions":[],"lastModifiedDate":"2022-03-23T13:44:44.222736","indexId":"70230006","displayToPublicDate":"2022-03-23T08:20:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Towards improving an Area of Concern: Main-channel habitat rehabilitation priorities for the Maumee River","docAbstract":"The Maumee River watershed in the Laurentian Great Lakes Basin has been impacted by decades of pollution and habitat modification due to human settlement and development. As such, the lower 35 km of the Maumee River and several smaller adjacent watersheds comprising over 2000 km2 were designated the Maumee Area of Concern (AOC) under the revised Great Lakes Water Quality Agreement in 1987. As part of pre-rehabilitation assessments in the Maumee AOC, we assessed fish and invertebrate communities in river km 24–11 of the Maumee River to identify: 1) areas that exhibit the highest biodiversity, 2) habitat characteristics associated with high biodiversity areas, 3) areas in need of protection from further degradation, and 4) areas that could feasibly be rehabilitated to increase biodiversity. Based on benthic trawl data, shallow water habitats surrounding large island complexes had the highest fish diversity and catch per unit effort (CPUE). Electrofishing displayed similar fish diversity and CPUE patterns across habitat types early in the study but yielded no discernable fish diversity or CPUE patterns towards the end of our study. Although highly variable among study sites, macroinvertebrate density was greatest in shallow water habitats <2.5 m and around large island complexes. Our results provide valuable baseline data that could act as a foundation for developing rehabilitation strategies in the lower Maumee River and for assessing the effectiveness of future aquatic habitat rehabilitation projects. In addition to increasing in-channel habitat, watershed-scale improvements of water quality might be necessary to ensure rehabilitation strategies are successful.
Habitat loss is a main contributor to fish fauna declines in the southwestern USA. Several studies have defined stream-specific habitat conditions that support the growth and survival of native fish in Arizona to inform stream restoration efforts, yet general habitat use of most individual species across the region is not established. Therefore, we evaluated habitat use of four native fishes, Speckled Dace Rhinichthys osculus, Sonora Sucker Catostomus insignis, Desert Sucker Catostomus clarkii, and Longfin Dace Agosia chrysogaster, across three Arizona streams through the development of habitat suitability criteria (HSC). We developed both stream-specific and generalized HSC for each species. Generalized HSC were calculated as the combination of stream-specific HSC for each species. We then assessed the utility of generalized HSC through transferability among study streams. Also, past HSC studies have not considered the occurrence of nonnative species, so we tested whether the presence of nonnative fishes influenced native fish habitat use through logistic regression models. Fish and habitat data were collected along the Mogollon Rim in Arizona during the 2017 summer field season at base flow conditions. We established minimum microhabitat use for four native Arizona fish species through developing HSC. Most generalized criteria did not transfer among study streams due to variation in habitat availability and fish community structure. Logistic regression analysis showed that the presence of nonnative fishes was inversely related to the presence of two native fish species, which could have influenced habitat use of both species. The lack of transferability across streams as demonstrated in this study confirms that only HSC developed in the stream of interest or in similar undegraded streams with comparable fish communities should be used for restoration efforts. For projects to restore native fishes in streams where nonnative competitors will not dominate, the least degraded similar streams without coexisting nonnative fishes can guide restoration efforts.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10575","usgsCitation":"Nemec, Z.C., Lee, L.N., and Bonar, S.A., 2021, Development and evaluation of habitat suitability criteria for native fishes in three Arizona streams: North American Journal of Fisheries Management, v. 41, no. 3, p. 661-677, https://doi.org/10.1002/nafm.10575.","productDescription":"17 p.","startPage":"661","endPage":"677","ipdsId":"IP-125478","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Blue River, Eagle Creek, Tonto Creek, Verde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.26904296874999,\n 32.82421110161336\n ],\n [\n -109.072265625,\n 32.82421110161336\n ],\n [\n -109.072265625,\n 35.40696093270201\n ],\n [\n -113.26904296874999,\n 35.40696093270201\n ],\n [\n -113.26904296874999,\n 32.82421110161336\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Nemec, Zach C.","contributorId":288222,"corporation":false,"usgs":false,"family":"Nemec","given":"Zach","email":"","middleInitial":"C.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":837576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Larissa N.","contributorId":288223,"corporation":false,"usgs":false,"family":"Lee","given":"Larissa","email":"","middleInitial":"N.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":837577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837575,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229418,"text":"70229418 - 2021 - Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone","interactions":[],"lastModifiedDate":"2022-03-07T14:55:02.548792","indexId":"70229418","displayToPublicDate":"2022-03-07T08:43:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10266,"text":"Geosphere (Geological Society of America)","active":true,"publicationSubtype":{"id":10}},"title":"Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone","docAbstract":"Lithology and microfossil biostratigraphy beneath the marshes of a central Oregon estuary limit geophysical models of Cascadia megathrust rupture during successive earthquakes by ruling out >0.5 m of coseismic coastal subsidence for the past 2000 yr. Although the stratigraphy in cores and outcrops includes as many as 12 peat-mud contacts, like those commonly inferred to record subsidence during megathrust earthquakes, mapping, qualitative diatom analysis, foraminiferal transfer function analysis, and 14C dating of the contacts failed to confirm that any contacts formed through subsidence during great earthquakes. Based on the youngest peat-mud contact’s distinctness, >400 m distribution, ∼0.6 m depth, and overlying probable tsunami deposit, we attribute it to the great 1700 CE Cascadia earthquake and(or) its accompanying tsunami. Minimal changes in diatom assemblages from below the contact to above its probable tsunami deposit suggest that the lower of several foraminiferal transfer function reconstructions of coseismic subsidence across the contact (0.1–0.5 m) is most accurate. The more limited stratigraphic extent and minimal changes in lithology, foraminifera, and(or) diatom assemblages across the other 11 peat-mud contacts are insufficient to distinguish them from contacts formed through small, gradual, or localized changes in tide levels during river floods, storm surges, and gradual sea-level rise. Although no data preclude any contacts from being synchronous with a megathrust earthquake, the evidence is equally consistent with all contacts recording relative sea-level changes below the ∼0.5 m detection threshold for distinguishing coseismic from nonseismic changes.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02254.1","usgsCitation":"Nelson, A., Hawkes, A.D., Sawai, Y., Hotron, B.P., Witter, R., Bradley, L., and Cahill, N., 2021, Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone: Geosphere (Geological Society of America), v. 17, p. 171-200, https://doi.org/10.1130/GES02254.1.","productDescription":"30 p.","startPage":"171","endPage":"200","ipdsId":"IP-120011","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":449915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02254.1","text":"Publisher Index Page"},{"id":396786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"central Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -132.275390625,\n 38.34165619279595\n ],\n [\n -122.08007812499999,\n 38.34165619279595\n ],\n [\n -122.08007812499999,\n 52.64306343665892\n ],\n [\n -132.275390625,\n 52.64306343665892\n ],\n [\n -132.275390625,\n 38.34165619279595\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2020-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Alan 0000-0001-7117-7098","orcid":"https://orcid.org/0000-0001-7117-7098","contributorId":216700,"corporation":false,"usgs":true,"family":"Nelson","given":"Alan","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":837343,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawkes, Andrea D.","contributorId":192811,"corporation":false,"usgs":false,"family":"Hawkes","given":"Andrea","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837344,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawai, Yuki","contributorId":127509,"corporation":false,"usgs":false,"family":"Sawai","given":"Yuki","email":"","affiliations":[{"id":6981,"text":"National Institute of Advanced Industrial Science and Technology, AIST, Japan","active":true,"usgs":false}],"preferred":false,"id":837345,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hotron, Ben P.","contributorId":288083,"corporation":false,"usgs":false,"family":"Hotron","given":"Ben","email":"","middleInitial":"P.","affiliations":[{"id":61708,"text":"Earth Observatory of Singapore and Asian School of the Environment, Nanyang Technological University, 639798, Singapore","active":true,"usgs":false}],"preferred":false,"id":837346,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":837347,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bradley, Lee-Ann","contributorId":193406,"corporation":false,"usgs":false,"family":"Bradley","given":"Lee-Ann","affiliations":[],"preferred":false,"id":837348,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cahill, Niamh","contributorId":150754,"corporation":false,"usgs":false,"family":"Cahill","given":"Niamh","email":"","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false},{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":837349,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229054,"text":"70229054 - 2021 - Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands","interactions":[],"lastModifiedDate":"2022-02-28T15:54:29.873881","indexId":"70229054","displayToPublicDate":"2022-02-28T09:41:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands","docAbstract":"Forests are often affected by management that could influence demographics of breeding and post-breeding birds that reside within. Numerous studies have focused on immediate effects from management on wildlife soon after forestry treatment (e.g., 0–5 years), however, fewer studies have examined changes in focal species abundance over longer durations as a forest regenerates after disturbance. We examined how forest management influenced 18 conifer-associated birds during breeding and post-breeding over the forest regeneration period in a landscape dominated by forestry. To achieve this, we paired avian detection data from point count surveys in lowland conifer and mixed-wood forests with Bayesian distance-removal models and an information-theoretic framework. We estimated abundance and associations with seven common forestry treatment categories applied at the stand scale, years-since-harvest (YSH; 5–120+), and seven vegetation variables measured within stands. Forestry treatment categories and YSH were poor predictors of abundance, and none of the 14 species with good-fitting models had associations with these covariates. Twelve of 13 species with good-fitting models had important associations between abundance and vegetation variables. All vegetation variables were associated with abundance of some species, irrespective of the forestry treatment in which the site occurred, including spruce-fir tree composition (seven species), tree basal area (six species), midstory cover (five species), live crown ratio (three species), shrub cover (three species), tree diameter at breast height (two species), and shrub composition (one species). In a companion study, several species assemblages were associated with vegetation variables (i.e., spruce-fir tree composition, tree basal area, and tree diameter at breast height) that varied with YSH and forestry treatments, suggesting that some forestry treatments may indirectly influence avian abundance when certain vegetation outcomes are achieved. Our results suggest that managers should target species-specific vegetation outcomes rather than more broadly categorized forestry treatment types when managing for individual focal species because of large variations in vegetative outcomes across stands within a forest treatment category. Our study informs management and conservation of biodiversity in regions such as the Atlantic Northern Forest where commercial forestry is the dominant human land use.","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119708","usgsCitation":"Rolek, B.W., Harrison, D.J., Linden, D.W., Loftin, C., and Wood, P.B., 2021, Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands: Forest Ecology and Management, v. 502, p. 1-15, https://doi.org/10.1016/j.foreco.2021.119708.","productDescription":"119708, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-123856","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119708","text":"Publisher Index 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management and the basis for Lake Erie’s Fish Community Goals and Objectives (FCOs) developed in 2020 (Francis et al. 2020). The 2021 USGS Lake Erie Biological Station annual report is responsive to these FCOs and the U.S. Geological Survey (USGS) obligations via a Memorandum of Understanding (MOU) in 2004 with the GLFC Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies were to monitor long-term changes in the fish community and population dynamics of key fishes of interest to management agencies (MOU 2004). Specific to Lake Erie, expectations of the MOU were sustained investigations of native percids, forage (prey) fish populations, and Lake Trout. Additionally, this work was conducted under the authority of the Great Lakes Fishery Research Authorization Act of 2019.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Dufour, M.R., Hilling, C.D., Keretz, K.R., Kraus, R.T., Oldham, R.C., Roberts, J., and Schmitt, J., 2021, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2021, 17 p.","productDescription":"17 p.","ipdsId":"IP-138896","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":425702,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://www.glfc.org/"},{"id":425722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.94328472654291,\n 42.45303920377259\n ],\n [\n -83.94328472654291,\n 41.175748153693036\n ],\n [\n -82.10856792966806,\n 41.175748153693036\n ],\n [\n -82.10856792966806,\n 42.45303920377259\n ],\n [\n -83.94328472654291,\n 42.45303920377259\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keretz, Kevin R. 0000-0002-4808-8350 kkeretz@usgs.gov","orcid":"https://orcid.org/0000-0002-4808-8350","contributorId":5859,"corporation":false,"usgs":true,"family":"Keretz","given":"Kevin","email":"kkeretz@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":894892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Richard T. 0000-0001-5280-6530 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0001-5280-6530","contributorId":334185,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldham, Richard Cole 0000-0002-2331-7612","orcid":"https://orcid.org/0000-0002-2331-7612","contributorId":294345,"corporation":false,"usgs":true,"family":"Oldham","given":"Richard","email":"","middleInitial":"Cole","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894898,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, James J. 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":894897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894894,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227786,"text":"70227786 - 2021 - Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization","interactions":[],"lastModifiedDate":"2022-01-31T15:46:01.075703","indexId":"70227786","displayToPublicDate":"2022-01-31T09:32:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10069,"text":"Frontiers in Soil Science","active":true,"publicationSubtype":{"id":10}},"title":"Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization","docAbstract":"Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected (U.S. Department of Agriculture, U.S. Environmental Protection Agency) and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset (NLCD). This allowed application of an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils less than 6%OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils greater than 6%OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm-3, ~56% mean RMSE). Disaggregation by vegetation type (woody v. emergent herbaceous), or region did not alter the breakpoint significantly (6% OC) nor improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered, harmonized CONUS wetland soil maps have now revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland, organic wetland soils, (35% lower than SSURGO-derived SOC stocks). Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0-30 cm and 30-100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). 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