Rising chloride concentrations pose critical risks to freshwater stream ecosystems in temperate regions like the Delaware River Basin (DRB), USA, where winter deicer applications (i.e., road salt) are common. Increasing chloride concentrations have been documented in the region, but the extent to which chloride exceeds regulatory benchmarks remains unclear because detection of exceedances requires continuous monitoring of chloride (i.e., hourly or daily). A network of 82 non-tidal continuous specific conductance (SC) monitoring sites, spanning varied land use and geological settings, was established across the DRB to address this research need. First, a cluster analysis was conducted to group sites based on their watershed characteristics. Next, regression models for sites and clusters were developed to predict chloride using SC as a proxy. Finally, daily mean and hourly mean chloride concentration predictions were made for a three-year period (2020–2022) at the 82 study sites and analyzed to determine where and when chloride exceeded federal regulatory benchmarks. Chloride exceedance events occurred at 35% of the sites, all of which had 5% impervious cover or greater. Seasonally elevated chloride also was predicted at sites with less than 5% impervious cover. Variability in chloride patterns likely was influenced by deicer material types, winter weather patterns, geological settings, and gaps in data coverage. This study demonstrated the value of SC as a proxy for predicting chloride concentrations and showed how SC-chloride regression relationships vary across settings. More broadly, this study highlighted the value of continuous water quality monitoring to assess effects of freshwater salinization at a regional scale.
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In the rain-snow transition zone of the Pacific Northwest, climate change is expected to alter the incidence of rain-on-snow and freeze-thaw events, which will change snow density and hardness dynamics. In winter, the ability of economically and ecologically important wildlife species, such as deer (Odocoileus spp.), to efficiently move through the landscape and access forage is mediated by snow conditions. Therefore, snow properties such as density and hardness can directly affect how energetically costly it is for these animals to survive. However, little is known about whether and how ungulates use habitats based on snow density and hardness. We deployed a stratified network of remote camera stations in complex forested terrain in Latah County, Idaho, USA, to remotely measure snow depth and detect deer. We also collected snow density and hardness measurements throughout the winter. We used these data to determine the degree to which the probability of deer presence at cameras could be explained by snow conditions and air temperature. Snow depth and density had negative relationships with the probability of deer presence, while ram resistance (a proxy for snow hardness) had a marginal positive effect. We were able to estimate snow conditions important to deer in winter 2020–2021 primarily using data obtained from cameras. This provides an important proof-of-concept that can be applied at different sites and climate conditions to gain a deeper understanding of how deer are affected by snowpack properties. These methods can be used by managers to determine how ungulates are affected by snow depth, density, and hardness collectively and subsequently inform ungulate management in a changing climate.
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U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
The former Stuart Ranch, now managed by the Bureau of Land Management, is transected by Meadow Valley Wash, where 4,600 feet of perennial stream and adjacent riparian vegetation provide critical habitat for several wildlife and aquatic species protected under the Endangered Species Act. The stream has been altered by prior construction of irrigation diversions, gravel mining, and removal of riparian vegetation, resulting in the loss of instream and riparian vegetation and disconnected floodplains. The stream alteration has also resulted in the loss of native species and increased non-native invasive species and changes in ecological cycles. With the goal of improving habitat extent and quality for native threatened and endangered species, the Bureau of Land Management (BLM) is considering establishing perennial streams through braided side channels by constructing beaver dam analogs, excavating side channel connectors, and grading an irrigation reservoir berm on the floodplain. The U.S. Geological Survey (USGS) provided hydraulic modeling to assist the BLM in evaluating how possible restoration modifications could affect the extent of aquatic, riparian, and other habitat types. Three two-dimensional (2-D) hydraulic models were developed to simulate 2021 conditions (when most of the topographic data were collected), minor restoration modifications (one excavated side channel and a beaver dam analog), and major restoration modifications (three excavated side channels, a beaver dam analog, and an excavated and graded area to remove the irrigation reservoir) to determine streamflow-inundation extents and hydraulic characteristics (depth and velocity) for base flow and various flood (50-, 20-, 10-, 4-, 2-, and 1-percent annual exceedance probability [AEP]) scenarios. An average summer base flow of 0.92 cubic feet per second was estimated based on data from a USGS streamgage in the study area. The 50-, 20-, 10-, 4-, 2-, and 1-percent AEP streamflows were estimated based on a flood-frequency analysis of data from the streamgage. The base flow and AEP floods were combined with surveyed topographic data to create a 2-D unsteady hydraulic model. The hydraulic model was used to simulate the base flow and flood-inundation extents and hydraulic characteristics under 2021 conditions and with two possible restoration modification scenarios. Under 2021 conditions, flow remains in a single channel until the most downstream end of the modeled reach, where flow then expands into slower velocity pools. During floods, streamflow begins to enter the side channels at the 50-percent flood, expands into the east floodplain at 20-percent flood, and flows in the irrigation reservoir at 4-percent flood. Compared to 2021 conditions with no terrain modification, base flow under the possible restoration modifications enters and remains in the side channels, thus increasing the likelihood of expanding riparian habitat. Additionally, during floods under the major restoration modifications, streamflow expands into the modified terrain surrounding the irrigation reservoir at 10-percent AEP, as opposed to 4-percent AEP under 2021 conditions. For all modeled streamflow scenarios, streamflow is deepest in the center of the main and side channels, as well as the downstream pooled areas. Streamflow is fastest in the narrow sections of the channels, especially in the upper 1,220 feet of the modeled reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255069","collaboration":"Prepared in cooperation with Bureau of Land Management","programNote":"Water Resources Mission Area","usgsCitation":"Dye, L.A., Morris, C.M., and Childres, H.K., 2025, Streamflow extents and hydraulic characteristics of Meadow Valley Wash at Stuart Ranch, near Rox, Nevada: U.S. Geological Survey Scientific Investigations Report 2025–5069, 24 p., https://doi.org/10.3133/sir20255069.","productDescription":"Report: vi, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124818","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":494320,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5069/images"},{"id":494319,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96HQ6F7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data, flood-frequency analysis, and surface-water model archive for streamflow extents and hydraulic characteristics of Meadow Valley Wash at Stuart Ranch, near Rox, Nevada"},{"id":494317,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5069/sir20255069.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5069"},{"id":494316,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5069/coverthb.jpg"},{"id":494318,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255069/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5069"},{"id":494321,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5069/sir20255069.XML"}],"country":"United States","state":"Nevada","city":"Rox","otherGeospatial":"Meadow Valley Wash at Stuart Ranch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.6611,\n 36.84\n ],\n [\n -114.6611,\n 36.8278\n ],\n [\n -114.65,\n 36.8278\n ],\n [\n -114.65,\n 36.84\n ],\n [\n -114.6611,\n 36.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road, Suite 3
Carson City, Nevada 89701
The development of environmental DNA (eDNA) methods for terrestrial arthropods could be transformative for the difficult task of assessing the status of species of conservation concern. The primary goal of this study was to investigate the efficacy of detecting the Dakota skipper (Hesperia dacotae) from its DNA left behind on inflorescences as a means of inferring species presence. We developed and tested a novel qPCR assay and validated the assay in both controlled and field contexts. Using captive animals at the Minnesota Zoo, we found that the number of skippers in an enclosure increased the probability of skipper DNA detection. In the field, Dakota skipper DNA was found on 14% (11 of 81) of inflorescences collected. All detections were from narrowleaf purple coneflower (Echinacea angustifolia). Known visitation of an inflorescence by Dakota skipper prior to sample collection was not a strong predictor of either skipper DNA presence or amount of DNA, but skipper eDNA was detected at 60% (3 of 5) of sites where skippers were observed and 33% (1 of 3) of sites where skippers were not observed. These findings demonstrate successful application of a targeted-species approach to eDNA sampling for butterflies in the field. Taken together, our findings indicate that this method could provide a novel and useful source of data for assessing occupancy trends of butterflies without capturing or even observing them in the wild.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2025.e03815","usgsCitation":"Pilliod, D.S., Grossklaus, M.R., Kageyama, S.A., Nordmeyer, C., Reinisch, J., Runquist, E., and Spear, S.F., 2025, A 21st Century butterfly net: Using eDNA to detect the imperiled Dakota skipper: Global Ecology and Conservation, v. 62, e03815, 12 p., https://doi.org/10.1016/j.gecco.2025.e03815.","productDescription":"e03815, 12 p.","ipdsId":"IP-180064","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":497023,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13JBGIU","text":"USGS data release","linkHelpText":"Detection of Dakota skipper eDNA from inflorescences in the Upper Midwest, June and July 2022 (ver. 1.1, November 2025)"},{"id":495733,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2025.e03815","text":"Publisher Index Page"},{"id":495517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United states","state":"Minnesota, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.4255937792456,\n 43.75282749088453\n ],\n [\n -95.82307671174901,\n 44.537984912524394\n ],\n [\n -95.85210368026306,\n 46.61850738417658\n ],\n [\n -102.74334512161774,\n 48.947800618009666\n ],\n [\n -103.87685016438193,\n 48.559342537992194\n ],\n [\n -103.84723638965521,\n 46.63167447613088\n ],\n [\n -101.82183750965085,\n 46.16543147358968\n ],\n [\n -99.3119300920362,\n 45.59635742673848\n ],\n [\n -96.4255937792456,\n 43.75282749088453\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":948761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grossklaus, Michaela Ray 0009-0002-0890-6520","orcid":"https://orcid.org/0009-0002-0890-6520","contributorId":342051,"corporation":false,"usgs":true,"family":"Grossklaus","given":"Michaela","email":"","middleInitial":"Ray","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":948762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kageyama, Stacie A. 0000-0003-4185-3627 skageyama@usgs.gov","orcid":"https://orcid.org/0000-0003-4185-3627","contributorId":195991,"corporation":false,"usgs":true,"family":"Kageyama","given":"Stacie","email":"skageyama@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":948763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nordmeyer, Cale 0000-0002-8826-251X","orcid":"https://orcid.org/0000-0002-8826-251X","contributorId":361407,"corporation":false,"usgs":false,"family":"Nordmeyer","given":"Cale","affiliations":[{"id":79104,"text":"Minnesota Zoo","active":true,"usgs":false}],"preferred":false,"id":948764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reinisch, Jerry","contributorId":361408,"corporation":false,"usgs":false,"family":"Reinisch","given":"Jerry","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":948765,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runquist, Erik","contributorId":335441,"corporation":false,"usgs":false,"family":"Runquist","given":"Erik","affiliations":[{"id":79104,"text":"Minnesota Zoo","active":true,"usgs":false}],"preferred":false,"id":948766,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spear, Stephen Frank 0000-0001-8351-9382","orcid":"https://orcid.org/0000-0001-8351-9382","contributorId":293162,"corporation":false,"usgs":true,"family":"Spear","given":"Stephen","email":"","middleInitial":"Frank","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":948767,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271730,"text":"70271730 - 2025 - New constraints on location and timing of the Great Lakes tectonic zone, central Upper Peninsula, Michigan, USA","interactions":[],"lastModifiedDate":"2025-09-22T14:18:30.472971","indexId":"70271730","displayToPublicDate":"2025-08-27T09:14:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"New constraints on location and timing of the Great Lakes tectonic zone, central Upper Peninsula, Michigan, USA","docAbstract":"The Great Lakes tectonic zone (GLTZ) forms the boundary between the Wawa–Abitibi and Minnesota River Valley subprovinces within the Archean Superior Province. The GLTZ is concealed for all of its 1100 km length, except for a segment in the central Upper Peninsula of Michigan. There, it is exposed as a northwest-striking mylonite zone along a 11 km segment, extending to the onlap of Paleozoic rocks to the east. Farther east, its location has been unknown. Here, we use aeromagnetic and gravity data to develop interpretations of the expression of the GLTZ and to define its extent under cover. Aeromagnetic gradients over the mylonite zone are interpreted to be produced by structurally juxtaposed rocks with varying magnetizations. Gravity data show a regional gradient along the GLTZ, produced by the juxtaposition of a dense greenstone belt on the north against lower-density gneisses and granites on the south. The GLTZ is interpreted to extend ∼55 km under cover to the east. The GLTZ is terminated on the east by the buried eastern arm of the ca. 1100 Ma Midcontinent Rift. An undeformed granitic dike that cuts the mylonitic foliation produces a U–Pb apatite age of 2523 ± 33 Ma, implying no major post-Archean shearing occurred, and is at odds with previous interpretations of major Proterozoic reactivation. A granite intrusion in the Minnesota River Valley subprovince produces a Pb–Pb zircon age of 2606.9 ± 3.6/7.4 Ma. This suggests that magmatism related to the Sacred Heart orogeny, previously known in Minnesota, extended to Michigan.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjes-2025-0021","usgsCitation":"Drenth, B.J., Souders, A., Cannon, W.F., and Thompson, J.M., 2025, New constraints on location and timing of the Great Lakes tectonic zone, central Upper Peninsula, Michigan, USA: Canadian Journal of Earth Sciences, v. 62, no. 9, p. 1459-1473, https://doi.org/10.1139/cjes-2025-0021.","productDescription":"15 p.","startPage":"1459","endPage":"1473","ipdsId":"IP-171166","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":495838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"central Upper Peninsula","volume":"62","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":949212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Souders, Amanda 0000-0002-1367-8924","orcid":"https://orcid.org/0000-0002-1367-8924","contributorId":296423,"corporation":false,"usgs":true,"family":"Souders","given":"Amanda","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":949213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cannon, William F. 0000-0002-2699-8118","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":201972,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949214,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Jay M. 0000-0003-3322-0870","orcid":"https://orcid.org/0000-0003-3322-0870","contributorId":329664,"corporation":false,"usgs":true,"family":"Thompson","given":"Jay","middleInitial":"M.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":949215,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273135,"text":"70273135 - 2025 - Desert ecosystems shape diversification in glossy snakes (genus Arizona) requiring a re-alignment of evolutionary and conservation units","interactions":[],"lastModifiedDate":"2025-12-16T15:09:11.907324","indexId":"70273135","displayToPublicDate":"2025-08-27T08:52:27","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2779,"text":"Molecular Phylogenetics and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Desert ecosystems shape diversification in glossy snakes (genus Arizona) requiring a re-alignment of evolutionary and conservation units","title":"Desert ecosystems shape diversification in glossy snakes (genus Arizona) requiring a re-alignment of evolutionary and conservation units","docAbstract":"Subspecies are often targets for conservation, yet many lack the genetic data necessary to validate their status as distinctive evolutionary lineages. In 2016, conservationists faced this issue when designating the California glossy snake, Arizona elegans occidentalis, as a Species of Special Concern in California, a decision prompted by population declines and habitat loss but absent of genetic information about its evolutionary integrity. To address this knowledge gap, we collected genomic and mitochondrial data from a rangewide sample of the Arizona elegans complex (n = 257) and characterized genetic structure at varying spatial scales. We confirmed an east–west phyletic division within the A. elegans complex that correlates with an ecotone between the Sonoran and Chihuahuan Deserts and pinpoint the separation to a ∼20 km area in southeastern Arizona, USA. Individuals recognized as A. e. occidentalis do not form a genetically cohesive unit within a more inclusive western clade that is sister to the endemic Arizona pacata in Baja California, México. We synonymize four subspecies circumscribed by the western clade and recognize a new species Arizona occidentalis to re-align the taxonomy with the phylogeographic structure. Most of the diversity within A. occidentalis occurs in California, with three major lineages corresponding separate desert biomes. We revise the conservation units within A. occidentalis to mirror these lineages and address concerns regarding habitat loss in transitional environments along the western edge of its range. This work underscores the importance of aligning taxonomy, evolutionary identity, and management units to design the most effective conservation strategies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ympev.2025.108441","usgsCitation":"Wood, D., Richmond, J.Q., Westphal, M.F., Hollingsworth, B.D., Fisher, R.D., and Vandergast, A.G., 2025, Desert ecosystems shape diversification in glossy snakes (genus Arizona) requiring a re-alignment of evolutionary and conservation units: Molecular Phylogenetics and Evolution, v. 213, 108441, 15 p., https://doi.org/10.1016/j.ympev.2025.108441.","productDescription":"108441, 15 p.","ipdsId":"IP-175218","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ympev.2025.108441","text":"Publisher Index Page"},{"id":497565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.28258728665597,\n 40.693035314631345\n ],\n [\n -119.78832300902783,\n 31.602553781844435\n ],\n [\n -111.36681184455207,\n 22.61258270236084\n ],\n [\n -97.05757848026627,\n 22.24916886918969\n ],\n [\n -94.40812045478627,\n 32.37973867254225\n ],\n [\n -95.39342062000813,\n 40.44807423817957\n ],\n [\n -125.28258728665597,\n 40.693035314631345\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"213","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Dustin 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":195223,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","affiliations":[],"preferred":true,"id":952412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":952413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Westphal, Michael F.","contributorId":364262,"corporation":false,"usgs":false,"family":"Westphal","given":"Michael","middleInitial":"F.","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":952414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hollingsworth, Bradford D.","contributorId":364265,"corporation":false,"usgs":false,"family":"Hollingsworth","given":"Bradford","middleInitial":"D.","affiliations":[{"id":16175,"text":"San Diego Natural History Museum","active":true,"usgs":false}],"preferred":false,"id":952415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fisher, Robert D. 0000-0002-2956-3240 rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":3913,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":952416,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":952417,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271410,"text":"70271410 - 2025 - Contribution of traffic emissions to PM2.5 concentrations at bus stops in Denver, Colorado","interactions":[],"lastModifiedDate":"2025-09-12T15:19:51.803063","indexId":"70271410","displayToPublicDate":"2025-08-27T08:09:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3504,"text":"Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"Contribution of traffic emissions to PM2.5 concentrations at bus stops in Denver, Colorado","docAbstract":"Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m3) and secondary organics (~3–5 μg/m3) contributed to PM2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m3).
","language":"English","publisher":"MDPI","doi":"10.3390/su17177707","usgsCitation":"deSouza, P., Hopke, P., L'Orange, C., Ibsen, P.C., Green, C., Graeber, B., Cicione, B., Mekonnen, R., Purushothama, S., Kinney, P., and Volckens, J., 2025, Contribution of traffic emissions to PM2.5 concentrations at bus stops in Denver, Colorado: Sustainability, v. 17, no. 17, 7707, 14 p., https://doi.org/10.3390/su17177707.","productDescription":"7707, 14 p.","ipdsId":"IP-176935","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":495724,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su17177707","text":"Publisher Index Page"},{"id":495442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Denver","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.32990609528034,\n 39.95761559533625\n ],\n [\n -105.32990609528034,\n 39.506686432213314\n ],\n [\n -104.5591800032328,\n 39.506686432213314\n ],\n [\n -104.5591800032328,\n 39.95761559533625\n ],\n [\n -105.32990609528034,\n 39.95761559533625\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"17","noUsgsAuthors":false,"publicationDate":"2025-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"deSouza, Priyanka","contributorId":353306,"corporation":false,"usgs":false,"family":"deSouza","given":"Priyanka","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":948628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hopke, Phillip","contributorId":361336,"corporation":false,"usgs":false,"family":"Hopke","given":"Phillip","affiliations":[{"id":12960,"text":"Clarkson University","active":true,"usgs":false}],"preferred":false,"id":948629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"L'Orange, Christian","contributorId":361337,"corporation":false,"usgs":false,"family":"L'Orange","given":"Christian","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":948630,"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":948631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Green, Carl Jr.","contributorId":361338,"corporation":false,"usgs":false,"family":"Green","given":"Carl","suffix":"Jr.","affiliations":[{"id":86239,"text":"Denver Regional Transportation District","active":true,"usgs":false}],"preferred":false,"id":948632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graeber, Brady","contributorId":361326,"corporation":false,"usgs":false,"family":"Graeber","given":"Brady","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":948633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cicione, Brendan","contributorId":361327,"corporation":false,"usgs":false,"family":"Cicione","given":"Brendan","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":948634,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mekonnen, Ruth","contributorId":361328,"corporation":false,"usgs":false,"family":"Mekonnen","given":"Ruth","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":948635,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Purushothama, Saadhana","contributorId":361329,"corporation":false,"usgs":false,"family":"Purushothama","given":"Saadhana","affiliations":[{"id":16824,"text":"University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":948636,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kinney, Patrick","contributorId":353314,"corporation":false,"usgs":false,"family":"Kinney","given":"Patrick","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":948637,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Volckens, John","contributorId":361331,"corporation":false,"usgs":false,"family":"Volckens","given":"John","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":948638,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70270830,"text":"ofr20251043 - 2025 - A crosswalk of the 2015 World Terrestrial Ecosystems to the International Union for the Conservation of Nature Global Ecosystem Typology Framework","interactions":[],"lastModifiedDate":"2026-02-03T15:15:11.657303","indexId":"ofr20251043","displayToPublicDate":"2025-08-26T14:15:00","publicationYear":"2025","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":"2025-1043","displayTitle":"A Crosswalk of the 2015 World Terrestrial Ecosystems to the International Union for the Conservation of Nature Global Ecosystem Typology Framework","title":"A crosswalk of the 2015 World Terrestrial Ecosystems to the International Union for the Conservation of Nature Global Ecosystem Typology Framework","docAbstract":"To support ecosystem mapping and accounting applications, we aligned the 2015 U.S. Geological Survey/Esri/The Nature Conservancy-World Terrestrial Ecosystems (WTEs) with the International Union for Conservation of Nature Global Ecosystem Typology (GET) framework. This process, known as “crosswalking,” enabled the development of a global map of GET level 3 Ecosystem Functional Groups (EFGs) at a 250-meter spatial resolution. Crosswalking involved manually assigning 1,781 biogeographically stratified WTEs to their most probable EFG based on similarities in climate, terrain, vegetation, and geographic distribution. We compared attributes of the WTE dataset with summary characteristics of the EFGs. The resulting crosswalked global map of International Union for Conservation of Nature GET ecosystems is intended to be useful for standardizing ecosystem classification and reporting under frameworks such as the Kunming-Montreal Global Biodiversity Framework and the United Nations System of Environmental-Economic Accounting. We discuss key challenges in reconciling non-identical classifications, such as many-to-one relationships and variation in data quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251043","programNote":"National Land Imaging Program","usgsCitation":"Sides, K.B., Naji, N., Kremer, A., Burton, D., and Sayre, R., 2025, A crosswalk of the 2015 World Terrestrial Ecosystems to the International Union for the Conservation of Nature Global Ecosystem Typology Framework: U.S. Geological Survey Open-File Report 2025–1043, 10 p., https://doi.org/10.3133/ofr20251043.","productDescription":"Report: iv, 10 p.; 2 Appendixes","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-174837","costCenters":[{"id":86069,"text":"National Land Imaging","active":true,"usgs":true}],"links":[{"id":494756,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2025/1043/ofr20251043_appendix.csv","text":"Appendix","size":"215 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2025-1043 Appendix CSV","linkHelpText":"- A Crosswalk of the U.S. Geological Survey/Esri/The Nature Conservancy World Terrestrial Ecosystems to the International Union for the Conservation of Nature Global Ecosystem Typology (GET) Ecosystem Functional Groups in a CSV file"},{"id":494750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1043/coverthb.jpg"},{"id":494755,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2025/1043/ofr20251043_appendix.xlsx","text":"Appendix","size":"70.4 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2025-1043 Appendix XLSX","linkHelpText":"- A Crosswalk of the U.S. Geological Survey/Esri/The Nature Conservancy World Terrestrial Ecosystems to the International Union for the Conservation of Nature Global Ecosystem Typology (GET) Ecosystem Functional Groups"},{"id":494754,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1043/images/"},{"id":494753,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1043/ofr20251043.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1043 XML"},{"id":494752,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251043/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1043 HTML"},{"id":494751,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1043/ofr20251043.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1043 PDF"}],"contact":"Director, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Cyanobacterial harmful algal blooms (cyanoHABs) and associated cyanotoxins are a concern for inland waters. Due to the extensive spatial coverage and frequent availability of satellite images, multispectral remote sensing tools demonstrate utility for monitoring these blooms. The next frontier for remote sensing of cyanoHABs in inland waters is hyperspectral data. Recent and upcoming hyperspectral satellite missions using narrow wavelength imaging spectrometers could have a major impact on advancing our ability to detect, quantify, and characterize cyanobacterial blooms. This study compares multispectral and hyperspectral remote sensing capabilities and processing tools for monitoring cyanoHAB dynamics. We evaluated the temporal trends of cyanoHABs in Clear Lake, California, a hypereutrophic lake with diverse cyanobacteria genera based on 38 sampling events over a five-year monitoring period (2019–2023). We validated the Sentinel-3 Ocean and Land Color Instrument (multispectral) Cyanobacteria Index algorithm for Clear Lake using in situ cyanobacteria measurements, which complemented our field-based evaluation of cyanobacteria trends in Clear Lake. We then demonstrate the advantages of hyperspectral data from both in situ spectroradiometer measurements and full-lake hyperspectral satellite images. We apply the Spectral Mixture Analysis for Surveillance of HABs (SMASH) workflow, a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm, to the hyperspectral images to assess the potential of satellite imaging spectrometer data to identify cyanobacteria genera – the first study to test this tool outside its original study sites. We developed a Clear Lake-specific cyanobacteria spectral library using our field spectroradiometer measurements to improve SMASH performance in Clear Lake, which supports the continued development of this tool.
","language":"English","publisher":"Elseiver","doi":"10.1016/j.rsase.2025.101704","usgsCitation":"Sharp, S.L., Cortes, A., Forrest, A.L., Legleiter, C.J., Guild, L.S., Jin, Y., and Schladow, S.G., 2025, Monitoring cyanobacteria temporal trends in a hypereutrophic lake using remote sensing: From multispectral to hyperspectral: Remote Sensing Applications: Society and Environment, v. 39, 101704, 17 p., https://doi.org/10.1016/j.rsase.2025.101704.","productDescription":"101704, 17 p.","ipdsId":"IP-174209","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":500065,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/5t83t0rw","text":"External Repository"},{"id":495280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.96267888555795,\n 39.16571325270124\n ],\n [\n -122.96267888555795,\n 38.914533541208556\n ],\n [\n -122.60070817606311,\n 38.914533541208556\n ],\n [\n -122.60070817606311,\n 39.16571325270124\n ],\n [\n -122.96267888555795,\n 39.16571325270124\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sharp, Samantha L.","contributorId":361094,"corporation":false,"usgs":false,"family":"Sharp","given":"Samantha","middleInitial":"L.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":948227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cortes, Alicia","contributorId":293333,"corporation":false,"usgs":false,"family":"Cortes","given":"Alicia","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":true,"id":948228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forrest, Alexander L.","contributorId":361096,"corporation":false,"usgs":false,"family":"Forrest","given":"Alexander","middleInitial":"L.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":948229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":948230,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guild, Liane S.","contributorId":361098,"corporation":false,"usgs":false,"family":"Guild","given":"Liane","middleInitial":"S.","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":948231,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jin, Yufang","contributorId":361101,"corporation":false,"usgs":false,"family":"Jin","given":"Yufang","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":948232,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schladow, S. Geoffrey","contributorId":361104,"corporation":false,"usgs":false,"family":"Schladow","given":"S.","middleInitial":"Geoffrey","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":948233,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270953,"text":"70270953 - 2025 - Favorability mapping for hydrothermal power resource assessments of the Great Basin, USA","interactions":[],"lastModifiedDate":"2025-08-27T15:00:56.506199","indexId":"70270953","displayToPublicDate":"2025-08-26T07:55:11","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Favorability mapping for hydrothermal power resource assessments of the Great Basin, USA","docAbstract":"The U.S. Geological Survey (USGS) is updating the 2008 assessment of conventional hydrothermal resources for the Great Basin in the western United States. As part of this work, the workflow for hydrothermal resource favorability maps is being modified to integrate modern data-driven machine learning (ML) methods. Improvements include: [1] using new and refined evidence layers (features); [2] using an order of magnitude more training sites (labeled examples); [3] utilizing simple but non-linear supervised ML algorithms; [4] representing positive training sites (wells with measured heat flow) with their ordinal value proportional to the magnitude of convective upflow (i.e., low, high, or very high convective signals instead of past strategies using positive-negative labels); [5] supplementing training sites with additional sites with low convective signals to represent diverse under-sampled areas where hydrothermal systems are unlikely to exist; [6] comparing with competing approaches; and [7] utilizing Monte Carlo cross-validation to estimate and evaluate prediction uncertainty.
For the new favorability map, over half of the power-producing systems (i.e., 15 of 28) are predicted in the 99th percentile of most favorable locations (i.e., the highest 1 % of favorability, corresponding to 1 % of the map area), exceeding the performance of past models that have explicitly used power plants as training sites. Previous favorability maps predicted approximately half of the power-producing hydrothermal systems above the 80th percentile (i.e., 20 % of the map area). For the new favorability map, 93 % of power-producing systems (i.e., 26 of 28) are above the 80th percentile. The power-producing systems for which the new model does not perform well are either comparatively small, low-temperature systems or systems also not predicted well by prior modeling approaches, suggesting that these few systems are unusual when compared with most power-producing systems. Focusing research on these known, seemingly different systems may yield new insights and subsequent discovery of new prospects.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2025.103450","usgsCitation":"Mordensky, S.P., Burns, E., Lipor, J., and DeAngelo, J., 2025, Favorability mapping for hydrothermal power resource assessments of the Great Basin, USA: Geothermics, v. 133, 103450, 24 p., https://doi.org/10.1016/j.geothermics.2025.103450.","productDescription":"103450, 24 p.","ipdsId":"IP-170174","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":495066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geothermics.2025.103450","text":"Publisher Index Page"},{"id":494947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.0202610934917,\n 42.55970962212865\n ],\n [\n -119.8746500530377,\n 37.88790476539039\n ],\n [\n -116.94256843416173,\n 36.91537025154052\n ],\n [\n -113.73824592248651,\n 36.98857095813982\n ],\n [\n -111.99293303262,\n 42.55970962212865\n ],\n [\n -115.98687680320434,\n 42.23211305382921\n ],\n [\n -118.48297767228134,\n 42.58841357223409\n ],\n [\n -121.0202610934917,\n 42.55970962212865\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"133","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":947427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":947428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":947429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":947430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270766,"text":"ofr20251038 - 2025 - Python Hyperspectral Analysis Tool (PyHAT) user guide","interactions":[],"lastModifiedDate":"2026-02-03T15:13:26.159322","indexId":"ofr20251038","displayToPublicDate":"2025-08-22T14:59:30","publicationYear":"2025","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":"2025-1038","displayTitle":"Python Hyperspectral Analysis Tool (PyHAT) User Guide","title":"Python Hyperspectral Analysis Tool (PyHAT) user guide","docAbstract":"This report is a user guide for the 0.1.2 release of the Python Hyperspectral Analysis Tool (PyHAT) and its graphical user interface (GUI). 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It also provides practical guidance on usage and example workflows for a wide variety of datasets.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251038","usgsCitation":"Anderson, R.B., Aneece, I.P., and Gabriel, T.S.J., 2025, Python Hyperspectral Analysis Tool (PyHAT) user guide: U.S. Geological Survey Open-File Report 2025–1038, 59 p., https://doi.org/10.3133/ofr20251038.","productDescription":"xi, 59 p.","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-120336","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":494678,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1038/images"},{"id":494677,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1038/ofr20251038.XML","description":"OFR 2025-1038 XML"},{"id":494676,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251038/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1038 HTML"},{"id":494675,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1038/ofr20251038.pdf","text":"Report","size":"12.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1038 PDF"},{"id":494653,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1038/coverthb.jpg"}],"contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
An ∼3 km long nodal array oriented approximately east–west was deployed in Chugiak, Alaska, by the U.S. Geological Survey during 2021. The array intersects with the permanent NetQuakes station NP.ARTY, where peak ground acceleration (PGA) value of 1.98g was recorded during the 2018 Mw 7.1 Anchorage, Alaska, earthquake, in sharp contrast to the PGA of ∼0.3g at a site just 4 km to the west. Seismic data for Mw 1.8–4.3 aftershocks from the Mw 7.1 event recorded by the nodal array confirm the anomalously large ground motions obtained at NP.ARTY as well as similar amplifications at nodes within ∼1 km to the east. Here, we performed 0–10 Hz 3D finite‐difference simulations, including high‐resolution surface topography, to explore the cause of the unexpectedly large amplification. As expected, the simulations computed with a regional 3D tomography velocity model severely underpredict the 0–10 Hz acceleration records at almost all sites. Adding a near‐surface low‐velocity taper to 300 m depth amplifies the accelerations by up to a factor of 5 and enables a reasonable match between the nodal data and simulations at sites to the west of NP.ARTY. However, this model still underpredicts the spectral energy in the area covered by glacial sediments by up to an order of magnitude. The addition of a till layer using a depth‐dependent shear‐wave velocity (Vs) profile along with a homogeneous, 8 m thick low‐velocity layer with Vs = 250 m/s representing the kame terraces improves the fit to data to within a factor of 2 at nodes located on top of the glacial sediments. Our study shows that the anomalously large high‐frequency amplification recorded at and near NP.ARTY can be explained by a combination of topographic effects and near‐surface low‐velocity material with amplification effects on the high‐frequency ground motion by up to about 40% and an order of magnitude, respectively.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240283","usgsCitation":"Yeh, T., Olsen, K.B., Steidl, J.H., and Haeussler, P., 2025, Near-surface material and topography generate anomalous high-frequency ground motion amplification in Chugiak, Alaska: Bulletin of the Seismological Society of America, v. 115, no. 6, p. 2793-2808, https://doi.org/10.1785/0120240283.","productDescription":"16 p.","startPage":"2793","endPage":"2808","ipdsId":"IP-173630","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":498350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Chugiak","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.0449585780233,\n 61.579337610698786\n ],\n [\n -150.32779349821365,\n 61.579337610698786\n ],\n [\n -150.32779349821365,\n 60.81067946634249\n ],\n [\n -149.0449585780233,\n 60.81067946634249\n ],\n [\n -149.0449585780233,\n 61.579337610698786\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Yeh, Te-Yang 0000-0002-9146-6804","orcid":"https://orcid.org/0000-0002-9146-6804","contributorId":364872,"corporation":false,"usgs":false,"family":"Yeh","given":"Te-Yang","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":953357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olsen, Kim B.","contributorId":364874,"corporation":false,"usgs":false,"family":"Olsen","given":"Kim","middleInitial":"B.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":953358,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":953359,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":353464,"corporation":false,"usgs":false,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":84407,"text":"USGS ASC retired","active":true,"usgs":false}],"preferred":false,"id":953360,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270361,"text":"dr1214 - 2025 - Revised marine bird collision and displacement vulnerability index for U.S. Pacific Outer Continental Shelf offshore wind energy development","interactions":[],"lastModifiedDate":"2026-02-03T15:11:59.483763","indexId":"dr1214","displayToPublicDate":"2025-08-21T06:59:57","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1214","displayTitle":"Revised Marine Bird Collision and Displacement Vulnerability Index for U.S. Pacific Outer Continental Shelf Offshore Wind Energy Development","title":"Revised marine bird collision and displacement vulnerability index for U.S. Pacific Outer Continental Shelf offshore wind energy development","docAbstract":"The installation of offshore wind energy infrastructure (OWEI) at sea may affect marine birds by increasing the risk of mortality from collision with OWEI (Collision Vulnerability) and causing disturbance and displacement from important habitats (Displacement Vulnerability). In 2017, we published the first comprehensive database quantifying marine bird Collision Vulnerability and Displacement Vulnerability to potential OWEI in the region of the U.S. Pacific Outer Continental Shelf (POCS; waters within the Exclusive Economic Zone of California, Oregon, and Washington). We have updated this Vulnerability Index with new research and data, additional species present in the POCS, and an evolved understanding of the application and utility of the Index. Of the species assessed, phalaropes and Red-billed Tropicbird have the highest Collision Vulnerability, and gulls, terns, jaegers, skuas, and pelicans have moderately high Collision Vulnerability. Boobies, sea ducks, and pelicans have the greatest Displacement Vulnerability. The overall trends in ranked Vulnerability among marine birds in the POCS were consistent between Version 1 and Version 2 although new data and revised calculations updated the outcomes. Alcids, loons, storm-petrels, Brant, and phalaropes ranked higher for Collision Vulnerability in Version 2 compared to Version 1; sea ducks, cormorants, skua, and jaegers ranked lower for Collision Vulnerability in Version 2 compared to Version 1. Displacement Vulnerability ranks were higher in Version 2 for gulls, pelicans, sea ducks, and alcids and lower for albatrosses, terns, and loons. Vulnerability Index Version 2 is an up-to-date, representative, and transparent assessment of marine bird vulnerability to potential offshore wind energy development. This updated Vulnerability Index can assist resource managers and others in understanding and addressing potential interactions between OWEI and marine bird species that inhabit the POCS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1214","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kelsey, E.C., Felis, J.J., Pereksta, D.M., and Adams, J., 2025, Revised marine bird collision and displacement vulnerability index for U.S. Pacific Outer Continental Shelf offshore wind energy development (ver. 1.1,\nNovember 2025): U.S. Geological Survey Data Report 1214, 32 p., https://doi.org/10.3133/dr1214.","productDescription":"viii, 32 p.","onlineOnly":"Y","ipdsId":"IP-167805","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":496435,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1214/dr1214.XML"},{"id":496432,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/dr/1214/VersionHistory.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":496434,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1214/images"},{"id":496433,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1OUOM9W","text":"USGS data release","description":"USGS data release","linkHelpText":"Data for the revised marine bird Collision and Displacement Vulnerability Index for Pacific Outer Continental Shelf offshore wind energy development"},{"id":496431,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1214/dr1214.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1214"},{"id":496429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1214/coverthb2.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.11122811871701,\n 46.836231292523934\n ],\n [\n -133.66298337147504,\n 43.87312626189197\n ],\n [\n -135.1639174719598,\n 38.18991249267404\n ],\n [\n -124.75213428275356,\n 21.70090202972672\n ],\n [\n -117.3903149351834,\n 19.187148095597664\n ],\n [\n -108.20584860772473,\n 21.105959128505745\n ],\n [\n -110.47687143079384,\n 23.789215318067903\n ],\n [\n -114.73093128440786,\n 29.394483151806185\n ],\n [\n -116.81948042658601,\n 32.732895836344994\n ],\n [\n -120.58898983505466,\n 35.78436076311384\n ],\n [\n -123.50560199570324,\n 40.66753444650692\n ],\n [\n -123.11122811871701,\n 46.836231292523934\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 19, 2025; Version 1.1: November 17, 2025","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Geological Survey (USGS) is using collaborative, interdisciplinary planning to develop data and tools needed to optimize the management of water resources and land use by resource management agencies during an ongoing, multidecadal drought in the Colorado River Basin. The USGS Actionable and Strategic Integrated Science and Technology team works to build relationships with resource management agencies and other stakeholders who can benefit from the use of USGS data and products. In 2023, the Actionable and Strategic Integrated Science and Technology team hosted a series of collaborative workshops to bring together representatives of resource management agencies and other stakeholders (any person or entity with interests in a resource or location) with USGS program managers, scientists, and multidisciplinary subject matter experts to codevelop concepts for interdisciplinary drought science and technology projects to address pressing needs related to drought in the Colorado River Basin. Workshop participants identified current and recent scientific data that could be shared through a centralized online data portal. Workshop participants also identified drought science and technology needs and developed project concepts to address those science needs. Participants categorized project concepts based on their potential to develop short-, mid-, and long-term drought science data and tools, provide for the spatial or temporal expansion of ongoing USGS science projects, and address high-priority science needs. Participants developed nine project concepts: (1) understanding shifting ecohydrologic baselines, (2) San Juan River Basin synthesis, (3) incorporating dynamic land cover into hydrologic models, (4) aridification compared to drought, (5) surface water-groundwater interactions, (6) cascading effects of drought on dust, (7) cascading effects of drought on water availability, (8) cascading effects of drought on socioeconomic factors, and (9) the value of water in the Colorado River Basin. This report provides an overview of the 2023 Codesign Workshop Series, synthesized outcomes from workshop materials and discussions, and science project concepts that emerged from the collaborative meetings that will continue to be refined into science project proposals through codevelopment processes. This report also highlights lessons learned and next steps needed to receive feedback and testing of the USGS Science Collaboration Portal, continue collaboration to develop detailed specifics and steps for short-term wins, develop interdisciplinary project proposals, and implement science planning and studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20251041","usgsCitation":"Anderson, P.J., Godaire, J.E., Jones, D.K., Andrews, W.J., Torregrosa, A.A., Bell, M.T., Holloway, J.M., Blakowski, M.A., Hevesi, J.A., and Qi, S.L., 2025, Collaborative drought science planning in the Colorado River Basin: U.S. Geological Survey Open-File Report 2025–1041, 32 p., https://doi.org/10.3133/ofr20251041.","productDescription":"vi, 32 p.","onlineOnly":"Y","ipdsId":"IP-165607","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":494357,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1041/ofr20251041.xml"},{"id":494378,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251041/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1041"},{"id":494325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1041/ofr20251041.pdf","text":"Report","size":"9.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1041"},{"id":494356,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1041/images"},{"id":494324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1041/coverthb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.23441985470737,\n 42.42360949558767\n ],\n [\n -110.0074572875208,\n 42.9273485741041\n ],\n [\n -110.88201818257588,\n 40.83215412591818\n ],\n [\n -112.09041488325958,\n 37.81270064609009\n ],\n [\n -113.86864235906498,\n 37.77076755792572\n ],\n [\n -113.94586057217214,\n 38.21912009189296\n ],\n [\n -115.10119317735365,\n 39.08121928179544\n ],\n [\n -115.47544949784407,\n 35.429353160164375\n ],\n [\n -115.29249888241867,\n 31.986896837542588\n ],\n [\n -110.42076682180414,\n 30.172954165166573\n ],\n [\n -108.95437388160886,\n 30.991312421045535\n ],\n [\n -108.56364522256465,\n 31.857948821439074\n ],\n [\n -107.84802514114666,\n 32.26017852956302\n ],\n [\n -107.22575341999277,\n 34.155285973008596\n ],\n [\n -107.68523996280838,\n 35.482296714195456\n ],\n [\n -106.46728549757393,\n 37.071939542790304\n ],\n [\n -105.6885671199549,\n 39.88037502712785\n ],\n [\n -106.23441985470737,\n 42.42360949558767\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
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Aeromagnetic and gravity surveys of the Skaergaard intrusion in East Greenland were carried out in July–August 1971 as part of a grant to the University of Oregon Center for Volcanology to refine the models of crystallization and differentiation of the intrusion, specifically to test whether the intrusion is underlain by dense rocks of a reservoir 20 kilometers (km) thick (referred to as a “hidden zone”). The Skaergaard intrusion is a source of platinum group elements that are critical mineral resources for many technologies, and because no new data have been collected these legacy datasets remain a valuable asset. The total-intensity aeromagnetic survey was flown in early July 1971 with a proton precession magnetometer at a constant barometric altitude of 1.5 km (5,000 feet) with a nominal line spacing of 1 km. Two gravimeters were used to acquire 168 stations of which 86 were at known altitudes (mainly sea level) and 82 had altitudes measured by altimetry in late July–August 1971. Finally, a north-south ground vertical-intensity magnetic traverse was completed across the intrusion together with collection of oriented hand specimens. The hand specimens were measured for remnant magnetization and density, along with density measurements of more specimens collected by expedition geologists for other purposes.
The intrusion is composed of layered gabbro with extensive crystal fractionation that is dense and strongly reversely polarized. After terrain correction and standard Bouguer gravity reduction, the gravity anomaly dataset was corrected for all rock above sea level using the density measurements of the various zones of the intrusion and the topographic and geologic maps (variable density Bouguer gravity reduction).
A large regional gradient in the gravity anomaly data was removed using orthogonal polynomial fitting to the gridded data. The zonal volumes of rock below sea level were calculated from the dipping polygonal layer gravity model of the intrusion below sea level and combined with elliptic cross–section cylinders for the various zones above sea level to approximate the original zonal volumes of the intrusion. The residual gravity anomaly of 18–20 milligals (mGal) was only about half of the expected anomaly if a large hidden zone proposed from petrologic considerations were present, and both two-dimensional and three-dimensional models imply that the exposed series of intrusion zones explain the gravity anomaly by their down-dip extension below sea level together with a small hidden-zone volume. A three-dimensional model of the exposed rocks and their down-dip extension below sea level also can account for the aeromagnetic anomaly with little or no requirement for hidden-zone rock. The middle and upper zone units of the intrusion contain the most magnetite and account for most of the aeromagnetic anomaly.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251030","programNote":"Mineral Resources Program","usgsCitation":"Gettings, M.E., 2025, Gravity and magnetic surveys of the Skaergaard intrusion, East Greenland: U.S. Geological Survey Open-File Report 2025–1030, 43 p., https://doi.org/10.3133/ofr20251030.","productDescription":"Report: ix, 43 p.; Data Release","numberOfPages":"43","onlineOnly":"Y","ipdsId":"IP-126792","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494352,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OVG7G","text":"USGS data release","description":"Gettings, M.E., and Parks, H.L., 2025, Aeromagnetic and gravity surveys of the Skaergaard intrusion in East Greenland, 1971: U.S. Geological Survey data release, https://doi.org/10.5066/P91OVG7G.","linkHelpText":"Aeromagnetic and gravity surveys of the Skaergaard intrusion in East Greenland, 1971"},{"id":494347,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1030/coverthb.jpg"},{"id":494348,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1030/ofr20251030.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1030 PDF"},{"id":494349,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251030/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1030 HTML"},{"id":494350,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1030/ofr20251030.XML","description":"OFR 2025-1030 XML"},{"id":494351,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1030/images"}],"country":"Greenland","otherGeospatial":"Skaergaard intrusion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -32.1667,\n 68.3\n ],\n [\n -32.1677,\n 68\n ],\n [\n -31.1667,\n 68\n ],\n [\n -31.1667,\n 68.3\n ],\n [\n -32.1667,\n 68.3\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, and Geophysics Science Center
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Many earthquakes occur along the North Coast of California in the vicinity of the Mendocino Triple Junction (MTJ), where the Pacific, Gorda, and North American (NA) plates meet, and on the adjacent plate boundaries. The MTJ marks the nexus of the Mendocino and San Andreas faults with the Cascadia subduction zone (CSZ). Historically, most large earthquakes around the MTJ have been within the offshore Gorda plate and its subducted portion beneath the NA plate. North of the MTJ, active faults mapped in the NA plate are part of the CSZ fold‐and‐thrust belt. Although some events have been detected in the NA plate, no large historic events have been associated with mapped surface faults. The 21 December 1954 Mw 6.5 earthquake in Humboldt County is one possible exception. Using published data from catalogs and articles, unpublished data from Berkeley’s archives, and S‐P times interpreted from two U.S. Coast and Geodetic Survey (USCGS) accelerometers, we determine a probability cloud for the earthquake’s hypocenter using NonLinLoc. The highest probability location lies beneath Fickle Hill just east of the city of Arcata, California, at 40.87° N, 124.03° W, and ∼11 km depth. Using P‐wave polarities from Berkeley stations and the digitized waveforms from the accelerometers, we find that the focal mechanism most consistent with the data indicates thrust movement with strike, dip, and rake of 350°, 10°, and 90°, respectively, at a depth of 14 km. Given the depth uncertainties of both this event and the megathrust, this implies that the earthquake most likely took place on the subduction interface rather than on the mapped faults in the Mad River fault zone that trend 322° and dip to the northeast. The revisited intensity in the epicentral region also supports a location beneath Fickle Hill to the east of the city of Arcata, California.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120250080","usgsCitation":"Hellweg, M., Lee, T.A., Dreger, D.S., Lomax, A., Hagos, L., Haddabi, H., McPherson, R.C., Dengler, L., Hough, S.E., and Patton, J.R., 2025, Revisiting an enigma on California's north coast: The Mw6.5 Fickle Hill earthquake of 21 December 1954: Bulletin of the Seismological Society of America, v. 115, no. 6, p. 2623-2639, https://doi.org/10.1785/0120250080.","productDescription":"17 p.","startPage":"2623","endPage":"2639","ipdsId":"IP-177949","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":494901,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.23082743259238,\n 46.45369999658712\n ],\n [\n -124.94108692827174,\n 41.87317683684783\n ],\n [\n -124.0036946792876,\n 37.27396300370703\n ],\n [\n -120.72594459506794,\n 33.05110616586563\n ],\n [\n -116.56731665360127,\n 33.7291335831041\n ],\n [\n -116.56731665360127,\n 46.45369999658712\n ],\n [\n -124.23082743259238,\n 46.45369999658712\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hellweg, Margaret","contributorId":360602,"corporation":false,"usgs":false,"family":"Hellweg","given":"Margaret","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":947294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Thomas A.","contributorId":360603,"corporation":false,"usgs":false,"family":"Lee","given":"Thomas","middleInitial":"A.","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":947295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dreger, Douglas S.","contributorId":360605,"corporation":false,"usgs":false,"family":"Dreger","given":"Douglas","middleInitial":"S.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":947296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lomax, Anthony","contributorId":355480,"corporation":false,"usgs":false,"family":"Lomax","given":"Anthony","affiliations":[{"id":84757,"text":"ALomax Scientific, Mouans Sartoux, France","active":true,"usgs":false}],"preferred":false,"id":947297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagos, Lijam","contributorId":300811,"corporation":false,"usgs":false,"family":"Hagos","given":"Lijam","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":947298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haddabi, Hamid","contributorId":360611,"corporation":false,"usgs":false,"family":"Haddabi","given":"Hamid","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":947299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McPherson, Robert C.","contributorId":350346,"corporation":false,"usgs":false,"family":"McPherson","given":"Robert","middleInitial":"C.","affiliations":[{"id":83721,"text":"Cal Poly Humboldt Univ.","active":true,"usgs":false}],"preferred":false,"id":947300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dengler, Lori","contributorId":197374,"corporation":false,"usgs":false,"family":"Dengler","given":"Lori","affiliations":[],"preferred":false,"id":947301,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":947302,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Patton, Jason R.","contributorId":317714,"corporation":false,"usgs":false,"family":"Patton","given":"Jason","email":"","middleInitial":"R.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":947334,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70271353,"text":"70271353 - 2025 - Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm","interactions":[],"lastModifiedDate":"2025-09-10T14:57:19.895523","indexId":"70271353","displayToPublicDate":"2025-08-19T07:53:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm","docAbstract":"Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R2 for annual 7-day maximum = 0.76; R2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.
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Spillover events (i.e., virus transfer from wild birds into poultry) are related to proximity to infected wild bird populations and environmental conditions. By accounting for such dynamics, we can take a combined approach to assess the impacts of biosecurity measures implemented at poultry farms while simultaneously accounting for their local risk levels. We implemented a Bayesian joint-likelihood logistic regression for the Continental U.S. comparing models of spatiotemporal risk according to land use, weather, and predicted waterfowl distributions followed by integrating a farm-level case-control questionnaire dataset focused on identifying trends in HPAI spillover risk associated with a farm's biosecurity practices. We found that estimates of waterfowl abundance, along with mean precipitation and temperature during winter, were most correlated with spatiotemporal HPAI risk. Additionally, we identified multiple biosecurity practices associated with reduced risk to HPAI, where the strongest relationships were related to litter decontamination treatments, vehicle wash stations, and avoiding shared dead-bird disposal sites with other farms. This model broadly guides surveillance of HPAI in wild and domestic populations, identifying when and where we are most likely to see increased instances of the virus while also providing insights into how poultry farms can better protect themselves from risk.","language":"English","publisher":"Elsevier","doi":"10.1016/j.onehlt.2025.101172","usgsCitation":"Gonnerman, M.B., Mullinax, J., Fox, A., Patyk, K.A., Fields, V., McCool, M., Torchetti, M.K., Lantz, K., Sullivan, J.D., and Prosser, D.J., 2025, Avian influenza spillover into poultry: Environmental influences and biosecurity protections: One Health, v. 21, 101172, 9 p., https://doi.org/10.1016/j.onehlt.2025.101172.","productDescription":"101172, 9 p.","ipdsId":"IP-178496","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":495069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.onehlt.2025.101172","text":"Publisher Index 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K.","contributorId":252830,"corporation":false,"usgs":false,"family":"Torchetti","given":"Mia","email":"","middleInitial":"K.","affiliations":[{"id":50437,"text":"US Department of Agriculture – Veterinary Services, Ames, Iowa, USA","active":true,"usgs":false}],"preferred":false,"id":947533,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lantz, Kristina","contributorId":317920,"corporation":false,"usgs":false,"family":"Lantz","given":"Kristina","email":"","affiliations":[{"id":69192,"text":"National Veterinary Services Laboratories, Animal and Plant Health Inspection Service, USDA","active":true,"usgs":false}],"preferred":false,"id":947534,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological 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Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5072","displayTitle":"Tracking Status and Trends in Seven Key Indicators of River and Stream Condition in the Chesapeake Bay Watershed","title":"Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed","docAbstract":"Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.
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1730 East Parham Road
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The ages and sizes at which organisms mature have significant implications for lifetime reproductive success. For species at risk of extinction, such as sea turtles, these attributes can ultimately impact probability of population persistence. Within the Northwest Atlantic Ocean, the broader loggerhead sea turtle (Caretta caretta) population comprises management units both along the US Gulf of America (formerly Gulf of Mexico) and Atlantic coasts. Although age, growth, and maturation have been more intensively studied along the US Atlantic, data specific to the Gulf of America have remained sparse. To address this data gap, we conducted skeletal growth mark analysis (skeletochronology) for 123 humerus bones collected from loggerheads found dead in the US Gulf of America from 1998 to 2021. We compared resulting age, growth, and maturation data with information from studies of US Atlantic coast loggerheads for similar size and year ranges, using the exact same skeletochronology methods, as well as with Gulf of America mark-recapture growth data. Results indicate that Gulf of America loggerheads exhibit significantly faster juvenile somatic growth. In addition, sizes at maturation were substantially smaller, corresponding with mean estimates of age at maturation 7.5 to 15 years earlier than US Atlantic counterparts. Finally, the maximum observed Gulf of America adult age estimate was 42.5 years, considerably less than the highest US Atlantic estimate of 77.0 years. These detailed data offer insights into regional variability in somatic growth dynamics and characteristics associated with maturation, which in turn can impact relative reproductive contributions and, ultimately, population trajectories.
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