The topography of New Hampshire ranges from the Coastal Lowlands to the Eastern New England Upland to the White Mountains region. High-quality statewide elevation data are useful in managing this very diverse landscape. For example, the short coastline, including the Great Bay estuary and the Hampton-Seabrook marshes, is of disproportionately high value to New Hampshire’s tourist economy. The vulnerability of the coast to the effects of sea-level rise underscores the need for accurate, high-quality nearshore topographic elevation data and offshore bathymetric data to effectively manage the coast’s valuable resources, which include important fisheries, habitat, and infrastructure. Another important use for accurate elevation data in New Hampshire is in the evaluation of flood hazards and their potential environmental and infrastructure effects. This evaluation includes mapping of inundation and sediment transport, and assessing the associated costs of flooding. Addressing this challenge requires detailed knowledge of both surface topography and inland bathymetry. Other important activities having a substantial economic element and needing accurate elevation data include geologic resource assessment and hazard mitigation, urban and regional planning, infrastructure and construction management, and cultural resources preservation and management. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional model of the Earth’s surface and aboveground features.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Wildfires in the western United States (US) have been demonstrated to affect water quality, including dissolved organic carbon (DOC), in streams. Elevated post-wildfire DOC concentration poses a potential risk to drinking water treatment systems. In-stream measurements of fluorescent dissolved organic matter (fDOM), a proxy for DOC, have shown potential to detect dynamic changes in DOC. High frequency monitoring of water temperature, turbidity, and fDOM was used in conjunction with discrete sampling during targeted storm events and at fixed intervals to estimate DOC in five western US streams following wildfires in 2020 and 2021 with the objective to characterise and compare responses to wildfire among sites. The elevated turbidity conditions typical after wildfire presented a challenge to fDOM measurements and there was a need to identify appropriate turbidity corrections at burned sites. A combination of established and novel methods corrected fDOM concentrations for turbidity effects up to 800 Formazin nephelometric units (FNU). Pre-wildfire high frequency water quality data in adjacent burned and unburned watersheds allowed for separation of climate effects on DOC at one of the sites. Hydrology, climate and landcover were more important drivers of post-wildfire DOC yield than wildfire characteristics. Seasonal patterns of DOC were unchanged by wildfire in snowmelt-driven watersheds. Large, transient spikes in DOC concentration following frontal and convective storms were observed post-wildfire at all burned sites, but not at the unburned site. These spikes often exceeded operational thresholds for drinking water treatment. This study highlights the ability to develop high frequency DOC estimates in surface waters up to 800 FNU using fDOM sensors and targeted storm sampling and emphasises the value of high frequency pre-wildfire data in adjacent burned and unburned watersheds for separating climate and wildfire effects.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70023","usgsCitation":"Akie, G.A., Clow, D.W., Murphy, S.F., Clark, G.D., Meador, M.R., and Ebel, B., 2024, An intercomparison of DOC estimated from fDOM sensors in wildfire affected streams of the western United States: Hydrological Processes, v. 38, no. 12, e70023, 20 p., https://doi.org/10.1002/hyp.70023.","productDescription":"e70023, 20 p.","ipdsId":"IP-164617","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":489141,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70023","text":"Publisher Index Page"},{"id":481139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, Montana, 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\"}}]}","volume":"38","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Akie, Garrett Alexander 0000-0002-6356-7106","orcid":"https://orcid.org/0000-0002-6356-7106","contributorId":290236,"corporation":false,"usgs":true,"family":"Akie","given":"Garrett","email":"","middleInitial":"Alexander","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":924966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Gregory D. 0000-0003-0066-8193 gmclark@usgs.gov","orcid":"https://orcid.org/0000-0003-0066-8193","contributorId":224364,"corporation":false,"usgs":true,"family":"Clark","given":"Gregory","email":"gmclark@usgs.gov","middleInitial":"D.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":219878,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":924968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":924969,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267704,"text":"70267704 - 2024 - Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations","interactions":[],"lastModifiedDate":"2025-05-29T16:45:22.117743","indexId":"70267704","displayToPublicDate":"2024-12-25T08:37:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations","docAbstract":"Genetic monitoring is important in small, fragmented populations that rely on gene flow to maintain genetic diversity. The Selkirk, Yaak, and Cabinet grizzly bear (Ursus arctos) populations are among the smallest in North America and are near the southernmost extent of the species’ range. These populations received little to no effective migration for generations but have recently experienced increased gene flow through natural migration and a population augmentation program. A long-term dataset of grizzly bear microsatellite genotypes from 1973 to 2021 presented a unique opportunity to examine genetic trends in these populations over time. We used this dataset of 464 bears to evaluate if gene flow affected observed heterozygosity (HO), expected heterozygosity (HE), allelic richness (AR), and average pairwise relatedness (r) in each of these populations. We also estimated effective population size (Ne) using the temporal and linkage disequilibrium (LD) methods. Post gene flow, AR increased in the Selkirk and Cabinet populations and r decreased in all three populations. We did not observe any significant changes in HE or HO, but HE values in our populations were significantly higher than those estimated using a model without gene flow. Our Ne estimates were consistent between the temporal and LD methods and ranged from 15.2 to 15.8, 15.4–17.5, and 5.6–8.9 for the Selkirk, Yaak, and Cabinet populations, respectively. Overall, our findings indicate that gene flow is increasing or maintaining genetic diversity in these populations. However, Ne remains low and additional connectivity or augmentation may be needed, particularly in the Cabinet population.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-024-01666-y","usgsCitation":"Turnock, M., Teisberg, J., Kasworm, W., Falcy, M.R., Proctor, M., and Waits, L., 2024, Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations: Conservation Genetics, v. 26, p. 279-291, https://doi.org/10.1007/s10592-024-01666-y.","productDescription":"13 p.","startPage":"279","endPage":"291","ipdsId":"IP-166778","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01666-y","text":"Publisher Index Page"},{"id":486760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Idaho, Montana, Washington","otherGeospatial":"British Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.3821304045668,\n 50.132013169608314\n ],\n [\n -118.3821304045668,\n 47.95130403023194\n ],\n [\n -114.75140702492735,\n 47.95130403023194\n ],\n [\n -114.75140702492735,\n 50.132013169608314\n ],\n [\n -118.3821304045668,\n 50.132013169608314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2024-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Turnock, Megan F.","contributorId":356036,"corporation":false,"usgs":false,"family":"Turnock","given":"Megan F.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":938583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teisberg, Justin E.","contributorId":356039,"corporation":false,"usgs":false,"family":"Teisberg","given":"Justin E.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kasworm, Wayne F.","contributorId":356042,"corporation":false,"usgs":false,"family":"Kasworm","given":"Wayne F.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falcy, Matthew Richard 0000-0002-3332-2239","orcid":"https://orcid.org/0000-0002-3332-2239","contributorId":288500,"corporation":false,"usgs":true,"family":"Falcy","given":"Matthew","email":"","middleInitial":"Richard","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938586,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Proctor, Michael F.","contributorId":356045,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael F.","affiliations":[{"id":84901,"text":"Birchdale Ecological, Ltd.","active":true,"usgs":false}],"preferred":false,"id":938587,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waits, Lisette P.","contributorId":356046,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":938588,"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":70261948,"text":"70261948 - 2024 - Sequoia groves of Yosemite: Visitor use and impacts monitoring","interactions":[],"lastModifiedDate":"2025-01-06T14:57:47.15225","indexId":"70261948","displayToPublicDate":"2024-12-22T07:51:11","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Sequoia groves of Yosemite: Visitor use and impacts monitoring","docAbstract":"Despite being long-lived and massive, giant sequoias (Sequoiadendron giganteum (Lindl.) J. Bucholz) are susceptible to erosion given their relatively shallow root structure. Human-caused soil compaction and vegetation loss through social trails are primary drivers of erosion in giant sequoia groves, particularly for trees that are near formal trails and access roads. We develop a method to observe and quantify the near-tree impacts from park visitors and to relate the overall amount of use with ground cover impact parameters to assess whether the desired conditions of each grove are being met for the park to maintain a spectrum of recreational opportunities. We collected data on visitation, ground cover, soil compaction, and social trailing using a combination of targeted surveys and observations at the three giant sequoia groves in Yosemite National Park. The Mariposa Grove receives the most visitation, and use levels among groves were consistent with relative size and facilities available. Selected parameters for ground cover data were analyzed by comparing values within undisturbed versus trampling-disturbed subplots at both 0–2 m and 2–8 m. Exposed soil cover and compaction were generally higher in anthropogenically disturbed subplots versus undisturbed subplots, and vegetation cover was reduced in some disturbed subplots. Each grove had one surveyed tree where average soil compaction was ≥2.2 kg/cm2, which may limit root growth and impact seedling regeneration. Each of the three groves had some trees with social trail presence, yet less than 7% of mature trees within any grove were impacted by social trails, and most social trails were rated as having low impairment. Coupling soil compaction measurements and estimates of trampling-disturbed areas with mapping of social trail conditions within groves provides a general assessment of visitor-associated impacts to sequoia groves and can facilitate a relatively rapid way to track hotspot (i.e., increasingly impacted) trees over time.
","language":"English","publisher":"MDPI","doi":"10.3390/f15122256","usgsCitation":"Shiflett, S., Jenkins, J., Mattos, R., Ibsen, P.C., and Athearn, N., 2024, Sequoia groves of Yosemite: Visitor use and impacts monitoring: Forests, v. 15, no. 12, 2256, 19 p., https://doi.org/10.3390/f15122256.","productDescription":"2256, 19 p.","ipdsId":"IP-170087","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":466702,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f15122256","text":"Publisher Index Page"},{"id":465665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.6799910466818,\n 37.9097240904418\n ],\n [\n -119.6799910466818,\n 37.729468979298204\n ],\n [\n -119.43926891028742,\n 37.729468979298204\n ],\n [\n -119.43926891028742,\n 37.9097240904418\n ],\n [\n -119.6799910466818,\n 37.9097240904418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Shiflett, Sheri A.","contributorId":347753,"corporation":false,"usgs":false,"family":"Shiflett","given":"Sheri A.","affiliations":[{"id":39509,"text":"National Park Service, Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":922376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Jeffery S.","contributorId":347754,"corporation":false,"usgs":false,"family":"Jenkins","given":"Jeffery S.","affiliations":[{"id":38695,"text":"University of California Merced","active":true,"usgs":false}],"preferred":false,"id":922377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mattos, Rachel F.","contributorId":347755,"corporation":false,"usgs":false,"family":"Mattos","given":"Rachel F.","affiliations":[{"id":39509,"text":"National Park Service, Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":922378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ibsen, Peter Christian 0000-0002-3436-9100","orcid":"https://orcid.org/0000-0002-3436-9100","contributorId":260735,"corporation":false,"usgs":true,"family":"Ibsen","given":"Peter","email":"","middleInitial":"Christian","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":922379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Athearn, Nicole D.","contributorId":347757,"corporation":false,"usgs":false,"family":"Athearn","given":"Nicole D.","affiliations":[{"id":39509,"text":"National Park Service, Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":922380,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267895,"text":"70267895 - 2024 - New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior","interactions":[],"lastModifiedDate":"2025-06-06T14:54:41.50936","indexId":"70267895","displayToPublicDate":"2024-12-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21812,"text":"Journal of Great Lakes Research (JGLR)","active":true,"publicationSubtype":{"id":10}},"title":"New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior","docAbstract":"Numerous mercury (Hg) sources can contribute to biological burdens within the Great Lakes, including atmospheric deposition (e.g., precipitation), non-point source land runoff (e.g., watershed), and legacy contamination. Due to these different environmental entry points, it is often difficult to ascertain if legacy Hg contamination contributes to contemporary fish consumption advisories within Areas of Concern (AOCs), as designated by the United States-Canada Great Lakes Water Quality Agreement. In this study, we aimed to assess the contributions of legacy Hg to sediments in nearshore wetland habitats and co-located prey items (dragonfly larvae and yellow perch) within the St. Louis River AOC using Hg stable isotopes. We observed that nearshore sediments had the same Hg source portfolio as previously examined main channel sites. Furthermore, this data confirmed that two major Hg sources were contributing to sediments within nearshore regions of the St. Louis River AOC: legacy and watershed Hg. The contribution of legacy Hg was estimated in biota and demonstrated that up to 64% of the Hg in fish tissue in the lower estuary (St. Louis Bay) was from legacy sources, but that this percentage declined substantially when examining upstream regions of the AOC. These data indicate the influence of legacy Hg to the food web varies spatially within the St. Louis River. We further found that watershed Hg sources are an important Hg contributor to the St. Louis River, which likely applies to other impacted and unimpacted tributaries across the Great Lakes region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102494","usgsCitation":"Janssen, S., Hoffman, J.C., and Krabbenhoft, D.P., 2024, New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior: Journal of Great Lakes Research (JGLR), v. 51, no. 1, 102494, 9 p., https://doi.org/10.1016/j.jglr.2024.102494.","productDescription":"102494, 9 p.","ipdsId":"IP-170550","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102494","text":"Publisher Index Page"},{"id":490198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River in Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.25467395030219,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 47.005307600372475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoffman, Joel C.","contributorId":84244,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":939290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":939291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261770,"text":"sir20245124 - 2024 - Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","interactions":[],"lastModifiedDate":"2025-08-15T16:13:12.075619","indexId":"sir20245124","displayToPublicDate":"2024-12-20T13:41:26","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":"2024-5124","displayTitle":"Iodine-129 in the Eastern Snake River Plain Aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","title":"Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","docAbstract":"Between the 1950s and 1980s, wastewater generated at the Idaho National Laboratory contained Iodine-129 (129I); this wastewater was discharged directly into the eastern Snake River Plain (ESRP) aquifer through a deep disposal well, unlined infiltration ponds, or leaked from distribution systems below industrial facilities. During 2021–22, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy and the Idaho Department of Environmental Quality Idaho National Laboratory Oversight Program, collected groundwater samples from 64 monitoring wells in the ESRP aquifer, 6 of which are part of a multilevel monitoring system, to determine the concentration of 129I in the groundwater. These samples were analyzed by accelerator mass spectrometry as part of a long-term ongoing study to track trends and occurrences of this carcinogenic, long-lived radionuclide in the environment. Concentrations ranged from slightly above the locally determined background concentration of 5.4×10−6 picocuries per liter, to just below the U.S. Environmental Protection Agency’s maximum contaminant level of 1 picocurie per liter. Discharge of wastewater containing 129I has been discontinued to the aquifer, and long-term trends from a subset (n=15) of sampled wells show decreasing 129I concentrations over the last three decades. Concentrations of 129I in groundwater from monitoring wells near facilities at the Idaho National Laboratory are affected by episodic recharge from an ephemeral surface-water source and by the fracture-flow dominated hydrologic regime in the ESRP aquifer. The spatially focused sampling effort has also identified a low-level 129I plume that affects long-term water quality near and downgradient from the Advanced Test Reactor Complex in the southwestern part of the facility that had not been clearly defined in previous sampling efforts, although the definition of the plume is somewhat limited by available data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245124","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22262","usgsCitation":"Treinen, K.C., Trcka, A.R., Krohe, N., and Lehotsky, G., 2024, Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22: U.S. Geological Survey Scientific Investigations Report 2024–5124 (DOE/ID 22262), 27 p., https://doi.org/10.3133/sir20245124.","productDescription":"Report: vii, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-150514","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":494219,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118236.htm","linkFileType":{"id":5,"text":"html"}},{"id":465410,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245124/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5124"},{"id":465409,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5124/sir20245124.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5124"},{"id":465413,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5124/sir20245124.XML"},{"id":465412,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5124/images"},{"id":465411,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UWRYR4","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets for the U.S. Geological Survey—Idaho National Laboratory groundwater and surface-water monitoring networks, v1.1"},{"id":465408,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5124/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.973611,\n 43.591667\n ],\n [\n -112.916667,\n 43.591667\n ],\n [\n -112.916667,\n 43.540278\n ],\n [\n -112.973611,\n 43.540278\n ],\n [\n -112.973611,\n 43.591667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4250
Great Salt Lake is a critical habitat for migratory birds that is threatened by elevated metal concentrations, including mercury (Hg) and lead (Pb), and is subject to severe hydrologic changes, such as declining lake level. When assessing metal profiles recorded in Great Salt Lake sediment, a large data gap exists regarding the sources of metals within the system, which is complicated by various source inputs to the lake and complex biogeochemistry. Here, we leverage Hg and Pb stable isotopes to track relative changes in metal source contributions to Great Salt Lake over time. Mercury and Pb concentrations increase in sediments deposited after 1920 and peak between 1965 and 1995, following closure of several local smelters and the onset of increased emission controls. The nominal associations above are confirmed via Hg stable isotopes in pre-1920 background sediments, which reflect atmospheric inputs from regional and global origin, whereas Hg and Pb stable isotopes together indicate that elevated metal concentrations in mid-late 20th century sediments reflect increased mining/smelting inputs. The observed minimal rebound towards pre-1920 Pb isotope signatures in 21st century sediments indicates that mining/smelting inputs, though reduced, remain a primary source of Pb to Great Salt Lake. In contrast, the more pronounced rebound of Hg stable isotope signatures to pre-1920 values indicate a greater contribution of atmospheric inputs of regional/global origin to current Hg inputs, though Hg concentrations are ∼10 times greater than pre-1920 background values due to global increases in atmospheric Hg concentrations or possibly slow recovery from local contamination. The importance of regional/global Hg sources to the system suggests that reductions in Hg bioaccumulation in the open water food webs of Great Salt Lake are more dependent on national and global reductions in Hg emissions and management strategies to limit methylmercury production within system. This work highlights the utility of using coupled Hg and Pb stable isotope values to assess trace metal pollution sources and pathways in aquatic systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.177374","usgsCitation":"Lopez, S.F., Janssen, S., Tate, M., Fernandez, D.P., Anderson, C.R., Armstrong, G.J., Wang, T.C., and Johnson, W.P., 2024, Using mercury and lead stable isotopes to assess mercury, lead, and trace metal source contributions to Great Salt Lake, Utah, USA: Science of the Total Environment, v. 957, 177374, 14 p., https://doi.org/10.1016/j.scitotenv.2024.177374.","productDescription":"177374, 14 p.","ipdsId":"IP-170245","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":488066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.177374","text":"Publisher Index Page"},{"id":464466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Great Salt Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.27532189699335,\n 41.764305005205784\n ],\n [\n -113.27532189699335,\n 40.550910675427446\n ],\n [\n -111.82239945429947,\n 40.550910675427446\n ],\n [\n -111.82239945429947,\n 41.764305005205784\n ],\n [\n -113.27532189699335,\n 41.764305005205784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"957","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lopez, Samuel Francisco 0000-0002-3544-7465","orcid":"https://orcid.org/0000-0002-3544-7465","contributorId":344607,"corporation":false,"usgs":true,"family":"Lopez","given":"Samuel","email":"","middleInitial":"Francisco","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernandez, Diego P.","contributorId":138701,"corporation":false,"usgs":false,"family":"Fernandez","given":"Diego","email":"","middleInitial":"P.","affiliations":[{"id":12499,"text":"Univ. of Utah","active":true,"usgs":false}],"preferred":false,"id":919348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Christopher R.","contributorId":346496,"corporation":false,"usgs":false,"family":"Anderson","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":919349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Armstrong, Grace Jane 0009-0009-8132-9011","orcid":"https://orcid.org/0009-0009-8132-9011","contributorId":332127,"corporation":false,"usgs":true,"family":"Armstrong","given":"Grace","email":"","middleInitial":"Jane","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Thomas Charng-Shuen 0009-0001-2214-4721","orcid":"https://orcid.org/0009-0001-2214-4721","contributorId":331024,"corporation":false,"usgs":true,"family":"Wang","given":"Thomas","email":"","middleInitial":"Charng-Shuen","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919351,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, William P.","contributorId":107288,"corporation":false,"usgs":false,"family":"Johnson","given":"William","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":919352,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"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":70261722,"text":"fs20243053 - 2024 - The 3D Elevation Program—Supporting the Kansas Economy","interactions":[],"lastModifiedDate":"2025-08-15T16:19:16.627318","indexId":"fs20243053","displayToPublicDate":"2024-12-19T14:20:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3053","displayTitle":"The 3D Elevation Program—Supporting the Kansas Economy","title":"The 3D Elevation Program—Supporting the Kansas Economy","docAbstract":"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 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245051","collaboration":"Prepared in cooperation with West Virginia Department of Transportation, Division of Highways","usgsCitation":"Messinger, T., Holmes, D.A., Scott, J.D., and Kirk, D.W., 2024, Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia: U.S. Geological Survey Scientific Investigations Report 2024–5051, 34 p., https://doi.org/10.3133/sir20245051.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135179","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":494223,"rank":7,"type":{"id":36,"text":"NGMDB Index 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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,
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6000 J Street, Placer Hall
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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.
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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":"Monitoring animal body condition can provide insight on population responses to environmental change. Pacific walruses (Odobenus rosmarus divergens) are experiencing loss of their sea ice habitat which has decreased the time that females spend foraging during a critical period of pregnancy and lactation. Here we investigate the potential for body condition to track demographic change and be monitored via two-dimensional aerial imagery by (1) examining whether walrus somatic growth and body mass data tracked estimated historic demographic changes, (2) collecting morphometric and body mass data and aerial imagery of walruses in human care to determine if sex, age group, and body size and condition can be determined from imagery, and (3) examining aerial imagery from a large coastal haulout used primarily by females and young to estimate potential sample sizes of measurable walruses. Body mass and growth in body length decreased between the last 1970s and early 1980s concurrent with a period when the population apparently approached carrying capacity and subsequently declined. Measures from aerial imagery (1) accurately distinguished reproductive age females from subadults and adult males and (2) enabled body mass estimates with 6-7% error using either areal footprint or a combination of length and width. We found a mean of 216 ± 77 walruses appropriately positioned for measurement from aerial surveys of the haulout enabling measurements of ≥7000 individuals annually via repeated daily imagery. Our results suggest that body mass of reproductive age females and growth of dependent young may be useful indicators to augment monitoring of the Pacific walrus population and can be achieved via non-invasive aerial imagery collections.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps14738","usgsCitation":"Rode, K.D., Fischbach, A.S., Synnott, M., Stewart, J., Northcraft, N., Allen, E., Trotto, K., Vancsok, C., Issenjou, N., Ploof, S., Rager, S., DiRocco, S., Owens, S., and Prahl, A., 2024, Photogrammetry-based body condition for monitoring an Arctic marine mammal experiencing habitat loss: Marine Ecology Progress Series, v. 751, p. 211-227, https://doi.org/10.3354/meps14738.","productDescription":"17 p.","startPage":"211","endPage":"227","ipdsId":"IP-167535","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466705,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14738","text":"Publisher Index Page"},{"id":465444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 71.04269895606964\n ],\n [\n -179.9,\n 57.55259327741362\n ],\n [\n -162.40817411659705,\n 57.55259327741362\n ],\n [\n -162.40817411659705,\n 71.04269895606964\n ],\n [\n -179.9,\n 71.04269895606964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"751","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science 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Staci","contributorId":347485,"corporation":false,"usgs":false,"family":"Owens","given":"Staci","email":"","affiliations":[{"id":83173,"text":"SeaWorld Orlando","active":true,"usgs":false}],"preferred":false,"id":921783,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Prahl, Adriane","contributorId":347500,"corporation":false,"usgs":false,"family":"Prahl","given":"Adriane","email":"","affiliations":[],"preferred":false,"id":921864,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70261662,"text":"ofr20241072 - 2024 - Topographic and bathymetric survey in support of the effectiveness assessment of the living shoreline restoration in Gandys Beach, New Jersey","interactions":[],"lastModifiedDate":"2025-08-15T16:24:45.607243","indexId":"ofr20241072","displayToPublicDate":"2024-12-19T09:47:32","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1072","displayTitle":"Topographic and Bathymetric Survey in Support of the Effectiveness Assessment of the Living Shoreline Restoration in Gandys Beach, New Jersey","title":"Topographic and bathymetric survey in support of the effectiveness assessment of the living shoreline restoration in Gandys Beach, New Jersey","docAbstract":"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":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 2 (April–June) of 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
This is the fourth quarterly report to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
This is the second quarterly report that does not include analysis results for Landsat 7 because Enhanced Thematic Mapper Plus imaging was suspended on January 19, 2024, after the satellite transitioned into full sunlight. The satellite has been drifting since early 2022 when it was lowered from the nominal orbit altitude, and the transition into full sunlight is a result of the satellite operating in its extended science mission. Additional information about the imaging suspension is available at https://www.usgs.gov/landsat-missions/news/landsat-7-imaging-suspended. Additional information about the Landsat 7 extended science mission is available at https://www.usgs.gov/landsat-missions/landsat-7-extended-science-mission.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241077","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 2, 2024: U.S. Geological Survey Open-File Report 2024–1077, 56 p., https://doi.org/10.3133/ofr20241077.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-168175","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":465270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1077/coverthb.jpg"},{"id":465271,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1077/ofr20241077.pdf","text":"Report","size":"5.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1077"},{"id":465272,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1077/ofr20241077.XML"},{"id":465273,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1077/images/"},{"id":465274,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1077/images/"},{"id":465275,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":465277,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241077/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
In 2021, the Tennessee Department of Environment and Conservation (TDEC) and the U.S. Geological Survey worked in cooperation to develop a geographic information system (GIS)-based methodology that systematically assesses the vulnerability of public-supply drinking water to potential contaminants consistent with the standards set forth in the Tennessee Source Water Assessment Program (SWAP). As of June 2022, public-supply water was provided by 643 active public water systems across Tennessee that withdrew water for public use from 1,378 individual water sources. With the newly developed methodology, referred to as “TN-SWAPyT,” TDEC can consistently delineate source water assessment zones and evaluate source susceptibility on the basis of information such as the proximity of contaminant sources, land-use activities, geologic information, and additional environmental spatial data. A major benefit of the TN-SWAPyT methodology is to provide TDEC with consistent reports that can be used as a starting point for assessing public supplies. Communities and public water systems can then build upon these reports by using local knowledge and site-specific information.
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":"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":"Water withdrawal from private groundwater wells is often unaccounted for in water planning studies, and water from private wells can be a source of exposure to environmental contaminants. The sizes of populations that depend on private wells for domestic water use and the amounts of water that are withdrawn from these wells are generally poorly represented in data collection efforts because of the challenges of locating, metering, or gathering withdrawal information from individual property owners. To address this problem, the U.S. Geological Survey, in cooperation with the Rhode Island Water Resources Board, estimated the volume of water withdrawn from domestic self-supply wells and the populations who use them for the State of Rhode Island at a 30-meter pixel spatial resolution and one-month temporal resolution between July 2014 and June 2021.
The number of people reliant on domestic self-supply wells has increased in Rhode Island over the study period; however, the statewide estimate of total water withdrawal has not statistically increased. Withdrawals from private wells are largest in the inland areas of the western part of the State, and the towns of Scituate and Charlestown have the highest estimated withdrawals. Statewide monthly withdrawals ranged from 3.987 million gallons per day in March 2018 to 7.767 million gallons per day in September 2016. The median per capita domestic water use rate was 46.0 gallons per capita per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245109","collaboration":"Prepared in cooperation with the Rhode Island Water Resources Board","usgsCitation":"Chamberlin, C.A., Armstrong, I.P., and Stagnitta, T.J., 2024, Estimating domestic self-supplied water use in Rhode Island, 2014–21: U.S. Geological Survey Scientific Investigations Report 2024–5109, 29 p., https://doi.org/10.3133/sir20245109.","productDescription":"Report: vii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152675","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":464969,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WU48KY","text":"USGS data release","linkHelpText":"Monthly and annual population and self-supplied domestic water withdrawal maps of Rhode Island, 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
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 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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}]}} ]}