In May 2021, the U.S. Geological Survey’s Grand Canyon Monitoring and Research Center acquired airborne multispectral high-resolution data for the Colorado River in the Grand Canyon, Arizona. The image data, which consist of four spectral bands (red, band 1; green, band 2; blue, band 3; and near infrared, band 4) with a ground resolution of 20 centimeters, are available as 16-bit unsigned-integer GeoTIFF files in Sankey and others (2024) (available online at https://doi.org/10.5066/P9BBGN6G). The image files are projected in the State Plane Coordinate System, using the central Arizona zone (202) with the North American Datum of 1983 National Adjustment of 2011. The assessed spatial accuracy for these data is based on 47 ground-control points that were independent from the ground-control points used by the contractor for aerotriangulation and is reported at the 95-percent confidence level as 0.514 meter (m) and a root mean square error of 0.297 m. The intended uses of this dataset are primarily in support of scientific research and monitoring applications. Examples of these applications include high-resolution spatial and temporal change detection of the river channel, geomorphic landforms, riparian vegetation, and backwater and nearshore habitat, as well as other ecosystem-wide mapping. These imagery data also serve as reference material for field science mission planning, as base data for field data collection including community science activities, and as a highly detailed guide for technical boat operation during science activities such as reconnaissance for nighttime missions and navigating rapids during low flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1202","collaboration":"Prepared in cooperation with Northern Arizona University","usgsCitation":"Sankey, J.B., Bransky, N.D., Pigue, L.M., Kohl, K.A., and Gushue, T.M., 2025, Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2021 airborne image acquisition: U.S. Geological Survey Data Report 1202, https://doi.org/10.3133/dr1202.","productDescription":"Report: HTML Document; Data Release","onlineOnly":"Y","ipdsId":"IP-162668","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":483089,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1202/dr1202.XML"},{"id":483585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":483091,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1202/full"},{"id":483090,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1202/images"},{"id":483526,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1027","description":"Durning, L.E., Sankey, J.B., Davis, P.A., and Sankey, T.T., 2016, Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2013 airborne image acquisition: U.S. Geological Survey Data Series 1027, https://doi.org/10.3133/ds1027.","linkHelpText":"- Four-band image mosaic of the Colorado River corridor downstream of Glen Canyon Dam in Arizona, derived from the May 2013 airborne image acquisition"},{"id":483066,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/780/","description":"Davis, P.A., 2013, Natural-color and color-infrared image mosaics of the Colorado River corridor in Arizona derived from the May 2009 airborne image collection: U.S. Geological Survey Data Series 780, https://pubs.usgs.gov/ds/780/.","linkHelpText":"- Natural-color and color-infrared image mosaics of the Colorado River corridor in Arizona derived from the May 2009 airborne image collection"},{"id":483527,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"http://pubs.usgs.gov/of/2012/1139/","description":"Davis, P.A., 2012, Airborne digital-image data for monitoring the Colorado River corridor below Glen Canyon Dam, Arizona, 2009—Image-mosaic production and comparison with 2002 and 2005 image mosaics: U.S. Geological Survey Open-File Report 2012–1139, 82 p. (Available at http://pubs.usgs.gov/of/2012/1139/.)","linkHelpText":"- Airborne digital-image data for monitoring the Colorado River corridor below Glen Canyon Dam, Arizona, 2009—Image-mosaic production and comparison with 2002 and 2005 image mosaics"},{"id":483065,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P93Y4FMJ","description":"Sankey, J.B., Bransky, N.B., Kohl, K.A., Gushue, T.M., Bedford, A.F., and Durning, L.E., 2025, Digital elevation model (DEM) and digital surface model (DSM) data for the Colorado River corridor in Grand Canyon National Park and Glen Canyon National Recreation Area (2002, 2009, 2013 and 2021), including accuracy assessment data: U.S. Geological Survey data release, https://doi.org/10.5066/P93Y4FMJ.","linkHelpText":"- Digital elevation model (DEM) and digital surface model (DSM) data for the Colorado River corridor in Grand Canyon National Park and Glen Canyon National Recreation Area (2002, 2009, 2013 and 2021), including accuracy assessment data"},{"id":483064,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BBGN6G","text":"USGS Data Release","description":"Sankey, J.B., Bransky, N., Pigue, L., Kohl, K., and Gushue, T.M., 2024, Four Band Image Mosaic of the Colorado River Corridor in Arizona—2021, including Accuracy Assessment Data: U.S. Geological Survey data release, https://doi.org/10.5066/P9BBGN6G.","linkHelpText":"Four band image mosaic of the Colorado River Corridor in Arizona—2021, including accuracy assessment data"},{"id":483525,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F7TX3CHS","description":"Durning, L.E., Sankey, J.B., Davis, P.A., and Sankey, T.T., 2016, Four band Image mosaic of the Colorado River Corridor in Arizona--2013, including accuracy assessment data: U.S. Geological Survey data release, https://doi.org/10.5066/F7TX3CHS","linkHelpText":"- Four band Image mosaic of the Colorado River Corridor in Arizona--2013, including accuracy assessment data"}],"country":"United States","state":"Arizona, Nevada, Utah","otherGeospatial":"Colorado River, Glen Canyon Dam, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.25503634497424,\n 37.07331588090071\n ],\n [\n -115.01927028989792,\n 37.07331588090071\n ],\n [\n -115.01927028989792,\n 35.44030487638608\n ],\n [\n -111.25503634497424,\n 35.44030487638608\n ],\n [\n -111.25503634497424,\n 37.07331588090071\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
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Wildfires are a rapidly increasing threat to boreal forests. While our understanding of the drivers behind wildfires and their environmental impact is growing, it is mostly limited to the observational period. Here we focus on the boreal forests of southern Siberia and exploit a U–Th-dated stalagmite from Botovskaya Cave, located in the upper Lena region of southern Siberia, to document wildfire activity and vegetation dynamics during parts of two warm periods: the Last Interglacial (LIG; specifically part of the Last Interglacial maximum between 124.1 and 118.8 ka) and the Holocene (10–0 ka). Our record is based on levoglucosan (Lev), a biomarker sensitive to biomass burning, and on lignin oxidation products (LOPs) that discriminate between open and closed forest and hard- or softwood vegetation. In addition, we used carbonate carbon stable isotope ratios (δ13C), which reflect a dominant control of the host rock, to evaluate soil respiration and local infiltration changes. Our LOP data suggest that, during the Last Interglacial, the region around Botovskaya Cave was characterised by open forest, which by ca. 121.5 ka underwent a transition from fire-resistant hardwood to fire-prone softwood. The Lev record indicates that fire activity was high and increased towards the end of Last Interglacial just before 119 ka. In contrast, the Holocene was characterised by a closed-forest environment with mixed hard- and softwood vegetation. Holocene fire activity varied but at a much lower level than during the Last Interglacial. We attribute the changes in wildfire activity during the intervals of interest to the interplay between vegetation and climate. The open forests of the Last Interglacial were more likely to ignite than their closed Holocene equivalents, and their flammability was aided by warmer and drier summers and a stronger seasonal temperature contrast due to the increase in seasonal insolation difference compared to the Holocene. Our comparison of the last two interglacial intervals suggests that, with increasing global temperatures, the boreal forest of southern Siberia may become progressively more vulnerable to higher wildfire activity.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-21-661-2025","usgsCitation":"Margerum, J., Homann, J., Umbo, S., Nehrke, G., Hoffmann, T., Vaks, A., Kononov, A., Osintsev, A., Giesche, A., Mason, A., Lechleitner, F., Henderson, G., Kwiecien, O., and Breitenbach, S., 2025, Reconstruction of Holocene and Last Interglacial vegetation dynamics and wildfire activity in Southern Siberia: Climate of the Past, v. 21, no. 3, p. 661-677, https://doi.org/10.5194/cp-21-661-2025.","productDescription":"17 p.","startPage":"661","endPage":"677","ipdsId":"IP-165953","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":488363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-21-661-2025","text":"Publisher Index Page"},{"id":483662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Botovskaya Cave, Siberia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 104.97249739324332,\n 55.0006548103033\n ],\n [\n 104.97249739324332,\n 54.87985401356909\n ],\n [\n 105.12004339826586,\n 54.87985401356909\n ],\n [\n 105.12004339826586,\n 55.0006548103033\n ],\n [\n 104.97249739324332,\n 55.0006548103033\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Margerum, Jade","contributorId":352494,"corporation":false,"usgs":false,"family":"Margerum","given":"Jade","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":931488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homann, Julia","contributorId":352495,"corporation":false,"usgs":false,"family":"Homann","given":"Julia","affiliations":[{"id":84241,"text":"Department Chemie, Johannes Gutenberg-Universität Mainz, Duesbergweg 10-14, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":931489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Umbo, Stuart","contributorId":352496,"corporation":false,"usgs":false,"family":"Umbo","given":"Stuart","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":931490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nehrke, Gernot","contributorId":352497,"corporation":false,"usgs":false,"family":"Nehrke","given":"Gernot","affiliations":[{"id":84242,"text":"Alfred Wegener Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Section Marine BioGeoSciences, 27570 Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":931491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoffmann, Thorsten","contributorId":352498,"corporation":false,"usgs":false,"family":"Hoffmann","given":"Thorsten","affiliations":[{"id":84241,"text":"Department Chemie, Johannes Gutenberg-Universität Mainz, Duesbergweg 10-14, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":931492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vaks, Anton","contributorId":352499,"corporation":false,"usgs":false,"family":"Vaks","given":"Anton","affiliations":[{"id":84243,"text":"Geological Survey of Israel, 32 Yeshayahu Leibowitz Street, 9692100 Jerusalem, Israel","active":true,"usgs":false}],"preferred":false,"id":931493,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kononov, Aleksandr","contributorId":352500,"corporation":false,"usgs":false,"family":"Kononov","given":"Aleksandr","affiliations":[{"id":84244,"text":"Irkutsk Nation al Research Technical University, Irkutsk, 664074, Russia; Institute of the Earth's Crust, Russian Academy of Sciences, Siberian Branch, Irkutsk, 664033, Russia","active":true,"usgs":false}],"preferred":false,"id":931494,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Osintsev, Alexander","contributorId":352501,"corporation":false,"usgs":false,"family":"Osintsev","given":"Alexander","affiliations":[{"id":84245,"text":"Speleoclub Arabika, St. Mamina-Sibiryaka 6a, 664058 Irkutsk, Russia","active":true,"usgs":false}],"preferred":false,"id":931495,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giesche, Alena Maria 0000-0003-3673-7269","orcid":"https://orcid.org/0000-0003-3673-7269","contributorId":344659,"corporation":false,"usgs":true,"family":"Giesche","given":"Alena Maria","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":931496,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mason, Andrew","contributorId":352502,"corporation":false,"usgs":false,"family":"Mason","given":"Andrew","affiliations":[{"id":84247,"text":"Department of Earth Sciences, University of Oxford, South Parks Road, OX1 3AN Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":931497,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lechleitner, Franziska A.","contributorId":352503,"corporation":false,"usgs":false,"family":"Lechleitner","given":"Franziska A.","affiliations":[{"id":84248,"text":"Department of Chemistry, Biochemistry and Pharmaceutical Sciences & Oeschger Centre for Climate Change Research, Universität Bern, Freiestrasse 3, 3012 Bern, Switzerland","active":true,"usgs":false}],"preferred":false,"id":931498,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Henderson, Gideon M.","contributorId":352504,"corporation":false,"usgs":false,"family":"Henderson","given":"Gideon M.","affiliations":[{"id":84247,"text":"Department of Earth Sciences, University of Oxford, South Parks Road, OX1 3AN Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":931499,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kwiecien, Ola","contributorId":352505,"corporation":false,"usgs":false,"family":"Kwiecien","given":"Ola","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":931500,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Breitenbach, Sebastian F.M.","contributorId":352506,"corporation":false,"usgs":false,"family":"Breitenbach","given":"Sebastian F.M.","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":931501,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70266530,"text":"70266530 - 2025 - A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales","interactions":[],"lastModifiedDate":"2025-05-09T14:47:14.01538","indexId":"70266530","displayToPublicDate":"2025-03-19T09:42:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales","docAbstract":"The use of quantitative real-time PCR (qPCR) to monitor pathogens is common; however, quantitative frameworks that consider the observation process, dynamics in pathogen presence, and pathogen load are lacking. This can be problematic in the early stages of disease progression, where low level detections may be treated as ‘inconclusive’ and excluded from analyses. Alternatively, a framework that accounts for imperfect detection would provide more robust inferences. To better estimate pathogen dynamics, we developed a hierarchical multi-scale dynamic occupancy hurdle model (MS-DOHM). The model used data gathered during sampling for Pseudogymnoascus destructans (Pd), the causative agent of white-nose syndrome, a fungal disease that has cause severe declines in several species of hibernating bats in North America. The model allowed us to estimate initial occupancy, colonization, persistence and prevalence of Pd at bat hibernacula. Additionally, utilizing the relationship between cycle threshold and pathogen load, we estimated pathogen detectability and modeled expected colony and bat pathogen loads. To assess the ability of MS-DOHM to estimate pathogen dynamics, we compared MS-DOHM’s results to those of a dynamic occupancy model and naïve detection/non-detection. MS-DOHM’s estimates of site-level pathogen presence were up to 11.9% higher than estimates from the dynamic occupancy model and 35.7% higher than naïve occupancy. Including prevalence and load in our modeling framework resulted in estimates of pathogen arrival that were two to three years earlier compared to the dynamic occupancy and naïve detection/non-detection, respectively. Compared to naïve values, MS-DOHM predicted greater pathogen loads on colonies; however, we found no difference between model estimates and naïve values of prevalence. While the model predicted no declines in site-level prevalence, there were instances where pathogen load decreased in colonies that had been Pd positive for longer periods of time. Our findings demonstrate that accounting for pathogen load and prevalence at multiple scales changes our understanding of Pd dynamics, potentially allowing earlier conservation intervention. Additionally, we found that accounting for pathogen load and prevalence within hibernacula and among individuals resulted in a better fitting model with greater predictive ability.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-93865-x","usgsCitation":"Gridder, J., Udell, B.J., Reichert, B., Foster, J., Kendall, W.L., Cheng, T., and Frick, W.F., 2025, A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales: Scientific Reports, v. 15, 9423, 10 p., https://doi.org/10.1038/s41598-025-93865-x.","productDescription":"9423, 10 p.","ipdsId":"IP-166127","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":490111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-93865-x","text":"Publisher Index Page"},{"id":485644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.26363777562113,\n 49.48504400263704\n ],\n [\n -97.26363777562113,\n 32.49418643417637\n ],\n [\n -70.38881830989425,\n 32.49418643417637\n ],\n [\n -70.38881830989425,\n 49.48504400263704\n ],\n [\n -97.26363777562113,\n 49.48504400263704\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Gridder, John F.","contributorId":354814,"corporation":false,"usgs":false,"family":"Gridder","given":"John F.","affiliations":[{"id":84669,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":936476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Udell, Bradley James 0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":271174,"corporation":false,"usgs":true,"family":"Udell","given":"Bradley","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":936477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":936478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, Jeffery T.","contributorId":351633,"corporation":false,"usgs":false,"family":"Foster","given":"Jeffery T.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":936479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendall, William Louis 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":257230,"corporation":false,"usgs":false,"family":"Kendall","given":"William","email":"","middleInitial":"Louis","affiliations":[{"id":51981,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, 201 J.V.K. Wagar Building 1484 Campus Delivery, Fort Collins, CO 80523, USA","active":true,"usgs":false}],"preferred":false,"id":936480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Tina L.","contributorId":127716,"corporation":false,"usgs":false,"family":"Cheng","given":"Tina L.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":936481,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frick, Winifred F. 0000-0002-9469-1839","orcid":"https://orcid.org/0000-0002-9469-1839","contributorId":337076,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred","email":"","middleInitial":"F.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":936482,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264723,"text":"70264723 - 2025 - Deterministic physics-based earthquake sequence simulators match empirical ground-motion models and enable extrapolation to data poor regimes: Application to multifault multimechanism ruptures","interactions":[],"lastModifiedDate":"2025-07-09T15:58:44.092141","indexId":"70264723","displayToPublicDate":"2025-03-19T07:56:41","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":"Deterministic physics-based earthquake sequence simulators match empirical ground-motion models and enable extrapolation to data poor regimes: Application to multifault multimechanism ruptures","docAbstract":"We use the deterministic earthquake simulator RSQSim to generate complex sequences of ruptures on fault systems used for hazard assessment. We show that the source motions combined with a wave propagation code create surface ground motions that fall within the range of epistemic uncertainties for the Next Generation Attenuation‐West2 set of empirical models. We show the model is well calibrated where there are good data constraints, and has good correspondence in regions with fewer data constraints. We show magnitude, distance, and mechanism dependence all arising naturally from the same underlying friction. The deterministic physics‐based approach provides an opportunity for better understanding the physical origins of ground motions. For example, we find that reduced stress drops in shallow layers relative to constant stress drop with depth lead to peak ground velocities in the near field that better match empirical models. The simulators may also provide better extrapolations into regimes that are poorly empirically constrained by data because physics, rather than surface shaking data parameterizations, is underlying the extrapolations. Having shown the model is credible, we apply it to a problem where observations are lacking. We examine the case of crustal faults above a shallow subduction interface seen to break coseismically in simulations of the New Zealand fault system. These types of events were left out of consideration in the most recent New Zealand national seismic hazard model due to the modeling complexity and lack of observational data to constrain ground‐motion models (GMMs). Here, we show that in the model, by breaking up the coseismic crustal and interface rupturing fault motions into two separate subevents, and then recombining the resulting ground‐motion measures in a square‐root‐of‐sum‐of‐squares incoherent manner, we reproduce well the ground‐motion measures from the full event rupture. This provides a new method for extrapolating GMMs to more complex multifault ruptures.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240141","usgsCitation":"Shaw, B.E., Milner, K., and Goulet, C.A., 2025, Deterministic physics-based earthquake sequence simulators match empirical ground-motion models and enable extrapolation to data poor regimes: Application to multifault multimechanism ruptures: Seismological Research Letters, v. 96, no. 4, p. 2431-2444, https://doi.org/10.1785/0220240141.","productDescription":"14 p.","startPage":"2431","endPage":"2444","ipdsId":"IP-170334","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":483583,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaw, Bruce E.","contributorId":194146,"corporation":false,"usgs":false,"family":"Shaw","given":"Bruce","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":931438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milner, Kevin Ross 0000-0002-9118-6378","orcid":"https://orcid.org/0000-0002-9118-6378","contributorId":352491,"corporation":false,"usgs":true,"family":"Milner","given":"Kevin Ross","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":931439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goulet, Christine A 0000-0002-7643-357X","orcid":"https://orcid.org/0000-0002-7643-357X","contributorId":336587,"corporation":false,"usgs":true,"family":"Goulet","given":"Christine","email":"","middleInitial":"A","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":931440,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272248,"text":"70272248 - 2025 - Decadal stability in stream fish communities and contemporary ecological drivers of species occupancy in two Appalachian U.S. National Parks","interactions":[],"lastModifiedDate":"2025-11-20T16:04:16.55833","indexId":"70272248","displayToPublicDate":"2025-03-18T08:53:36","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Decadal stability in stream fish communities and contemporary ecological drivers of species occupancy in two Appalachian U.S. National Parks","docAbstract":"Objective
Although conserving fish biodiversity in lotic systems is challenging, protected areas can provide refuge from certain environmental stressors. In the Appalachian region, USA, the National Park Service manages Delaware Water Gap National Recreation Area (DEWA) and New River Gorge National Park & Preserve (NERI), which contain abundant and diverse freshwater resources. To assess the effectiveness of these protected areas in conserving stream fishes, we evaluated decadal changes and ecological drivers of species occupancy and detection.
Methods
Using fish assemblage data from backpack electrofishing surveys conducted in both parks during 2013–2014 and 2022–2023, we quantified temporal differences in species occupancy and detection probabilities using a Bayesian hierarchical multispecies occupancy modeling approach. For the 2022–2023 survey, we included habitat variables as predictors of occupancy and detection.
Results
Community composition and occupancy probabilities for species in both parks remained similar through time, with the most recent occupancy estimates ranging from 0.07 (90% CI = 0.02, 0.14) for Variegate Darter Etheostoma variatum and Rainbow Darter E. caeruleum to 0.73 (90% credible interval = 0.59, 0.85) for Blacknose Dace Rhinichthys atratulus. Changes in occupancy were more prominent at Delaware Water Gap National Recreation Area than New River Gorge National Park & Preserve, with Yellow Perch Perca flavescens having a posterior mean difference of −0.17 [90% credible interval = −0.35, −0.01] and American Eel Anguilla rostrata having a high posterior probability (>80%) of occupancy increasing by at least 1%. Habitat variables were related to community structure, but effects varied in significance, magnitude, and direction among species and parks. Conversely, species-specific detection probabilities were comparatively less affected by environmental and sampling effort predictors.
Conclusions
Between 2013 and 2023, occupancy estimates for 44 fish species across two protected, ecologically diverse landscapes remained relatively stable. Furthermore, we highlight the efficacy of national parks in maintaining freshwater fish biodiversity amidst rapid global change.
Many species have a variety of adaptations to winter weather, but these adaptations could become maladaptive if winter snowfall and temperatures are more variable. Snowshoe hares (Lepus americanus) molt from a brown summer coat to a white winter coat, but reductions in snow cover could result in phenotypic mismatch, which in turn could reduce survival. Hare populations near the southern extent of their range might be especially sensitive to phenotypic mismatch because of variable winter weather, but variation in winter coat coloration could allow for these populations to persist in inconsistent snow cover conditions. Using capture data (n = 59 individual hares) spanning 8 years, we document the prevalence of three atypical winter coat color phenotypes (brown bodies, brown-ringed eyes, and brown ears) in a snowshoe hare population in Pennsylvania. The majority of hares in our study (84.7%) exhibited at least one of these atypical winter phenotypes, with a high probability of hares having brown-ringed eyes or brown ears, and four hares remaining brown during the winter. The presence and high prevalence of non-white winter phenotypes could be beneficial for hares in this population if winters are mild with low snow cover. If these phenotypes have a genetic basis, there may be evolutionary potential for hares to persist near the southern extent of their range, even in the face of changing winters.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70217","usgsCitation":"Gigliotti, L., Boyd, E.S., and Diefenbach, D.R., 2025, Atypical winter coat coloration of snowshoe hares near the southern extent of their range: Ecosphere, v. 16, no. 3, e70217, 7 p., https://doi.org/10.1002/ecs2.70217.","productDescription":"e70217, 7 p.","ipdsId":"IP-170108","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":492471,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70217","text":"Publisher Index Page"},{"id":492128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Monroe County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.4944445855176,\n 41.110769966630414\n ],\n [\n -75.4944445855176,\n 40.90104058418078\n ],\n [\n -75.14502818574874,\n 40.90104058418078\n ],\n [\n -75.14502818574874,\n 41.110769966630414\n ],\n [\n -75.4944445855176,\n 41.110769966630414\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Laura Christine 0000-0002-6390-4133","orcid":"https://orcid.org/0000-0002-6390-4133","contributorId":348259,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Laura Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":942731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyd, Emily S.","contributorId":342971,"corporation":false,"usgs":false,"family":"Boyd","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":942732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":942733,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264525,"text":"sir20255007 - 2025 - A model uncertainty quantification protocol for evaluating the value of observation data","interactions":[],"lastModifiedDate":"2025-07-23T17:09:46.818291","indexId":"sir20255007","displayToPublicDate":"2025-03-17T11:20:49","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":"2025-5007","displayTitle":"A Model Uncertainty Quantification Protocol for Evaluating the Value of Observation Data","title":"A model uncertainty quantification protocol for evaluating the value of observation data","docAbstract":"The history-matching approach to parameter estimation with models enables a powerful offshoot analysis of data worth—using the uncertainty of a model forecast as a metric for the worth of data. Adding observation data will either have no impact on forecast uncertainty or will reduce it. Removing existing data will either have no impact on forecast uncertainty or will increase it. The history-matching framework makes it possible to perform this quantitative analysis leveraging the connections among observations, model parameters, and model forecasts. We show this behavior on a specific groundwater flow model of the Mississippi Alluvial Plain and show where the analysis can be informative for considering the potential design of an observation network based on existing or potential observations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255007","usgsCitation":"Fienen, M.N., Schachter, L.A., and Hunt, R.J., 2025, A model uncertainty quantification protocol for evaluating the value of observation data: U.S. Geological Survey Scientific Investigations Report 2025–5007, 12 p., https://doi.org/10.3133/sir20255007.","productDescription":"vi; 12 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171702","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":483399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.pdf","text":"Report","size":"7.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025–5007"},{"id":483398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5007/coverthb.jpg"},{"id":483404,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255007/full","text":"Report"},{"id":483400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.XML","text":"Report","description":"SIR 2025–5007"},{"id":483403,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5007/images"},{"id":492790,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118495.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, Wisconsin 53726
Population monitoring is essential to document recovery efforts for threatened and endangered species. Mexican wolves (Canis lupus baileyi) are an endangered subspecies of gray wolves that historically occupied large portions of the American Southwest and Mexico. Recently, the Mexican wolf population in the United States has been growing rapidly and traditional approaches for population monitoring (e.g., capture and radio collaring) are becoming difficult and expensive as wolves expand into new areas. We developed predictive models of pup-rearing habitat (i.e., den and rendezvous sites) that could help guide future population monitoring efforts. We located 255 den sites and 129 rendezvous sites in Arizona and New Mexico, USA (1998–2023) using tracking collars and site visits. We sampled habitat conditions in wolf-occupied regions of Arizona and New Mexico and fit logistic regressions to these data following a use–available study design to estimate resource selection functions (RSF) for den and rendezvous sites. We hypothesized wolves would select areas that offered greater physical protection, lower human-disturbance, and access to reliable water sources for pup-rearing but that the relative importance of these features would differ between the denning and rendezvous site seasons. Mexican wolves selected den sites at higher elevations in steeper and rougher terrain that were closer to permanent waterbodies but farther from rural roads. Selection of rendezvous sites was also associated with higher elevations and proximity to waterbodies but varied with availability of green leaf biomass on the landscape. While still highly predictive, our rendezvous site model was less predictive than our den model (Spearman's correlation averaged 0.81 [SE = 0.05] vs. 0.90 [SE = 0.03], respectively), possibly because water and green leaf biomass are more spatially diffuse and variable because of monsoonal rains during the rendezvous site season. Our results suggest that terrain features associated with physical protection and access to reliable water were most important in characterizing suitable pup-rearing habitat for Mexican wolves. By predicting suitable den and rendezvous site habitat across portions of the Mexican Wolf Experimental Population Area, our models can help guide future population monitoring by reducing the total search area when surveying for wolves and increase the probability of detecting all members of a pack.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70017","usgsCitation":"Bassing, S., Oakleaf, J., Cain, J.W., Greenleaf, A., Gardner, C., and Ausband, D.E., 2025, Predicting pup-rearing habitat for Mexican wolves: Journal of Wildlife Management, v. 89, no. 5, e70017, 19 p., https://doi.org/10.1002/jwmg.70017.","productDescription":"e70017, 19 p.","ipdsId":"IP-169774","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486238,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":488960,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70017","text":"Publisher Index Page"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.15656068155297,\n 37.15236143729469\n ],\n [\n -114.79334387076136,\n 36.19839033323856\n ],\n [\n -114.82092420356022,\n 32.28431539821898\n ],\n [\n -111.22371663071222,\n 31.529018437535207\n ],\n [\n -108.37781874007106,\n 31.259432273304338\n ],\n [\n -108.00163404937481,\n 31.805280742585737\n ],\n [\n -103.03648352957026,\n 31.93273826397835\n ],\n [\n -103.13422840316065,\n 37.15236143729469\n ],\n [\n -114.15656068155297,\n 37.15236143729469\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Bassing, Sarah B.","contributorId":355638,"corporation":false,"usgs":false,"family":"Bassing","given":"Sarah B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oakleaf, John K.","contributorId":355639,"corporation":false,"usgs":false,"family":"Oakleaf","given":"John K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937836,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greenleaf, Allison R.","contributorId":355640,"corporation":false,"usgs":false,"family":"Greenleaf","given":"Allison R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937837,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gardner, Colby M.","contributorId":355641,"corporation":false,"usgs":false,"family":"Gardner","given":"Colby M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937838,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937839,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264717,"text":"70264717 - 2025 - Climate and dispersal ability limit future habitats for Gila monsters in the Mojave Desert","interactions":[],"lastModifiedDate":"2025-03-20T14:58:11.540958","indexId":"70264717","displayToPublicDate":"2025-03-17T09:50:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Climate and dispersal ability limit future habitats for Gila monsters in the Mojave Desert","docAbstract":"Describing future habitat for sensitive species can be helpful in planning conservation efforts to ensure species persistence under new climatic conditions. The Gila monster (Heloderma suspectum) is an iconic lizard of the southwestern United States. The northernmost range of Gila monsters is the Mojave Desert, an area experiencing rapid human population growth and urban sprawl. To understand current and potential future habitat for Gila monsters in the Mojave Desert, we fit ensemble species distribution models using known locations and current environmental variables known to be important to the species' biology. We then projected future suitable habitat under different climate forecasts based on IPCC emission scenarios. To ensure that Gila monsters would be able to disperse to newly suitable habitat, we fit Brownian Bridge movement models using telemetry data from two locations in Nevada. This model indicated that Gila monsters prefer to move through areas with a moderate slope and higher shrub cover. Modeled current suitable habitat for Gila monsters in Nevada was primarily in rugged bajadas and lower elevations at the bases of mountain ranges. Predictions of potential future habitat suggested that overall habitat suitability through 2082 would remain relatively stable throughout the study area in the lower emissions scenario, but in the high emissions scenario potential habitat is greatly reduced in many lower-elevation areas. Future habitat areas at higher elevations under the high emissions scenario showed moderate increases in suitability, though occupancy would likely be limited by Gila monster dispersal capabilities. Finally, we determined how well the protected area network of our study area encompassed future Gila monster habitat to highlight potential opportunities to protect this important species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71008","usgsCitation":"Hromada, S.J., Jones, J., Stalker, J., Wood, D.A., Vandergast, A.G., Tracy, C.R., Gienger, C., and Nussear, K.E., 2025, Climate and dispersal ability limit future habitats for Gila monsters in the Mojave Desert: Ecology and Evolution, v. 15, e71008, 15 p., https://doi.org/10.1002/ece3.71008.","productDescription":"e71008, 15 p.","ipdsId":"IP-166958","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71008","text":"Publisher Index Page"},{"id":483582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, Utah","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.33194255435204,\n 37.76713352893536\n ],\n [\n -117.13833621353872,\n 37.76713352893536\n ],\n [\n -117.13833621353872,\n 33.75898076102743\n ],\n [\n -113.33194255435204,\n 33.75898076102743\n ],\n [\n -113.33194255435204,\n 37.76713352893536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hromada, Steven J.","contributorId":245147,"corporation":false,"usgs":false,"family":"Hromada","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":931420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Jason L.","contributorId":352480,"corporation":false,"usgs":false,"family":"Jones","given":"Jason L.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":931421,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stalker, Jocelyn B.","contributorId":352484,"corporation":false,"usgs":false,"family":"Stalker","given":"Jocelyn B.","affiliations":[{"id":84237,"text":"Austin Peay State University","active":true,"usgs":false}],"preferred":false,"id":931422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":931423,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":931424,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tracy, C. Richard","contributorId":31515,"corporation":false,"usgs":true,"family":"Tracy","given":"C.","email":"","middleInitial":"Richard","affiliations":[],"preferred":false,"id":931425,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gienger, C.M.","contributorId":352486,"corporation":false,"usgs":false,"family":"Gienger","given":"C.M.","affiliations":[{"id":84237,"text":"Austin Peay State University","active":true,"usgs":false}],"preferred":false,"id":931426,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":931427,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273260,"text":"70273260 - 2025 - Optimizing sampling across transect-based methods improves the power of agroecological monitoring data","interactions":[],"lastModifiedDate":"2025-12-29T15:30:48.878215","indexId":"70273260","displayToPublicDate":"2025-03-17T09:25:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing sampling across transect-based methods improves the power of agroecological monitoring data","docAbstract":"Transect-based monitoring has long been a valuable tool in ecosystem monitoring to measure multiple ecosystem attributes. The line-point intercept (LPI), vegetation height, and canopy gap intercept methods comprise a set of core methods, which provide indicators of ecosystem condition. However, users often struggle to design a sampling strategy that optimizes the ability to detect ecological change using transect-based methods. We assessed the sensitivity of each of these core methods to transect length, number, and sampling interval in 1-ha plots to determine: (1) minimum sampling required to describe ecosystem characteristics and detect change; and (2) optimal transect length and number to make recommendations for future analyses and monitoring efforts. We used data from 13 National Wind Erosion Research Network locations, including five LTAR sites, spanning the western United States, which included 151 plot sampling events over time across five biomes. We found that longer and increased replicates of transects were more important for reducing sampling error than increased sample intensity along fewer transects per plot. For all methods and indicators across biomes plots, three 100-m transects reduced sampling error such that indicator estimates fell within a 95% confidence interval of ±5% for canopy gap intercept and LPI-total foliar cover, ±5 cm for height, and ±2 species for LPI-species counts. For the same criteria at 80% confidence intervals, two 100-m transects are needed. Site-scale inference was strongly affected by sample design, consequently our understanding of ecological dynamics may be influenced by sampling decisions.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/jeq2.20678","usgsCitation":"McCord, S.E., Webb, N.P., Van Zee, J.W., Courtright, E.M., Billings, B., Duniway, M.C., Edwards, B.L., Kachergis, E., Moriasi, D.N., Morra, B., Nafus, A., Newingham, B.A., Scott, D.A., and Toledo, D., 2025, Optimizing sampling across transect-based methods improves the power of agroecological monitoring data: Journal of Environmental Quality, v. 54, no. 3, p. 706-719, https://doi.org/10.1002/jeq2.20678.","productDescription":"14 p.","startPage":"706","endPage":"719","ipdsId":"IP-170564","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498293,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jeq2.20678","text":"Publisher Index Page"},{"id":498101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.0289560089564,\n 48.97409076449233\n ],\n [\n -121.35700711516505,\n 48.97409076449233\n ],\n [\n -121.35700711516505,\n 31.451111657107248\n ],\n [\n -97.0289560089564,\n 31.451111657107248\n ],\n [\n -97.0289560089564,\n 48.97409076449233\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"McCord, Sarah E.","contributorId":364571,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","middleInitial":"E.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Nicholas P.","contributorId":364574,"corporation":false,"usgs":false,"family":"Webb","given":"Nicholas","middleInitial":"P.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Zee, Justin W.","contributorId":364577,"corporation":false,"usgs":false,"family":"Van Zee","given":"Justin","middleInitial":"W.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courtright, Ericha M.","contributorId":364580,"corporation":false,"usgs":false,"family":"Courtright","given":"Ericha","middleInitial":"M.","affiliations":[{"id":79445,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Billings, Benjamin J","contributorId":169763,"corporation":false,"usgs":false,"family":"Billings","given":"Benjamin J","affiliations":[{"id":25582,"text":"Bureau of Land Management, San Luis Valley Field Office, Monte Vista, CO 81144","active":true,"usgs":false}],"preferred":false,"id":952904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":952905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Edwards, Brandon L.","contributorId":364583,"corporation":false,"usgs":false,"family":"Edwards","given":"Brandon","middleInitial":"L.","affiliations":[{"id":86850,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA; New Mexico State University, Jornada Experimental Range, PO Box 30003, MSC 3JER, Las Cruces, NM, 88003, USA","active":true,"usgs":false}],"preferred":false,"id":952906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kachergis, Emily","contributorId":195930,"corporation":false,"usgs":false,"family":"Kachergis","given":"Emily","affiliations":[],"preferred":false,"id":952907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moriasi, Daniel N","contributorId":270209,"corporation":false,"usgs":false,"family":"Moriasi","given":"Daniel","email":"","middleInitial":"N","affiliations":[{"id":56110,"text":"USDA-ARS USDA-ARS Grazinglands Research Laboratory, El Reno, OK 73036","active":true,"usgs":false}],"preferred":false,"id":952908,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Morra, Brian","contributorId":364584,"corporation":false,"usgs":false,"family":"Morra","given":"Brian","affiliations":[{"id":86853,"text":"USDA-ARS, Great Basin Rangelands Research Unit, 920 Valley Road, Reno, NV 89512, USA","active":true,"usgs":false}],"preferred":false,"id":952909,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nafus, Aleta","contributorId":167781,"corporation":false,"usgs":false,"family":"Nafus","given":"Aleta","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":952910,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Newingham, Beth A.","contributorId":364585,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","middleInitial":"A.","affiliations":[{"id":86853,"text":"USDA-ARS, Great Basin Rangelands Research Unit, 920 Valley Road, Reno, NV 89512, USA","active":true,"usgs":false}],"preferred":false,"id":952911,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Scott, Drew A.","contributorId":364586,"corporation":false,"usgs":false,"family":"Scott","given":"Drew","middleInitial":"A.","affiliations":[{"id":86854,"text":"USDA-ARS Northern Great Plains Research Laboratory, 1701 10th Av. SW Mandan, ND 58454, USA","active":true,"usgs":false}],"preferred":false,"id":952912,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Toledo, David","contributorId":195936,"corporation":false,"usgs":false,"family":"Toledo","given":"David","email":"","affiliations":[],"preferred":false,"id":952913,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70267458,"text":"70267458 - 2025 - Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA","interactions":[],"lastModifiedDate":"2025-05-23T14:22:38.733654","indexId":"70267458","displayToPublicDate":"2025-03-17T09:16:47","publicationYear":"2025","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA","docAbstract":"This field trip focuses on the geology of the central Appalachian Valley and Ridge province near Harrisonburg, Virginia, USA. Recent geologic mapping utilizing 1-m resolution lidar data has revealed new insights into the Paleozoic stratigraphy, structural geology, and Neogene landscape evolution of the region. The detailed mapping reveals the presence of the Big Spring Station Member and multiple thrombolite zones in the Cambrian Conococheague Formation extending as far south as the Briery Branch 7.5 min quadrangle, providing insights into Late Cambrian sea-level fluctuations. Multiple outcrop exposures in the study area of this guidebook confirm recent work in Pennsylvania, USA, showing that the Ordovician Reedsville Shale overlies the Martinsburg Formation and that the two are distinct and mappable as separate formations rather than laterally equivalent units as previously interpreted. Our work extends the Silurian Williamsport Sandstone into Shenandoah County, Virginia, and describes its facies relationships with the Bloomsburg Formation along strike and across the Adams Run anticline. Mapping within the thick Devonian siliciclastic sequence reveals the presence of the Mahantango Formation on the western limb of Supin Lick syncline and illustrates its complex facies relationship with the Millboro Shale. In addition, we highlight new mapping criteria for the Brallier and Foreknobs Formations and demonstrate how the specific changes to the placement of the contact between them addresses previous challenges in their differentiation. We present cosmogenic burial ages of broad alluvial fan sediments in the Shenandoah Valley near Timberville and Briery Branch, Virginia, and erosion rates estimated for the Briery Branch stream basin. Both analyses provide new constraints on the timing of landscape evolution and karst development since the middle Pliocene. This field guide also highlights some significant structural features within the North Mountain fault zone, such as evidence of imbricated thrust sheets cut by cross-strike faults that have been exploited by Eocene igneous intrusions. Map-scale horses of Silurian and Ordovician rocks hold up ridges that are oblique to the regional strike. Deformation internal to one of these horse blocks is shown to be non-coaxial with respect to the main regional northwest directed transport.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 GSA south-central and southeastern section meetings","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2025.0072(06)","usgsCitation":"Doctor, D.H., Gray, A., and Odom, W.E., 2025, Stratigraphy, structure, and geomorphology of the central Appalachians across the North Mountain fault zone near Harrisonburg, Virginia, USA, chap. of From the Ozark Plateaus and Arkansas River Valley to the Shenandoah Valley: Field guides for the 2025 GSA south-central and southeastern section meetings, v. 72, p. 93-142, https://doi.org/10.1130/2025.0072(06).","productDescription":"50 p.","startPage":"93","endPage":"142","ipdsId":"IP-175153","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":486501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Harrisonburg","otherGeospatial":"North Mountain fault zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.5,\n 39\n ],\n [\n -79.5,\n 38\n ],\n [\n -78.5,\n 38\n ],\n [\n -78.5,\n 39\n ],\n [\n -79.5,\n 39\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"72","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","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":938298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Alexander Addison 0009-0008-3071-2179","orcid":"https://orcid.org/0009-0008-3071-2179","contributorId":350945,"corporation":false,"usgs":true,"family":"Gray","given":"Alexander Addison","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":938300,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264626,"text":"70264626 - 2025 - Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","interactions":[],"lastModifiedDate":"2025-03-18T16:47:13.564948","indexId":"70264626","displayToPublicDate":"2025-03-16T11:34:04","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":"Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","docAbstract":"Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon (Oncorhynchus tshawytscha) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index (α) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038045","usgsCitation":"Harrison, L.R., Legleiter, C.J., Overstreet, B., and White, J., 2025, Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry: Water Resources Research, v. 3, no. 61, e2024WR038045, 21 p., https://doi.org/10.1029/2024WR038045.","productDescription":"e2024WR038045, 21 p.","ipdsId":"IP-163184","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038045","text":"Publisher Index Page"},{"id":483481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"North Santiam River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.01923518970605,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.68226153883313\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"61","noUsgsAuthors":false,"publicationDate":"2025-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":930994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":930995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":930997,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264474,"text":"sir20255011 - 2025 - Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","interactions":[],"lastModifiedDate":"2025-07-23T17:07:13.510916","indexId":"sir20255011","displayToPublicDate":"2025-03-14T15:26:55","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":"2025-5011","displayTitle":"Comparison of Hydrologic Data and Water Budgets Between 2003–08 and 2018–23 for the Eastern Part of the Arbuckle-Simpson Aquifer, South-Central Oklahoma","title":"Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","docAbstract":"The Arbuckle-Simpson aquifer is divided spatially into three parts (eastern, central, and western). The largest groundwater withdrawals are from the eastern part of the Arbuckle-Simpson aquifer, which provides water to approximately 39,000 people in Ada and Sulphur, Oklahoma, and surrounding areas. The Arbuckle-Simpson aquifer, including the eastern part, is designated a sole source aquifer for its service area. Based primarily on data collected between 2003 and 2008, a series of comprehensive hydrologic studies of the Arbuckle-Simpson aquifer was published to provide the information necessary to perform groundwater-flow model simulations so that the Oklahoma Water Resources Board could determine how much water could be withdrawn from the aquifer while maintaining flow to springs and streams. As part of the Phase 1 studies, an aquifer water budget was developed from a numerical model for the period 2003–08. For this report, Phase 1 refers to the 2003–08 data collection period, although for some of the analyses, data collected prior to 2003 were used to inform model development work. Allocation of water from this aquifer was then established by the Oklahoma Water Resources Board in 2013. Additional well-spacing rules were also established by the Oklahoma Water Resources Board for sensitive sole source groundwater basins. To determine how the water budget for the eastern part of the Arbuckle-Simpson aquifer has changed over time, recently collected hydrologic data (2018–23) were compared to data collected during 2003–08. The analysis of changes in the aquifer water budget from 2003–08 to 2018–23 could help resource managers better understand changes in the overall balance of water in storage and the potential effects on streamflow, changes in groundwater levels, and the effects of different water uses in the aquifer area on available water in the eastern part of the Arbuckle-Simpson aquifer and streams overlying the eastern part of the Arbuckle-Simpson aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and the Wyoming Water Development Office, has developed standard methods of peak-flow frequency analysis for studies in Montana, North Dakota, South Dakota, and Wyoming. These methods describe the implementation of national flood frequency guidelines described in Bulletin 17C (https://doi.org/10.3133/tm4B5) for the four States and deviations from Bulletin 17C standard procedures to accommodate unusual hydrologic conditions. A U.S. Geological Survey data release accompanying this report (https://doi.org/10.5066/P1WHRK8H) provides example peak-flow frequency analyses for selected streamgages in the study area. The methods described in this report can be used to publish similar data releases for other streamgages in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255019","collaboration":"Prepared in cooperation with the Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wyoming Water Development Office","usgsCitation":"Siefken, S.A., Williams-Sether, T., Barth, N.A., Chase, K.J., and Cedar Face, M.A., 2025, Methods for peak-flow frequency analysis for streamgages in or near Montana, North Dakota, South Dakota, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2025–5019, 19 p., https://doi.org/10.3133/sir20255019.","productDescription":"Report: vii, 19 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148119","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":483328,"rank":4,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
The U.S. Geological Survey assessed the Quaternary evolution of Seven Mile Island, New Jersey, to quantify coastal sediment availability, which is crucial for establishing sediment budgets, understanding sediment dispersal, and managing coastlines. This report presents preliminary interpretations of seismic profiles, maps of Holocene sand thickness from the shoreline to 2 kilometers offshore, and tables quantifying the volume of available sediment along the coastal margin based on data collected during 2021 and 2022. The results reveal spatial variability in the thickness and cross-shore extent of Holocene sand. The study area was separated into northern, central, and southern zones by using underlying stratigraphy and geomorphic features. The characteristics and spatial extent of the Holocene sand deposit indicate that hydrodynamic processes contribute to its spatial variability. Northern Seven Mile Island contains the thickest deposits of Holocene sand that were formed by sediment bypass around the Townsends Inlet ebb-tidal delta. Specifically, swash bars have welded to the updrift end of Seven Mile Island and have formed thick deposits of Holocene sand that thicken landward and taper seaward. Despite their thickness, these deposits have the smallest cross-shore extent; therefore, northern Seven Mile Island has the smallest volume of Holocene sand of the three geomorphic zones. Central Seven Mile Island has the thinnest Holocene sand deposits because this section of the barrier island is outside the influence of ebb-tidal deltas. Southern Seven Mile Island has the greatest volumes of Holocene sand because of increased accommodation and deposition adjacent to the Hereford Inlet ebb-tidal delta. Even though tidal inlets exert variable influence on the three geomorphic zones, sediment is distributed fairly uniformly within each geomorphic zone; each of the three zones contains 31.05–36.48 percent of the volume of available Holocene sand.
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey, in cooperation with the Suffolk County Water Authority, evaluated the aquifer transmissivity and storage properties at the Alvahs Lane well field north of the village of Cutchogue, New York. This analysis of aquifer properties provides the Suffolk County Water Authority with hydrogeologic information needed to develop water supplies to meet the increasing water demands of the residents of Suffolk County, New York.
An aquifer test was conducted at the Alvahs Lane well field from October 18 through October 21, 2022, when a production well was pumped at 550 gallons per minute for about 24 hours, and groundwater-level drawdown and recovery were measured in two monitoring wells. The three wells are screened in a glaciofluvial aquifer under unconfined (water table) conditions. Drawdown and recovery data were analyzed with an analytical solution for partial penetration and delayed yield in an unconfined aquifer to provide estimates of the glaciofluvial aquifer properties. Inclusion of lateral aquifer boundaries was not necessary for the analysis to result in satisfactory matches with the observed water-level responses. Aquifer transmissivity was estimated at 32,000 feet squared per day. Assuming a saturated aquifer thickness of 120 feet, this result is equivalent to a horizontal hydraulic conductivity value of 270 feet per day. Specific yield was estimated at 0.15 (dimensionless). The estimated properties are consistent with those of a highly transmissive unconfined aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245128","collaboration":"Prepared in cooperation with the Suffolk County Water Authority","usgsCitation":"Misut, P.E., 2025, Analysis of aquifer framework and properties, Alvahs Lane well field, Cutchogue, New York: U.S. Geological Survey Scientific Investigations Report 2024–5128, 9 p., https://doi.org/10.3133/sir20245128.","productDescription":"iv, 9 p.","numberOfPages":"9","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154731","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492784,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118489.htm","linkFileType":{"id":5,"text":"html"}},{"id":483275,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5128/images/"},{"id":483274,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5128 XML"},{"id":483273,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/preview/sir20245128/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5128 HTML"},{"id":483272,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5128/sir20245128.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5128 PDF"},{"id":483271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5128/coverthb.jpg"}],"country":"United States","state":"New York","city":"Cutchogue","otherGeospatial":"Alvahs Lane well field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.5463729228096,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 40.987047126294755\n ],\n [\n -72.46023151369637,\n 41.03435851242847\n ],\n [\n -72.5463729228096,\n 41.03435851242847\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
Excessive nitrogen discharge is a major concern for the Long Island Sound. Programs have been implemented to reduce point sources of nitrogen to the sound, but little is known about the nonpoint sources. This study aims to better understand the current groundwater contributions of nitrogen from nonpoint sources in the Long Island Sound watershed.
During the spring and summer of 2022, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, collected water-quality samples to analyze nutrients (nitrogen and phosphorus), chloride, and bromide at 45 stations in the Long Island Sound watershed in Connecticut, New York, and Rhode Island. The stations were in small drainage watersheds (5 to 30 square kilometers) in the southern part of the Long Island Sound watershed. During two separate synoptic sampling events, water-quality samples and instantaneous streamflow measurements were collected under base-flow conditions (where the streamflow is dominated by groundwater inputs rather than overland flow or runoff flow). One sampling event was in the nongrowing season (April 24–25, 2022), and the other was in the growing season (June 30–July 1, 2022). To calculate instantaneous nitrogen loads and yields, streamflow was measured at the time of sample collection.
Nitrogen concentrations, loads, and yields varied among sampling stations and by season. Total filtered nitrogen concentrations were generally lower in the nongrowing season (from less than 0.14 to 1.9 milligrams per liter) than in the growing season (from less than 0.23 to 3.0 milligrams per liter). Nitrate plus nitrite concentrations showed little variation between the nongrowing and growing seasons. Unfiltered ammonia plus organic nitrogen concentrations were generally lower in the nongrowing season (from less than 0.07 to 0.83 milligram per liter) than in the growing season (from 0.11 to 0.98 milligram per liter). In contrast, total filtered and unfiltered nitrogen loads and yields were higher in the nongrowing season than during the growing season, likely because streamflows were higher during the nongrowing season. Total unfiltered nitrogen yields during the nongrowing season ranged from less than 0.15 to 5.0 kilograms per square kilometer per day. Total unfiltered nitrogen yields during the growing season ranged from less than 0.12 to 2.5 kilograms per square kilometer per day. Total filtered nitrogen yields during the nongrowing season ranged from less than 0.13 to 5.2 kilograms per square kilometer per day. Total filtered nitrogen yields during the growing season ranged from less than 0.06 to 2.5 kilograms per square kilometer per day.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1206","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Laabs, K.L., Barclay, J.R., and Mullaney, J.R., 2025, Base-flow sampling to enhance understanding of the groundwater flow component of nitrogen loading in small watersheds draining into Long Island Sound: U.S. Geological Survey Data Report 1206, 23 p., https://doi.org/10.3133/dr1206.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161925","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":492783,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118484.htm","linkFileType":{"id":5,"text":"html"}},{"id":483188,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1206/full","linkFileType":{"id":5,"text":"html"},"description":"DR 1206 HTML"},{"id":483186,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1206/coverthb.jpg"},{"id":483187,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.pdf","text":"Report","size":"7.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1206 PDF"},{"id":483189,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1206/dr1206.XML","linkFileType":{"id":8,"text":"xml"},"description":"DR 1206 XML"},{"id":483190,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1206/images/"},{"id":483191,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99IAXUI","text":"USGS data release","linkHelpText":"Nitrogen loads, yields, and associated field data collected during baseflow conditions and site attributes for small basins draining to Long Island Sound"}],"country":"United States","state":"Connecticut, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.91277546117404,\n 40.875888202775144\n ],\n [\n -73.5833363685162,\n 40.98693216297761\n ],\n [\n -72.40741106161018,\n 41.2504046305547\n ],\n [\n -71.4646381344502,\n 41.35702020040523\n ],\n [\n -71.40893145316154,\n 41.57350712794573\n ],\n [\n -71.49014822027934,\n 41.724945464467424\n ],\n [\n -72.43277552970126,\n 41.78157938825299\n ],\n [\n -72.757106016586,\n 41.53934120640511\n ],\n [\n -73.08115837726446,\n 41.93608782758082\n ],\n [\n -73.30909534441503,\n 41.8870827192506\n ],\n [\n -73.51694453574096,\n 41.73992349657644\n ],\n [\n -73.50192428145502,\n 41.280885844515296\n ],\n [\n -73.88228764759546,\n 40.963994488494365\n ],\n [\n -73.91277546117404,\n 40.875888202775144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The mummichog Fundulus heteroclitus is a trophically important fish inhabiting Atlantic coastal salt marshes, with few in situ estimates of overwinter survival throughout the species range. We estimated overwinter apparent survival rates of F. heteroclitus at the approximate mid-latitudinal species range [coastal North Carolina (USA)] in four tidal creeks that experience variable winter water temperatures. To estimate apparent survival, we fitted a Cormack-Jolly-Seber model to daily mark-resight data autonomously obtained from fish marked with passive integrated transponder tags. Creek, year, mean daily water temperature, change in mean daily temperature, fish length and fish condition were considered for effects on the modelled parameters: apparent survival (Φ) (product of true survival and site fidelity) and detection probability (p). Modelling showed that water temperature and fish metrics were not related to Φ. Water temperature was directly related to p, indicating reduced fish activity and thus reduced detection probability or poor antenna detection performance at low temperatures. Creek was related to Φ and p, and the creek most open to its downstream estuary (lacking a culvert) had lower rates than the others. Greater loss (fish mortality plus emigration) in this one creek may more effectively transfer production of F. heteroclitus to larger waterbodies via emigration or predation. Conversely, lower Φ may reflect reduced detection efficiency. The results suggest that F. heteroclitus survival is insensitive to variable winter water temperatures typical of thermal dynamics in shallow estuaries in this region of its range. Median creek-specific overwinter Φ rates (range of median values, 2 × 10−8, 0.04) were roughly equal to previously published rates for these creeks during the growing season (April–October). At these latitudes and with increasingly moderate winters, the results indicate that natural mortality could arise equally or more so from predation during the growing season than mechanisms such as starvation, direct mortality, thermal morbidity and stress-related susceptibility to predation resulting from intermittently low water temperatures during the overwinter season.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70020","usgsCitation":"Rudershausen, P.J., and O'Donnell, M.J., 2025, Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks: Journal of Fish of Biology, v. 107, no. 1, p. 188-200, https://doi.org/10.1111/jfb.70020.","productDescription":"13 p.","startPage":"188","endPage":"200","ipdsId":"IP-168105","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488323,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70020","text":"Publisher Index Page"},{"id":483454,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.61589538108844,\n 34.739737828981234\n ],\n [\n -76.80342045072118,\n 34.74057409854757\n ],\n [\n -76.80219234343787,\n 34.68681714274115\n ],\n [\n -76.61593931817359,\n 34.68548980814643\n ],\n [\n -76.61589538108844,\n 34.739737828981234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rudershausen, P. J.","contributorId":352331,"corporation":false,"usgs":false,"family":"Rudershausen","given":"P.","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":930816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930817,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269914,"text":"70269914 - 2025 - Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","interactions":[],"lastModifiedDate":"2025-08-07T17:05:49.242218","indexId":"70269914","displayToPublicDate":"2025-03-13T09:31:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change","docAbstract":"1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce.
2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies.
3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58.
4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.70025","usgsCitation":"Şen, B., Che-Castaldo, C., LaRue, M., Krumhardt, K., Landrum, L., Holland, M., Lynch, H., Delord, K., Barbraud, C., and Jenouvrier, S., 2025, Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change: Journal of Animal Ecology, v. 94, no. 5, p. 932-942, https://doi.org/10.1111/1365-2656.70025.","productDescription":"11 p.","startPage":"932","endPage":"942","ipdsId":"IP-170330","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":496436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.70025","text":"Publisher Index Page"},{"id":493730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Pointe Géologie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 154.44140675588028,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -70.08242540623479\n ],\n [\n 161.81002372233837,\n -67.88310899484881\n ],\n [\n 154.44140675588028,\n -67.88310899484881\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"94","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Şen, Bilgecan","contributorId":359058,"corporation":false,"usgs":false,"family":"Şen","given":"Bilgecan","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":944928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Che-Castaldo, Christian Joseph 0000-0002-7670-2178","orcid":"https://orcid.org/0000-0002-7670-2178","contributorId":347906,"corporation":false,"usgs":true,"family":"Che-Castaldo","given":"Christian Joseph","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":944929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRue, Michelle A.","contributorId":348627,"corporation":false,"usgs":false,"family":"LaRue","given":"Michelle A.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":944930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krumhardt, Kristen M.","contributorId":359059,"corporation":false,"usgs":false,"family":"Krumhardt","given":"Kristen M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landrum, Laura","contributorId":359060,"corporation":false,"usgs":false,"family":"Landrum","given":"Laura","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holland, Marika M.","contributorId":359062,"corporation":false,"usgs":false,"family":"Holland","given":"Marika M.","affiliations":[{"id":85742,"text":"NSF National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":944933,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lynch, Heather J.","contributorId":347911,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":944934,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delord, Karine 0000-0001-6720-951X","orcid":"https://orcid.org/0000-0001-6720-951X","contributorId":197702,"corporation":false,"usgs":false,"family":"Delord","given":"Karine","email":"","affiliations":[],"preferred":false,"id":944935,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Barbraud, Christophe","contributorId":197701,"corporation":false,"usgs":false,"family":"Barbraud","given":"Christophe","email":"","affiliations":[],"preferred":false,"id":944936,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jenouvrier, Stéphanie","contributorId":359063,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stéphanie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":944937,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70264400,"text":"70264400 - 2025 - MTAB 111, March 2025","interactions":[],"lastModifiedDate":"2025-03-14T14:08:10.828956","indexId":"70264400","displayToPublicDate":"2025-03-13T09:05:42","publicationYear":"2025","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":13451,"text":"Memo to All Banders (MTAB)","active":true,"publicationSubtype":{"id":30}},"title":"MTAB 111, March 2025","docAbstract":"This Memo to All Banders (MTAB 111) was released in March 2025. Subjects in this this memo are 1. The Chief’s Chirp; 2. Alerts – Highly Pathogenic Avian Influenza; 3. Staff updates – celebrating Karen Jone’s remarkable career and retirement, meeting reports and a field trip; 4. News – BandIt end of life! (starting February 1st, 2025 the BBL will no longer be accepting BandIt files), Notes From the Field: Black-bellied Whistling Ducks, Longevity records update, ABA Bird of the Year the Common Loon, EESC signs partnership with Audubon Society, and what 100 years of USGS bird monitoring data tells us about hummingbirds; 5. A note from the permitting shelves – changes to the application process for new master personal or station permits and don’t wait to submit authorization requests; 6. A note from the supply room – best practices for band supply; 7. Data management – banding data submission for birds released from rehabilitation; 8. Frequently asked questions – I had to replace a federal metal band or auxiliary marker, how should I submit this data to the BBL? 9. Banding and encounter highlights; 10. Message to the Flyways; 11. Recent literature; 12. Moments in history; 13. Upcoming events; and 14. Request for information.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Harvey, K., and McKay, J.L., 2025, MTAB 111, March 2025: Memo to All Banders (MTAB), 14 p.","productDescription":"14 p.","ipdsId":"IP-176859","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/mtab-111-march-2025"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Kyra 0000-0003-4781-1874","orcid":"https://orcid.org/0000-0003-4781-1874","contributorId":296250,"corporation":false,"usgs":true,"family":"Harvey","given":"Kyra","email":"","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKay, Jennifer L. 0000-0002-8893-0231","orcid":"https://orcid.org/0000-0002-8893-0231","contributorId":296562,"corporation":false,"usgs":true,"family":"McKay","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930751,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264321,"text":"sir20255002 - 2025 - Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba","interactions":[],"lastModifiedDate":"2025-07-23T16:49:39.233509","indexId":"sir20255002","displayToPublicDate":"2025-03-12T13:16:46","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":"2025-5002","displayTitle":"Evaluating Drought Risk of the Red River of the North Basin Using Historical and Stochastic Streamflow Upstream from Emerson, Manitoba","title":"Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba","docAbstract":"Drought and its effect on streamflow are important to understand because of the potential to adversely affect water supply, agricultural production, and ecological conditions. The Red River of the North Basin in north-central United States and central Canada is susceptible to dry conditions. During an extended drought, streamflow conditions in the Red River of the North may become inadequate to support existing water supply needs in the basin for agriculture, industry, human use, and aquatic life. To understand potential future low-streamflow conditions in the Red River of the North Basin, the U.S. Geological Survey, in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board, developed a water-balance model of the Red River of the North Basin upstream from Emerson, Manitoba, Canada, and coupled the model with stochastic weather inputs to simulate possible future low-streamflow conditions.
Historical changes in low-streamflow conditions were characterized across the Red River of the North Basin using multiple change-point analysis for 12 streamgages. Across these stations, significant change-point years in 1943 and 1994 marked increases in the magnitude of low-streamflow conditions. During 1920–2015, conversion of primary land (not affected by human use) to agricultural and secondary land was followed by a conversion from smalls grains to corn and soybeans as the dominant crop type. From land-use analysis, 1940–2000 was determined to have relatively stable land use and therefore was used as the calibration period for the water-balance model.
A deterministic water-balance model was developed for the Red River of the North Basin upstream from Emerson, Manitoba. The water-balance model was calibrated with data from 37 U.S. Geological Survey streamgages for 1940–2000 and verified using data for 2001–15. The calibrated water-balance model simulated streamflow distributions that mirrored the seasonal patterns of the observed mean monthly streamflow and the standard deviation of the monthly streamflow data, especially during the fall and winter months when streamflow was lowest. For the verification period, during the low-streamflow months of December through January, the difference between simulated and observed data was similar to the calibration comparison and successfully reproduced seasonal trends in the distribution of streamflow, even when using weather data that were outside the calibration period.
To determine the future risk of low-streamflow conditions in the Red River of the North Basin, a block-bootstrap method was used to generate multiple possible future climates. These stochastically generated weather time series were then input to a water-balance model to simulate a distribution of possible streamflows. Three sets of experiments were performed, with each experiment containing a set of scenarios. The first set of experiments from the stochastic streamflow model were designed to investigate how changes in reservoir management would affect the distribution of low streamflow. Relative to scenario 1 (present-day [2023] reservoir operation), scenario 2 (no reservoir operation) shifted the low-streamflow frequency curves downward, reducing the annual minimum monthly streamflow for the Emerson subbasin. Subbasins were defined by the contributing area upstream from a selected streamgage station. Relative to scenario 1, scenario 3 (regulated streamflow with an increased reservoir capacity of 10 percent) shifted the low-streamflow frequency curves upward for the Emerson subbasin. The magnitude of this upward shift, caused by increased reservoir capacity, was lower than the magnitude of the shift caused by the absence of the reservoirs, which indicates that the streamflow was most affected when the reservoirs were first constructed.
The second set of experiments from the stochastic streamflow model included two scenarios that were performed to better understand how the Red River of the North Basin responds to long periods of low or high precipitation. The results indicate that the model consistently overestimated streamflow, but the relative change between a wet and dry climate state of simulated streamflow distribution reasonably matched the relative change of historical streamflow. Across the subbasins, the model was most accurate for low-streamflow conditions associated with nonexceedance probabilities between 20 and 40 percent.
The third set of experiments from the stochastic streamflow model were done to investigate low-streamflow response across the basin to several drought events. Low-end streamflow was reduced when the basin was exposed to a drought, and the magnitude of the reduction increased with longer or more intense droughts. Compared to the low-intensity drought scenarios, the range of percent reductions (as indicated by the interquartile range) was larger for the high-intensity drought scenarios for all subbasins, and the subbasins of Grand Forks and Emerson had a smaller range of reductions compared to the other three subbasins. The larger drainage area—combined with the large contribution of the Red Lake River and several other Minnesota tributaries that generally experience wetter climate conditions—upstream from the Emerson and Grand Forks subbasins may contribute to the smaller range in reductions under the high intensity scenarios. Comparison of the percent reduction in low-end streamflow among subbasins also indicated that the effects of drought duration and intensity could be cumulative. Combining factors of time and intensity produced a larger reduction in streamflow than when each effect was isolated. The array of drought scenarios can be used to determine how a subbasin would respond to multiple possible future conditions. Based on climate predictions, the drought scenario that best matches a future anticipated drought scenario can be used to estimate a low streamflow response for a given subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255002","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Water Resources, Red River Joint Water Resource District, and Red River Watershed Management Board","usgsCitation":"Redoloza, F.S., Glas, R.L., Nustad, R.A., and Ryberg, K.R., 2025, Evaluating drought risk of the Red River of the North Basin using historical and stochastic streamflow upstream from Emerson, Manitoba: U.S. Geological Survey Scientific Investigations Report 2025–5002, 58 p., https://doi.org/10.3133/sir20255002.","productDescription":"Report: viii, 58 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160450","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":492779,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118481.htm","linkFileType":{"id":5,"text":"html"}},{"id":483206,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13XEN8U","text":"USGS data release","linkHelpText":"Red River of the North low flow water-balance model and supporting data"},{"id":483203,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.XML"},{"id":483202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5002/sir20255002.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5002"},{"id":483201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5002/coverthb.jpg"},{"id":483204,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5002/images/"},{"id":483207,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":483205,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255002/full"}],"country":"Canada, United States","state":"Manitoba, Minnesota, North Dakota","otherGeospatial":"Red River of the North Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.08881710576394,\n 49.037592614274644\n ],\n [\n -97.61189514667993,\n 49.237246321777064\n ],\n [\n -97.98430773681044,\n 49.25792746144549\n ],\n [\n -98.47156466708684,\n 49.30311814027007\n ],\n [\n -98.18869515177104,\n 48.806510092341696\n ],\n [\n -98.92510212635796,\n 48.563256003263746\n ],\n [\n -98.41426650704827,\n 48.10818045779425\n ],\n [\n -97.49283622534884,\n 48.05240253638604\n ],\n [\n -97.3523048621935,\n 47.017146908752025\n ],\n [\n -97.10429716720503,\n 45.72316825195222\n ],\n [\n -95.52291599413297,\n 45.59412827523792\n ],\n [\n -94.77240339220764,\n 46.995377381657505\n ],\n [\n -94.76454577568065,\n 47.45566932791883\n ],\n [\n -93.68493211560182,\n 47.83688445747143\n ],\n [\n -94.11770692662546,\n 48.33322840096503\n ],\n [\n -95.73784376997673,\n 48.92757937919353\n ],\n [\n -97.08881710576394,\n 49.037592614274644\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
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This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030S","usgsCitation":"Kim, M., Park, S., and Anderson, C., 2025, System characterization report on the Environmental Mapping and Analysis Program (EnMAP), chap. S of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors:U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030S.","productDescription":"vi, 28 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167720","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":483138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/s/coverthb.jpg"},{"id":483141,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/s/images/"},{"id":483142,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030S/full"},{"id":483139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/s/ofr20211030s.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–S"},{"id":483140,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/s/ofr20211030s.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
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1. Patterns of migratory connectivity are increasingly used to understand and manage threats throughout the annual cycle of migratory species. Strong migratory connectivity refers to when individuals from different populations remain spatially separated across the annual cycle, which may expose populations to unique sets of threats and conditions that cause differential population trends. However, the populations or groups used for species’ management are often defined a priori based on expert knowledge and/or management units, which may mask important population segregation and obscure differential population trends and their drivers.
2. We compared three approaches to defining management groups of a declining shorebird, the long-billed curlew (Numenius americanus), for annual cycle management: by expert-opinion, according to management flyways, and with unsupervised clustering of satellite tracking data that maximizes the strength of migratory connectivity.
3. Despite the curlews having a continuous breeding range and a pattern of parallel migration, all three approaches identified groups with different population trends, movement behaviours and habitat selection across the annual cycle, suggesting these are meaningful ecological groups. The expert and clustering approaches resulted in similar group structure, strong estimates of migratory connectivity (measured as MC = 0.64 across seasons), movement behaviour and habitat selection; however, the expert approach identified an additional divide between the easternmost grouping, which revealed strongly negative population trends in the group occupying the Chihuahuan desert during the stationary nonbreeding season. In contrast, the flyway delineation resulted in weaker estimates of migratory connectivity, marginal differences in population trends and less between-group differences in movement behaviour and habitat selection.
4. Synthesis and applications. Using measurements of migratory connectivity in concert with expert opinion can define ecologically distinct groups for wildlife management that differ in the environmental conditions they experience across seasons of the annual cycle, which is a key component for understanding and reversing declines of migratory species.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14885","usgsCitation":"Knight, E., Carlisle, J.D., Boyce, A., Bradley, D., Cimprich, P., Coates, S., Dinsmore, S., Gregory, C., Jorgensen, J., Kelly, J., Newstead, D., Olalla, A., Powell, L., Scarpignato, A., Tibbitts, T., Warnock, N., Wehtje, W., Marra, P., and Harrison, A., 2025, Delineating ecologically-distinct groups for annual cycle management of a declining shorebird: Journal of Applied Ecology, v. 62, no. 5, p. 1152-1165, https://doi.org/10.1111/1365-2664.14885.","productDescription":"14 p.","startPage":"1152","endPage":"1165","ipdsId":"IP-162500","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488308,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.14885","text":"Publisher Index Page"},{"id":483352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -130.61742203243398,\n 55.06220192129385\n ],\n [\n -125.78642691212771,\n 37.96714257094378\n ],\n [\n -112.79332255145798,\n 19.6607075263222\n ],\n [\n -96.55986873218092,\n 19.42104945978697\n ],\n [\n -98.16768517783267,\n 27.617354166317778\n ],\n [\n -93.88189635105832,\n 30.28935268993333\n ],\n [\n -80.97632580516836,\n 30.315920278541377\n ],\n [\n -95.50678240297971,\n 43.55365071845705\n ],\n [\n -100.13328021048312,\n 54.69226523920972\n ],\n [\n -130.61742203243398,\n 55.06220192129385\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Knight, Elly C.","contributorId":352283,"corporation":false,"usgs":false,"family":"Knight","given":"Elly C.","affiliations":[{"id":84154,"text":"Migratory Bird Center, Smithsonian’s National Zoo and Conservation Biology Institute, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":930653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, J. 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The U.S. Geological Survey has a new decadal-scale coastal change assessment product that synthesizes nearly two dozen coastal datasets. A supervised machine-learning framework is used to combine existing datasets that describe the landscape and the hazards that affect it to determine the coastal change likelihood (CCL) in the coming decade at a resolution of 10 m per pixel for the NE United States from Maine to Virginia. Here, results from a series of statistical tests conducted on source data, the supervised classification, and the CCL outcomes as compared with historical land-cover change are presented. The overall accuracy of the aggregated land-cover dataset that serves as the foundation to which other source datasets are appended is 94%. The supervised learning classification that determines the final CCL output has an overall accuracy of 92%. The CCL predictions of high expected coastal change were consistent with 95% of the coastal and low-elevation landscape change in the last 20 years, as recorded by the Coastal Change Analysis Program land-cover change atlas. Results suggest that CCL provides accurate estimates of coastal landscape change in the next decade that are consistent with recent observed change. Additionally, best practices for applying CCL for planning purposes are outlined, and citing limitations, knowledge gaps, and opportunities for improved accuracy and further investigation are considered.
","language":"English","publisher":"BioOne","doi":"10.2112/JCOASTRES-D-24-00072.1","usgsCitation":"Pendleton, E.A., Lentz, E.E., Henderson, R.E., Heslin, J.L., Bartlett, M., and Sterne, T.K., 2025, Assessing decadal-scale coastal change likelihood to define the accuracy and application of scientific information: Journal of Coastal Research, v. 41, no. 5, p. 770-785, https://doi.org/10.2112/JCOASTRES-D-24-00072.1.","productDescription":"16 p.","startPage":"770","endPage":"785","ipdsId":"IP-163674","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":495794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.13790802852529,\n 44.7147016542408\n ],\n [\n -67.76738265729634,\n 46.02637816149995\n ],\n [\n -75.35960353633556,\n 40.88331130667447\n ],\n [\n -77.15417882407351,\n 35.009269531921106\n ],\n [\n -76.77058869230837,\n 34.739931782196365\n ],\n [\n -67.13790802852529,\n 44.7147016542408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pendleton, Elizabeth A. 0000-0002-1224-4892 ependleton@usgs.gov","orcid":"https://orcid.org/0000-0002-1224-4892","contributorId":174845,"corporation":false,"usgs":true,"family":"Pendleton","given":"Elizabeth","email":"ependleton@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lentz, Erika E. 0000-0002-0621-8954 elentz@usgs.gov","orcid":"https://orcid.org/0000-0002-0621-8954","contributorId":173964,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Rachel E. 0000-0001-5810-7941 rehenderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5810-7941","contributorId":194022,"corporation":false,"usgs":true,"family":"Henderson","given":"Rachel","email":"rehenderson@usgs.gov","middleInitial":"E.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heslin, Julia L. 0000-0002-6895-800X","orcid":"https://orcid.org/0000-0002-6895-800X","contributorId":292929,"corporation":false,"usgs":true,"family":"Heslin","given":"Julia","email":"","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartlett, Marie Kathleen 0000-0003-1335-4484","orcid":"https://orcid.org/0000-0003-1335-4484","contributorId":305975,"corporation":false,"usgs":true,"family":"Bartlett","given":"Marie Kathleen","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":949031,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sterne, Travis K. 0000-0002-8626-5151","orcid":"https://orcid.org/0000-0002-8626-5151","contributorId":302689,"corporation":false,"usgs":false,"family":"Sterne","given":"Travis","email":"","middleInitial":"K.","affiliations":[{"id":65531,"text":"Texas Parks and Wildlife Dept.","active":true,"usgs":false}],"preferred":false,"id":949032,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}