Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90.
Geographical distributions of waterfowl exhibit annual variation in response to spatiotemporal variation in weather conditions, habitat availability, and other factors. Continuing changes in climate and land use could lead to persistent shifts of waterfowl distributions, potentially causing a mismatch with habitat conservation planning, wetland restoration efforts, and harvest management decisions informed by historical distributions. We used band recoveries and harvest records (i.e., hunter-harvested wings) from the United States Fish and Wildlife Service Waterfowl Parts Collection Survey as indices of duck distribution in autumn and winter, and quantified intra-annual, interannual, and interspecific variation in their geographic distributions across 6 decades (1960–2019) for 15 duck species in the Central and Mississippi flyways in North America. Specifically, we tested for annual and decadal shifts in mean latitude and longitude of recoveries for each month (Oct–Jan) by species and taxonomic guild (i.e., dabbling, diving ducks). Overall, species varied in the extent, timing, and sometimes direction, of distributional change in recoveries. From 1960–2019, mean recovery locations for dabbling ducks shifted south 105–296 km in October and 27 km in November (wings only), whereas mean latitudes shifted north 144–234 km in December and 186–301 km in January. Mean recovery locations for diving ducks shifted north 162 km in October (wings only), 84–173 km in December, and 66–120 km in January, but shifted 99–512 km south in November. Shifts in longitude were less consistent between guilds and data types. Finally, distributional change rarely accelerated during recent decades, except for southward shifts of band recoveries of diving ducks in November and northward shifts of band and wing recoveries of dabbling ducks in January. Although anecdotal accounts of large-scale northward shifts in duck distributions are prolific in the land management and hunting communities, our data demonstrate more subtle shifts that vary considerably by species and month. Observed changes in recovery distributions could necessitate changes in timing of habitat management practices throughout the Central and Mississippi flyways and may result in fewer hunting and recreational opportunities for some species in southern states. Quantifying patterns of historical change is a necessary first step to understanding temporal and interspecific variation in waterfowl distributions, which will help with landscape-scale conservation and management efforts in the future and enable effective communication to core constituencies regarding ongoing changes and their implications for recreational engagement.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.22521","usgsCitation":"Verheijen, B., Webb, E.B., Brasher, M., and Hagy, H.M., 2024, Spatiotemporal dynamics of duck harvest distributions in the Central and Mississippi flyways, 1960–2019: The Journal of Wildlife Management, v. 88, no. 2, e22521, 18 p., https://doi.org/10.1002/jwmg.22521.","productDescription":"e22521, 18 p.","ipdsId":"IP-151486","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432886,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.31640625,\n 28.92163128242129\n ],\n [\n -85.4296875,\n 28.92163128242129\n ],\n [\n -85.4296875,\n 51.069016659603896\n ],\n [\n -99.31640625,\n 51.069016659603896\n ],\n [\n -99.31640625,\n 28.92163128242129\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-11-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Verheijen, Bram H. F.","contributorId":274514,"corporation":false,"usgs":false,"family":"Verheijen","given":"Bram H. F.","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":907872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907873,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brasher, Michael G.","contributorId":338627,"corporation":false,"usgs":false,"family":"Brasher","given":"Michael G.","affiliations":[{"id":81180,"text":"Ducks Unlimited, Inc","active":true,"usgs":false}],"preferred":false,"id":907874,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hagy, Heath M.","contributorId":172326,"corporation":false,"usgs":false,"family":"Hagy","given":"Heath","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":907875,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256492,"text":"70256492 - 2024 - Both Landsat- and LiDAR-derived measures predict forest bee response to large-scale wildfire","interactions":[],"lastModifiedDate":"2024-08-19T17:28:46.841111","indexId":"70256492","displayToPublicDate":"2024-02-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Both Landsat- and LiDAR-derived measures predict forest bee response to large-scale wildfire","docAbstract":"Large-scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post-fire habitat assessment. We evaluated the extent to which LiDAR-derived forest structure measurements can predict forest bee communities after a large, mixed-severity fire. We hypothesized that LiDAR measurements linked to post-fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite-based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed-canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post-wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large-scale wildfire.
","language":"English","doi":"10.1002/rse2.354","usgsCitation":"Galbraith, S.M., Valente, J., Dunn, C.J., and Rivers, J.W., 2024, Both Landsat- and LiDAR-derived measures predict forest bee response to large-scale wildfire: Remote Sensing in Ecology and Conservation, v. 10, no. 1, p. 24-38, https://doi.org/10.1002/rse2.354.","productDescription":"15 p.","startPage":"24","endPage":"38","ipdsId":"IP-144537","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.354","text":"Publisher Index Page"},{"id":432888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Douglas Fire Complex, southwestern Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.576158064366,\n 43.53770092737449\n ],\n [\n -123.576158064366,\n 42.89081714418663\n ],\n [\n -123.04040779515724,\n 42.89081714418663\n ],\n [\n -123.04040779515724,\n 43.53770092737449\n ],\n [\n -123.576158064366,\n 43.53770092737449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Galbraith, Sara M.","contributorId":340887,"corporation":false,"usgs":false,"family":"Galbraith","given":"Sara","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":907638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":910913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Christopher J.","contributorId":340888,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":907640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rivers, James W.","contributorId":23072,"corporation":false,"usgs":false,"family":"Rivers","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":7005,"text":"Department of Forest Ecosystems and Society, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":907641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251343,"text":"70251343 - 2024 - Top-predator recovery abates geomorphic decline of a coastal ecosystem","interactions":[],"lastModifiedDate":"2024-02-07T01:15:12.912408","indexId":"70251343","displayToPublicDate":"2024-01-31T19:12:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Top-predator recovery abates geomorphic decline of a coastal ecosystem","docAbstract":"The recovery of top predators is thought to have cascading effects on vegetated ecosystems and their geomorphology1,2, but the evidence for this remains correlational and intensely debated3,4. Here we combine observational and experimental data to reveal that recolonization of sea otters in a US estuary generates a trophic cascade that facilitates coastal wetland plant biomass and suppresses the erosion of marsh edges—a process that otherwise leads to the severe loss of habitats and ecosystem services5,6. Monitoring of the Elkhorn Slough estuary over several decades suggested top-down control in the system, because the erosion of salt marsh edges has generally slowed with increasing sea otter abundance, despite the consistently increasing physical stress in the system (that is, nutrient loading, sea-level rise and tidal scour7,8,9). Predator-exclusion experiments in five marsh creeks revealed that sea otters suppress the abundance of burrowing crabs, a top-down effect that cascades to both increase marsh edge strength and reduce marsh erosion. Multi-creek surveys comparing marsh creeks pre- and post-sea otter colonization confirmed the presence of an interaction between the keystone sea otter, burrowing crabs and marsh creeks, demonstrating the spatial generality of predator control of ecosystem edge processes: densities of burrowing crabs and edge erosion have declined markedly in creeks that have high levels of sea otter recolonization. These results show that trophic downgrading could be a strong but underappreciated contributor to the loss of coastal wetlands, and suggest that restoring top predators can help to re-establish geomorphic stability.
Forest thinning on public lands in the Pacific Northwest USA is an important tool for restoring diversity in forest stands with a legacy of simplified structure from decades of intensive management for timber production. A primary application of thinning in young (< 50-year-old) stands is to accelerate forest development to mitigate loss of late-seral habitat to decades of logging. However, thinning may have short-term negative effects for some species associated with mature forest that are expected to benefit from the practice over the long term. An increased risk of nest predation is a primary concern to managers charged with stewardship of habitat for the federally threatened Marbled Murrelet (Brachyramphus marmoratus), a species that nests in older forests. Predation by corvids is the greatest cause of nest failure for the Marbled Murrelet, and corvids are known to respond positively to forest disturbance, but quantitative information is lacking on the potential impacts of thinning on risk of nest predation. We investigated the response of two common corvid nest predators, Steller’s Jay (Cyanocitta stelleri) and Canada Jay (Perisoreus canadensis), to variation in thinning intensity in young forest (< 50 years old) using data from a long-term silviculture experiment. We used a Before-After-Control-Impact (BACI) design, linear mixed modeling, and occupancy modeling to quantify differences in corvid observation rates among varying levels of thinning intensity, and to assess changes in jay response over more than a decade following thinning. We found an increase in observation rates of both species in the heavily thinned treatment during the first 5 to 7 years following thinning, and some evidence of a short-term increase in Steller’s Jay activity in the thinning-with-gaps treatment. Neither jay species responded to the least intensive thinning treatment, which reduced average canopy cover by < 30%. By approximately a decade after thinning, observation rates of jays did not differ between unthinned controls and any of the thinning treatments. Incorporating our quantitative information into landscape-level planning can help managers balance short- and long-term conservation goals.
","language":"English","publisher":"Avian Conservation and Ecology","doi":"10.5751/ACE-02578-190103","usgsCitation":"Hagar, J., Owen, T.K., Stevens, T.K., and Waianuhea, L.K., 2024, Response of corvid nest predators to thinning: implications for balancing short- and long-term goals for restoration of forest habitat: Avian Conservation and Ecology, v. 19, no. 1, 3, 11 p., https://doi.org/10.5751/ACE-02578-190103.","productDescription":"3, 11 p.","ipdsId":"IP-110816","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":440576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02578-190103","text":"Publisher Index Page"},{"id":425446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.31669804329832,\n 43.72071322185553\n ],\n [\n -122.22828499277165,\n 43.72071322185553\n ],\n [\n -122.22828499277165,\n 46.34467865412955\n ],\n [\n -124.31669804329832,\n 46.34467865412955\n ],\n [\n -124.31669804329832,\n 43.72071322185553\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hagar, Joan 0000-0002-3044-6607 joan_hagar@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-6607","contributorId":3369,"corporation":false,"usgs":true,"family":"Hagar","given":"Joan","email":"joan_hagar@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":894180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Owen, Theodore K","contributorId":333872,"corporation":false,"usgs":false,"family":"Owen","given":"Theodore","email":"","middleInitial":"K","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":894181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Thomas K.","contributorId":333873,"corporation":false,"usgs":false,"family":"Stevens","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":894182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waianuhea, Lorraine K 0000-0001-9697-6857","orcid":"https://orcid.org/0000-0001-9697-6857","contributorId":333874,"corporation":false,"usgs":false,"family":"Waianuhea","given":"Lorraine","email":"","middleInitial":"K","affiliations":[{"id":79996,"text":"Pacific Biosciences Research Center, University of Hawai’i at Mānoa","active":true,"usgs":false}],"preferred":false,"id":894183,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251195,"text":"sir20235137 - 2024 - Isolation and identification of microcystin-degrading bacteria in Lake Erie source waters and drinking-water plant sand filters","interactions":[],"lastModifiedDate":"2026-01-30T19:40:43.248539","indexId":"sir20235137","displayToPublicDate":"2024-01-31T15:40:00","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":"2023-5137","displayTitle":"Isolation and Identification of Microcystin-Degrading Bacteria in Lake Erie Source Waters and Drinking-Water Plant Sand Filters","title":"Isolation and identification of microcystin-degrading bacteria in Lake Erie source waters and drinking-water plant sand filters","docAbstract":"The increasing prevalence of cyanobacterial harmful algal blooms and the toxins they produce is a global water-quality issue. In the Western Basin of Lake Erie, high microcystin concentrations have led to water-quality advisories, process adjustments for treating drinking water, and increased water-quality monitoring. Biodegradation is an environmentally friendly and cost-effective way to reduce concentrations of microcystins in drinking water; however, few studies have been done to determine biodegradation potential of bacteria indigenous to the Lake Erie watershed. As part of a cooperative program between the U.S. Geological Survey and the U.S. Environmental Protection Agency, this study aimed to identify naturally occurring microcystin-degrading bacteria in source waters and in the sand filters of drinking-water treatment plants in the Western Basin of Lake Erie. Biodegradation of microcystin-LR was found to occur in microcosms developed with three different Lake Erie-area sources—Lake Erie water, water from storage reservoirs supplied by inland streams, and water or solid medium from sand/anthracite filters at drinking-water plants. In microplates with microcystin-LR as the sole carbon source, 10 isolates exhibited cellular respiration and were, therefore, identified as promising microcystin biodegraders; 4 of those isolates subsequently were found to have potential to form biofilms. The 10 promising isolates along with 14 additional isolates from the microcosms were identified by 16S ribosomal RNA sequencing: 15 isolates were γ-proteobacteria, 6 isolates were β-proteobacteria, 1 isolate was an α-proteobacterium, 1 isolate was a flavobacterium in the phylum Bacteroidetes, and 1 isolate was in the phylum Actinobacteria. Isolates were screened for possession of the mlrA gene (found to encode for the protein responsible for cleaving the cyclic structure of microcystin), and results indicate that, for Lake Erie source waters and elsewhere, more work would be required to identify microcystin-biodegradation pathways and products and to confirm biodegradation rates in pure culture isolates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235137","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Cicale, J.R., Stelzer, E.A., Reano, D.C., and Ecker, C.D., 2024, Isolation and identification of microcystin-degrading bacteria in Lake Erie source waters and drinking-water plant sand filters: U.S. Geological Survey Scientific Investigations Report 2023–5137, 23 p., https://doi.org/10.3133/sir20235137","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095884","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":499399,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115978.htm","linkFileType":{"id":5,"text":"html"}},{"id":425054,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DL080Y","text":"USGS data release","linkHelpText":"Microcosm experiment data of microcystin-degrading bacteria in Lake Erie source waters and drinking-water plants, 2015–18"},{"id":425053,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5137/images/"},{"id":425052,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5137/sir20235137.XML"},{"id":425051,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235137/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5137"},{"id":425050,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5137/sir20235137.pdf","text":"Report","size":"1.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5137"},{"id":425049,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5137/coverthb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.035308272771,\n 41.1710613373711\n ],\n [\n -78.41030827277076,\n 41.1710613373711\n ],\n [\n -78.41030827277076,\n 43.25385984825451\n ],\n [\n -84.035308272771,\n 43.25385984825451\n ],\n [\n -84.035308272771,\n 41.1710613373711\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
The widespread application of pesticides in agricultural and urban areas leads to their presence in surface waters. Presence of these biologically active chemicals in environmental waters potentially has adverse effects on nontarget organisms. To better understand the environmental fate of these contaminants, a robust method to capture chemicals with wide-ranging physicochemical properties has been developed. The method was developed by the U.S. Geological Survey’s Organic Chemistry Research Laboratory to monitor pesticides, pesticide degradates, and other agrochemicals in environmental surface waters throughout the country. The analysis involves a multiresidue method to determine 183 pesticides and pesticide degradates in filtered water samples and 178 pesticides and pesticide degradates in paired suspended sediment samples. After the filtration of whole water, contaminants are individually measured in the filtered water and the collected suspended sediment. Filtered water is extracted via solid-phase extraction, whereas suspended sediment is extracted using an ultrasonication, solid-liquid extraction. Samples are analyzed by liquid chromatography-tandem mass spectrometry using an electrospray ionization source in positive and negative modes and analyzed by gas chromatography-tandem mass spectrometry using an advanced electron ionization source in positive mode. Instrument parameters were optimized for the highest sensitivity, and at least two transitions (quantifier and qualifier) were monitored for each analyte.
Recoveries in test filtered water (n=9; 183 analytes) from the American River, California, and suspended sediment (n=9; 178 analytes) samples fortified at 15 nanograms per liter (ng/L) ranged from 70.1 to 121.0 and 71.1 to 117.0 percent in water and suspended sediment filter samples, respectively. Method detection limits of pesticides and pesticide degradates ranged from 0.5 to 10.6 ng/L in water and 0.7 to 11.8 ng/L in suspended sediment filters. Reporting limits were 1.1–21.1 ng/L and 1.5–23.7 ng/L in water and filter samples, respectively. The developed method is applied to surface-water samples for the analysis of pesticides, pesticide degradates, and other agrochemicals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm5A12","programNote":"Water Resources Mission Area—Water Availability and Use Science Program","usgsCitation":"Gross, M.S., Sanders, C.J., De Parsia, M.D., and Hladik, M.L., 2024, Methods of analysis—Determination of pesticides in filtered water and suspended sediment using liquid chromatography- and gas chromatography-tandem mass spectrometry: U.S. Geological Survey Techniques and Methods, book 5, chap. A12, 33 p., https://doi.org/10.3133/tm5A12.","productDescription":"Report: vi, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","ipdsId":"IP-139193","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425118,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J8E544","text":"USGS Data Release","description":"Gross, M.S., Sanders, C.J., De Parsia, M.D., and Hladik, M.L., 2023, A multiresidue method for the analysis of pesticides in water using solid-phase extraction with gas and liquid chromatography-tandem mass spectrometry (ver. 2.0, April 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9J8E544.","linkHelpText":"A multiresidue method for the analysis of pesticides in water using solid-phase extraction with gas and liquid chromatography-tandem mass spectrometry (ver. 2.0, April 2023)"},{"id":425662,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm5A12/full"},{"id":425113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/05/a12/tm5a12.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425114,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/05/a12/tm5a12.xml"},{"id":425112,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/05/a12/covrthb.jpg"},{"id":425661,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/05/a12/images/"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Millions of small (< 10 ha) waterbodies embedded in grassland and agroecosystems in midcontinental North America provide breeding habitat to an estimated 50–80% of North America’s migratory ducks. Tens of millions of dollars are invested annually to conserve and enhance upland and wetland habitats for breeding ducks by prioritizing locations predicted to have high densities of breeding pairs under average precipitation conditions. An implicit assumption of this approach is that the distribution of breeding habitat remains relatively static. Climate change is an identified risk to this strategy. To assess this assumption and plan for potential forthcoming conditions, we estimated changes in potential breeding duck pairs under different climate scenarios by combining results of 1) a mechanistic hydrology model that simulates ecosystem processes for a subset of wetlands distributed across the U.S. Prairie Pothole Region (USPPR); 2) four downscaled climate model projections at mid- and late-century time horizons; and 3) U.S. Fish and Wildlife Service multi-decadal datasets and predictive breeding waterfowl pair statistical models. We conducted virtual and in-person informational sessions with partners to inform them on the best practices of using downscaled global circulation models and approaches for climate scenario planning. This close coordination led to a joint presentation at a monthly North Central Climate Adaptation Science Center seminar. We are also co-developing simulated wetland- waterfowl responses under different climate futures for wetlands. Information from these robust predictions of waterfowl habitat and settling patterns in this region provides land-management agencies insights in prioritizing current conservation actions given uncertainty. In addition, understanding how many breeding pairs the USPPR might support in coming decades will likely influence overall breeding population sizes and sustainable harvest objectives across North America.
","language":"English","publisher":"North Central Climate Adaptation Science Center","usgsCitation":"McKenna, O.P., and Rangwala, I., 2024, The impact of future changes in climate on breeding waterfowl pairs in the US Prairie Pothole Region: Final Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-160169","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":501397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501396,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/65c3d314d34ef4b119cae715"}],"country":"United States","state":"Iowa, Minnesota, Nebraska, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.05393441199925,\n 48.93957527305318\n ],\n [\n -108.27686560641123,\n 49.13252538326782\n ],\n [\n -108.40976144212098,\n 47.91192273687099\n ],\n [\n -106.07828064784712,\n 47.88578029277039\n ],\n [\n -101.59819304168207,\n 47.10144151067459\n ],\n [\n -100.41457783182686,\n 42.307798494527646\n ],\n [\n -96.89275640038099,\n 41.01374950027804\n ],\n [\n -96.60513562002711,\n 43.75532782507912\n ],\n [\n -94.98321580753591,\n 41.5817220442664\n ],\n [\n -94.20767561860225,\n 41.328376780271384\n ],\n [\n -93.63954575827131,\n 42.51490897209834\n ],\n [\n -93.78977025276507,\n 43.36437770770755\n ],\n [\n -94.24637551611141,\n 47.33263848178828\n ],\n [\n -95.05393441199925,\n 48.93957527305318\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":893736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangwala, Imtiaz 0000-0002-4313-9374","orcid":"https://orcid.org/0000-0002-4313-9374","contributorId":148973,"corporation":false,"usgs":false,"family":"Rangwala","given":"Imtiaz","email":"","affiliations":[{"id":34534,"text":"Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado","active":true,"usgs":false}],"preferred":true,"id":957215,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256560,"text":"70256560 - 2024 - Lesser prairie-chicken dispersal after translocation: Implications for restoration and population connectivity","interactions":[],"lastModifiedDate":"2024-08-22T15:57:44.93115","indexId":"70256560","displayToPublicDate":"2024-01-31T10:43:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Lesser prairie-chicken dispersal after translocation: Implications for restoration and population connectivity","docAbstract":"Conservation translocations are frequently inhibited by extensive dispersal after release, which can expose animals to dispersal-related mortality or Allee effects due to a lack of nearby conspecifics. However, translocation-induced dispersals also provide opportunities to study how animals move across a novel landscape, and how their movements are influenced by landscape configuration and anthropogenic features. Translocation among populations is considered a potential conservation strategy for lesser prairie-chickens (Tympanuchus pallidicinctus). We determined the influence of release area on dispersal frequency by translocated lesser prairie-chickens and measured how lesser prairie-chickens move through grassland landscapes through avoidance of anthropogenic features during their dispersal movements. We translocated 411 lesser prairie-chickens from northwest Kansas to southeastern Colorado and southwestern Kansas in 2016–2019. We used satellite GPS transmitters to track 115 lesser prairie-chickens throughout their post-release dispersal movements. We found that almost all lesser prairie-chickens that survived from their spring release date until June undergo post-translocation dispersal, and there was little variation in dispersal frequency by release area (96% of all tracked birds, 100% in Baca County, Colorado, 94% in Morton County, Kansas, n = 55). Dispersal movements (male: 103 ± 73 km, female: 175 ± 108 km, n = 62) led to diffusion across landscapes, with 69% of birds settling >5 km from their release site. During dispersal movements, translocated lesser prairie-chickens usually travel by a single 3.75 ± 4.95 km dispersal flight per day, selecting for steps that end far from roads and in Conservation Reserve Program (CRP) grasslands. Due to this “stepping stone” method of transit, landscape connectivity is optimized when <5 km separates grassland patches on the landscape. Future persistence of lesser prairie-chicken populations can be aided through conservation of habitat and strategic placement of CRP to maximize habitat connectivity. Dispersal rates suggest that translocation is better suited to objectives for regional, rather than site-specific, population augmentation for this species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10871","usgsCitation":"Berigan, L.A., Aulicky, C.S., Teige, E., Sullins, D., Fricke, K., Reitz, J.H., Rossi, L.G., Schultz, K.A., Rice, M., Tanner, E., Fuhlendorf, S., and Haukos, D.A., 2024, Lesser prairie-chicken dispersal after translocation: Implications for restoration and population connectivity: Ecology and Evolution, v. 14, no. 2, e10871, 14 p., https://doi.org/10.1002/ece3.10871.","productDescription":"e10871, 14 p.","ipdsId":"IP-155685","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440577,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10871","text":"Publisher Index Page"},{"id":433066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": 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A.","contributorId":341142,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent A.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":907995,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reitz, Jonathan H.","contributorId":341143,"corporation":false,"usgs":false,"family":"Reitz","given":"Jonathan","email":"","middleInitial":"H.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rossi, Liza G.","contributorId":341144,"corporation":false,"usgs":false,"family":"Rossi","given":"Liza","email":"","middleInitial":"G.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907997,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schultz, Kraig A.","contributorId":341145,"corporation":false,"usgs":false,"family":"Schultz","given":"Kraig","email":"","middleInitial":"A.","affiliations":[{"id":81167,"text":"Kansas Department of Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":907998,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rice, Mindy","contributorId":341146,"corporation":false,"usgs":false,"family":"Rice","given":"Mindy","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907999,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tanner, Evan","contributorId":341147,"corporation":false,"usgs":false,"family":"Tanner","given":"Evan","affiliations":[{"id":81708,"text":"Caesar Kleberg Wildlife Research Institute","active":true,"usgs":false}],"preferred":false,"id":908000,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fuhlendorf, Samuel D.","contributorId":341148,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel D.","affiliations":[{"id":81709,"text":"Oklahoma State University,","active":true,"usgs":false}],"preferred":false,"id":908001,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907994,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70252677,"text":"70252677 - 2024 - Short-term sediment dispersal on a large retreating coastal river delta via 234Th and 7Be sediment geochronology: The Mississippi River Delta Front","interactions":[],"lastModifiedDate":"2024-04-02T14:44:14.447864","indexId":"70252677","displayToPublicDate":"2024-01-31T09:43:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Short-term sediment dispersal on a large retreating coastal river delta via 234Th and 7Be sediment geochronology: The Mississippi River Delta Front","title":"Short-term sediment dispersal on a large retreating coastal river delta via 234Th and 7Be sediment geochronology: The Mississippi River Delta Front","docAbstract":"Many Mississippi River Delta studies have shown recent declines in fluvial sediment load from the river and associated land loss. In contrast, recent sedimentary processes on the subaqueous delta are less documented. To help address this knowledge gap, multicores were collected offshore from the three main river outlets at water depths of 25–280 m in June 2017 just after the peak river discharge period, with locations selected based on 2017 U.S. Geological Survey seabed mapping. The coring locations included the undisturbed upper foreset, mudflow lobes, gullies, and the undisturbed prodelta. Nine multicores were analyzed for Beryllium-7 activity, and four cores were analyzed for excess Thorium-234 activity via gamma spectrometry, granulometry and X-radiography. Our results indicate a general trend of declining 7Be and 234Th activities and inventories with increasing distance from sources and in deeper water. The core X-radiographs are graded from the predominantly physically stratified nearshore to the more bioturbated offshore, consistent with the sedimentation patterns. Sediment focusing assessed via the 7Be and 234Th sediment inventories shows preferential sedimentation in gully and lobe environments, whereas the upper foreset and prodelta focusing factors are relatively depleted. Overall, short-term sediment deposition from the main fluvial source remains active offshore from all three major river outlets, despite the overall declining river load.
","language":"English","publisher":"MDPI","doi":"10.3390/w16030463","usgsCitation":"Courtois, A., Bentley, S., Maloney, J., Xu, K., Chaytor, J., Georgiou, I.Y., Miner, M., Obelcz, J., Jafari, N., and Damour, M., 2024, Short-term sediment dispersal on a large retreating coastal river delta via 234Th and 7Be sediment geochronology: The Mississippi River Delta Front: Water, v. 16, no. 3, 463, 18 p., https://doi.org/10.3390/w16030463.","productDescription":"463, 18 p.","ipdsId":"IP-161126","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440581,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w16030463","text":"Publisher Index Page"},{"id":427313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Delta Front","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.14856845740192,\n 30.086965664332396\n ],\n [\n -91.2450587263565,\n 30.086965664332396\n ],\n [\n -91.2450587263565,\n 29.065454996104577\n ],\n [\n -89.14856845740192,\n 29.065454996104577\n ],\n [\n -89.14856845740192,\n 30.086965664332396\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Courtois, Andrew","contributorId":205364,"corporation":false,"usgs":false,"family":"Courtois","given":"Andrew","affiliations":[{"id":37089,"text":"Pontchartrain Institute for Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":897884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bentley, Samuel J.","contributorId":150402,"corporation":false,"usgs":false,"family":"Bentley","given":"Samuel J.","affiliations":[],"preferred":false,"id":897885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maloney, Jillian","contributorId":304141,"corporation":false,"usgs":false,"family":"Maloney","given":"Jillian","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":897886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xu, Kehui","contributorId":223696,"corporation":false,"usgs":false,"family":"Xu","given":"Kehui","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":897887,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chaytor, Jason 0000-0001-8135-8677 jchaytor@usgs.gov","orcid":"https://orcid.org/0000-0001-8135-8677","contributorId":140095,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason","email":"jchaytor@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":897888,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Georgiou, Ioannis Y.","contributorId":205361,"corporation":false,"usgs":false,"family":"Georgiou","given":"Ioannis","email":"","middleInitial":"Y.","affiliations":[{"id":37089,"text":"Pontchartrain Institute for Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":897889,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miner, Michael","contributorId":223694,"corporation":false,"usgs":false,"family":"Miner","given":"Michael","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":897890,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Obelcz, Jeffery","contributorId":335257,"corporation":false,"usgs":false,"family":"Obelcz","given":"Jeffery","email":"","affiliations":[{"id":80360,"text":"United States Naval Research Lab, Stennis Space Center","active":true,"usgs":false}],"preferred":false,"id":897891,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jafari, Navid H.","contributorId":214730,"corporation":false,"usgs":false,"family":"Jafari","given":"Navid H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":897892,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Damour, Melanie","contributorId":223691,"corporation":false,"usgs":false,"family":"Damour","given":"Melanie","email":"","affiliations":[{"id":25296,"text":"BOEM","active":true,"usgs":false}],"preferred":false,"id":897893,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70251310,"text":"70251310 - 2024 - Predicting the spatial distribution of wintering golden eagles to inform full annual cycle conservation in western North America","interactions":[],"lastModifiedDate":"2024-02-03T15:25:37.454332","indexId":"70251310","displayToPublicDate":"2024-01-31T09:21:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the spatial distribution of wintering golden eagles to inform full annual cycle conservation in western North America","docAbstract":"Wildlife conservation strategies focused on one season or population segment may fail to adequately protect populations, especially when a species’ habitat preferences vary among seasons, age-classes, geographic regions, or other factors. Conservation of golden eagles (Aquila chrysaetos) is an example of such a complex scenario, in which the distribution, habitat use, and migratory strategies of this species of conservation concern vary by age-class, reproductive status, region, and season. Nonetheless, research aimed at mapping priority use areas to inform management of golden eagles in western North America has typically focused on territory-holding adults during the breeding period, largely to the exclusion of other seasons and life-history groups. To support population-wide conservation planning across the full annual cycle for golden eagles, we developed a distribution model for individuals in a season not typically evaluated–winter–and in an area of the interior western U.S. that is a high priority for conservation of the species. We used a large GPS-telemetry dataset and library of environmental variables to develop a machine-learning model to predict spatial variation in the relative intensity of use by golden eagles during winter in Wyoming, USA, and surrounding ecoregions. Based on a rigorous series of evaluations including cross-validation, withheld and independent data, our winter-season model accurately predicted spatial variation in intensity of use by multiple age- and life-history groups of eagles not associated with nesting territories (i.e., all age classes of long-distance migrants, and resident non-adults and adult “floaters”, and movements of adult territory holders and their offspring outside their breeding territories). Important predictors in the model were wind and uplift (40.2% contribution), vegetation and landcover (27.9%), topography (14%), climate and weather (9.4%), and ecoregion (8.7%). Predicted areas of high-use winter habitat had relatively low spatial overlap with nesting habitat, suggesting a conservation strategy targeting high-use areas for one season would capture as much as half and as little as one quarter of high-use areas for the other season. The majority of predicted high-use habitat (top 10% quantile) occurred on private lands (55%); lands managed by states and the Bureau of Land Management (BLM) had a lower amount (33%), but higher concentration of high-use habitat than expected for their area (1.5–1.6x). These results will enable those involved in conservation and management of golden eagles in our study region to incorporate spatial prioritization of wintering habitat into their existing regulatory processes, land-use planning tasks, and conservation actions.
Streams that flow throughout summer (“permanent” streams) provide critical habitat for aquatic species and serve as an important water supply. Streams that go dry seasonally or only flow after rainfall or snowmelt are a natural feature of mountain systems, including Mount Rainier National Park. However, in years with substantially less than normal snowfall, like 2015, more streams go dry, resulting in less water for Mount Rainier National Park infrastructure and unknown consequences for stream ecology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233051","collaboration":"Prepared in cooperation with the U.S. National Park Service","usgsCitation":"Jaeger, K.L., 2024, Streamflow permanence in Mount Rainier National Park, Washington: U.S. Geological Survey Fact Sheet 2023–3051, 2 p., https://doi.org/10.3133/fs20233051.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-158621","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499114,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116004.htm"},{"id":424658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3051/fs20233051.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3051"},{"id":424657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3051/fs20233051.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.23285025723396,\n 47.201628005617295\n ],\n [\n -122.23285025723396,\n 46.441316165691745\n ],\n [\n -121.05824525132806,\n 46.441316165691745\n ],\n [\n -121.05824525132806,\n 47.201628005617295\n ],\n [\n -122.23285025723396,\n 47.201628005617295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
No abstract available.
","language":"English","publisher":"Hudson River Foundation","usgsCitation":"Finkelstein, K.M., 2024, 24 hours on the Arthur Kill: Tidal Exchange News, no. January 2024, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-161537","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426492,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.hudsonriver.org/tidal-exchange-news"}],"country":"United States","state":"New Jersey","otherGeospatial":"Arthur Kill","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.2645433330953,\n 40.50334734453406\n ],\n [\n -74.24828559031762,\n 40.50293528101173\n ],\n [\n -74.23419554657706,\n 40.521063681284545\n ],\n [\n -74.18217076968922,\n 40.609163731683935\n ],\n [\n -74.17295804878228,\n 40.64618050898363\n ],\n [\n -74.19355118963331,\n 40.650292217652606\n ],\n [\n -74.21468625524412,\n 40.617391455312145\n ],\n [\n -74.2612917845396,\n 40.54659992634237\n ],\n [\n -74.26562718261341,\n 40.51117607050753\n ],\n [\n -74.2645433330953,\n 40.50334734453406\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","issue":"January 2024","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Finkelstein, Kaitlyn M. 0000-0003-1588-3312","orcid":"https://orcid.org/0000-0003-1588-3312","contributorId":202727,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Kaitlyn","email":"","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896305,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70251178,"text":"mcs2024 - 2024 - Mineral commodity summaries 2024","interactions":[],"lastModifiedDate":"2026-01-27T18:13:29.201677","indexId":"mcs2024","displayToPublicDate":"2024-01-31T08:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":323,"text":"Mineral Commodity Summaries","code":"MCS","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024","displayTitle":"Mineral Commodity Summaries 2024","title":"Mineral commodity summaries 2024","docAbstract":"Each mineral commodity chapter of the 2024 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production, reserves, and resources. The MCS is the earliest comprehensive source of 2023 mineral production data for the world. More than 90 individual minerals and materials are covered by 2-page synopses.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2024 are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2024","isbn":"978-1-4113-4544-7","usgsCitation":"U.S. Geological Survey, 2024, Mineral commodity summaries 2024: U.S. Geological Survey, 212 p., https://doi.org/10.3133/mcs2024.","productDescription":"Report: 212 p.; Data Release","numberOfPages":"212","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-160630","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":425029,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://tableau.usgs.gov/views/MCSDashboardWorkbook_2024-01-30/MCSDashboard?%3Aembed=y&%3AisGuestRedirectFromVizportal=y#7","text":"Data visualization"},{"id":424958,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/commodity-statistics-and-information","text":"Commodity Statistics and Information"},{"id":424956,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2024/mcs2024.pdf","text":"Report","size":"13.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2024"},{"id":424955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2024/coverthb.jpg"},{"id":424959,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P144BA54","text":"USGS data release","linkHelpText":"U.S. Geological Survey Mineral Commodity Summaries 2024 Data Release"},{"id":424957,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/national-minerals-information-center/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2024"},{"id":499130,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115979.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Species with extensive geographical ranges pose special challenges to assessing drivers of wildlife disease, necessitating collaborative and large-scale analyses. The imperilled foothill yellow-legged frog (Rana boylii) inhabits a wide geographical range and variable conditions in rivers of California and Oregon (USA), and is considered threatened by the pathogen Batrachochytrium dendrobatidis (Bd). To assess drivers of Bd infections over time and space, we compiled over 2000 datapoints from R. boylii museum specimens (collected 1897–2005) and field samples (2005–2021) spanning 9° of latitude. We observed a south-to-north spread of Bd detections beginning in the 1940s and increase in prevalence from the 1940s to 1970s, coinciding with extirpation from southern latitudes. We detected eight high-prevalence geographical clusters through time that span the species' geographical range. Field-sampled male R. boylii exhibited the highest prevalence, and juveniles sampled in autumn exhibited the highest loads. Bd infection risk was highest in lower elevation rain-dominated watersheds, and with cool temperatures and low stream-flow conditions at the end of the dry season. Through a holistic assessment of relationships between infection risk, geographical context and time, we identify the locations and time periods where Bd mitigation and monitoring will be critical for conservation of this imperilled species.
Avian eggshell thickness is an important life history metric in birds and has broad applications across disciplines ranging from animal behavior to toxicology. Empirical eggshell thickness values for songbirds (Order Passeriformes) are under-represented in the literature due to the difficulty of measuring smaller eggs using traditional methods. We used a Hall-effect thickness gauge to measure eggs of five focal songbird species from California’s Central Valley: House Wren (Troglodytes aedon; n = 567), Tree Swallow (Tachycineta bicolor; n = 297), Ash-throated Flycatcher (Myiarchus cinerascens; n = 21), Western Bluebird (Sialia mexicana; n = 13), and Bewick’s Wren (Thryomanes bewickii; n = 5). We compared minimum eggshell thickness measurements at the equator and sharp pole, and we related eggshell thickness to other egg morphometrics and adult body mass. Eggshell thickness at the equator was 5.6% thicker in Ash-throated Flycatchers and 3.5% thinner in Tree Swallows compared with eggshell thickness at the sharp pole. Among species, eggshell thickness at the sharp pole was greater in species with larger eggs, whereas, within species, larger eggs were thinner at the sharp pole. Eggshells were 8% and 11% thinner in late incubation eggs (≥75% of total incubation duration) than early incubation (≤10% of total incubation duration) for House Wren and Tree Swallow eggs, respectively. Whenever possible, it is preferable to use empirical eggshell thickness data that are specific to the species and geographic region being studied, and a relatively new method used in this study allows accurate measurement of small eggs without having to compromise the integrity of preserved eggshell specimens.
","language":"English","publisher":"Journal of Field Ornithology","doi":"10.5751/JFO-00410-950103","usgsCitation":"Schacter, C., Peterson, S.H., Hartman, C.A., Herzog, M.P., and Ackerman, J.T., 2024, Eggshell thickness and egg morphometrics in five songbird species from the Central Valley, California: Journal of Field Ornithology, v. 95, no. 1, 3, 10 p., https://doi.org/10.5751/JFO-00410-950103.","productDescription":"3, 10 p.","ipdsId":"IP-154263","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":440588,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5751/jfo-00410-950103","text":"Publisher Index Page"},{"id":435054,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GL21VQ","text":"USGS data release","linkHelpText":"Eggshell Thickness in 5 Songbird Species"},{"id":425282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schacter, Carley R. 0000-0001-5493-2768","orcid":"https://orcid.org/0000-0001-5493-2768","contributorId":333758,"corporation":false,"usgs":false,"family":"Schacter","given":"Carley R.","affiliations":[{"id":79969,"text":"USFWS; Former USGS employee","active":true,"usgs":false}],"preferred":false,"id":893844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Sarah H. 0000-0003-2773-3901 sepeterson@usgs.gov","orcid":"https://orcid.org/0000-0003-2773-3901","contributorId":167181,"corporation":false,"usgs":true,"family":"Peterson","given":"Sarah","email":"sepeterson@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":893845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":893846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":893847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":893848,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254428,"text":"70254428 - 2024 - Earthquake rupture forecast model construction for the 2023 U.S. 50‐State National Seismic Hazard Model Update: Central and eastern U.S. fault‐based source model","interactions":[],"lastModifiedDate":"2024-05-24T11:59:35.15136","indexId":"70254428","displayToPublicDate":"2024-01-31T06:56:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Earthquake rupture forecast model construction for the 2023 U.S. 50‐State National Seismic Hazard Model Update: Central and eastern U.S. fault‐based source model","docAbstract":"As part of the U.S. Geological Survey’s 2023 50‐State National Seismic Hazard Model (NSHM), we make modest revisions and additions to the central and eastern U.S. (CEUS) fault‐based seismic source model that result in locally substantial hazard changes. The CEUS fault‐based source model was last updated as part of the 2014 NSHM and considered new information from the Seismic Source Characterization for Nuclear Facilities (CEUS‐SSCn) Project. Since then, new geologic investigations have led to revised fault and fault‐zone inputs, and the release of databases of fault‐based sources in the CEUS. We have reviewed these databases and made minor revisions to six of the current fault‐based sources in the NSHM, as well as added five new fault‐based sources. Implementation of these sources follows the current NSHM methodology for CEUS fault‐based sources, as well as the incorporation of a new magnitude–area relationship and updated maximum magnitude and recurrence rate estimates following the methods used by the CEUS‐SSCn Project. Seismic hazard sensitivity calculations show some substantial local changes in hazard (−0.4g to 1.1g) due to some of these revisions and additions, especially from the addition of the central Virginia, Joiner ridge, and Saline River sources and revisions made to the Meers and New Madrid sources.
Arundo donax, commonly called Arundo cane, giant reed, or Carrizo cane, is an invasive bamboo-like perennial grass common in riparian areas throughout the southwestern United States. In Texas, not only does it negatively affect riparian ecosystems, but it has also become a problem for border security because it reduces visibility along the Rio Grande. To address these problems, in 2015 the Texas State Soil and Water Conservation Board was authorized by the Texas State Legislature to develop a program to eradicate Arundo cane along the Rio Grande. In 2020, the Texas State Soil and Water Conservation Board applied imazapyr and glyphosate herbicides along a 19.3-kilometer reach of the Rio Grande, northwest of Laredo, Texas. The U.S. Geological Survey, in cooperation with the Texas State Soil and Water Conservation Board and the Webb Soil and Water Conservation District, used WorldView-3 Standard high-resolution satellite imagery to map Arundo cane extent along the reach before, during, and after the herbicide-treatment period on June 30, 2020, September 26, 2020, and May 7, 2021, respectively. A maximum likelihood supervised classification analysis was computed on the images to map the spatial extent and estimate the area covered by Arundo cane. The estimated area covered by Arundo cane in the before classification was 1,282,000 square meters, in the during classification was 1,064,000 square meters, and in the after classification was 1,108,000 square meters. The qualitative comparison of the three images shows that there was an overall decrease in vegetation classified as Arundo cane throughout the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3512","issn":"2329-132X","collaboration":"Prepared in cooperation with the Texas State Soil and Water Conservation Board and the Webb Soil and Water Conservation District","programNote":"Water Resources Research Act Program","usgsCitation":"Villa, J., 2024, Mapping Arundo donax (Arundo cane) with multispectral imagery before, during, and after herbicide treatment along the Rio Grande in Webb County, Texas, 2020–21: U.S. Geological Survey Scientific Investigations Map 3512, 1 sheet, includes 7-p. pamphlet, https://doi.org/10.3133/sim3512.","productDescription":"Report: viii, 7 p.; 1 Sheet: 32.00 × 34.00 inches; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-144135","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424979,"rank":7,"type":{"id":30,"text":"Data 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more information about this publication, contact
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Changes in the quantity of sand stored within river segments can affect aquatic and riparian habitat, archeological resources, and recreation. Since summer to fall of 2002, gaging stations on the Colorado River in Grand Canyon National Park and on its major tributaries and selected lesser tributaries have measured the mass of sand transported past each station, which allows for changes in the mass of sand stored between gaging stations to be calculated. Sand mass balances on six Colorado River segments are currently measured; the upstream two segments measure sand mass balance in Marble Canyon, the middle three segments measure sand mass balance within the majority of Grand Canyon, and the downstream-most segment—western Grand Canyon and the Lake Mead delta—measures the quantity of sand transported past Diamond Creek and ultimately deposited in Lake Mead.
Between July 1, 2017, and June 30, 2020, the amount of sand stored in the Colorado River in Marble Canyon decreased, whereas the sand mass balance in Grand Canyon was indeterminate. Of the 3 years of study presented herein, sand was eroded from Marble Canyon during sediment year 2018 (July 1, 2017–June 30, 2018), a year with less than 40 percent of the 2003–2020 mean Paria River sand input, and sediment year 2020 (July 1, 2019–June 30, 2020), a year with negligible Paria River sand input. During sediment year 2018, when the Little Colorado River supplied negligible sand, sand was also eroded from Grand Canyon. The sand mass balance was indeterminate for Grand Canyon during sediment year 2020. During sediment year 2019 (July 1, 2018–June 30, 2019) sand accumulated in both Marble Canyon and Grand Canyon. This sediment year had sand inputs from both the Paria River and the Little Colorado River of more than 170 percent the 2003–2020 mean, coupled with below post-1964 mean discharge from Glen Canyon Dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231093","usgsCitation":"Griffiths, R.E., Topping, D.J., and Unema, J.A., 2024, Changes in sand storage in the Colorado River in Grand Canyon National Park from July 2017 through June 2020: U.S. Geological Survey Open-File Report 2023–1093, 9 p., https://doi.org/10.3133/ofr20231093.","productDescription":"v, 9 p.","numberOfPages":"9","onlineOnly":"Y","ipdsId":"IP-147171","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":425105,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1093/images"},{"id":425103,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1093/ofr20231093.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":425102,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1093/covrthb.jpg"},{"id":425104,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1093/ofr20231093.xml","linkFileType":{"id":8,"text":"xml"}},{"id":499200,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116003.htm","linkFileType":{"id":5,"text":"html"}},{"id":425106,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231093/full"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.67941991219553,\n 37.29250555492341\n ],\n [\n -114.67941991219553,\n 35.64936002497116\n ],\n [\n -111.03195897469551,\n 35.64936002497116\n ],\n [\n -111.03195897469551,\n 37.29250555492341\n ],\n [\n -114.67941991219553,\n 37.29250555492341\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
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Flagstaff, AZ 86001
In 2012, the Secretary of the U.S. Department of the Interior placed a 20-year limit on mineral extraction on Federal lands in the Grand Canyon watershed to permit further study of the environmental effects of uranium mining. Tribal concerns were also noted by the U.S. Department of the Interior and included in the rationale for the decision stating Tribal resource impacts could not be mitigated and cultural degradation may result should mining occur within sacred and traditional places of Tribal peoples. The U.S. Geological Survey previously developed a conceptual framework for a uranium mine in the region that defined contaminant sources and physical, chemical, and biological processes that affect contaminant transport to ecological receptors. However, published risk models have largely ignored exposure pathways relevant to Tribal communities in terms of traditional uses and existential values of the resources included. This report presents an updated conceptual risk framework for uranium mining that includes indigenous knowledge components informed by the Havasupai Tribe perspective.
The expansion of the framework relied on connecting to the foundations of the Havasupai ceremonial wheel—food, environment, belief system, and ceremony. The framework is applied to uranium development near Red Butte, an important gathering place for multiple federally recognized Tribes including the Havasupai, Hopi, Navajo, and Zuni. Plants and animals important to the Havasupai for subsistence, ceremonial, and medicinal practices and how mining affects these practices are described. The final framework is presented in English and Havasupai to aid Tribal members in understanding how the framework relates to their community and to help preserve the language and historical cultural practices for future generations. New or expanded exposure pathways include inhalation, ingestion, and absorption from traditional food and medicines as well as ceremonial practices. The updated framework has allowed the U.S. Geological Survey to take first steps in understanding resources important to the Havasupai and to build relationships to improve co-production in our research. Ideally, the framework and other research can be used, along with indigenous knowledge, in Federal research and decision making for mining in the Grand Canyon region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231092","usgsCitation":"Tilousi, C., and Hinck, J.E., 2024, Expanded conceptual risk framework for uranium mining in Grand Canyon watershed—Inclusion of the Havasupai Tribe perspective (ver. 1.1, February 2024): U.S. Geological Survey Open-File Report 2023–1092, 25 p., https://doi.org/10.3133/ofr20231092.","productDescription":"vi, 25 p.","numberOfPages":"36","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-157227","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":499197,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116005.htm","linkFileType":{"id":5,"text":"html"}},{"id":425864,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip241","text":"General Information Product 241"},{"id":425233,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1092/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":424862,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231092/full"},{"id":424859,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1092/images/"},{"id":424858,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1092/ofr20231092.XML"},{"id":424857,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1092/ofr20231092.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1092"},{"id":424856,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1092/coverthb2.jpg"},{"id":425791,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip240","text":"General Information Product 240"},{"id":425790,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip239","text":"General Information Product 239"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.19147841939832,\n 38.32491175913418\n ],\n [\n -117.19147841939832,\n 32.88011313999995\n ],\n [\n -110.33600966939846,\n 32.88011313999995\n ],\n [\n -110.33600966939846,\n 38.32491175913418\n ],\n [\n -117.19147841939832,\n 38.32491175913418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 30, 2024; Version 1.1: February 1, 2024","contact":"Associate Director, Natural Hazards Mission Area
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Many studies use landscape form to determine spatial patterns of tectonic deformation, and these are particularly effective when paired with independent measures of rock uplift and erosion. Here, we use morphometric analyses and 10Be catchment-averaged erosion rates, together with reverse slip rates from the Sierra Madre−Cucamonga fault zone, to reveal patterns in uplift, erosion, and fault activity in the range front of the San Gabriel Mountains in southern California, USA. Our analysis tests two prevailing hypotheses: (1) the range front of the San Gabriel Mountains is at steady state, in which rock uplift balances erosion and topographic elevations are stable throughout time, and (2) that west-to-east increases in elevation, relief, erosion rate, and stream-channel steepness across the interior of the massif reflect a parallel reverse-slip rate gradient on the range-bounding Sierra Madre−Cucamonga fault zone. We show that although deviations from steady state occur, the range-front hillslopes and stream channels are typically both well-connected and adjusted to patterns in Quaternary uplift driven by motion on the range-front fault network. Accordingly, landscape morphometrics, 10Be erosion rates, and model erosion rates effectively image spatial and temporal patterns in uplift. Interpreted jointly, these data reveal comparable peak slip rates on the Sierra Madre−Cucamonga fault zone and show that they do not monotonically increase from west to east. Thus, the eastward-increasing gradients developed within the interior of the massif are not solely related to reverse slip on the range-front faults. Evaluated on shorter length scales (<10 km), morphometric data corroborate earlier descriptions of the Sierra Madre−Cucamonga fault zone as multiple individual faults or fault sections, with slip rates tapering toward fault tips. We infer that these patterns imply the predominance of independent fault or fault section ruptures throughout the Quaternary, though data cannot rule out the possibility of large, connected Sierra Madre−Cucamonga fault zone ruptures. Deeper in the hanging wall of the Sierra Madre−Cucamonga fault zone, secondary faults accommodate range-front uplift. Motion on these faults may contribute to active uplift of the highest topography within the massif, in addition to partly reconciling differences between geologic and geodetic Sierra Madre−Cucamonga fault zone reverse-slip rates. This study provides a new, unified perspective on tectonics and landscape evolution in the San Gabriel Mountains.
The fracture of Earth materials occurs over a wide range of time and length scales. Physical conditions, particularly the stress field and Earth material properties, may condition rupture in a specific fracture regime. In nature, fast and slow fractures occur concurrently: tectonic tremor events are fast enough to emit seismic waves and frequently accompany slow earthquakes, which are too slow to emit seismic waves and are referred to as aseismic slip events. In this study, we generate simultaneous seismic and aseismic processes in a laboratory setting by driving a penny-shaped crack in a transparent sample with pressurized fluid. We leverage synchronized high-speed imaging and high-frequency acoustic emission (AE) sensing to visualize and listen to the various sequences of propagation (breaks) and arrest (sticks) of a fracture undergoing stick-break instabilities. Slow radial crack propagation is facilitated by fast tangential fractures. Fluid viscosity and pressure regulate the fracture dynamics of slow and fast events, and control the inter-event time and the energy released during individual fast events. These AE signals share behaviors with observations of episodic tremors in Cascadia, United States; these include: (a) bursty or intermittent slow propagation, and (b) nearly linear scaling of radiated energy with area. Our laboratory experiments provide a plausible model of tectonic tremor as an indicative of hydraulic fracturing facilitating shear slip during slow earthquakes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023AV001002","usgsCitation":"Yuan, C., Cochard, T., Denolle, M.A., Gomberg, J.S., Wech, A., Lizhi, X., and Weitz, D., 2024, Laboratory hydrofractures as analogs to tectonic tremors: AGU Advances, v. 5, no. 1, e2023AV001002, 15 p., https://doi.org/10.1029/2023AV001002.","productDescription":"e2023AV001002, 15 p.","ipdsId":"IP-155902","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023av001002","text":"Publisher Index Page"},{"id":480848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Yuan, Congcong","contributorId":349711,"corporation":false,"usgs":false,"family":"Yuan","given":"Congcong","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochard, Thomas","contributorId":349712,"corporation":false,"usgs":false,"family":"Cochard","given":"Thomas","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Denolle, Marine A.","contributorId":345689,"corporation":false,"usgs":false,"family":"Denolle","given":"Marine","email":"","middleInitial":"A.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":924630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lizhi, Xiao","contributorId":349713,"corporation":false,"usgs":false,"family":"Lizhi","given":"Xiao","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weitz, David","contributorId":349714,"corporation":false,"usgs":false,"family":"Weitz","given":"David","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":924634,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70251180,"text":"fs20233028 - 2024 - Assessment of undiscovered conventional oil and gas resources in presalt reservoirs of the West-Central Coastal Province of Africa, 2022","interactions":[],"lastModifiedDate":"2024-01-30T18:36:25.793382","indexId":"fs20233028","displayToPublicDate":"2024-01-29T11:45:00","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":"2023-3028","displayTitle":"Assessment of Undiscovered Conventional Oil and Gas Resources in Presalt Reservoirs of the West-Central Coastal Province of Africa, 2022","title":"Assessment of undiscovered conventional oil and gas resources in presalt reservoirs of the West-Central Coastal Province of Africa, 2022","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean conventional resources of 12.1 billion barrels of oil and 50 trillion cubic feet of gas in presalt reservoirs within the West-Central Coastal Province of Africa.
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U.S. Geological Survey
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