Background
The number of telemetry studies focused on lake whitefish (Coregonus clupeaformis) in the Laurentian Great Lakes has steadily increased over the last decade, but field tests of immobilization methods used for tag implantation, which have the potential to affect survival and behavior of fish after release, are lacking. We compared post-tagging survival and behavior of lake whitefish that were immobilized for tag implantation using electroimmobilization via a transcutaneous electrical nerve stimulation (TENS) unit or by chemical immobilization via exposure to 10% eugenol.
Results
Acoustic tags were implanted into 126 adult lake whitefish (N = 126; N = 67 TENS treatment group, N = 59 eugenol treatment group) collected from the Fox River, Wisconsin, during the spawning period in November 2021. We found no significant differences between treatments in the number of days that lake whitefish spent in the Fox River following tagging (TENS mean = 13.4 days, eugenol mean = 14.7), and also found that the proportions of fish within each treatment group that returned to the Fox River during fall 2022 (51% from TENS treatment group, 49% from eugenol treatment group) did not differ from the proportions for all fish that were confirmed to be alive at that time. The best Cormack–Jolly–Seber model indicated no differences in survival between the two treatment groups (monthly survival = 0.980, 95% CI 0.970–0.987). Fish immobilized using TENS underwent almost immediate induction and recovery from surgeries, while fish immobilized using eugenol had induction times that ranged 167–487 s (mean = 347 s) and recovery times that ranged 51–2358 s (mean = 1242 s).
Conclusions
Short- and long-term behavior (time to exit of Fox River, return to Fox River in the next spawning season) and monthly survival estimates of lake whitefish did not differ between the immobilization treatments. Either method may be suitable for immobilization during tag implantation, but the additional time needed for induction and recovery of fish when using eugenol may be a limiting factor in some field-based tagging situations.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-024-00393-y","usgsCitation":"Izzo, L., Dembkowski, D., Binder, T., Hansen, S., Vandergoot, C., and Isermann, D.A., 2024, A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization: Animal Biotelemetry, v. 12, 39, 10 p., https://doi.org/10.1186/s40317-024-00393-y.","productDescription":"39, 10 p.","ipdsId":"IP-169427","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-024-00393-y","text":"Publisher Index Page"},{"id":480989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River, Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.05808785412178,\n 44.69153434457186\n ],\n [\n -88.05808785412178,\n 44.4951220149938\n ],\n [\n -87.86450882150862,\n 44.4951220149938\n ],\n [\n -87.86450882150862,\n 44.69153434457186\n ],\n [\n -88.05808785412178,\n 44.69153434457186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":349826,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":924889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":349827,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":924890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binder, Thomas R.","contributorId":349828,"corporation":false,"usgs":false,"family":"Binder","given":"Thomas R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":924891,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Scott P.","contributorId":349829,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924892,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergoot, Christopher S.","contributorId":349830,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":924893,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924894,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261520,"text":"sim3514 - 2024 - Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","interactions":[],"lastModifiedDate":"2025-08-15T16:11:32.54982","indexId":"sim3514","displayToPublicDate":"2024-12-23T10:32:03","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3514","displayTitle":"Geologic Map and Structure Sections Along the Southern Part of the Bartlett Springs Fault Zone and Adjacent Areas from Cache Creek to Lake Berryessa, Northern Coast Ranges, California","title":"Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","docAbstract":"Located in the Coast Ranges of northern California, the Bartlett Springs Fault Zone is the easternmost fault in the San Andreas Fault system in northern California. The fault is a right-lateral, strike-slip structure considered capable of producing an earthquake of moment magnitude 7. The purpose of this mapping is to better characterize the geology and earthquake hazards associated with the southern part of the Bartlett Springs Fault Zone and to help identify any evidence of active uplift on the faults bounding the Coast Ranges. Although the area immediately surrounding the Bartlett Springs Fault Zone is sparsely populated, its southern segment presents a potential seismic hazard to northern California communities as far away as the San Francisco Bay region and Sacramento. There are also nearby water resources, mineral resources, and public lands used for public recreation.
The Coast Ranges of northern California are a series of northwest-southeast-oriented mountain ranges and valleys located north of the San Francisco Bay region, between the Pacific Ocean to the west and the Sacramento Valley to the east. The region has rugged terrain, high mountain peaks that reach more than 2,400 meters above sea level, isolated and narrow valley bottoms on which most human settlements are located, and large drainage systems that tend to follow the northwest-southeast-oriented topographic grain. The physiographic character of the region is shaped by its bedrock geology, deformational history, and active faulting.
The basement rocks of the northern Coast Ranges consist of the Franciscan Complex and the Great Valley complex, the latter of which consists of two informal units, the Coast Range ophiolite and the Great Valley sequence. The Franciscan Complex and the Great Valley complex are in structural contact along the Coast Range Fault, a regional-scale structure and fundamental crustal boundary.
The Franciscan Complex and the Great Valley complex are superposed by active, northwest-southeast-striking strike-slip faults that are associated with seismicity swarms. These active strike-slip faults can produce moderate to large earthquakes that have moment magnitudes of 7–8. In places, these active structures bound large ranges and valleys, suggesting that much of the modern topographic expression is the result of active deformation processes.
This report contains new 1:24,000-scale geologic mapping along the southern part of the Bartlett Springs Fault Zone between Clear Lake and Lake Berryessa. The map area spans 738 square kilometers in northern Napa County, southern Lake County, and parts of Yolo and Colusa Counties. The south and east borders of the map are 90 kilometers north of San Francisco and 70 kilometers west of Sacramento, respectively. The map area is within the Knoxville mining district, which has a history of mercury and gold mining dating back to the mid-19th century. The two main towns in the region, Lower Lake and Clearlake, California, are west-northwest of the map area. Approximately 71,000 people live in the cities and rural communities located within a 40-kilometer radius of the center of the map area.
The bedrock geology, cross sections, and structural data presented herein are critical for evaluating the long-term evolution of the Bartlett Springs Fault Zone. This work will supplement studies on local seismic hazards, liquefaction potential, landslide hazards, earthquake geology, natural resources, groundwater resources, engineering geology, and tectonic history by providing the background information for site-specific investigations on these subjects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3514","usgsCitation":"Melosh, B.L., Bodtker, J.W., and Valin, Z.C., 2024, Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey Scientific Investigations Map 3514, 2 sheets, scale 1:24,000, 20 p. pamphlet, https://doi.org/10.3133/sim3514.","productDescription":"Pamphlet: vi, 20 p.; 2 Sheets: 46.15 x 78.86 inches and 58.26 x 41.78 inches; Data Release","numberOfPages":"20","additionalOnlineFiles":"Y","ipdsId":"IP-128914","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494218,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118060.htm","linkFileType":{"id":5,"text":"html"}},{"id":465095,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YJRCZD","text":"USGS Data Release","description":"Melosh, B.L., Bodtker, J.W., Valin, Z.C., and Sullivan, K., 2024, Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey data release, https://doi.org/10.5066/P1YJRCZD.","linkHelpText":"Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California"},{"id":465094,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3514/covrthb.jpg"},{"id":465093,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet2.pdf","text":"Sheet 2","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465092,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet1.pdf","text":"Sheet 1","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465091,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_pamphlet.pdf","text":"Pamphlet","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Northern Coast Ranges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5457,\n 39.0012\n ],\n [\n -122.5457,\n 38.6099\n ],\n [\n -122.2368,\n 38.6099\n ],\n [\n -122.2368,\n 39.0012\n ],\n [\n -122.5457,\n 39.0012\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
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Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles (Chelonia mydas). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence-only, presence-absence, mark-recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70136","usgsCitation":"Cullen, J., Domit, C.A., Lamont, M., Marshall, C., Santos, A.J., Sasso, C.R., Al Ansi, M., Hart, K., and Fuentes, M.M., 2024, A comparative framework to develop transferable species distribution models for animal telemetry data: Ecosphere, v. 15, no. 12, e70136, 20 p., https://doi.org/10.1002/ecs2.70136.","productDescription":"e70136, 20 p.","ipdsId":"IP-155304","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70136","text":"Publisher Index Page"},{"id":486722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil, Qatar, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.95593264608854,\n 32.350190091301116\n ],\n [\n -98.95593264608854,\n 20.378665663035946\n ],\n [\n -80.62400900088807,\n 20.378665663035946\n ],\n [\n -80.62400900088807,\n 32.350190091301116\n ],\n [\n -98.95593264608854,\n 32.350190091301116\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -42.715717605076065,\n -0.3517422249989295\n ],\n [\n -42.715717605076065,\n -9.92936026959562\n ],\n [\n -27.825824873144256,\n -9.92936026959562\n ],\n [\n -27.825824873144256,\n -0.3517422249989295\n ],\n [\n -42.715717605076065,\n -0.3517422249989295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 51.04983792747635,\n 26.326840048554942\n ],\n [\n 51.04983792747635,\n 24.360208529614056\n ],\n [\n 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83255000","active":true,"usgs":false}],"preferred":false,"id":938643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, Christopher D.","contributorId":356063,"corporation":false,"usgs":false,"family":"Marshall","given":"Christopher D.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":938645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Santos, Armando J.B.","contributorId":174284,"corporation":false,"usgs":false,"family":"Santos","given":"Armando","email":"","middleInitial":"J.B.","affiliations":[],"preferred":false,"id":938646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sasso, Christopher R.","contributorId":296894,"corporation":false,"usgs":false,"family":"Sasso","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":64230,"text":"NOAA-NMFS Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":938647,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Al Ansi, Mehsin","contributorId":356065,"corporation":false,"usgs":false,"family":"Al Ansi","given":"Mehsin","affiliations":[{"id":54794,"text":"Qatar University","active":true,"usgs":false}],"preferred":false,"id":938648,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938649,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fuentes, Mariana M.P.B.","contributorId":331394,"corporation":false,"usgs":false,"family":"Fuentes","given":"Mariana","email":"","middleInitial":"M.P.B.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":938650,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262849,"text":"70262849 - 2024 - Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA","interactions":[],"lastModifiedDate":"2025-01-24T15:33:05.221746","indexId":"70262849","displayToPublicDate":"2024-12-20T08:23:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA","docAbstract":"The Magnet Cove alkaline‑carbonatite complex (MCC), located in the Ouachita Mountains of south-central Arkansas in the United States, hosts an extensive variety of rare rock types and critical mineral resources with physical properties (density and magnetization) that contrast significantly with the sedimentary rocks into which they have intruded. Newly acquired ground-based gravity and magnetic data were used to develop two-dimensional and three-dimensional geophysical models of the Cretaceous-aged Magnet Cove intrusive complex. The models reveal that the MCC: (1) widens out at middle crustal depths to as much 22 km across, and may reach a depth of 20 km; (2) has a total volume (exposed and subsurface) that may be over 800 km3; (3) is likely connected at depth to other intrusions in the Arkansas alkaline province; and (4) has a geometry that is aligned with pre-existing structures such as the Reelfoot rift and the Ouachita orogenic belt, some of which were likely structurally controlled by the Precambrian crystalline basement and the continent-ocean transition zone buried beneath the Ouachita orogen. For the first time, the magnetic models of the MCC account for the presence of strong remanent magnetization. This results in a geophysical workflow necessary to accurately interpret magnetic anomalies over the much larger Arkansas alkaline province, its geologic and structural framework, and critical mineral potential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2024.230545","usgsCitation":"Amaral, C.M., Lamb, A., and Dumond, G., 2024, Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA: Tectonophysics, v. 893, 230545, 17 p., https://doi.org/10.1016/j.tecto.2024.230545.","productDescription":"230545, 17 p.","ipdsId":"IP-155830","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":489907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2024.230545","text":"Publisher Index Page"},{"id":481137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Magnet Cove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.87450349583777,\n 34.46740461224745\n ],\n [\n -92.87450349583777,\n 34.43682956355562\n ],\n [\n -92.80310027946261,\n 34.43682956355562\n ],\n [\n -92.80310027946261,\n 34.46740461224745\n ],\n [\n -92.87450349583777,\n 34.46740461224745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"893","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Amaral, Chelsea Morgan 0000-0003-4632-4097","orcid":"https://orcid.org/0000-0003-4632-4097","contributorId":313539,"corporation":false,"usgs":true,"family":"Amaral","given":"Chelsea","email":"","middleInitial":"Morgan","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":925001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Andrew P. 0000-0001-7214-516X","orcid":"https://orcid.org/0000-0001-7214-516X","contributorId":349870,"corporation":false,"usgs":false,"family":"Lamb","given":"Andrew P.","affiliations":[{"id":83523,"text":"University of Arkansas Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":925002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dumond, Gregory 0000-0002-3296-0976","orcid":"https://orcid.org/0000-0002-3296-0976","contributorId":349871,"corporation":false,"usgs":false,"family":"Dumond","given":"Gregory","affiliations":[{"id":83523,"text":"University of Arkansas Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":925003,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261721,"text":"sir20245117 - 2024 - Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24","interactions":[],"lastModifiedDate":"2025-08-15T16:14:31.530381","indexId":"sir20245117","displayToPublicDate":"2024-12-20T08:15:47","publicationYear":"2024","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":"2024-5117","displayTitle":"Hydrologic and Hydraulic Analyses of Silver Creek and Selected Tributaries Associated with Scott Air Force Base, Illinois, 2022–24","title":"Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24","docAbstract":"A hydrologic model of the Silver Creek Basin in southwest Illinois, and a hydraulic model of a selected reach of Silver Creek and local tributaries on and near Scott Air Force Base, Illinois, were developed to assess the effects of temporal land-use development in the Silver Creek Basin, the potential effects of projected changes based on future precipitation, and the effects of added detention storage in selected tributaries near Scott Air Force Base. The hydrologic model consists of a total of 52 scenarios—24 scenarios for an assessment of basin-wide changes in hydrology, and 28 scenarios for the hydraulic analysis of a focus area of Silver Creek and tributaries on and near Scott Air Force Base. Scenarios were run for precipitation events of 2-year through 500-year recurrence intervals (50-percent through 0.2-percent annual exceedance probability) and 24-hour durations.
The effects of detention structures added to Silver Creek tributaries throughout Scott Air Force Base were greater on water-level profiles (about 1 to 3 feet) than the effects of projected (2050) changes in precipitation (about 1 foot or less) in these basins. The results indicated that despite the increases in water-surface elevations resulting from projected increases in precipitation, the detention structures could provide a net reduction in water-surface elevations in the flood-prone western tributaries on the base. The effects of detention structures and projected precipitation also were assessed using the mapped extent of inundation for the simulated probabilistic precipitation scenarios. As an example, limited inundation of a residential area along Ash Creek was evident in the 5-year recurrence interval event for the scenarios without detention storage, whereas the first indications of flooding in the residential area from the scenario with detention storage were in the 50-year recurrence interval event.
Changes in hydrologic conditions followed a spatial pattern similar to that of the changes in land-cover development, with the greatest changes in the downstream one-half of the Silver Creek Basin and most pronounced in subbasins on and surrounding Scott Air Force Base. There was up to an estimated 54.6-percent increase in peak streamflows in subbasins on or near Scott Air Force Base from historical (1992) to current (2019) conditions, but changes in peak streamflows of as much as 144 percent are anticipated under the planned (to about 2050) land cover plus projected (2050) precipitation. The changes in the timing of peak streamflows were towards earlier peaks, with cumulative changes between historical and projected conditions approaching 0.75 hour (45 minutes) for a 2-year recurrence interval event. Results of the percentage change in cumulative event volume were similar to those of percentage change in peak streamflows in terms of magnitude of change and temporal and spatial distribution of changes. The greatest magnitude of percentage change in the assessed hydrologic properties was associated with the 2-year recurrence interval event, and the magnitude of the percentage change decreased with increasing probabilistic event recurrence interval. Subbasins with a substantial change in runoff yield between historical and current conditions were primarily in the downstream one-half of the Silver Creek Basin and most were within or adjacent to Scott Air Force Base. The magnitude of runoff yield changes increased with recurrence interval, and maximum changes were associated with subbasins on base and with the changes between the historical and current conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245117","collaboration":"Prepared in cooperation with Scott Air Force Base","usgsCitation":"Cigrand, C.V., Heimann, D.C., and Rydlund, P.H., Jr., 2024, Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24: U.S. Geological Survey Scientific Investigations Report 2024–5117, 87 p., https://doi.org/10.3133/sir20245117.","productDescription":"Report: x, 87 p.; Data Release; 2 Datasets","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135331","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494220,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118090.htm","linkFileType":{"id":5,"text":"html"}},{"id":465320,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GBYP2K","text":"USGS data release","linkHelpText":"Archive of hydrologic and hydraulic models used in the analyses of Silver Creek Basin and selected tributaries associated with Scott Air Force Base, Illinois, 1992–2050"},{"id":465321,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://datagateway.nrcs.usda.gov/GDGOrder.aspx","text":"U.S. Department of Agriculture, Natural Resources Conservation Service database","linkHelpText":"- GeoSpatial data gateway"},{"id":465322,"rank":8,"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":465316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5117/sir20245117.pdf","text":"Report","size":"97.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5117"},{"id":465317,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5117/sir20245117.XML"},{"id":465318,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5117/images/"},{"id":465315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5117/coverthb.jpg"},{"id":465319,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245117/full"}],"country":"United States","state":"Illinois","otherGeospatial":"Scott Air Force Base, Silver Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.8831289278596,\n 38.57664573726461\n ],\n [\n -89.8831289278596,\n 38.50342611477362\n ],\n [\n -89.77601466850366,\n 38.50342611477362\n ],\n [\n -89.77601466850366,\n 38.57664573726461\n ],\n [\n -89.8831289278596,\n 38.57664573726461\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
High-resolution elevation data for Kansas inform decision making to improve the State’s economy. Existing elevation data coverage is used to support State water planning initiatives, facilitate infrastructure management, and improve resilience to natural disasters. The expanding availability of current and more accurate elevation data helps better support natural resources conservation, agriculture and precision farming, flood risk management, water supply planning, infrastructure and construction management, and geologic resource assessment and hazard mitigation. 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.
The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Kansas. The status of available and in-progress 3DEP baseline lidar data in Kansas is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $14.41 million in new benefits annually to the State. The top nine Kansas business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Reed canarygrass (Phalaris arundinacea L.) is one of the most common invaders of floodplains and wetlands in North America. In the Upper Mississippi River floodplain, invasion by reed canarygrass in forest understories can inhibit forest regeneration when gaps form in the overstory. Understanding the distribution of reed canarygrass in forest understories is essential for effective management and control. We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). Data from forest inventory study plots with reed canarygrass presence and absence were combined with 10 hypothesized environmental predictors of reed canarygrass invasion. We applied three approaches to better understand and incorporate the influence of spatial autocorrelation among our predictor variables, including random cross-validation, spatial cross-validation, and spatial cross-validation with Euclidean distance fields. Flood frequency, distance to contiguous floodplain, distance to forest edge, and distance to invaded wet meadow were among the most important environmental predictors across the three algorithms. Generally, the mean probability of reed canarygrass presence decreased with increasing flood depth, distance to contiguous floodplain, distance to invaded wet meadow, forest cover, and forest height, while relationships with other predictors were more variable. The ensemble of the three models (i.e., the average prediction) was used to map and summarize potential reed canary grass habitat suitability across the landscape. The maps generated quantified the habitat suitability for reed canarygrass and areas of agreement among the models in forest understories across the floodplain. This information can be used to better understand the extent of invasion, prioritize restoration efforts, and develop further research.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70138","usgsCitation":"Delaney, J., Van Appledorn, M., De Jager, N.R., Bouska, K.L., and Rohweder, J.J., 2024, Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories: Ecosphere, v. 15, no. 12, e70138, 19 p., https://doi.org/10.1002/ecs2.70138.","productDescription":"e70138, 19 p.","ipdsId":"IP-151054","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70138","text":"Publisher Index Page"},{"id":482505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.10323647292768,\n 46.99782038374508\n ],\n [\n -95.10323647292768,\n 39.14905192203409\n ],\n [\n -86.81143448094211,\n 39.14905192203409\n ],\n [\n -86.81143448094211,\n 46.99782038374508\n ],\n [\n -95.10323647292768,\n 46.99782038374508\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, John T. 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928663,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261666,"text":"sir20245051 - 2024 - Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia","interactions":[],"lastModifiedDate":"2025-08-15T16:20:41.023018","indexId":"sir20245051","displayToPublicDate":"2024-12-19T13:25:00","publicationYear":"2024","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":"2024-5051","displayTitle":"Improving Time of Concentration Estimates for Small Rural Watersheds in the Appalachian Plateaus Physiographic Province, West Virginia","title":"Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the West Virginia Department of Transportation, Division of Highways, compared time of concentration (Tc) and related runoff characteristics measured at four field sites in West Virginia to estimates of these values made using accepted methods. These four sites were selected to represent a range of basin size, length, and slope, and a range of estimated Tc. Instrumentation included a rain gage and a streamgage at all sites. Two streamgages, USGS station number (no.) 03159718 Grasslick Creek tributary above Interstate 77 near Fairplain, West Virginia, (referred to as Fairplain in this report) and USGS station no. 03159823 Grass Run tributary above Interstate 77 near Ripley, W. Va., (referred to as Ripley in this report) were near each other in northwestern West Virginia at the outlets of small basins with moderate slope. The largest, longest, and flattest basin in the study was upstream from USGS station no. 03190307 Hedricks Creek Tributary above US–19 near Hico, W. Va. (Hico). The final gaged basin in the study, that of USGS station no. 03197062 Cookman Fork at Interstate 79 near Wallback, W. Va., (Wallback) in central West Virginia, had a drainage area nearly as large as Hico, but the basin was more compact.
Precipitation and streamflow data were collected at the streamgages between October 2017 and July 2020. Storms were identified and classified through an iterative process relying on inspecting graphs created from the precipitation and streamflow data. Three hydrograph time metrics that represent Tc were computed for this study: time to rise, time to recede from a high point on the hydrograph to an inflection on the recession, and the time between an inflection on the hyetograph and an inflection on the recession of the hydrograph (precipitation inflection to recession inflection or PI-to-RI).
Hico had the slowest time metrics: the streamgage had an average Tc of 34 and 32 minutes for time to rise and time to recede, respectively. The time between the PI-to-RI at Hico, 38 minutes, was the longest for any of the characteristics at any of the streamgages. Wallback had the second slowest time metrics. At Wallback, average Tc for time to rise and time to recede was similar, 23 and 25 minutes, respectively. The average time between the PI-to-RI for Wallback was greater than its time to rise or time to recede, 32 minutes. At Fairplain and Ripley, time to rise was 18 and 19 minutes, time to recede was 14 and 16 minutes, and time between the PI-to-RI was 22 and 27 minutes, respectively. At Ripley, PI-to-RI and time to rise were significantly different from each other. Differences in metrics were not statistically significant (p ≤0.05) among streamgages.
At all streamgages, predictions made with the “Rational Method” were within one average standard deviation of the overall mean Tc. The Rational Method was applied following two different procedures— (1) channel geometry was estimated using professional judgment and (2) channel geometry estimates were adjusted using regional equations. The three different time metrics had an inconsistent relation with the estimates. Some of the predictions differed from individual hydrograph time metrics by more than one standard deviation. Predicted values for the 10-year storm were within the interquartile range (IQR) for 4 of 12 combinations of streamgages and time metrics. Adjusted Tc predictions were within the IQR of PI-to-RI for Fairplain and Wallback, longer than the IQR of observed PI-to-RI at Hico, and shorter than the IQR of observed PI-to-RI at Ripley. The adjusted predictions of Tc were within the IQR of time-to-rise for Hico and Fairplain and were longer than the IQR for Ripley and Wallback. At Ripley, the predictions were not within the IQR for either PI-to-RI or time to rise, but instead, were between them. These lines of evidence do not indicate large, systematic errors in Tc estimates.
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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
The Russian River watershed is in northern Sonoma County and southern Mendocino County, California, in the northern part of the California Coast Ranges. The Russian River serves as a supply for agricultural irrigation and for municipal, domestic, and commercial uses. Through a cooperative agreement with the California State Water Resources Control Board and Sonoma County Water Agency, the U.S. Geological Survey has completed studies to better understand the hydrogeologic system and develop numerical hydrologic modeling tools to evaluate and aid in managing groundwater resources. This report focuses on the development of a digital three-dimensional hydrogeologic framework model of the Russian River watershed for use in groundwater resource assessment and numerical models.
The digital three-dimensional hydrogeologic framework model of the Russian River watershed portrays the altitude, thickness, and extent of five hydrogeologic units. These five hydrogeologic units include (1) a basement unit, (2) the Sonoma Volcanics, (3) a consolidated sedimentary rock unit, (4) an unconsolidated sediment unit, and (5) channel alluvium. Model input data were compiled from published geologic maps, interpreted well data, and a model of the top of basement derived from gravity data. These data were used to construct surfaces that represent the upper and lower subsurface boundaries of each hydrogeologic unit. Top surfaces were created for the five hydrogeologic units and then stacked in three dimensions to create a solid-volume digital model.
The digital three-dimensional hydrogeologic framework model described in this report and the corresponding data represent the generalized geometry of the subsurface geologic units; the model reproduces the input geologic data with reasonable accuracy and is consistent with previously published subsurface conceptualizations of the region. The model indicates the overall geometry of the basement within the watershed and the spatial extent, altitude, and thickness of the basin-filling units. The hydrogeologic framework model is at a scale and resolution appropriate for use as the foundation for a numerical hydrologic model of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245083","collaboration":"Prepared in cooperation with the California State Water Resources Control Board and Sonoma County Water Agency","programNote":"Water Availability and Use Science Program—Water Resources Mission Area","usgsCitation":"Cromwell, G., Sweetkind, D.S., Langenheim, V.E., and Ely, C.P., 2024, Three-dimensional hydrogeologic framework model of the Russian River watershed, California: U.S. Geological Survey Scientific Investigations Report 2024–5083, 25 p., https://doi.org/10.3133/sir20245083.","productDescription":"Report: viii, 25 p.; Data Release","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-122963","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":465343,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GPH5FW","text":"USGS Data Release","description":"Fenton, N.C., Cromwell, G., Sweetkind, D.S., Langenheim, V.E., Ely, C.P., Kohel, C.A., Martin, A.J., Morita, A.Y., Peterson, M.F., and Roberts, M.A., 2022, Hydrogeologic data of the Russian River watershed, Sonoma and Mendocino Counties, California (ver. 1.1, July 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9GPH5FW.","linkHelpText":"Hydrogeologic data of the Russian River watershed, Sonoma and Mendocino Counties, California (ver. 1.1, July 2023)"},{"id":465348,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5083/images"},{"id":465346,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5083/sir20245083.xml","linkFileType":{"id":8,"text":"xml"}},{"id":465347,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245083/full"},{"id":494221,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118084.htm"},{"id":465345,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5083/sir20245083.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465344,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5083/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Russian River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.25,\n 39.25\n ],\n [\n -123.25,\n 38.333\n ],\n [\n -122.667,\n 38.333\n ],\n [\n -122.667,\n 39.25\n ],\n [\n -123.25,\n 39.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time as thermal images are being acquired from an uncrewed aircraft system (UAS). The sensitivity of thermal image velocimetry to environmental conditions was quantified by conducting 20 flights over four days and assessing the accuracy of image-derived velocity estimates via comparison to direct field measurements made with an acoustic Doppler current profiler (ADCP). This analysis indicated that velocity mapping was most reliable when the air was cooler than the water. We also introduced a workflow for River Velocity Measurement in Approximately Real Time (RiVMART) that involved transferring brief image sequences from the UAS to a ground station as distinct data packets. The resulting velocity fields were as accurate as those generated via post-processing. A new particle image velocimetry (PIV) algorithm based on staggered image sequences increased the number of image pairs available for a given image sequence duration and slightly improved accuracy relative to a standard PIV implementation. Direct, automated geo-referencing of image-derived velocity vectors based on information on the position and orientation of the UAS acquired during flight led to poor alignment with vectors that were geo-referenced manually by selecting ground control points from an orthophoto. This initial proof-of-concept investigation suggests that our workflow could enable highly efficient characterization of flow fields in rivers and might help support applications that require rapid response to changing conditions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16244746","usgsCitation":"Legleiter, C.J., Kinzel, P.J., Dille, M., Vespignani, M., Wong, U., Anderson, I.E., Hyde, E., Gazoorian, C.L., and Cramer, J.M., 2024, Mapping river flow from thermal images in approximately real time: Proof of concept on the Sacramento River, California, USA: Remote Sensing, v. 16, no. 24, 4746, 32 p., https://doi.org/10.3390/rs16244746.","productDescription":"4746, 32 p.","ipdsId":"IP-170700","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16244746","text":"Publisher Index Page"},{"id":465404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.98941342940537,\n 39.5356924740195\n ],\n [\n -122.01174409601143,\n 39.5356924740195\n ],\n [\n -122.01174409601143,\n 39.51821864106586\n ],\n [\n -121.98941342940537,\n 39.51821864106586\n ],\n [\n -121.98941342940537,\n 39.5356924740195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"24","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - 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Existing elevation data coverage is used to improve resiliency to natural disasters, manage energy infrastructure, and assess natural resources. The expanding availability of current and more accurate elevation data helps better support natural resources conservation, agriculture and precision farming, flood risk management, infrastructure and construction management, geologic resource assessment and hazard mitigation, coastal zone management, and identification of features of interest or concern, such as archaeological and historic sites. 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.
The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Texas. The status of available and in-progress 3DEP baseline lidar data in Texas is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $53.1 million in new benefits annually to the State. The top 10 Texas business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"High resolution topobathymetric field surveys were conducted by the U.S. Geological Survey in collaboration with Northeastern University and in cooperation with the U.S. Fish and Wildlife Service and The Nature Conservancy in a selected shoreline along Gandys Beach, New Jersey, from January to April 2018. These data are a critical model input for hydrodynamic and wave models and can affect the accuracy of model outputs such as wave height, water surface elevation, current velocity, and sediment transport. Gandys Beach is a living shoreline where constructed oyster reefs (CORs) were built to protect the shoreline and enhance habitat for oyster and other species. Because of the complex topography and bathymetry of the study area, higher spatial resolution topobathymetric data are required to resolve the vertical variations near the CORs. During the field survey, the global navigation satellite system positioning method was used to establish the elevation of a benchmark referenced to the North American Vertical Datum of 1988. The topobathymetric data were collected using a total station. Horizontal accuracy of plus or minus 0.05 foot (ft) and vertical accuracy of plus or minus 0.10 ft were calculated using root mean square error between duplicate surveys. Two existing datasets were integrated with the survey data to create an updated topobathymetric dataset for model input and analysis: (1) the U.S. Geological Survey Coastal National Elevation Database 1-meter resolution data developed after Hurricane Sandy and (2) The Nature Conservancy 2017 elevation monitoring data at 10-meter resolution. A root mean square error analysis comparing survey data with the new topobathymetric dataset versus the survey data compared to the original Coastal National Elevation Data dataset showed errors of 0.31 and 2.61 ft, respectively. This improved dataset can be used for wave and hydrodynamic modeling in support of the effectiveness assessment of the CORs and living shoreline restoration along Gandys Beach.
Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
Contact Us- USGS Publications Warehouse
","tableOfContents":"Lithium-rich brine deposits occur throughout the United States, including in the Smackover Formation. The concentration of lithium in Smackover Formation brines was predicted across southern Arkansas by using a machine-learning model that incorporated lithium concentration data and geologic information. Between 5.1 and 19.0 million metric tons of lithium are calculated to be present in the brines of the Smackover Formation in southern Arkansas. The range in possible total lithium reflects the uncertainty in machine-learning predictions of lithium concentrations and the range of Smackover Formation porosity. This estimate quantifies the in-place lithium resource and does not consider the technological and economic feasibility of extracting the lithium from the brines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243052","issn":"2327-6916, 2327-6932","collaboration":"Prepared in cooperation with the Arkansas Department of Energy and Environment, Office of the State Geologist","programNote":"Energy Resources Program","usgsCitation":"Knierim, K.J., Masterson, A.L., Freeman, P.A., McDevitt, B., Herzberg, A.H., Li, P., Mills, C., Doolan, C., Jubb, A.M., Ausbrooks, S.M., and Chenault, J., 2024, Lithium resource in the Smackover Formation brines of southern Arkansas: U.S. Geological Survey Fact Sheet 2024–3052, 4 p., https://doi.org/10.3133/fs20243052.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-172337","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":494229,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118086.htm","linkFileType":{"id":5,"text":"html"}},{"id":465210,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3052/fs20243052.pdf","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3052"},{"id":465209,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3052/images"},{"id":465208,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3052/coverthb.jpg"},{"id":465231,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243052/full","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3052 HTML"},{"id":465230,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3052/fs20243052.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3052 XML"},{"id":465228,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/70259385","text":"Evaluation of the lithium resource in the Smackover Formation brines of southern Arkansas using machine learning"},{"id":465219,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QPRYZN","text":"USGS Data Release","linkHelpText":"- Lithium observations, machine-learning predictions, and mass estimates from the Smackover Formation brines in southern Arkansas"}],"country":"United States","state":"Arkansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.00896297632752,\n 33.862680632060474\n ],\n [\n -94.00896297632752,\n 33.04929982263086\n ],\n [\n -92.24865389098971,\n 33.04929982263086\n ],\n [\n -92.24865389098971,\n 33.862680632060474\n ],\n [\n -94.00896297632752,\n 33.862680632060474\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Understanding the causes of mortality for a declining species is essential for developing effective conservation and management strategies, particularly when anthropogenic activities are the primary threat. Using a competing hazards framework allows for robust estimation of the cause-specific variation in risk that may exist across multiple dimensions, such as time and individual. Here, we estimated cause-specific rates of severe injury and mortality for North Atlantic right whales (Eubalaena glacialis), a critically endangered species that is currently in peril due to human-caused interactions. We developed a multistate capture–recapture model that leveraged 30 years of intensive survey effort yielding sightings of individuals with injury assessments and necropsies of carcass recoveries. We examined variation in the hazard rates of severe injury and mortality due to entanglements in fishing gear and vessel strikes as explained by temporal patterns and the age and reproductive status of the individual. We found strong evidence for increased rates of severe entanglement injuries after 2013 and for females with calves, with consequently higher marginal mortality. The model results also suggested that despite vessel strikes causing a lower average rate of severe injuries, the higher mortality rate conditional on injury results in significant total mortality risk, particularly for females resting from a recent calving event. Large uncertainty in the estimation of carcass recovery rate for vessel strike deaths permeated into the apportionment of mortality causes. The increased rates of North Atlantic right whale mortality in the last decade, particularly for reproducing females, has been responsible for the severe decline in the species. By apportioning the human-caused threats using a quantitative approach with estimation of relevant uncertainty, this work can guide development of conservation and management strategies to facilitate species recovery. Our approach is relevant to other monitored populations where cause-specific injuries from multiple threats can be observed in live and dead individuals.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70086","usgsCitation":"Linden, D., Hostetler, J.A., Pace, R., Garrison, L.P., Knowlton, A., Lesage, V., Williams, R., and Runge, M.C., 2024, Quantifying uncertainty in anthropogenic causes of injury and mortality for an endangered baleen whale: Ecosphere, v. 15, no. 12, e70086, 12 p., https://doi.org/10.1002/ecs2.70086.","productDescription":"e70086, 12 p.","ipdsId":"IP-157942","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488298,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70086","text":"Publisher Index Page"},{"id":483337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Linden, Daniel W.","contributorId":229525,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":930730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, J. A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":11319,"corporation":false,"usgs":true,"family":"Hostetler","given":"J.","middleInitial":"A.","affiliations":[],"preferred":true,"id":930731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pace, Richard M III","contributorId":352277,"corporation":false,"usgs":false,"family":"Pace","given":"Richard M","suffix":"III","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":930732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garrison, Lance P.","contributorId":296893,"corporation":false,"usgs":false,"family":"Garrison","given":"Lance","email":"","middleInitial":"P.","affiliations":[{"id":64230,"text":"NOAA-NMFS Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":930733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knowlton, Amy R.","contributorId":352046,"corporation":false,"usgs":false,"family":"Knowlton","given":"Amy R.","affiliations":[{"id":37373,"text":"New England Aquarium","active":true,"usgs":false}],"preferred":false,"id":930734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lesage, Veronique","contributorId":352311,"corporation":false,"usgs":false,"family":"Lesage","given":"Veronique","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":930735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Robert A. 0000-0002-2973-8493","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":203802,"corporation":false,"usgs":false,"family":"Williams","given":"Robert A.","affiliations":[{"id":36721,"text":"USGS-Emeritus","active":true,"usgs":false}],"preferred":false,"id":930736,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":930737,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261711,"text":"70261711 - 2024 - A benchmark for computational analysis of animal behavior, using animal-borne tags","interactions":[],"lastModifiedDate":"2024-12-19T15:23:34.579209","indexId":"70261711","displayToPublicDate":"2024-12-18T09:18:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A benchmark for computational analysis of animal behavior, using animal-borne tags","docAbstract":"Animal-borne sensors (‘bio-loggers’) can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data. It also makes it difficult to evaluate the effectiveness of novel methods developed by the machine learning community.
To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, as well as a modeling task and evaluation metrics. BEBE is to date the largest, most taxonomically diverse, publicly available benchmark of this type, and includes 1654 h of data collected from 149 individuals across nine taxa. Using BEBE, we compare the performance of deep and classical machine learning methods for identifying animal behaviors based on bio-logger data. As an example usage of BEBE, we test an approach based on self-supervised learning. To apply this approach to animal behavior classification, we adapt a deep neural network pre-trained with 700,000 h of data collected from human wrist-worn accelerometers.
We find that deep neural networks out-perform the classical machine learning methods we tested across all nine datasets in BEBE. We additionally find that the approach based on self-supervised learning out-performs the alternatives we tested, especially in settings when there is a low amount of training data available.
In light of these results, we are able to make concrete suggestions for designing studies that rely on machine learning to infer behavior from bio-logger data. Therefore, we expect that BEBE will be useful for making similar suggestions in the future, as additional hypotheses about machine learning techniques are tested. Datasets, models, and evaluation code are made publicly available at https://github.com/earthspecies/BEBE, to enable community use of BEBE.
","language":"English","publisher":"BMC","doi":"10.1186/s40462-024-00511-8","usgsCitation":"Hoffmann, B., Cusimano, M., Baglione, V., Canestrari, D., Chevallier, D., DeSantis, D.L., Jeantet, L., Ladds, M., Maekawa, T., Vicente, M., Moreno-Gonzalez, V., Pagano, A.M., Trapote, E., Vainio, O., Vehkaoja, A., Yoda, K., Zacarian, K., and Friedlaender, A., 2024, A benchmark for computational analysis of animal behavior, using animal-borne tags: Movement Ecology, v. 12, 78, 25 p., https://doi.org/10.1186/s40462-024-00511-8.","productDescription":"78, 25 p.","ipdsId":"IP-152357","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466709,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00511-8","text":"Publisher Index Page"},{"id":465332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoffmann, Benjamin","contributorId":179259,"corporation":false,"usgs":false,"family":"Hoffmann","given":"Benjamin","affiliations":[],"preferred":false,"id":921541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cusimano, Maddie","contributorId":347362,"corporation":false,"usgs":false,"family":"Cusimano","given":"Maddie","email":"","affiliations":[{"id":83145,"text":"Earth Species Project","active":true,"usgs":false}],"preferred":false,"id":921542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baglione, Vittorio","contributorId":347363,"corporation":false,"usgs":false,"family":"Baglione","given":"Vittorio","email":"","affiliations":[{"id":83146,"text":"Universidad de León","active":true,"usgs":false}],"preferred":false,"id":921543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Canestrari, Daniela","contributorId":347364,"corporation":false,"usgs":false,"family":"Canestrari","given":"Daniela","email":"","affiliations":[{"id":83146,"text":"Universidad de León","active":true,"usgs":false}],"preferred":false,"id":921544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chevallier, Damien","contributorId":347365,"corporation":false,"usgs":false,"family":"Chevallier","given":"Damien","email":"","affiliations":[{"id":49035,"text":"French National Centre for Scientific Research","active":true,"usgs":false}],"preferred":false,"id":921545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeSantis, Dominic L.","contributorId":347366,"corporation":false,"usgs":false,"family":"DeSantis","given":"Dominic","email":"","middleInitial":"L.","affiliations":[{"id":83147,"text":"Georgia College and State University","active":true,"usgs":false}],"preferred":false,"id":921546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jeantet, Lorene","contributorId":347367,"corporation":false,"usgs":false,"family":"Jeantet","given":"Lorene","email":"","affiliations":[{"id":39919,"text":"Stellenbosch University","active":true,"usgs":false}],"preferred":false,"id":921547,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ladds, Monique","contributorId":347368,"corporation":false,"usgs":false,"family":"Ladds","given":"Monique","email":"","affiliations":[{"id":38703,"text":"New Zealand Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":921548,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Maekawa, Takuya","contributorId":347369,"corporation":false,"usgs":false,"family":"Maekawa","given":"Takuya","email":"","affiliations":[{"id":83148,"text":"Osaka University","active":true,"usgs":false}],"preferred":false,"id":921549,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vicente, Mata-Silva","contributorId":347370,"corporation":false,"usgs":false,"family":"Vicente","given":"Mata-Silva","email":"","affiliations":[{"id":37164,"text":"University of Texas, El Paso","active":true,"usgs":false}],"preferred":false,"id":921550,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Moreno-Gonzalez, Victor","contributorId":347371,"corporation":false,"usgs":false,"family":"Moreno-Gonzalez","given":"Victor","email":"","affiliations":[{"id":83146,"text":"Universidad de León","active":true,"usgs":false}],"preferred":false,"id":921551,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pagano, Anthony M. 0000-0003-2176-0909 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Antti","contributorId":347374,"corporation":false,"usgs":false,"family":"Vehkaoja","given":"Antti","email":"","affiliations":[{"id":83150,"text":"Tampere University","active":true,"usgs":false}],"preferred":false,"id":921555,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yoda, Ken 0000-0002-8346-3291","orcid":"https://orcid.org/0000-0002-8346-3291","contributorId":317214,"corporation":false,"usgs":false,"family":"Yoda","given":"Ken","email":"","affiliations":[{"id":27745,"text":"Nagoya University","active":true,"usgs":false}],"preferred":false,"id":921556,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zacarian, Katherine","contributorId":347375,"corporation":false,"usgs":false,"family":"Zacarian","given":"Katherine","email":"","affiliations":[{"id":83145,"text":"Earth Species Project","active":true,"usgs":false}],"preferred":false,"id":921557,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Friedlaender, Ari","contributorId":207721,"corporation":false,"usgs":false,"family":"Friedlaender","given":"Ari","email":"","affiliations":[],"preferred":false,"id":921558,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70261876,"text":"70261876 - 2024 - The potential of remote sensing for improved infectious disease ecology research and practice","interactions":[],"lastModifiedDate":"2024-12-31T15:09:58.148732","indexId":"70261876","displayToPublicDate":"2024-12-18T09:05:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The potential of remote sensing for improved infectious disease ecology research and practice","docAbstract":"Outbreaks of Covid-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of emerging infectious diseases on public health, ecosystems, and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission, and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we 1) conduct a systematic review of primary literature to quantify current uses of remote sensing in disease ecology and 2) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are promoting early detection of forest pathogens and that satellite data is contributing to the study of geographically widespread human diseases. We discuss opportunities for increased use of data products that characterize vegetation, surface water, and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal movement telemetry can provide novel insights into wildlife disease. Integrating these opportunities will advance research and management of infectious diseases.","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2024.1712","usgsCitation":"Teitelbaum, C., Ferraz, A., De La Cruz, S.E., Gilmour, M., and Brosnan, I., 2024, The potential of remote sensing for improved infectious disease ecology research and practice: Proceedings of the Royal Society B: Biological Sciences, v. 291, 20241712, 12 p., https://doi.org/10.1098/rspb.2024.1712.","productDescription":"20241712, 12 p.","ipdsId":"IP-170705","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466710,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2024.1712","text":"Publisher Index Page"},{"id":465563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"291","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Teitelbaum, Claire S.","contributorId":337675,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":922111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferraz, António","contributorId":347661,"corporation":false,"usgs":false,"family":"Ferraz","given":"António","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":922112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":922113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilmour, Morgan E.","contributorId":245099,"corporation":false,"usgs":false,"family":"Gilmour","given":"Morgan E.","affiliations":[],"preferred":false,"id":922114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brosnan, Ian G.","contributorId":347663,"corporation":false,"usgs":false,"family":"Brosnan","given":"Ian G.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":922115,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272012,"text":"70272012 - 2024 - Managing to survive despite the weather: Seeding decisions affecting simulated dryland restoration outcomes","interactions":[],"lastModifiedDate":"2025-09-30T15:53:37.174326","indexId":"70272012","displayToPublicDate":"2024-12-18T08:46:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Managing to survive despite the weather: Seeding decisions affecting simulated dryland restoration outcomes","docAbstract":"Limited favorable weather windows for post-germination early seedling survival are associated with low restoration success in drylands. We examined whether post-fire seeding decisions could alter early seedling emergence and restoration success across western North American sagebrush ecosystems with a simulation approach. Seedling emergence estimates were based on germination of a specified percentile of sown seeds followed by favorable conditions for seedling development. We asked how three categories of seeding decisions affected emergence: post-fire seeding year, seasonal seeding delay (via seed coating technologies or later seeding timing), or native perennial grass seed source (via differing germination behavior). We also tested potential effects of mean annual precipitation on seeding decision outcomes (interactive effect) and included topographic microclimate and site (random effect) in models. Emergence was highest when the best seed source (of 10) was seeded in the best post-fire year (of 5). High emergence also resulted from seeding in the best post-fire year with an average seed source or seeding with the best source in the first post-fire year. Emergence was higher overall with higher mean precipitation. Seeding decision outcomes were sensitive to annual mean precipitation, with greater differences observed due to decisions at lower precipitation levels. While sensitive to assumptions related to seedling development and germination speed, these results suggest that restoration approaches linked to germination behavior and weather variability could improve restoration success rates in variable drylands like sagebrush ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.14362","usgsCitation":"Copeland, S.M., Baughman, O.W., Bradford, J., Hardegree, S.P., Larson, J.E., Schlaepfer, D.R., and Badik, K.J., 2024, Managing to survive despite the weather: Seeding decisions affecting simulated dryland restoration outcomes: Restoration Ecology, v. 33, no. 6, e14362, 14 p., https://doi.org/10.1111/rec.14362.","productDescription":"e14362, 14 p.","ipdsId":"IP-163398","costCenters":[{"id":49226,"text":"Northwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":496333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.14362","text":"Publisher Index Page"},{"id":496270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.574531556712,\n 43.9234644270079\n ],\n [\n -120.04179285934424,\n 38.43059054059461\n ],\n [\n -111.74305139404657,\n 38.21318759330562\n ],\n [\n -111.10879379526153,\n 44.028804617635814\n ],\n [\n -119.574531556712,\n 43.9234644270079\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Copeland, Stella M. 0000-0001-6707-4803","orcid":"https://orcid.org/0000-0001-6707-4803","contributorId":361962,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella","middleInitial":"M.","affiliations":[{"id":86403,"text":"USDA-Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, OR, USA","active":true,"usgs":false}],"preferred":false,"id":949718,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baughman, Owen W.","contributorId":361963,"corporation":false,"usgs":false,"family":"Baughman","given":"Owen","middleInitial":"W.","affiliations":[{"id":86404,"text":"The Nature Conservancy, Eastern Oregon Agricultural Research Center, Burns, OR, USA","active":true,"usgs":false}],"preferred":false,"id":949719,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949720,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hardegree, Stuart P.","contributorId":361964,"corporation":false,"usgs":false,"family":"Hardegree","given":"Stuart","middleInitial":"P.","affiliations":[{"id":86405,"text":"USDA-Agricultural Research Service, Northwest Watershed Research Center, Boise, ID, USA","active":true,"usgs":false}],"preferred":false,"id":949721,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larson, Julie E.","contributorId":361965,"corporation":false,"usgs":false,"family":"Larson","given":"Julie","middleInitial":"E.","affiliations":[{"id":86403,"text":"USDA-Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, OR, USA","active":true,"usgs":false}],"preferred":false,"id":949722,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949723,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Badik, Kevin J.","contributorId":361966,"corporation":false,"usgs":false,"family":"Badik","given":"Kevin","middleInitial":"J.","affiliations":[{"id":86407,"text":"The Nature Conservancy Reno, NV, USA","active":true,"usgs":false}],"preferred":false,"id":949724,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266299,"text":"70266299 - 2024 - Food habits of nonnative Walleyes in Lake Pend Oreille, Idaho","interactions":[],"lastModifiedDate":"2025-05-05T15:36:39.499182","indexId":"70266299","displayToPublicDate":"2024-12-17T10:34:03","publicationYear":"2024","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":"Food habits of nonnative Walleyes in Lake Pend Oreille, Idaho","docAbstract":"Walleye Sander vitreus is an important species that has been widely introduced outside of its native distribution. The goal of this study was to assess the effects of an established Walleye population in the Lake Pend Oreille (LPO) system, Idaho.
Food habits of Walleyes were described using stomach contents and stable isotopes (δ15N, δ13C). Trophic structure of the LPO system's food web was identified using stable isotopes. Annual consumption by Walleyes of important prey items was estimated using a bioenergetics model.
Walleyes consumed a diversity of prey items, including macroinvertebrates and fishes. Kokanee Oncorhynchus nerka, the most frequently consumed prey item, occurred in 23% of all Walleye diets. Combined, native cyprinids and catostomids occurred in 31% of all Walleye stomachs. Select taxa (e.g., native cyprinids, kokanee) were consistently consumed by Walleyes across seasons, regions, and cohorts, whereas other taxa (e.g., Westslope Cutthroat Trout O. lewisi, Smallmouth Bass Micropterus dolomieu) were consumed inconsistently. Stable isotope analysis suggested that Walleyes occupied similar trophic positions as other top‐level piscivores in the system. As Walleye age increased, δ15N increased and δ13C decreased, indicating increased consumption of pelagic prey resources and prey at higher trophic positions. The estimated biomass of kokanee consumed annually by Walleyes was 27,121 kg (95% confidence interval = 9178–61,603). Comparatively, native cyprinids represented about 46% of the total biomass of kokanee consumed by Walleyes, whereas native catostomids represented about 11% and native salmonids represented about 15% of the total biomass of kokanee consumed by Walleyes.
This study revealed that Walleyes consumed various fishes across the LPO system. Although kokanee were the most frequently consumed prey item, native cyprinids and catostomids (combined) occurred at similar proportions. This study contributes to our growing knowledge of the effects of nonnative Walleyes on important salmonids and native fishes in western systems.
Digital flood-inundation maps for a 9.9-mile reach of the Cuyahoga River in and near Independence, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees. Water-surface profiles were computed for the stream reach by using a one-dimensional steady-state step-backwater model. The model was calibrated to the current (2024) stage-streamflow relation (rating curve 43.0) for the USGS streamgage 04208000, Cuyahoga River at Independence, Ohio. The resulting hydraulic model was then used to compute 13 water-surface profiles for water levels (flood stages) ranging from 14.00 to 26.00 feet. The flood stages range from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a digital elevation model derived from light detection and ranging data to map the inundated areas associated with each flood profile.
The flood-inundation maps and the supporting hydraulic model produced by this study can be used by emergency managers and local officials to assess flood mitigation strategies and to define flood hazard areas to protect life and property, to coordinate flood response activities such as evacuations and road closures, and to aid postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245122","collaboration":"Prepared in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees","usgsCitation":"Ostheimer, C.J., and Whitehead, M.T., 2024, Flood-inundation maps for the Cuyahoga River in and near Independence, Ohio, 2024: U.S. Geological Survey Scientific Investigations Report 2024–5122, 16 p., https://doi.org/10.3133/sir20245122.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158401","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":464970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5122/coverthb.jpg"},{"id":464971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5122/sir20245122.pdf","text":"Report","size":"1.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5122 PDF"},{"id":464972,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245122/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5122 HTML"},{"id":464976,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245115","text":"Scientific Investigations Report 2024–5115","linkHelpText":"Flood-Inundation Maps for the Cuyahoga River at Jaite, Ohio, 2024"},{"id":464973,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5122/sir20245122.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5122 XML"},{"id":464974,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5122/images/"},{"id":464975,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFZK0L","text":"USGS data release","linkHelpText":"Geospatial data sets and hydraulic model for the Cuyahoga River in and near the city of Independence, Ohio"}],"country":"United States","state":"Ohio","city":"Independence","otherGeospatial":"Cuyahoga River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.7238278506537,\n 41.48\n ],\n [\n -81.7238278506537,\n 41.373775959357204\n ],\n [\n -81.61067566475016,\n 41.373775959357204\n ],\n [\n -81.61067566475016,\n 41.48\n ],\n [\n -81.7238278506537,\n 41.48\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Objective
Growth is one of the primary drivers of fish population dynamics and understanding factors influencing growth is vital to effective management of fish populations. This study investigated potential factors influencing growth of a recently established, non-native population of Walleye Sander vitreus in the Lake Pend Oreille system in northern Idaho.
Methods
We used relative growth index to describe growth of Walleyes relative to populations across North America. Mixed‐effects modeling was used to relate growth to abiotic (i.e., mean summer water temperature, river inflow) and biotic (i.e., kokanee Oncorhynchus nerka abundance and biomass; opossum shrimp Mysis diluviana density) variables. Models were ranked using Akaike's information criterion corrected for small sample size. Individual variability in growth was related to diet represented by stable isotopes (i.e., δ15N, δ13C) using linear regression for age‐1, age‐2, age‐3, and age‐5 individuals. Subsequently, for each age‐class, we evaluated differences in δ15N and δ13C between fast‐growing (i.e., 75th and higher percentiles of growth) and slow‐growing (i.e., 25th and lower percentiles of growth) individuals.
Results
The relative growth index suggested that Walleye grew fast relative to other populations, particularly those at similar latitudes to the Lake Pend Oreille system. Mixed-effects regression modeling indicated that growth of Walleyes was positively associated with temperature as well as abundance and biomass of kokanee; growth was negatively associated with inflow from the Clark Fork River and Mysis diluviana density. The top model explaining growth of Walleyes contained temperature and abundance of kokanee as environmental variables. The second equally plausible (i.e., within 2 AICc) model contained temperature. Growth of Walleyes varied among individuals. Generally, fast-growing Walleyes had higher δ15N than slow-growing Walleyes. Similarly, δ13C was more depleted in the fast-growing individuals for all age classes, except age 1, suggesting that age-1 individuals used higher proportions of littoral prey items compared to other age classes.
Conclusion
This study showed that kokanee abundance and temperature appeared to be important factors influencing growth of Walleyes in the Lake Pend Oreille system. Additionally, variability in growth appeared to be related to variability in diet, particularly for age-1 Walleyes. Impact statement Growth of Walleyes has been extensively studied, yet few studies have evaluated growth of Walleyes in novel systems or assessed individual variability in growth. Our research adds to the understanding of individual variability in growth and factors influencing population dynamics of non-native Walleyes.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11056","collaboration":"Idaho Department of Fish and Game","usgsCitation":"Frawley, S., Corsi, M., Dux, A.M., Hardy, R.S., and Quist, M.C., 2024, Abiotic and biotic factors related to growth of non-native Walleyes in Lake Pend Oreille, Idaho: North American Journal of Fisheries Management, v. 44, no. 6, p. 1325-1341, https://doi.org/10.1002/nafm.11056.","productDescription":"17 p.","startPage":"1325","endPage":"1341","ipdsId":"IP-163539","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Pend Oreille","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.79187320079157,\n 48.33908577891279\n ],\n [\n -116.79187320079157,\n 47.933452975307574\n ],\n [\n -116.12337645556701,\n 47.933452975307574\n ],\n [\n -116.12337645556701,\n 48.33908577891279\n ],\n [\n -116.79187320079157,\n 48.33908577891279\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Frawley, Susan","contributorId":354288,"corporation":false,"usgs":false,"family":"Frawley","given":"Susan","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Matthew P.","contributorId":171811,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew P.","affiliations":[],"preferred":false,"id":935347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dux, Andrew M.","contributorId":175256,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":935348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hardy, Ryan S.","contributorId":167032,"corporation":false,"usgs":false,"family":"Hardy","given":"Ryan","email":"","middleInitial":"S.","affiliations":[{"id":6764,"text":"Idaho Department of Fish and Game, Nampa, Idaho","active":true,"usgs":false}],"preferred":false,"id":935349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935350,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261643,"text":"70261643 - 2024 - Controls on lake pelagic primary productivity: Formalizing the nutrient-color paradigm","interactions":[],"lastModifiedDate":"2024-12-18T14:16:18.362641","indexId":"70261643","displayToPublicDate":"2024-12-15T12:08:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Controls on lake pelagic primary productivity: Formalizing the nutrient-color paradigm","docAbstract":"Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We used data from 58 diverse and globally distributed temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. Across these diverse systems, and given relatively limited inputs, model predictions of primary production were highly correlated with observed values derived from high-frequency sensor data. Our analysis provides a model structure, including calibrated parameter estimates, that may be broadly useful for understanding current and future patterns in lake primary production.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JG008140","usgsCitation":"Oleksy, I., Solomon, C.T., Jones, S.E., Olson, C., Bertolet, B., Adrian, R., Bansal, S., Baron, J., Brothers, S., Chandra, S., Chou, H., Colom-Montero, W., Culpeper, J., de Eyto, E., Farragher, M., Hilt, S., Holeck, K.T., Kazanjian, G., Klaus, M., Klug, J., Kohler, J., Laas, A., Lundin, E., Parkes, A., Rose, K.C., Rustam, L., Rusak, J.A., Scordo, F., Vanni, M.J., Verburg, P., and Weyhenmeyer, G.A., 2024, Controls on lake pelagic primary productivity: Formalizing the nutrient-color paradigm: JGR Biogeosciences, v. 129, no. 12, e2024JG008140, 15 p., https://doi.org/10.1029/2024JG008140.","productDescription":"e2024JG008140, 15 p.","ipdsId":"IP-154658","costCenters":[{"id":291,"text":"Fort Collins 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University","active":true,"usgs":false}],"preferred":false,"id":921254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Stuart E.","contributorId":203400,"corporation":false,"usgs":false,"family":"Jones","given":"Stuart","email":"","middleInitial":"E.","affiliations":[{"id":36611,"text":"Notre Dame","active":true,"usgs":false}],"preferred":false,"id":921255,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olson, Carly","contributorId":347287,"corporation":false,"usgs":false,"family":"Olson","given":"Carly","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":921256,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bertolet, Brittni","contributorId":347288,"corporation":false,"usgs":false,"family":"Bertolet","given":"Brittni","email":"","affiliations":[{"id":13696,"text":"University of California 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magnitude of Earth’s subsurface endowment has not yet been assessed. Knowledge of the occurrence and behavior of natural hydrogen on Earth has been combined with information from geologic analogs to construct a mass balance model to predict the resource potential. Given the associated uncertainty, stochastic model results predict a wide range of values for the potential in-place hydrogen resource [103 to 1010 million metric tons (Mt)] with the most probable value of ~5.6 × 106 Mt. Although most of this hydrogen is likely to be impractical to recover, a small fraction (e.g., 1 × 105 Mt) would supply the projected hydrogen needed to reach net-zero carbon emissions for ~200 years. This amount of hydrogen contains more energy (~1.4 × 1016 MJ) than all proven natural gas reserves on Earth (~8.4 × 1015 MJ). Study results demonstrate that further research into understanding the potential for geologic hydrogen resources is merited.
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