Contamination from acid mine drainage affects ecosystems and usability of groundwater for domestic and municipal purposes. The Captain Jack Superfund Site outside of Ward, Boulder County, Colorado, USA, hosts a draining mine adit that was remediated through emplacement of a hydraulic bulkhead to preclude acid mine drainage from entering nearby Lefthand Creek. During impoundment of water within the mine workings in 2020, a diverse and novel dataset of stable isotopes of water, sulfate, and carbon (δ2H, δ18OH2O, δ18OSO4, δ34S, δ13CDIC), rare earth elements, and environmental tracers (noble gases and tritium) were collected to understand groundwater recharge and mixing, mechanisms of sulfide oxidation and water-rock interaction, and the influence of remediation on the hydrologic and geochemical system. Water isotopes indicate that groundwater distal from the mine workings has seasonally variable recharge sources whereas water within the workings has a distinctive composition with minimal temporal variability. Sulfate isotopes indicate that sulfide oxidation occurs both within the mine workings and in adjacent igneous dikes, and that sulfide oxidation may occur under suboxic conditions with ferric iron as the oxidant. Carbon isotopes track the neutralization of acidic waters and the carbon mass budget of the system. Rare earth elements corroborate stable isotopes in indicating groundwater compartmentalization, and additionally illustrate enhanced mineral weathering in the mine workings. Environmental tracers indicate mixing of modern and pre-modern groundwater and inform timelines that active remediation may be needed. Together these datasets provide a useful template for similar investigations of abandoned mine sites where physical mixing processes, sources of solute loading, or remediation timeframes are of importance.
Floodplains provide critical ecosystem services to people by regulating floodwaters and retaining sediments and nutrients. Geospatial analyses, field data collection, and modeling were integrated to quantify a portfolio of services that floodplains provide to downstream communities within the Chesapeake Bay and Delaware River watersheds. The portfolio of services included floodplain sediment and nutrient retention and flood regulation. Sediment and nutrient retention were quantified and valued for all non-tidal wadable streams in the Chesapeake Bay and Delaware River watersheds. Predicted nitrogen fluxes from measurements of streambanks and floodplain geomorphic changes were summarized at various scales (river basin, state, and county) and valued using a benefits transfer approach. Floodplain flood regulation services were assessed through a pilot study focused on the Schuylkill River watershed in the Delaware River watershed. Geospatial analysis and published flood frequency estimates were used to assess baseline and counterfactual (i.e., floodplain storage removed) scenarios. Flood regulation was valued using the Federal Emergency Management Agency's Hazus model to compare differences in structural damage to private residences under baseline and counterfactual scenarios. The estimated value of floodplain sediment and nutrient retention was \\$223 million United States dollars (USD) per year in the Chesapeake Bay watershed and \\$38 million USD per year in the Delaware River watershed. Sediment and nutrient retention benefits were offset by a streambank erosion cost of \\$123 million and \\$14 million USD annually in the Chesapeake and Delaware watersheds, respectively. In the Schuylkill River watershed floodplain flood regulation was valued at \\$860,000 USD per year, with an additional \\$7.2 million USD annually provided through floodplain sediment and nutrient retention. Together this portfolio of floodplain ecosystem services indicates that floodplains provide substantial benefits to people by trapping nutrients and storing floodwaters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.118747","usgsCitation":"Hopkins, K.G., Welles, J.S., Pindilli, E., Noe, G.E., Claggett, P., Ahmed, L., and Metes, M.J., 2023, Societal benefits of floodplains in the Chesapeake Bay and Delaware River watersheds: Sediment, nutrient, and flood regulation ecosystem services: Journal of Environmental Management, v. 345, 118747, 16 p., https://doi.org/10.1016/j.jenvman.2023.118747.","productDescription":"118747, 16 p.","ipdsId":"IP-152558","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":442357,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2023.118747","text":"Publisher Index Page"},{"id":435217,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YPYM5M","text":"USGS data release","linkHelpText":"Depth grids for floodplain flood attenuation baseline and 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Center","active":true,"usgs":true}],"preferred":true,"id":880731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":880732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":880733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Claggett, Peter 0000-0002-5335-2857","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":238920,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":880734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahmed, Labeeb 0000-0003-4524-9611","orcid":"https://orcid.org/0000-0003-4524-9611","contributorId":303117,"corporation":false,"usgs":true,"family":"Ahmed","given":"Labeeb","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Metes, Marina J. 0000-0002-6797-9837","orcid":"https://orcid.org/0000-0002-6797-9837","contributorId":204835,"corporation":false,"usgs":true,"family":"Metes","given":"Marina","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":880736,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249844,"text":"70249844 - 2023 - Ground‐motion variability from kinematic rupture models and the implications for nonergodic probabilistic seismic hazard analysis","interactions":[],"lastModifiedDate":"2023-11-02T14:54:05.009294","indexId":"70249844","displayToPublicDate":"2023-08-18T09:46:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Ground‐motion variability from kinematic rupture models and the implications for nonergodic probabilistic seismic hazard analysis","docAbstract":"The variability of earthquake ground motions has a strong control on probabilistic seismic hazard analysis (PSHA), particularly for the low frequencies of exceedance used for critical facilities. We use a crossed mixed‐effects model to partition the variance components from simulated ground motions of
Methane (CH4) is a potent greenhouse gas and its concentrations have tripled in the atmosphere since the industrial revolution. There is evidence that global warming has increased CH4 emissions from freshwater ecosystems1,2, providing positive feedback to the global climate. Yet for rivers and streams, the controls and the magnitude of CH4 emissions remain highly uncertain3,4. Here we report a spatially explicit global estimate of CH4 emissions from running waters, accounting for 27.9 (16.7–39.7) Tg CH4 per year and roughly equal in magnitude to those of other freshwater systems5,6. Riverine CH4 emissions are not strongly temperature dependent, with low average activation energy (EM = 0.14 eV) compared with that of lakes and wetlands (EM = 0.96 eV)1. By contrast, global patterns of emissions are characterized by large fluxes in high- and low-latitude settings as well as in human-dominated environments. These patterns are explained by edaphic and climate features that are linked to anoxia in and near fluvial habitats, including a high supply of organic matter and water saturation in hydrologically connected soils. Our results highlight the importance of land–water connections in regulating CH4 supply to running waters, which is vulnerable not only to direct human modifications but also to several climate change responses on land.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41586-023-06344-6","usgsCitation":"Rocher-Ros, G., Stanley, E.H., Loken, L.C., Casson, N.J., Raymond, P.A., Liu, S., Amatulli, G., and Sponseller, R.A., 2023, Global methane emissions from rivers and streams: Nature, v. 621, p. 530-535, https://doi.org/10.1038/s41586-023-06344-6.","productDescription":"6 p.","startPage":"530","endPage":"535","ipdsId":"IP-146294","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":442410,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41586-023-06344-6","text":"Publisher Index Page"},{"id":421888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"621","noUsgsAuthors":false,"publicationDate":"2023-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Rocher-Ros, Gerard","contributorId":329670,"corporation":false,"usgs":false,"family":"Rocher-Ros","given":"Gerard","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":886050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":886051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loken, Luke C. 0000-0003-3194-1498 lloken@usgs.gov","orcid":"https://orcid.org/0000-0003-3194-1498","contributorId":195600,"corporation":false,"usgs":true,"family":"Loken","given":"Luke","email":"lloken@usgs.gov","middleInitial":"C.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casson, Nora J.","contributorId":169271,"corporation":false,"usgs":false,"family":"Casson","given":"Nora","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":886053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raymond, Peter A.","contributorId":172876,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":17883,"text":"Yale School of Forestry and Environmental Studies, New Haven, CT","active":true,"usgs":false}],"preferred":false,"id":886054,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Shaoda","contributorId":257246,"corporation":false,"usgs":false,"family":"Liu","given":"Shaoda","email":"","affiliations":[{"id":51989,"text":"Yale School of Forestry and Environmental Studies, 195 Prospect Street, New Haven, CT, USA","active":true,"usgs":false}],"preferred":false,"id":886055,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amatulli, Giuseppe","contributorId":330856,"corporation":false,"usgs":false,"family":"Amatulli","given":"Giuseppe","email":"","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":886056,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sponseller, Ryan A.","contributorId":329667,"corporation":false,"usgs":false,"family":"Sponseller","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":24847,"text":"Umea University","active":true,"usgs":false}],"preferred":false,"id":886057,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247900,"text":"70247900 - 2023 - A multi-ecosystem prioritization framework to balance competing habitat conservation needs of multiple species in decline","interactions":[],"lastModifiedDate":"2023-11-07T15:44:25.329372","indexId":"70247900","displayToPublicDate":"2023-08-16T06:37:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A multi-ecosystem prioritization framework to balance competing habitat conservation needs of multiple species in decline","docAbstract":"Individual species often drive habitat restoration action; however, management under this paradigm may negatively affect non-target species. Prioritization frameworks which explicitly consider benefits to target species while minimizing consequences for non-target species may improve management strategies and outcomes.
We examined extents to which conifer removal, an approach frequently implemented to restore sagebrush ecosystems, can be conducted without detrimental effects to conifer-associated species, including the imperiled Pinyon Jay (Gymnorhinus cyanocephalus). Additionally, we prioritized sites for conifer removal, and predicted abundance responses for multiple species following simulated conifer removal at selected sites to achieve variable management objectives.
We used model-predicted changes in species’ densities following simulated conifer removal to identify optimal removal sites under single species, multi-species (ecosystem), and multi-ecosystem management scenarios. We simulated conifer removal at prioritized sites and evaluated resulting changes in abundance for six passerine species.
Management prioritized for a single species (Brewer’s Sparrow) provided the greatest per-unit-effort benefits for that species but resulted in the lowest population outcomes for all other species considered. In comparison, prioritizations for multiple species within a single ecosystem (i.e., pinyon–juniper or sagebrush) resulted in larger population benefits for species associated with that ecosystem and reduced detrimental effects on non-target species associated with another ecosystem. For example, single species management for Brewer’s Sparrow resulted in an average increase of 1.38% for sagebrush-associated species and a 4.58% decrease for pinyon–juniper associated species. In contrast, when managing for multiple sagebrush-associated species sagebrush-associated songbird populations increased by 3.98% and pinyon–juniper associated species decreased by 2.36%, on average.
Our results illustrate single species management can result in detrimental outcomes and/or opportunity costs for non-target species compared to management designed to benefit multiple species. Our framework can be used to balance undesired consequences for non-target species and is adaptable for other systems and taxa.
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","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220132","usgsCitation":"Kwong, N.S., and Jaiswal, K.S., 2023, A methodology to combine shaking and ground failure models for forecasting seismic damage to buried pipeline networks: Bulletin of the Seismological Society of America, v. 113, no. 6, p. 2574-2595, https://doi.org/10.1785/0120220132.","productDescription":"22 p.","startPage":"2574","endPage":"2595","ipdsId":"IP-142244","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":420906,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kwong, N. Simon 0000-0003-3017-9585","orcid":"https://orcid.org/0000-0003-3017-9585","contributorId":241863,"corporation":false,"usgs":true,"family":"Kwong","given":"N.","email":"","middleInitial":"Simon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883285,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883286,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247388,"text":"fs20233029 - 2023 - The 3D Elevation Program—Supporting Oregon's economy","interactions":[],"lastModifiedDate":"2023-08-22T15:10:07.494221","indexId":"fs20233029","displayToPublicDate":"2023-08-15T10:30:00","publicationYear":"2023","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":"2023-3029","displayTitle":"The 3D Elevation Program—Supporting Oregon’s Economy","title":"The 3D Elevation Program—Supporting Oregon's economy","docAbstract":"Oregon’s physical environments and vegetation are diverse. The varied geologic and climatic conditions combined with increasing population have created the need for high-quality elevation data that can be used for infrastructure management, forestry and wildfire management, agriculture, natural resources conservation, and other business uses. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"The effects of interior headland restoration on estuarine sediment transport processes were assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) were modeled using Delft3D to understand impacts on suspended sediment concentrations, bed level morphology, and sediment fluxes under present-day conditions and a sea level rise (SLR) of 0.5 m, representing a high projection of SLR by the year 2050. Model results showed localized differences in bed levels near the restored features after a year of simulated morphologic change. The restored headland features acted as a sediment source to the immediate surroundings while also providing some non-significant sheltering effect of backshore shoals and marsh shorelines. Sediment fluxes were sensitive to wind directions and the presence of the restored headlands. However, regardless of wind direction, mean sea level, or restoration action, the greatest sediment fluxes were always export fluxes from the estuary, which were further increased with increased sea level. Suspended sediment concentrations were highly influenced by SLR in a non-linear manner. Sediment concentrations both increased and decreased depending on depth under SLR. Furthermore, SLR allowed for the suspension and deposition of sediments on the marsh platform. Overall, the influence of SLR was more impactful to changing sediment dynamics than the influence of the restoration features.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2023.1217830","usgsCitation":"Jenkins, R., Passeri, D., Smith, C., Thompson, D.M., and Smith, K., 2023, Modeling the effects of interior headland restoration on estuarine sediment transport processes in a marine-dominant estuary: Frontiers in Marine Science, v. 10, 1217830, 20 p.; Data Release, https://doi.org/10.3389/fmars.2023.1217830.","productDescription":"1217830, 20 p.; Data Release","ipdsId":"IP-153408","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":420007,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":435223,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P986ZR6B","text":"USGS data release","linkHelpText":"Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output"},{"id":442425,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/fmars.2023.1217830","text":"Publisher Index Page"}],"country":"United States","state":"Alabama","otherGeospatial":"Grand Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.42970223559698,\n 30.42582727404016\n ],\n [\n -88.42970223559698,\n 30.33819705788089\n ],\n [\n -88.27115514836353,\n 30.33819705788089\n ],\n [\n -88.27115514836353,\n 30.42582727404016\n ],\n [\n -88.42970223559698,\n 30.42582727404016\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2023-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Passeri, Davina L. 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880795,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Kathryn E.L. 0000-0002-7521-7875 kelsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-7521-7875","contributorId":173264,"corporation":false,"usgs":true,"family":"Smith","given":"Kathryn","email":"kelsmith@usgs.gov","middleInitial":"E.L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":880796,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247706,"text":"sir20235067 - 2023 - Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming","interactions":[],"lastModifiedDate":"2026-03-09T16:56:31.872382","indexId":"sir20235067","displayToPublicDate":"2023-08-14T17:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5067","displayTitle":"Geology and Assessment of Coal Resources for the Cherokee Coal Bed in the Fort Union Formation, South-Central Wyoming","title":"Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming","docAbstract":"The Cherokee coal bed is a locally thick and laterally continuous coal bed in the Overland Member of the Paleocene Fort Union Formation in south-central Wyoming. It represents a significant resource that is easily accessible and may be extractable through both surface and underground mining methods. A database of more than 600 data points, comprising coalbed methane wells, coal exploration drill holes, and measured sections, was compiled from a previously released geologic database and reinterpreted to provide a more detailed geologic model for the Cherokee coal bed. The thickest part of the Cherokee coal bed lies along the crest of the Wamsutter arch, an east-west trending anticlinal feature that separates the Great Divide subbasin to north from the Washakie subbasin to the south. The Cherokee coal bed consists of several laterally persistent benches separated by partings that range in thickness from one inch to greater than 100 feet. A series of detailed geologic cross sections through the study area show both the structural geology and the distribution and areal extent of the individual coal benches of the Cherokee coal bed.
Data generated from the geologic model were used in stochastic geostatistical analyses to estimate the remaining or in-place coal resources. Certain parameters, as described later in the text, were applied to calculate available coal resources for surface and underground mining. This study is part of an ongoing process by the U.S. Geological Survey (USGS) to transition from a distance-based approach to a probabilistic approach for determining uncertainty in coal resource assessment. This probabilistic approach uses quantitative statistical methods to determine the potential range of uncertainty in coal resource estimates, whereas the distance-based approach does not provide any mathematical method to determine the range of uncertainty. Using stochastic geostatistical methods, utilizing 100 realizations or gridding iterations of the data, in-place resources were calculated, with a 90 percent probability, to be 15.261 ± 0.464 billion short tons (bst). Available coal resources tonnages were calculated using separate sets of criteria for surface and underground mining methods, based on probable mining parameters. Tonnage values were calculated based on estimated coal densities determined from available coal quality data. Available coal resources that meet the parameters for surface mining methods were calculated, with a 90 percent probability, to be 0.813 ± 0.038 bst.
Available coal resources that meet the parameters for underground mining methods were calculated, with a 90 percent probability, to be 2.393 ± 0.055 bst. The calculations were based on estimates of the resources that meet the parameters for the optimum mining of the thickest coal benches of the Cherokee coal bed. This is depicted in a series of cross sections through the study area that show projected underground mining horizons in the Cherokee coal bed, based on the thickest combinations of individual coal benches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235067","programNote":"Energy Resources Program","usgsCitation":"Shaffer, B.N., and Olea, R.A., 2023, Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming: U.S. Geological Survey Scientific Investigations Report 2023–5067, 29 p., https://doi.org/10.3133/sir20235067.","productDescription":"Report: vii, 30 p.; 6 Figures: 36.00 x 24.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-132141","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":419765,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92K1UT6","text":"USGS data release","linkHelpText":"Cherokee coal bed drill hole data from the Fort Union Formation in the Little Snake River coal field and Red Desert area, Wyoming"},{"id":419763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5067/coverthb2.jpg"},{"id":419764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067.pdf","text":"Report","size":"6.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067"},{"id":419766,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig07.pdf","text":"Figure 7","size":"108 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 7"},{"id":419768,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig09.pdf","text":"Figure 9","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 9"},{"id":419769,"rank":7,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig16.pdf","text":"Figure 16","size":"96 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 16"},{"id":419770,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig17.pdf","text":"Figure 17","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 17"},{"id":419771,"rank":9,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig18.pdf","text":"Figure 18","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 18"},{"id":419767,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig08.pdf","text":"Figure 8","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 8"},{"id":420209,"rank":10,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5067/images"},{"id":420210,"rank":11,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067.xml"},{"id":420217,"rank":12,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235067/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5067"},{"id":500948,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115199.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Cherokee Coal Bed, Fort Union Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.3333,\n 42\n ],\n [\n -108.3333,\n 41.4167\n ],\n [\n -107.5,\n 41.4167\n ],\n [\n -107.5,\n 42\n ],\n [\n -108.3333,\n 42\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Digital flood-inundation maps for a 3.4-mile reach of Fourmile Creek at Silver Grove, Kentucky, were created by the U.S. Geological Survey (USGS) in cooperation with the City of Silver Grove and the U.S. Army Corps of Engineers Louisville District. Because the City of Silver Grove is subject to flooding from Fourmile Creek and the Ohio River (backwater flooding up Fourmile Creek), a set of flood-inundation maps was created, including maps for each flooding source considered independently and for possible scenarios involving flooding from both sources combined. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to a range of gage heights (gage height is commonly referred to as “stage,” or the water-surface elevation at a streamgage) at the USGS streamgage on Fourmile Creek at Grays Crossing at Silver Grove, Ky. (station number 03238785), and the USGS streamgage on Fourmile Creek at Highway 8 at Silver Grove, Ky. (station number 03238798). Near-real-time stages at these streamgages can be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/. The USGS streamgage on the Ohio River at Cincinnati, Ohio (station number 03255000), is also important in this study because the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS; https://water.weather.gov/ahps/) forecasts flood hydrographs for this site (NWS AHPS site CCNO1). The peak-stage information forecast by the NWS AHPS can be used in conjunction with the maps developed in this study to show predicted areas of flood inundation.
Flood profiles were computed for the Fourmile Creek study reach by means of a one-dimensional, step-backwater hydraulic model (HEC-RAS) developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the current stage-discharge relation (USGS rating number 1.1) at USGS streamgage 03238785, Fourmile Creek at Grays Crossing at Silver Grove, Ky. The model was then used to compute water-surface profiles for 83 combinations of flood stages on the Ohio River and Fourmile Creek ranging from approximately base flow to greater than a 2-percent annual exceedance probability flood in the model reach. An additional 50 water-surface profiles were computed for backwater-only flooding (from the Ohio River) for flood elevations (referenced to the North American Vertical Datum of 1988 [NAVD 88]) at 1-foot intervals referenced to USGS streamgage 03238798, Fourmile Creek at Highway 8 at Silver Grove, Ky.; these elevations ranged from approximately normal pool (460 ft, NAVD 88) to approximately a 0.2-percent annual exceedance probability flood (509 ft, NAVD 88) on the Ohio River. The computed water-surface profile information was then combined with a digital elevation model derived from light detection and ranging (lidar) data to delineate the approximate flooded areas.
The digital flood-inundation maps are available through the USGS Flood Inundation Mapper application (https://fim.wim.usgs.gov/fim/), which presents map libraries and provides detailed information on flood extent and depths for selected sites. The flood-inundation maps developed in this study, in conjunction with the real-time stage data from the USGS streamgages on Fourmile Creek at Silver Grove, Ky., and forecasted stream stages from the NWS AHPS, are intended to provide information that can help inform the public about potential flooding and provide emergency management personnel with a tool to efficiently manage emergency flood operations, such as evacuations and road closures, and assist in postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235068","collaboration":"Prepared in cooperation with the City of Silver Grove and the U.S. Army Corps of Engineers Louisville District","usgsCitation":"Boldt, J.A., 2023, Flood-inundation maps for Fourmile Creek at Silver Grove, Kentucky: U.S. Geological Survey Scientific Investigations Report 2023–5068, 22 p., https://doi.org/10.3133/sir20235068.","productDescription":"Report: vii, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130251","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":500949,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115179.htm","linkFileType":{"id":5,"text":"html"}},{"id":418995,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJSH7D","text":"USGS data release","linkHelpText":"Geospatial datasets and model for the flood-inundation study of Fourmile Creek at Silver Grove, Kentucky"},{"id":418994,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5068/sir20235068.XML"},{"id":418993,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5068/images/"},{"id":418992,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235068/full","text":"Report","description":"SIR 2023-5068"},{"id":418991,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5068/sir20235068.pdf","text":"Report","size":"5.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5068"},{"id":418990,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5068/coverthb.jpg"}],"country":"United States","state":"Kentucky","city":"Silver Grove","otherGeospatial":"Fourmile Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.5333,\n 39.1333\n ],\n [\n -84.5333,\n 39.0167\n ],\n [\n -84.3583,\n 39.0167\n ],\n [\n -84.3583,\n 39.1333\n ],\n [\n -84.5333,\n 39.1333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Water withdrawn from rivers and streams accounts for approximately 80 percent of the public water supply in West Virginia. Localized and (or) seasonal droughts may threaten future water availability in the state, particularly in rural communities located in the headwaters of unregulated watersheds. Monthly water withdrawal data obtained from the West Virginia Department of Environmental Protection’s Large Quantity User program’s regulatory database was used to calculate all-time, seasonal, and monthly 75th quantile withdrawal rates for 109 public water system (PWS) intakes withdrawing from surface waters in West Virginia. A drought-vulnerability assessment value was calculated by comparing PWS withdrawal rates to the 1-day, 10-year hydrologically based streamflow statistic (1Q10) for 71 of the 109 PWS in locations with valid streamflow statistics. Withdrawal rates were evaluated against thresholds representing different levels of drought-related impacts from the West Virginia interagency drought plan and ecological-flow literature. The drought-vulnerability assessment found 33 of 71 PWS have 75th quantile withdrawal rates greater than 100 percent of 1Q10 streamflow. Forty-five of 71 PWS have 75th quantile withdrawal rates more than 10 percent of 1Q10 streamflow, suggesting some level of ecological impairment during severe drought. Additionally, a publicly available, near real-time drought-awareness web tool was created to compare the estimated withdrawal rate for 109 PWS to forecast streamflows from the National Water Model to support decision-making for emergency and water managers.
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Virginia\",\"nation\":\"USA \"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Background: Burn severity significantly increases the likelihood and volume of post-wildfire debris flows. Pre-fire severity predictions can expedite mitigation efforts because precipitation contributing to these hazards often occurs shortly after wildfires, leaving little time for post-fire planning and management.
Aim: The aim of this study was to predict burn severity using pre-fire conditions of individual wildfire events and estimate potential post-fire debris flow to unburned areas.
Methods: We used random forests to model dNBR from pre-fire weather, fuels, topography, and remotely sensed data. We validated our model predictions against post-fire observations and potential post-fire debris-flow hazard estimates.
Key results: Fuels, pre-fire weather, and topography were important predictors of burn severity, although predictor importance varied between fires. Post-fire debris-flow hazard rankings from predicted burn severity (pre-fire) were similar to hazard assessments based on observed burn severity (post-fire).
Conclusion: Predicted burn severity can serve as an input to post-fire debris-flow models before wildfires occur, antecedent to standard post-fire burn severity products. Assessing a larger set of fires under disparate conditions and landscapes will be needed to refine predictive models.
Implications: Burn severity models based on pre-fire conditions enable the prediction of fire effects and identification of potential hazards to prioritise response and mitigation.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF22200","usgsCitation":"Wells, A.G., Hawbaker, T., Hiers, J.K., Kean, J.W., Loehman, R.A., and Steblein, P.F., 2023, Predicting burn severity for integration with post-fire debris-flow hazard assessment: A case study from the Upper Colorado River Basin, USA: International Journal of Wildland Fire, v. 32, no. 9, p. 1315-1331, https://doi.org/10.1071/WF22200.","productDescription":"17 p.","startPage":"1315","endPage":"1331","ipdsId":"IP-139674","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":442431,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wf22200","text":"Publisher Index Page"},{"id":423322,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.06565163962557,\n 43.427546253490476\n ],\n [\n -110.98798557839956,\n 42.01861589248239\n ],\n [\n -111.167880059579,\n 41.798766051959774\n ],\n [\n -111.44306126894347,\n 39.908461537613874\n ],\n [\n -112.53658323354853,\n 37.68116371252778\n ],\n [\n -111.48694518463799,\n 36.98448253816319\n ],\n [\n -108.9707627610722,\n 35.73546875520603\n ],\n [\n -106.21544190659625,\n 35.95406463803488\n ],\n [\n -105.24197446984596,\n 37.061352239951816\n ],\n [\n 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tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":889877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hiers, John Kevin 0000-0002-6813-8941","orcid":"https://orcid.org/0000-0002-6813-8941","contributorId":332282,"corporation":false,"usgs":true,"family":"Hiers","given":"John","email":"","middleInitial":"Kevin","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":889878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":889879,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":false,"id":889880,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steblein, Paul F. 0000-0001-7856-5106","orcid":"https://orcid.org/0000-0001-7856-5106","contributorId":213237,"corporation":false,"usgs":true,"family":"Steblein","given":"Paul","email":"","middleInitial":"F.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":889881,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256607,"text":"70256607 - 2023 - Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub","interactions":[],"lastModifiedDate":"2024-08-26T15:28:07.371737","indexId":"70256607","displayToPublicDate":"2023-08-11T10:19:49","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub","docAbstract":"A central challenge in applied ecology is understanding how organisms are spatially and temporally distributed and how management might be tailored to maintain or restore species distributions. The niche concept is central to understanding species distributions, but the diversity of niche definitions requires that multiple dimensions be considered. For example, the Grinnellian niche concept focuses on environmental conditions that allow species to persist, the Eltonian niche concept stresses the influence of biotic interactions, and the fundamental niche concept considers both abiotic and biotic environmental features to define spaces that organisms could occupy.
We combined abiotic (A), biotic (B), and movement (M) information (collectively, BAM model) to map the multidimensional niche of Prairie Chub Macrhybopsis australis, a regionally endemic freshwater fish currently under review for listing under the Endangered Species Act. We estimated A using remotely sensed environmental riverscape variables, B using the spatial distribution of a hybridization zone between Prairie Chub and Shoal Chub M. hyostoma, and M using data from a mark–recapture study.
The BAM model estimated the spatial extent of multiple niches, including the Grinnellian (A; extent = 944 km of river), Eltonian (B; 2974 km), and fundamental niche (overlap of A + B; 645 km) niches. When A, B, and M components were combined, the estimated extent of the Prairie Chub niche was 645 km.
Our work shows that the realized, multidimensional niche of Prairie Chub includes medium to large rivers with high habitat connectivity in the upper–middle Red River basin upstream of the distribution of Shoal Chub. The current Prairie Chub distribution could be maintained by preventing further habitat fragmentation and maintaining the environmental gradient separating Prairie Chub from Shoal Chub. Expansion of the species distribution may be possible through restoration of longitudinal fluvial connectivity.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10860","usgsCitation":"Steffensmeier, Z.D., Brewer, S.K., Wedgeworth, M., Starks, T.A., Rodger, A.W., Nguyen, E., and Perkin, J., 2023, Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub: North American Journal of Fisheries Management, v. 43, no. 5, p. 1205-1224, https://doi.org/10.1002/nafm.10860.","productDescription":"20 p.","startPage":"1205","endPage":"1224","ipdsId":"IP-142909","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433158,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Steffensmeier, Zachary D.","contributorId":341344,"corporation":false,"usgs":false,"family":"Steffensmeier","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wedgeworth, Maeghen","contributorId":341345,"corporation":false,"usgs":false,"family":"Wedgeworth","given":"Maeghen","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starks, Trevor A.","contributorId":145640,"corporation":false,"usgs":false,"family":"Starks","given":"Trevor","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rodger, Anthony W.","contributorId":302586,"corporation":false,"usgs":false,"family":"Rodger","given":"Anthony","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":908274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nguyen, Erin","contributorId":341346,"corporation":false,"usgs":false,"family":"Nguyen","given":"Erin","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perkin, Joshuah S.","contributorId":238286,"corporation":false,"usgs":false,"family":"Perkin","given":"Joshuah S.","affiliations":[{"id":47708,"text":"Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":908276,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250395,"text":"70250395 - 2023 - Automated mapping of culverts, bridges, and dams","interactions":[],"lastModifiedDate":"2023-12-21T16:35:54.551553","indexId":"70250395","displayToPublicDate":"2023-08-11T10:15:52","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Automated mapping of culverts, bridges, and dams","docAbstract":"Accurate maps of built structures around stream channels, such as dams, culverts, and bridges, are vital in monitoring infrastructure, risk management, and hydrologic modeling. Hydrologic modeling is essential for research and decisionmaking related to infrastructure and development planning, emergency management, ecology, and developing hydrographic data. Technological advances in remote sensing afford increasingly fine-scale elevation data, such as the U.S. Geological Survey 1-meter digital elevation models (DEMs), that can accurately model the Earth’s surface characteristics and related hydrologic dynamics. A long-standing challenge in flow modeling is the presence of built structures in an elevation model that resist flow in a way that does not reflect actual dynamics, such as culverts, bridges, and dams. This challenge is exacerbated in fine-scale elevation data as more built structures are resolved. Here we present a test of the extensibility of a culvert and dam detection workflow, culvert-net (CN). CN was developed using a large dataset of field-validated culverts, bridges, and dam locations for Alexander County, North Carolina, USA, supplemented by manual review and identification of additional features. In this workflow, the CN model is tested on a new study area in western Michigan, USA, where culverts and associated hydrography have recently been manually compiled.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Abstracts of the International Cartographic Association","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"31st International Cartographic Conference (ICC 2023)","conferenceDate":"August 13-18, 2023","conferenceLocation":"Cape Town, South Africa","language":"English","publisher":"Copernicus","doi":"10.5194/ica-abs-6-231-2023","usgsCitation":"Shavers, E.J., Stanislawski, L., Schott, J., and Brosseau, Z., 2023, Automated mapping of culverts, bridges, and dams, in Abstracts of the International Cartographic Association, v. 6, Cape Town, South Africa, August 13-18, 2023, 231, 2 p., https://doi.org/10.5194/ica-abs-6-231-2023.","productDescription":"231, 2 p.","ipdsId":"IP-149048","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":442442,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5194/ica-abs-6-231-2023","text":"Publisher Index Page"},{"id":423839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2023-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":889749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":889750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schott, Joel","contributorId":332235,"corporation":false,"usgs":false,"family":"Schott","given":"Joel","email":"","affiliations":[{"id":79425,"text":"Missouri University of Science and Technology, under contract to the U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":889751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brosseau, Zachary","contributorId":332236,"corporation":false,"usgs":false,"family":"Brosseau","given":"Zachary","email":"","affiliations":[{"id":79425,"text":"Missouri University of Science and Technology, under contract to the U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":889752,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247703,"text":"70247703 - 2023 - AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management","interactions":[],"lastModifiedDate":"2023-08-14T12:32:07.473699","indexId":"70247703","displayToPublicDate":"2023-08-11T07:31:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management","docAbstract":"To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the ‘Automated Interactive Monitoring System (AIMS) for Wildlife’. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.
No abstract available.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230003","usgsCitation":"Pollitz, F., Garza-Giron, R., and Lay, T., 2023, Comment on \"Multi-Event explosive seismic source for the 2022 Mw 6.3 Hunga Tonga submarine volcanic eruption\" by Julien Thurin, Carl Tape, and Ryan Modrak: The Seismic Record, v. 3, no. 3, p. 210-2014, https://doi.org/10.1785/0320230003.","productDescription":"5 p.","startPage":"210","endPage":"2014","ipdsId":"IP-149376","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":442457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230003","text":"Publisher Index Page"},{"id":426238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tonga","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.9,\n 15.707465011804047\n ],\n [\n 139.84794943809027,\n 20.668627791297567\n ],\n [\n 143.4623513761436,\n -40.646297636757645\n ],\n [\n 179.9,\n -39.74397803815904\n ],\n [\n 179.9,\n 15.707465011804047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 16.290847903689155\n ],\n [\n -179.9,\n -39.1693171545614\n ],\n [\n -150,\n -39.1693171545614\n ],\n [\n -150,\n 17.303337805258792\n ],\n [\n -179.9,\n 16.290847903689155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":895781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garza-Giron, Ricardo","contributorId":334466,"corporation":false,"usgs":false,"family":"Garza-Giron","given":"Ricardo","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":895782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":334467,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":895783,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247678,"text":"70247678 - 2023 - Conservation decision support for Silver Chub habitat in Lake Erie","interactions":[],"lastModifiedDate":"2023-11-20T17:35:54.449622","indexId":"70247678","displayToPublicDate":"2023-08-10T09:43:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Conservation decision support for Silver Chub habitat in Lake Erie","docAbstract":"Conservation and restoration of aquatic species is difficult, especially for rare species, because their habitats are typically disturbed, obscuring the natural ability of the habitat to support each species. The Lake Erie population of Silver Chub Macrhybopsis storeriana struggles to sustain itself in a habitat disturbed by a wide spectrum of anthropogenic factors. Application of multiple model predictions can provide indications of conservation or restoration opportunities for this species.
A combination of models that predict the best potential for Lake Erie habitat to support Silver Chub and the effects of anthropogenic disturbances on that population were used to identify habitat conditions throughout the western aquatic lake unit.
As many as 76 combinations of best habitat potential and disturbance conditions were present, but the best opportunities occurred in <12% of the study area. Some of the best protection opportunities were farthest offshore, and extensive areas of least disturbed habitat for restoration were near the southern and western shores. The location-specific model predictions provide fine-scale decision support for Silver Chub habitat protection or restoration.
The approach applied here may help identify compatibilities among species to achieve the desirable fish community for Lake Erie and reconcile conflicting management actions.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10843","usgsCitation":"McKenna, J.E., 2023, Conservation decision support for Silver Chub habitat in Lake Erie: North American Journal of Fisheries Management, v. 43, no. 5, p. 1151-1165, https://doi.org/10.1002/nafm.10843.","productDescription":"15 p.","startPage":"1151","endPage":"1165","ipdsId":"IP-137742","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":419749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ohio, Ontario","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.90370229732227,\n 41.50076537943562\n ],\n [\n -82.56348384906306,\n 41.989542296201876\n ],\n [\n -82.63835267762897,\n 42.04516549693972\n ],\n [\n -82.95186589725073,\n 41.98606423085357\n ],\n [\n -83.12032076152461,\n 42.093795884914755\n ],\n [\n -83.10160355438312,\n 42.26024574328264\n ],\n [\n -83.17179308116394,\n 42.235999094028784\n ],\n [\n -83.190510288306,\n 42.08337822384061\n ],\n [\n -83.46190979185857,\n 41.87118148731366\n ],\n [\n -83.53677862042505,\n 41.68624000731879\n ],\n [\n -83.30749283294051,\n 41.59531904873529\n ],\n [\n -83.0454519329587,\n 41.38150113199248\n ],\n [\n -82.62899407405826,\n 41.34286871970326\n ],\n [\n -81.90370229732227,\n 41.50076537943562\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-08-10","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":880015,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70248406,"text":"70248406 - 2023 - Evaluation of hydrodynamic mixing in an afterbay reservoir","interactions":[],"lastModifiedDate":"2023-09-12T14:01:11.219703","indexId":"70248406","displayToPublicDate":"2023-08-10T08:54:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2255,"text":"Journal of Environmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of hydrodynamic mixing in an afterbay reservoir","docAbstract":"This study focused on the mixing of a solute, assumed to be conservative, introduced to one arm of an afterbay reservoir, between Keswick and Shasta Dams on the Sacramento River near Redding, California. Rhodamine water tracer (WT) dye served as the solute in a field experiment, and was introduced over 4.5 h and monitored for 4 days by sondes moored in the reservoir. The scenario was modeled numerically using the Delft3D flexible mesh (FM) hydrodynamic and mixing model, with measured inflows, outflows, water level, water temperatures, and bathymetry as input. Manning’s
Diet analysis is a vital tool for understanding trophic interactions and is frequently used to inform conservation and management. Molecular approaches can identify diet items that are impossible to distinguish using more traditional visual-based methods. Yet, our understanding of how different variables, such as predator species or prey ration size, influence molecular diet analysis is still incomplete. Here, we conducted a large feeding trial to assess the impact that ration size, predator species, and temperature had on digestion rates estimated with visual identification, qPCR, and metabarcoding. Our trial was conducted by feeding two rations of Chinook salmon (Oncorhynchus tshawytscha) to two piscivorous fish species (largemouth bass [Micropterus salmoides] and channel catfish [Ictalurus punctatus]) held at two different temperatures (15.5 and 18.5°C) and sacrificed at regular intervals up to 120 h from the time of ingestion to quantify the prey contents remaining in the digestive tract. We found that ration size, temperature, and predator species all influenced digestion rate, with some indication that ration size had the largest influence. DNA-based analyses were able to identify salmon smolt prey in predator gut samples for much longer than visual analysis (~12 h for visual analysis vs. ~72 h for molecular analyses). Our study provides evidence that modelling the persistence of prey DNA in predator guts for molecular diet analyses may be feasible using a small set of controlling variables for many fish systems.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13849","usgsCitation":"Dick, C., Larson, W., Karpan, K., Baetscher, D.S., Shi, Y., Sethi, S., Fangue, N., and Henderson, M., 2023, Prey ration, temperature, and predator species influence digestion rates of prey DNA inferred from qPCR and metabarcoding: Molecular Ecology Resources, v. 00, p. 1-17, https://doi.org/10.1111/1755-0998.13849.","productDescription":"17 p.","startPage":"1","endPage":"17","ipdsId":"IP-148203","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":442471,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/1755-0998.13849","text":"External Repository"},{"id":433116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"00","noUsgsAuthors":false,"publicationDate":"2023-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dick, Cory","contributorId":342431,"corporation":false,"usgs":false,"family":"Dick","given":"Cory","email":"","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":910100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Wesley A.","contributorId":342433,"corporation":false,"usgs":false,"family":"Larson","given":"Wesley A.","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpan, Kirby","contributorId":342435,"corporation":false,"usgs":false,"family":"Karpan","given":"Kirby","email":"","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baetscher, Diana S.","contributorId":342437,"corporation":false,"usgs":false,"family":"Baetscher","given":"Diana","email":"","middleInitial":"S.","affiliations":[{"id":37482,"text":"National Oceanographic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":910103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Yue","contributorId":342439,"corporation":false,"usgs":false,"family":"Shi","given":"Yue","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910104,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910105,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fangue, Nann A.","contributorId":342441,"corporation":false,"usgs":false,"family":"Fangue","given":"Nann A.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":910106,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":910107,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247499,"text":"70247499 - 2023 - SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline","interactions":[],"lastModifiedDate":"2023-08-10T11:47:28.20107","indexId":"70247499","displayToPublicDate":"2023-08-09T06:45:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"SaTSeaD: Satellite Triangulated Sea Depth open-source bathymetry module for NASA Ames Stereo Pipeline","docAbstract":"Native pinyon (Pinus spp.) and juniper (Juniperus spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush (Artemisia spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network (www.SageSTEP.org), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III.
Increased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82 m min−1 in mechanical and 106 m min−1 in prescribed fire treatments by 10 years post-treatment compared to 5 m min−1 in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments.
While prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-023-00201-7","usgsCitation":"Williams, C.L., Ellsworth, L., Strand, E., Reeves, M.C., Shaff, S.E., Short, K., Chambers, J., Newingham, B., and Tortorelli, C., 2023, Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior: Fire Ecology, v. 19, 46, 21 p., https://doi.org/10.1186/s42408-023-00201-7.","productDescription":"46, 21 p.","ipdsId":"IP-154672","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":442492,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-023-00201-7","text":"Publisher Index Page"},{"id":419748,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.242321499979,\n 37.26614922694803\n ],\n [\n -111.73150627380747,\n 40.33982762810214\n ],\n [\n -117.02080093108165,\n 40.473878098349985\n ],\n [\n -116.74789132368525,\n 38.56409342781134\n ],\n [\n -112.242321499979,\n 37.26614922694803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.35613130086793,\n 41.4092957538588\n ],\n [\n -117.52494090332212,\n 43.41419744406673\n ],\n [\n -120.12652755099501,\n 45.42305748463039\n ],\n [\n -122.07668627870967,\n 43.86124338845653\n ],\n [\n -121.35613130086793,\n 41.4092957538588\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Claire L.","contributorId":328374,"corporation":false,"usgs":false,"family":"Williams","given":"Claire","email":"","middleInitial":"L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":880021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellsworth, Lisa M.","contributorId":328375,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa M.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":880022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Strand, Eva","contributorId":328376,"corporation":false,"usgs":false,"family":"Strand","given":"Eva","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":880023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeves, Matt C.","contributorId":328377,"corporation":false,"usgs":false,"family":"Reeves","given":"Matt","email":"","middleInitial":"C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaff, Scott E. 0000-0001-8978-9260","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":219813,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":880025,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Short, Karen","contributorId":328378,"corporation":false,"usgs":false,"family":"Short","given":"Karen","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880026,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chambers, Jeanne C.","contributorId":328379,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":880027,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Newingham, Beth","contributorId":328380,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":880028,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tortorelli, Claire","contributorId":328381,"corporation":false,"usgs":false,"family":"Tortorelli","given":"Claire","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":880029,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249617,"text":"70249617 - 2023 - Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams","interactions":[],"lastModifiedDate":"2024-09-16T16:08:11.965666","indexId":"70249617","displayToPublicDate":"2023-08-07T09:15:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams","docAbstract":"Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, whereas nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16905","usgsCitation":"DeVilbiss, S.E., Taylor, J.M., and Hicks, M.B., 2023, Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment-bound bacterial communities in agricultural streams: Global Change Biology, v. 29, no. 19, p. 5615-5633, https://doi.org/10.1111/gcb.16905.","productDescription":"19 p.","startPage":"5615","endPage":"5633","ipdsId":"IP-147797","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":442494,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.16905","text":"Publisher Index Page"},{"id":421998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Mississippi Alluvial Plain ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.5140953224701,\n 31.05055950304515\n ],\n [\n -90.8551102985312,\n 32.36507651262579\n ],\n [\n -89.83254733034994,\n 33.34811466354185\n ],\n [\n -89.71892922277418,\n 33.94399111104305\n ],\n [\n -90.08250716701652,\n 35.020996431931664\n ],\n [\n -90.25293432837982,\n 34.97445988995358\n ],\n [\n -90.4347233005013,\n 34.82536524774929\n ],\n [\n -90.54834140807708,\n 34.67600021318641\n ],\n [\n -90.61651227262215,\n 34.376461081479576\n ],\n [\n -90.76421581247044,\n 34.29202179657514\n ],\n [\n -90.95736659534946,\n 34.13229367636508\n ],\n [\n -91.0255374598945,\n 33.91570969239025\n ],\n [\n -91.15051737822778,\n 33.54719821260997\n ],\n [\n -91.11643194595524,\n 33.06291844822118\n ],\n [\n -91.13915556747027,\n 32.709900045262685\n ],\n [\n -90.95736659534946,\n 32.38426808119267\n ],\n [\n -91.57090437625834,\n 31.361530547535793\n ],\n [\n -91.6731606730759,\n 31.0213536083742\n ],\n [\n -91.5140953224701,\n 31.05055950304515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"19","noUsgsAuthors":false,"publicationDate":"2023-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"DeVilbiss, Stephen E.","contributorId":316291,"corporation":false,"usgs":false,"family":"DeVilbiss","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":886463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Jason M.","contributorId":100678,"corporation":false,"usgs":true,"family":"Taylor","given":"Jason","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":886464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886465,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247508,"text":"70247508 - 2023 - A spatially explicit modeling framework to guide management of subsidized avian predator densities","interactions":[],"lastModifiedDate":"2023-08-10T11:43:08.209226","indexId":"70247508","displayToPublicDate":"2023-08-07T06:39:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A spatially explicit modeling framework to guide management of subsidized avian predator densities","docAbstract":"Anthropogenic resource subsidization across western ecosystems has contributed to widespread increases in generalist avian predators, including common ravens (Corvus corax; hereafter, raven). Ravens are adept nest predators and can negatively impact species of conservation concern. Predation effects from ravens are especially concerning for greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), which have experienced prolonged population decline. Our objectives were to quantify spatiotemporal patterns in raven density, evaluate sage-grouse nest success concurrent with fluctuating raven densities, and demonstrate a spatially explicit decision support tool to guide management applications to appropriate conflict areas. We combined ~28,000 raven point count surveys with data from more than 900 sage-grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage-grouse nest survival using a hierarchical frailty model as a function of raven density and other environmental covariates that influence the risk of nest failure. Raven density commonly exceeded 0.5 ravens km−2 and increased at low elevations with more anthropogenic development and/or agriculture. Reduced sage-grouse nest survival was strongly associated with elevated raven density (e.g., >0.5 ravens km−2) and varied with topographic ruggedness, shrub cover, and burned areas. For conservation application, we developed a spatially explicit planning tool that predicts nest survival under current and reduced raven numbers within the Great Basin to help direct management actions to localized areas where sage-grouse nests are at highest risk of failure. Our modeling framework can be generalized to multiple species where spatially registered abundance and demographic data are available.