Studies involving individually marked animals provide insights predicated on the assumption marked individuals are accurate models of their unmarked counterparts. Taxa-specific and marker-specific examinations are needed to determine if marked animals are suitable models for the parameter(s) being measured. Our objective was to determine if brightly colored ear tags influenced the probability of predation for neonatal ungulates. We captured 94 neonatal pronghorn (Antilocapra americana (Ord, 1815)), fitted each neonate with a tracking collar, and attached a yellow ear tag to 49 (52.1%) of the captured neonates. We monitored the survival of each neonate during 2023–2024 in Oklahoma, USA. Predation was the leading cause of mortality during our monitoring period and accounted for 29 (82.9%) of the 35 mortalities with a known cause. Coyotes (Canis latrans Say, 1823) were the predominant predator of neonatal pronghorn in our study area. Presence of a yellow ear tag seemingly did not influence the probability of predation, even though coyotes can distinguish yellow objects from most natural backgrounds. A larger sample size may be needed to validate our results, but neonatal ungulates with an ear tag appear to be accurate models of neonatal ungulates without an ear tag when examining predation risk.
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This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation (r = 0.85) than solely using elevation (r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. 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","language":"English","publisher":"Nature","doi":"10.1038/s41597-025-05131-4","usgsCitation":"Reis Ely, C., Perakis, S.S., Cleveland, C., Menge, D., Reed, S.C., Batterman, S., Crews, T., Dynarski, K.A., Gei, M., Gundale, M., Jovan, S.E., Kou-Giesbrecht, S., Peoples, M., Rodriguez-Caballero, E., Salmon, V., Soper, F.M., Staccone, A., Taylor, B., Weber, B., and Wurzburger, N., 2025, A global dataset of terrestrial biological nitrogen fixation: Scientific Data, v. 12, 1362, 10 p., https://doi.org/10.1038/s41597-025-05131-4.","productDescription":"1362, 10 p.","ipdsId":"IP-169686","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05131-4","text":"Publisher Index 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are used in a parametric inversion for source, path, and site effects. Five earthquakes are selected for analysis during the installation of the United States National Seismic Network (US), Earthscope’s USArray Transportable Array (TA), and other temporary arrays to maximize station coverage. A global search algorithm is used to solve for site response from 0.1 to 15 Hz, corner frequency, geometrical spreading (r-γ), and frequency dependent anelastic attenuation in the form Q(f) = Qof α. Tradeoff between moment and geometric spreading is handled by fixing the moment. The tradeoff between corner frequency and Q(f) is solved by selecting the value of corner frequency that minimizes an objective function defined over all stations. Values of site response and attenuation parameters show a strong spatial correlation with the physiographic provinces of the eastern United States. Site response for the Atlantic Coastal Plain is consistent with previous work using spectral ratios relative to a reference site, defined by strong resonance peaks correlated with the thickness of sediments. Site response for the other physiographic provinces is markedly different from the coastal plain, with a lack of distinct resonance peaks and a broad moderate high at frequences from 0.1 to 0.5 Hz consistent with the hard-rock geology of the regions. Like site response, Q(f) has a strong correlation with physiographic province, showing lower values on the coastal plain and higher values inland. Geometric spreading exponent, γ, decreases with increasing hypocenter distance from just above 1 at a few tens of kilometers to 0.9 at 500 km. The limited range in geometric spreading values is attributed to starting the Fourier transform window at the S‐wave arrival for all distances and averaging over multiple wave types.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0120250066","usgsCitation":"Hartzell, S.H., Martinetti, L., Mendoza, C., and Schmitt, R.G., 2025, Site response and wave propagation effects in the eastern United States: Bulletin of the Seismological Society of America, v. 115, no. 5, p. 2485-2506, https://doi.org/10.1785/0120250066.","productDescription":"22 p.","startPage":"2485","endPage":"2506","ipdsId":"IP-174795","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":493930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.07834108451145,\n 43.347066936734876\n ],\n [\n -83.07190566074532,\n 41.15850918749996\n ],\n [\n -84.72974599179413,\n 38.49572818331108\n ],\n [\n -87.6040403599389,\n 37.557442613196955\n ],\n [\n -90.35205007389611,\n 32.79825745697784\n ],\n [\n -81.70066250884963,\n 32.45943477635677\n ],\n [\n -76.33120640624651,\n 37.93006949063302\n ],\n [\n -74.11435019028751,\n 44.90250530044207\n ],\n [\n -74.96201722230823,\n 45.03628280801624\n ],\n [\n -78.07834108451145,\n 43.347066936734876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinetti, Luis B.","contributorId":359446,"corporation":false,"usgs":false,"family":"Martinetti","given":"Luis B.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":945457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendoza, Carlos 0000-0002-2428-7064","orcid":"https://orcid.org/0000-0002-2428-7064","contributorId":343872,"corporation":false,"usgs":false,"family":"Mendoza","given":"Carlos","email":"","affiliations":[{"id":18923,"text":"Universidad Nacional Autonoma de Mexico","active":true,"usgs":false}],"preferred":false,"id":945458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Robert G. 0000-0001-8060-1954 rschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-1954","contributorId":5611,"corporation":false,"usgs":true,"family":"Schmitt","given":"Robert","email":"rschmitt@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945459,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269910,"text":"70269910 - 2025 - High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon","interactions":[],"lastModifiedDate":"2025-08-06T14:47:01.818797","indexId":"70269910","displayToPublicDate":"2025-08-05T07:41:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon","docAbstract":"The distribution of river-bed grain sizes plays a foundational role in river morphology and ecology. River-bed grain size is a key driver of channel form and process, and has first order effects on aquatic macroinvertebrate assemblages, fish nesting, and biogeochemical processes. Despite this importance, tools to spatially quantify grain-size distributions, particularly submerged grain-size distributions, are lacking. Efforts to address this knowledge gap include developing optical and sonographic tools, however, these approaches have limitations, especially in shallow rivers and over large spatial extents. This study quantifies submerged grain size at high resolution (1 m2) across 260 km of geomorphically diverse river corridors in the Santiam River Basin, Oregon, by pairing bathymetric Lidar point clouds with georeferenced pebble counts. Results suggest that derivatives of Lidar point clouds are able to accurately estimate measured median grain size across seven of the eight river reaches investigated, including reaches above and below high-head dams. Spatial analysis of predicted grain-sizes in the context of Chinook salmon spawning habitat suggests that suitable size sediment patches in the upper, unregulated reaches the study basin is typically small and unorganized. In contrast, the larger rivers downstream of high-head dams typically have larger areas of suitable spawning gravels. This method may be useful for quantification of fish and macroinvertebrates habitats, surface grain-size metrics for sediment transport models, and monitoring of natural and anthropogenic changes in river systems.
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In south Florida, most reefs are already net eroding, and without intervention, valuable ecosystem services may be lost. Coral restoration holds the potential to reverse those trends; however, typical restoration monitoring does not adequately capture key geo-ecological functions. We addressed this knowledge gap using carbonate budgets and Structure-from-Motion models to evaluate the impact of coral restoration on reef-accretion potential and structural complexity at eight offshore and three inshore coral reefs in the Lower Florida Keys. Within 2–6 years following outplanting, restoration of rapidly growing A. cervicornis populations increased reef-accretion potential to 2.8 mm y− 1 and drove significant increases in structural complexity. There was no measurable impact of restoring slower-growing, massive corals on reef-accretion potential inshore; however, whereas the severe 2023 coral-bleaching event immediately following our study caused near-complete mortality of A. cervicornis, 59% of massive corals survived, highlighting potential trade-offs between coral growth and survival on future restoration efficacy. We conclude that although restoration can produce rapid, small-scale increases in reef-accretion potential, there remain important uncertainties about how and whether ecosystem-scale benefits of restoration on important geo-ecological reef functions can persist long term.
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lithium in rhyolitic melt during decompression and ascent","interactions":[],"lastModifiedDate":"2025-12-16T15:54:26.382187","indexId":"70273115","displayToPublicDate":"2025-08-01T09:48:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Variable partitioning of lithium in rhyolitic melt during decompression and ascent","docAbstract":"The partitioning behavior of Li in magmatic systems is increasingly being investigated due to the economic importance of Li in the transition to sustainable energy resources (e.g., batteries). However, at upper crustal pressures, it remains uncertain whether Li preferentially partitions into the vapor or liquid (brine) phase or remains in the silicate melt. This complicates our ability to determine where Li resides—silicate melt, minerals, or fluid phase—upon eruption, a crucial factor for understanding its postdepositional movement and concentration into a brine or volcano-sedimentary deposit. Here, we present a novel investigation into the behavior of Li within natural evolved melts during continuous magma decompression and ascent using melt embayments (open melt inclusions). Mineral-hosted melt embayments preserve records of the evolving composition of the exterior melt, including degassing pathways and ascent timescales, when paired with appropriate diffusion coefficients. Lithium concentration profiles were measured in quartz-hosted melt embayments from the rapidly quenched eruptive phases of five rhyolitic, caldera-forming eruptions to investigate the behavior of Li during magma decompression and ascent, where vapor partitioning and ascent dynamics were previously established by investigating H2O and CO2 profiles. We find that in four systems, embayments contain lower interior Li concentrations than the coerupted melt inclusions; the fifth system contains the same Li concentrations in embayments and melt inclusions. However, many of these embayments contain gradients, with 84% preserving Li enrichment near the melt-bubble interface, as compared to their interior concentration. We interpret these characteristics to represent two distinct stages of Li partitioning during magma decompression and ascent, in contrast to existing literature that proposes only one type of partitioning behavior. The first stage is interpreted as melt depletion of Li, likely driven by partitioning into an exsolved supercritical fluid phase, supported by the strong correlation between the extent of Li depletion and Cl concentration in the melt, as well as the decompression rate. This behavior then fundamentally shifts, where Li reenriches in the melt, postulated to be driven by the unmixing of the supercritical fluid phase at shallow pressures. For the one system that did not develop Li gradients through decompression, we attribute this to the lower values of Na and Cl in the melt, potentially inhibiting the partitioning of Li into a fluid phase. Importantly, the behavior of Li during decompression is not consistent within or between volcanic centers, highlighting the need for systematic experimental investigation in variable composition melts at pressures relevant to conduit dynamics. This knowledge would improve our ability to model Li profiles to understand magma decompression, and predict where Li resides (e.g., stored in volcanic glass, gas, or crystals) upon eruption prior to any later extraction.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.5171","usgsCitation":"Myers, M., Spallanzani, R., Schwartz, D., Mercer, C.N., and Hosseini, B., 2025, Variable partitioning of lithium in rhyolitic melt during decompression and ascent: Economic Geology, v. 120, no. 5, p. 1191-1206, https://doi.org/10.5382/econgeo.5171.","productDescription":"16 p.","startPage":"1191","endPage":"1206","ipdsId":"IP-169836","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497728,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.5171","text":"Publisher Index Page"},{"id":497573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Myers, Madison 0000-0003-2271-4445","orcid":"https://orcid.org/0000-0003-2271-4445","contributorId":331812,"corporation":false,"usgs":false,"family":"Myers","given":"Madison","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":952376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spallanzani, Roberta","contributorId":364231,"corporation":false,"usgs":false,"family":"Spallanzani","given":"Roberta","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":952377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwartz, Darin","contributorId":364233,"corporation":false,"usgs":false,"family":"Schwartz","given":"Darin","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":952378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mercer, Celestine N. 0000-0001-8359-4147 cmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-8359-4147","contributorId":4006,"corporation":false,"usgs":true,"family":"Mercer","given":"Celestine","email":"cmercer@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":952379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hosseini, Behnaz","contributorId":364237,"corporation":false,"usgs":false,"family":"Hosseini","given":"Behnaz","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":952380,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269626,"text":"sir20255060 - 2025 - Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island","interactions":[],"lastModifiedDate":"2026-02-03T14:41:55.782053","indexId":"sir20255060","displayToPublicDate":"2025-08-01T09:30:00","publicationYear":"2025","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":"2025-5060","displayTitle":"Random Forest Regression Models for Estimating Low-Streamflow Statistics at Ungaged Locations in New York, Excluding Long Island","title":"Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island","docAbstract":"Models to estimate low-streamflow statistics at ungaged locations in New York, excluding Long Island and including hydrologically connected basins from bordering States, were developed for the first time by the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation. A total of 224 basin characteristics were developed for 213 unaltered streamgages (locations where the human effects on streamflow were limited), across the following categories: basin geometry, climate, land cover, soils, surficial geology, and other characteristics. The basins with unaltered streamgages were evaluated for potential redundancy, and streamgages in close proximity and with similar drainage areas were flagged and removed from the testing and cross-validation datasets to prevent data leaking from the training dataset to the testing dataset.
Random forest regression models were created by using basin characteristics as predictor variables and by developing a workflow to train, tune, and test the model. Models were developed to estimate the ungaged lowest annual 7-day and 30-day average streamflow that occurs (on average) once every 10 years (7Q10 and 30Q10). The top four basin characteristics used for the 7Q10 and 30Q10 models were drainage area, total stream length, perimeter of the basin, and length of the longest flow path. Results for the 7Q10 and 30Q10 models had coefficients of determination (R2) of 0.796 and 0.853, respectively. The output model results were bias-corrected for ungaged locations across New York and are available within the interactive StreamStats tool.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255060","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stagnitta, T.J., Woda, J.C., and Graziano, A.P., 2025, Random forest regression models for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island: U.S. Geological Survey Scientific Investigations Report 2025–5060, 23 p., https://doi.org/10.3133/sir20255060.","productDescription":"Report: v, 23 p.; 2 Data Releases","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167540","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492989,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://streamstats.usgs.gov/ss/","text":"StreamStats"},{"id":492988,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245055","text":"Scientific Investigations Report 2024–5055","linkHelpText":"- Low-Flow Statistics for Selected Streams in New York, Excluding Long Island"},{"id":492987,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P146MTRS","text":"USGS data release","linkHelpText":"Random forest regression model archive for estimating low-streamflow statistics at ungaged locations in New York, excluding Long Island"},{"id":492986,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NOM6FR","text":"USGS data release","linkHelpText":"Low-flow statistics for New York State, excluding Long Island, computed through March 2022"},{"id":492985,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5060/images/"},{"id":492984,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5060 XML"},{"id":492983,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5060 HTML"},{"id":492982,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5060/sir20255060.pdf","text":"Report","size":"8.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5060 PDF"},{"id":492981,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5060/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"New York excluding Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.8668360740237,\n 40.82174116460561\n ],\n [\n -73.64101370770591,\n 40.97341618058218\n ],\n [\n -73.641675229166,\n 41.369110620239354\n ],\n [\n -73.44442715633755,\n 41.42737341824312\n ],\n [\n -73.23400688811246,\n 42.735562128301694\n ],\n [\n -73.28689303884832,\n 45.063167069767246\n ],\n [\n -74.92263898531354,\n 45.049241271408505\n ],\n [\n -76.59790269295956,\n 44.152600935032865\n ],\n [\n -76.27472597825448,\n 43.63979148650773\n ],\n [\n -76.9594903941915,\n 43.33322253590592\n ],\n [\n -78.50182350844328,\n 43.459456447836146\n ],\n [\n -79.39789785747713,\n 43.19859290777666\n ],\n [\n -78.90328998597391,\n 42.86522759603616\n ],\n [\n -79.82520290314585,\n 42.24436339597355\n ],\n [\n -79.76467975802953,\n 41.964093469327736\n ],\n [\n -75.37056382699097,\n 42.00382656329654\n ],\n [\n -74.98586389700988,\n 41.54435731278656\n ],\n [\n -73.999903714982,\n 41.001270702844295\n ],\n [\n -73.96336292917533,\n 40.82357491793678\n ],\n [\n -73.8668360740237,\n 40.82174116460561\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Background
Physics-based three-dimensional (3D) fire behavior models improve planning for prescribed fire application and wildfire mitigation, but require high spatial resolution 3D fuel models as inputs. While multiple methods and data sources for realistically representing 3D, heterogeneous fuels are available, no unifying framework exists to guide the use of these tools to create 3D fuel models across gradients of vegetation characteristics and data availability. Existing data and methods are most uncertain for mid-level fuels (e.g., shrubs and small trees), due to canopy obstruction of remotely sensed data and a relative lack of modeling efforts. Yet, mid-level fuels are especially important as potential ladder fuels and increasingly common as the dominant fuel in type-converted, post-fire, shrub-dominated landscapes.
Results
Here we introduce the Framework for Representing 3D Fuels (FR3D), a general framework for combining multiple data sources and methods to construct 3D fuel models for forested and unforested landscapes. We then demonstrate FR3D in a case study to build a 3D fuelbed model in a post-fire, shrub-dominated landscape using three new methods for deriving mid-level shrub fuels from: (1) Airborne Laser Scanning (ALS), (2) imputation of Terrestrial Laser Scanning (TLS), and (3) generative modeling of TLS. We compare the resulting fuel models and examine how they affected simulated 3D fire behavior using QUIC-Fire. While each method represented the broad landscape patterning of shrubs, differences in shrub loading, height, and cover highlighted advantages and drawbacks of the different methods. Modeled fire behavior was realistic for all fuel representation methods, but rate of spread and fine fuel consumption was sensitive to the different arrangements of shrubs.
Conclusions
The sensitivity of fire behavior to shrub modeling methods emphasizes the need for fuel models that faithfully represent local fuelbed characteristics and conditions, and highlights the value in testing a range of modeled fuels to understand the potential range of prescribed fire outcomes. FR3D and novel methods of modeling mid-level fuel provide a foundation for tool integration efforts and increased site-specificity of fuel representation for physics-based fire models.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-025-00383-2","usgsCitation":"Tutland, N., Wion, A.P., May, C.J., Hutchings, G.C., Nowak, H., Gattiker, J.R., Hiers, J.K., Linn, R.R., Pokswinski, S.M., and Margolis, E.Q., 2025, Representing 3-dimensional fuels for physics-based fire behavior models: A general framework and case study in a type-converted post-fire shrubfield: Fire Ecology, v. 21, 43, 18 p., https://doi.org/10.1186/s42408-025-00383-2.","productDescription":"43, 18 p.","ipdsId":"IP-176508","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":495062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-025-00383-2","text":"Publisher Index Page"},{"id":494909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Sanchez Canyon, Santa Fe National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.03368207720807,\n 36.27913419354171\n ],\n [\n -107.03368207720807,\n 35.6434439241678\n ],\n [\n -106.074466969809,\n 35.6434439241678\n ],\n [\n -106.074466969809,\n 36.27913419354171\n ],\n [\n -107.03368207720807,\n 36.27913419354171\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationDate":"2025-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Tutland, Niko","contributorId":360588,"corporation":false,"usgs":false,"family":"Tutland","given":"Niko","affiliations":[{"id":86045,"text":"New Mexico Consortium","active":true,"usgs":false}],"preferred":false,"id":947236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wion, Andreas Paul 0000-0002-0701-2843","orcid":"https://orcid.org/0000-0002-0701-2843","contributorId":335166,"corporation":false,"usgs":true,"family":"Wion","given":"Andreas","email":"","middleInitial":"Paul","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"May, Carolina Jasmine 0009-0005-1667-109X","orcid":"https://orcid.org/0009-0005-1667-109X","contributorId":360589,"corporation":false,"usgs":true,"family":"May","given":"Carolina","middleInitial":"Jasmine","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":947238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutchings, Grant C.","contributorId":360590,"corporation":false,"usgs":false,"family":"Hutchings","given":"Grant","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, Hope","contributorId":360591,"corporation":false,"usgs":false,"family":"Nowak","given":"Hope","affiliations":[{"id":7197,"text":"Unaffiliated","active":true,"usgs":false}],"preferred":false,"id":947240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gattiker, James R.","contributorId":360592,"corporation":false,"usgs":false,"family":"Gattiker","given":"James","middleInitial":"R.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":947241,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiers, J. 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The Obed Wild and Scenic River is managed by the National Park Service and covers a protected area of the Obed River headwaters (including four contributing tributaries). The Obed Wild and Scenic River supports a unique ecosystem with eight federally listed species. The National Park Service is responsible for preserving the baseline free-flowing condition of the river and associated outstandingly remarkable values (ORVs). Previous studies have been mostly project-based with differing methods, thus complicating efforts to quantify long-term changes in environmental conditions. This report presents a science plan summarizing (1) ORV conditions, (2) recent results of a decision-support hydrologic model for OBRI, and (3) possible future research priorities. The decision-support model was created to model streamflow conditions and changes in the ORVs since park establishment in 1976 and during three additional time periods. Established baseline conditions could help with management of ORVs not dependent on streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251035","issn":"2331-1258","collaboration":"Prepared in cooperation with the National Park Service","programNote":"Water Availability and Use Science Program","usgsCitation":"Crowley-Ornelas, E.R., Schapansky, R., Blount, T., and Nicholas, N.S., 2025, Decision-support modeling and research priorities for establishing baseline conditions for outstandingly remarkable values, Obed Wild and Scenic River, Tennessee: U.S. Geological Survey Open-File Report 2025–1035, 18 p., https://doi.org/10.3133/ofr20251035.","productDescription":"viii, 18 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-160489","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":493199,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251035/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1035 HTML"},{"id":493198,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1035/ofr20251035.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1035 XML"},{"id":493197,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1035/ofr20251035.pdf","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1035"},{"id":493200,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1035/images"},{"id":493196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1035/coverthb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Obed Wild and Scenic River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.95125744767695,\n 36.150994941624745\n ],\n [\n -84.95125744767695,\n 36.049079144332424\n ],\n [\n -84.64800767968804,\n 36.049079144332424\n ],\n [\n -84.64800767968804,\n 36.150994941624745\n ],\n [\n -84.95125744767695,\n 36.150994941624745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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Contact Us- USGS Publications Warehouse
","tableOfContents":"Population growth and recurring droughts in the Black Hills region raised interest in water resources and future availability. The Black Hills hydrology study (BHHS) was initiated in the early 1990s to address questions regarding water resources. Since completion of the BHHS in the early 2000s, the population of the Black Hills region increased by about 39 percent, which has renewed interest in water demand and availability in the Black Hills. The U.S. Geological Survey, in cooperation with the Western Dakota Regional Water System, completed a study to update hydrologic budgets from the BHHS for six of the most used aquifers in the Black Hills. Water availability was determined by comparing results from hydrologic budgets to modern well withdrawals (2003–22) and water rights information. Key updates to the BHHS budgets included adding available data from 1999 to 2022 and determining hydrologic budgets for six aquifers in nine smaller areas (called “subareas”).
Inflows for the hydrologic budget included recharge from precipitation and streamflow losses to aquifers. Total mean annual recharge for the six aquifers in the study area was estimated at 278,900 acre-feet, with 205,100 acre-feet from precipitation recharge and 73,800 acre-feet from streamflow recharge. Mean annual precipitation recharge for the Madison and Minnelusa aquifers together accounted for 76 percent of the total mean annual precipitation recharge, with the Madison aquifer contributing 57,000 acre-feet and the Minnelusa aquifer contributing 98,100 acre-feet. Outflow components estimated for the hydrologic budget include artesian springflow and well withdrawals. Total mean annual artesian springflow in the study area was estimated as 166,100 acre-feet for the combined Madison and Minnelusa aquifers. Mean total annual well withdrawals for 2003–22 in the study area were about 50,000 acre-feet. No increased well withdrawal patterns corresponding to population increases were observed between 2003 and 2022.
Water availability was determined by comparing total annual appropriations and mean and maximum annual well withdrawals for 2003–22 to mean annual recharge for 1931–2022 for each aquifer in subareas 1–9. Modern well withdrawals (mean and maximum for 2003–22) exceeded mean annual recharge for only the Deadwood and Inyan Kara aquifers in subareas 9 and 4, respectively. Additionally, total annual appropriations did not exceed mean annual recharge in most subareas, except most notably in subarea 4 (Rapid City area) where appropriations exceeded recharge for the Madison, Minnelusa, and Inyan Kara aquifers. Total annual appropriations also exceeded mean annual recharge for the Inyan Kara aquifer in subareas 3 and 5. In addition to recharge, water availability includes the water stored in pore spaces of aquifer materials. Estimates of total volume of recoverable water in storage were updated as part of this study to include the portion of aquifers in Wyoming, which were omitted during the BHHS. In total, the estimated total amount of recoverable water in storage in the study area was 356.9 million acre-feet for six major aquifers in the Black Hills area of South Dakota and Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255067","collaboration":"Prepared in cooperation with the Western Dakota Regional Water System","usgsCitation":"Medler, C.J., Anderson, T.M., and Eldridge, W.G., 2025, Hydrologic budgets and water availability of six bedrock aquifers in the Black Hills area, South Dakota and Wyoming, 1931–2022: U.S. Geological Survey Scientific Investigations Report 2025–5067, 87 p., https://doi.org/10.3133/sir20255067.","productDescription":"Report: ix, 87 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-169475","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":493206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QWKUKP","text":"USGS data release","linkHelpText":"Datasets used in constructing hydrologic budgets for six bedrock aquifers in the Black Hills area of South Dakota and Wyoming, 1931–2022"},{"id":493201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5067/coverthb.jpg"},{"id":493202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.pdf","text":"Report","size":"27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Sir 2025–5067"},{"id":493203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5067/sir20255067.XML"},{"id":493204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5067/images/"},{"id":493205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255067/full"}],"country":"United States","state":"South Dakota, Wyoming","otherGeospatial":"Black Hills area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.5,\n 44.75\n ],\n [\n -104.5,\n 43.25\n ],\n [\n -103,\n 43.25\n ],\n [\n -103,\n 44.75\n ],\n [\n -104.5,\n 44.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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The State of Tennessee has an area of approximately 42,100 square miles and includes six physiographic regions: Blue Ridge, Valley and Ridge, Appalachian Plateaus, Highland Rim, Nashville Basin, and the Gulf Coastal Plains. Up-to-date elevation data support key activities across the State, such as economic development, infrastructure and construction management, agriculture and precision farming, forest resources management, natural resources conservation, flood risk management, emergency management, and urban and regional planning. The State experiences frequent landslides affecting major roadways. High-resolution elevation data can help identify potential landslide-prone areas. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
The 3D Elevation Program (3DEP; refer to sidebar) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at qual-ity level 2 or better to meet the many needs of the Nation and Tennessee. The status of available and in-progress 3DEP baseline lidar data in Tennessee is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $6.32 million in new benefits annually to the State. The top 10 Tennessee business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.
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U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Objective
Yellow Perch Perca flavescens was first encountered in Lake Cascade, Idaho, in 1957. Since its introduction, the abundance of Yellow Perch in Lake Cascade has been highly variable. Historically, declines in Yellow Perch abundance were attributed to predation by Northern Pikeminnow Ptychocheilus oregonensis. In the 1990s, Smallmouth Bass Micropterus dolomieu became established in Lake Cascade. Although Yellow Perch abundance has been high and currently supports a world-class fishery, the Idaho Department of Fish and Game has documented poor Yellow Perch recruitment in recent years, concerning regional managers. An investigation into the food habits of Yellow Perch, Smallmouth Bass, and Northern Pikeminnow was conducted to better understand the importance of juvenile Yellow Perch to predator diets.
Methods
In Lake Cascade, gill nets were deployed monthly from April 2022 to May 2023. Ageing structures and stomach contents were removed from Yellow Perch, Smallmouth Bass, and Northern Pikeminnow. Diet composition was quantified and then summarized by species, cohort, and season. Bioenergetics modeling, coupled with estimates of predator abundance, was used to evaluate overall consumption of Yellow Perch.
Results
In total, stomach contents were extracted from 1,099 Yellow Perch, 440 Smallmouth Bass, and 980 Northern Pikeminnow. Food habits varied by season and predator length, but Yellow Perch was a primary prey item of all study species. The energetic contribution of Yellow Perch to Yellow Perch varied from 10% to 80% across seasons. Smallmouth Bass consumed fewer Yellow Perch than either Yellow Perch or Northern Pikeminnow. The highest average per capita consumption of Yellow Perch (i.e., across seasons) was observed for 500–600-mm Northern Pikeminnow (five Yellow Perch/Northern Pikeminnow). Energetic contribution of Yellow Perch to Northern Pikeminnow diets was highest in spring 2023, yet Northern Pikeminnow consumed the highest average number of Yellow Perch in July. Bioenergetics modeling estimated that per-capita annual consumption of Yellow Perch was 6.0 kg for Yellow Perch, 3.4 kg for Smallmouth Bass, and 9.9 kg for Northern Pikeminnow. In total, estimated consumption of all study species was as high as 37% of the estimated total number age-0 Yellow Perch produced.
Conclusions
Yellow Perch was an important prey item for all three species, particularly Yellow Perch and Northern Pikeminnow. Results from this study provide important information on the predation of Yellow Perch in Lake Cascade. Further, this work will bolster a growing body of research on the food habits of and interactions between native and nonnative predators in western reservoir systems.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/najfmt/vqaf062","usgsCitation":"Marciniak, B., Thomas, M., Messner, J., Corsi, M., Quist, M., 2025, Food habits of Yellow Perch, Smallmouth Bass, and Northern Pikeminnow in Lake Cascade, Idaho: North American Journal of Fisheries Management, v. 45, no. 5, p. 795-812, https://doi.org/10.1093/najfmt/vqaf062.","productDescription":"18 p.","startPage":"795","endPage":"812","ipdsId":"IP-173300","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Cascade","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.20393006754492,\n 44.763754267268894\n ],\n [\n -116.20393006754492,\n 44.4319010241793\n ],\n [\n -115.97067657023298,\n 44.4319010241793\n ],\n [\n -115.97067657023298,\n 44.763754267268894\n ],\n [\n -116.20393006754492,\n 44.763754267268894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","issue":"5","noUsgsAuthors":false,"plainLanguageSummary":"The participants of the 12th International Atomic Energy Agency (IAEA) meeting on stable isotope reference materials reached a consensus, acknowledging the existence and use of two carbon isotope delta scales: the VPDB (Vienna Peedee belemnite) scale and the VPDB-LSVEC (LSVEC - lithium carbonate prepared by H. J. Svec). Conversion models between the two scales can be established and used but introduce uncertainty. A format for isotope delta scale definition was agreed upon and was used to define the two carbon isotope delta scales and the two main oxygen isotope delta scales, VSMOW-SLAP (Vienna Standard Mean Ocean Water–Standard Light Antarctic Precipitation) and VPDB. Confirmation or identification of a second-scale–defining point is still necessary for the nitrogen and sulfur isotope delta scales.
Efforts are encouraged to improve consistency among laboratories in the isotopic analysis of “non-exchangeable hydrogen” in bulk organic materials and oxygen in carbonates using the phosphoric acid reaction. Additional topics discussed include (1) need for improvement in reference materials for accurate greenhouse gas isotopic analyses; (2) reference materials under production by the IAEA, the US Geological Survey (USGS), and the US National Institute of Standards and Technology (NIST); (3) methods for value and uncertainty assignment of reference materials; and (4) calculation of carbon-13 isotope delta and oxygen-18 isotope delta of CO2 measured by dual-inlet isotope ratio mass spectrometry.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.10018","usgsCitation":"Camin, F., Besic, D., Brewer, P.J., Allison, C.E., Coplen, T.B., Dunn, P.J., Gehre, M., Gröning, M., Meijer, H.A., Hélie, J., Iacumin, P., Kraft, R., Krajnc, B., Kümmel, S., Lee, S., Meija, J., Mester, Z., Mohn, J., Moossen, H., Qi, H., Skrzypek, G., Sperlich, P., Viallon, J., Wassenaar, L.I., and Wielgosz, R.I., 2025, Stable isotope reference materials and scale definitions – Outcomes of the 2024 IAEA experts meeting: Rapid Communications in Mass Spectrometry, v. 39, no. 14, e10018, 11 p., https://doi.org/10.1002/rcm.10018.","productDescription":"e10018, 11 p.","ipdsId":"IP-167836","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":496933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rcm.10018","text":"Publisher Index 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Certain mineral-derived elements act as catalysts and reaction intermediaries, facilitating early-stage hydrocarbon formation in potential source rocks. This study investigated the role of sulfur contributed from pyrite as an accelerant in thermal reaction, focusing on its effects on early maturation and consequent hydrocarbon generation from gilsonite (low-sulfur solid petroleum). Hydrous pyrolysis (HP) experiments were conducted on mixtures of gilsonite and pyrite in varying ratios (1:0.1, 1:0.5, 1:1, 1:2, and 1:10 w/w gilsonite:pyrite) at 320, 350, and 370 °C for 72 h. Untreated and thermally altered residues were analyzed using solid bitumen reflectance (BRo, %), total organic carbon (TOC) content, programmed temperature pyrolysis, scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) to evaluate the potential accelerant role of pyritic sulfur in hydrocarbon formation. The results show HP residues at 320 and 350 °C with greater pyrite concentrations had higher BRo, while reflectance values were similar in the 370 °C residues, regardless of pyrite concentration, suggesting enhanced reaction at lower thermal conditions. Increasing pyrite content systematically decreased hydrogen index (HI) values while increasing the transformation ratio (TR) and production index (PI), indicating enhanced conversion of organic matter to hydrocarbons with increasing pyrite concentrations. Gas yields increased with pyrite addition, particularly at 350 °C, confirming secondary cracking effects. However, gas production stabilized or declined at higher pyrite loadings (1:10), suggesting alternative reaction pathways such as coke formation. Our data indicate the presence of pyrite lowers the activation energy for thermal cracking, shifting peak experimental hydrocarbon generation temperatures downward by 20–30 °C, with the most pronounced accelerant effects observed at moderate pyrite concentrations (1:0.5 and 1:1). The thermodynamic framework reveals that pyrite stability is influenced by experimental conditions, with pyrrhotite formation favored in the presence of gilsonite due to reduced oxygen fugacity. Pyrite transformation to pyrrhotite, as observed through XRD, SEM-EDS, and predicted by thermodynamic data, further supports the accelerant role of S, as pyrrhotite exhibits a higher hydrogen transfer potential, promoting early oil generation. These findings highlight the importance of pyrite in modulating hydrocarbon generation pathways in organic-rich systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaap.2025.107307","usgsCitation":"Adsul, T., Hackley, P.C., Hatcherian, J.J., McAleer, R.J., Green, C.J., Burnham, A.K., Ghosh, S., Werne, J.P., and Varma, A.K., 2025, Reaction kinetics and accelerant effects of sulfides in early mature hydrocarbon generation using hydrous pyrolysis: Journal of Analytical and Applied Pyrolysis, v. 192, 107307, https://doi.org/10.1016/j.jaap.2025.107307.","productDescription":"107307","ipdsId":"IP-176736","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":496496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"192","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adsul, Tushar","contributorId":330815,"corporation":false,"usgs":false,"family":"Adsul","given":"Tushar","email":"","affiliations":[{"id":79028,"text":"Indian Institute of Technology (Indian School of Mines), India","active":true,"usgs":false}],"preferred":false,"id":949842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":949843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":949844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":949845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Green, Carlin J. 0000-0002-6557-6268 cjgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6557-6268","contributorId":193013,"corporation":false,"usgs":true,"family":"Green","given":"Carlin","email":"cjgreen@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burnham, Alan K.","contributorId":362030,"corporation":false,"usgs":false,"family":"Burnham","given":"Alan","middleInitial":"K.","affiliations":[{"id":86422,"text":"Stratify//MH Chew Associates, 7633 Southfront Rd #170, Livermore, California 94551, USA","active":true,"usgs":false}],"preferred":false,"id":949847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ghosh, Santanu","contributorId":330824,"corporation":false,"usgs":false,"family":"Ghosh","given":"Santanu","email":"","affiliations":[{"id":79037,"text":"Mizoram University, India","active":true,"usgs":false}],"preferred":false,"id":949848,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Werne, Josef P.","contributorId":362031,"corporation":false,"usgs":false,"family":"Werne","given":"Josef","middleInitial":"P.","affiliations":[{"id":86423,"text":"Organic and Stable Isotope Biogeochemistry Laboratory, Department of Geology and Environmental Science, University of Pittsburgh, 200 Space Research Coordination Center, 4107 O’Hara Street, Pittsburgh, PA 15260, USA","active":true,"usgs":false}],"preferred":false,"id":949849,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Varma, Atul K.","contributorId":290219,"corporation":false,"usgs":false,"family":"Varma","given":"Atul","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":949850,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70269987,"text":"70269987 - 2025 - Climate and land use drivers of freshwater fish biodiversity in the northeastern United States","interactions":[],"lastModifiedDate":"2025-08-07T15:19:03.836084","indexId":"70269987","displayToPublicDate":"2025-07-28T10:10:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Climate and land use drivers of freshwater fish biodiversity in the northeastern United States","docAbstract":"Freshwater habitats can sustain high biodiversity, but habitat degradation, species invasion, and overexploitation have imperiled freshwater species. The multiple threats to freshwater habitats and changing stream characteristics due to climate change make it challenging to identify the drivers of fish vulnerability, especially given that the importance of drivers may vary by the biodiversity endpoint. The goals of this study were to 1) describe freshwater (lotic) fish biodiversity across northeastern states in the United States, and 2) identify geographic, climate, and land use drivers of fish biodiversity, toward improving freshwater fish conservation. We predicted habitat suitability for 53 fish species using survey data and corresponding geographic, climate, and land use data. After model fitting, we grouped species using a traditional approach based on traits and a new approach using modeled streamflow and stream temperature to create clusters. We found that climate and land use vulnerable groups did not always have similar geographic patterns or relationships to the climate or land use variables, suggesting that biota vulnerable to changing conditions may be overlooked by reliance on predefined guilds to set conservation goals. Biodiversity groups that were vulnerable to climate variables were also related to land use variables that can be used to identify restoration opportunities. This approach may be useful for managers interested in holistic freshwater management under changing stream conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2025.111337","usgsCitation":"Rogers, J.B., DiRenzo, G.V., Quiñones, R.M., Richards, T., and Roy, A.H., 2025, Climate and land use drivers of freshwater fish biodiversity in the northeastern United States: Biological Conservation, v. 310, 111337, 18 p., https://doi.org/10.1016/j.biocon.2025.111337.","productDescription":"111337, 18 p.","ipdsId":"IP-172448","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, 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This premise served as motivation to collect sediment cores during the summer of 2022 at four sites on the Canadian continental shelf and Alaskan upper continental slope to reconstruct changes in the marine and terrestrial environments to provide a comprehensive picture of the ocean environment during the preindustrial period before anthropogenic influences. We dated the sediments based on the 137Cs radioactivity of bulk sediments and the 14C concentrations of mollusk shells. The 137Cs radioactivity shows a distinct onset corresponding to 1950 Common Era (CE) and the most prominent peak corresponding to 1963 CE. Multiple peaks appeared above the most prominent one, coinciding with nuclear power plant accidents in 1986 and 2011. Inventories of excess 210Pb in all cores exceed the estimated supply of excess 210Pb from atmospheric deposition, likely due to the scavenging supply of excess 210Pb. By comparing 137Cs and radiocarbon conventional ages, we estimated the local radiocarbon reservoir age value of each site. Using these local radiocarbon reservoir age and the conventional ages of mollusk shell samples, we established the age-depth models by the Bayesian method. The optimal ΔR values were 598, 511, 65, and –60 years at the MT1, MT2, BC2, and BC2-2 sites, respectively. The cores consist of clayey silts continuously deposited with uniquely high sedimentation rates of 0.17 to 0.74 cm y−1. Variation in the Ca/Ti ratio indicates ~ 20, ~ 30, 50–60, 100–125, and 300-year cycles, likely attributed to the variation in the Aleutian Low that controls the Bering Strait inflow of Pacific waters influencing our core sites. These sediments will be used for further high-resolution, multi-proxy studies with forthcoming results.
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The Neoproterozoic Timanian, Paleozoic Caledonian and Uralian, and late Mesozoic Verkhoyansk–Kolyma, Chukotkan and Brookian orogenies formed several large fold-and-thrust belts (FTBs). The FTBs are exposed across vast areas of continents and continue offshore to form a complex tectonic basement for thick sedimentary basins, playing an important role in the history of accumulation and deformation of younger unmetamorphosed sedimentary successions that are the subject of this volume. Recognition of the importance of FTBs in the Arctic geological history and their role as a controlling factor of development of Arctic sedimentary basins resulted in this chapter, in which we review the current state of knowledge about Arctic FTBs and highlight questions that remain to be addressed. Enclosure D, a map showing boundaries of the FTB and their internal first-order structural fabric, is a part of the overview.
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Understanding how U is taken up by organisms can aid in evaluating the potential for bioaccumulation and toxicity. Although the importance of aqueous geochemical speciation is well recognized for U bioavailability after dissolved exposures, far less is known about the processes controlling U bioavailability after dietary exposures. This study characterizes the biogeochemical drivers of dietary U uptake in the freshwater snail Lymnaea stagnalis in laboratory experiments. Solids tested included benthic diatoms pre-exposed to dissolved U(VI), soils from contaminated U mine sites, and colloidal hydrous ferric oxide (HFO) synthesized in the presence of dissolved U(VI) or with U complexed by natural organic matter (NOM). Results showed that U was bioavailable from all solids. Uranium assimilation efficiency (AE), a proxy for dietary U bioavailability, varied among solids. AE was lowest for the U-contaminated soils (25 ± 17%) and highest for the U-laden diatoms (71 ± 13%). AE varied slightly among HFO preparations, suggesting modest influences of NOM and iron on U bioavailability. Increases in dietary U exposures reduced feeding rates, and the extent of feeding inhibition appeared inversely related to U bioavailability. The high U assimilation and range of bioavailability have implications for toxicity risks inferred without considering dietary uptake.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5c05140","usgsCitation":"Croteau, M.N., Fuller, C.C., Cain, D.J., and Campbell, K.M., 2025, Dietary bioavailability of uranium to a model freshwater invertebrate: Environmental Science and Technology, v. 59, no. 31, p. 16641-16651, https://doi.org/10.1021/acs.est.5c05140.","productDescription":"11 p.","startPage":"16641","endPage":"16651","ipdsId":"IP-172293","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":493091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"31","noUsgsAuthors":false,"publicationDate":"2025-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":944277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":944278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, Daniel J. 0000-0002-3443-0493 djcain@usgs.gov","orcid":"https://orcid.org/0000-0002-3443-0493","contributorId":1784,"corporation":false,"usgs":true,"family":"Cain","given":"Daniel","email":"djcain@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":944279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":944280,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269556,"text":"ofr20251022 - 2025 - Contributions of erosion, deposition, and human activities to a change in sand storage in the bed of San Francisco Bay, California, 1980s to 2010s","interactions":[],"lastModifiedDate":"2026-02-03T14:38:01.331405","indexId":"ofr20251022","displayToPublicDate":"2025-07-28T08:39:19","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":"2025-1022","displayTitle":"Contributions of Erosion, Deposition, and Human Activities to a Change in Sand Storage in the Bed of San Francisco Bay, California, 1980s to 2010s","title":"Contributions of erosion, deposition, and human activities to a change in sand storage in the bed of San Francisco Bay, California, 1980s to 2010s","docAbstract":"This study by the U.S. Geological Survey (USGS) provides estimates of the change in sand storage in bed sediments from the 1980s to 2010s in the San Francisco Bay area, California. The study is part of a larger project called “Research to Understand Impacts of Bay Sand Mining on Sand Transport in San Francisco Bay and the Outer Coast” that has the goal of providing information for the California Coastal Conservancy to inform decision making regarding sand mining activities. Information from this study will contribute to the sand budget for the San Francisco Bay system by accounting for sand made available by erosion of bay sediment and sequestered by deposition in the bay.
Sediment budgets for estuaries typically account for change in sediment storage in the bed without discriminating for sediment size. However, the physics of mud and sand erosion, deposition, and transport differ. Sediment budgets that treat mud and sand separately give a more complete understanding of the system, including how human activities related to sediment size, such as sand mining, affect the system. We used bathymetric change analysis in combination with a three-dimensional model to generate estimates of net change in sand storage within the San Francisco Bay floor. We document sediment volume change from a 1980s bathymetric surface to a 2010s bathymetric surface, in combination with information on the sand content of the bed sediment derived from sediment cores and surface samples from six different sediment studies, to estimate the net change in sand volume in the bed of San Francisco Bay. This analysis includes areas heavily affected by human activities (such as sand mining, dredging, and sediment disposal) as well as regions more representative of natural transport processes.
Overall, the sediment bed of San Francisco Bay is losing sand. Across the total area surveyed in San Francisco Bay, including areas affected by natural processes, oyster shell beds, and human activities, a net loss of about 17 million cubic meters (Mm3) of sand from the sediment bed occurred from the 1980s to 2010s, at a rate of about 0.8 Mm3 per year. For the period of this study, sand loss from bed level changes in permitted sand-lease mining areas (about 11 Mm3) accounts for about two-thirds of the total sand loss throughout the study area. It is important to consider potential uncertainty bounds when interpreting these findings. A key part of the report is an assessment of the uncertainties in our estimates of sand volumes. We estimate that variability in modeled sand content values of Bay floor sediments could result in an uncertainty of approximately 25 percent of the net sand volume change. Even larger uncertainty amounts may be associated with uncertainty in the systematic errors in the bathymetric surveys. Further refining estimates of uncertainty in bathymetric change is important in guiding the use of this study. The results presented here can fill a critical gap that may enable the creation of the first comprehensive sand budget of San Francisco Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251022","collaboration":"Prepared in cooperation with the San Francisco Estuary Institute","usgsCitation":"Fregoso, T.A., Jaffe, B.E., Foxgrover, A.C., Woodrow, D.L., Kharrazi, B., and Orzech, K., 2025, Contributions of erosion, deposition, and human activities to a change in sand storage in the bed of San Francisco Bay, California, 1980s to 2010s: U.S. Geological Survey Open-File Report 2025–1022, 30 p., https://doi.org/10.3133/ofr20251022.","productDescription":"Report: vii, 30 p.; 4 Data Releases","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-154253","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":492954,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XG9PB0","text":"USGS data release","description":"Woodrow, D.L., Chin, J.L., Wong, F.L., Fregoso, T.A., Jaffe, B.E., 2017, Gravity cores from San Pablo Bay and Carquinez Strait, San Francisco Bay, California: U.S. Geological Survey data release, https://doi.org/10.5066/F7XG9PB0.","linkHelpText":"Gravity cores from San Pablo Bay and Carquinez Strait, San Francisco Bay, California"},{"id":492953,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BDEB3K","text":"USGS data release","description":"Takesue, R.K., McGann, M.L., Lorenson, T.D., and Watt, J.T., 2021, Geophysical properties, geochronologic, and geochemical data of sediment cores collected from San Pablo Bay, California, October 17–20, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/P9BDEB3K.","linkHelpText":"Geophysical properties, geochronologic, and geochemical data of sediment cores collected from San Pablo Bay, California, October 17–20, 2016"},{"id":492950,"rank":5,"type":{"id":34,"text":"Image 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2885 Mission St.
Santa Cruz, CA 95060
Recreation demand analysis has relied on mail and internet surveys to collect information on individual recreators. However, conducting these surveys is costly and time-consuming. Alternative sources that report aggregate visitation may go unused due to a lack of information about trip starting points. We set up and solve a system of equations that predict reservoir visits and the home locations of recreational anglers. Using mode-level effort statistics from Nebraska creel surveys, we separate the effects of travel cost and site attributes between bank and boat anglers, which allows us to measure heterogenous values for public reservoir access.
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\"}}]}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Boehm, Luke","contributorId":360175,"corporation":false,"usgs":false,"family":"Boehm","given":"Luke","affiliations":[{"id":33535,"text":"Loyola University Chicago","active":true,"usgs":false}],"preferred":false,"id":946877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Melstrom, Richard T.","contributorId":360179,"corporation":false,"usgs":false,"family":"Melstrom","given":"Richard","middleInitial":"T.","affiliations":[{"id":33535,"text":"Loyola University Chicago","active":true,"usgs":false}],"preferred":false,"id":946878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946879,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269752,"text":"70269752 - 2025 - The conundrum of taxonomic uniformitarianism in planktic foraminifera","interactions":[],"lastModifiedDate":"2025-07-31T14:23:30.840961","indexId":"70269752","displayToPublicDate":"2025-07-27T09:18:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2998,"text":"Palaeontology","active":true,"publicationSubtype":{"id":10}},"title":"The conundrum of taxonomic uniformitarianism in planktic foraminifera","docAbstract":"Planktic foraminiferal species distributions in the modern ocean track environmental features like sea surface temperature (SST). Species shift their distributions as the marine environment changes, providing an analogue for past behaviour. Stationarity of species' ecological tolerances is therefore a first-order assumption of all palaeoenvironmental reconstructions based upon modern analogue methods. In this paper we test the hypothesis that planktic foraminifer species temperature preferences did not change between the Late Pliocene and present, using a dataset which contains faunal abundance data and alkenone palaeotemperature data from the same samples. Our dataset includes 463 samples from 29 localities. Pliocene relative abundances of four taxa (Globigerina bulloides, Globigerinita glutinata, Neogloboquadrina pachyderma and Neogloboquadrina incompta) are compared to SST estimates of the same age derived using the alkenone unsaturation ratio (UK'37) palaeothermometer. Core-top abundances of the same taxa were compared to pre-industrial SST. Our Pliocene data are generally concordant with previous work. Pliocene SST responses and those of the pre-industrial are similar, supporting the hypothesis that temperature preferences of planktic foraminifera have been relatively stable since the Late Pliocene. This documentation of stationarity of planktic foraminiferal species' temperature tolerances is helpful in identifying situations where environmental variables other than temperature (e.g. salinity or productivity) exhibit a first-order control on faunal diversity. Our results support the notion of taxonomic uniformitarianism and therefore provide additional confidence in using planktic foraminifera to evaluate both regional and global climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/pala.70018","usgsCitation":"Dowsett, H., Robinson, M.M., Foley, K.M., and Spivey, W., 2025, The conundrum of taxonomic uniformitarianism in planktic foraminifera: Palaeontology, v. 68, no. 4, e70018, 10 p., https://doi.org/10.1111/pala.70018.","productDescription":"e70018, 10 p.","ipdsId":"IP-172361","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":493297,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/pala.70018","text":"Publisher Index Page"},{"id":493238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":261665,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":944564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":332062,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":944565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":944566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":944567,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270212,"text":"70270212 - 2025 - Real-time oil spill concentration assessment through fluorescence imaging and deep learning","interactions":[],"lastModifiedDate":"2025-08-18T15:29:16.381499","indexId":"70270212","displayToPublicDate":"2025-07-27T09:15:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Real-time oil spill concentration assessment through fluorescence imaging and deep learning","docAbstract":"Oil spills may pose severe ecological and socioeconomic threats, necessitating rapid and accurate environmental assessment. Traditional assessment methods used to determine the extent of a spill including gas chromatography-mass spectrometry, satellite imaging, and visual surveys, are often time-consuming, expensive, and limited by weather conditions or sampling constraints. Furthermore, these methods frequently struggle to provide real-time data crucial for prompt decision-making during spill emergencies. This study addresses these limitations by combining fluorescence imaging, deep learning, a mobile application, and a data management system for automated and real-time oil spill assessment. Our approach leverages a convolutional neural network architecture for feature extraction coupled with a custom regression model, trained and evaluated on a self-curated comprehensive dataset of 1,530 fluorescence images from two distinct oil types, a napthalenic crude oil and an aromatic-napthalenic crude oil, at concentrations ranging from 0 to 500 mg/L. The proposed approach demonstrates superior performance compared to both traditional machine learning models and more complex deep learning architectures, achieving an R² score of 0.9958 and RMSE of 9.28. The application enables rapid, cost-effective field measurements with robust data tracking and analysis capabilities. This research advances oil spill monitoring technology with a scalable solution that balances accuracy, speed, and accessibility for real-time environmental assessment and emergency response.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2025.139374","usgsCitation":"Poudel, B., Xie, J., Guo, C., Watt, O., Pulster, E.L., Patel, R.J., Steevens, J.A., and Xu, D., 2025, Real-time oil spill concentration assessment through fluorescence imaging and deep learning: Journal of Hazardous Materials, v. 496, 139374, 10 p., https://doi.org/10.1016/j.jhazmat.2025.139374.","productDescription":"139374, 10 p.","ipdsId":"IP-177565","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":494021,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"496","noUsgsAuthors":false,"publicationDate":"2025-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Poudel, Biplab","contributorId":359506,"corporation":false,"usgs":false,"family":"Poudel","given":"Biplab","affiliations":[{"id":39687,"text":"University of Missouri, Columbia","active":true,"usgs":false}],"preferred":false,"id":945730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Jiacheng","contributorId":331598,"corporation":false,"usgs":false,"family":"Xie","given":"Jiacheng","email":"","affiliations":[],"preferred":false,"id":945731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guo, Congyu","contributorId":359509,"corporation":false,"usgs":false,"family":"Guo","given":"Congyu","affiliations":[{"id":39687,"text":"University of Missouri, Columbia","active":true,"usgs":false}],"preferred":false,"id":945732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Watt, Olivia","contributorId":359518,"corporation":false,"usgs":false,"family":"Watt","given":"Olivia","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":945733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pulster, Erin L. 0000-0003-4574-8613","orcid":"https://orcid.org/0000-0003-4574-8613","contributorId":300266,"corporation":false,"usgs":true,"family":"Pulster","given":"Erin","email":"","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":945734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Patel, Rishi J.","contributorId":359520,"corporation":false,"usgs":false,"family":"Patel","given":"Rishi","middleInitial":"J.","affiliations":[{"id":16806,"text":"Missouri State University","active":true,"usgs":false}],"preferred":false,"id":945735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":945736,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Xu, Dong","contributorId":305418,"corporation":false,"usgs":false,"family":"Xu","given":"Dong","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":945737,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70269604,"text":"70269604 - 2025 - Reflections on a trio of North American earthquakes in 1925","interactions":[],"lastModifiedDate":"2025-12-15T16:24:36.85827","indexId":"70269604","displayToPublicDate":"2025-07-25T08:49:44","publicationYear":"2025","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":"Reflections on a trio of North American earthquakes in 1925","docAbstract":"In 1925, three moderately large damaging earthquakes occurred in North America over four months: the 28 February (local time; LT) M 6.2 Charlevoix, 27 June (LT) M 6.6 Montana, and 29 June M 6.5 Santa Barbara earthquakes. The centennial anniversaries of these events motivated this retrospective consideration focused on the ground motions generated by the three events, including a reconsideration of early intensity assignments for the Montana earthquake. At the time, these three earthquakes appeared to support the arguments of some geologists who downplayed the severity of seismic hazard in southern California relative to other parts of the country. Some of the arguments advanced at that time, for example that Los Angeles “has the least to fear from ‘Acts of God’ of any city under the American flag,” (Hill, 1928) sound naïve if not laughable now, but a comparison of well‐constrained shaking distributions for the three earthquakes reveals the dramatic difference in wave propagation efficiency in western versus eastern North America (ENAM), which leads to moderate ENAM events being felt to much larger distances. At M 6.2, the 1925 Charlevoix earthquake was a notably large event in ENAM. This earthquake was the largest event in eastern Canada since 1870 and caused damage in the epicentral region in addition to towns as far away as 200 km, with felt shaking extending over 1000 km. In contrast, felt shaking from the Santa Barbara earthquake barely extended beyond ∼200 km. Compiling published intensity distributions for larger ENAM earthquakes, we show that perceptible earthquake shaking is not uncommon in ENAM over century time scales, but experience with weakly felt shaking may incline people to downplay potential earthquake risk.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220250149","usgsCitation":"Hough, S., Lamontagne, M., Ebel, J.E., and Baise, L., 2025, Reflections on a trio of North American earthquakes in 1925: Seismological Research Letters, v. 97, no. 1, p. 548-563, https://doi.org/10.1785/0220250149.","productDescription":"16 p.","startPage":"548","endPage":"563","ipdsId":"IP-177706","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":492992,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493792,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0220250149","text":"Publisher Index Page"}],"country":"Canada, United States","state":"California, Montana, Quebec","city":"Charlevoix, Santa Barabara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -59.53926709002974,\n 53.87081636094325\n ],\n [\n -83.28222707125245,\n 53.87081636094325\n ],\n [\n -83.28222707125245,\n 38.33111191824375\n ],\n [\n -59.53926709002974,\n 38.33111191824375\n ],\n [\n -59.53926709002974,\n 53.87081636094325\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.68351578110878,\n 50.1420000300991\n ],\n [\n -118.68351578110878,\n 41.07790483235203\n ],\n [\n -105.88082499225033,\n 41.07790483235203\n ],\n [\n -105.88082499225033,\n 50.1420000300991\n ],\n [\n -118.68351578110878,\n 50.1420000300991\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.32391559009457,\n 34.119859058363375\n ],\n [\n -116.7888148930596,\n 34.119859058363375\n ],\n [\n -116.7888148930596,\n 37.37154662231801\n ],\n [\n -122.49989970320146,\n 37.667148547520824\n ],\n [\n -120.32391559009457,\n 34.119859058363375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":350979,"corporation":false,"usgs":true,"family":"Hough","given":"Susan E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":944166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamontagne, Maurice","contributorId":358790,"corporation":false,"usgs":false,"family":"Lamontagne","given":"Maurice","affiliations":[{"id":85683,"text":"Canadian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":944167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebel, John E.","contributorId":198671,"corporation":false,"usgs":false,"family":"Ebel","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":944168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baise, L.","contributorId":358791,"corporation":false,"usgs":false,"family":"Baise","given":"L.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":944169,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}