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.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01712-z","usgsCitation":"Van Lanen, N.J., Shyvers, J.E., Duchardt, C.J., and Aldridge, C.L., 2023, A multi-ecosystem prioritization framework to balance competing habitat conservation needs of multiple species in decline: Landscape Ecology, v. 38, p. 2795-2813, https://doi.org/10.1007/s10980-023-01712-z.","productDescription":"19 p.","startPage":"2795","endPage":"2813","ipdsId":"IP-147313","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":442419,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-023-01712-z","text":"Publisher Index Page"},{"id":435222,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QFTK1D","text":"USGS data release","linkHelpText":"Prioritized sites for conifer removal within the Utah portion of Bird Conservation Region 16, 2020"},{"id":435221,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJHTMQ","text":"USGS data release","linkHelpText":"Predicted 2020 densities for 11 songbird species across the western United States"},{"id":420062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationDate":"2023-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":880924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shyvers, Jessica E. 0000-0002-4307-0004","orcid":"https://orcid.org/0000-0002-4307-0004","contributorId":288929,"corporation":false,"usgs":true,"family":"Shyvers","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":880925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duchardt, Courtney J. 0000-0003-4563-0199","orcid":"https://orcid.org/0000-0003-4563-0199","contributorId":239754,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney","middleInitial":"J.","affiliations":[{"id":48000,"text":"U Wyoming","active":true,"usgs":false}],"preferred":false,"id":880926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":880927,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247744,"text":"ofr20231061 - 2023 - Fall contributions of phosphorus and nitrogen in stormwater runoff through weekly street cleaning","interactions":[],"lastModifiedDate":"2026-02-11T21:36:13.210009","indexId":"ofr20231061","displayToPublicDate":"2023-08-15T15:34:48","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1061","displayTitle":"Fall Contributions of Phosphorus and Nitrogen in Stormwater Runoff through Weekly Street Cleaning","title":"Fall contributions of phosphorus and nitrogen in stormwater runoff through weekly street cleaning","docAbstract":"This report describes a study that characterized reductions in total and dissolved forms of phosphorus and nitrogen in stormwater runoff through implementation of a municipal leaf collection and street cleaning program in two medium-density residential catchments in Madison, Wisconsin. One catchment was established as a control in which no effort was made to remove leaf litter and other organic detritus from streets. The second catchment served as the test catchment in which leaf litter was removed through a combination of biweekly leaf collection and weekly mechanical-broom street cleaning. Loads of total and dissolved phosphorus in the test catchment were reduced by 46 and 51 percent (probability less than 0.20), respectively, and total and dissolved nitrogen by 42 and 52 percent (probability less than 0.20), respectively, with an active leaf collection and street cleaning program compared to no program in the control catchment. Results from this study support an ongoing effort to characterize how various combinations of municipal leaf collection and street cleaning can affect nutrient loads in urban runoff.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231061","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources and League of Wisconsin Municipalities","usgsCitation":"Selbig, W.R., and Stenehjem, K.J., 2023, Fall contributions of phosphorus and nitrogen in stormwater runoff through weekly street cleaning: U.S. Geological Survey Open-File Report 2023–1061, 11 p., https://doi.org/10.3133/ofr20231061.","productDescription":"Report: vi, 11 p.; Dataset","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-150684","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419832,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":499786,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115215.htm","linkFileType":{"id":5,"text":"html"}},{"id":419833,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231061/full"},{"id":419831,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1061/images"},{"id":419830,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1061/ofr20231061.XML"},{"id":419829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1061/ofr20231061.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1061"},{"id":419828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1061/coverthb.jpg"}],"country":"United States","state":"Wisconsin","county":"Dane County","city":"Madison","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.38888315075111,\n 43.151755311064505\n ],\n [\n -89.38888315075111,\n 43.13561018325166\n ],\n [\n -89.36103939056075,\n 43.13561018325166\n ],\n [\n -89.36103939056075,\n 43.151755311064505\n ],\n [\n -89.38888315075111,\n 43.151755311064505\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
How does an earthquake affect buried pipeline networks? It is well known that the seismic performance of buried pipelines depends on ground failures (GFs) as well as strong ground shaking (SGS), but it is unclear how the various types of earthquake hazards should be collectively combined, as existing methodologies tend to examine each of the earthquake hazards separately. In this article, we develop a probability‐based methodology to consistently combine SGS with three types of GF (surface faulting, liquefaction, and landslide) for forecasting seismic damage in buried pipeline networks from a given earthquake rupture scenario. Using a gas transmission pipeline example, we illustrate how the proposed methodology enables others (e.g., researchers, pipeline operators who manage distribution lines, and consultants) to modularly combine various models such as those for estimating probability of GF, permanent ground displacements, and pipeline fragility. Finally, we compare the proposed methodology against the Hazus methodology to explore implications from considering each hazard one at a time.
","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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233029","usgsCitation":"Carlson, T., 2023, The 3D Elevation Program—Supporting Oregon's economy: U.S. Geological Survey Fact Sheet 2023–3029, 2 p., https://doi.org/10.3133/fs20233029.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-122479","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":419966,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3029/images/"},{"id":419965,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3029/fs20233029.XML"},{"id":419964,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 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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":"Every year in the United States, natural hazards threaten lives and livelihoods, resulting in thousands of casualties and billions of dollars in damage. The U.S. Geological Survey (USGS) Natural Hazards Mission Area works with many partners to monitor, assess, and research a wide range of natural hazards, including earthquakes and volcanic eruptions. These efforts aim to enhance community preparedness, response, and resilience. The USGS Earthquake Hazards Program (EHP) provides earthquake monitoring and notifications, assesses seismic hazards, and conducts targeted research to reduce the risk of earthquake hazards nationwide. The USGS Volcano Hazards Program (VHP) delivers forecasts, warnings, and information about volcanic hazards based on proactive monitoring of the nation’s active volcanoes and scientific understanding of volcanic processes. The VHP also conducts targeted research on volcanic processes and creates hazards assessments that inform the level of monitoring required at each of the nation’s active volcanoes. Earthquake and volcano early warning systems are essential to disaster risk reduction: they can save lives and reduce property damage by quickly distributing messages and warnings to communities in harm’s way.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233033","programNote":"Volcano Hazards Program","usgsCitation":"Wilkins, A., Mandeville, C., Power, J., and Given, D., 2023, Comparison of earthquake early warning systems and the national volcano early warning system at the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2023-3033, 4 p., https://doi.org/10.3133/fs20233033.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-139283","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":419798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3033/fs20233033.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3033"},{"id":419797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3033/coverthb.jpg"},{"id":419968,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3033/images/"},{"id":419967,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233033/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3033"},{"id":419969,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3033/fs20233033.XML"}],"contact":"Associate Director, Natural Hazards
U.S. Geological Survey
12201 Sunrise Valley Dr., Mail Stop 905
Reston, VA 20192
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 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":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
Porosity, the fraction of soil volume not occupied by solids, is relatively easy to conceptualize and measure. Pore-size distribution is a complex topic, in part from the lack of a clear and unique concept of a soil pore as a discrete object. Available tools for evaluating pore-size distribution involve traditional conventions and operational definitions applied to hydraulic property characterizations, as well as more recently developed imaging techniques. Application of these tools can provide effective characterizations of the pores and pore space that lead to an understanding of the relation between pore characteristics and soil functions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Soils in the Environment","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-822974-3.00139-7","usgsCitation":"Nimmo, J.R., Katuwal, S., and Lucas, M., 2023, Porosity and pore-size distribution, chap. of Encyclopedia of Soils in the Environment, v. 5, p. 16-24, https://doi.org/10.1016/B978-0-12-822974-3.00139-7.","productDescription":"9 p.","startPage":"16","endPage":"24","ipdsId":"IP-142777","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":420121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":875699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katuwal, Sheela","contributorId":310444,"corporation":false,"usgs":false,"family":"Katuwal","given":"Sheela","email":"","affiliations":[{"id":67189,"text":"U. Arkansas","active":true,"usgs":false}],"preferred":false,"id":875700,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucas, Maik","contributorId":310445,"corporation":false,"usgs":false,"family":"Lucas","given":"Maik","email":"","affiliations":[{"id":67191,"text":"Michigan State U.","active":true,"usgs":false}],"preferred":false,"id":875701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246691,"text":"sir20235068 - 2023 - Flood-inundation maps for Fourmile Creek at Silver Grove, Kentucky","interactions":[],"lastModifiedDate":"2026-03-09T17:08:29.267739","indexId":"sir20235068","displayToPublicDate":"2023-08-14T09:50:00","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":"2023-5068","displayTitle":"Flood-Inundation Maps for Fourmile Creek at Silver Grove, Kentucky","title":"Flood-inundation maps for Fourmile Creek at Silver Grove, Kentucky","docAbstract":"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
No abstract available.
","language":"English","publisher":"Polk County Conservation","usgsCitation":"Burch, T., Stokdyk, J.P., Firnstahl, A.D., Opelt, S., Cook, R.M., and Borchardt, M.A., 2023, Des Moines water trails: Health risk from waterborne pathogens during recreational water use, 55 p.","productDescription":"55 p.","ipdsId":"IP-152824","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":430569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430568,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.polkcountyiowa.gov/conservation/water-quality/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Iowa","city":"Des Moines","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.9020694474879,\n 41.79712862176632\n ],\n [\n -93.9020694474879,\n 41.46537191589431\n ],\n [\n -93.43046961673721,\n 41.46537191589431\n ],\n [\n -93.43046961673721,\n 41.79712862176632\n ],\n [\n -93.9020694474879,\n 41.79712862176632\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":905026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opelt, Sarah A.","contributorId":300168,"corporation":false,"usgs":false,"family":"Opelt","given":"Sarah","middleInitial":"A.","affiliations":[],"preferred":false,"id":905029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Rachel M.","contributorId":300167,"corporation":false,"usgs":false,"family":"Cook","given":"Rachel","middleInitial":"M.","affiliations":[],"preferred":false,"id":905030,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":905031,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256081,"text":"70256081 - 2023 - MTAB 105, August 2023","interactions":[],"lastModifiedDate":"2024-07-19T13:09:35.767659","indexId":"70256081","displayToPublicDate":"2023-08-14T08:08:07","publicationYear":"2023","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":13451,"text":"Memo to All Banders (MTAB)","active":true,"publicationSubtype":{"id":30}},"title":"MTAB 105, August 2023","docAbstract":"This Memo to All Banders (MTAB 105) was released in August 2023. Subjects in this memo are 1. The Chief’s Chirp – a reminder to review ethics guidelines; 2. Alerts – Highly Pathogenic Avian Influenza, reminder that banders can only manage data not submit data through Bandit, reminder to be mindful of nighttime migration; 3. Staff updates – Kyra Harvey joins the BBL and the BBL welcomes student contractor; 4. News – updates to the data templates, BWBI hosts Rob Robinson from British Trust for Ornithology; 5. A note from the permitting shelves – Multi-permit collaborative project instructions; 6. A note from the supply room – 1C bands available; 7. Data management – Bander Portal video tutorials, Bander Portal Portal Office Hours, Resolving “Warnings” on the Bander Portal’s Data Cleaning/ Submission Page; 8. Frequently asked questions – Is there an easier way to load lots of locations into the Bander Portal?; 9. Auxiliary marker corner - information on how to submit data with transmitters attached to color markers; 10. Banding and encounter highlights; 11. Message to the Flyways; 12. Moments in history; 13. Recent Publications; 14. Upcoming events; 15. Request for information; and 16. Appendix - NABBP database species updates.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Harvey, K., 2023, MTAB 105, August 2023: Memo to All Banders (MTAB), 17 p.","productDescription":"17 p.","ipdsId":"IP-156792","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":431239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":431230,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/mtab-105-august-2023"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Kyra 0000-0003-4781-1874","orcid":"https://orcid.org/0000-0003-4781-1874","contributorId":296250,"corporation":false,"usgs":true,"family":"Harvey","given":"Kyra","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":906630,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70247673,"text":"ofr20231057 - 2023 - Drought-vulnerability assessment of public water systems in West Virginia","interactions":[],"lastModifiedDate":"2026-02-11T21:30:53.996113","indexId":"ofr20231057","displayToPublicDate":"2023-08-14T07:23:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1057","displayTitle":"Drought-Vulnerability Assessment of Public Water Systems in West Virginia","title":"Drought-vulnerability assessment of public water systems in West Virginia","docAbstract":"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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231057","usgsCitation":"Kearns, M.R., Faunce, K.E., and Messinger, T., 2023, Drought-vulnerability assessment of public water systems in West Virginia: U.S. Geological Survey Open-File Report 2023–1057, 14 p., https://doi.org/10.3133/ofr20231057.","productDescription":"Report: viii, 14 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151563","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":420078,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GHK4Y0","text":"USGS data release","linkHelpText":"Water withdrawal data of selected public water systems in West Virginia, 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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
Between 38.5 ka cal BP and 32.4 ka cal BP, a dacitic Volcanic Explosivity Index 5 eruption at Misti volcano emplaced the Sacarosa tephra-fall deposit. Its biotite phenocrysts, fine grain size, scarce lithics, and abundant loose crystals characterize the deposit at locations sampled. The eruption’s ~ 800 °C magma rose rapidly from ~ 10 km depth, culminating in a Plinian eruption which reached a mass eruption rate of 7.7 × 106–4.1 × 107 kg/s and emplaced about 3 km3 of tephra within tens of hours. The unit comprises two layers of subequal thickness separated by a diffuse contact with the upper distinguished by being slightly coarser and less well sorted than the lower. The deposit’s coarser upper layer indicates either climactic conditions or a lesser degree of fragmentation during the latter half of the eruption. Strong winds distributed the deposit southwest of Misti, where it crops out over at least 800 km2 and drapes the present site of Arequipa with up to 100 cm of tephra. The Sacarosa deposit is the first among the Cayma stage deposits, a distinctive group of felsic, biotite-bearing units, to be carefully described and its eruption characterized. Several Cayma stage deposits were emplaced by voluminous explosive eruptions similar to the Sacarosa eruption, representing a ~ 8.9–15.5 ky interval of powerful eruptions. Such an explosive eruption today would threaten Arequipa’s over 1,100,000 residents, many of whom live within the Sacarosa deposit’s distribution.
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 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","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2023.1252360","usgsCitation":"Alves, F., Queiroz, N., and Jodice, P.G., 2023, Editorial: Ecological and behavioral traits of apex predators in oceanic insular ecosystems: Advances and challenges in research and conservation: Frontiers in Marine Science, v. 10, 1252360, 3 p., https://doi.org/10.3389/fmars.2023.1252360.","productDescription":"1252360, 3 p.","ipdsId":"IP-154439","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":442436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2023.1252360","text":"Publisher Index Page"},{"id":432042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2023-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Alves, Filipe","contributorId":340554,"corporation":false,"usgs":false,"family":"Alves","given":"Filipe","email":"","affiliations":[{"id":81629,"text":"MARE – Marine and Environmental Sciences Centre","active":true,"usgs":false}],"preferred":false,"id":907356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Queiroz, Nuno","contributorId":340555,"corporation":false,"usgs":false,"family":"Queiroz","given":"Nuno","affiliations":[{"id":81630,"text":"CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos","active":true,"usgs":false}],"preferred":false,"id":907357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907358,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247828,"text":"70247828 - 2023 - Assessing the suitability of YY males and ZZ females as an invasive species population control method across life histories","interactions":[],"lastModifiedDate":"2023-11-07T15:33:59.683477","indexId":"70247828","displayToPublicDate":"2023-08-12T07:06:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the suitability of YY males and ZZ females as an invasive species population control method across life histories","docAbstract":"Natural resource managers use tools to control invasive species. In theory, stocking YY males or ZZ females would allow managers to skew sex ratios until populations collapse. In combination with other suppression methods, such as removal, this approach could be incorporated into Integrated Pest Management plans. For example, fishery managers have stocked YY males to control isolated non-native brook trout (Salvelinus fontinalis) populations. However, life histories and demographic factors (e.g., lifespans) vary across species and could affect the feasibility of skewing sex ratios as an effective control strategy for a given population. Likewise, some species may have sex determinations that do not allow population control through sex-skewing methods. We compared five representative aquatic invasive species with global invasion ranges for potential control by skewing the sex ratio through closed population simulations: red swamp crayfish (Procambarus clarkii), zebra mussels (Dreissena polymorpha), lake trout (Salvelinus namaycush), silver carp (Hypophthalmichthys molitrix), and Nile tilapia (Oreochromis niloticus). We determined that Nile tilapia, red swamp crayfish, and zebra mussels would be the most suitable to control through skewing the sex ratio assuming appropriate sex determination exists in the species. Lake trout could be eliminated by stocking YY males but would require either long stocking periods or high stocking numbers because of the long lifespan of the species. Silver carp populations were more difficult to crash because they live longer and produce many recruits. Broadly, these patterns demonstrated that short lived species lend themselves to control by skewing the sex ratio.
The U.S. Geological Survey (USGS) has a long history of working cooperatively with the South Carolina Department of Transportation to develop methods for estimating the magnitude and frequency of floods for rural and urban streams that have minimal to no regulation or tidal influence. As part of those previous investigations, flood-frequency estimates also have been generated for selected streamgages on regulated streams. This report assesses the effects of impoundments on flood-frequency characteristics by comparing annual exceedance probability (AEP) streamflows from pre- and post-regulated (before and after impoundment) periods at 18 long-term USGS streamgages, which is defined as a streamgage with 30 or more years of record, in Georgia, South Carolina, and North Carolina. For an assessment of how differences in such statistics can be influenced by period of record and hydrologic conditions captured in those records, which could be considered as natural variability, AEP streamflows at an additional 18 long-term USGS streamgages that represent unregulated conditions in those three States were computed and compared for the first and last half of those records.
Of the 18 long-term streamgages with pre- and post-regulated periods of record, 17 streamgages had both peak streamflows and daily mean streamflows available. To further assess how impoundments may influence a broader range of streamflow characteristics, The Nature Conservancy’s Indicators of Hydrologic Alteration software was used to compare selected streamflow characteristics generated from daily mean streamflows for pre- and post-regulated periods of record at 16 of those long-term streamgages. For comparison of the natural variability of such streamflow statistics, two periods of record (first half and last half) also were compared at 17 of the 18 long-term streamgages on unregulated streams. The remaining long-term streamgage on an unregulated stream included in this report had only annual peak streamflows and, therefore, was not included in the hydrologic alteration analysis.
In a separate USGS investigation completed in 2023, flood-frequency statistics for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP streamflows (also known as the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence interval streamflows, respectively) were computed for 72 streamgages on regulated streams in Georgia, South Carolina, and North Carolina. Of those 72 streamgages, 29 streamgages were found to be redundant, which is a situation where the drainage basin of one streamgage is contained inside another (nested) and the two basins are of similar size. For the remaining 43 streamgages, 39 had basins where 75 percent or more of the drainage area was above the Fall Line. Those 39 streamgages were included in this investigation to develop regional regression equations that can be used to estimate the flood-frequency statistics at ungaged locations on regulated streams in Georgia, South Carolina, and North Carolina in which 75 percent or more of the drainage basin is located above the Fall Line. The flood-frequency regression equations are functions of drainage area and maximum storage index computed for upstream reservoirs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235065","issn":"2328-0328","collaboration":"Prepared in cooperation with South Carolina Department of Transportation","usgsCitation":"Feaster, T.D., and Musser, J.W., 2023, Effects of impoundments on selected flood-frequency and daily mean streamflow characteristics in Georgia, South Carolina, and North Carolina: U.S. Geological Survey Scientific Investigations Report 2023–5065, 43 p., https://doi.org/10.3133/sir20235065.","productDescription":"Report: viii, 43 p.; 2 Data Releases","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-135158","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":419669,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5065/images"},{"id":419929,"rank":7,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Lead mining in the Southeast Missouri Lead Mining District began in the 1700s and continued for nearly 300 years; the waste piles associated with smelting, mining, and milling of lead ores have released metal residues that have contaminated soil and water in the region. Previous studies in the district have indicated potential harm to wildlife, including birds, because of elevated lead concentrations associated with mining. Exposure to soil-borne lead was correlated with elevated lead concentrations in tissues, inhibition of δ-aminolevulinic acid dehydratase (δALAD), and renal lesions in birds foraging on ground-dwelling invertebrates at contaminated sites (compared to reference sites) in the Southeast Missouri Lead Mining District.
This study assessed reproductive outcomes for songbirds exposed to soil-borne lead in the district, examined the relation between lead concentrations in soils and in tissues of ground-feeding birds and prey species, and compared the results to literature-based toxicity thresholds for lead that are associated with negative effects in birds. Three lead-contaminated sites and three reference sites (with background concentrations of lead and no known mining inputs) were compared in two ways: individually to all other sites or by site type. Additional effects of lead exposure were evaluated by examining concentrations of biomarkers (oxidative stress, lipid peroxidation, and deoxyribonucleic acid damage) in liver tissues, δALAD inhibition, and renal and hepatic microscopic lesions in birds from lead-contaminated and reference sites.
Lead concentrations in soil were site-dependent and were also generally heterogeneous within the lead-contaminated sites. Between 17 and 74 percent of all soil samples at contaminated sites had lead concentrations that exceeded a threshold (1,000 milligrams per kilogram [mg/kg] lead in soil) previously associated with adverse physiological effects in birds in the Southeast Missouri Lead Mining District. Lead concentrations in mixed invertebrates from lead-contaminated sites (282 to 2,230 mg/kg dry weight [dw]) indicated that consuming soil-dwelling prey species is a potential exposure pathway for adult birds and their broods. At lead-contaminated sites, lead concentrations in 40.5 percent of blood samples (adults and their broods) were within a subclinical effects range (0.9 to 2.3 mg/kg dw), and 18.7 percent of samples had lead concentrations that exceeded clinical effects criteria (greater than 2.3 mg/kg dw). In contrast, only 2.6 percent of blood samples from reference sites were within the subclinical effects range for lead; all other blood samples from the reference sites had lead concentrations representative of background concentrations (less than 0.9 mg/kg dw). Subclinical and clinical threshold exceedances for lead concentrations in livers and kidneys were similarly more prevalent at the contaminated sites compared to the reference sites.
Lead concentrations in blood were positively correlated with lead concentrations in soil, livers, and kidneys. Lead concentrations in blood were negatively correlated with δALAD activity; greater than 50 percent of the birds collected at lead-contaminated sites exhibited injury via greater than 50 percent inhibition of δALAD in blood compared to birds at reference sites. Birds with elevated lead concentrations in tissues also exhibited enhanced oxidative stress. Microscopic lesions in the livers and kidneys of birds had similar rates of occurrence at the contaminated and reference sites, and lesion prevalence could not be directly linked to lead exposure. Reproductive success was monitored at 585 nests, and 3 out of 5 species had reduced nest success associated with elevated lead concentrations in soil; habitat measures did not help explain nest success. Reduced nest success may have resulted from greater nest predation resulting from neurological and behavioral effects of lead exposure. Ultimately, these lines of evidence indicate that bird health and reproduction have been negatively affected by exposure to lead-contaminated soils in the Southeast Missouri Lead Mining District.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235032","usgsCitation":"Brasso, R., Cleveland, D., Thompson, F.R., III, Mosby, D.E., Hixson, K., Roach, M., Rattner, B.A., Karouna-Renier, N.K., and Lankton, J.S., 2023, Effects of lead exposure on birds breeding in the Southeast Missouri Lead Mining District: U.S. Geological Survey Scientific Investigations Report 2023–5032, 127 p., https://doi.org/10.3133/sir20235032.","productDescription":"Report: xi, 127 p.; Data Release","numberOfPages":"144","onlineOnly":"Y","ipdsId":"IP-134142","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419666,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RV1D60","text":"USGS data release","linkHelpText":"Breeding songbird tissue analyses and metal concentrations in tissues, soil and invertebrates collected near nesting sites within the Southeast Missouri Lead Mining District, 2016–19"},{"id":419662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5032/coverthb.jpg"},{"id":419663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5032/sir20235032.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5032"},{"id":419664,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5032/sir20235032.XML"},{"id":500894,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115182.htm","linkFileType":{"id":5,"text":"html"}},{"id":419755,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235032/full"},{"id":419665,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5032/images"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.66252506041675,\n 39.51237957711933\n ],\n [\n -92.66252506041675,\n 36.74875309799043\n ],\n [\n -89.01662840507522,\n 36.74875309799043\n ],\n [\n -89.01662840507522,\n 39.51237957711933\n ],\n [\n -92.66252506041675,\n 39.51237957711933\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
A goal of U.S. Geological Survey (USGS) research is to create actionable science for U.S. Department of the Interior managers and partners. Properly managed and released software tools can quickly, accurately, and easily translate scientific results to aid managers’ decision-making. The USGS policies on software development and distribution affect how authors of USGS software products must navigate the review and approval process. Understanding these rules and procedures will save time and effort in making science useful and visible through software development. This document outlines USGS policies on software development as they pertain to Shiny applications, which use R code to create interactive web-based applications. The requirements and procedures described here will also be relevant for other USGS software applications.
This document is not intended to be a step-by-step guide on building a Shiny application or to go into detail about any of the referenced software programs. An understanding of the referenced software programs, including R, Git, and hypertext markup language/cascading style sheets (HTML/CSS) is needed to use this document efficiently, though links for more thorough guidance are included throughout.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7B2","programNote":"Ecosystems Mission Area—Land Management Research Program","usgsCitation":"Shryock, D.F., Wright, M.C., van Mantgem, P.J., and Esque, T.C., 2023, Best practices for distributing and deploying U.S. Geological Survey Shiny applications: U.S. Geological Survey Techniques and Methods, book 7, chap. B2, 10 p., https://doi.org/10.3133/tm7B2.","productDescription":"iv, 10 p.","numberOfPages":"10","onlineOnly":"Y","ipdsId":"IP-141440","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":419717,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/07/b02/images"},{"id":419716,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/07/b02/tm7B2.xml"},{"id":419715,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm7B2/full"},{"id":419713,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/b02/covrthb.jpg"},{"id":419714,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/b02/tm7B2.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
No abstract available.
","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":"International Cartographic Association","doi":"10.5194/ica-abs-6-10-2023","usgsCitation":"Arundel, S., Gaurav, S., Li, W., Martin, D., McKeehan, K.G., and Thiem, P.T., 2023, Historical maps inform landform cognition in machine learning, in Abstracts of the international cartographic association, v. 6, Cape Town, South Africa, August 13-18, 2023, 10, 2 p., https://doi.org/10.5194/ica-abs-6-10-2023.","productDescription":"10, 2 p.","ipdsId":"IP-148964","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":442441,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5194/ica-abs-6-10-2023","text":"Publisher Index Page"},{"id":421276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2023-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":884336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaurav, Sinha 0000-0002-1280-6269","orcid":"https://orcid.org/0000-0002-1280-6269","contributorId":330211,"corporation":false,"usgs":false,"family":"Gaurav","given":"Sinha","email":"","affiliations":[{"id":12807,"text":"Ohio University","active":true,"usgs":false}],"preferred":false,"id":884337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":884338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, David P.","contributorId":330212,"corporation":false,"usgs":false,"family":"Martin","given":"David P.","affiliations":[{"id":48960,"text":"Duke Energy","active":true,"usgs":false}],"preferred":false,"id":884339,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKeehan, Kevin G 0000-0002-7242-6954","orcid":"https://orcid.org/0000-0002-7242-6954","contributorId":330206,"corporation":false,"usgs":true,"family":"McKeehan","given":"Kevin","email":"","middleInitial":"G","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":884340,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":884341,"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":70247538,"text":"ofr20231058 - 2023 - Lock 19 underwater acoustic deterrent system study—Interim project update, through 2022","interactions":[],"lastModifiedDate":"2023-08-18T12:21:24.733583","indexId":"ofr20231058","displayToPublicDate":"2023-08-11T07:40:55","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1058","displayTitle":"Lock 19 Underwater Acoustic Deterrent System Study—Interim Project Update, through 2022","title":"Lock 19 underwater acoustic deterrent system study—Interim project update, through 2022","docAbstract":"Invasive carp (Hypophthalmichthys nobilis [Richardson, 1845; Bighead Carp], H. molitrix [Valenciennes in Cuvier and Valenciennes, 1844; Silver Carp], Ctenopharyngodon idella [Valenciennes in Cuvier and Valenciennes, 1844; Grass Carp], and Mylopharyngodon piceus [Richardson, 1846; Black Carp]) expansion threatens the Laurentian Great Lakes and other major waterways. Numerous tools and techniques are being tested or developed to curtail the upstream expansion of invasive carps while minimizing the effect to native species. Underwater sound is one technology that has shown promise as a deterrent in the laboratory and ponds and is being considered for use at bottleneck locks and dams to reduce the risk of invasive carps moving to uninvaded areas. To test this technology at a management-relevant scale, an underwater acoustic deterrent system (UADS) was designed and installed at Lock 19 on the Mississippi River in 2021. The experimental UADS operates on a continuous schedule of 80 hours on and 80 hours off to allow for comparisons of fish behavior during varying environmental, operational, and navigation traffic conditions. Two telemetry systems, operating at 69 and 307 kilohertz, were deployed to evaluate how the UADS may affect movement and behavior of invasive carps and native fish species. During 2021–22, Silver Carp were twice as likely to pass over the UADS and into the lock when it was off compared to on; however, Bigmouth Buffalo, a native fish, were 1.2 times more likely to make upstream passage when the UADS was off compared to on.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231058","usgsCitation":"Brey, M.K., Woodley, C.M., Stanton, J.C., Fritts, A.K., Sholtis, M., Castro-Santos, T., Vallazza, J.M., and Albers, J.L., 2023, Lock 19 underwater acoustic deterrent system study—Interim project update, through 2022: U.S. Geological Survey Open-File Report 2023–1058, 11 p., https://doi.org/10.3133/ofr20231058.","productDescription":"Report: vi, 11 p.; 2 Data Releases; Dataset","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-153233","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":419707,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HOPS3O","text":"USGS data release","linkHelpText":"2017–2018 telemetry data for Asian carp and native fish species at Lock and Dam 19 in the Upper Mississippi River Basin"},{"id":419706,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PGL147","text":"USGS data release","linkHelpText":"Data release for Lock 19 underwater acoustic deterrent system (UADS) study—Interim project update"},{"id":419702,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1058/ofr20231058.pdf","text":"Report","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1058"},{"id":419703,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1058/ofr20231058.XML"},{"id":419705,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":419704,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1058/images"},{"id":419701,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1058/coverthb.jpg"},{"id":419720,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231058/full"}],"country":"United States","state":"Illinois, Iowa","otherGeospatial":"Lock 19","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.40921032418395,\n 40.41896333943944\n ],\n [\n -91.40921032418395,\n 40.368258319993515\n ],\n [\n -91.33577076429651,\n 40.368258319993515\n ],\n [\n -91.33577076429651,\n 40.41896333943944\n ],\n [\n -91.40921032418395,\n 40.41896333943944\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
Invasive carp (Hypophthalmichthys nobilis [Bighead Carp], Mylopharyngodon piceus [Black Carp], Ctenopharyngodon idella [Grass Carp], and H. molitrix [Silver Carp]) continue to spread in the United States and deterrents at river navigation locks are one emerging control strategy for slowing the spread. High-head navigation dams on large rivers serve as impediments to the upstream spread of these populations. One possible control technique is using a multimodal deterrent that utilizes a combination of lights, sound, and air bubbles to guide fish away from a location. Laboratory tests and small-scale field deployments of similar multi-stimuli deterrents have demonstrated potential for deterring invasive carp. These earlier studies led to the deployment and initiation of a field study of a multimodal deterrent in 2019 at the downstream lock approach at Barkley Lock and Dam on the Cumberland River near Grand Rivers, Kentucky. We are using two types of telemetry systems to evaluate how the multimodal deterrent affects movement and behavior of Silver Carp, Grass Carp, and four native species: Aplodinotus grunniens (Freshwater Drum), Polyodon spathula (Paddlefish), Ictiobus bubalus (Smallmouth Buffalo), and Acipenser fulvescens (Lake Sturgeon). We are evaluating fish movements in response to the multimodal deterrent by using a study design that cycles between 1 week with the multimodal deterrent on and 1 week with it off. This weekly cycling provides baseline comparisons across variable environmental conditions. When the multimodal deterrent is on, the number of Silver Carp completing upstream passage through Barkley Lock is reduced by about one-half. This study that began in 2019 can provide insights into the effectiveness of a multimodal deterrent for limiting upstream movement of invasive carp or effects on native fishes through strategic navigation locks on large rivers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231051","usgsCitation":"Fritts, A., Gibson-Reinemer, D., Stanton, J., Mosel, K., Brey, M., Vallazza, J., Appel, D., Faulkner, J., Tompkins, J., Castro-Santos, T., Sholtis, M., Turnpenny, A., Sorensen, P., and Simmonds, R., 2023, Multimodal invasive carp deterrent study at Barkley Lock and Dam—Status update through 2022: U.S. Geological Survey Open-File Report 2023–1051, 7 p., https://doi.org/10.3133/ofr20231051.","productDescription":"Report: vi, 7 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-152060","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419719,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231051/full"},{"id":419570,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKGK94","text":"USGS data release","linkHelpText":"Data release for multimodal invasive carp deterrent study at Barkley Lock and Dam—Status update through 2022"},{"id":419569,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1051/images/"},{"id":419568,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1051/ofr20231051.XML"},{"id":419567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1051/ofr20231051.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1051"},{"id":419566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1051/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Barkley Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.06563176427669,\n 36.91369487844129\n ],\n [\n -88.06563176427669,\n 36.769782774646316\n ],\n [\n -87.88100333801793,\n 36.769782774646316\n ],\n [\n -87.88100333801793,\n 36.91369487844129\n ],\n [\n -88.06563176427669,\n 36.91369487844129\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
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.