The Annual Brome Adaptive Management (ABAM) project is a consortium of seven parks in the Northern Great Plains working together to better understand how to control invasive annual grasses (including Bromus species) through an adaptive management approach. This approach is supported by a quantitative model that uses current data from standardized vegetation monitoring plots in all seven parks to annually update the model's parameters and predictions regarding the effects of different management actions on invasive annual grasses and other components of the mixed-grass prairie plant community. This updating is called \"learning.\" Currently, the ABAM model has little information about the effects of the herbicide indaziflam, applied alone or together with the herbicide imazapic, at different times during the growing season, on target invasive annual grasses and other components of the vegetation. The purpose of this study is to increase the amount of information about this herbicide and therefore accelerate the rate of learning accomplished in the adaptive management cycle.
","language":"English","publisher":"National Park Service","usgsCitation":"Symstad, A., 2022, Fort Laramie National Historic Site 2022 ABAM Investigator Annual Report: Annual Report, 3 p.","productDescription":"3 p.","ipdsId":"IP-152071","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":415836,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/RPRS/IAR/Profile/573318","linkFileType":{"id":5,"text":"html"}},{"id":426324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Fort Laramie National Historic Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.56770704017384,\n 42.210821091328\n ],\n [\n -104.56770704017384,\n 42.19287280305102\n ],\n [\n -104.52383325080481,\n 42.19287280305102\n ],\n [\n -104.52383325080481,\n 42.210821091328\n ],\n [\n -104.56770704017384,\n 42.210821091328\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":869741,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70242767,"text":"70242767 - 2022 - Supplemental vegetation monitoring plots at Wind Cave National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","interactions":[],"lastModifiedDate":"2024-03-05T16:41:17.236689","indexId":"70242767","displayToPublicDate":"2022-12-01T09:55:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":7577,"text":"Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Supplemental vegetation monitoring plots at Wind Cave National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","docAbstract":"The Annual Brome Adaptive Management (ABAM) project is a consortium of seven parks in the Northern Great Plains (NGP) working together to better understand how to control invasive annual grasses (including Bromus species) through an adaptive management approach. This approach is supported by a quantitative model that uses current data from standardized vegetation monitoring plots in all seven parks to annually update the model’s parameters and predictions regarding the effects of different management actions on invasive annual grasses and other components of the mixed-grass prairie plant community. This updating of the model is called “learning.”
The ABAM model includes treatments in which the herbicides indaziflam and imazapic are applied alone or in combination with or without a prescribed fire preceding or following their application. However, the original ABAM model did not have field data for the effects of those treatments on target invasive annual grasses and other components of the vegetation in conditions like those that frequently occur in ABAM parks (i.e., ungrazed). The purpose of this study is to increase the amount of information about these treatments and therefore accelerate the rate of learning accomplished in the adaptive management cycle.
","language":"English","publisher":"National Park Service","usgsCitation":"Symstad, A., and Richardson, T., 2022, Supplemental vegetation monitoring plots at Wind Cave National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model: Annual Report, 4 p.","productDescription":"4 p.","ipdsId":"IP-152075","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":415835,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/RPRS/IAR/Profile/573317","linkFileType":{"id":5,"text":"html"}},{"id":426321,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Wind Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.49938816977327,\n 43.633694711675986\n ],\n [\n -103.5024470931983,\n 43.55944982284214\n ],\n [\n -103.52141241843475,\n 43.54383294926339\n ],\n [\n -103.52202420312013,\n 43.52023368800823\n ],\n [\n -103.45748091884613,\n 43.51534889803226\n ],\n [\n -103.45657280095472,\n 43.53372182682589\n ],\n [\n -103.43975828125109,\n 43.5356829718161\n ],\n [\n -103.44038918420713,\n 43.58528414983906\n ],\n [\n -103.41717960271762,\n 43.58473704935065\n ],\n [\n -103.41664429111844,\n 43.56545928494155\n ],\n [\n -103.38062546778514,\n 43.56346760240271\n ],\n [\n -103.38009015618593,\n 43.56812536845743\n ],\n [\n -103.36058951934952,\n 43.56694573600919\n ],\n [\n -103.3588306383801,\n 43.592360590123576\n ],\n [\n -103.34185361336944,\n 43.59180514516018\n ],\n [\n -103.33680638971731,\n 43.60876454212675\n ],\n [\n -103.33665344354603,\n 43.62660987781496\n ],\n [\n -103.3485832449052,\n 43.62505981511873\n ],\n [\n -103.35317163004311,\n 43.63059510828057\n ],\n [\n -103.43729202423958,\n 43.63170210623065\n ],\n [\n -103.45365726456492,\n 43.640225573499706\n ],\n [\n -103.48019342527975,\n 43.641830445497\n ],\n [\n -103.49938816977327,\n 43.633694711675986\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":869740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, Timm","contributorId":334581,"corporation":false,"usgs":false,"family":"Richardson","given":"Timm","email":"","affiliations":[],"preferred":false,"id":895967,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238720,"text":"70238720 - 2022 - PHREEQ-N-AMDTreat+REYs water-quality modeling tools to evaluate acid mine drainage treatment strategies for recovery of rare-earth elements","interactions":[],"lastModifiedDate":"2024-02-23T16:04:23.105819","indexId":"70238720","displayToPublicDate":"2022-12-01T09:55:30","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"PHREEQ-N-AMDTreat+REYs water-quality modeling tools to evaluate acid mine drainage treatment strategies for recovery of rare-earth elements","docAbstract":"The PHREEQ-N-AMDTreat+REYs water-quality modeling tools have the fundamental capability to simulate aqueous chemical reactions and predict the formation of metal-rich solids during the treatment of acid mine drainage (AMD). These new user-friendly, publicly available tools were expanded from the PHREEQ-N-AMDTreat tools to include the precipitation of rare-earth elements plus yttrium (REYs) and the adsorption of REYs onto hydrous Fe, Al, and Mn oxides. The tool set consists of a caustic titration model that indicates equilibrium surface and aqueous speciation of REYs as functions of pH and caustic agent, and a kinetics+adsorption model that simulates progressive changes in pH, major ions, and REYs in water and solids during sequential steps through passive and/or active treatment. Each model has a user interface (UI) that facilitates the input of water-quality data and adjustment to geochemical or treatment system variables; for example, retention time and aeration rate are adjustable parameters in the kinetics model. On-screen graphs display results of changes in metals and associated solute concentrations as functions of pH or retention time; details are summarized in output tables. A goal of such modeling is to identify strategies that could produce a concentrated REYs extract from AMD or mine waste leachate. For example, if REYs could be concentrated after first removing substantial Fe and Al, the final REYs-bearing phase(s) could be more efficiently processed for REYs recovery and, therefore, may represent a more valuable commodity. Preliminary modeling supports the hypothesis that Fe and Al can be removed at pH < 5.5 using conventional sequential oxidation and neutralization treatment processes without removing REYs, and that further increasing pH can promote the adsorption of REYs by hydrous Mn oxides. Alternatively, chemicals such as oxalate or phosphate may be added to precipitate REYs compounds following initial steps to decrease Fe and Al concentrations. The aqueous geochemical model framework is comprehensive and permits evaluation of effects from interactive chemical and physical variables. Field studies that demonstrate REYs attenuation from AMD and corresponding solid-phase formation during specific treatment steps plus laboratory studies of aqueous/solid interactions are helpful to corroborate, refine, and constrain modelin parameters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 12th International Conference on Acid Mine Drainage","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th International Conference on Acid Mine Drainage","conferenceDate":"September 18-24, 2022","language":"English","publisher":"University of Queensland","usgsCitation":"Cravotta, C., 2022, PHREEQ-N-AMDTreat+REYs water-quality modeling tools to evaluate acid mine drainage treatment strategies for recovery of rare-earth elements, in Proceedings of the 12th International Conference on Acid Mine Drainage, September 18-24, 2022, p. 788-804.","productDescription":"7 p.","startPage":"788","endPage":"804","ipdsId":"IP-137202","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":410097,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://smi.uq.edu.au/conferences/international-conference-acid-rock-drainage-2022","linkFileType":{"id":5,"text":"html"}},{"id":425945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":858359,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263323,"text":"70263323 - 2022 - Understory structure and heterospecifics influence the occupancy of a ground-nesting species of conservation concern, the Canada Warbler","interactions":[],"lastModifiedDate":"2025-02-06T16:00:27.11247","indexId":"70263323","displayToPublicDate":"2022-12-01T09:54:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Understory structure and heterospecifics influence the occupancy of a ground-nesting species of conservation concern, the Canada Warbler","docAbstract":"Forest structure and composition in eastern U.S. forests are changing because of forest regeneration after farmland abandonment, less frequent occurrence of severe disturbances, and climate change. Some of these changes may disproportionally affect birds that rely on gap dynamics or other forest canopy disturbances to create understory habitat. The Canada Warbler (Cardellina canadensis) is one such understory specialist that has undergone consistent declines. We assessed environmental and interspecific factors associated with Canada Warbler space use in its southern breeding distribution to understand potential causes of population declines and inform conservation efforts. We evaluated Canada Warbler occupancy from 840 point count surveys conducted in 2017 and 2018 at 470 unique locations (79% of locations surveyed in both years) throughout Monongahela National Forest, West Virginia, USA. We modeled Canada Warbler occupancy probability as a function of environmental variables and included Black-throated Blue Warbler (Setophaga caerulescens) and Hermit Thrush (Catharus guttatus) as interacting species because all three species exhibit similar habitat preferences. Canada Warblers were most likely to occur in areas with rhododendron (Rhododendron maximum) density > 0.27 stems/m² and within 3 m of riparian areas (streams and wetlands). They were also more likely to occur in mid-elevation (highest occupancy at 930 m) northern hardwood forests when Black-throated Blue Warblers were also present. Black-throated Blue Warblers were most likely to occupy mid-elevation sites with high shrub density, whereas Hermit Thrushes were more likely to occupy high-elevation, old-age forests. Potential management actions could focus on conserving riparian areas in northern hardwood forests, especially those with dense rhododendron thickets. Such potential actions could also be beneficial across the entire elevation range we explored within the region (500–1300 m). Canada Warblers may be benefiting from the recent spread of rhododendron habitats and northern hardwood forest types within West Virginia.
","language":"English","publisher":"Resilience Alliance Publications","doi":"10.5751/ace-02079-170120","usgsCitation":"Dimmig, G., Rota, C., Wood, P.B., and Lituma, C., 2022, Understory structure and heterospecifics influence the occupancy of a ground-nesting species of conservation concern, the Canada Warbler: Avian Conservation and Ecology, v. 17, no. 1, 20, 16 p., https://doi.org/10.5751/ace-02079-170120.","productDescription":"20, 16 p.","ipdsId":"IP-123270","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":487031,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02079-170120","text":"Publisher Index Page"},{"id":481747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United sTates","state":"West Virginia","otherGeospatial":"Monongahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.58889045378262,\n 37.463537077379684\n ],\n [\n -80.3485425975682,\n 37.51121532612136\n ],\n [\n -79.62749902892529,\n 38.552413228981266\n ],\n [\n -79.33707870266615,\n 38.442685588318085\n ],\n [\n -78.8463684962287,\n 39.0674460432719\n ],\n [\n -79.48729611280025,\n 39.1995023289459\n ],\n [\n -79.48729611280025,\n 39.455139390658985\n ],\n [\n -79.8978903671664,\n 39.37777230668391\n ],\n [\n -81.23983256436323,\n 37.88364086385796\n ],\n [\n -81.01951369616684,\n 37.54298391685913\n ],\n [\n -80.58889045378262,\n 37.463537077379684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dimmig, Gordon W.","contributorId":350556,"corporation":false,"usgs":false,"family":"Dimmig","given":"Gordon W.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":926339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rota, Christopher T.","contributorId":350557,"corporation":false,"usgs":false,"family":"Rota","given":"Christopher T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":926340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Petra B. 0000-0002-8575-1705 pbwood@usgs.gov","orcid":"https://orcid.org/0000-0002-8575-1705","contributorId":199090,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":926338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lituma, Christopher M.","contributorId":350558,"corporation":false,"usgs":false,"family":"Lituma","given":"Christopher M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":926341,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238719,"text":"70238719 - 2022 - Determination and prediction of rare earth element eeochemical associations in acid mine drainage treatment wastes","interactions":[],"lastModifiedDate":"2024-02-23T15:37:15.333066","indexId":"70238719","displayToPublicDate":"2022-12-01T09:36:32","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Determination and prediction of rare earth element eeochemical associations in acid mine drainage treatment wastes","docAbstract":"Acid mine drainage (AMD) has been proposed by various researchers as a novel source of rare earth elements (REE), a group of elements that include critical metals for clean energy and modern technologies. REE tend to be sequestered in the Fe-Al-Mn-rich solids produced during the treatment of AMD. These solids are typically managed as waste, but could be a low-cost, readily available REE source. Here, results from field sampling, solids characterization, and geochemical modeling are presented to identify the mechanism(s) of REE attenuation and determine the minerals/solid phases in AMD solids that are enriched in REE.
This study reveals that solids produced from low-pH AMD that was passively treated by limestone contain elevated concentrations of REE with Al, Fe, and/or Mn. AMD solid characterization via sequential extraction demonstrated that Al and Mn oxides were more abundant than Fe oxides and that the REEs are mainly associated with Al/Mn phases. Additionally, sequential extractions demonstrate that for the AMD solids evaluated, acidic and/or reducing extractions are required to mobilize the REE. Finally, the “CausticTitrationREYs.exe” geochemical equilibrium model demonstrated in this study indicates that the observed dissolved REE attenuation can be explained via surface complexation on Fe, Al, and Mn oxides/hydroxides and not by REE compound precipitation. The model accurately predicts the pH dependent removal of dissolved REE and that Al and Mn oxides/hydroxides are largely responsible for dissolved REE removal for the systems evaluated. The modeling results are consistent with the characterization results that show that Al and Mn hydroxides are important hosting phases of REEs in AMD treatment systems.
The results presented here can be used to identify conditions favorable for accumulation of REE-enriched AMD solids and possible chemical treatment(s) to mobilize REE. The geochemical model can be applied to active and/or passive AMD treatment systems to predict REE attenuation with Fe, Al, and Mn during treatment and what phases may be enriched in REE. This information can be used to engineer AMD systems to produce specific phases enriched in REE. The recovery of REE from AMD solids is an opportunity to transform the environmental and economical challenge of polluted mine drainage into an asset.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 12th International Conference on Acid Rock Drainage","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th International Conference on Acid Rock Drainage","conferenceDate":"September 18-24, 2022","language":"English","publisher":"University of Queensland","usgsCitation":"Hedin, B., Cravotta, C., Stuckman, M., Lopano, C., Capo, R., and Hedin, R., 2022, Determination and prediction of rare earth element eeochemical associations in acid mine drainage treatment wastes, in Proceedings of the 12th International Conference on Acid Rock Drainage, September 18-24, 2022, p. 626-633.","productDescription":"8 p.","startPage":"626","endPage":"633","ipdsId":"IP-141129","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":425944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":410096,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://smi.uq.edu.au/conferences/international-conference-acid-rock-drainage-2022"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hedin, B.C.","contributorId":299679,"corporation":false,"usgs":false,"family":"Hedin","given":"B.C.","email":"","affiliations":[{"id":64931,"text":"Hedin Environmental Inc.","active":true,"usgs":false}],"preferred":false,"id":858353,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":858354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stuckman, M.Y.","contributorId":299680,"corporation":false,"usgs":false,"family":"Stuckman","given":"M.Y.","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":858355,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopano, C.L.","contributorId":299681,"corporation":false,"usgs":false,"family":"Lopano","given":"C.L.","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":858356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Capo, R.C.","contributorId":299682,"corporation":false,"usgs":false,"family":"Capo","given":"R.C.","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":858357,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hedin, R.S.","contributorId":299683,"corporation":false,"usgs":false,"family":"Hedin","given":"R.S.","email":"","affiliations":[{"id":64931,"text":"Hedin Environmental Inc.","active":true,"usgs":false}],"preferred":false,"id":858358,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230030,"text":"70230030 - 2022 - Geologic setting and geomorphic history of La Botica and surrounding area","interactions":[],"lastModifiedDate":"2026-03-18T14:06:14.010431","indexId":"70230030","displayToPublicDate":"2022-12-01T08:54:09","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":23617,"text":"Research Contributions","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"115","title":"Geologic setting and geomorphic history of La Botica and surrounding area","docAbstract":"La Botica is located on the gently east-dipping marginal area between the high San Juan Mountains to the west and the San Luis Basin to the east in south-central Colorado. The site is positioned on a topographic bench perched about 70 to 80 m above La Jara Creek (figure 2.1), a tributary to the Rio Grande. The unique floral assemblage at La Botica has resulted in intermittent occupation over the last several thousand years. The physical environment supporting this assemblage is a result of Quaternary surface processes that have modified the underlying Tertiary bedrock. Underlying bedrock at the site consists of Oligocene to Pliocene volcanic and sedimentary deposits related to the Rio Grande rift and the San Juan volcanic locus of the Southern Rocky Mountains volcanic field. Local bedrock is mildly deformed by normal faulting and eastward tilting due to the onset of Oligocene extensional deformation and initial formation of the San Luis Basin. The geomorphic evolution and incision history of La Jara Creek are directly linked to middle to late Pleistocene evolution of the Rio Grande and to regional alpine glacial cycles over the last 500 k.y. (thousand years). Subsequent degradation of surrounding bedrock and development of mass-wasting deposits, such as landslides and talus slopes, have strongly influenced the incision history of La Jara Creek and the local environment at La Botica. In addition, local talus slopes and blockfields can host processes that actively modify the local environment, and these processes may have contributed to establishment of the floral assemblage.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Multidisciplinary research at the La Botica site, Conjeos County, Colorado","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Paleocultural Research Group","usgsCitation":"Turner, K.J., Ruleman, C.A., and Mahan, S.A., 2022, Geologic setting and geomorphic history of La Botica and surrounding area: Research Contributions 115, 20 p.","productDescription":"20 p.","startPage":"9","endPage":"28","ipdsId":"IP-123501","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501237,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501236,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://paleocultural.org/Research/la-botica/"}],"country":"United States","state":"Colorado","otherGeospatial":"La Botica","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.79256393659836,\n 37.70573798045494\n ],\n [\n -106.79256393659836,\n 37.02301823232551\n ],\n [\n -105.74755024473933,\n 37.02301823232551\n ],\n [\n -105.74755024473933,\n 37.70573798045494\n ],\n [\n -106.79256393659836,\n 37.70573798045494\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Turner, Kenzie J. 0000-0002-4940-3981 kturner@usgs.gov","orcid":"https://orcid.org/0000-0002-4940-3981","contributorId":496,"corporation":false,"usgs":true,"family":"Turner","given":"Kenzie","email":"kturner@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruleman, Chester A. 0000-0002-1503-4591 cruleman@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-4591","contributorId":1264,"corporation":false,"usgs":true,"family":"Ruleman","given":"Chester","email":"cruleman@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838790,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242763,"text":"70242763 - 2022 - Supplemental vegetation monitoring plots at Badlands National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","interactions":[],"lastModifiedDate":"2024-03-05T15:12:59.931116","indexId":"70242763","displayToPublicDate":"2022-12-01T08:53:45","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":7577,"text":"Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Supplemental vegetation monitoring plots at Badlands National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","docAbstract":"The annual Brome Adaptive Management (ABAM) project is a consortium of seven parks in the Northern Great Plains working together to better understand how to control invasive annual grasses (including Bromus species) through an adaptive management approach. This approach is supported by a quantitative model that uses current data from standardized vegetation monitoring plots in all seven parks to annually update the model’s parameters and predictions regarding the effects of different management actions on invasive annual grasses and other components of the mixed-grass prairie plant community. This updating of the model is called “learning.” The ABAM model includes treatments in which the herbicides indaziflam and imazapic are applied alone or in combination with or without a prescribed fire preceding or following their application. However, the ABAM model currently does not have field data for the effects of those treatments on target invasive annual grasses and other components of the vegetation in conditions like those that frequently occur in ABAM parks. This annual report provides raw results of these treatments applied to plots at Badlands National Park established specifically to accumulate this type of data and therefore accelerate the rate of learning accomplished in the adaptive management cycle.","language":"English","publisher":"National Park Service","usgsCitation":"Symstad, A., 2022, Supplemental vegetation monitoring plots at Badlands National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model: Annual Report, 4 p.","productDescription":"4 p.","ipdsId":"IP-152066","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":415833,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/RPRS/IAR/Profile/573319"},{"id":426319,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Badlands National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.9816413781004,\n 43.81545397317089\n ],\n [\n -102.10404473861178,\n 43.820721999747335\n ],\n [\n -102.18038072914308,\n 43.900094036554435\n ],\n [\n -102.43056276472474,\n 43.93869274314153\n ],\n [\n -102.46588237645057,\n 43.87808438644481\n ],\n [\n -102.46227631796151,\n 43.81240136470154\n ],\n [\n -102.52583495138943,\n 43.76271118438757\n ],\n [\n -102.61056188331933,\n 43.745312984118215\n ],\n [\n -102.61512488487168,\n 43.697319743127245\n ],\n [\n -102.79687064513114,\n 43.69924298806647\n ],\n [\n -102.80984003809536,\n 43.66341493167664\n ],\n [\n -102.90154396748794,\n 43.662470844462945\n ],\n [\n -102.91146582849841,\n 43.47194100980886\n ],\n [\n -102.80317094893881,\n 43.47397686938032\n ],\n [\n -102.71249040143672,\n 43.53766576763433\n ],\n [\n -102.60922465539478,\n 43.48525504065212\n ],\n [\n -102.55509069073659,\n 43.49459163360207\n ],\n [\n -102.49109164390808,\n 43.49135456209459\n ],\n [\n -102.40294493885204,\n 43.47379006618429\n ],\n [\n -102.3098505724263,\n 43.465209713416414\n ],\n [\n -102.27255837742683,\n 43.48077466407841\n ],\n [\n -102.25880034575657,\n 43.582995671627835\n ],\n [\n -102.35279341761728,\n 43.577534191075756\n ],\n [\n -102.44677360300271,\n 43.541331165393316\n ],\n [\n -102.54267701175816,\n 43.56638417465828\n ],\n [\n -102.58354318324504,\n 43.60646020158184\n ],\n [\n -102.53234630194093,\n 43.63244149516539\n ],\n [\n -102.49724178055375,\n 43.716432342349705\n ],\n [\n -102.34297147119695,\n 43.748511777562356\n ],\n [\n -102.2746784068271,\n 43.781173073417534\n ],\n [\n -102.25598001505656,\n 43.80871222310375\n ],\n [\n -102.08569317423665,\n 43.7448827308326\n ],\n [\n -101.86229425624181,\n 43.732224058910504\n ],\n [\n -101.87763233485761,\n 43.800704970828534\n ],\n [\n -101.9816413781004,\n 43.81545397317089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":869738,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239795,"text":"70239795 - 2022 - Osmoregulation and acid-base balance.","interactions":[],"lastModifiedDate":"2023-01-20T14:50:53.947059","indexId":"70239795","displayToPublicDate":"2022-12-01T08:49:30","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"9","title":"Osmoregulation and acid-base balance.","docAbstract":"Maintaining relatively constant levels of internal cellular ions is critical to the normal function of all animals. For many organisms this is achieved primarily by regulating the ion and acid-base composition of the blood within narrow limits. This understanding of the importance of “le milieu interior,” first espoused by Claude Bernard in the mid-1800s and later described as “homeostasis” by Walter Cannon, is a cornerstone of modern physiology. “It was Bernard’s view that we achieve a free and independent life, physically and mentally, because of the constancy of the composition of our internal environment” (Smith 1961:1). Direct contact between the gills and water makes ion, water, and acid-base balance especially challenging and important to fish and, in turn, makes fish important subjects for understanding the evolution and control of all of these homeostatic processes.
Several strategies exist within fishes for regulating ion concentrations in the blood relative to external (environmental) salt concentrations. Hagfishes, which are one extant group representing the ancestral jawless condition of vertebrates, are restricted to seawater (SW) and have an osmoconforming strategy in which the internal (blood) and external osmotic concentrations are very similar (Currie and Edwards 2010), but important differences do exist (Sardella et al. 2009). Lampreys are the other group of extant jawless fishes and either live wholly in freshwater (FW) or are anadromous. Lampreys have an osmoregulatory strategy in which the internal concentrations of ions are approximately one-third that of SW (Reis-Santos et al. 2008). Their underlying mechanisms of ion transport and osmoregulation appear to be nearly identical to those of the more recently evolved ray-finned fishes (Figure 9.1), which have adopted a similar osmoregulatory strategy. Elasmobranchs and coelacanths in SW retain high levels of urea in their plasma and are osmoconformers (Figure 9.2), whereas in the relatively rare instances elasmobranchs are found in FW, they are hyperosmoregulators, maintaining plasma ion levels in excess of environmental levels via mechanisms similar to FW ray-finned fishes (Ballantyne and Robinson 2010).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Methods for fish biology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Fisheries Society","doi":"10.47886/9781934874615.ch9","usgsCitation":"McCormick, S.D., Schultz, E., and Brauner, C., 2022, Osmoregulation and acid-base balance., chap. 9 of Methods for fish biology, p. 275-308, https://doi.org/10.47886/9781934874615.ch9.","productDescription":"34 p.","startPage":"275","endPage":"308","ipdsId":"IP-108481","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":412128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"2nd edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":861975,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schultz, Eric T.","contributorId":298956,"corporation":false,"usgs":false,"family":"Schultz","given":"Eric T.","affiliations":[{"id":64738,"text":"University of CT, Storrs","active":true,"usgs":false}],"preferred":false,"id":861976,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brauner, Colin","contributorId":301092,"corporation":false,"usgs":false,"family":"Brauner","given":"Colin","affiliations":[{"id":36484,"text":"UBC","active":true,"usgs":false}],"preferred":false,"id":861977,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240308,"text":"70240308 - 2022 - Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream","interactions":[],"lastModifiedDate":"2023-02-03T14:45:16.85921","indexId":"70240308","displayToPublicDate":"2022-12-01T08:24:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5112,"text":"Environmental Science: Water Research & Technology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream","docAbstract":"Wastewater effluent-dominated streams are becoming increasingly common worldwide, including in temperate regions, with potential impacts on ecological systems and drinking water sources. We recently quantified the occurrence/spatiotemporal dynamics of pharmaceutical mixtures in a representative temperate-region wastewater effluent-dominated stream (Muddy Creek, Iowa) under baseflow conditions and characterized relevant fate processes. Herein, we quantified the ecological risk quotients (RQs) of 19 effluent-derived contaminants of emerging concern (CECs; including: 14 pharmaceuticals, 2 industrial chemicals, and 3 neonicotinoid insecticides) and 1 run-off-derived compound (atrazine) in the stream under baseflow conditions, and estimated the probabilistic risks of effluent-derived CECs under all-flow conditions (i.e., including runoff events) using stochastic risk modeling. We determined that 11 out of 20 CECs pose medium-to-high risks to local ecological systems (i.e., algae, invertebrates, fish) based on literature-derived acute effects under measured baseflow conditions. Stochastic risk modeling indicated decreased, but still problematic, risk of effluent-derived CECs (i.e., RQ ≥ 0.1) under all-flow conditions when runoff events were included. Dilution of effluent-derived chemicals from storm flows thus only minimally decreased risk to aquatic biota in the effluent-dominated stream. We also modeled in-stream transport. Thirteen out of 14 pharmaceuticals persisted along the stream reach (median attenuation rate constant k < 0.1 h−1) and entered the Iowa River at elevated concentrations. Predicted and measured concentrations in the drinking water treatment plant were below the human health benchmarks. This study demonstrates the application of probabilistic risk assessments for effluent-derived CECs in a representative effluent-dominated stream under variable flow conditions (when measurements are less practical) and provides an enhanced prediction tool transferable to other effluent-dominated systems.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D2EW00157H","usgsCitation":"Zhi, H., Webb, D.T., Schnoor, J.L., Kolpin, D., Klaper, R.D., Iwanowicz, L., and LeFevre, G.H., 2022, Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream: Environmental Science: Water Research & Technology, v. 8, p. 1408-1422, https://doi.org/10.1039/D2EW00157H.","productDescription":"15 p.","startPage":"1408","endPage":"1422","ipdsId":"IP-129637","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":445736,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9431852","text":"External 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L","contributorId":302047,"corporation":false,"usgs":false,"family":"Schnoor","given":"Jerald","email":"","middleInitial":"L","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":863355,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":863356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Klaper, Rebecca D.","contributorId":218114,"corporation":false,"usgs":false,"family":"Klaper","given":"Rebecca","email":"","middleInitial":"D.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":false,"id":863357,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Iwanowicz, Luke 0000-0002-1197-6178 liwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":302048,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke","email":"liwanowicz@usgs.gov","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":863358,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":863359,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238615,"text":"70238615 - 2022 - Atmospheric circulation drivers of extreme high water level events at Foggy Island Bay, Alaska","interactions":[],"lastModifiedDate":"2022-12-01T14:28:00.803404","indexId":"70238615","displayToPublicDate":"2022-12-01T08:20:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5634,"text":"Atmosphere","active":true,"publicationSubtype":{"id":10}},"title":"Atmospheric circulation drivers of extreme high water level events at Foggy Island Bay, Alaska","docAbstract":"The northern coast of Alaska is experiencing significant climatic change enhancing hazards from reduced sea ice and increased coastal erosion. This same region is home to offshore oil/gas activities. Foggy Island Bay is one region along the Beaufort Sea coast with planned offshore oil/gas development that will need to account for the changing climate. High water levels impact infrastructure through coastal erosion and flooding hazards. In this study, 21 high water level events exceeding the top 95th percentile were identified at the gauge in Prudhoe Bay, Alaska (adjacent to Foggy Island Bay) over 1990-2018. All events were associated with strong westerly winds according to weather station records. Low pressure storm systems were found to be a key driver of westerly winds in the region according to downscaled reanalysis and storm track data. A dynamically downscaled global climate model projection from CMIP5 indicates that days with westerly wind events will become frequent by 2100 in the Foggy Island Bay region. Coupled with the anticipated continued decline in sea ice, the northern coast of Alaska may experience more frequent high water events over the next ~80 years.","language":"English","publisher":"MDPI","doi":"10.3390/atmos13111791","usgsCitation":"Bieniek, P., Erikson, L.H., and Kasper, J., 2022, Atmospheric circulation drivers of extreme high water level events at Foggy Island Bay, Alaska: Atmosphere, v. 13, 1791, 17 p., https://doi.org/10.3390/atmos13111791.","productDescription":"1791, 17 p.","ipdsId":"IP-144924","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":445740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/atmos13111791","text":"Publisher Index Page"},{"id":409922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea, Foggy Island Bay, Prudhoe Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.80741465739465,\n 70.39723931268995\n ],\n [\n -148.70029566028296,\n 70.4000035945075\n ],\n [\n -148.54373712604283,\n 70.3677303272334\n ],\n [\n -148.4970442298659,\n 70.33540601843683\n ],\n [\n -148.5245106393817,\n 70.31321124176867\n ],\n [\n -148.5217639984302,\n 70.29284489806011\n ],\n [\n -148.3404856956258,\n 70.29377107971973\n ],\n [\n -148.1015279328383,\n 70.39816078157423\n ],\n [\n -148.37344538704474,\n 70.47541577367042\n ],\n [\n -148.7222687878956,\n 70.46715246793207\n ],\n [\n -148.80741465739465,\n 70.39723931268995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2022-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Bieniek, Peter A.","contributorId":209850,"corporation":false,"usgs":false,"family":"Bieniek","given":"Peter A.","affiliations":[{"id":38014,"text":"Alaska Climate Science Center, University of Alaska, Fairbanks, AK","active":true,"usgs":false}],"preferred":false,"id":858103,"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":858104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":858105,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226741,"text":"70226741 - 2022 - OpenET: Filling a critical data gap in water management for the western United States","interactions":[],"lastModifiedDate":"2024-05-17T16:01:54.302021","indexId":"70226741","displayToPublicDate":"2022-12-01T06:52:20","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":"OpenET: Filling a critical data gap in water management for the western United States","docAbstract":"The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.
Understanding the ecology of endangered taxa and the factors affecting their population growth and decline is imperative for their recovery. In the southeastern USA, the Everglades wetland ecosystem supports a high diversity of species and communities, including many endemic and imperiled taxa, such as the federally endangered Cape Sable seaside sparrow Ammospiza maritima mirabilis (CSSS). The Everglades, once a completely connected wetland with a slow-moving sheet flow of water, is now compartmentalized into separated wetland units where water distribution is managed year-round. The CSSS is affected by, and at the crux of, many Everglades ecosystem restoration decisions. The CSSS faces conservation challenges, including limited habitat availability, low population numbers, dispersal limitations, and constraints on suitable breeding conditions owing to wetland water levels. Despite these challenges, ecological knowledge of the factors affecting CSSS population numbers in the context of ongoing ecosystem-level restoration can help inform protection of this bird while restoring the Everglades. Existing research shows target hydroperiods between 90 and 210 days, a minimum of 90 consecutive dry days during the breeding season, and non-breeding season fires approximately every 5-10 years may aid in CSSS recovery. There are numerous tools and models to support habitat and water management for the CSSS, and the most recent ecosystem-level water operations plan for the Everglades indicates potential for increased CSSS habitat. Here, we provide a review on the ecology of the CSSS, factors affecting population decline, and ecosystem-level restoration actions that may aid in CSSS recovery.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01212","usgsCitation":"Benscoter, A., and Romanach, S., 2022, Endangered Cape Sable seaside sparrow ecology: Actions towards recovery through landscape-scale ecosystem restoration: Endangered Species Research, v. 49, p. 199-215, https://doi.org/10.3354/esr01212.","productDescription":"17 p.","startPage":"199","endPage":"215","ipdsId":"IP-138298","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":445757,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01212","text":"Publisher Index Page"},{"id":410150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Dlorida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.00201430502638,\n 26.691780992327622\n ],\n [\n -82.00201430502638,\n 24.87241063952405\n ],\n [\n -80.13513135216013,\n 24.87241063952405\n ],\n [\n -80.13513135216013,\n 26.691780992327622\n ],\n [\n -82.00201430502638,\n 26.691780992327622\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benscoter, Allison 0000-0003-4205-3808","orcid":"https://orcid.org/0000-0003-4205-3808","contributorId":216194,"corporation":false,"usgs":true,"family":"Benscoter","given":"Allison","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220761,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858458,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247379,"text":"70247379 - 2022 - Hybrid broadband ground-motion simulation validation of small magnitude active shallow crustal earthquakes in New Zealand","interactions":[],"lastModifiedDate":"2023-07-31T18:45:24.809351","indexId":"70247379","displayToPublicDate":"2022-11-30T13:27:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Hybrid broadband ground-motion simulation validation of small magnitude active shallow crustal earthquakes in New Zealand","docAbstract":"This article presents a comprehensive validation of the hybrid broadband ground-motion simulation approach (via the commonly used Graves and Pitarka method) in a New Zealand context with small magnitude point source ruptures using an extensive set of 5218 ground motions recorded at 212 sites from 479 active shallow crustal earthquakes across the country. Modifications to the simulation method inferred from a previous New Zealand validation are implemented, and the improvements are explicitly quantified. Empirical ground-motion models are also considered to provide a benchmark for simulation prediction accuracy and precision. Examination of intensity measure residuals identifies that the simulation method modifications lead to reduced model prediction bias and within-event variability and provides evidence toward the use of spatially varying coefficient models for simulation parameters, such as the high-frequency Brune stress parameter. Additional biases identified include, among others, underprediction of significant durations at soft soil sites and overprediction of short-period pseudo-spectral accelerations at stiff alluvial gravel and rock sites due to low-estimated 30 m time-averaged shear-wave velocity values.
","language":"English","publisher":"SAGE publishing","doi":"10.1177/87552930221109297","usgsCitation":"Lee, R.L., Bradley, B.A., Stafford, P.J., Graves, R., and Rodriguez-Marek, A., 2022, Hybrid broadband ground-motion simulation validation of small magnitude active shallow crustal earthquakes in New Zealand: Earthquake Spectra, v. 38, no. 4, p. 2548-2579, https://doi.org/10.1177/87552930221109297.","productDescription":"32 p.","startPage":"2548","endPage":"2579","ipdsId":"IP-128104","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[173.02037,-40.91905],[173.24723,-41.332],[173.95841,-40.9267],[174.24759,-41.34916],[174.24852,-41.77001],[173.87645,-42.23318],[173.22274,-42.97004],[172.71125,-43.37229],[173.08011,-43.85334],[172.30858,-43.86569],[171.45293,-44.24252],[171.18514,-44.8971],[170.6167,-45.90893],[169.83142,-46.35577],[169.33233,-46.64124],[168.41135,-46.61994],[167.76374,-46.2902],[166.67689,-46.21992],[166.50914,-45.8527],[167.04642,-45.11094],[168.30376,-44.12397],[168.94941,-43.93582],[169.66781,-43.55533],[170.52492,-43.03169],[171.12509,-42.51275],[171.56971,-41.76742],[171.94871,-41.51442],[172.09723,-40.9561],[172.79858,-40.49396],[173.02037,-40.91905]]],[[[174.61201,-36.1564],[175.33662,-37.2091],[175.3576,-36.52619],[175.80889,-36.79894],[175.95849,-37.55538],[176.7632,-37.88125],[177.43881,-37.96125],[178.01035,-37.57982],[178.51709,-37.69537],[178.27473,-38.58281],[177.97046,-39.16634],[177.20699,-39.14578],[176.93998,-39.44974],[177.03295,-39.87994],[176.88582,-40.06598],[176.50802,-40.60481],[176.01244,-41.28962],[175.23957,-41.68831],[175.0679,-41.42589],[174.65097,-41.28182],[175.22763,-40.45924],[174.90016,-39.90893],[173.82405,-39.50885],[173.85226,-39.1466],[174.5748,-38.79768],[174.74347,-38.02781],[174.69702,-37.38113],[174.29203,-36.71109],[174.319,-36.53482],[173.841,-36.12198],[173.05417,-35.23713],[172.63601,-34.52911],[173.00704,-34.45066],[173.5513,-35.00618],[174.32939,-35.2655],[174.61201,-36.1564]]]]},\"properties\":{\"name\":\"New Zealand\"}}]}","volume":"38","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Robin L.","contributorId":261917,"corporation":false,"usgs":false,"family":"Lee","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":879375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Brendon A.","contributorId":202814,"corporation":false,"usgs":false,"family":"Bradley","given":"Brendon","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":879376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stafford, Peter J.","contributorId":261918,"corporation":false,"usgs":false,"family":"Stafford","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":24608,"text":"Imperial College London","active":true,"usgs":false}],"preferred":false,"id":879377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879378,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rodriguez-Marek, Adrian","contributorId":261919,"corporation":false,"usgs":false,"family":"Rodriguez-Marek","given":"Adrian","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":879379,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238613,"text":"70238613 - 2022 - The influence of drying on the aeolian transport of river-sourced sand","interactions":[],"lastModifiedDate":"2022-12-15T15:56:24.73617","indexId":"70238613","displayToPublicDate":"2022-11-30T08:07:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6503,"text":"Journal of Geophysical Research Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"The influence of drying on the aeolian transport of river-sourced sand","docAbstract":"Transgression and regression of water levels (stages) have impacted the evolution of aeolian landforms and sedimentary deposits throughout geologic history. We studied this phenomenon over a five-day period of reduced flow on the Colorado River in Grand Canyon National Park, AZ, USA, in March 2021. These transient low flows exposed river-channel sand deposits to the air, causing progressive desiccation (drying) and thereby making these deposits susceptible to aeolian transport. We measured aeolian threshold friction velocities (u*t) for sand saltation and PM10 dust emissions, as well as other characteristics, on a subaerially exposed sandbar and downwind aeolian dunefield during each day of the low river flow. The sandbar transitioned from supply-limited to transport-limited aeolian sediment transport conditions during the regression in river water stage. A possible tipping point between the two transport conditions occurred approximately 48 hours after the drop in river flow. The empirically measured u*t decreased as the sandbar sediment dried with increased subaerial exposure time. Theoretical estimates and empirical measurements of u*t corresponded closely on the aeolian dunefield and on the sandbar when it was drier during the third and fourth day of the experiment. Eighty-seven percent of the variability in u*t was explained by empirical models that provide practical estimates of aeolian transport potential of subaerial river sediment deposits using monitoring data that are commonly available in this and other river systems. The work provides theoretical insight into the response of aeolian processes to sediment supply changes driven by periods of anthropogenic activity, drought, and climate change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006816","usgsCitation":"Sankey, J., Caster, J., Kasprak, A., and Fairley, H.C., 2022, The influence of drying on the aeolian transport of river-sourced sand: Journal of Geophysical Research Earth Surface, v. 127, no. 12, e2022JF006816, 24 p., https://doi.org/10.1029/2022JF006816.","productDescription":"e2022JF006816, 24 p.","ipdsId":"IP-142498","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445764,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006816","text":"Publisher Index Page"},{"id":435603,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WBUYO","text":"USGS data release","linkHelpText":"Threshold friction velocities for aeolian transport of river-sourced sand, with related moisture content, grain size, topographic, and wind data from Lees Ferry, Arizona"},{"id":409919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.35659478532732,\n 36.965267960408156\n ],\n [\n -114.03110966943333,\n 36.965267960408156\n ],\n [\n -114.03110966943333,\n 35.544550609550456\n ],\n [\n -111.35659478532732,\n 35.544550609550456\n ],\n [\n -111.35659478532732,\n 36.965267960408156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":204162,"corporation":false,"usgs":true,"family":"Kasprak","given":"Alan","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fairley, Helen C. 0000-0001-6151-4804 hfairley@usgs.gov","orcid":"https://orcid.org/0000-0001-6151-4804","contributorId":3040,"corporation":false,"usgs":true,"family":"Fairley","given":"Helen","email":"hfairley@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":858102,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238688,"text":"70238688 - 2022 - Defining biologically relevant and hierarchically nested population units to inform wildlife management","interactions":[],"lastModifiedDate":"2022-12-05T13:03:45.51127","indexId":"70238688","displayToPublicDate":"2022-11-30T06:52:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Defining biologically relevant and hierarchically nested population units to inform wildlife management","docAbstract":"Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial “K”luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse (Centrocercus urophasianus), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.
Recent studies have documented benefits of small, prescribed fire and wildfire for grassland-dependent wildlife, such as lesser prairie-chickens (Tympanuchus pallidicintus), but wildlife demographic response to the scale and intensity of megafire (wildfire >40,000 ha) in modern, fragmented grasslands remains unknown. Limited available grassland habitat makes it imperative to understand if increasing frequency of megafires could further reduce already declining lesser prairie-chicken populations, or if historical evolutionary interactions with fire make lesser prairie-chickens resilient. To evaluate lesser prairie-chicken demographic response to megafires, we compared lek counts, nest density, and survival rates of adults, nests, and chicks before (2014–2016) and after (2018–2020) a 2017 megafire in the mixed-grass prairie of Kansas, USA (Starbuck fire ~254,000 ha). There was a 67% decline in attending males on leks post-fire and a 57% decline in occupied leks post-fire. Despite population declines as indicated by lek counts, adult female breeding season survival (Ŝ) was similar pre- (Ŝ = 0.65 ± 0.08 [SE]) and post-fire (0.61 ± 0.08), as was chick survival (pre-fire: 0.23 ± 0.07; post-fire: 0.27 ± 0.11). Nest survival appeared lower post-fire (pre-fire: 0.38 ± 0.06; post-fire: 0.20 ± 0.06), but did not differ at the 95% confidence interval. Nest density of marked females declined 73% in areas burned by megafire. Although lesser prairie-chickens persisted in the study area and we documented minimal effects on most demographic rates, reduced lesser prairie-chicken abundance and reproductive output suggests full recovery may take >3 years. Increased propensity for megafire resulting from suppression of smaller fires, compounded by climate change and woody encroachment, may impose a short-term (3–5 year) threat to already declining lesser prairie-chicken populations.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9544","usgsCitation":"Parke, N.J., Sullin, D.S., Haukos, D.A., Fricke, K., Hagen, C., and Ahlers, A.A., 2022, Demographic effects of a megafire on a declining prairie grouse in the mixed-grass prairie: Ecology and Evolution, v. 12, no. 12, e9544, 16 p., https://doi.org/10.1002/ece3.9544.","productDescription":"e9544, 16 p.","ipdsId":"IP-142934","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445778,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9544","text":"Publisher Index Page"},{"id":432681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.54073970102226,\n 37.75193745545033\n ],\n [\n -101.54073970102226,\n 36.24283843115835\n ],\n [\n -98.50851313852192,\n 36.24283843115835\n ],\n [\n -98.50851313852192,\n 37.75193745545033\n ],\n [\n -101.54073970102226,\n 37.75193745545033\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Parke, Nicholas J.","contributorId":341309,"corporation":false,"usgs":false,"family":"Parke","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":908215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullin, Daniel S.","contributorId":341310,"corporation":false,"usgs":false,"family":"Sullin","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":908216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fricke, Kent A.","contributorId":341311,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent A.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":908218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagen, Christian A.","contributorId":341312,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":908219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahlers, Adam A.","contributorId":341313,"corporation":false,"usgs":false,"family":"Ahlers","given":"Adam","email":"","middleInitial":"A.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":908220,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238538,"text":"fs20223051 - 2022 - U.S. Geological Survey Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)—Information Management Technology Plan","interactions":[],"lastModifiedDate":"2022-11-30T16:55:55.585497","indexId":"fs20223051","displayToPublicDate":"2022-11-29T13:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3051","displayTitle":"U.S. Geological Survey Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)—Information Management Technology Plan","title":"U.S. Geological Survey Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)—Information Management Technology Plan","docAbstract":"More than 840 publications, 575 data releases, and 330 project web pages from the U.S. Geological Survey (USGS) pertain to the Colorado River Basin. Limited interconnections between Colorado River Basin publications, data, and web pages restrict the ability to synthesize and interpret scientific resources. Currently, these pieces are spread across multiple isolated locations, internal systems, data repositories, and local offices. The increasing size, complexity, and diversity of Colorado River Basin data creates additional need for integration. These different data types—including discrete, continuous, aerial, remote sensing, geophysical, geospatial, and other types in varied formats—are collected over numerous time and space scales and require data-intensive science and technology to integrate.
Information management technology (IMT) resources are enterprise capabilities that the USGS workforce can leverage at multiple scales with consistent interoperable solutions to better facilitate integrated science. The USGS 21st Century Science Strategy directs the USGS to establish enterprise IMT capabilities that support integrated work through interoperable software and database solutions at multiple scales. This Information Management Technology Plan identifies nine steps to leverage new and existing technologies, data, models, and scientific knowledge to support integrated science projects conducted across the Colorado River Basin. These steps are transferable to integrated-science studies in other locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223051","usgsCitation":"Anderson, E.D, Erxleben, J.R., Qi, S.L., Monroe, A.P., and Dahm, K.G., 2022, U.S. Geological Survey Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)—Information Management Technology Plan: U.S. Geological Survey Fact Sheet 2022-3051, 4 p., https://doi.org/10.3133/fs20223051.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-132808","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":409861,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223051/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3051"},{"id":409757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3051/coverthb.jpg"},{"id":409758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3051/fs20223051.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3051"},{"id":409760,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223010","text":"USGS Fact Sheet 2022-3010—","linkHelpText":"Addressing Stakeholder Science Needs for Integrated Drought Science in the Colorado River Basin Fact Sheet 2022-3010"},{"id":409803,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3051/images"},{"id":409804,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3051/fs20223051.xml"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.00488281250001,\n 32.65787573695528\n ],\n [\n -114.78515624999999,\n 31.840232667909365\n ],\n [\n -113.99414062499999,\n 31.541089879585808\n ],\n [\n -113.2470703125,\n 31.015278981711266\n ],\n [\n -112.0166015625,\n 30.14512718337613\n ],\n [\n -110.654296875,\n 29.878755346037977\n ],\n [\n -109.86328125,\n 29.99300228455108\n ],\n [\n -108.720703125,\n 30.600093873550072\n ],\n [\n -108.28125,\n 31.653381399664\n ],\n [\n -108.28125,\n 32.54681317351514\n ],\n [\n -107.9736328125,\n 33.87041555094183\n ],\n [\n -107.40234375,\n 34.23451236236987\n ],\n [\n -106.9189453125,\n 35.88905007936091\n ],\n [\n -106.69921875,\n 36.35052700542763\n ],\n [\n -106.3916015625,\n 37.23032838760387\n ],\n [\n -106.3916015625,\n 38.272688535980976\n ],\n [\n -106.3916015625,\n 39.13006024213511\n ],\n [\n -106.12792968749999,\n 40.84706035607122\n ],\n [\n -106.3037109375,\n 41.47566020027821\n ],\n [\n -107.09472656249999,\n 41.96765920367816\n ],\n [\n -108.67675781249999,\n 42.32606244456202\n ],\n [\n -109.7314453125,\n 43.03677585761058\n ],\n [\n -110.654296875,\n 43.45291889355465\n ],\n [\n -111.09374999999999,\n 43.389081939117496\n ],\n [\n -111.1376953125,\n 42.61779143282346\n ],\n [\n -111.005859375,\n 42.16340342422401\n ],\n [\n -110.830078125,\n 41.376808565702355\n ],\n [\n -111.0498046875,\n 40.51379915504413\n ],\n [\n -111.4013671875,\n 39.740986355883564\n ],\n [\n -111.533203125,\n 37.68382032669382\n ],\n [\n -112.19238281249999,\n 37.43997405227057\n ],\n [\n -113.203125,\n 37.3002752813443\n ],\n [\n -114.2138671875,\n 37.37015718405753\n ],\n [\n -114.521484375,\n 38.20365531807149\n ],\n [\n -115.13671875,\n 38.51378825951165\n ],\n [\n -115.400390625,\n 37.16031654673677\n ],\n [\n -115.1806640625,\n 35.92464453144099\n ],\n [\n -114.82910156249999,\n 34.994003757575776\n ],\n [\n -114.697265625,\n 33.7243396617476\n ],\n [\n -115.00488281250001,\n 32.65787573695528\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS-911
Denver, CO 80225-0046
The Conservation Reserve Enhancement Program is a program administered by the U.S. Department of Agriculture’s Farm Service Agency. The Secretary of Agriculture is required to submit an annual report to Congress on Conservation Reserve Enhancement Program agreements that, among other things, reports on the progress made towards fulfilling commitments outlined in the agreements. The U.S. Geological Survey developed an online reporting form designed to ensure that consistent information is submitted to the Farm Service Agency from Conservation Reserve Enhancement Program State partners. Combined with the automated importation of text from partner-provided forms to word-processing documents, individual State reports and annual reports to Congress can now be produced efficiently and in a standardized format. Use of a standardized reporting format will also assist the Farm Service Agency in collecting information needed to support ecosystem service quantifications that go beyond the quantifications required from partners to document progress towards meeting the specific purposes and objectives identified in each agreement. Addition of these overarching conservation effect quantifications builds upon past ecosystem services modeling efforts based on the Integrated Valuation of Ecosystem Services and Tradeoffs suite of open-source software models; these offer a spatially explicit means to quantify additional ecosystem services across diverse partners in a consistent manner. Data sources are currently available to provide much of the information needed to run these models and complete simulations that would facilitate the quantification and reporting of the societal values of conservation actions taken under the Conservation Reserve Enhancement Program. It is the aim of this report to provide the information needed to move towards widescale monitoring of the Nation’s ecosystem services in a natural accounting framework, similar to the framework used to value financial and human capital.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221104","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture’s Farm Production and Conservation Business Center and Farm Service Agency","usgsCitation":"Mushet, D.M., and McKenna, O.P., 2022, Development of an online reporting format to facilitate the inclusion of ecosystem services into Conservation Reserve Enhancement Program reports: U.S. Geological Survey Open-File Report 2022–1104, 19 p., https://doi.org/10.3133/ofr20221104.","productDescription":"Report: vi, 19 p.; 5 Appendixes","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141507","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":409698,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221104/full","text":"Report"},{"id":409675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1104/coverthb.jpg"},{"id":409676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104.pdf","text":"Report","size":"725 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1104"},{"id":409677,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104.XML"},{"id":409678,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix1.pdf","text":"Appendix 1","description":"OFR 2022–1104, Appendix 1","linkHelpText":"—Farm Service Agency Notice Implementing Use of Online Reporting Form"},{"id":409679,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix2.pdf","text":"Appendix 2","description":"OFR 2022–1104, Appendix 2","linkHelpText":"—A Guide for Completing Conservation Reserve Enhancement Program Annual Reports Using the New Online Reporting Form"},{"id":409681,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix4.pdf","text":"Appendix 4","description":"OFR 2022–1104, Appendix 4","linkHelpText":"—Microsoft Word Mail Merge State Report Template"},{"id":409682,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix5.pdf","text":"Appendix 5","description":"OFR 2022–1104, Appendix 5","linkHelpText":"—Draft Text Produced for 2020 Report to Congress"},{"id":409683,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix6.pdf","text":"Appendix 6","description":"OFR 2022–1104, Appendix 6","linkHelpText":"—Draft Text Produced for 2021 Report to Congress"},{"id":409687,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1104/images"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
White-nose syndrome has been decimating populations of several bat species since its first occurrence in the Northeastern United States in the winter 2006–2007. The spread of the disease has been monitored across the continent through the collaboration of many organizations. Inferring the rate of spread of the disease and predicting its arrival at new locations is critical when assessing the current and predicting the future status and trends of bat species. We developed a model of disease spread that simultaneously achieves high-predictive performance, computational efficiency, and interpretability. We modeled white-nose syndrome spread using Gaussian process variations to infer the spread rate of the disease front, identify areas of anomalous time of arrival, and provide future forecasts of the expected time of arrival throughout North America. Cross-validation of model predictive performance identified a stationary Gaussian process without an additional residual error process as the best-supported model. Results indicated that white-nose syndrome is likely to spread throughout the entire continental United States by 2030. These annually updatable model predictions will be useful in determining the horizon over which disease management actions must take place as well as in status and trend assessments of disease-affected bats.
It is important to routinely estimate loads from an entire watershed to describe current conditions and evaluate how watershed-wide management efforts have affected the nutrient and sediment export that affect downstream water quality. However, monitoring in most areas, including the Great Lakes watershed, consists of sampling at a limited number of sites that are only periodically used to estimate total watershed loading. Here, we describe a technique to extrapolate loads measured at a limited number of reference sites to the total load from a large watershed using load ratios between monitored sites and unmonitored areas obtained from a watershed model (i.e., model load ratio, MLR, approach). In this study, modeled nonpoint-source load ratios between monitored tributaries (reference sites) and nearby unmonitored areas and point-source delivery factors for all areas were obtained from a Spatially Referenced Regression On Watershed attributes (SPARROW) model and used to extrapolate the measured loads from an ongoing monitoring program (Great Lakes Restoration Initiative Tributary monitoring program) to the entire Great Lakes watershed. The MLR approach incorporates spatial variability in nonpoint- and point-source delivery, watershed characteristics, and hydrology that are often not considered when estimating loads from unmonitored areas, such as using the unit area load (UAL) extrapolation approach. The MLR approach provided smaller watershed loads than the UAL approach because yields from monitored sites, in general, were larger than from unmonitored areas. When both approaches were used to estimate loads at adjacent monitored sites, the MLR approach provided more accurate estimates than the UAL approach.
Quantitative, broadly applicable metrics of resilience are needed to effectively manage tidal marshes into the future. Here we quantified three metrics of temporal marsh resilience: time to marsh drowning, time to marsh tipping point, and the probability of a regime shift, defined as the conditional probability of a transition to an alternative super-optimal, suboptimal, or drowned state. We used organic matter content (loss on ignition, LOI) and peat age combined with the Coastal Wetland Equilibrium Model (CWEM) to track wetland development and resilience under different sea-level rise scenarios in the Sacramento-San Joaquin Delta (Delta) of California. A 100-year hindcast of the model showed excellent agreement (R2 = 0.96) between observed (2.86 mm/year) and predicted vertical accretion rates (2.98 mm/year) and correctly predicted a recovery in LOI (R2 = 0.76) after the California Gold Rush. Vertical accretion in the tidal freshwater marshes of the Delta is dominated by organic production. The large elevation range of the vegetation combined with high relative marsh elevation provides Delta marshes with resilience and elevation capital sufficiently great to tolerate centenary sea-level rise (CLSR) as high as 200 cm. The initial relative elevation of a marsh was a strong determinant of marsh survival time and tipping point. For a Delta marsh of average elevation, the tipping point at which vertical accretion no longer keeps up with the rate of sea-level rise is 50 years or more. Simulated, triennial additions of 6 mm of sediment via episodic atmospheric rivers increased the proportion of marshes surviving from 51% to 72% and decreased the proportion drowning from 49% to 28%. Our temporal metrics provide critical time frames for adaptively managing marshes, restoring marshes with the best chance of survival, and seizing opportunities for establishing migration corridors, which are all essential for safeguarding future habitats for sensitive species.
Real-time monitoring is crucial to assess hazards and mitigate risks of sustained volcanic eruptions that last hours to months or more. Sustained eruptions have been shown to produce a low frequency (infrasonic) form of jet noise. We analyze the lava fountaining at fissure 8 during the 2018 Lower East Rift Zone eruption of Kīlauea volcano, Hawaii, and connect changes in fountain properties with recorded infrasound signals from an array about 500 m from the fountain using jet noise scaling laws and visual imagery. Video footage from the eruption reveals a change in lava fountain dynamics from a tall, distinct fountain at the beginning of June to a low fountain with a turbulent, out-pouring lava pond surrounded by a tephra cone by mid-June. During mid-June, the sound pressure level reaches a maximum, and peak frequency drops. We develop a model that uses jet noise scaling relationships to estimate changes in volcanic jet diameter and jet velocity from infrasound sound pressure levels and peak frequencies. The results of this model indicate a decrease in velocity in mid-June which coincides with the decrease in fountain height. Furthermore, the model results suggest an increase in jet diameter, which can be explained by the larger width of the fountain that resembles a turbulent lava pond compared to the distinct fountain at the beginning of June. The agreement between the infrasound-derived and visually observed changes in fountain dynamics suggests that jet noise scaling relationships can be used to monitor lava fountain dynamics using infrasound recordings.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.1027408","usgsCitation":"Gestrich, J., Fee, D., Matoza, R., Lyons, J.J., Dietterich, H., Cigala, V., Kueppers, U., Patrick, M.R., and Parcheta, C., 2022, Lava fountain jet noise during the 2018 eruption of fissure 8 of Kīlauea volcano: Frontiers Earth Science Journal, v. 10, 1027408, 18 p., https://doi.org/10.3389/feart.2022.1027408.","productDescription":"1027408, 18 p.","ipdsId":"IP-144544","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467142,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.1027408","text":"Publisher Index Page"},{"id":462663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.40616179843389,\n 19.510647106982844\n ],\n [\n -155.40616179843389,\n 19.352324463279487\n ],\n [\n -155.20934216439622,\n 19.352324463279487\n ],\n [\n -155.20934216439622,\n 19.510647106982844\n ],\n [\n -155.40616179843389,\n 19.510647106982844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Gestrich, Julia","contributorId":268787,"corporation":false,"usgs":false,"family":"Gestrich","given":"Julia","affiliations":[{"id":50446,"text":"UAF-GI","active":true,"usgs":false}],"preferred":false,"id":915202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":915203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matoza, Robin","contributorId":268788,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":915204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":915205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915206,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cigala, Valerie","contributorId":344976,"corporation":false,"usgs":false,"family":"Cigala","given":"Valerie","affiliations":[{"id":62362,"text":"LMU","active":true,"usgs":false}],"preferred":false,"id":915207,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kueppers, Ulrich","contributorId":178534,"corporation":false,"usgs":false,"family":"Kueppers","given":"Ulrich","affiliations":[],"preferred":false,"id":915208,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915209,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Parcheta, Carolyn 0000-0001-6556-4630 cparcheta@usgs.gov","orcid":"https://orcid.org/0000-0001-6556-4630","contributorId":215617,"corporation":false,"usgs":true,"family":"Parcheta","given":"Carolyn","email":"cparcheta@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915210,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}